An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36065262", "kind": "science-record", "revision": 3, "content_hash": "sha256:45649947f0c1715b20411b72e93f8093f6a1624c58d77095516411f479ae13c8", "created_at": "2026-11-01T18:15:16Z", "updated_at": "2026-11-01T21:28:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis.", "doi": "10.1155/2022/3640589", "pmid": "36065262", "authors": ["Qing Lai", "Haifei Feng"], "journal": "Evidence-based complementary and alternative medicine : eCAM", "year": 2022, "abstract": "Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan-Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.", "claims": [{"id": "claim-method", "statement": "Cox regression analysis of the multistep AIC (stepAIC) algorithm [22] was used to screen the prognosis-related DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36065262", "locator": "sections[8] \"2.6. Identification of Prognosis-Related Genes\" paragraph 2", "quote": "Cox regression analysis of the multistep AIC (stepAIC) algorithm [22] was used to screen the prognosis-related DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36065262", "locator": "sections[19] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six-gene immune-related prognostic model distinguished colon cancer patients with significantly different survival probabilities in both TCGA and GSE39582.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36065262", "locator": "sections[15] \"3.4. Identification of Prognosis-Related DEGs\" paragraph 2", "quote": "KM survival analysis showed significant differences in the survival probabilities between patients of high- and low-risk groups in TCGA (p=0.00065, Figure 5(a)) and GSE39582 (p=0.047, Figure 5(b))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-gene immune-related prognostic model distinguished colon cancer patients with significantly different survival probabilities in both TCGA and GSE39582.", "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:36065262"], "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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"title": "Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, molecular and survival characteristics [Expression]", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE39582 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Cox regression analysis of the multistep AIC (stepAIC) algorithm [22] was used to screen the prognosis-related DEGs."}}, {"id": "method-2", "family": "survival_analysis", "specific": 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