Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.
Journal:
Medicine
Published Date:
May 30, 2025
Abstract
Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cancer (CRC). However, the mechanisms that sustain CSCs in CRC remain unclear. This study aimed to identify and characterize gene expressions associated with CRC stemness. We applied a 1-class logistic regression machine learning model to calculate the mRNA expression-based stemness index (mRNAsi) for CRC samples from The Cancer Genome Atlas and cBioPortal databases, adjusting the mRNAsi by tumor purity. Clinical features of CRC were considered in assessing both mRNAsi and adjusted mRNAsi levels. Using DESeq2, we screened differentially expressed genes between high and low mRNAsi groups. Enrichment analysis provided functional annotation for these differentially expressed genes. Key genes linked to mRNAsi were identified using the Kaplan-Meier plotter and Cytoscape software, followed by an evaluation of their prognostic significance. Potential small-molecule compounds targeting the CRC stemness signature were explored via L1000FWD, DGIdb, and CMap databases. CRC samples with higher mRNAsi or adjusted mRNAsi values showed improved disease-free survival (DSS) and progression-free survival (PFS). Strong correlation between clinical characteristics of CSCs and mRNAsi was observed; CMS4 subtype CRC patients had lower mRNAsi with worse DSS and PFS. Ten key genes associated with mRNAsi were identified: collagen type I alpha 1, fibrillin 1, matrix metalloproteinase 9, SPP1, BGN, COL5A1, FN1, elastin, matrix metalloproteinase 2, collagen type I alpha 2. Lower expression of these genes correlated with better PFS and DSS. High correlation among these genes was confirmed in the protein-protein interaction network. This study identifies potential small-molecule drugs targeting stemness in CRC and highlights the prognostic value of the 10 key genes, offering insights into therapeutic targets for CRC treatment.
