Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36915600", "kind": "science-record", "revision": 2, "content_hash": "sha256:9d8291b55e9e9bb8cd0768fcb35b4967a6fc975529967f7c4bdb8e29ad859d83", "created_at": "2026-11-01T19:01:33Z", "updated_at": "2026-11-01T21:30:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment.", "doi": "10.21037/tcr-22-2036", "pmid": "36915600", "authors": ["Ruoyang Chai", "Zhengjia Su", "Yajie Zhao", "Wei Liang"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: The extracellular matrix (ECM) plays a vital role in progression, expansion, and prognosis of malignancies. In this study, we aimed to explore a novel ECM-based prognostic model for patients with colon cancer (CC).\nMethods: ECM-related genes were obtained from Molecular Signatures database. Differential expression analysis was performed using the CC dataset from The Cancer Genome Atlas (TCGA) database. Four ECM-related genes related to overall survival were identified using the Cox regression and LASSO analysis. Then an ECM-related signature was developed and verified in three independent CC cohorts (GSE33882, GSE39582 and GSE29621) from the Gene Expression Omnibus (GEO). A prognostic nomogram was developed incorporating the ECM-related gene signature with clinical risk factors. CIBERSORT was used to explore the immune cell infiltration level. Human Protein Atlas (HPA) database was utilized to validate the expression levels of identified prognostic ECM genes.\nResults: Four ECM-related genes (CXCL13, CXCL14, SFRP5 and THBS4) were identified to develop an ECM-based gene signature and demarcated CC patients into the high- and low-risk groups. In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001). In addition, ECM-related signature was significantly associated with consensus molecular subtype 4 (CMS4) as well as other known clinical risk factors such as a higher Tumor, Nodal Involvement, Metastasis (TNM) stage. Moreover, the risk score derived from the ECM-based gene signature could be utilized as an independent prognostic factor for CC patients. A nomogram including the ECM-related gene signature, age and stage was developed to serve clinical practice. CIBERSORT analysis showed immune cell infiltration was different between high- and low-risk groups. The immunohistochemical results derived from HPA indicated differential expression of prognosis-related ECM genes in CC and normal tissues.\nConclusions: In the present study, a novel risk model based on ECM-signature could effectively reflect individual risk classification and provide potential therapeutic targets for CC patients. Moreover, the prognostic nomogram may help predict individualized survival.", "claims": [{"id": "claim-method", "statement": "Four ECM-related genes related to overall survival were identified using the Cox regression and LASSO analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36915600", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Four ECM-related genes related to overall survival were identified using the Cox regression and LASSO analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-2036/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36915600", "locator": "sections[26] \"Footnotes\" paragraph 2", "quote": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-2036/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001)."}, "evidence": [{"asset": "source:pmid:36915600", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001).", "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:36915600"], "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:GSE29621"], "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:GSE29621"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36915600", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-22-2036", "sha256": 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