ArticleBiochemistry and biophysics reports2026
Integrative single-cell and bulk transcriptomic analysis identifies lesion-associated gene signatures for prognostic stratification and therapeutic guidance in head and neck squamous cell carcinoma.
Article in Biochemistry and biophysics reports, 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
Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis and variable response to therapy. Effective biomarkers for prognosis and treatment guidance remain limited. Methods: We performed integrative analysis of single-cell and bulk RNA-seq data to dissect the tumor microenvironment and identify lesion-associated transcriptional changes. Gene modules related to lesion progression were identified using weighted gene co-expression network analysis (WGCNA) and integrated with differential expression and genomic data. Prognostic modeling was conducted using multiple machine learning algorithms, and the best-performing model was validated across independent cohorts. A nomogram combining the risk score and clinical features was developed. Multi-omics analyses were applied to characterize the genomic landscape, immune infiltration, and treatment response associated with the risk groups. Results: The final prognostic model effectively stratified patients by survival and was validated in an external cohort. The nomogram showed high predictive accuracy and clinical utility. High-risk tumors were enriched in oncogenic pathways such as hypoxia, glycolysis, and epithelial-mesenchymal transition, and displayed elevated genomic instability. Immune profiling revealed suppressed antitumor activity and increased infiltration of immunosuppressive cells in high-risk patients. Risk score was negatively associated with immunotherapy response and correlated with differential sensitivity to chemotherapy and targeted agents. Conclusion: This study presents a robust prognostic model based on lesion-associated gene expression signatures in HNSCC. It offers valuable tools for individualized prognosis and therapeutic decision-making, with implications for advancing precision oncology in this challenging cancer type.
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