Genes de transporte de membrana como ferramentas de prognóstico e preditores de resposta à quimioradioterapia em pacientes com câncer cervical

Abstract

Introduction: Cervical cancer is women's fourth most common cancer worldwide. Improving survival remains the goal of the treatment in the clinical setting. Although some advances have been made in cervical cancer screening technologies, the survival rate and response to treatment remain poor. Membrane transporters encompass many proteins responsible for maintaining proper amounts of molecules inside and outside the cells. Among them, the family of ATPase and ATP synthase stands out for their involvement in the process of resistance to chemotherapy and platinum accumulation in whole cells. Solute carrier proteins (SLC) have also been highlighted for playing critical roles in the movement of cytotoxic agents across the neoplastic cell membrane. The potential to determine efficacy, toxicity, and resistance to chemotherapy makes these transporters promising predictive biomarkers for cervical cancer. Objective: The present study aims to identify the expression profile of genes related to membrane transport as biomarkers to chemoradiotherapy in CC. Methods: Fluorescence-activated cell sorting (FACS) was used to isolate non-stem cells (NCCSCs) from cervical cancer biopsies. The cDNA library of samples from 21 responders (R) and 10 non-responders (NR) patients were constructed and sequenced using the Illumina platform. Expression analysis was performed using the DESeq2 package in the R program, considering differentially expressed genes (DEGs) for those with log fold change > 1 or < -1 and padj ≤ 0.05. Wald's statistical test and p-value adjustment with Benjamini's Hochberg method were also performed with the same tool. Decision trees with differentially expressed genes (DEGs) related to membrane transporters were analyzed using WEKA software. Results: Two large groups of genes with opposite differential expression profiles were revealed. The first group, composed mainly of the SLC35, ATP1, and ATP5 families, showed overexpressed genes in NR patients compared to R patients. The second group, mainly represented by the SLC25 and ATP6 families, was overexpressed in R patients compared to NR patients. Decision tree methods define the profile of ATP1B3 and SLCO1B3 genes as capable of classifying patients into responders and non-responders and cut-off of counts is necessary to this with an accuracy of 90% in LOOCV. The results suggest ATP1B3 and SLCO1B3 signature-based are candidate predictors of response to chemoradiotherapy.