Clinical and molecular assessment of an onco-immune signature with prognostic significance in patients with colorectal cancer.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35137551", "kind": "science-record", "revision": 3, "content_hash": "sha256:d363c0bd7288f2de6036edcfa5e4b465ec76e0a0207c547977da44f341de57b8", "created_at": "2026-11-01T17:36:53Z", "updated_at": "2026-11-01T21:27:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Clinical and molecular assessment of an onco-immune signature with prognostic significance in patients with colorectal cancer.", "doi": "10.1002/cam4.4568", "pmid": "35137551", "authors": ["Pankaj Ahluwalia", "Ashis K Mondal", "Meenakshi Ahluwalia", "Nikhil S Sahajpal", "Kimya Jones", "Yasmeen Jilani", "Gagandeep K Gahlay", "Amanda Barrett", "Vamsi Kota", "Amyn M Rojiani", "Ravindra Kolhe"], "journal": "Cancer medicine", "year": 2022, "abstract": "Understanding the complex tumor microenvironment is key to the development of personalized therapies for the treatment of cancer including colorectal cancer (CRC). In the past decade, significant advances in the field of immunotherapy have changed the paradigm of cancer treatment. Despite significant improvements, tumor heterogeneity and lack of appropriate classification tools for CRC have prevented accurate risk stratification and identification of a wider patient population that may potentially benefit from targeted therapies. To identify novel signatures for accurate prognostication of CRC, we quantified gene expression of 12 immune-related genes using a medium-throughput NanoString quantification platform in 93 CRC patients. Multivariate prognostic analysis identified a combined four-gene prognostic signature (TGFB1, PTK2, RORC, and SOCS1) (HR: 1.76, 95% CI: 1.05-2.95, *p < 0.02). The survival trend was captured in an independent gene expression data set: GSE17536 (177 patients; HR: 3.31, 95% CI: 1.99-5.55, *p < 0.01) and GSE14333 (226 patients; HR: 2.47, 95% CI: 1.35-4.53, *p < 0.01). Further, gene set enrichment analysis of the TCGA data set associated higher prognostic scores with epithelial-mesenchymal transition (EMT) and inflammatory pathways. Comparatively, a lower prognostic score was correlated with oxidative phosphorylation and MYC and E2F targets. Analysis of immune parameters identified infiltration of T-reg cells, CD8+ T cells, M2 macrophages, and B cells in high-risk patient groups along with upregulation of immune exhaustion genes. This molecular study has identified a novel prognostic gene signature with clinical utility in CRC. Therefore, along with prognostic features, characterization of immune cell infiltrates and immunosuppression provides actionable information that should be considered while employing personalized medicine.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox proportional hazard models were used to analyze clinicopathological variables. Patients were stratified into low‐ and high‐risk groups according to the median risk score, and survival was assessed using the Kaplan–Meier method and the log‐rank test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35137551", "locator": "sections[7] \"Identification of prognostic gene signature\" paragraph 2", "quote": "Univariate and multivariate Cox proportional hazard models were used to analyze clinicopathological variables."}, {"asset": "source:pmid:35137551", "locator": "sections[7] \"Identification of prognostic gene signature\" paragraph 2", "quote": "Patients were stratified into low‐ and high‐risk groups according to the median risk score, and survival was assessed using the Kaplan–Meier method and the log‐rank test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data sets used and/or analyzed in the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35137551", "locator": "sections[26] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The data sets used and/or analyzed in the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The survival trend was captured in an independent gene expression data set: GSE17536 (177 patients; HR: 3.31, 95% CI: 1.99–5.55, *p < 0.01) and GSE14333 (226 patients; HR: 2.47, 95% CI: 1.35–4.53, *p < 0.01).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Multivariate prognostic analysis identified a combined four‐gene prognostic signature (TGFB1, PTK2, RORC, and SOCS1) (HR: 1.76, 95% CI: 1.05–2.95, *p < 0.02)."}, "evidence": [{"asset": "source:pmid:35137551", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Multivariate prognostic analysis identified a combined four‐gene prognostic signature (TGFB1, PTK2, RORC, and SOCS1) (HR: 1.76, 95% CI: 1.05–2.95, *p < 0.02)."}, {"asset": "source:pmid:35137551", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The survival trend was captured in an independent gene expression data set: GSE17536 (177 patients; HR: 3.31, 95% CI: 1.99–5.55, *p < 0.01) and GSE14333 (226 patients; HR: 2.47, 95% CI: 1.35–4.53, *p < 0.01)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The survival trend was captured in an independent gene expression data set: GSE17536 (177 patients; HR: 3.31, 95% CI: 1.99–5.55, *p < 0.01) and GSE14333 (226 patients; HR: 2.47, 95% CI: 1.35–4.53, *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:35137551"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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