Membrane transporter genes predict chemoradiotherapy response in patients with cervical cancer
This study aimed to explore membrane transporter gene expression as a predictive biomarker of chemoradiotherapy response in cervical cancer. The differential expression of ATP1B3 and SLCO1B3 accurately classified patients as responders or non-responders with 90% accuracy, highlighting their potential for personalized treatment strategies. Two gene groups with contrasting expression profiles were identified. The ATP1B3 and SLCOB3 gene profiles classified patients with 90% accuracy. The ATP1B3 and SLCOB3 gene signature is a potential predictor of treatment response. Cervical cancer is the fourth most common cancer in women worldwide. Resistance to chemoradiotherapy in cervical cancer has been widely associated with membrane transport-related genes, particularly those encoding efflux transport proteins, such as the ATP-binding cassette family members (including P-glycoprotein), which act by expelling chemotherapeutic agents from tumor cells, as well as solute carrier proteins, whose expression impairs the uptake of antineoplastic drugs by cancer cells. This study aimed to identify specific membrane transport-related gene expression profiles as potential biomarkers for predicting chemoradiotherapy response in cervical cancer. Cervical biopsies were collected from 31 patients (21 responders and 10 non-responders) at Hospital Luxemburgo - Instituto Mário Penna. Fluorescence-activated cell sorting was used to separate non-stem cancer cells from cervical cancer biopsies. cDNA libraries from the 21 responders and 10 non-responders were sequenced using the Illumina platform. Expression analysis was performed using R and the DESeq2 package, with differentially expressed genes identified based on log fold change >1 or <-1 and padj ≤0.05. WEKA software and decision tree methods were used to analyze membrane transporters. The results revealed two major gene groups with contrasting differentially expressed genes profiles. The first group, comprising SLC35 and ATP13, was overexpressed in non-responders, while the second group, consisting of SLC25 and ATP6, was overexpressed in responders. Decision tree analysis revealed that ATP1B3 and SLCOB3 expression profiles accurately classified patients into responder and non-responder groups with 90% accuracy, indicating that ATP1B3 and SLCOB3 are potential predictors of chemoradiotherapy response. Our results strongly suggest the presence of a candidate gene signature comprising ATP1B3 and SLCO1B3 that holds predictive value for chemoradiotherapy response in cervical cancer.