Identification And Clinicopathological Analysis Of Potential P73-regulated Biomarkers In Colorectal Cancer Via Integrative Bioinformatics

This study utilizes integrative bioinformatics and expression profiling techniques to identify key biomarkers for the early detection of colorectal cancer (CRC). Through transcriptome profiling and analysis of gene ontology and KEGG, 20 hub genes were identified as potential biomarkers. Machine/deep learning algorithms achieved high accuracy in TNM-stage classification using these hub genes. This study represents a pioneering effort in combining transcriptomics, publicly available datasets, and machine learning for the identification of CRC-associated genes with clinical relevance.