UCN-Centric Prognostic Model for Predicting Overall Survival and Immune Response in Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39336730", "kind": "science-record", "revision": 3, "content_hash": "sha256:2c3094a279600a694f47c743351c2e57f46322981047d5ad913ca69b26fac456", "created_at": "2026-11-01T20:29:32Z", "updated_at": "2026-11-01T22:12:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "UCN-Centric Prognostic Model for Predicting Overall Survival and Immune Response in Colorectal Cancer.", "doi": "10.3390/genes15091139", "pmid": "39336730", "authors": ["Jia Liu", "Feiliang Zhong", "Yue Chen"], "journal": "Genes", "year": 2024, "abstract": "Colorectal cancer (CRC), a prevalent malignancy, ranks third in global incidence and second in mortality rates. Despite advances in screening methods such as colonoscopy, the accurate diagnosis of CRC remains challenging due to the absence of reliable biomarkers. This study aimed to develop a robust prognostic model for precise CRC outcome prediction. Employing weighted co-expression network analysis (WGCNA) and Cox regression analysis on data from The Cancer Genome Atlas (TCGA), we identified a panel of 12 genes strongly associated with patient survival. This gene panel facilitated accurate CRC outcome predictions, which is also validated via the external validation cohort GSE17536. We conducted further investigations into the key gene, urocortin (UCN), using single-cell transcriptomic data and immune infiltration analysis in CRC patients. Our results revealed a significant correlation between high UCN expression and the reduced prevalence of key immune cells, including B cells, CD4+ cytotoxic T cells, CD8+ T cells, and NKT cells. Functional experiments showed that UCN gene interference in the CRC cell lines significantly decreased cancer cell proliferation, underscoring UCN's role in intestinal immunity modulation. The UCN-centric prognostic model developed enhances prognosis prediction accuracy and offers critical insights for CRC diagnosis and therapeutic interventions.", "claims": [{"id": "claim-method", "statement": "Employing weighted co-expression network analysis (WGCNA) and Cox regression analysis on data from The Cancer Genome Atlas (TCGA), we identified a panel of 12 genes strongly associated with patient survival.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39336730", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Employing weighted co-expression network analysis (WGCNA) and Cox regression analysis on data from The Cancer Genome Atlas (TCGA), we identified a panel of 12 genes strongly associated with patient survival."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The bulk RNA-seq dataset TCGA-COAD was obtained from The Cancer Genome Atlas (https://portal.gdc.cancer.gov/, accessed on 1 January 2023). 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