Comprehensive analysis reveals prognostic gene set that could impacts the response to chemotherapy and immunotherapy outcomes in malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is a rare but aggressive thoracic malignancy primarily linked to asbestos exposure. Despite advances in research, the prognosis of MPM remains poor, and there is a lack of efficient and precise prognostic assessment tools. Its insidious onset, limited treatment options, and high resistance to therapy contribute to poor clinical outcomes and underscore the urgent need for reliable prognostic biomarkers. To address this gap, we integrated multi-omics data to identify a prognostic gene set for MPM and subsequently developed a prognostic model aiming to improve the clinical outcomes of this disease. Transcriptomic data from TCGA were used to identify genes related to prognosis and tumor stage in malignant pleural mesothelioma (MPM), followed by construction of a prognostic model via LASSO-Cox regression and external validation using a GEO dataset. The model was then integrated with DNA methylation and SNP data for GO enrichment analysis. Chemotherapy drug IC50 data from MPM cell lines were correlated with model gene expression to evaluate associations between risk scores and drug sensitivity, which were further validated using TCGA clinical response data. Finally, the model's impact on the immune microenvironment was assessed using single-cell RNA-seq data, and its predictive value for immunotherapy response was validated in an independent MPM immunotherapy cohort. Our novel prognostic model demonstrated consistent performance in both the training and validation cohorts. Patients with high-risk scores had poorer outcomes, with AUC values exceeding 0.8 and reaching 0.9 for 3 year survival prediction. The risk score accurately reflected biological processes such as tumor proliferation and metastasis in mesothelioma. It was also closely associated with clinical responses to radiotherapy and chemotherapy, with low-risk patients showing greater sensitivity and lower drug IC50 values. Additionally, the risk score correlated positively with tumor immune activity and was predictive of immunotherapy response. In conclusion, our prognostic model shows strong and reliable predictive power for patient survival and treatment response in malignant pleural mesothelioma. It not only reflects key tumor biological processes but also serves as a potential tool for guiding personalized therapy, including chemotherapy, radiotherapy, and immunotherapy.