Transcriptomic Profiling of Gastric Cancer Reveals Key Biomarkers and Pathways via Bioinformatic Analysis
This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, <i>CST1</i>, <i>TIMP1</i>, <i>KRT16</i>, and <i>ATP4A</i> 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.
Authors
- İpek Balıkçı Çiçek (ORCID: https://orcid.org/0000-0002-3805-9214)
- Zeynep Küçükakçalı (ORCID: https://orcid.org/0000-0001-7956-9272)
Institutions
- Inonu University (TR)
Publication Details
- Journal
- Genes
- Published
- 2025-07-16
- DOI
- https://doi.org/10.3390/genes16070829
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00