Identification of Novel Pyroptosis-Related Gene Signatures to Predict Prostate Cancer Recurrence.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35669428", "kind": "science-record", "revision": 4, "content_hash": "sha256:97f27263d9f0628946d2d18bdfda9fd23b1bedc10b5c42c7c6d12dd6c9be3eb2", "created_at": "2026-11-01T18:14:37Z", "updated_at": "2026-11-01T22:06:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Novel Pyroptosis-Related Gene Signatures to Predict Prostate Cancer Recurrence.", "doi": "10.3389/fonc.2022.814912", "pmid": "35669428", "authors": ["Chun Li", "Jie Zhu", "Hexi Du", "Chaozhao Liang"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Prostate cancer (PCa) is a common malignant type of urogenital tract tumor with poor prognosis. Despite therapeutic advances, the recurrence and mortality rates of PCa have continued to increase with poor prognoses. Pyroptosis, also known as inflammatory cell necrosis, is a recently identified type of programmed cell death that can regulate the invasiveness, differentiation, proliferation, and metastasis of tumor cells; thus, it has a profound effect on the prognosis of patients with tumors. However, the relationship between pyroptosis and PCa remains unclear. We first identified 25 pyroptosis-related genes (PRGs) that were differentially expressed between PCa tissues and matched normal tissues in The Cancer Genome Atlas (TCGA) cohort. Based on the expression levels of 25 PRGs, PCa patients were clearly divided into two clusters and 17 PRGs were found to be significantly different between the two clusters, suggesting probable roles for these genes in the progression and recurrence of PCa. Therefore, the GSE40272 dataset with recurrence follow-up information was used to verify their value. Univariate analysis suggested that 5/17 genes were associated with recurrence, the number of genes did not decrease after least absolute shrinkage and selection operator (LASSO) regression analysis, and 5 PRGs constituted the risk score formula. Low-risk and high-risk subgroups identified using the recurrence model showed different disease-free survival (DFS) times (P<0.001) and the risk score of five PRGs was a factor of independence for recurrence in patients with PCa. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that these pathways, and comprising PRGs might be closely related to carcinogenesis and invasion of tumors, tumor microenvironment, and immune response. In conclusion, the expression signatures of PRGs play an important role in predicting PCa recurrence.", "claims": [{"id": "claim-method", "statement": "First, univariate Cox regression analysis was used to assess the association between DFS status and 17 PRGs to evaluate their prognostic value. With 0.05 as the cut-off value, 5/17 genes involved in recurrence were identified for further analysis. Subsequently, the LASSO Cox regression model (R package “glmnet”) was used to construct a refined recurrence prediction model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35669428", "locator": "sections[13] \"Establishment and Validation of the PRG Model of Recurrence and Prognosis\" paragraph 2", "quote": "First, univariate Cox regression analysis was used to assess the association between DFS status and 17 PRGs to evaluate their prognostic value. With 0.05 as the cut-off value, 5/17 genes involved in recurrence were identified for further analysis. Subsequently, the LASSO Cox regression model (R package “glmnet”) was used to construct a refined recurrence prediction model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35669428", "locator": "sections[26] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Low-risk and high-risk subgroups identified using the recurrence model showed different disease-free survival (DFS) times (P<0.001) and the risk score of five PRGs was a factor of independence for recurrence in patients with PCa.", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "Low-risk and high-risk subgroups identified using the recurrence model showed different disease-free survival (DFS) times (P<0.001) and the risk score of five PRGs was a factor of independence for recurrence in patients with PCa."}, "evidence": [{"asset": "source:pmid:35669428", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Low-risk and high-risk subgroups identified using the recurrence model showed different disease-free survival (DFS) times (P<0.001) and the risk score of five PRGs was a factor of independence for recurrence in patients with PCa."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Low-risk and high-risk subgroups identified using the recurrence model showed different disease-free survival (DFS) times (P<0.001) and the risk score of five PRGs was a factor of independence for recurrence in patients with PCa.", "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:35669428"], "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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All PC cell lines were cultured in RP1640 medium (RP1640, Gibco) supplemente", ", Manassas, VA, USA). All PC cell lines were cultured in RP1640 medium (RP1640, Gibco) supplemented w"], "order_basis": "paper_text_order:2207", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["First, univariate Cox regression analysis was used to assess the association between DFS status and 17 PRGs to evaluate their prognostic value. With 0.05 as the cut-off value, 5/17 genes involved in recurrence were identified for further analysis. Subsequently, the LASSO Cox regression model (R package “glmnet”) was used to construct a refined recurrence prediction model."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}