Integrative transcriptomic analysis identifies shared coding and non-coding biomarkers in tongue squamous cell carcinoma across diverse populations.

Authors

Ronit Roy, Monika Rajput, Manoj Pandey

Published in

Discover oncology. Volume 17. Issue 1. Aug 27, 2026. Epub Aug 27, 2026.

Abstract

Tongue cancer, a prevalent subtype of oral malignancy, remains a significant global health burden with high morbidity and mortality, particularly in developing countries. Understanding its molecular landscape is crucial for developing targeted diagnostics and therapeutics.
This systematic review aimed to identify common differentially expressed protein-coding genes (DEGs) in tongue cancer across multiple populations and explore their functional significance through integrative bioinformatic analyses.
We performed an integrative meta-analysis of transcriptomic datasets from China, the USA, and Australia to identify common differentially expressed genes (DEGs). Functional enrichment, protein-protein interaction (PPI), and regulatory network analyses were conducted. Crucially, the clinical relevance of identified hub genes was validated using the TCGA-HNSC cohort to assess correlations with tumor stage and overall survival.
A total of 133 common DEGs were identified, primarily enriched in extracellular matrix (ECM) disassembly, collagen catabolism, and IL-17 signaling. Key hub genes included MMP3, MMP10, COL1A1, CDKN2A, and CXCL11. Network analysis uncovered novel lncRNAs, including TENM3-AS1 and DUXAP10, regulating immune and epithelial pathways. Clinical validation demonstrated that high expression of STC2 and MMP3 significantly correlated with advanced tumor stage (p < 0.05). Furthermore, STC2 upregulation was identified as a significant predictor of poor overall survival (p = 0.0037).
This study defines a cross-population gene signature driving TSCC through ECM remodeling and immune modulation. The validation of STC2 as a prognostic marker highlights its potential for clinical risk stratification.

PMID:
42658318
Bibliographic data and abstract were imported from PubMed on 28 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.