Wei Gu, Chuan Liu, Jinglei Li, Jian Wang
Background: Head and neck squamous cell carcinoma (HNSCC) responds poorly (<20%) to immune checkpoint blockade. Since sialylation suppresses anti-tumour immunity through Siglec signalling independently of the PD-L1/PD-1 axis, we mapped its prognostic landscape in HNSCC by integrating prognostic modelling, molecular subtyping, and AI-guided drug discovery. Methods: Transcriptomic data from seven GEO cohorts and TCGA-HNSC (n = 501) were analysed with a 1204-gene sialylation compendium. The Sialylation Niche Index (SNI) was built in TCGA-HNSC from 117 machine-learning algorithm combinations (nominally 101), with survival-based feature selection and model fitting confined to the training set; model selection used TCGA-HNSC and the external model-selection cohort GSE42743 (n = 74 with overall survival), and the three independent test cohorts were scored without re-fitting (E-MTAB-8588, n = 83; GSE65858, n = 270; and GSE41613, n = 97). Key model genes were characterised by single-cell RNA sequencing (168,742 cells), spatial transcriptomics, and AI-guided screening of 249,455 compounds. Results: Thirty-one prognostic sialylation-associated DEGs defined an immunosuppressive subtype (C1) and an immune-active subtype (C2) with divergent survival. The SNI achieved a C-index of 0.88 in TCGA-HNSC, 0.68 in GSE42743, and 0.58-0.62 in the three independent test cohorts and remained independently prognostic after adjustment for age and other clinicopathological variables in multivariable analysis. SHAP analysis identified HSPH1 (risk-associated) and ST6GALNAC1 (protective) as principal contributors with opposing microenvironmental associations. Conclusions: Sialylation constitutes a distinct immunosuppressive axis in HNSCC, complementary to PD-1 blockade. The SNI provides a biologically anchored prognostic framework whose cross-platform transfer requires recalibration; putative candidate compounds require experimental validation.