Costa et al. Acta Neuropathologica Communications (2021) 9:183 https://doi.org/10.1186/s40478-021-01273-9 RESEARCH Chemogenetic modulation of sensory neurons reveals their regulating role in melanoma progression Pedro A. C. Costa1, Walison N. Silva1, Pedro H. D. M. Prazeres1, Caroline C. Picoli1, Gabriela D. A. Guardia2, Alinne C. Costa1, Mariana A. Oliveira3, Pedro P. G. Guimarães4, Ricardo Gonçalves1, Mauro C. X. Pinto5, Jaime H. Amorim6, Vasco A. C. Azevedo7, Rodrigo R. Resende3, Remo C. Russo4 , Thiago M. Cunha8, Pedro A. F. Galante2, Akiva Mintz9 and Alexander Birbrair1,9* Abstract Sensory neurons have recently emerged as components of the tumor microenvironment. Nevertheless, whether sensory neuronal activity is important for tumor progression remains unknown. Here we used Designer Receptors Exclusively Activated by a Designer Drug (DREADD) technology to inhibit or activate sensory neurons’ firing within the melanoma tumor. Melanoma growth and angiogenesis were accelerated following inhibition of sensory neurons’ activity and were reduced following overstimulation of these neurons. Sensory neuron-specific overactivation also induced a boost in the immune surveillance by increasing tumor-infiltrating anti-tumor lymphocytes, while reducing immune-suppressor cells. In humans, a retrospective in silico analysis of melanoma biopsies revealed that increased expression of sensory neurons-related genes within melanoma was associated with improved survival. These findings suggest that sensory innervations regulate melanoma progression, indicating that manipulation of sensory neurons’ activity may provide a valuable tool to improve melanoma patients’ outcomes. Keywords: Sensory neurons, Tumor microenvironment, Melanoma, Neuronal activity, Chemogenetics © The Author(s) 2021. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Introduction Melanoma represents one of the leading causes of cancer-related deaths, being the most aggressive skin cancer type worldwide [124]. It emerges from molecu- larly altered melanocytes, which are  the producers of melanin in the skin [48]. These cancer cells are embed- ded within the cutaneous microenvironment where they reside and interact dynamically with its constitu- ents during disease progression [15, 54]. Understanding the interplay between the different components within the tumor microenvironment is crucial for the success of therapeutic applications, since each component can be influenced by the others, resulting in impacts on the cancer cells [9, 10, 45, 52, 88, 106]. The presence of individual nerve fibers within the tumor microenviron- ment was ignored for many years as they are difficult to detect in classical histology. For a long time, only large nerve trunks were detected within tumors, and they were always associated with perineural invasion of cancer cells, a process in which these cells grow and migrate along native passive tissue nerves [84]. Recently, a different phenomenon was described, by which the tumor itself is infiltrated pro-actively by newly developed peripheral nerve projections [32, 36, 71, 89, 108, 115, 116, 121, 154, 158]. To understand how peripheral innervations behave within the tumors, functional studies, in which Open Access *Correspondence: birbrair@icb.ufmg.br 1 Departamento de Patologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brasil Full list of author information is available at the end of the article http://orcid.org/0000-0002-1715-3834 http://orcid.org/0000-0003-1015-2561 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://crossmark.crossref.org/dialog/?doi=10.1186/s40478-021-01273-9&domain=pdf Page 2 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 intra-tumoral nerves were eliminated, have relied on the surgical or pharmacological manipulation of nerves. Each such strategy, however, has its disadvantages. Peripheral nerves contain mixtures of different nerve fiber types [41, 82], and therefore, surgical denervation of a peripheral nerve leads to the disruption of all the nerve fibers pre- sent within that specific nerve [101]. Consequently, the role of particular nerve projections in the tumor cannot be isolated, as other nerve fibers are also affected. On the other hand, pharmacological drugs cause systemic reac- tions in several organs and indirect effects on unexpected targets. Thus, achieving the neuronal type-specificity that is needed to understand the role that specific nerve fib- ers perform in the tumor microenvironment is difficult with these methods, and the observed outcomes could be due to the unwanted effects on other innervations in addition to the targeted neurons. Wherefore, conclusions drawn from studies based on surgical or pharmacologi- cal denervation may be imprecise. These are some of the reasons, in addition to tumor tissue specificity, for some of the ambiguity about the roles of specific nerve fibers in cancer behavior. Accordingly, contradictory reports have been published: while some studies have claimed that certain neuronal types promote cancer progression [57, 158], others concluded that they suppress tumorigenesis [32, 116]. Therefore, to study the role of specific innervations, these should be directly manipulated in a nerve-fiber- type-specific manner. Recently, this approach became possible with the advent of powerful genetically-based tools, that precisely allow the targeting and elimina- tion of specific peripheral nerve fibers for studying their functions in  vivo  [13]. Our group showed that specific genetic depletion of sensory neurons promotes mela- noma growth [108]. Nevertheless, genetic ablation of these innervations may result in the generation of a pro- inflammatory microenvironment, secondary to cell death in the site where the neurons were ablated (Männ et al. 2016; Christiaansen, Boggiatto, and Varga 2014; Ben- nett et  al. 2005), which itself is strongly tumor growth promoting [49, 67], and can affect cancer cells’ behavior [50]. Thus, it remains unclear which facets of the sensory neuron-ablated tumor phenotype are due to the loss of sensory innervations, rather than indirect effects due to the local inflammation caused by the death of these neu- rons. To circumvent this issue, in the present study, we used chemogenetics, an experimental strategy that has empowered neuroscience studies [131, 147], to deter- mine the precise role of sensory neurons in the regulation of melanoma progression. Designer Receptors Exclu- sively Activated by Designer Drugs (DREADDs) enable the silencing or overactivation of genetically defined neuronal populations upon binding to small-molecule designer drugs [119]. This approach allowed for highly selective and non-invasive modulation of sensory neu- rons’ activity in the tumor. Here, we revealed that silenc- ing of sensory neurons’ activity, without ablating them, is sufficient to trigger increase in melanoma growth and in intra-tumoral new blood vessel formation. In contrast, chemogenetic stimulation of sensory neurons coun- teracted melanoma progression, by regulating tumoral growth, angiogenesis and immunosurveillance. Our results provide unequivocal evidence of the influence of sensory neurons in cancer progression. Materials and methods Animals Generation of Nav1.8-Cre  mice, in which Nav1.8 + sen- sory neurons express Cre recombinase, have been pre- viously described. These animals were obtained from Infrafrontier (EMMA ID: 04 582). R26-LSL-hM4Di- DREADD (hM4Di) and CAG-LSL-hM3Dq-DREADD (hM3Dq) mice were purchased from the Jackson Labora- tory (Jax) (Bar Harbor, ME). To silence neuronal activity in sensory innervations in  vivo, Nav1.8-Cre mice were crossed with R26-LSL- hM4Di-DREADD (hM4Di), a mouse line conditionally expressing a Gi-coupled engineered human muscarinic 4 receptor (hM4Di) [159]. hM4Di is a mutant G pro- tein-coupled receptor which induces the canonical Gi  pathway following binding to the pharmacologically inert drug clozapine-N-oxide (CNO). In Nav1.8-Cre + / hM4Di + mice, upon removal of the loxP-stop-loxP cas- sette by Cre recombination, the Gi-coupled hM4Di is expressed only in Nav1.8 + sensory neurons. Thus, sen- sory neuronal activity can be silenced by the administra- tion of CNO. Nav1.8-Cre-/hM4Di + mice were used as controls. To promote sensory neuron overactivation in  vivo, Nav1.8-Cre  mice were crossed with CAG-LSL-hM3Dq- DREADD (hM3Dq) animals, a mouse line condition- ally expressing an evolved Gq protein-coupled receptor (hM3Dq), to generate Nav1.8-Cre + /hM3Dq + mice. In Nav1.8-Cre + /hM3Dq + animals, upon removal of loxP- stop-loxP cassette by Cre recombination, the Gq-cou- pled hM3Dq is expressed specifically in Nav1.8-sensory neurons. hM3Dq is a mutant G protein-coupled recep- tor which induces the canonical Gq  pathway following the binding to CNO. Thus, sensory neuron firing can be chemically induced by administration of CNO. Nav1.8- Cre-/ hM3Dq + animals were used as controls. All animal care and experimental procedures were approved by the Ethics Animal Care and Use Commit- tee (CEUA), in accordance with the Guide for the Care and Use of Laboratory Animals from the Federal Uni- versity of Minas Gerais. All colonies were housed in Page 3 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 a pathogen-free animal facility of the Department of Pathology, UFMG, under controlled light cycle (12:12-h light/dark cycle) and fed ad  libitum. Age-matched 8- to 12-week-old mice were used for all experiments. All experiments used mice heterozygous for both NaV1.8- Cre and DREADD receptors. Cell culture Murine B16-F10 melanoma is a common cell line that naturally originated in melanin-producing epithelia of C57BL6 mice. These cells were originally obtained from ATCC (USA), and were used to study melanoma devel- opment in  vivo. The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% (v/v) fetal calf serum/2 mM L-glutamine/100 U/ml penicillin/100  μg/ml streptomycin. The cells have been tested and found negative for mycoplasma. Cells were cultured in a humidified atmosphere of 95% air and 5% (v/v) CO2 at 37 °C. Melanoma tumor implantation B16-F10 cells were suspended in PBS and examined for viability using trypan blue staining. B16-F10 cells were used for transplantation  only when  more than 90% of cells were viable. For subcutaneous injection, the skin of all mice at an age of 8–12  weeks was shaved at the place of application. 1 × 105 cells in 100 μL were injected subcutaneously into the right flank of each mouse and the growth of the tumors was monitored until sacri- fice. Growth of the tumors was assessed over time with a caliper as previously reported [12]. For determination of tumor volume, tumor-bearing animals were anes- thetized with isoflurane in O2 by manually restraining the mice and placing their heads in in-house-built nose cones. Tumors were removed 16  days after transplanta- tion and weighted. Length (L) and width (W) were cal- culated to measure tumor volume (V) using the formula V = 0.5 × (L × W2) [40].  Tumor area was determined using calibrated photographs of each tumor using  Fiji software®, version 1.53 (National Institute of Health, Bethesda, MD). CNO treatment The DREADD ligand clozapine-N-oxide (CNO) (1  mg/ kg in saline) (Sigma-Aldrich, St Louis, MO, USA) [7] was administered intra-peritoneally using a 25-gauge needle daily to test the effect of neuronal inhibition or activation on melanoma progression in Nav1.8-Cre + /hM4Di + and Nav1.8-Cre + /hM3Dq + animals, respectively. Control Nav1.8-Cre-/hM4Di + and Nav1.8-Cre-/hM3Dq + mice were similarly injected with CNO. Capsaicin‑induced spontaneous behavior To confirm sensory neurons inhibition efficiency, following acclimation, Nav1.8-Cre-/hM4Di + and Nav1.8-Cre + /hM4Di + mice were injected with an intra-plantar subcutaneous dose of 10  μl of capsaicin (1 μg/10 μl; Sigma-Aldrich). A video recording was taken for 5 min post-capsaicin injection. The time that the ani- mals spent performing spontaneous behaviors of licking, lifting, and flicking the paw were measured for 5  min after injection of capsaicin from these videos. Immunohistochemistry and microscopy Adult mice were deeply anesthetized with isoflurane and transcardially perfused with saline followed by 4% buff- ered paraformaldehyde (PFA, pH = 7.4). After dissec- tion, B16F10 tumors were fixed overnight at 4  °C in 4% buffered paraformaldehyde, incubated overnight at 4  °C with 30% sucrose diluted in PBS, embedded and frozen in optimal cutting temperature compound (OCT, Tissue‐ Tek). Embedded tumors were stored at − 80  °C. 20  μm cryosections were cut and blocked for 2 h in 3% BSA in PBS + 0.5% Triton and immunostained with the follow- ing antibodies: CD31‐PE (dilution 1:100) (BioLegend), Ki67 (dilution 1:100) (BD Biosciences), and anti‐Guinea pig‐AlexaFluor‐647 (1:1000) (Life Technologies) [10, 23]. After this, the sections were washed with PBS contain- ing 4’,6-diamidino-2-phenylindole (DAPI, 5 μg/ml, Invit- rogen) and mounted using Dako fluorescence mounting medium (Dako, Santa Clara, CA). Stained tumor sec- tions were imaged and analyzed by confocal microscopy using an inverted Zeiss LSM 880 confocal microscope (Oberkochen, Germany). CD31 area and the number of Ki67+  cells were quantified using Fiji software®, ver- sion 1.53 (National Institute of Health). Multiple random fields of each section were used for quantification. Tumor‑infiltrating leukocytes immunophenotyping and intracellular cytokine measurement Tumors, their draining lymph nodes and non-tumor draining lymph nodes were harvested. Tissues were macerated and filtrated trough cell strainers of 40 um (Falcon) to isolate the cells used for immunophenotyp- ing. Cells were washed in phosphate-buffered saline (PBS), incubated with Live/Dead solution (Invitrogen), for dead cell exclusion, and with monoclonal antibodies, washed, fixed, and permeabilized (FoxP3 staining buffer set, eBioscience) according to manufacture’s instruc- tions. Antibodies are listed in Table  1. Acquisition was realized on a LSR-FORTESSA. For analyses, to exclude debris, combinations of fluorochromes was done, to remove doublets a forward scatter area (FSC-A) versus forward scatter height (FSC-H) gate was used, and then cells were gated in function of time versus FSC-A to Page 4 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 avoid a possible interference of flux interruptions. Only live leukocytes were used using a Live/Dead gate versus CD45. We assessed different immune cell subpopulations based on molecular markers of each cell subset: CD4 + T cells (CD4+/CD3+), CD8 + T cells (CD8+/CD3+), γδ T cells (CD3+/CD4−/CD8−/TCRγδ+),  NKT cells (CD3+/ NK+), regulatory T cells (Foxp3+/CD4+/CD3+), NK cells (CD3−/NK+), neutrophils (CD11b+/CD11c−/Ly6C−/ LyG6+), PMN/MDSCs (CD11b+/Ly6C−/LyG6+) and dendritic cells (CD11b−/CD11c+/Ly6C−/LyG6−). In each T-cell subset, frequencies of cells expressing checkpoint inhibitors CTLA-4 and PD1 were evaluated. Cytokine analyses in lymphocytes from the tumor microenviron- ment and lymph nodes were done using intracellular staining. Briefly, cells were isolated from tumor samples and lymph nodes and cultivated for 4 h at 37  °C in 10% FBS RPMI supplemented with 2  mM L-glutamine, 50 units/mL penicillin, and 50  μg/mL streptomycin, in the presence of Brefeldin A (ThermoFisher) and Monen- sin (ThermoFisher). Following this, cells were washed in FACS buffer and stained for cell surface markers. Cells were then fixed for 35  min at 4  °C with eBiosciences Cytofix/Cytoperm buffer and, subsequently, washed once in eBioscience Perm/Wash buffer. Then, cells were stained for 45  min at 4  °C with anti-IFN-γ and anti- IL-17 (Table  1) antibodies diluted in eBioscience Perm/ Wash [78]. Cells were washed twice and the data was acquired. Ki67 is a nuclear factor transcript in the late G1, S, G2, and M of cell cycle, therefore marks prolifer- ating cells [44, 128]. Thus, we evaluated proliferation in viable CD45 negative cells, suggesting tumoral prolif- eration. GraphPad Prism V7.0 (GraphPad software) and FlowJo V10.4.11 (TreeStar) were used for data analysis and graphic presentation. Quantification of CGRP within tumors Tumor samples from Nav1.8-Cre + /hM4Di + and Nav1.8-Cre + /hM3Dq + animals, as well as from their respective controls (Nav1.8-Cre-/hM4Di + and Nav1.8- Cre-/hM3Dq +) were analyzed to measure the amount of CGRP using commercially available Sandwich-CGRP ELISA kit purchased from Elabscience (Catalog # E-EL- M2744). Briefly, tumor pieces were weighed and then homogenized in PBS (0.01  M, pH = 7.4) with a glass homogenizer on ice. The homogenates were centrifuged for 5 min at 5000 × g at 4 ℃ to get the supernatant. ELISA of CGRP were performed according to manufacturer’s instructions. After ELISA, Optical Density (OD) was measured using Varioskan Flash (Thermo) set at 450 nm. In silico analysis RNA sequencing count data of 103 Skin Cutaneous Melanoma (SKCM) patients was downloaded from The Cancer Genome Atlas (TCGA) repository (https:// por- tal. gdc. cancer. gov/). Differential gene expression analy- ses were performed between samples of alive and dead patients (considering a 5-year interval) using DESeq2 [83]. We stratified patients in these two groups, alive or dead, based on their vital status in a 5-year interval of their tumor diagnosis (clinical data available at TCGA and curated by Liu et  al. (2018) [85]. Genes with abso- lute log2(Fold-change) ≥ 1 and False Discovery Rate (FDR) adjusted P-value < 0.05 were considered differen- tially expressed. To identify biological processes associ- ated with genes differentially expressed, we performed a Gene Ontology (GO) enrichment analysis using ShinyGO [42]. We used the STRING database [132] (parameters: full STRING network, considering only text-mining, databases and experiments interactions with score > 0.400, and only genes with 3 or more interactions) and Cytoscape (https:// cytos cape. org/) to construct protein–protein interactions (PPIs) among our manu- ally curated list of 34 gene related to sensory neurons selected based on the literature [29, 37, 51, 114, 141, 144]. The set of 18 genes showing at least two PPI interactions are shown. For the remaining analyses, RNA sequencing counts were first Transcripts Per Million (TPM)-normal- ized using a local R script. To identify a gene signature associated with SKCM cancer patient survival we used Reboot [31] with parameters "-B 100 -G 5 -P 0.3 -V 0.01". Table 1 Antibodies used in flow cytometry Antigen Fluorochrome Clone Company CD3 eFluor450 145-2C11 ThermoFhisher CD8a eFluor 450 53–6.7 ThermoFhisher CD11c eFluor 450 N418 ThermoFhisher LIVE/DEAD Acqua ThermoFhisher Streptavidin Pacific Orange ThermoFhisher CD45 Super Bright 600 30-F11 ThermoFhisher TCR gamma/delta Super Bright 645 eBioGL3 ThermoFhisher CD4 Alexa Fluor 488 GK1.5 ThermoFhisher F4/80 FITC BM8 Hycult NK1.1 PE-eFluor 610 PK136 ThermoFhisher CD8a PerCP-Cyanine5.5 53–6.7 ThermoFhisher Ly-6G PerCP-eFluor 710 1A8-Ly6g ThermoFhisher IL-17A PerCP-Cyanine5.5 eBio17B7 ThermoFhisher CTLA-4 PE-Cyanine7 UC10-4B9 ThermoFhisher FoxP3 Alexa Fluor 647 3G3 ThermoFhisher CD3e Cyanine5 500A2 ThermoFhisher Ki67 AlexaFluor 700 SolA15 ThermoFhisher PD-1 APC-eFluor 780 J43 ThermoFhisher Ly-6C APC-eFluor 780 HK1.4 ThermoFhisher IFN-γ APC-eFluor 780 XMG1.2 ThermoFhisher CD11b Biotin M1/70 Biolegend https://portal.gdc.cancer.gov/ https://portal.gdc.cancer.gov/ https://cytoscape.org/ Page 5 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Briefly, Reboot finds genes associated with cancer patient prognosis using multivariate penalized Cox regression combined with a bootstrap approach. In the first step of Reboot, it produces regression coefficients (numerical values) that determine the contribution of each submit- ted gene to patients’ survival. These coefficients may be positive or negative values indicating that high expres- sion of a particular gene potentially contributes to worse or better prognosis, respectively. Once these coefficients are produced, Reboot then calculates the score of each patient (sample) as the sum of each gene coefficient mul- tiplied by the corresponding gene expression level in that patient. Finally, when all patients’ scores are calculated, we then stratify them into groups with high/low scores based on the median score of all patients to create the survival curve (Kaplan–Meier). For further information, see [31]. SCN10A box plots and survival curves were cre- ated using R (https:// www.r- proje ct. org/) scripts. Statistical analysis Graphs were plotted using GraphPad Prism 7 (San Diego, CA). Shapiro‐Wilk normality test was performed, and unpaired  t  test was used to determine statistical significance. Results Chemogenetic inhibition of Nav1.8 + neurons accelerates melanoma progression We have previously demonstrated that melanomas are infiltrated by Nav1.8 + sensory innervations, and that those tumors grow slower when these neurons are pharmacologically or genetically ablated [108]. How- ever, these investigations were performed using Nav1.8- Cre + /DTA + mice, in which a diphtheria toxin fragment A is constitutively activated in Nav1.8 + sensory neu- rons, resulting in the toxin induced-death of these cells. Therefore, this technique lacks temporal control of neu- ronal ablation, and enables compensatory effects dur- ing the development of these animals. Importantly, the approach by which specific neurons are ablated from the tissue microenvironment is also limited because of the secondary consequences, such as inflammation, caused in the tissue where sensory neurons are elimi- nated. Thus, it remains unclear whether these damage- induced changes in the tissue may influence the observed cancer outcomes. Here, we applied a chemogenetic approach to specifically inhibit the activity of Nav1.8- expressing sensory neurons without killing these cells. We used DREADDs to specifically control sensory neu- ron activity. DREADDs are derived from different types of mutated muscarinic receptors that have been engi- neered to lose affinity to their endogenous ligand ace- tylcholine [5], but to  gain responsiveness to a synthetic ligand, clozapine-N-oxide (CNO). Inhibitory DREADDs (hM4Di) elicit an intracellular cascade that results in the silencing of neuronal activity [113], without changing the number of innervations as previously reported [68]. This method allows for the selective silencing of specific types of neurons in  vivo without physical manipulation or destruction in the tissue. DREADDs were expressed spe- cifically in sensory neurons, using a transgenic murine approach: mice carrying the construct encoding for Cre- dependent expression of hM4Di were crossed to Nav1.8- Cre animals. In the resulting mice, Nav1.8-Cre + / hM4Di + , only Nav1.8 + sensory neurons expressed inhibitory DREADDs. As controls in this study, we used littermate mice carrying Cre-dependent hM4Di, but lacking the Cre gene (Nav1.8-Cre-/hM4Di +) (Fig.  1A). This allowed us to control for any potential side effects from CNO administration. In order to ascertain that the expression of DREADD receptors was driven to intra-tumoral Nav1.8-express- ing neurons in Nav1.8-Cre + /hM3Di + mice, we used the solid tumor model B16F10. We assessed tumor sec- tions from melanoma grown in Nav1.8-Cre + /TdTo- mato + mice and detected Nav1.8 + neurons expressing TdTomato present within the tumor microenvironment (Fig.  1B). To validate sensory neuronal inhibition fol- lowing daily CNO injection, we used a behavioral test to evaluate the sensitivity to capsaicin, confirming the silencing of sensory neurons, as previously described [2]. Indeed, Nav1.8-Cre + /hM4Di + animals spent less time (25.53 ± 2.27  s) licking their paws after intra‐plan- tar injection of capsaicin, compared to control animals (56.53 ± 3.92 s) (Fig. 1C). To analyze the effect of sensory neurons silencing on tumor growth, we subcutaneously transplanted B16F10 cells to the lower right flank of both inhibitory DREADD-expressing mice (Nav1.8-Cre + / hM4Di +) and their controls (Nav1.8-Cre-/hM4Di +). Following cancer cell injection, we treated the animals daily with CNO to induce sensory neuronal activity inhi- bition (controls were also treated with CNO) (Fig.  1D). After 14  days of continuous  sensory inhibition, tumor volume was significantly enhanced in the sensory neu- ron-silenced mice when compared to the controls (tumor volume was increased from 82.1 ± 29.6 to 319.6 ± 72.8 mm3; Fig. 1E). After 16 days of repeated sensory inhibi- tion, tumor weight was also significantly enhanced in the sensory neuron-silenced mice when compared to the controls (tumor weight was increased from 0.50 ± 0.04 to 0.98 ± 0.23  g; Fig.  1F, G). Animal weights were not affected by sensory inhibition (data not shown). Increase in neoangiogenesis within melanoma tumors is correlated with worse outcomes in these patients [111]. We detected, in melanoma-bearing animals with silenced sensory neurons, an enhancement in the https://www.r-project.org/ Page 6 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Fig. 1 Chemogenetic inhibition of neuronal activity in sensory Nav1.8 + nerve fibers triggers melanoma growth. A Schematic diagram of the Nav1.8-Cre + /hM4Di + experimental mouse model. Cre recombinase directs the expression of hM4Di specifically to sensory neurons in those mice. After the administration of CNO to those mice, neuronal activity in sensory neurons is inhibited. B Tumor-infiltrating sensory neurons are targeted in Nav1.8-Cre mice. 1 × 105 B16F10 melanoma cells were subcutaneously injected into Nav1.8-Cre/TdTomato mice, and tumor tissues were surgically removed 16 days later. Representative image of a Nav1.8-Cre/TdTomato mouse tumoral section with sensory nerve fibers infiltrating the tumor labelled with TdTomato fluorescence (red) and nuclei with DAPI (blue). C Capsaicin-induced spontaneous behavior test corroborates chemogenetic inhibition of sensory Nav1.8 + nerve fibers in Nav1.8‐Cre+/hM4Di+ mice after CNO treatment. Column charts show the licking time after capsaicin application of Nav1.8‐Cre−/hM4Di+ (n = 5) and Nav1.8‐Cre+/hM4Di+ (n = 5) animals. D Representation of the protocol for subcutaneous allograft melanoma growth. 1 × 105 B16F10 melanoma cells were subcutaneously injected into Nav1.8‐Cre−/hM4Di + (n = 5) and Nav1.8‐Cre + /hM4Di + (n = 5) mice, followed by tumors removal for analysis after 16 days. CNO was daily intra-peritoneal injected at 1 mg/kg. E Development curve of tumor growth from Nav1.8‐Cre−/hM3Dq+ and Nav1.8‐Cre+/hM3Dq+. Tumor volumes were assessed over time with a caliper. F Representative macroscopic image of B16F10 melanoma after dissection, left panel (Nav1.8‐Cre−/hM4Di+) and right panel (Nav1.8‐Cre+/ hM4Di+). G Tumor weight. (Nav1.8‐Cre−/hM4Di+: 0.50 ± 0.04 g; Nav1.8‐Cre+/hM4Di+: 0.98 ± 0.23 g). Data are shown as mean ± SEM. Unpaired t test (ns P > 0.05; *P < 0.05; **P < 0.01) Page 7 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 intra‐tumoral blood vessels’ area (from 0.02 ± 0.00 to 0.03 ± 0.01 µm2) (Fig. 2A, B). Expression of Ki67 is used to determine the proliferation rate of malignant can- cer cells [139], which is also associated with melanoma aggressiveness [76]. We found that genetic silencing of sensory innervations led to an increase in the prolif- eration rate within the melanoma (from 2074 ± 55.32 to 2454 ± 168.4 Ki67 + cells per μm2) (Fig.  2C, D). We also observed after inhibition of sensory neurons fir- ing a decrease in tumor-infiltrating CD4 + T cells (from 4.47 × 107 ± 1.15 × 107 to 1.73 × 107 ± 7.92 × 106 cells per mg of tumor) (Fig.  2E), in special, in IL-17-pro- ducing CD4 + T cells (from 1.63 × 107 ± 1.30 × 106 to 3.77 × 106 ± 3.27 × 106 cells per mg of tumor) (Fig.  2F), Fig. 2 Chemogenetic inhibition of neuronal activity in sensory Nav1.8 + innervations increases intra-tumoral proliferation and angiogenesis, and blocks anti-tumoral immune response. 1 × 105 B16F10 melanoma cells were subcutaneously injected into Nav1.8‐Cre−/hM4Di + (n = 5) and Nav1.8‐Cre + /hM4Di + (n = 5) mice, followed by tumors removal for analysis after 16 days. A Representative immunofluorescence images of tumors labelled for endothelial cells (CD31; red) to identify blood vessels and nuclei (DAPI; blue). B Quantification of angiogenesis in melanomas by blood vessel area. C Representative immunofluorescence images of tumors labelled for Ki67 (Ki67; green) to identify cell proliferation and nuclei (DAPI; blue). D Quantification of proliferation in melanomas by the counting of Ki67 + cells per μm2. Absolute number of CD4 + E and CD8 + G T cells from the melanomas of B16F10–inoculated mice. F Graph shows absolute numbers of CD4 + T cells producers of IL-17. IL-17 levels were measured in cells isolated from tumors of B16F10–inoculated Nav1.8-Cre−/hM4Di+ and Nav1.8-Cre+/hM4Di+ animals. Data are shown as mean ± SEM. Unpaired t test (ns P > 0.05; *P < 0.05) Page 8 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 and a decrease in melanoma-infiltrating CD8 + T cells (from 3.27 × 106 ± 5.22 × 105 to 7.62 × 105 ± 6.78 × 105 cells per mg of tumor) (Fig. 2G). Our results indicate that inhibition of neuronal activity in sensory neurons pro- motes melanoma tumor advancement. Chemogenetic activation of hM3Dq excitatory DREADD receptors in Nav1.8 + neurons promotes melanoma regression As we found that inhibition of sensory neuron activ- ity promotes melanoma advancement, we hypothesized that increasing sensory excitability would result in the reverse: blockage of melanoma progression. To test this hypothesis, we used  again chemogenetics, by which  we induced the expression of excitatory hM3Dq DREADDs [127] only in Nav1.8 + sensory neurons. We crossed Nav1.8-Cre mice to a mouse line with a Cre-dependent evolved Gq protein-coupled receptor (hM3Dq) expres- sion. In the resulting Nav1.8-Cre + /hM3Dq + mice, upon removal of loxP-stop-loxP cassette by Cre recom- bination, the Gq-coupled hM3Dq is expressed specifi- cally in Nav1.8-sensory nerve fibers. Sensory neurons in those mice can thus be overactivated by the adminis- tration of CNO. It has been shown previously that Gq- DREADD activation by CNO increases neuronal activity in the targeted neurons, including sensory neurons [68, 90], without changing the number of neurons [110]. To evaluate the role of sensory stimulation on tumor growth, we transplanted subcutaneously B16F10 mela- noma cells to the lower right flank of both stimulatory DREADD-expressing mice (Nav1.8-Cre + /hM3Dq +) and their controls (Nav1.8-Cre-/hM3Dq +). Follow- ing the cancer cell implantation, we treated mice daily with CNO to induce Nav1.8 + sensory neuron activa- tion (controls were also treated with CNO) (Fig. 3A, B). After repeated sensory neuron activation, melanoma development was decreased in the sensory neuron-over- activated mice when compared to the controls (at day 14, tumor volume per body weight was reduced from 3.51 ± 0.89 to 0.71 ± 0.20 mm3; at day 16, tumor weight was reduced from 0.38 ± 0.07 to 0.17 ± 0.03 g; Fig. 3C–F). Animal weights were not affected by genetic stimula- tion of sensory neurons in melanoma‐bearing mice (data not shown). Moreover, genetic overactivation of sensory neurons led to a decrease in proliferating cells within the tumor (from 3050 ± 203 to 1292 ± 367 Ki67 + cells per μm2, analyzed by immunohistochemistry) (Fig.  3G, H), corroborated by flow cytometry analysis of CD45- cells for Ki67 expression (the was a decrease from 8.13 ± 1.00 to 5.07 ± 0.70% of CD45-/Ki67 + cells within the popu- lation of CD45- cells) (Fig. 3I). Additionally, there was a decrease in the intra‐tumoral blood vessels’ area (from 0.010 ± 0.001 to 0.006 ± 0.001 µm2 of CD31 + area / µm2 of tumor area) (Fig. 3J, K). Our data suggest that increase in neuronal activity in sensory neurons counteracts mela- noma development. Increase in sensory neuron activty affects melanoma immunosurveillance Functional studies in combination with histological anal- ysis have demonstrated that tumor-infiltrating immune cells modulate melanoma cells’ behavior, altering can- cer outcomes [38, 72, 79, 83, 112, 130, 133, 134, 152, 153]. Given that sensory neurons may influence immune responses in non-cancer contexts, we sought to probe whether sensory neurons stimulation alters immune sur- veillance within the tumor. Accumulating evidence has demonstrated that tumor- infiltrating neutrophils and PMN-MDSCs promote tumor development and progression [21, 39, 65, 107, 136, 138, 140, 150]. Thus, we evaluated whether these cells are affected by sensory neurons’ overactivation. We found that the number of melanoma-infiltrating neu- trophils and PMN-MDSCs was significantly decreased in the sensory neuron-overactivated mice (Nav1.8- Cre + /hM3Dq +) when compared to the controls (Nav1.8-Cre-/hM3Dq +) (from 12.02 × 107 ± 3.45 × 107 to 4.69 × 107 ± 7.10 × 106 PMN-MDSCs per mg of tumor; and from 10.45 × 107 ± 3.70 × 107 to 2.78 × 107 ± 5.65 × 106 neutrophils per mg of tumor) (See figure on next page.) Fig. 3 Overstimulation of sensory Nav1.8 + nerve fibers decreases melanoma growth. A Schematic diagram of the Nav1.8-Cre + / hM3Dq + experimental mouse model. Cre recombinase directs the expression of hM3Dq specifically to sensory neurons in those mice. After the administration of CNO to those mice, neuronal activity in sensory neurons is overactivated. B Representation of the protocol for subcutaneous allograft melanoma growth. 1 × 105 B16F10 melanoma cells were subcutaneously injected into Nav1.8‐Cre−/hM3Dq + (n = 14) and Nav1.8‐ Cre + /hM3Dq + (n = 13) mice, and tumors were removed for analysis after 16 days. CNO was injected daily intra-peritoneally at 1 mg/kg. C Development curve of tumor growth from Nav1.8‐Cre−/hM3Dq+ and Nav1.8‐Cre+/hM3Dq+. Tumor volumes were assessed over time with a caliper. D Representative macroscopic images of B16F10 melanoma tumors after dissection, left panel (Nav1.8‐Cre−/hM3Dq+) and right panel (Nav1.8‐Cre+/hM3Dq+). E Tumor weight. (Nav1.8‐Cre−/hM3Dq+: 0.38 ± 0.07; Nav1.8‐Cre+/hM3Dq+: 0.17 ± 0.03). F Tumor weight corrected by animal body weight. G Representative immunofluorescence images of tumors labelled for Ki67 (Ki67; green) to identify cell proliferation and nuclei (DAPI; blue). H Quantification of proliferation in melanomas from Nav1.8‐Cre−/hM3Dq+ and Nav1.8‐Cre+/hM3Dq+ animals. I Quantification of proliferation (Ki67 +) by flow cytometry in CD45- cells from tumors of Nav1.8‐Cre−/hM3Dq+ and Nav1.8‐Cre+/hM3Dq+ mice. J Representative immunofluorescence images of tumor sections labelled for endothelial cells (CD31; red) to identify blood vessels and nuclei (DAPI; blue). K Quantification of angiogenesis in melanomas by blood vessel area. Data are shown as mean ± SEM. Unpaired t test (ns P > 0.05; *P < 0.05; **P < 0.01) Page 9 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 (Fig. 4A, B). On the other hand, we found that the num- ber of tumor-infiltrating dendritic cells, which coun- teract the proliferation of melanoma cells [137], was significantly increased (from 5.53 × 107 ± 8.80 × 106 to 1.07 × 108 ± 2.27 × 107 dendritic cells per mg of tumor) (Fig. 4C). Recent breakthroughs in cancer immunotherapy have revealed  the remarkable ability of the immune system Fig. 3 (See legend on previous page.) Page 10 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 to fight different types of cancers, including melanoma. The phenotypes and numbers of prevalent tumor-infil- trating lymphocytes are predictive of response to immu- notherapy and key modulators of disease progression. Thus, we examined how tumor-infiltrating lymphocytes are affected by sensory neurons’ overstimulation. We detected an increase in tumor-infiltrating CD4 + T cells (from 2.91 × 106 ± 1.04 × 106 to 1.09 × 107 ± 2.92 × 106 cells per mg of tumor), CD8 + T cells (from 8.94 × 106 ± 1.60 × 106 to 1.96 × 107 ± 5.20 × 106 cells per mg of tumor), γδ T cells  (from 31.76 × 107 ± 7.32 × 107 to 74.14 × 107 ± 20.40 × 107 cells per mg of tumor), NKT cells (from 16.34 × 107 ± 4.6 × 107 to 34.92 × 107 ± 10.42 × 107 cells per mg of tumor) and NK cells (from 1.72 × 107 ± 2.90 × 106 to 3.47 × 107 ± 4.40 × 106 cells per mg of tumor) (Fig.  4D– H), while regulatory T cells, which mediate immuno- suppression in the tumor microenvironment [66], were not altered (Fig.  4I). Immune checkpoint molecules, such as cytotoxic T lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1), act fine-tuning the intense immune responses that might kill healthy cells [27, 55, 122]. Their expression in cytotoxic T cells may lead to dysfunction of these cells, affecting their effector function [11, 146]. We found that increase in the firing of sensory neurons prevented the increase of immune checkpoint markers of tumor infiltrating CD8 + T cells and CD4 + T cells (Fig. 5 and Additional file 1: Figure 1). The percentage of CTLA-4-expressing CD4 + tumor- infiltrating lymphocytes decreased from 29.43 ± 4.04% in Nav1.8-Cre−/hM3Dq+ to 19.08 ± 2.80% in Nav1.8-Cre+/ hM3Dq+ animals (Fig.  5A); similarly, the percentage of PD-1-expressing CD4 + tumor-infiltrating lymphocytes decreased from 15.02 ± 2.62% in Nav1.8-Cre−/hM3Dq+ to 7.85 ± 1.43% in Nav1.8-Cre+/hM3Dq+ mice (Fig.  5B, C). The percentage of PD-1-expressing CD8 + tumor- infiltrating cytotoxic lymphocytes also decreased from 22,03 ± 2,66% in Nav1.8-Cre−/hM3Dq+ to 12.99 ± 3.85% in Nav1.8-Cre+/hM3Dq+ animals (Fig.  5E), while the expression of CTLA-4 did not vary in these cells (Fig. 5D, F). In addition, no differences were found in CTLA-4 and PD-1 expression on γδ T cells (Fig.  5G, H and I), NKT Fig. 4 Sensory neurons overactivation improves anti-tumor immunity by decreasing tumor-infiltrating immunosuppressive cells, increasing dendritic cells and by promoting CD4 + T, CD8 + T, γδT, NKT, and NK-cell infiltration. Immune cells from B16F10–inoculated mice were analyzed ex vivo in Nav1.8-Cre−/hM3Dq+ (n = 14) and Nav1.8-Cre+/hM3Dq+ (n = 13) mice. Column charts show the proportion of PMN/MDSC (A) Neutrophils (B) and Dendritic cells (C) quantified in the tumor microenvironment. (D-I) TIL from B16F10–inoculated Nav1.8-Cre-/hM3Dq + (n = 14) and Nav1.8-Cre + /hM3Dq + (n = 13) mice were analyzed ex vivo. Absolute number of CD4 + T cells (D), CD8 + T cells (E), γδ T cells (F), NKT cells (G), NK cells (H), and Treg cells (I) from the melanomas of B16F10–inoculated mice. Data are shown as mean ± SEM, Unpaired t test, *.01 < P < .05; **.001 < P < .01 Page 11 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 cells (Fig. 5J, K and L), NK cells (Fig. 5M, N and O) and Treg cells (Fig.  5P, Q and R). Overall, our data suggest that sensory neurons overactivation induces improve- ment of T cells effector functions within the tumor microenvironment. It has been reported that CD4 + and CD8 + lympho- cytes secreting IL-17 promote melanoma regression [91, 99]. Here, we detected in response to sensory neuron firing an increase in melanoma-infiltrating IL-17-pro- ducing CD4 + T cells (from 2.45 × 107 ± 6.05 × 106 to Fig. 5 Sensory neurons overstimulation prevent the increase of immune checkpoint markers in tumor infiltrating CD8 + T cells and CD4 + T cells. Immune cells from tumors of B16F10–inoculated mice were analyzed ex vivo in Nav1.8-Cre−/hM3Dq+ (n = 14) and Nav1.8-Cre+/hM3Dq+ (n = 13) mice. Column charts show proportion of CTLA-4 (A, D, G, J, M, P), PD-1 (B, E, H, K, N, Q) and CTLA-4/PD-1 co-expressing (C, F, I, L, O, R) CD4 + T cells (A, B, C), CD8 + T cells (D, E, F), γδ T cells (G, H, I), NKT cells (J, K, L), NK cells (M, N, O), and Treg cells (P, Q, R) from tumors of B16F10–inoculated mice. Data are shown as mean ± SEM, Unpaired t test, *.01 < P < .05; **.001 < P < .01 Page 12 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 30.78 × 107 ± 9.20 × 107 cells per mg of tumor) as well as in melanoma-infiltrating IL-17-producing CD8 + T cells (from 5.02 × 107 ± 0.90 × 107 to 20.08 × 107 ± 5.92 × 107 cells per mg of tumor) (Fig.  6A). In parallel, we did not detect significant changes in the number of other tumor-infiltrating lymphocytes producing IL-17 or in IFN-γ-producing lymphocytes after sensory neuron overactivation (Fig.  6B). Altogether, our results suggest that sensory neurons induce a Th17-immune response in the melanoma microenvironment. Lymph nodes are an integral part of the adaptive immune system in our organism and are essential for the effective immune responses. Melanoma draining lymph nodes are influenced by the primary tumor, but may also prime the immune suppressive microenvironment, play- ing critical roles in promoting cancer immune escape [33, 86, 87, 97]. It is completely unknown whether sensory neuron overactivation may affect the immune cells also within the tumor draining lymph nodes. Herein, we ana- lyzed immune cells from tumor draining and non-tumor- draining lymph nodes from CNO-treated stimulatory DREADD-expressing animals (Nav1.8-Cre + /hM3Dq +) and their controls (Nav1.8-Cre-/hM3Dq +). Tumor draining and non-tumor-draining lymph nodes were iso- lated from the ipsilateral and contralateral side, respec- tively, of the implanted melanoma (Fig. 7A, B). We found that the effect of sensory neurons stimulation in tumor draining lymph nodes mimics the immune response within the primary tumor, but not in non-tumor-drain- ing lymph nodes. In the tumor-draining lymph nodes, we found an increase in the number of CD8 + cytotoxic T cells after sensory neurons’ overstimulation (from 13.68 × 107 ± 3.50 × 107 to 22.41 × 107 ± 2.17 × 107 cells per mg of tumor) (Fig. 7C); while we did not detect any differences in the numbers of T cells in the tumor non- draining lymph nodes (Fig.  7D). These data indicate a possible priming effect of tumor on the adjacent draining lymph nodes. We also observed, in the tumor-draining lymph nodes, increases in IFN-γ-producing CD4 + T cells (from 8.31 × 106 ± 2.65 × 106 to 1.94 × 107 ± 3.36 × 106 cells per lymph node), in IFN-γ-producing NK cells (from 4.72 × 104 ± 2.46 × 104 to 8.44 × 105 ± 2.22 × 105 cells per lymph node), in IL-17-producing CD8 + T cells (from 2.38 × 106 ± 4.27 × 105 to 1.28 × 107 ± 1.64 × 106 cells per lymph node), and in IL-17-producing NKT cells (from 2.35 × 106 ± 5.19 × 105 to 4.08 × 106 ± 5.00 × 105 cells per lymph node) after sensory neurons CNO-stimula- tion (Fig.  7E). In tumor non-draining lymph nodes, we detected increases in IFN-γ-producing CD4 + T cells (from 5.73 × 106 ± 2.17 × 106 to 1.92 × 107 ± 4.23 × 106 cells per lymph node), in IFN-γ-producing CD8 + T cells (from 1.36 × 107 ± 5.57 × 106 to 4.77 × 107 ± 1.19 × 107 cells per lymph node), in IFN-γ-producing γδ T cells (from 2.27 × 106 ± 8.55 × 105 to 1.03 × 107 ± 3.22 × 106 cells per lymph node), and in IFN-γ-producing NK cells (from 1.80 × 105 ± 4.70 × 104 to 6.82 × 105 ± 1.53 × 105 cells per lymph node) (Fig.  7F). Our data suggest that lymphocytes at the lymph nodes may be contributing to the response against the melanoma observed within the tumor microenvironment after sensory neurons’ over- activation as both, IFN-γ and IL-17, may contribute to enhance the anti-tumoral response in the context of mel- anoma [92, 109, 151]. Altogether, our data suggest that Fig. 6 Sensory neurons overactivation promote an increase in tumor-infiltrating IL-17-producing CD4 + and CD8 + T cells. TIL from melanomas of B16F10–inoculated Nav1.8-Cre-/hM3Dq + (n = 14) and Nav1.8-Cre + /hM3Dq + (n = 13) mice were analyzed. TIL from B16F10–inoculated mice were analyzed after 4 h of culture. A Column charts show absolute numbers of CD4 + and CD8 + T cells producers of IFN-γ and IL-17. B Column charts show absolute number of γδ T cells, NKT cells and NK cells producing IFN-γ and IL-17. Cytokines levels were measured in cells isolated from tumors of B16F10–inoculated Nav1.8-Cre−/hM3Dq+ and Nav1.8-Cre+/hM3Dq+ mice. Data are shown as mean ± SEM, Unpaired t test, *.01 < P < .05; **.001 < P < .01 Page 13 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 sensory neurons stimulation alters immune surveillance that impacts melanoma development. High expression of genes related to sensory neurons correlates with best prognosis in human melanoma patients In order to investigate our findings also in human tumors, we analyzed The Cancer Genome Atlas (TCGA) samples Fig. 7 Tumor-draining lymph nodes present an increase in effector CD8 + T-cells after overstimulation of sensory neurons, while the number of lymphocytes in tumor non-draining lymph nodes doesnt change. A Schematic representation of the collected tumor draining lymph nodes. TIL from tumor-draining lymph nodes of B16F10–inoculated Nav1.8-Cre-/hM3Dq + (n = 14) and Nav1.8-Cre + /hM3Dq + (n = 13) mice were analyzed. B Absolute number of CD4 + , CD8 + , γδ, NKT and NK cells from tumor-draining lymph nodes of B16F10–inoculated mice. C IFN-γ and IL-17 were quantified in CD4, CD8, γδ, NKT and NK cells. D Schematic representation of the collected tumor non-draining lymph nodes. TIL from tumor non-draining lymph nodes of B16F10–inoculated Nav1.8-Cre-/hM3Dq + (n = 14) and Nav1.8-Cre + /hM3Dq + (n = 13) mice were analyzed. E Absolute number of CD4 + , CD8 + , γδ, NKT and NK cells from tumor non-draining lymph nodes of B16F10–inoculated mice. F IFN-γ and IL-17 were quantified in CD4, CD8, γδ, NKT and NK cells. Cytokines levels were measured in cells from tumor-draining and tumor non-draining lymph nodes of B16F10–inoculated Nav1.8-Cre-/hM3Dq + and Nav1.8-Cre + /hM3Dq + mice. Data are shown as mean ± SEM. Unpaired t test, *.01 < P < .05; **.001 < P < .01; ****P < 0.0001 Page 14 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 from 103 Skin Cutaneous Melanoma (SKCM) patients. First, we stratified SKCM patients in two groups, alive or dead based on a 5-year interval. Next, we searched for differentially expressed genes between these groups. We found 112 up-regulated and 195 down-regulated genes (|log2(Fold change)|≥ 1 and adjusted P-value < 0.05; Table  2). Next, we performed a Gene Ontology (GO) analysis of Biological Processes (BP) in which these genes are involved. The up-regulated genes set are enriched for three biological processes, while the down-regulated genes are enriched to a wide variety of processes (Fig. 8A; Table 3). Curiously, for the former (up-regulated genes), two out of three Biological Processes represent nervous system development (Fig. 8A), indicating the importance of neuronal networks in melanoma outcomes. Next, we investigated the interactions (and putative regulation) of 34 sensory neurons-related genes which were selected from the literature [29, 37, 51, 114, 141, 144] (Table  4). Figure 8B shows a strong connection among 18 of these genes, suggesting that they work on the same cellular pathways or cell types. Next, using the expression levels of these 34 gene markers for sensory neurons we inves- tigated their potential to be "a signature" associated with SKCM cancer patient survival. Figure 8C shows that high expression of these genes (lower patient scores) are asso- ciated with a better overall survival of SKCM patients. Next, we investigated the expression of these genes in the two sample sets (alive and dead patients). We found that SCN10A, which codifies Nav1.8, a key gene based on which our mouse models target sensory neurons, is more expressed in alive than in dead patients (Fig. 8D). Finally, we investigated the impact of SCN10A expres- sion on SKCM patients’ survival. Figure  8E shows that high expression of SCN10A trends to be associated with a better overall survival of SKCM patients, even with- out statistical support (P-value = 0.26). Taken together, these results confirm that genes related to nervous sys- tem development are enriched in samples from live Skin Cutaneous Melanoma patients. Focusing on gene mark- ers for sensory neurons, we confirmed that these genes are strongly connected, suggesting a synergistic activity, and that the higher expression of some of these genes are associated with a higher overall survival. Strikingly, the high expression of SCN10A is potentially associated with better SKCM patient survival, indicating that the presence of sensory neurons within melanoma counter- acts cancer progression. We also found that TCGA sam- ples from tumors with a worst prognosis (dead patients) have an enrichment of genes promoting angiogenesis (Tables  3 and 5; 15 genes related to angiogenesis). We focused on this set of 15 genes related to angiogen- esis and we confirmed that they are strongly connected (Additional file 2: Fig. 2), indicating a synergic function in promoting angiogenesis. Our results indicate that there is an increase in genes related with angiogenesis in tumors with worst prognosis (from dead patients) and a decrease in their expression in tumors from alive patients which show an increased expression of SCN10a, a sensory neu- ron marker used in this study (Fig. 8C, D, E). By using the CIBERSORT tool [102], we investigated immune infil- trated cells in the same TCGA cohort of alive vs. dead patients (Additional file 3: Fig. 3). CIBERSORT uses gene expression (RNA sequencing data) and support vector regression combined with prior knowledge of expression profiles from purified leukocyte subsets (gene signatures) to produce an estimation of the abundances of immune infiltrated cells subpopulations in a sample. In line with our data presented in this manuscript, we have checked the enrichment of immune infiltrated cells in the tumors of patients alive vs. dead (Additional file  4: Fig.  4). We found an increase of CD4 + T cells, CD8 + T cells, NK cells and dendritic cells in patients with better prognosis (alive). Thus, tumors showing a better prognosis (alive) have an increased infiltration of some key immune cells. Additionally, microarray data evidenced a down-regu- lation of genes related to the Th17 immune response in melanoma patients (Additional file 5: Fig. 5). These analy- ses are consistent with the data generated in our mouse models: that the overactivation of sensory innervations in the tumor microenvironment was associated with sup- pressed melanoma progression. Albeit gene  expression in tumor biopsies  from human cancer patients is used as a tool to define novel biomarkers and  to contribute to prognosis, the obtained data should be also validated by the quantification of sensory neuron-related proteins in human melanoma biopsies and correlation with clini- cal outcomes in future research. Discussion In the present study, we examined how melanoma pro- gression is affected by sensory neurons activity. Our chemogenetic approach revealed that inhibition of sen- sory activity promotes tumor growth and intra-tumoral angiogenesis. In contrast, excitation of sensory neurons induces melanoma regression with decrease in tumor growth and in new blood vessel formation, as well as a boost in the anti-tumor immune surveillance (Fig.  9). This work indicates that induction of hyperactivation in Nav1.8-expressing sensory neurons represents a poten- tial new therapeutic path in the battle against melanoma. Just as the role of particular genes in a specific bio- logical process can be examined by evaluating examin- ing the assessable consequences that  result from their removal (e.g. using knockout animals), the role of neu- rons in the tumor microenvironment was previously assessed by eliminating them [32, 36, 71, 89, 108, 115, Page 15 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 2 Analyzes of genes from The Cancer Genome Atlas (TCGA) samples from 103 Skin Cutaneous Melanoma (SKCM) patients Up‑regulated genes in alive × dead Gene.symbol log2FC FDR SLC5A4 4.35910 1.29E−09 VGF 3.26059 1.06E−05 NPPC 3.24826 2.60E−05 LINC00698 3.76414 3.90E−05 SPACA3 2.67598 1.97E−04 VCX3A 4.95714 2.21E−04 PRSS56 4.13411 4.15E−04 ARHGAP8 2.75929 4.15E−04 VCX 3.63292 4.28E−04 LINC01287 3.48781 7.19E−04 NGFR 1.99998 1.16E−03 NAT16 2.23940 1.16E−03 RP13-143G15.4 2.26728 1.34E−03 HLA-J 1.67323 1.38E−03 LHFPL4 2.80159 2.04E−03 RP11-376N17.4 2.27953 2.10E−03 ZNF689 1.08305 2.33E−03 DCD 6.91950 2.44E−03 SLITRK5 2.38821 2.59E−03 ARPP21 3.84108 2.67E−03 TFAP2B 2.73943 2.68E−03 VIT 2.42509 3.21E−03 HPCAL4 1.70772 3.30E−03 LINC00645 3.54408 4.05E−03 KLHL32 1.29974 4.09E−03 TRIML2 2.32595 4.80E−03 GFAP 2.04141 5.21E−03 MYOZ2 2.33172 6.94E−03 PPY 2.13167 7.88E−03 ARX 2.02055 8.39E−03 LRRTM2 1.75474 8.39E−03 C20orf203 1.95612 8.43E−03 LSMEM2 1.36377 8.43E−03 NRTN 1.74226 8.58E−03 RP11-809C18.3 1.54681 8.89E−03 FOSB 1.08702 9.10E−03 PASD1 4.57206 9.14E−03 UNC93B3 1.84987 9.14E−03 RP11-469H8.6 2.35144 9.14E−03 BCO1 2.17260 9.80E−03 XKR7 1.93913 1.00E−02 RDH5 1.36688 1.04E−02 PAGE1 3.69526 1.10E−02 RP5-907D15.4 1.73346 1.10E−02 AC003092.1 2.31155 1.20E−02 IGHV1-58 3.09550 1.20E−02 FOXG1 3.56195 1.36E−02 GBA3 2.67229 1.39E−02 Table 2 (continued) Up‑regulated genes in alive × dead Gene.symbol log2FC FDR FKSG51 1.89383 1.39E−02 BTNL8 1.87248 1.46E−02 ACHE 1.18749 1.52E−02 KCNJ11 1.81031 1.52E−02 ENPP7P2 1.45231 1.64E−02 CCKBR 2.06353 1.73E−02 PCSK1N 1.87835 1.76E−02 RP11-114G11.5 3.04186 1.76E−02 EFTUD1P1 2.13819 1.77E−02 OR2N1P 5.08848 1.83E−02 CA10 2.21148 1.85E−02 SLCO5A1 1.55420 1.88E−02 IBSP 1.81676 1.91E−02 RP11-88I21.1 3.56590 1.94E−02 MAGEA9 5.10603 1.96E−02 SYP 1.03514 2.15E−02 NBEAP1 1.55393 2.17E−02 RP5-965G21.4 1.14894 2.18E−02 MPZ 1.95997 2.22E−02 RP11-299H22.3 2.95030 2.22E−02 FBXO2 1.34819 2.31E−02 LGSN 2.41190 2.31E−02 RDH8 2.14341 2.32E−02 AC073325.2 1.90901 2.38E−02 GAGE1 3.92238 2.44E−02 MYB 1.16344 2.55E−02 AATK 1.23997 2.63E−02 DOK7 1.48390 2.72E−02 AC068580.7 2.45353 2.72E−02 RP11-36D19.9 1.99056 2.72E−02 HAPLN2 1.51979 2.73E−02 TDRD12 2.24362 3.09E−02 RP11-159H10.3 1.83751 3.18E−02 CHGB 1.62908 3.32E−02 RCN3 1.25033 3.32E−02 RP5-1171I10.5 1.33948 3.32E−02 NMRK2 2.15724 3.33E−02 TNNI3 1.50305 3.33E−02 DPEP3 1.81124 3.33E−02 DPYSL5 2.12990 3.33E−02 ZNF365 1.53513 3.39E−02 KCNQ2 1.98246 3.51E−02 PMP2 2.27248 3.53E−02 HAVCR1 2.17185 3.56E−02 RP1-140K8.5 1.72113 3.72E−02 ROR1-AS1 2.03339 3.75E−02 C1QTNF1-AS1 1.82857 3.75E−02 WFDC1 1.74139 3.99E−02 DEFB126 2.40171 3.99E−02 LBP 1.71514 3.99E−02 Page 16 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 2 (continued) Up‑regulated genes in alive × dead Gene.symbol log2FC FDR CDH12 2.39970 3.99E−02 FABP7 2.11963 3.99E−02 MAGEB17 1.73309 3.99E−02 MYBPC1 1.99301 3.99E−02 RP4-764D2.1 1.03824 4.07E−02 CD5L 1.77129 4.21E−02 CPN2 1.81842 4.21E−02 ZNF727 2.20430 4.21E−02 CST1 2.16587 4.35E−02 RP11-9G1.3 2.55148 4.48E−02 LL22NC03-22D1.1 2.49624 4.50E−02 NPFFR1 1.60868 4.54E−02 MYRIP 1.45769 4.67E−02 RP11-369C8.1 2.61906 4.95E−02 Down‑regulated genes in alive × dead Gene.symbol log2FC FDR AVPR1A − 2.06066 8.65E−08 KRT16P2 − 5.08308 1.00E−07 CHST8 − 3.38063 7.38E−07 ST6GAL2 − 2.68516 1.74E−06 PRSS35 − 3.35395 4.71E−6 SLC8A3 − 3.18840 9.15E−06 CRABP1 − 3.07142 9.15E−6 HSPB3 − 3.24770 2.15E−05 TREX2 − 2.43750 2.15E−05 B3GNT4 − 1.77784 2.26E−05 NPTX1 − 2.61603 1.97E−04 PI3 − 3.60121 2.70E−04 HEYL -1.67534 2.99E−04 ID3 − 1.32736 3.67E−04 ADRB2 − 1.81924 3.91E−04 C6orf223 − 2.13191 4.15E−04 AJAP1 − 1.92423 4.56E−04 CHRNA4 − 2.95110 4.69E−04 RHCG − 2.92093 5.10E−04 PART1 − 2.14901 5.50E−04 ALOX12 − 1.23574 5.50E−04 RSPO3 − 1.48997 6.13E−04 GDPD3 − 1.74062 7.38E−04 CNFN − 2.84236 8.27E−04 ANO2 − 1.70534 9.14E−04 OVOL1 − 2.75293 9.14E−04 CLDN4 − 2.52180 9.56E−04 GREB1L − 2.35313 1.12E−03 IGLV9-49 − 3.84182 1.20E−03 TGM1 − 2.85878 1.28E−03 CYSRT1 − 2.10472 1.34E−03 LYPD5 − 2.27815 1.34E−03 Table 2 (continued) Down‑regulated genes in alive × dead Gene.symbol log2FC FDR CYP19A1 − 1.84923 1.41E−03 ACTG2 − 1.63424 1.41E−03 ABCG4 − 2.12582 1.41E−03 FAM3D − 2.12972 1.41E−03 PAPSS2 − 1.50601 1.41E−03 AC124789.1 − 2.07689 1.41E−03 RASL11B − 1.54784 1.43E−03 MAFB − 1.28214 1.43E−03 NGF − 1.73693 2.01E−03 AC006116.20 − 1.47472 2.01E−03 S100A12 − 2.64402 2.10E−03 KLK14 − 2.29647 2.39E−03 IL1RN − 2.09008 2.39E−03 B3GNT8 − 1.78028 2.39E−03 GPX3 − 1.88978 2.39E−03 CDA − 2.22423 2.68E−03 CHN1 − 1.30585 2.68E−03 PRSS27 − 1.71351 2.68E−03 TMEM79 − 1.43989 3.52E−03 NOTCH3 − 1.22297 3.57E−03 FGF18 − 1.35348 3.80E−03 HGF − 1.19158 4.28E−03 SH3RF3 − 1.23159 4.49E−03 KRT37 − 4.22818 4.57E−03 SH3RF3-AS1 − 1.31293 4.57E−03 ZC3H12A − 1.35742 4.67E−03 TBX4 − 1.84817 5.39E−03 CLIC3 − 2.31078 5.58E−03 LRRC43 − 1.34142 5.69E−03 NRARP − 1.42862 5.97E−03 B3GNT3 − 1.97566 6.61E−03 ELF3 − 2.07351 6.73E−03 LRRN2 − 1.90993 6.80E−03 NFE4 − 2.76901 6.80E−03 KLK10 − 2.72909 6.94E−03 LINC01121 − 1.68460 7.15E−03 SDCBP2 − 1.48707 7.88E−03 FAM83G − 1.50253 7.88E−03 ZNF469 − 1.06394 7.88E−03 ROR2 − 1.15831 7.92E−03 LYNX1 − 1.75074 7.92E−03 VSIG10L − 1.54237 7.92E−03 PITX1 − 2.50531 8.39E−03 PNMA5 − 2.07397 8.43E−03 LTB4R2 − 1.38009 8.43E−03 GTSF1 − 1.95376 8.89E−03 RP3-449H6.1 − 2.80745 8.89E−03 PKDCC − 1.47224 9.03E−03 Page 17 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 2 (continued) Down‑regulated genes in alive × dead Gene.symbol log2FC FDR C11orf87 − 1.86276 9.10E−03 C9orf47 − 1.63063 9.10E−03 KLK12 − 3.43996 9.10E−03 SPNS2 − 1.33949 9.43E−03 TCHH − 1.56077 1.00E−02 ADRA1D − 1.45232 1.01E−02 LINC00675 − 2.28274 1.01E−02 GLP1R − 1.93142 1.04E−02 DLX5 − 1.61403 1.05E−02 JUP − 1.81687 1.10E−02 GREM1 − 1.36182 1.10E−02 FUT2 − 1.65558 1.10E−02 FLJ43879 − 1.73000 1.10E−02 RP4-530I15.9 − 1.37950 1.10E−02 ADAMTSL4 − 1.15965 1.10E−02 LGALS9C − 1.74166 1.11E−02 RP11-145A3.1 − 1.28593 1.20E−02 LINC00689 − 2.44100 1.20E−02 RP11-57C13.6 − 2.13756 1.20E−02 WNT11 − 1.67218 1.23E−02 SOX11 − 1.31510 1.27E−02 SMCO2 − 1.86262 1.29E−02 AC104654.2 − 1.54674 1.34E−02 RP11-715H19.2 − 2.54830 1.45E−02 MALL − 1.65870 1.50E−02 SLPI − 2.56276 1.50E−02 ZNF385B − 1.82165 1.52E−02 AC079305.10 − 1.07337 1.52E−02 RARRES1 − 1.09119 1.56E−02 C1orf177 − 1.80915 1.64E−02 MIR4635 − 1.81095 1.68E−02 KCNK12 − 1.74033 1.70E−02 CTC-525D6.2 − 3.65998 1.75E−02 FGF7 − 1.14265 1.76E−02 KRT82 − 2.71344 1.76E−02 CTD-2554C21.3 − 2.09957 1.76E−02 VNN3 − 1.87348 1.94E−02 KRT17P6 − 2.54509 1.94E−02 CD36 − 1.47332 1.98E−02 ALDH1L1 − 1.84277 1.99E−02 KRT25 − 3.64328 2.04E−02 GLIS3 − 1.24797 2.06E−02 SPRR2D − 2.65861 2.06E−02 CEACAM19 − 1.48405 2.11E−02 ZNF154 − 1.19730 2.12E−02 FBLIM1 − 1.00682 2.15E−02 OR7E11P − 3.14060 2.15E−02 ZNF185 − 1.43741 2.27E−02 Table 2 (continued) Down‑regulated genes in alive × dead Gene.symbol log2FC FDR MYOC − 3.14651 2.30E−02 SULT2B1 − 2.16618 2.31E−02 PLA2G4E−AS1 − 1.49669 2.32E−02 IL36G − 2.54342 2.39E−02 IGKV2-29 − 3.58127 2.43E−02 PADI3 − 1.60173 2.45E−02 WDR87 − 2.67496 2.45E−02 CTD-2555C10.3 − 1.62741 2.45E−02 SMPD3 − 1.51129 2.63E−02 LYPD3 − 2.15151 2.63E−02 SPINK9 − 2.18753 2.63E−02 RP3-405J10.2 − 1.89008 2.63E−02 KRT17 − 2.51044 2.65E−02 RP11-845M18.6 − 2.34005 2.70E−02 CPXM2 − 1.33321 2.72E−02 GDPD2 − 1.69177 2.79E−02 LINC01482 − 1.58713 2.84E−02 KRT8P13 − 2.24864 3.07E−02 ANGPT2 − 1.42767 3.23E−02 TMEM45B − 1.81149 3.23E−02 KLK13 -2.41601 3.23E−02 IGHE − 2.55145 3.23E−02 SPRR2A − 2.94281 3.23E−02 RP11-91J3.3 − 1.87453 3.23E−02 CTC-490G23.2 − 2.34066 3.32E−02 ADAMTS15 − 1.20759 3.33E−02 RHOD − 1.51767 3.33E−02 COL28A1 − 1.56903 3.33E−02 RP11-752L20.3 − 1.07531 3.33E−02 AC133785.1 − 2.02852 3.47E−02 GNA15 − 1.37100 3.49E−02 FAM46B − 1.22702 3.53E−02 KRT80 − 2.07094 3.53E−02 TWIST2 − 1.11079 3.53E−02 KRT42P − 2.45179 3.53E−2 PRSS50 − 1.86776 3.64E−02 TBX1 − 1.26122 3.69E−2 KRT81 − 1.69382 3.75E−02 ALOX12B − 2.41352 3.75E−02 KCNMA1-AS1 − 1.82562 3.79E−02 HS3ST3A1 − 1.28721 3.84E−02 USP2 − 1.07928 3.99E−02 BMP4 − 1.23615 3.99E−02 G6PC2 − 2.36196 3.99E−02 RP11-169K16.4 − 1.98903 3.99E−02 COL8A1 − 1.22669 4.09E−02 SIX2 − 1.02697 4.09E−02 KRT17P2 − 2.21877 4.21E−02 Page 18 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 116, 121, 154, 158]. Nevertheless, although biomedi- cal research has gained some insights into the function of intra-tumoral neurons using loss-of-function studies with surgical or pharmacologic denervation, these strat- egies are mostly not specific to a given neuronal type. Importantly, a disadvantage of all these studies is that neuronal killing may generate secondary undesirable indirect side effects caused by the inflammatory tissu- lar response which may influence the observed pheno- types (Männ et  al. 2016; Christiaansen, Boggiatto, and Varga 2014; Bennett et  al. 2005). To circumvent these limitations, novel powerful technologies have been cre- ated in the field of modern neuroscience which allow to manipulate the firing of specific neurons without killing them: optogenetics and chemogenetics. These methods use genetic strategies to deliver the expression of light- sensitive proteins or designer receptors exclusively acti- vated by designer drugs, respectively, to the membrane of defined neuronal populations. Therefore, by using these techniques, manipulation of neurons by exposure to light or to designer drugs, without killing them, became feasible. As melanoma is a chronic disease, the use of optogenetics for longer periods, may not be the best approach, as the unavoidable surgical preparation with the chronic implantation of hardware for stimulation and prolonged exposure to highly energetic laser light will eventually culminate in confounding regional inflamma- tion and tissue degradation. Therefore, we chose to use chemogenetics to examine the participation of sensory neurons in melanoma development, as it is more suit- able to evaluate the long-term effects of sensory stimula- tions with less side-effects. Future studies may use similar approaches to explore the role of sensory neurons and other innervations in other cancers. Our findings suggest that sensory neurons’ overactiva- tion affects the immune response to melanoma. Mela- noma progression is influenced by the complex interplay between cancer cells and different components of the immune system [8]. Melanoma cells may cause disrup- tion of the organism’s immunity to overrun and escape the immune system control [96, 104]. The role of sensory innervations in these interactions remains completely unknown. Lymphocytes are the dominant immune elements found infiltrating the melanoma microenvi- ronment. Their composition correlates with patients’ survival [38]. While regulatory T cells play pro-tumo- rigenic roles; CD8 + T cells, CD4 + T cells, γδ T cells, and NK cells have been shown to act against the trans- formed cells, [38, 43, 46, 47, 58, 64, 73, 118]. Conversely neutrophils and myeloid-derived suppressor cells have been associated with poor prognosis and are largely pro- tumorigenic [22, 28, 69, 126, 129, 145, 155]. Our data shows that sensory overactivation induce an increase in the number of tumor-infiltrating anti-cancer lympho- cytes (CD8 + T cells, CD4 + T cells, γδ T cells, and NK cells), while we did not detect changes in the number of tumor-infiltrating regulatory T cells. We also found that there is a decrease in the number of neutrophils and myeloid-derived suppressor cells within the tumor. We found that these changes were tumor-specific, as we did not detect any alterations in the number of lymphocytes in the non-draining lymph nodes. Tumor-draining lymph nodes presented an increase in some of the anti-tumor lymphocytes probably because of the tumor-priming effect previously reported [135]. Signals transmitted to T cells via PD-1 or CTLA-4 (considered markers for T cells “exhaustion”) promote T cell dysfunction, thereby turn- ing off the immune response [59, 98, 149]. We found that the tumor-infiltrating lymphocytes decrease their expres- sion of PD1 and CTLA-4, possibly indicating that these cells are “less exhausted” within the melanoma micro- environment after sensory hyperactivation. The more active phenotypes of lymphocytes have been associated to the increase in the production of cytokines. A variety of lymphocytes are capable of producing IL-17 [14, 20, 24, 53, 56, 77, 94, 103] which has presented anti-tumo- rigenic effects in melanoma [3, 63, 74, 75, 91, 99, 100]. We found an increase in tumor-infiltrating lymphocytes producing IL-17 after sensory stimulation. Thus, in light of our overall findings, we suggest that induced increase in firing of sensory innervations contributes to boosting Table 2 (continued) Down‑regulated genes in alive × dead Gene.symbol log2FC FDR BDKRB1 − 1.22248 4.35E−02 LINC00857 − 1.30729 4.35E−02 CREB3L1 − 1.04632 4.44E−02 CCBE1 − 1.04094 4.44E−02 PRR15L − 1.31182 4.48E−02 ST8SIA2 − 1.70296 4.50E−02 S100A9 − 2.02615 4.55E−02 SIK1 − 1.83293 4.60E−02 EPN3 − 1.93269 4.65E−02 ZBTB16 − 1.22227 4.67E−02 ATP8B5P − 1.00252 4.76E−02 DUOX1 − 1.72011 4.78E−02 TRIM53CP − 2.79481 4.80E−02 C6orf132 − 1.81165 4.87E−02 MDFI − 1.36942 4.91E−02 CATSPERB − 1.85527 4.94E−02 HSPE1P5 − 1.47888 4.94E−02 DEFB4A − 3.10613 4.95E−02 SDC1 − 1.36628 4.96E−02 Page 19 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 of the immune response against melanoma. Future stud- ies will need to explore the exact molecular mechanisms involved in the interactions of sensory neurons and immune cells in the melanoma microenvironment. A variety of cellular and molecular mechanisms may be involved in the effect of sensory neurons’ modula- tion on melanoma behavior. For instance, it has been documented that the same drug that is used to denervate sensory neurons, resiniferatoxin (RTX), an analogue of capsaicin, also induces stress by causing hyperactivation of the sympathetic nervous system  [16, 62, 157]. These studies also revealed that sensory nerves may tune down sympathetic nerve activity [16, 62, 105]. Sympathetic neurons release norepinephrine [70, 123], which has been shown to strongly induce tumorigenesis [1, 60, 154]. It remains open the important question whether the effect of sensory innervations in the tumor microenvironment depend also on the modulation of the sympathetic tone. Future perspectives The present study reveals the short-term impact of chemogenetic modulation of sensory neurons on mela- noma behavior. It remains to be examined what are the long-term effects of this manipulations. In the current study, the sensory neurons’ activity is being continuously inhibited or overactivated. Are changes in sensory neu- rons’ activity at specific time points sufficient to influence cancer outcomes? Also, it remains to be determined what are the changes within the tumor microenvironment at different stages of cancer progression. Are some stages more sensible to changes in the activity of sensory neu- rons? Moreover, this study focuses on melanoma tumors. Future studies should explore what is the role of sensory neurons in the development of other solid tumors. A variety of factors secreted from sensory neurons may be implicated in the regulation of the melanoma microenvironment described here [18]. The overactiva- tion of sensory nerve fibers may induce the release of neuropeptides, such as substance P, CGRP, VIP, GRP, neurokinin A, neurokinin B, neuropeptide Y (NPY) and adrenomedullin, which have been shown to interact Fig. 8 Overexpression of genes related to sensory nerves is associated with Skin Cutaneous Melanomas (SKCM) patients improved survival. A Biological Processes of genes overexpressed in Skin Cutaneous Melanomas (SKCM) samples from alive patients versus dead patients. Patients were stratified (alive or dead) based on their vital status in a 5-year interval of their tumor diagnosis (clinical data available at TCGA and curated by Liu et al. (2018) [85]. We stratified patients in two groups, alive or dead, based on a 5-year interval of their tumor diagnosis. B Interactions among genes related to sensory neurons. C Gene signature using sensory neurons-related genes. High expression of these genes (lower patient scores) is associated with a better overall survival of SKCM patients (patients were stratified based on their median Reboot score). Overall survival of patients showing expression of sensory neurons-related genes. More negative scores are associated with higher gene expression. D Expression of SNC10A (Nav1.8) in SKCM samples. Only patients presenting tumors expressing SNC10A were used. E We evaluated the survival probability of patients with melanoma based on their tumor transcriptome. Patients were stratified based on lower/upper quartiles of SCN10A expression values. Overall survival of SKCM patients based on the level of expression of SCN10A. High expression of SCN10A (Nav1.8) correlates with best outcomes in patients with melanoma Page 20 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 3 Gene Ontology (GO) and analysis of Biological Processes (BP) in which specific genes are involved Up‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Nervous system development 22 2474 0.004805005 ARX HAPLN2 SLITRK5 VCX3A VCX ZNF365 NRTN TFAP2B NGFR MYB GFAP LRRTM2 LHFPL4 DPYSL5 FABP7 FOXG1 VIT MPZ CA10 KCNQ2 ACHE HPCAL4 Central nervous system development 12 1054 0.029644095 HAPLN2 VCX3A VCX ARX GFAP ZNF365 FABP7 SLITRK5 FOXG1 VIT CA10 HPCAL4 Regulation of hormone levels 9 583 0.029644095 ACHE PCSK1N MYB VGF RDH5 BCO1 TFAP2B KCNJ11 MYRIP Down‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Cornification 17 125 8.55E−15 TMEM79 TGM1 KRT37 PI3 KRT17 KLK14 TCHH KRT82 SPRR2D KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 SPRR2A Epithelium development 42 1386 9.40E−15 KLK14 SPRR2D OVOL1 NRARP SPRR2A HGF TGM1 BMP4 FGF7 AJAP1 WNT11 CNFN DLX5 ALOX12 SDC1 ID3 TBX4 KRT17 GREB1L ADAMTSL4 RSPO3 TCHH ELF3 TMEM79 HEYL GREM1 ROR2 SIX2 SOX11 KRT25 SULT2B1 TBX1 RHCG KRT37 PI3 KRT82 KLK13 KRT80 JUP KLK12 SPINK9 KRT81 Epithelial cell differentiation 33 831 1.67E−14 SPRR2D OVOL1 SPRR2A TGM1 BMP4 AJAP1 CNFN DLX5 SDC1 ID3 KRT17 ADAMTSL4 TCHH ELF3 TMEM79 GREM1 SIX2 SOX11 WNT11 SULT2B1 TBX1 RHCG KRT37 PI3 KLK14 KRT82 KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 Tissue development 51 2168 2.40E−14 KLK14 SPRR2D OVOL1 NRARP SPRR2A HGF TGM1 ZBTB16 BMP4 FGF7 AJAP1 PITX1 WNT11 SMPD3 CNFN DLX5 ALOX12 SDC1 ID3 TBX4 KRT17 GREB1L ADAMTSL4 RSPO3 FGF18 TCHH PKDCC ELF3 TMEM79 HEYL GREM1 ROR2 ADRB2 SIX2 SOX11 KRT25 COL8A1 SULT2B1 SIK1 ACTG2 TBX1 RHCG KRT37 PI3 KRT82 KLK13 KRT80 JUP KLK12 SPINK9 KRT81 Skin development 23 464 1.11E−11 SPRR2D SPRR2A TGM1 CNFN ALOX12 KRT17 FGF7 TCHH TMEM79 OVOL1 JUP ALOX12B KRT25 CLDN4 KRT37 PI3 KLK14 KRT82 KLK13 KRT80 KLK12 SPINK9 KRT81 Keratinization 18 268 4.20E−11 TGM1 CNFN KRT17 TCHH SPRR2D TMEM79 SPRR2A KRT37 PI3 KLK14 KRT82 KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 Animal organ development 62 3779 4.72E−11 SPRR2D HEYL SIX2 SPRR2A HGF TGM1 ZBTB16 BMP4 FGF7 GREB1L AJAP1 MAFB MYOC USP2 PITX1 NOTCH3 WNT11 ANGPT2 SLC8A3 SMPD3 CNFN DLX5 ALOX12 MDFI SDC1 ID3 TBX4 KRT17 CYP19A1 COL8A1 RSPO3 FGF18 TCHH PKDCC ELF3 TMEM79 ZC3H12A AVPR1A GREM1 ROR2 ADRB2 OVOL1 JUP SOX11 ALOX12B CCBE1 NRARP PAPSS2 KRT25 TBX1 CLDN4 SIK1 ACTG2 KRT37 PI3 KLK14 KRT82 KLK13 KRT80 KLK12 SPINK9 KRT81 Epidermal cell differentiation 21 410 5.29E−11 SPRR2D OVOL1 SPRR2A TGM1 BMP4 CNFN KRT17 TCHH TMEM79 SULT2B1 KRT37 PI3 KLK14 KRT82 KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 Epidermis development 23 521 6.96E−11 KLK14 SPRR2D OVOL1 SPRR2A TGM1 BMP4 CNFN KRT17 FGF7 TCHH TMEM79 KRT25 SULT2B1 ELF3 KRT37 PI3 KRT82 KLK13 KRT80 JUP KLK12 SPINK9 KRT81 Keratinocyte differentiation 18 346 1.87E−09 SPRR2D SPRR2A TGM1 CNFN KRT17 TCHH TMEM79 KRT37 PI3 KLK14 KRT82 KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 Ossification 18 396 1.56E−08 MYOC ZBTB16 BMP4 TWIST2 WNT11 SMPD3 DLX5 ID3 GDPD2 FGF18 PKDCC ROR2 ADRB2 SIX2 SOX11 GREM1 CREB3L1 HGF Cellular developmental process 64 4671 3.21E−08 WNT11 DLX5 ID3 BMP4 NGF SPRR2D ELF3 HEYL OVOL1 RHOD SOX11 SPRR2A MYOC TGM1 ZC3H12A GREM1 AJAP1 MAFB TWIST2 HGF PITX1 NOTCH3 ANGPT2 SLC8A3 SMPD3 CNFN ZBTB16 MDFI SDC1 KRT17 CHN1 GDPD2 CATSPERB CD36 SIK1 ADAMTSL4 FGF18 TCHH FBLIM1 PKDCC TMEM79 AVPR1A ROR2 ADRB2 SIX2 GTSF1 NRARP COL8A1 SULT2B1 CREB3L1 S100A9 TBX1 RHCG KRT37 PI3 KLK14 KRT82 KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 Cell differentiation 62 4459 3.79E−08 WNT11 DLX5 ID3 BMP4 NGF SPRR2D ELF3 HEYL OVOL1 SOX11 SPRR2A MYOC TGM1 ZC3H12A AJAP1 MAFB TWIST2 HGF PITX1 NOTCH3 ANGPT2 SLC8A3 SMPD3 CNFN ZBTB16 MDFI SDC1 KRT17 CHN1 GDPD2 CATSPERB CD36 SIK1 ADAMTSL4 FGF18 TCHH PKDCC TMEM79 AVPR1A GREM1 ROR2 ADRB2 SIX2 GTSF1 NRARP COL8A1 SULT2B1 CREB3L1 S100A9 TBX1 RHCG KRT37 PI3 KLK14 KRT82 KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 Page 21 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 3 (continued) Down‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Anatomical structure morphogenesis 46 2785 1.00E−07 PITX1 DLX5 KLK14 NGF HEYL RHOD NRARP HGF MYOC BMP4 IL1RN FGF7 ZC3H12A GREM1 AJAP1 NOTCH3 WNT11 ANGPT2 SMPD3 ALOX12 ZBTB16 MDFI SDC1 ID3 TBX4 KRT17 CHN1 CD36 GREB1L COL8A1 RSPO3 FGF18 FBLIM1 PKDCC ELF3 TMEM79 ROR2 SIX2 SOX11 CCBE1 MAFB KRT25 CREB3L1 JUP TBX1 ACTG2 Tissue morphogenesis 21 719 8.87E−07 KLK14 NRARP HGF BMP4 AJAP1 WNT11 ALOX12 TBX4 KRT17 FGF7 GREB1L RSPO3 TMEM79 HEYL GREM1 ROR2 SIX2 SOX11 KRT25 ACTG2 TBX1 Regulation of cartilage development 8 68 1.39E−06 ZBTB16 BMP4 SMPD3 FGF18 PKDCC SIX2 GREM1 WNT11 Tube development 25 1062 1.93E−06 NRARP BMP4 FGF7 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 SMPD3 ALOX12 SDC1 TBX4 GREB1L COL8A1 RSPO3 FGF18 PKDCC HEYL GREM1 SIX2 SOX11 CCBE1 CREB3L1 JUP TBX1 Morphogenesis of an epithelium 18 581 3.91E−06 KLK14 NRARP HGF BMP4 AJAP1 WNT11 ALOX12 TBX4 KRT17 FGF7 GREB1L RSPO3 TMEM79 GREM1 ROR2 SIX2 SOX11 KRT25 Urogenital system development 14 346 4.57E−06 SIX2 BMP4 GREB1L NOTCH3 WNT11 ANGPT2 ZBTB16 SDC1 ID3 CYP19A1 HEYL GREM1 OVOL1 SOX11 Kidney development 13 291 4.57E−06 SIX2 BMP4 GREB1L NOTCH3 WNT11 ANGPT2 ZBTB16 SDC1 ID3 HEYL GREM1 OVOL1 SOX11 Regulation of anatomical structure morpho- genesis 25 1125 4.57E−06 NGF RHOD HGF MYOC BMP4 IL1RN FGF7 ZC3H12A AJAP1 WNT11 ANGPT2 ALOX12 CHN1 CD36 FBLIM1 GREM1 ROR2 SIX2 CCBE1 NRARP FGF18 CREB3L1 JUP TBX1 RSPO3 Regulation of ossification 11 193 4.57E−06 ZBTB16 BMP4 TWIST2 ID3 GDPD2 PKDCC ADRB2 SIX2 SOX11 GREM1 HGF Skeletal system development 17 541 6.10E−06 ZBTB16 BMP4 MYOC PITX1 SMPD3 DLX5 MDFI FGF18 PKDCC ROR2 SIX2 SOX11 PAPSS2 WNT11 TBX4 GREM1 TBX1 Renal system development 13 307 7.35E−06 SIX2 BMP4 GREB1L NOTCH3 WNT11 ANGPT2 ZBTB16 SDC1 ID3 HEYL GREM1 OVOL1 SOX11 Animal organ morphogenesis 24 1099 1.06E−05 HEYL HGF BMP4 FGF7 AJAP1 WNT11 SMPD3 DLX5 MDFI SDC1 ID3 GREB1L COL8A1 FGF18 ELF3 GREM1 ROR2 SIX2 SOX11 MAFB TBX4 TBX1 ACTG2 RSPO3 Osteoblast differentiation 11 218 1.18E−05 MYOC BMP4 TWIST2 WNT11 DLX5 ID3 GDPD2 GREM1 SOX11 CREB3L1 HGF Regulation of developmental process 41 2763 1.21E−05 ID3 NGF HEYL RHOD HGF MYOC ZBTB16 BMP4 IL1RN FGF7 ZC3H12A AJAP1 MAFB TWIST2 NOTCH3 WNT11 ANGPT2 SMPD3 ALOX12 KRT17 CHN1 GDPD2 CD36 FGF18 FBLIM1 PKDCC TMEM79 GREM1 ROR2 ADRB2 SIX2 SOX11 CCBE1 NRARP CREB3L1 JUP SULT2B1 SIK1 S100A9 TBX1 RSPO3 Anatomical structure formation involved in morphogenesis 24 1164 2.67E−05 DLX5 NRARP BMP4 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 SMPD3 TBX4 CD36 COL8A1 RSPO3 FGF18 HEYL GREM1 ROR2 SIX2 SOX11 CCBE1 MAFB CREB3L1 JUP TBX1 Skeletal system morphogenesis 11 245 3.39E−05 SMPD3 DLX5 MDFI BMP4 FGF18 ROR2 SIX2 SOX11 TBX4 GREM1 TBX1 Regulation of chondrocyte differentiation 6 49 3.91E−05 BMP4 FGF18 PKDCC SIX2 ZBTB16 GREM1 Appendage morphogenesis 9 155 3.91E−05 PITX1 DLX5 ZBTB16 TBX4 BMP4 PKDCC GREM1 ROR2 SOX11 Limb morphogenesis 9 155 3.91E−05 PITX1 DLX5 ZBTB16 TBX4 BMP4 PKDCC GREM1 ROR2 SOX11 Tube morphogenesis 20 860 3.91E−05 NRARP BMP4 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 ALOX12 TBX4 GREB1L COL8A1 RSPO3 FGF18 GREM1 SIX2 SOX11 CCBE1 CREB3L1 JUP TBX1 Regulation of morphogenesis of an epithelium 10 204 4.32E−05 HGF BMP4 AJAP1 WNT11 ALOX12 FGF7 GREM1 ROR2 SIX2 RSPO3 Epidermis morphogenesis 5 29 5.24E−05 KLK14 KRT17 FGF7 TMEM79 KRT25 Angiogenesis 15 511 5.42E−05 NRARP BMP4 ZC3H12A HGF NOTCH3 ANGPT2 TBX4 COL8A1 RSPO3 FGF18 GREM1 CCBE1 CREB3L1 JUP TBX1 Cell migration 27 1506 5.42E−05 HGF SDC1 IL36G IL1RN DEFB4A RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 S100A9 ZC3H12A ROR2 SIX2 WNT11 BDKRB1 SMPD3 ALOX12 CYP19A1 GREM1 CCBE1 LTB4R2 JUP TWIST2 TBX1 S100A12 Cell death 36 2415 5.45E−05 HGF BMP4 NGF ADAMTSL4 ZNF385B S100A9 ZC3H12A WNT11 ALOX12 ZBTB16 ID3 CREB3L1 TMEM79 GREM1 SOX11 TWIST2 CD36 SIK1 TBX1 PNMA5 TGM1 KRT37 PI3 KRT17 KLK14 TCHH KRT82 SPRR2D KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 SPRR2A Respiratory system development 10 215 6.07E−05 BMP4 FGF7 SMPD3 DLX5 TBX4 FGF18 PKDCC SOX11 CCBE1 WNT11 Cartilage development 10 216 6.17E−05 ZBTB16 BMP4 PITX1 SMPD3 FGF18 PKDCC ROR2 SIX2 GREM1 WNT11 Page 22 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 3 (continued) Down‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Positive regulation of ossification 7 88 6.78E−05 ZBTB16 BMP4 GDPD2 PKDCC ADRB2 SOX11 HGF Regulation of multicellular organismal develop- ment 33 2138 7.25E−05 ID3 NGF HEYL HGF ZBTB16 BMP4 IL1RN FGF7 ZC3H12A AJAP1 MAFB NOTCH3 WNT11 ANGPT2 SMPD3 ALOX12 KRT17 CHN1 FGF18 PKDCC GREM1 ROR2 ADRB2 SIX2 SOX11 CCBE1 NRARP CREB3L1 JUP SULT2B1 S100A9 TBX1 RSPO3 Blood vessel morphogenesis 16 603 7.46E−5 NRARP BMP4 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 TBX4 COL8A1 RSPO3 FGF18 GREM1 CCBE1 CREB3L1 JUP TBX1 Programmed cell death 34 2257 8.00E−05 HGF BMP4 NGF ADAMTSL4 ZNF385B S100A9 WNT11 ALOX12 ZBTB16 ID3 CREB3L1 TMEM79 ZC3H12A GREM1 TWIST2 SIK1 TBX1 PNMA5 TGM1 KRT37 PI3 KRT17 KLK14 TCHH KRT82 SPRR2D KLK13 KRT80 JUP KLK12 KRT25 SPINK9 KRT81 SPRR2A Blood vessel development 17 687 8.58E−05 HEYL NRARP BMP4 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 TBX4 COL8A1 RSPO3 FGF18 GREM1 CCBE1 CREB3L1 JUP TBX1 Embryonic morphogenesis 16 615 8.98E−05 BMP4 IL1RN PITX1 WNT11 DLX5 ZBTB16 MDFI TBX4 RSPO3 GREM1 ROR2 SIX2 SOX11 MAFB COL8A1 TBX1 Cell motility 28 1670 9.30E−05 HGF SDC1 IL36G IL1RN DEFB4A RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 S100A9 ZC3H12A ROR2 SIX2 WNT11 BDKRB1 SMPD3 ALOX12 CYP19A1 GREM1 CCBE1 LTB4R2 DUOX1 JUP TWIST2 TBX1 S100A12 Localization of cell 28 1670 9.30E−05 HGF SDC1 IL36G IL1RN DEFB4A RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 S100A9 ZC3H12A ROR2 SIX2 WNT11 BDKRB1 SMPD3 ALOX12 CYP19A1 GREM1 CCBE1 LTB4R2 DUOX1 JUP TWIST2 TBX1 S100A12 Cellular response to growth factor stimulus 17 694 9.30E−05 HGF BMP4 NGF FGF18 CREB3L1 GREM1 CCBE1 FAM83G ANGPT2 SMPD3 DLX5 RASL11B FGF7 HEYL ROR2 SOX11 TBX1 Cell–cell signaling 29 1774 9.65E−05 WNT11 CHRNA4 NGF RSPO3 NRARP JUP HGF CYP19A1 GREM1 FAM3D SLC8A3 SMPD3 DLX5 SDC1 BMP4 IL1RN FGF7 ROR2 ADRB2 SOX11 LYNX1 MYOC G6PC2 MDFI GLP1R IL36G FGF18 S100A9 ADRA1D Tissue migration 11 293 1.10E−04 ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ALOX12 GREM1 CCBE1 LTB4R2 JUP ACTG2 Appendage development 9 186 1.10E−04 PITX1 DLX5 ZBTB16 TBX4 BMP4 PKDCC GREM1 ROR2 SOX11 Limb development 9 186 1.10E−04 PITX1 DLX5 ZBTB16 TBX4 BMP4 PKDCC GREM1 ROR2 SOX11 Lung development 9 188 1.18E−04 BMP4 FGF7 SMPD3 TBX4 FGF18 PKDCC SOX11 CCBE1 WNT11 Vasculature development 17 715 1.20E−04 HEYL NRARP BMP4 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 TBX4 COL8A1 RSPO3 FGF18 GREM1 CCBE1 CREB3L1 JUP TBX1 Respiratory tube development 9 192 1.34E−04 BMP4 FGF7 SMPD3 TBX4 FGF18 PKDCC SOX11 CCBE1 WNT11 Response to growth factor 17 723 1.34E−04 HGF BMP4 NGF FGF18 CREB3L1 GREM1 CCBE1 FAM83G ANGPT2 SMPD3 DLX5 RASL11B FGF7 HEYL ROR2 SOX11 TBX1 Cardiovascular system development 17 724 1.34E−04 HEYL NRARP BMP4 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 TBX4 COL8A1 RSPO3 FGF18 GREM1 CCBE1 CREB3L1 JUP TBX1 Locomotion 30 1921 1.34E−04 HGF SDC1 IL36G IL1RN DEFB4A RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 S100A9 ZC3H12A ROR2 SIX2 WNT11 BDKRB1 SMPD3 DLX5 ALOX12 CHN1 CYP19A1 GREM1 CCBE1 LTB4R2 DUOX1 JUP TWIST2 TBX1 S100A12 Mesonephros development 7 104 1.40E−04 BMP4 ZBTB16 SDC1 GREB1L GREM1 SIX2 WNT11 Positive regulation of developmental process 25 1433 1.40E−04 NGF MYOC ZBTB16 BMP4 FGF7 ZC3H12A HGF ANGPT2 ALOX12 KRT17 GDPD2 CD36 FGF18 PKDCC TMEM79 HEYL GREM1 ROR2 ADRB2 SOX11 CCBE1 JUP SULT2B1 S100A9 TBX1 Nephron development 8 147 1.41E−04 BMP4 NOTCH3 ANGPT2 GREB1L HEYL GREM1 SIX2 WNT11 Positive regulation of cell communication 30 1937 1.48E−04 BMP4 IL36G IL1RN RSPO3 NRARP JUP HGF MYOC KLK14 CYP19A1 FGF18 S100A9 S100A12 ZC3H12A ADRB2 ALOX12B CCBE1 SLC8A3 DLX5 CHN1 CD36 GREM1 ROR2 SOX11 WNT11 SH3RF3 NGF TBX1 FGF7 ELF3 Positive regulation of signaling 30 1945 1.56E−04 BMP4 IL36G IL1RN RSPO3 NRARP JUP HGF MYOC KLK14 CYP19A1 FGF18 S100A9 S100A12 ZC3H12A ADRB2 ALOX12B CCBE1 SLC8A3 DLX5 CHN1 CD36 GREM1 ROR2 SOX11 WNT11 SH3RF3 NGF TBX1 FGF7 ELF3 Circulatory system development 21 1077 1.56E−04 HEYL NRARP BMP4 ZC3H12A HGF NOTCH3 WNT11 ANGPT2 ID3 TBX4 GREB1L COL8A1 RSPO3 FGF18 GREM1 SOX11 CCBE1 CREB3L1 JUP TBX1 SIK1 Positive regulation of signal transduction 28 1762 1.91E−04 BMP4 IL36G IL1RN RSPO3 NRARP JUP HGF MYOC KLK14 FGF18 S100A9 S100A12 ZC3H12A ADRB2 ALOX12B CCBE1 DLX5 CHN1 CD36 GREM1 ROR2 SOX11 WNT11 SH3RF3 NGF TBX1 FGF7 ELF3 Page 23 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 3 (continued) Down‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Ameboidal-type cell migration 12 392 2.39E−04 ANGPT2 BMP4 FGF7 FGF18 ZC3H12A WNT11 ALOX12 GREM1 CCBE1 LTB4R2 JUP TBX1 Regulation of osteoblast differentiation 7 115 2.44E−04 BMP4 TWIST2 ID3 GDPD2 GREM1 SOX11 HGF Bone mineralization 7 116 2.55E−04 BMP4 WNT11 SMPD3 PKDCC ROR2 ADRB2 GREM1 Establishment of skin barrier 4 22 2.65E−04 ALOX12 TMEM79 ALOX12B CLDN4 Regulation of cell motility 19 946 2.69E−04 HGF RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 SMPD3 ALOX12 CYP19A1 GREM1 CCBE1 DUOX1 JUP TWIST2 WNT11 Mesenchymal cell proliferation 5 46 2.69E−04 BMP4 FGF7 WNT11 SIX2 TBX1 Positive regulation of cell motility 14 544 2.73E−04 HGF MYOC BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 ALOX12 CCBE1 DUOX1 TWIST2 WNT11 RHOD Response to BMP 8 165 2.73E−04 BMP4 GREM1 FAM83G SMPD3 DLX5 HEYL ROR2 SOX11 Cellular response to BMP stimulus 8 165 2.73E−04 BMP4 GREM1 FAM83G SMPD3 DLX5 HEYL ROR2 SOX11 Biomineral tissue development 8 167 2.93E−04 BMP4 WNT11 SMPD3 PKDCC ROR2 ADRB2 GREM1 TBX1 Connective tissue development 10 280 3.18E−04 ZBTB16 BMP4 PITX1 SMPD3 FGF18 PKDCC ROR2 SIX2 GREM1 WNT11 Regulation of animal organ morphogenesis 10 281 3.22E−04 HGF BMP4 FGF7 AJAP1 WNT11 GREM1 ROR2 SIX2 TBX1 RSPO3 Embryo development 20 1054 3.22E−04 BMP4 IL1RN PITX1 WNT11 DLX5 ZBTB16 MDFI ID3 TBX4 RSPO3 PKDCC ELF3 GREM1 ROR2 SIX2 SOX11 NRARP MAFB COL8A1 TBX1 Chondrocyte differentiation 7 123 3.22E−04 SMPD3 BMP4 FGF18 PKDCC SIX2 ZBTB16 GREM1 Epithelial cell migration 10 284 3.33E−04 ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ALOX12 GREM1 CCBE1 LTB4R2 JUP Regulation of cell migration 18 883 3.33E−04 HGF RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 SMPD3 ALOX12 CYP19A1 GREM1 CCBE1 JUP TWIST2 WNT11 Positive regulation of cellular component movement 14 558 3.33E−04 HGF MYOC BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 ALOX12 CCBE1 DUOX1 TWIST2 WNT11 RHOD Epithelium migration 10 287 3.60E−04 ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ALOX12 GREM1 CCBE1 LTB4R2 JUP Hair follicle morphogenesis 4 25 3.68E−04 KRT17 FGF7 TMEM79 KRT25 Regulation of water loss via skin 4 25 3.68E−04 ALOX12 TMEM79 ALOX12B CLDN4 Embryonic limb morphogenesis 7 130 4.20E−04 PITX1 DLX5 ZBTB16 TBX4 BMP4 GREM1 ROR2 Embryonic appendage morphogenesis 7 130 4.20E−04 PITX1 DLX5 ZBTB16 TBX4 BMP4 GREM1 ROR2 Positive regulation of locomotion 14 576 4.34E−04 HGF MYOC BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 ALOX12 CCBE1 DUOX1 TWIST2 WNT11 RHOD Endochondral ossification 4 27 4.78E−04 SMPD3 DLX5 BMP4 FGF18 Replacement ossification 4 27 4.78E−04 SMPD3 DLX5 BMP4 FGF18 Positive regulation of MAPK cascade 14 583 4.79E−04 IL36G IL1RN BMP4 FGF18 ZC3H12A ADRB2 ALOX12B HGF CD36 ROR2 SH3RF3 TBX1 NGF S100A12 Poly-N-acetyllactosamine biosynthetic process 3 10 5.48E−04 B3GNT4 B3GNT8 B3GNT3 Regulation of multicellular organismal process 41 3382 5.95E−04 ID3 NGF IL36G IL1RN HEYL AVPR1A ADRB2 HGF ANGPT2 ZBTB16 BMP4 FGF7 FGF18 ZC3H12A CCBE1 AJAP1 MAFB TWIST2 NOTCH3 WNT11 SLC8A3 SMPD3 ALOX12 GLP1R KRT17 CHN1 GDPD2 CD36 PKDCC GREM1 ROR2 SIX2 SOX11 NRARP CREB3L1 JUP SULT2B1 ALOX12B S100A9 TBX1 RSPO3 Response to endogenous stimulus 26 1704 5.97E−04 BMP4 NGF AVPR1A IL1RN FGF18 CREB3L1 HEYL GREM1 JUP FAM83G SLC8A3 CHRNA4 SMPD3 DLX5 GLP1R SDC1 RASL11B CD36 FGF7 ROR2 ADRB2 SOX11 CLDN4 TBX1 GNA15 CATSPERB Positive regulation of cell migration 13 521 6.09E−04 HGF MYOC BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 ALOX12 CCBE1 TWIST2 WNT11 RHOD Chemotaxis 15 680 6.18E−04 IL36G IL1RN DEFB4A HGF ANGPT2 FGF7 FGF18 S100A9 DLX5 BMP4 CHN1 CYP19A1 GREM1 S100A12 LTB4R2 Regulation of cellular component movement 19 1028 6.21E−04 HGF RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 SMPD3 ALOX12 CYP19A1 GREM1 CCBE1 DUOX1 JUP TWIST2 WNT11 Movement of cell or subcellular component 30 2139 6.25E−04 HGF SDC1 IL36G IL1RN DEFB4A RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 S100A9 ZC3H12A ROR2 SIX2 WNT11 BDKRB1 SMPD3 DLX5 ALOX12 CHN1 CYP19A1 GREM1 CCBE1 LTB4R2 DUOX1 JUP TWIST2 TBX1 S100A12 Taxis 15 683 6.25E−04 IL36G IL1RN DEFB4A HGF ANGPT2 FGF7 FGF18 S100A9 DLX5 BMP4 CHN1 CYP19A1 GREM1 S100A12 LTB4R2 Page 24 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 3 (continued) Down‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Nephron tubule development 6 96 6.25E−04 BMP4 GREB1L HEYL GREM1 SIX2 WNT11 Embryonic organ development 12 452 6.34E−04 WNT11 DLX5 MDFI ID3 BMP4 RSPO3 PKDCC ROR2 SIX2 SOX11 MAFB TBX1 Morphogenesis of a branching epithelium 8 194 6.34E−04 NRARP HGF BMP4 FGF7 GREB1L RSPO3 GREM1 SIX2 Kidney epithelium development 7 143 6.55E−04 BMP4 SDC1 GREB1L HEYL GREM1 SIX2 WNT11 Poly-N-acetyllactosamine metabolic process 3 11 6.64E−04 B3GNT4 B3GNT8 B3GNT3 Positive regulation of intracellular signal transduction 20 1133 6.72E−04 IL36G IL1RN HGF MYOC BMP4 FGF18 S100A9 S100A12 ZC3H12A ADRB2 ALOX12B CD36 GREM1 ROR2 SOX11 WNT11 SH3RF3 NGF TBX1 FGF7 Regulation of locomotion 19 1041 6.74E−04 HGF RHOD MYOC ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ROR2 BDKRB1 SMPD3 ALOX12 CYP19A1 GREM1 CCBE1 DUOX1 JUP TWIST2 WNT11 Ureteric bud development 6 99 6.85E−04 BMP4 SDC1 GREB1L GREM1 SIX2 WNT11 Renal tubule development 6 99 6.85E−04 BMP4 GREB1L HEYL GREM1 SIX2 WNT11 Positive regulation of cartilage development 4 31 6.94E−04 ZBTB16 BMP4 FGF18 PKDCC Mesonephric epithelium development 6 100 7.06E−04 BMP4 SDC1 GREB1L GREM1 SIX2 WNT11 Mesonephric tubule development 6 100 7.06E−04 BMP4 SDC1 GREB1L GREM1 SIX2 WNT11 Positive regulation of gene expression 29 2060 7.22E−04 DLX5 ZBTB16 HEYL JUP SOX11 BMP4 ELF3 ZC3H12A CCBE1 PITX1 NOTCH3 ALOX12 KRT17 CD36 FGF7 CREB3L1 GREM1 ROR2 ADRB2 SIX2 OVOL1 MAFB WNT11 NGF ALOX12B GLIS3 ACTG2 TBX1 HGF Regulation of morphogenesis of a branching structure 5 63 7.98E−04 HGF BMP4 FGF7 GREM1 SIX2 BMP signaling pathway 7 152 8.66E−04 BMP4 GREM1 FAM83G SMPD3 DLX5 ROR2 SOX11 Endothelial cell migration 8 206 8.66E−04 ANGPT2 BMP4 FGF18 ZC3H12A ALOX12 GREM1 CCBE1 JUP Morphogenesis of a branching structure 8 208 9.13E−04 NRARP HGF BMP4 FGF7 GREB1L RSPO3 GREM1 SIX2 Mesenchymal to epithelial transition involved in metanephros morphogenesis 3 13 1.01E−03 BMP4 GREM1 SIX2 Mesenchymal cell differentiation 8 212 1.01E−03 WNT11 BMP4 HEYL SIX2 SOX11 GREM1 TBX1 HGF Mesenchyme development 9 273 1.01E−03 WNT11 BMP4 HEYL SIX2 SOX11 GREM1 ACTG2 TBX1 HGF Ureteric bud morphogenesis 5 67 1.01E−03 BMP4 GREB1L GREM1 SIX2 WNT11 Mesonephric tubule morphogenesis 5 68 1.08E−03 BMP4 GREB1L GREM1 SIX2 WNT11 Regulation of cell-substrate adhesion 8 216 1.13E−03 MYOC CD36 AJAP1 ANGPT2 COL8A1 GREM1 RHOD JUP Regulation of endothelial cell migration 7 161 1.16E−03 ANGPT2 BMP4 FGF18 ZC3H12A ALOX12 CCBE1 JUP Positive regulation of multicellular organismal process 27 1911 1.17E−03 NGF IL36G IL1RN AVPR1A ZBTB16 BMP4 FGF7 FGF18 ZC3H12A CCBE1 HGF WNT11 ANGPT2 ALOX12 KRT17 GDPD2 CD36 PKDCC HEYL GREM1 ROR2 ADRB2 SOX11 JUP ALOX12B S100A9 TBX1 Nephron epithelium development 6 112 1.18E−03 BMP4 GREB1L HEYL GREM1 SIX2 WNT11 Regulation of epithelial cell migration 8 223 1.36E−03 ANGPT2 BMP4 FGF7 FGF18 ZC3H12A ALOX12 CCBE1 JUP Positive regulation of epidermis development 4 38 1.36E−03 BMP4 KRT17 TMEM79 SULT2B1 Aminoglycan biosynthetic process 6 116 1.39E−03 B3GNT4 B3GNT8 B3GNT3 SMPD3 SDC1 HS3ST3A1 Positive regulation of angiogenesis 7 167 1.39E−03 ZC3H12A HGF ANGPT2 GREM1 CCBE1 FGF18 JUP Sprouting angiogenesis 6 117 1.45E−03 NRARP BMP4 RSPO3 GREM1 CREB3L1 CCBE1 Cell-substrate adhesion 10 358 1.46E−03 LYPD3 LYPD5 MYOC CD36 AJAP1 ANGPT2 COL8A1 GREM1 RHOD JUP Sulfate assimilation 2 3 1.52E−03 PAPSS2 SULT2B1 Cuticle development 2 3 1.52E−03 TMEM79 DUOX1 Cloacal septation 2 3 1.52E−03 BMP4 WNT11 Aminoglycan metabolic process 7 171 1.53E−03 B3GNT4 B3GNT8 B3GNT3 HGF SMPD3 SDC1 HS3ST3A1 Cardiac septum morphogenesis 5 75 1.53E−03 HEYL WNT11 BMP4 SOX11 TBX1 Cellular response to endogenous stimulus 22 1432 1.61E−03 BMP4 NGF AVPR1A FGF18 CREB3L1 HEYL GREM1 JUP FAM83G SLC8A3 CHRNA4 SMPD3 DLX5 GLP1R RASL11B FGF7 ROR2 ADRB2 SOX11 CD36 TBX1 GNA15 Nephron tubule morphogenesis 5 76 1.61E−03 BMP4 GREB1L GREM1 SIX2 WNT11 Metanephric renal vesicle morphogenesis 3 16 1.65E−03 BMP4 GREM1 SIX2 Regulation of cell–matrix adhesion 6 122 1.68E−03 MYOC CD36 AJAP1 GREM1 RHOD JUP Outflow tract morphogenesis 5 77 1.68E−03 WNT11 BMP4 HEYL SOX11 TBX1 Nephron epithelium morphogenesis 5 78 1.77E−03 BMP4 GREB1L GREM1 SIX2 WNT11 Page 25 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 3 (continued) Down‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Ventricular septum morphogenesis 4 42 1.79E−03 HEYL WNT11 BMP4 SOX11 Cell–matrix adhesion 8 239 1.91E−03 LYPD3 LYPD5 MYOC CD36 AJAP1 GREM1 RHOD JUP Embryonic organ morphogenesis 9 305 1.91E−03 WNT11 DLX5 MDFI BMP4 ROR2 SIX2 SOX11 MAFB TBX1 Renal tubule morphogenesis 5 80 1.91E−03 BMP4 GREB1L GREM1 SIX2 WNT11 Nephron morphogenesis 5 80 1.91E−03 BMP4 GREB1L GREM1 SIX2 WNT11 Nephric duct development 3 17 1.91E−03 BMP4 GREB1L WNT11 Regulation of cell communication 43 3903 2.15E−03 BMP4 NGF IL36G IL1RN SIK1 RSPO3 NRARP JUP HGF MYOC KLK14 CYP19A1 FGF18 CREB3L1 S100A9 S100A12 ZC3H12A HEYL GREM1 ADRB2 ALOX12B LYNX1 CCBE1 FAM3D WNT11 SLC8A3 CHRNA4 DLX5 RASL11B CHN1 CD36 FGF7 AVPR1A ROR2 SOX11 G6PC2 SH3RF3 MDFI TBX1 NOTCH3 GLP1R ELF3 RHOD Enzyme linked receptor protein signaling pathway 18 1072 2.17E−03 HGF BMP4 NGF ROR2 ANGPT2 CREB3L1 GREM1 CCBE1 FAM83G MYOC SMPD3 DLX5 RASL11B FGF7 FGF18 SOX11 CHN1 ADRB2 Regulation of MAPK cascade 15 793 2.17E−03 BMP4 IL36G IL1RN MYOC FGF18 ZC3H12A ADRB2 ALOX12B HGF CD36 ROR2 SH3RF3 TBX1 NGF S100A12 Embryonic skeletal system development 6 130 2.21E−03 MDFI BMP4 SIX2 SOX11 WNT11 TBX1 Positive regulation of vasculature development 7 186 2.29E−03 ZC3H12A HGF ANGPT2 GREM1 CCBE1 FGF18 JUP Response to organic substance 40 3547 2.30E−03 HGF BMP4 NGF IL36G IL1RN AVPR1A CD36 DUOX1 FGF18 CREB3L1 ZC3H12A HEYL GREM1 JUP CCBE1 FAM83G ANGPT2 SLC8A3 BDKRB1 CHRNA4 SMPD3 DLX5 ALOX12 GLP1R SDC1 ID3 SLPI RASL11B FGF7 ROR2 ADRB2 ABCG4 SOX11 CLDN4 WNT11 TBX1 GNA15 CATSPERB HSPB3 GPX3 Positive chemotaxis 5 84 2.30E−03 ANGPT2 FGF7 HGF BMP4 DEFB4A Response to psychosocial stress 2 4 2.56E−03 GLP1R ADRB2 Cloaca development 2 4 2.56E−03 BMP4 WNT11 Positive regulation of epithelial cell proliferation 7 190 2.56E−03 NRARP BMP4 FGF7 DLX5 SOX11 TGM1 TBX1 Regulation of branching involved in salivary gland morphogenesis by mesenchymal-epithe- lial signaling 2 4 2.56E−03 HGF FGF7 Myoblast differentiation 5 87 2.62E−03 BMP4 PITX1 SDC1 ID3 GREM1 Regulation of signaling 43 3952 2.64E−03 BMP4 NGF IL36G IL1RN SIK1 RSPO3 NRARP JUP HGF MYOC KLK14 CYP19A1 FGF18 CREB3L1 S100A9 S100A12 ZC3H12A HEYL GREM1 ADRB2 ALOX12B LYNX1 CCBE1 FAM3D WNT11 SLC8A3 CHRNA4 DLX5 RASL11B CHN1 CD36 FGF7 AVPR1A ROR2 SOX11 G6PC2 SH3RF3 MDFI TBX1 NOTCH3 GLP1R ELF3 RHOD Negative regulation of transcription, DNA- templated 20 1298 2.73E−03 ZBTB16 BMP4 SIX2 OVOL1 SOX11 ZNF154 ID3 CREB3L1 ELF3 HEYL PITX1 NOTCH3 CD36 GREM1 NRARP USP2 WNT11 GLIS3 MDFI TWIST2 Positive regulation of chondrocyte differentia- tion 3 20 2.78E−03 FGF18 PKDCC ZBTB16 Mesenchymal to epithelial transition 3 20 2.78E−03 BMP4 GREM1 SIX2 Renal vesicle morphogenesis 3 20 2.78E−03 BMP4 GREM1 SIX2 Response to oxygen-containing compound 24 1725 2.88E−03 IL36G IL1RN HGF DUOX1 ZC3H12A JUP ANGPT2 SLC8A3 BDKRB1 CHRNA4 SMPD3 GLP1R SDC1 ID3 SLPI CD36 AVPR1A ADRB2 CLDN4 WNT11 TBX1 GNA15 CATSPERB GPX3 Regulation of vasculature development 9 328 2.89E−03 BMP4 ZC3H12A HGF ANGPT2 GREM1 CCBE1 FGF18 CREB3L1 JUP Digestive tract morphogenesis 4 50 3.01E−03 BMP4 SIX2 SOX11 WNT11 Metanephros development 5 91 3.04E−03 BMP4 ID3 GREB1L GREM1 SIX2 Positive regulation of epithelial cell migration 6 141 3.04E−03 BMP4 FGF7 FGF18 ZC3H12A ALOX12 CCBE1 Regulation of cell differentiation 26 1954 3.04E−03 ID3 NGF HEYL MYOC BMP4 ZC3H12A AJAP1 MAFB TWIST2 HGF NOTCH3 ZBTB16 CHN1 GDPD2 CD36 FGF18 PKDCC GREM1 ROR2 SIX2 SOX11 NRARP SULT2B1 SIK1 S100A9 TBX1 Copulation 3 21 3.06E−03 KLK14 PI3 AVPR1A Positive regulation of cell differentiation 17 1018 3.06E−03 NGF MYOC BMP4 ZC3H12A HGF ZBTB16 GDPD2 CD36 FGF18 PKDCC HEYL GREM1 ROR2 SOX11 SULT2B1 S100A9 TBX1 Renal vesicle development 3 21 3.06E−03 BMP4 GREM1 SIX2 Cell surface receptor signaling pathway involved in cell–cell signaling 13 654 3.16E−03 WNT11 RSPO3 NRARP JUP GREM1 CHRNA4 DLX5 SDC1 ROR2 ADRB2 MYOC BMP4 MDFI Page 26 of 41Costa et al. Acta Neuropathologica Communications (2021) 9:183 Table 3 (continued) Down‑regulated genes in alive × dead Functional category Genes in list Total genes FDR Genes Roof of mouth development 5 93 3.25E−03 WNT11 DLX5 PKDCC SOX11 TBX1 Leukocyte migration 11 491 3.36E−03 IL36G IL1RN S100A9 BDKRB1 SMPD3 CYP19A1 GREM1 ROR2 ANGPT2 SDC1 S100A12 Cell chemotaxis 9 339 3.45E−03 IL36G IL1RN DEFB4A HGF FGF18 S100A9 CYP19A1 GREM1 S100A12 Sensory organ morphogenesis 8 270 3.53E−03 DLX5 BMP4 COL8A1 ROR2 SIX2 SOX11 MAFB TBX1 Negative regulation of cellular biosynthetic process 23 1658 3.67E−03 ZBTB16 BMP4 SIX2 OVOL1 SOX11 ZNF154 ID3 CREB3L1 ELF3 ZC3H12A HEYL PITX1 NOTCH3 SMPD3 CD36 GREM1 NRARP USP2 WNT11 GLIS3 MDFI SIK1 TWIST2 Hepoxilin metabolic process 2 5 3.67E−03 ALOX12 ALOX12B Hepoxilin biosynthetic process 2 5 3.67E−03 ALOX12 ALOX12B Glomerulus vasculature morphogenesis 2 5 3.67E−03 NOTCH3 BMP4 Glomerular capillary formation 2 5 3.67E−03 NOTCH3 BMP4 Negative regulation of RNA metabolic process 21 1446 3.68E−03 ZBTB16 BMP4 SIX2 OVOL1 SOX11 ZNF154 ID3 CREB3L1 ELF3 ZC3H12A HEYL PITX1 NOTCH3 CD36 GREM1 NRARP USP2 WNT11 GLIS3 MDFI TWIST2 Embryonic skeletal system morphogenesis 5 97 3.70E−03 MDFI BMP4 SIX2 SOX11 TBX1 Kidney morphogenesis 5 97 3.70E−03 BMP4 GREB1L GREM1 SIX2 WNT11 Cellular response to chemical stimulus 39 3536 3.70E−03 HGF BMP4 NGF IL36G IL1RN AVPR1A DEFB4A DUOX1 FGF18 CREB3L1 S100A9 S100A12 ZC3H12A HEYL GREM1 JUP CCBE1 FAM83G ANGPT2 SLC8A3 CHRNA4 SMPD3 DLX5 ALOX12 GLP1R ID3 RASL11B CD36 CYP19A1 FGF7 ROR2 ADRB2 ABCG4 SOX11 GPX3 WNT11 TBX1 GNA15 SDC1 Positive regulation of endothelial cell migration 5 98 3.86E−03 BMP4 FGF18 ZC3H12A ALOX12 CCBE1 Regulation of cell adhesion 14 765 3.94E−03 MYOC CD36 IL1RN AJAP1 ANGPT2 ALOX12 ZBTB16 COL8A1 ZC3H12A GREM1 RHOD NRARP JUP BMP4 Negative regulation of nucleic acid-templated transcription 20 1353 3.96E−03 ZBTB16 BMP4 SIX2 OVOL1 SOX11 ZNF154 ID3 CREB3L1 ELF3 HEYL PITX1 NOTCH3 CD36 GREM1 NRARP USP2 WNT11 GLIS3 MDFI TWIST2 Cellular response to organic substance 34 2938 3.99E−03 HGF BMP4 NGF IL36G IL1RN AVPR1A DUOX1 FGF18 CREB3L1 ZC3H12A HEYL GREM1 JUP CC