Identification of immune subtypes and their prognosis and molecular implications in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36417424", "kind": "science-record", "revision": 3, "content_hash": "sha256:2c416cb39b12a345cf456d28f1971369d3e0af0633501ef4a71d69300fa845a5", "created_at": "2026-11-01T19:00:53Z", "updated_at": "2026-11-01T22:50:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune subtypes and their prognosis and molecular implications in colorectal cancer.", "doi": "10.1371/journal.pone.0278114", "pmid": "36417424", "authors": ["Yan Sun", "Hongping Li", "Zhiming Ma", "Jianfei Wang", "Huiyu Yang", "Xiaopeng Zhang", "Bingrong Liu"], "journal": "PloS one", "year": 2022, "abstract": "Immune composition is commonly heterogeneous and varies among colorectal cancer (CRC) patients. A comprehensive immune classification may act as important characteristics to predict CRC prognosis. Thus, we aimed to identify novel immune specific subtypes to guide future therapies. Unsupervised clustering was used to classify CRC samples into different immune subtypes based on abundances of immune cell populations, during which TCGA and GSE17536 datasets were used as training and validation sets, respectively. The associations between the immune subtypes and patient prognosis were investigated. Further, we identified differentially expressed genes (DEGs) between immune high and low subtypes, followed by functional enrichment analyses of DEGs. The expression levels of 74 immunomodulators (IMs) across immune subtypes were analyzed. As a result, we clustered CRC samples into three distinct immune subtypes (immune high, moderate, and low). Patients with immune-high subtype showed the best prognosis, and patients with immune-low subtype had the worst survival in both TCGA and GSE17536 cohorts. A group of 2735 up-regulated DEGs were identified across immune high and low subtypes. The main DEGs were the members of complement components, chemokines, immunoglobulins, and immunosuppressive genes that are involved in immune modulation-related pathways (e.g., cytokine-cytokine receptor interaction) or GO terms (e.g., adaptive immune response and T cell activation). The expression levels of 63 IMs were significantly varied across immune subtypes. In conclusion, this study provides a conceptual framework and molecular characteristics of CRC immune subtypes, which may accurately predict prognosis and offer novel targets for personalized immunotherapy through modifying subtype-specific tumor immune microenvironment.", "claims": [{"id": "claim-method", "statement": "Kaplan-Meier analysis was conducted to explore the prognostic associations of immune subtypes using the “survival” package (version 3.1–11) in R and the Kaplan-Meier curves were plotted by the “survmine” package (version 0.4.6).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36417424", "locator": "sections[5] \"Prognostic associations of immune subtypes\" paragraph 2", "quote": "Kaplan-Meier analysis was conducted to explore the prognostic associations of immune subtypes using the “survival” package (version 3.1–11) in R and the Kaplan-Meier curves were plotted by the “survmine” package (version 0.4.6)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA-sequencing data of 433 colon adenocarcinoma (COAD) samples and related clinical information including age, sex, stage, vital status, and time to last contact were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) as the training set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36417424", "locator": "sections[3] \"Public transcriptomic data acquirement and preprocessing\" paragraph 2", "quote": "The RNA-sequencing data of 433 colon adenocarcinoma (COAD) samples and related clinical information including age, sex, stage, vital status, and time to last contact were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) as the training set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CRC samples were classified into three immune subtypes, with the immune-high subtype having the best prognosis and the immune-low subtype the worst; 2735 genes were up-regulated and 63 immunomodulators varied significantly across the subtypes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36417424", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "As a result, we clustered CRC samples into three distinct immune subtypes (immune high, moderate, and low)."}, {"asset": "source:pmid:36417424", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Patients with immune-high subtype showed the best prognosis, and patients with immune-low subtype had the worst survival in both TCGA and GSE17536 cohorts."}, {"asset": "source:pmid:36417424", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A group of 2735 up-regulated DEGs were identified across immune high and low subtypes."}, {"asset": "source:pmid:36417424", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The expression levels of 63 IMs were significantly varied across immune subtypes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CRC samples were classified into three immune subtypes, with the immune-high subtype having the best prognosis and the immune-low subtype the worst; 2735 genes were up-regulated and 63 immunomodulators varied significantly across the subtypes.", "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:36417424"], "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. 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