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

Our plain-English summary of Comprehensive analysis reveals prognostic gene set that could impacts the response to chemotherapy and immunotherapy outcomes in malignant pleural mesothelioma, by J. Li, H. He, Y. Zhang, et al (Clinical and Experimental Medicine, 2025).

The study

• Authors: J. Li, H. He, Y. Zhang, M.S. Azhar, Y. Wang
• Journal: Clinical and Experimental Medicine
• Published: 2025;26(1):22
• Cited by: 2 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

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.

© 2025. The Author(s).

In plain English

This was a computer analysis of existing public genetic data on malignant pleural mesothelioma; the authors did no laboratory experiments. Using tumor gene-activity data from 84 patients in The Cancer Genome Atlas, they built a risk score from 12 genes and tested it on 39 patients from a second dataset. Patients with low scores lived longer than those with high scores (reported hazard ratios 4.61 and 5.87 in the two datasets). Accuracy (AUC; higher means better prediction) was 0.90 for 3-year survival in the first dataset, and 0.80 at 1 year and 0.95 at 2 years in the second. In existing drug-test data, mesothelioma cell lines with low scores had lower IC50 values (less drug needed) for vincristine, teniposide, gemcitabine and entospletinib. The article states its immunotherapy findings, based partly on 10 treated patients, in conflicting directions. History of asbestos exposure appeared only as one variable in a disease-specific survival chart (nomogram). The authors concluded the model predicts prognosis efficiently. They noted that the genes' functions were not tested experimentally and that there were too few immunotherapy patients.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Bioinformatics (computer) analysis of existing public genomic datasets to build and validate a 12-gene prognostic risk model; no new patients or laboratory experiments
• Who was studied: Tumor samples from public databases: 84 malignant pleural mesothelioma samples from TCGA (model building), 39 from GEO dataset GSE2549 (validation), a mesothelioma single-cell dataset (GSE172155), a mesothelioma immunotherapy dataset (GSE99070, including 10 samples with immunotherapy response), and 348 bladder cancer samples (EGAS00001002556) used for PD-L1 information. Countries and years of patient collection are not given.
• Exposure: Not an asbestos exposure study. The 'exposure' is the 12-gene risk score. History of asbestos exposure was one of several variables in the disease-specific survival nomogram; no separate result for asbestos is reported.
• Main results:
  • TCGA training set: low-risk patients survived longer than high-risk patients (p = 4.3e-9, HR = 4.61)
  • TCGA training set: AUC for predicting 1-, 2- and 3-year prognosis was 0.80, 0.89 and 0.90
  • GSE2549 validation set: better prognosis in the low-risk group (p = 1.4e-5, HR = 5.87)
  • GSE2549 validation set: AUC 0.80 for 1-year and 0.95 for 2-year prognosis
  • TCGA patients receiving chemotherapy: low-risk group had significantly longer overall survival (p = 4.6e-3, HR = 3.08). For chemotherapy combined with targeted therapy the difference was p = 0.08 (HR = 3.96).
  • Clinical response in TCGA: lower risk scores went with better response to targeted therapy, but not to chemotherapy
  • Mesothelioma cell lines (GDSC drug data): IC50 of vincristine, teniposide and gemcitabine was lower in the low-risk group; entospletinib IC50 was also lower in the low-risk group
  • PD-L1 protein expression in MPM was positively correlated with the risk score (cor = 0.32, p = 0.01)
  • Immunotherapy response: the article states that response increased with higher risk score, but also that risk scores were lower in the clinical benefit group than in the progression groups; the two statements conflict
• The authors' conclusion: The authors concluded that their model is an efficient prognostic model for MPM patients. They suggested it can guide the choice of chemotherapy drugs and immunotherapy.
• Limitations the authors state:
  • Too many genes were included to validate their functions experimentally in this study
  • No normal pleura sequencing data, so expression differences between cancerous and adjacent tissue could not be shown
  • The sample used to test prediction of immunotherapy response was too small

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer, Mesothelioma.

Medical terms in this study, explained in our Lexicon: Chemotherapy, Immunotherapy, Pleural Mesothelioma, Asbestos Exposure, Prognosis, Biomarker, Metastasis, Mesothelioma.

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