Gastric Cancer Biomarker Candidates Identified by Machine Learning and Integrative Bioinformatics: Toward Personalized Medicine

Abstract

Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein-protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that EXO1, DTL, KIF14, and TRIP13 genes are significant and poised as potential diagnostic biomarkers in relation to GC. The receiver operating characteristic curve analysis found KIF14 and TRIP13 are strongly associated with diagnosis of GC. We suggest KIF14 and TRIP13 are considered as biomarker candidates that might potentially inform future research on diagnosis, prognosis, or therapeutic targets for GC. These findings collectively offer new future possibilities for precision/personalized medicine research and development for patients with GC.

Keywords: biomarkers; diagnosis; digital transformation and omics; gastric cancer; machine learning; prognosis.