Exploring Immune-Related Prognostic Signatures in the Tumor Microenvironment of Colon Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35281839", "kind": "science-record", "revision": 5, "content_hash": "sha256:0568fc7ef1076dc720eca8a8676e51768dd509ad5b60a0bd478858c1ce6d102a", "created_at": "2026-11-01T18:14:03Z", "updated_at": "2026-11-01T22:48:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring Immune-Related Prognostic Signatures in the Tumor Microenvironment of Colon Cancer.", "doi": "10.3389/fgene.2022.801484", "pmid": "35281839", "authors": ["Lichao Cao", "Tong Li", "Ying Ba", "Erfei Chen", "Jin Yang", "Hezi Zhang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Colon cancer is a common malignant tumor with poor prognosis. The aim of this study is to explore the immune-related prognostic signatures and the tumor immune microenvironment of colon cancer. Methods: The mRNA expression data of TCGA-COAD from the UCSC Xena platform and the list of immune-related genes (IRGs) from the ImmPort database were used to identify immune-related differentially expressed genes (DEGs). Then, we constructed an immune-related risk score prognostic model and validated its predictive performance in the test dataset, the whole dataset, and two independent GEO datasets. In addition, we explored the differences in tumor-infiltrating immune cell types, tumor mutation burden (TMB), microsatellite status, and expression levels of immune checkpoints and their ligands between the high-risk and low-risk score groups. Moreover, the potential value of the identified immune-related signature with respect to immunotherapy was investigated based on an immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent. Results: Seven immune-related DEGs were identified as prognostic signatures. The areas under the curves (AUCs) of the constructed risk score model for overall survival (OS) were calculated (training dataset: 0.780 at 3 years, 0.801 at 4 years, and 0.766 at 5 years; test dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years; and the whole dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years). In the high-risk score group of the whole dataset, patients had worse OS, higher TMN stages, advanced pathological stages, and a higher TP53 mutation rate (p < 0.05). In addition, a high level of resting NK cells or M0 macrophages, and high TMB were significantly related to poor OS (p < 0.05). Also, we observed that high-risk score patients had a high expression level of PD-L1, PD-1, and CTLA-4 (p < 0.05). The patients with high-risk scores demonstrated worse prognosis than those with low-risk scores in multiple datasets (GSE39582: p = 0.0023; GSE17536: p = 0.0008; immunotherapeutic cohort without platinum treatment: p = 0.0014; immunotherapeutic cohort with platinum treatment: p = 0.0027). Conclusion: We developed a robust immune-related prognostic signature that performed great in multiple cohorts and explored the characteristics of the tumor immune microenvironment of colon cancer patients, which may give suggestions for the prognosis and immunotherapy in the future.", "claims": [{"id": "claim-method", "statement": "First, we identified the candidate prognostic signature using the univariable Cox proportional hazards regression model and Survival R package. To avoid over-fitting, all genes with p-value < 0.05 were involved in the subsequent least absolute shrinkage and selection operator (LASSO) analysis using the glmnet R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35281839", "locator": "sections[8] \"Construction and Validation of the Immune-Related Prognostic Model for Colon Cancer\" paragraph 2", "quote": "First, we identified the candidate prognostic signature using the univariable Cox proportional hazards regression model and Survival R package. To avoid over-fitting, all genes with p-value < 0.05 were involved in the subsequent least absolute shrinkage and selection operator (LASSO) analysis using the glmnet R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mRNA sequencing data, mutation profiling data, and clinical information were downloaded from the UCSC Xena platform (https://xenabrowser.net/datapages/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35281839", "locator": "sections[4] \"TCGA Cohorts and the List of Immune-Related Genes\" paragraph 2", "quote": "The mRNA sequencing data, mutation profiling data, and clinical information were downloaded from the UCSC Xena platform (https://xenabrowser.net/datapages/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The seven-gene immune-related risk score had time-dependent OS AUCs of 0.780, 0.801, and 0.766 at 3, 4, and 5 years in the training dataset, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35281839", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The areas under the curves (AUCs) of the constructed risk score model for overall survival (OS) were calculated (training dataset: 0.780 at 3 years, 0.801 at 4 years, and 0.766 at 5 years; test dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years; and the whole dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The seven-gene immune-related risk score had time-dependent OS AUCs of 0.780, 0.801, and 0.766 at 3, 4, and 5 years in the training dataset, respectively.", "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:35281839"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE17536"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE17536"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35281839", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.801484", "sha256": 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