Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39439891", "kind": "science-record", "revision": 3, "content_hash": "sha256:bd276765ce41210912de55f14defb97bd3a89806e22bbee42c1c29585d491415", "created_at": "2026-11-01T20:29:42Z", "updated_at": "2026-11-01T22:13:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients.", "doi": "10.3389/fphar.2024.1295687", "pmid": "39439891", "authors": ["Zewei Chen", "Ruopeng Zhang", "Zhoukai Zhao", "Baiwei Zhao", "Feiyang Zhang", "Guoming Chen", "Xiaojiang Chen", "Chengzhi Wei", "Jun Lin", "Feizhi Lin", "Ziqi Zheng", "Kaiming Jiang", "Runcong Nie", "Yingbo Chen"], "journal": "Frontiers in pharmacology", "year": 2024, "abstract": "Background: Melanoma, a malignant tumor of the skin, presents challenges in its treatment process involving modalities such as surgery, chemotherapy, and targeted therapy. However, there is a need for an ideal model to assess prognosis and drug sensitivity. Programmed cell death (PCD) modes play a crucial role in tumor progression and has the potential to serve as prognostic and drug sensitivity indicators for melanoma.\nMethods: We analyzed 13 PCD modes including apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, disulfidptosis, and alkaliptosis. These modes were used to construct a model that incorporated genes related to these 13 PCD modes to establish a cell death index (CDI) to conduct prognosis analysis. Transcriptomic, genomic, and clinical data were collected from cohorts including TCGA-SKCM, GSE19234, and GSE65904 to validate this model.\nResults: A CDI consisting of ten gene signatures was established using machine learning algorithms and divided into two groups based on CDI values. The high CDI group exhibited relatively lower numbers of immune-infiltrating cells and showed resistance to commonly used drugs such as docetaxel and axitinib. Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01).\nConclusion: The CDI model, incorporating multiple PCD modes, accurately predicts the clinical prognosis and drug sensitivity of melanoma patients.", "claims": [{"id": "claim-method", "statement": "Furthermore, we employed the LASSO Cox regression method to narrow down the candidate gene set and construct the optimal gene signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39439891", "locator": "sections[5] \"2.3 Development of the PCD-related gene signature\" paragraph 2", "quote": "Furthermore, we employed the LASSO Cox regression method to narrow down the candidate gene set and construct the optimal gene signature."}], "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 authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39439891", "locator": "sections[22] \"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 authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01).", "kind": "quantitative", "target": {"value": 0.01, "unit": "p-value", "tolerance": null, "source": "Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01)."}, "evidence": [{"asset": "source:pmid:39439891", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01).", "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:39439891"], "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:GSE19234"], "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:GSE19234"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39439891", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2024.1295687", "sha256": 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