Single-cell profiling of immunogenic cell death in melanoma reveals prognostic signatures and therapeutic targets.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41543608", "kind": "science-record", "revision": 3, "content_hash": "sha256:f18cbfecdd2a2e87a99b4f8925cd1bd68171aff676738d8e9e4be8fe351b7140", "created_at": "2026-11-01T20:31:30Z", "updated_at": "2026-11-01T22:15:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell profiling of immunogenic cell death in melanoma reveals prognostic signatures and therapeutic targets.", "doi": "10.1007/s12672-026-04406-5", "pmid": "41543608", "authors": ["Jingwen Huang", "Taoxuan Jiang", "Yiyang Han", "Chunsheng Yang"], "journal": "Discover oncology", "year": 2026, "abstract": "BACKGROUND: Cutaneous melanoma is one of the most aggressive forms of skin cancer, marked by rapid progression, early metastasis, and high resistance to conventional therapies. Although targeted therapies and immune checkpoint blockade have improved patient outcomes, many still relapse or fail to respond. This underscores the need for novel biomarkers to refine risk stratification and predict therapeutic responses. Immunogenic cell death (ICD) is a critical process linking tumor cell death to immune activation, which could enhance melanoma treatment responses.\nOBJECTIVE: This study investigates the role of ICD in melanoma by integrating bulk transcriptomic data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model based on ICD, and to explore its clinical relevance.\nMETHODS: Bulk transcriptomic data were sourced from TCGA and GEO, while single-cell RNA-seq data were obtained from GSE215120. ICD-related genes were curated from the literature, and ICD scores were calculated using ssGSEA and GSVA. Machine learning algorithms were used to construct and validate a prognostic model. Immune infiltration and tumor microenvironment (TME) were analyzed to assess the relationship between ICD activity and immune responses.\nRESULTS: High ICD scores correlated with poor prognosis, and ICD activity was predominantly observed in stromal and myeloid cells. MYO10, identified as a key gene in the ICD signature, was associated with immune evasion and poor clinical outcomes. The ICD-based risk score (ICDRS) demonstrated superior prognostic power in multiple melanoma cohorts and reflected immune activation and TME characteristics.\nCONCLUSION: The ICDRS, incorporating MYO10, provides a robust prognostic tool for melanoma and highlights MYO10 as a potential therapeutic target for improving immune responses in melanoma therapy.", "claims": [{"id": "claim-method", "statement": "In particular, the RSF + plsRcox combination achieved the highest overall discrimination, with a C-index of about 0.71 in the gse19234 cohort and approximately 0.62–0.64 in the remaining cohorts, giving the best average C-index (0.632).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41543608", "locator": "sections[19] \"Performance comparison of 101 machine-learning–based prognostic models\" paragraph 2", "quote": "In particular, the RSF + plsRcox combination achieved the highest overall discrimination, with a C-index of about 0.71 in the gse19234 cohort and approximately 0.62–0.64 in the remaining cohorts, giving the best average C-index (0.632)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study were obtained from public repositories, including The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE19234, GSE22153, GSE53118, GSE54467, GSE59455, GSE65904.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41543608", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "The datasets analyzed in this study were obtained from public repositories, including The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE19234, GSE22153, GSE53118, GSE54467, GSE59455, GSE65904."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The RSF + plsRcox combination achieved the highest overall discrimination, with a C-index of about 0.71 in the gse19234 cohort and approximately 0.62–0.64 in the remaining cohorts, giving the best average C-index (0.632).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41543608", "locator": "sections[19] \"Performance comparison of 101 machine-learning–based prognostic models\" paragraph 2", "quote": "In particular, the RSF + plsRcox combination achieved the highest overall discrimination, with a C-index of about 0.71 in the gse19234 cohort and approximately 0.62–0.64 in the remaining cohorts, giving the best average C-index (0.632)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The RSF + plsRcox combination achieved the highest overall discrimination, with a C-index of about 0.71 in the gse19234 cohort and approximately 0.62–0.64 in the remaining cohorts, giving the best average C-index (0.632).", "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:41543608"], "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": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41543608", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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