Development and Validation of a Prognostic Signature for Malignant Pleural Mesothelioma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30828567", "kind": "science-record", "revision": 8, "content_hash": "sha256:9da81dac42945f4cefcac251d5034011d6482096b924e6e7d9e4f90e56f33a0f", "created_at": "2026-11-01T15:40:43Z", "updated_at": "2026-11-01T22:00:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and Validation of a Prognostic Signature for Malignant Pleural Mesothelioma.", "doi": "10.3389/fonc.2019.00078", "pmid": "30828567", "authors": ["Jian-Guo Zhou", "Hua Zhong", "Juan Zhang", "Su-Han Jin", "Raheleh Roudi", "Hu Ma"], "journal": "Frontiers in oncology", "year": 2019, "abstract": "Introduction: Dysregulated genes play a critical role in the development and progression of cancer, suggesting their potential as novel independent biomarkers for cancer diagnosis and prognosis. Prognostic model-based gene expression profiles are not widely utilized in clinical medicine. We investigated the prognostic significance of an expression profile-based gene signature for outcome prediction in patients with malignant pleural mesothelioma (MPM). Methods: The gene expression profiles of a large cohort of patients with MPM were obtained and analyzed by repurposing publicly available microarray data. A gene-based risk score model was developed with the training dataset and then validated with the TCGA-MESO (mesothelioma) dataset. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance of survival prediction. The biological function of the prognostic genes was predicted using bioinformatics analysis. Results: Three genes in the training dataset (GSE2549) were identified as significantly associated with the overall survival (OS) of patients with MPM and were combined to develop a three-gene prognostic signature to stratify patients into low-risk and high-risk groups. The MPM patients of the training dataset in the low-risk group exhibited longer OS than those in the high-risk group (HR = 0.25, 95% CI = 0.11-0.56, P < 0.001). Similar prognostic values for the three-gene signature were observed in the validated TCGA-MESO cohort (HR = 0.53 95% CI = 0.33-0.85, P = 0.008). ROC analysis also demonstrated the good performance in predicting 3-year OS in the GEO and TCGA cohorts (KM-AUC for GEO = 0.989, KM-AUC for TCGA = 0.618). The C-statistic for the 3-gene model was 0.761. Validation with TCGA-MESO confirmed the model's ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic: 0.68). Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to MPM. Conclusions: This study has identified and validated a novel 3-gene model to reliably discriminate patients at high and low risk of death in unselected populations of patients with MPM. Further larger, prospective multi-institutional cohort studies are necessary to validate this model.", "claims": [{"id": "claim-method", "statement": "Kaplan-Meier analysis with the log-rank test for difference was performed by the R package survival (25).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30828567", "locator": "sections[4] \"Identification of Prognosis-Related Genes\" paragraph 2", "quote": "Kaplan-Meier analysis with the log-rank test for difference was performed by the R package survival (25)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The 49 RNA expression profiles included data from MPM surgical specimens (n = 40), normal pleura specimens (n = 5), and normal lung specimens (n = 4) in GSE2549 (Affymetrix Human Genome U133A Array) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE2549) (21).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30828567", "locator": "sections[3] \"Data Processing and Computational Analysis\" paragraph 2", "quote": "The 49 RNA expression profiles included data from MPM surgical specimens (n = 40), normal pleura specimens (n = 5), and normal lung specimens (n = 4) in GSE2549 (Affymetrix Human Genome U133A Array) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE2549) (21)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:30828567"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE2549"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE2549"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30828567", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2019.00078", "sha256": 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