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Dong-Mei Zhou

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Open access Aug 2026

Post-Translational Modification–Driven Metabolic Reprogramming Shapes Melanoma Progression and Immune Microenvironment

Background Post-translational modifications (PTMs) critically regulate protein function, yet their intratumoral heterogeneity and clinical relevance in melanoma remain poorly characterized. Elucidating PTM-driven programs may uncover novel mechanisms of tumor progression and therapeutic vulnerability. Methods We implemented an integrative multi-scale framework combining single-cell RNA sequencing (GSE215120), bulk transcriptomic cohorts (TCGA-SKCM and GSE19234), and spatial transcriptomics (VISDS000459). Twenty curated PTM functional programs were quantified using AUCell and ssGSEA. PTM-defined melanoma subpopulations were interrogated for transcription factor activity, cell-cell communication, copy number variation, and metabolic reprogramming. A PTM-based prognostic signature was constructed using LASSO-Cox regression, validated across independent cohorts, and interpreted using SHAP and LIME. Spatial transcriptomics was used to resolve the tissue localization of prognostic genes. Drug sensitivity was predicted with oncoPredict and evaluated by molecular docking. Results PTM-based stratification identified melanoma subtypes with distinct proliferative, metabolic, and immune states. Melanoma-high cells exhibited elevated PTM-associated transcriptional enrichment, increased CNV burden, enhanced glycolysis/PPP/TCA metabolism, and dominant VEGF, MIF, GALECTIN, CXCL, and PDGF signaling, whereas Melanoma-low cells were enriched in antigen presentation and IFN-γ-related pathways. A 15-gene PTM risk score robustly stratified overall survival in TCGA-SKCM (p = 4.52×10−12) and GSE19234 (p = 0.027), with predictive performance comparable to clinical stage and superior to age and sex. High-risk tumors showed increased tumor mutation burden, reduced immune and stromal infiltration, lower immunophenoscores, widespread immune checkpoint activation, and suppressed cytolytic activity. SHAP and LIME highlighted FOXM1, FOXK1, TTYH2, SLC25A15, and FCGR2A as key contributors. Drug modeling suggested heightened sensitivity of high-risk tumors to kinase and cell-cycle inhibitors, supported by favorable TTYH2-drug docking. Conclusion This PTM-centered integrative framework delineates metabolic and immune remodeling in melanoma, establishes an interpretable prognostic model, and identifies candidate therapeutic vulnerabilities for precision oncology.

Man-Ning Wu, Dong-Mei Zhou, Yue-Min Zou et al. · 0 citations
Review Open access Feb 2026

Development and validation of an interpretable machine learning model for predicting hypertension risk in patients with psoriasis

Objective Hypertension is a common yet frequently underdiagnosed comorbidity in psoriasis patients. Early identification and blood pressure control are critical to improving outcomes. Although machine learning (ML) is widely used in disease prediction, a model for hypertension risk within the psoriasis population remains unavailable. This study aims to develop and validate such a model in patients with psoriasis. Methods In this retrospective study, 2,957 psoriasis patients from a single tertiary center were used for model development and internal validation, and 567 psoriasis participants from the National Health and Nutrition Examination Survey (NHANES) served as the external validation cohort. After missForest imputation and consensus feature selection, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training set only. Nine machine learning algorithms were trained and evaluated for discrimination, calibration, and clinical utility. Shapley Additive Explanations (SHAP) were used for model interpretation. Results Nine nonredundant predictors were retained. The SMOTE-enhanced logistic regression model showed the most balanced and generalizable performance, with area under the receiver operating characteristic curve values of 0.850, 0.816, and 0.789 in the training, internal validation, and external validation cohorts, respectively, together with acceptable calibration and favorable net clinical benefit. SHAP identified age, dyslipidemia, and type 2 diabetes mellitus as the leading contributors. The final model was deployed as a publicly accessible web application. Conclusions This interpretable and externally validated machine learning model provides a practical tool for hypertension risk stratification in psoriasis patients and may support earlier identification and individualized preventive management in clinical practice.

Guo-Hua Xue, Xiao-Yang Guo, Jia-Qi Chen et al. · 0 citations

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