Interferon regulatory factor family influences tumor immunity and prognosis of patients with colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34488791", "kind": "science-record", "revision": 7, "content_hash": "sha256:2a2da199682de1d5853c81006d0134c724c67460c6cb90f6fd685ed91e2cce82", "created_at": "2026-11-01T15:41:50Z", "updated_at": "2026-11-01T22:04:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Interferon regulatory factor family influences tumor immunity and prognosis of patients with colorectal cancer.", "doi": "10.1186/s12967-021-03054-3", "pmid": "34488791", "authors": ["Yan-Jie Chen", "Shu-Neng Luo", "Ling Dong", "Tao-Tao Liu", "Xi-Zhong Shen", "Ning-Ping Zhang", "Li Liang"], "journal": "Journal of translational medicine", "year": 2021, "abstract": "BACKGROUND: Since interferon regulatory factor (IRF) family functions in immune response to viral infection, its role in colorectal cancer (CRC) has not been inspected before. This study tries to investigate members of IRF family using bioinformatics approaches in aspect of differential expressions, biological function, tumor immune infiltration and clinical prognostic value for patients with CRC.\nMETHODS: Transcriptome profiles data, somatic mutations and clinical information of CRC were obtained from COAD/READ dataset of The Cancer Genome Atlas (TCGA) as a training set. Gene expression data (GSE17536 and GSE39582) were downloaded from the Gene Expression Omnibus as a validating set. A random forest algorithm was used to score the risk for every case. Analyzing gene and function enrichment, constructing protein-protein interaction and noncoding RNA network, identifying hub-gene, characterizing tumor immune infiltration, evaluating differences in tumor mutational burden (TMB) and sensitivity to chemotherapeutics or immunotherapy were performed by a series of online tools and R packages. Immunohistochemical (IHC) examinations were carried out validation in tissue samples.\nRESULTS: Principal-component analysis (PCA) suggested that the transcript expression levels of nine members of IRF family differed between normal colorectum and CRC. The risk score constructed by IRF family not only acted as an independent factor for predicting survival in CRC patients with different biological processes, signaling pathways and TMB, but also indicated different immunotherapy response with diverse immune and stromal cells infiltration. IRF3 and IRF7 were upregulated in CRC and suggested a shorter survival time in patients with CRC. Differentially expressed members of IRF family exhibited varying degrees of immune cell infiltration. IHC analysis showed a positive association between IRF3 and IRF7 expression and tumor-infiltrating immune cells, including CD4+ T cell and CD68+ macrophages.\nCONCLUSIONS: On account of differential expression, IRF family members can help to predict both response to immunotherapy and clinical prognosis of patients with CRC. Our bioinformatic investigation not only gives a preliminary picture of the genetic features as well as tumor microenvironment, but it may provide a clue for further experimental exploration and verification on IRF family members in CRC.", "claims": [{"id": "claim-method", "statement": "Correlation coefficients between different genes were calculated via Pearson correlation analysis. We downloaded gene expression data from cell lines and IC50 values to analyze correlations between differentially expressed IRF genes and anticancer drug sensitivities.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34488791", "locator": "sections[14] \"Statistical analysis\" paragraph 2", "quote": "Correlation coefficients between different genes were calculated via Pearson correlation analysis."}, {"asset": "source:pmid:34488791", "locator": "sections[10] \"Analysis of anticancer therapy sensitivity\" paragraph 2", "quote": "We downloaded gene expression data from cell lines and IC50 values to analyze correlations between differentially expressed IRF genes and anticancer drug sensitivities."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during our study are included in the published article or its Additional files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34488791", "locator": "sections[31] \"notes\" paragraph 1", "quote": "All data generated or analyzed during our study are included in the published article or its Additional files."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:34488791"], "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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TCGA", "rows": [["Variable", "Univariate Cox analysis", "Multivariate Cox analysis"], ["HR (95% CI)", "P value", "HR (95% CI)", "P value"], ["Age (≥ 60 vs. < 60)", "1.00 (0.60–1.69)", "0.975", "1.02 (0.59–1.78)", "0.934"], ["Gender (male vs. female)", "1.15 (0.72–1.84)", "0.567", "0.75 (0.46–1.24)", "0.263"], ["T stage (T3 and T4 vs. T1 and T2)", "8.40 (2.06–34.31)", "0.003", "3.41 (0.82–14.17)", "0.091"], ["N stage (N1 and N2 vs. N0)", "4.77 (2.77–8.24)", "< 0.001", "0.74 (0.28–1.96)", "0.547"], ["M stage (M1 and MX vs. M0)", "5.57 (3.45–8.99)", "< 0.001", "2.99 (1.72–5.18)", "0.001"], ["Stage (III + IV vs. I + II)", "6.52 (3.57–11.91)", "< 0.001", "3.32 (1.04–10.61)", "0.043"], ["IRF score (high vs. low)", "1585.65 (372.03–6758.19)", "< 0.001", "1855.25 (335.16–10,269.55)", "< 0.001"]], "row_count": 9, "column_count": 5, "numeric_cells": 31, "header_rows": 2, "locator": "section[18]/table-wrap[2]#Tab3"}}], "gaps": ["asset:data:geo:GSE17536 not downloaded", "asset:data:geo:GSE17536 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