Integrative Multi-Dataset Gene Expression Analysis Reveals Novel Ion Transport Biomarkers in Colorectal Cancer

Background: In 2022, the World Health Organization announced colorectal cancer as the third most common cancer worldwide and the second leading cause of cancer-related deaths. CRC progression is driven by cumulative genetic and epigenetic alterations which can transformation from benign adenomas into invasive adenocarcinomas. Considering the mortality rate and the challenge of early detection, computational analyses utilizing public datasets provide a powerful approach to suggest novel biomarkers and the underlying molecular basis of the disease. Methods: We applied an integrative gene expression analysis approach to four CRC NCBI GEO gene expression datasets that include human tumor and normal samples. The module detects the common up/downregulated differentially expressed genes (DEGs) across the four datasets. Next, functional enrichment analysis using Gene Ontology (GO) and KEGG pathways was performed to identify key biological processes and potential biomarkers. Then, core candidate biomarkers were evaluated using The Cancer Genome Atlas (TCGA-COAD/READ) datasets, with diagnostic and prognostic utility evaluated via Receiver Operating Characteristic (ROC) curves and a Kaplan–Meier survival analysis. Results: A robust set of 14 downregulated genes and eight upregulated genes common across all four datasets and successfully validated through TCGA datasets. Notably, the batch of the 14 downregulated genes were functionally linked to the regulation of epithelial ion and electrolyte transport. Conclusions: This study provides a robust multi-dataset computational analysis pointing out that reduced epithelial electrolyte transport is a characteristic molecular change in tumor samples. The identified 22 genes are supported by the existing experimental literature and are proposed as potential diagnostic and prognostic biomarkers for future colorectal cancer diagnostics.