Identification and Validation of Prognostic Biomarker Signatures Associated with Overall Survival in Colorectal...

Colorectal cancer (CRC) is one of the most common gastrointestinal cancers worldwide. Thus, genes targeting is useful for the prognosis and therapy of CRC. This study aimed to identify a promising and valuable signature model of prognostic biomarkers associated with overall survival (OS) in CRC.

Two mRNA microarray datasets (GSE18105 and GSE113513) from the Gene Expression Omnibus (GEO) database were screened to extract key genes from differentially expressed genes (DEGs) to establish a multiscale embedded gene co-expression network, protein-protein interaction network, and survival analysis. Univariate Cox analysis was conducted to construct a prognostic signature for OS using Kaplan-Meier analysis. Then, we constructed and analyzed the protein-protein interaction network using STRING and Cytoscape, respectively to establish the key genes. Finally, the selected potential prognostic genes were validated in tissue samples of CRC by quantitative real-time PCR (qRT-PCR).

In the present study, among 340 identified DEGs, four key genes (SPP1, CHEK1, KIF18A, and MAD2L1) were detected. The prognostic gene signature model demonstrated strong performance in the prognosis of CRC (AUC > 0.9). Moreover, the four key genes were also used to construct a risk-score prognostic model for OS and the findings showed that the prognostic gene signature model was highly effective in predicting the OS in CRC patients. The Gene Ontology (GO) enrichment analysis indicated the key genes were significantly associated with several CRC-related signaling pathways such as calcium-independent cell-cell adhesion. Finally, the results of qRT-PCR showed that the upregulation of SPP1, CHEK1, KIF18A, and MAD2L1 was associated with poor prognosis and served as risk factors for CRC patients compared to controls.

The findings of the present study provided a set of four key genes with valid clinical utility that can serve as an alternative tool for prognosis and identification of new targets in CRC treatment.