A multi-omics pipeline integrating machine learning and spatial-cellular analysis identifies SASH1 as a prognostic biomarker and therapeutic target in head and neck squamous cell carcinoma

作者：Ziwei Dai, Xiaofeng Shan, Yifan Kang, Yutong Chen, Qiushi Feng, Zhigang Cai, Shang Xie · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000003647 · 被引用次数：10 · 研究领域：Ferroptosis and cancer prognosis、RNA regulation and disease、Chromatin Remodeling and Cancer

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with a poor prognosis, necessitating the discovery of novel and reliable molecular biomarkers for improved clinical management. Traditional bulk transcriptomic analyses often mask the cellular heterogeneity and spatial complexity of the tumor microenvironment, limiting the identification of robust biomarkers. This study aimed to identify and validate key driver genes in HNSCC through a comprehensive multi-omics and machine learning-based approach. MATERIALS AND METHODS: Transcriptomic data from multiple GEO datasets (GSE29330, GSE6631, GSE138206) and the TCGA-HNSC cohort were integrated and analyzed to identify consensus differentially expressed genes (DEGs). A suite of four machine learning algorithms (LASSO, SVM-RFE, XGBoost, Boruta) was employed to screen for core candidate genes. The cellular origins and spatial distribution of these core genes were subsequently dissected using public single-cell (GSE215403) and spatial transcriptomics (GSE252265) data. Finally, the expression of the key gene, SAM and SH3 domain-containing 1 (SASH1), was validated at the protein level via Western blot in HNSCC cell lines, and its clinical and therapeutic value was assessed through survival, clinical correlation, and drug sensitivity analyses. RESULTS: An integrated analysis of bulk transcriptomic data identified 159 consensus DEGs, from which four core genes (COL1A1, EMP1, MYH11, SASH1) were robust...