Transcriptomic Profiling of Gastric Cancer Reveals Key Biomarkers and Pathways via Bioinformatic Analysis

TL;DR

This study uses transcriptomic data and machine learning to identify key biomarkers and pathways in gastric cancer, offering potential for early detection and treatment.

Contribution

The study integrates multiple transcriptomic datasets and machine learning to identify consistent, biologically relevant biomarkers for gastric cancer.

Findings

627 differentially expressed genes were identified in the discovery dataset, with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A.

A random forest model achieved excellent classification performance with an AUC of 0.952 for gastric cancer detection.

Cross-platform validation confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT16, and ATP4A as strong diagnostic candidates.

Abstract

Background/Objectives: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. Methods: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, n = 108) as the discovery dataset and GSE248612 (RNA-seq, n = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted p < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. Results: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A. The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT17, ATP4A, CHIA, KRT16, and CRABP2. Enrichment analyses revealed involvement in ECM–receptor interaction, PI3K-Akt signaling, EMT, and cell cycle. Conclusions: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, CST1, TIMP1, KRT16, and ATP4A emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer.

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