ArticleDiscover oncology2026
Integrative transcriptomic analysis identifies shared coding and non-coding biomarkers in tongue squamous cell carcinoma across diverse populations.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTongue 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.
objectiveThis 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.
methodsWe 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.
resultsA 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).
conclusionThis 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.
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