Public omics data reuse in gastric cancer: from discovery abundance to translational evidence
4 affiliations
- 1Department of Gastroenterology, Guangxi Medical University Cancer Hospital
- 2Department of Chemotherapy, Guangxi Medical University First Affiliated Hospital
- 3Department of Oncology, The People’s Hospital of Yichun City
- 4Department of Radiation Oncology, Sun Yat-sen University Cancer Center
Abstract
Public-omics data are now widely reused in gastric cancer bioinformatics. Resources such as The Cancer Genome Atlas stomach adenocarcinoma cohort (TCGA-STAD), the Gene Expression Omnibus (GEO), cBioPortal, proteogenomic cohorts, single-cell atlases, and spatial datasets make secondary analysis fast and inexpensive. They are used to study tumor biology, biomarkers, prognosis, therapy-response hypotheses, and tumor-microenvironment states. A major limitation in this literature is not data access. It is evidentiary dilution. Many studies reuse similar cohorts and analytical pipelines. They then produce additional gene lists or risk signatures without a corresponding improvement in independent validation, interpretation, or clinical relevance. This methodological narrative review synthesizes public-omics secondary analysis in gastric cancer from methodological, translational, and reproducibility-oriented perspectives, supported by reproducible PubMed database scans and public metadata checks conducted through 2026-06-30; both are descriptive signals and are not formal quality appraisals. The review has three aims. The first aim is to classify reusable public data resources and the recurring analytical workflows built on them. The second aim is to use prognostic modeling as a stress test for leakage, overfitting, weak validation, and overinterpreted clinical value. The third aim is to propose GC-PROVE, a five-domain appraisal framework covering problem anchoring, resource provenance, omics workflow transparency, validation hierarchy, and evidence restraint. Public-omics research is most useful when it produces auditable evidence, external validation, biological triangulation, and a clear clinical context.
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BibTeX
@article{su2026public,
title = {{Public omics data reuse in gastric cancer: from discovery abundance to translational evidence}},
author = {Shuqiong Su and Cong Huang and Bo Yang and Yueli Huang and Panyang Yang and Aiqun Liu},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1974260},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1974260/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Public omics data reuse in gastric cancer: from discovery abundance to translational evidence
AU - Shuqiong Su
AU - Cong Huang
AU - Bo Yang
AU - Yueli Huang
AU - Panyang Yang
AU - Aiqun Liu
PY - 2026
DA - 2026/10/26
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1974260
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1974260/full
AB - Public-omics data are now widely reused in gastric cancer bioinformatics. Resources such as The Cancer Genome Atlas stomach adenocarcinoma cohort (TCGA-STAD), the Gene Expression Omnibus (GEO), cBioPortal, proteogenomic cohorts, single-cell atlases, and spatial datasets make secondary analysis fast and inexpensive. They are used to study tumor biology, biomarkers, prognosis, therapy-response hypotheses, and tumor-microenvironment states. A major limitation in this literature is not data access. It is evidentiary dilution. Many studies reuse similar cohorts and analytical pipelines. They then produce additional gene lists or risk signatures without a corresponding improvement in independent validation, interpretation, or clinical relevance. This methodological narrative review synthesizes public-omics secondary analysis in gastric cancer from methodological, translational, and reproducibility-oriented perspectives, supported by reproducible PubMed database scans and public metadata checks conducted through 2026-06-30; both are descriptive signals and are not formal quality appraisals. The review has three aims. The first aim is to classify reusable public data resources and the recurring analytical workflows built on them. The second aim is to use prognostic modeling as a stress test for leakage, overfitting, weak validation, and overinterpreted clinical value. The third aim is to propose GC-PROVE, a five-domain appraisal framework covering problem anchoring, resource provenance, omics workflow transparency, validation hierarchy, and evidence restraint. Public-omics research is most useful when it produces auditable evidence, external validation, biological triangulation, and a clear clinical context.
SN - 2234-943X
ER -