Research trends and future perspectives in gastric cancer omics technologies

Gastric cancer (GC) ranks fifth in global incidence and third in cancer-related mortality. Omics technologies have become essential for decoding intratumoral heterogeneity and the spatial architecture of tumors, leading to a surge in omics-based research on gastric cancer. This study systematically maps the landscape, evolving trends, and emerging frontiers of omics applications in gastric cancer through a comprehensive bibliometric analysis. Publications were retrieved from the Web of Science Core Collection and Scopus databases, covering the period from the inception of these databases to December 31, 2025. A total of 1,382 documents were analyzed using CiteSpace, VOSviewer, and the Bibliometrix R package, focusing on six dimensions: country/region, institution, author, source journal, reference, and keyword. The annual publication output has consistently increased, with China, the United States, and Japan at the forefront of productivity. High-frequency keywords such as gastric cancer, radiomics, biomarker, and proteomics delineate the core thematic structure of the research. Co-occurrence clustering analysis revealed six distinct clusters, prominently featuring radiomics, epigenetics, and gastric cancer. Additionally, keyword burst detection analysis identified machine learning and radiomics as current research frontiers, while citation analysis underscored therapeutic strategies as a central hotspot in the field. The number of publications in the field of GC omics research has shown a sustained increase, reflecting the growing interest in omics technologies. With the advancement of omics technologies, research efforts have gradually expanded from single-omics analysis to encompass epigenetics, radiomics, and multi-omics integration, with genomics and proteomics consistently playing central roles. In this context, the development of novel biomarkers through omics analysis provides key evidence for elucidating the mechanisms underlying GC initiation and progression, predicting disease risk, and optimizing therapeutic strategies. Current research priorities include biomarker screening and exploration of pathogenic mechanisms based on omics technologies to enable early GC screening, facilitate drug development, and enable efficacy evaluation. The integration of multi-omics data with deep learning to construct early risk prediction models for GC, with subsequent application to targeted therapy and immunotherapy, represents a promising research direction.