Work

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

Year: 2025

Type: article

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.

Source: Genes

DOI: 10.3390/genes16070829

Authors: Ipek Balikci Cicek, Zeynep Kucukakcali

Institution: Inonu University

Language: English

Topic: Gastric Cancer Management and Outcomes

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137