Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33441161", "kind": "science-record", "revision": 5, "content_hash": "sha256:7a10fcb2f56f71ef413c211fc3f87165368e27b4e881a1501b8152c0e9fbf6d5", "created_at": "2026-11-01T15:41:22Z", "updated_at": "2026-11-01T21:24:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis.", "doi": "10.1186/s12957-020-02116-y", "pmid": "33441161", "authors": ["Zhengyu Fang", "Sumei Xu", "Yiwen Xie", "Wenxi Yan"], "journal": "World journal of surgical oncology", "year": 2021, "abstract": "BACKGROUND: Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer.\nMETHODS: The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram.\nRESULTS: Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients' age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions.\nCONCLUSIONS: The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.", "claims": [{"id": "claim-method", "statement": "The common DEGs across the TCGA, GSE44861, and GSE44076 datasets were identified using the MetaDE.ES methods in the R MetaDE package", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33441161", "locator": "sections[5] \"DEG identification by meta-analysis\" paragraph 2", "quote": "The common DEGs across the TCGA, GSE44861, and GSE44076 datasets were identified using the MetaDE.ES methods in the R MetaDE package"}], "state": "complete"}, {"id": "claim-data-access", "statement": "Besides, the RNA-seq data of colon cancer and the corresponding clinical information were downloaded from TCGA (https://gdc-portal.nci.nih.gov/). After sample selection, 473 samples including 432 tumor samples with clinical information and 41 normal samples were retained in this study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33441161", "locator": "sections[3] \"Data extraction\" paragraph 3", "quote": "Besides, the RNA-seq data of colon cancer and the corresponding clinical information were downloaded from TCGA (https://gdc-portal.nci.nih.gov/). After sample selection, 473 samples including 432 tumor samples with clinical information and 41 normal samples were retained in this study."}], "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:33441161"], "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:GSE17538"], "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:GSE17538"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33441161", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12957-020-02116-y", "sha256": "sha256:2427fd9f4c124abf2adc109578004dcf69518f36d845296b840a18a4f6f93d45", "bytes": 37272, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12957-020-02116-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7807455", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17538", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17538", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17538", "version": null, "note": "Besides, the RNA-seq data of colon cancer and the corresponding clinical information were downloaded from TCGA (https://gdc-portal.nci.nih.gov/). 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