Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37214439", "kind": "science-record", "revision": 3, "content_hash": "sha256:5e55eb3549b0fa006402fcc1e2c599ca8aa96652ee48bcd8c69b111c927aa42a", "created_at": "2026-11-01T19:01:54Z", "updated_at": "2026-11-01T22:09:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.", "doi": "10.3389/fphar.2023.1188473", "pmid": "37214439", "authors": ["Mengmeng Liu", "Quan Li", "Yao Liang"], "journal": "Frontiers in pharmacology", "year": 2023, "abstract": "Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.", "claims": [{"id": "claim-method", "statement": "Moreover, the predictive significance of PRGs was examined using LASSO cox regression analysis for evaluating correlations among the genes as well as their status of survival in the TCGA-SARC cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37214439", "locator": "sections[5] \"2.3 Development and validation of the PRGs prognostic model\" paragraph 2", "quote": "Moreover, the predictive significance of PRGs was examined using LASSO cox regression analysis for evaluating correlations among the genes as well as their status of survival in the TCGA-SARC cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37214439", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used LASSO Cox regression to develop a five-gene PRGs survival risk model and validated it in TCGA-SARC and three independent GEO cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37214439", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used LASSO Cox regression to develop a five-gene PRGs survival risk model and validated it in TCGA-SARC and three independent GEO cohorts.", "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:37214439"], "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. 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