Integrative multi-omics reveal a CD4⁺ T cell-derived six-gene signature linking the immune-stromal ecosystem to prognosis and immunotherapy selection in pancreatic cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41572218", "kind": "science-record", "revision": 3, "content_hash": "sha256:100f47c1f957032cd155d4c165800cb433449332608956439391c59d0cb932b5", "created_at": "2026-11-01T20:31:32Z", "updated_at": "2026-11-01T22:15:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative multi-omics reveal a CD4⁺ T cell-derived six-gene signature linking the immune-stromal ecosystem to prognosis and immunotherapy selection in pancreatic cancer.", "doi": "10.1186/s12885-026-15613-2", "pmid": "41572218", "authors": ["Yuxin Liang", "Shuoshuo Ma", "Xing Chen", "Yuhao Su", "Deyuan Zhong", "Hongtao Yan", "Yahui Chen", "Ming Wang", "Zhengwei Leng", "Xiaolun Huang"], "journal": "BMC cancer", "year": 2026, "abstract": "BACKGROUND: Pancreatic cancer (PC) exhibits dismal outcomes and an immune-excluded microenvironment that blunts immunotherapy. Robust, immune-anchored biomarkers are needed to stratify risk and inform treatment design.\nMETHODS: We integrated The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) dataset and validated findings in Gene Expression Omnibus (GEO) dataset (GSE57495). Immune infiltration, survival modeling, pathway activity, tumor microenvironment, and immunotherapy responses were analyzed. Single-cell RNA sequencing (GSE212966) informed CD4⁺ T-cell differentially expressed genes (DEGs), pseudotime, and intercellular communication (CellChat). Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot in H6C7, CAPAN-1, and PANC-1 cell lines were performed.\nRESULTS: Among 28 immune subsets, CD4⁺ T-cell populations most strongly associated with overall survival (OS). A six-gene signature (KLF3, EZR, SMDT1, JPT1, ISG15, MT1X) derived from 111 CD4⁺ T-cell DEGs successfully stratified OS with time-dependent AUCs ranging from 0.637 to 0.838 across the training and validation cohorts. The high-risk group showed significantly poorer OS, higher stromal scores, broad checkpoint upregulation, and greater immune exclusion (P < 0.05), indicating an immune-stromal ecosystem favoring tolerance. Single-cell RNA sequencing localized model genes to fibroblasts, ductal cells, and CD4⁺ T cells subsets. CellChat revealed globally intensified crosstalk in PC and strengthened CD4⁺ T-cell interactions with myeloid and stromal compartments. Western blot and qRT-PCR results confirmed up-expression of all model gene except SMDT1 in PC cell lines.\nCONCLUSIONS: CD4⁺ T-cell subsets serve as central determinant of the immune landscape and clinical outcome in PC. The CD4⁺ T cell-anchored six-gene signature enables risk stratification, links immune contexture to stromal ecology, and motivates biomarker-guided trials and rational combinations that alleviate immunosuppression while targeting metabolic-stress pathways.", "claims": [{"id": "claim-method", "statement": "In the training set, univariate Cox proportional hazards regression analysis was applied to the DEGs combined with survival information, with a significance threshold set at P < 0.01. Genes satisfying the PH assumption were subsequently included in the least absolute shrinkage and selection operator (LASSO) regression analysis to identify prognostic genes. Multivariate Cox regression model was then constructed based on the expression levels of these prognostic genes in the TCGA dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41572218", "locator": "sections[8] \"Construction of the prognostic risk model\" paragraph 2", "quote": "In the training set, univariate Cox proportional hazards regression analysis was applied to the DEGs combined with survival information, with a significance threshold set at P < 0.01. Genes satisfying the PH assumption were subsequently included in the least absolute shrinkage and selection operator (LASSO) regression analysis to identify prognostic genes. 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A Western blot validation of protein expression for the six"], "order_basis": "paper_text_order:865", "id": "assay:3"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["h GAPDH as loading control. 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