Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38352883", "kind": "science-record", "revision": 3, "content_hash": "sha256:30a643704a46905b37ecf65e4c1956dbdbe0d72efdd49a162799e9fa052d3373", "created_at": "2026-11-01T19:20:17Z", "updated_at": "2026-11-01T22:11:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion.", "doi": "10.3389/fimmu.2024.1258475", "pmid": "38352883", "authors": ["Pengping Li", "Shaowen Wang", "Hong Wan", "Yuqing Huang", "Kexin Yin", "Ke Sun", "Haigang Jin", "Zhenyu Wang"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Given the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity.\nMethods: Based on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer. SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET.\nResults: The expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells.\nConclusion: To summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs. C-MET was screened and validated for its tumor driver gene and immune regulation function (inducing t-cell exhaustion) in glioma.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the gene signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38352883", "locator": "sections[6] \"Prognosis model construction\" paragraph 2", "quote": "Univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the 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:38352883", "locator": "sections[29] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE22138"], "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:GSE22138"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38352883", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1258475", "sha256": "sha256:05b53adc440500b1088fba52427b16ee337deb44c728b46e003afb469c1df752", "bytes": 47958, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2024.1258475", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10862485", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE22138", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE22138", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE22138", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material. 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The siRNA transfection, FACS, ELISA, etc., were employed to validat", "ced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employ", "ating detail for PBMC co-cultured with c-MET-knockdown ln299 cell for 48h.Click here for additional"], "order_basis": "paper_text_order:937", "id": "assay:1"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the"], "order_basis": "paper_text_order:957", "id": "assay:2"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["de for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the functio"], "order_basis": "paper_text_order:963", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["bank with STR matching analysis. Cells were cultured in recommended conditions. Co-culture was done", "ementary Figure 1Gating strategy for PBMC co-cultured with Ln299 cells. (A) The gating detail for PBMC", "PBMC only. (B) The gating detail for PBMC co-cultured with ln299 cell for 48h. (C) The gating detail fo", "l for 48h. (C) The gating detail for PBMC co-cultured with c-MET-knockdown ln299 cell for 48h.Click her"], "order_basis": "paper_text_order:2651", "id": "assay:4"}, {"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": ["Univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the gene signature."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}