Integrative multi-omics and machine learning reveal PLAUR as a Pan-Cancer prognostic biomarker and potential therapeutic target

Résumé fourni par la source

Tumor heterogeneity remains a major barrier to effective cancer therapy, driving the need for novel biomarkers and targeted strategies. A bibliometric analysis underscored the lack of a systematic pan-cancer study of PLAUR. By integrating functional experiments, multi-omics analyses, and machine learning, we systematically investigated urokinase-type plasminogen activator receptor (PLAUR) across malignancies. PLAUR overexpression was observed in 10 cancer types, with significant prognostic value in 7 of these. Epigenetic analysis revealed its association with specific methylation patterns. Functionally, PLAUR drives malignant phenotypes by mediating tumor-cancer-associated fibroblast (CAF) crosstalk, remodeling the immunosuppressive microenvironment, and is implicated in therapy resistance. We identified four FDA-approved drugs (5-Fluorouracil, Irinotecan, Talazoparib, Gemcitabine) that may have potential binding affinity to PLAUR through in silico analyses, and in vitro functional assays using the human tongue squamous cell carcinoma cell line CAL-27. We further validated that Irinotecan can significantly reduce PLAUR expression and suppress malignant phenotypes (including survival, proliferation, migration, and invasion), which consolidates the preliminary findings from in silico analyses. Furthermore, we characterized peroxidasin (PXDN) as a core PLAUR-interacting hub and propose five candidate drugs (IGF1R-3801, Taselisib, Dasatinib, 5-Fluorouracil, and Alpelisib) for a synergistic dual-target strategy. Our work establishes PLAUR as a pivotal pan-cancer biomarker and potential therapeutic target, providing a rational framework for precision oncology.

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