Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39182081", "kind": "science-record", "revision": 3, "content_hash": "sha256:0289a42583031de9902a8773e3444eaceb9da483f8bc0d37e140234a65494906", "created_at": "2026-11-01T20:29:18Z", "updated_at": "2026-11-01T22:12:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer.", "doi": "10.1186/s12935-024-03462-7", "pmid": "39182081", "authors": ["Ming Wang", "Bangshun Dai", "Qiushi Liu", "Xiansheng Zhang"], "journal": "Cancer cell international", "year": 2024, "abstract": "BACKGROUND: Prostate cancer is one of the most common cancers in men with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles in tumor progression and can potentially serve as prognostic and therapeutic biomarkers in PCa. This study aimed to develop a prognostic signature for BCR in PCa using PCD-related genes.\nMATERIALS AND METHODS: We conducted an analysis of 19 different modes of PCD to develop a comprehensive model. Bulk transcriptomic, single-cell transcriptomic, genomic, and clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, and GSE193337. We analyzed the expression and mutations of the 19 PCD modes and constructed, evaluated, and validated the model.\nRESULTS: Ten PCD modes were found to be associated with BCR in PCa, with specific PCD patterns exhibited by various cell components within the tumor microenvironment. Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature. High PCDI values were validated in five independent datasets and were found to be associated with an increased risk of BCR in PCa patients. Notably, older age and advanced T and N staging were associated with higher PCDI values. By combining PCDI with T staging, we constructed a nomogram with enhanced predictive performance. Additionally, high PCDI values were significantly correlated with decreased drug sensitivity, including drugs such as Docetaxel and Methotrexate. Patients with lower PCDI values demonstrated higher immunophenoscores (IPS), suggesting a potentially higher response rate to immune therapy. Furthermore, PCDI was associated with immune checkpoint genes and key components of the tumor microenvironment, including macrophages, T cells, and NK cells. Finally, clinical specimens validated the differential expression of PCDI-related PCDRGs at both the gene and protein levels.\nCONCLUSION: In conclusion, we developed a novel PCD-based prognostic feature that successfully predicted BCR in PCa patients and provided insights into drug sensitivity and potential response to immune therapy. These findings have significant clinical implications for the treatment of PCa.", "claims": [{"id": "claim-method", "statement": "To further refine the candidate signature, we utilized the LASSO Cox regression method with the glmnet package, employing the deviance method for loss in cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39182081", "locator": "sections[6] \"Construction of PCD prognostic model\" paragraph 2", "quote": "To further refine the candidate signature, we utilized the LASSO Cox regression method with the glmnet package, employing the deviance method for loss in cross-validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39182081", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "Data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39182081", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature.", "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:39182081"], "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:GSE116918"], "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:GSE116918"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39182081", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12935-024-03462-7", "sha256": 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