Image_1_Cervical Cancer Stem-Like Cell Transcriptome Profiles Predict Response to Chemoradiotherapy.tif (1.91 MB)

Image_1_Cervical Cancer Stem-Like Cell Transcriptome Profiles Predict Response to Chemoradiotherapy.tif

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posted on 2021-05-07, 05:00 authored by Luciana W. Zuccherato, Christina M. T. Machado, Wagner C. S. Magalhães, Patrícia R. Martins, Larissa S. Campos, Letícia C. Braga, Andrea Teixeira-Carvalho, Olindo A. Martins-Filho, Telma M. R. F. Franco, Sálua O. C. Paula, Israel Tojal da Silva, Rodrigo Drummond, Kenneth J. Gollob, Paulo Guilherme O. Salles

Cervical cancer (CC) represents a major global health issue, particularly impacting women from resource constrained regions worldwide. Treatment refractoriness to standard chemoradiotheraphy has identified cancer stem cells as critical coordinators behind the biological mechanisms of resistance, contributing to CC recurrence. In this work, we evaluated differential gene expression in cervical cancer stem-like cells (CCSC) as biomarkers related to intrinsic chemoradioresistance in CC. A total of 31 patients with locally advanced CC and referred to Mário Penna Institute (Belo Horizonte, Brazil) from August 2017 to May 2018 were recruited for the study. Fluorescence-activated cell sorting was used to enrich CD34+/CD45- CCSC from tumor biopsies. Transcriptome was performed using ultra-low input RNA sequencing and differentially expressed genes (DEGs) using Log2 fold differences and adjusted p-value < 0.05 were determined. The analysis returned 1050 DEGs when comparing the Non-Responder (NR) (n=10) and Responder (R) (n=21) groups to chemoradiotherapy. These included a wide-ranging pattern of underexpressed coding genes in the NR vs. R patients and a panel of lncRNAs and miRNAs with implications for CC tumorigenesis. A panel of biomarkers was selected using the rank-based AUC (Area Under the ROC Curve) and pAUC (partial AUC) measurements for diagnostic sensitivity and specificity. Genes overlapping between the 21 highest AUC and pAUC loci revealed seven genes with a strong capacity for identifying NR vs. R patients (ILF2, RBM22P2, ACO16722.1, AL360175.1 and AC092354.1), of which four also returned significant survival Hazard Ratios. This study identifies DEG signatures that provide potential biomarkers in CC prognosis and treatment outcome, as well as identifies potential alternative targets for cancer therapy.