The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34398900", "kind": "science-record", "revision": 8, "content_hash": "sha256:7f9168284cf3d062b5784f0c777945654f4604fa8fe6471e7e05ff5ac1ec149f", "created_at": "2026-11-01T15:41:48Z", "updated_at": "2026-11-01T22:04:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases.", "doi": "10.1371/journal.pone.0251323", "pmid": "34398900", "authors": ["Weijun Shi", "Xincan Li", "Xu Su", "Hexin Wen", "Tianwen Chen", "Huazhang Wu", "Mulin Liu"], "journal": "PloS one", "year": 2021, "abstract": "The recent advances in gene chip technology have led to the identification of multiple metabolism-related genes that are closely associated with colorectal cancer (CRC). Nevertheless, none of these genes could accurately diagnose or predict CRC. The prognosis of CRC has been made by previous prognostic models constructed by using multiple genes, however, the predictive function of multi-gene prognostic models using metabolic genes for the CRC prognosis remains unexplored. In this study, we used the TCGA-CRC cohort as the test dataset and the GSE39582 cohort as the experimental dataset. Firstly, we constructed a prognostic model using metabolic genes from the TCGA-CRC cohort, which were also associated with CRC prognosis. We analyzed the advantages of the prognostic model in the prognosis of CRC and its regulatory mechanism of the genes associated with the model. Secondly, the outcome of the TCGA-CRC cohort analysis was validated using the GSE39582 cohort. We found that the prognostic model can be employed as an independent prognostic risk factor for estimating the CRC survival rate. Besides, compared with traditional clinical pathology, it can precisely predict CRC prognosis as well. The high-risk group of the prognostic model showed a substantially lower survival rate as compared to the low-risk group. In addition, gene enrichment analysis of metabolic genes showed that genes in the prognostic model are enriched in metabolism and cancer-related pathways, which may explain its underlying mechanism. Our study identified a novel metabolic profile containing 11 genes for prognostic prediction of CRC. The prognostic model may unravel the imbalanced metabolic microenvironment, and it might promote the development of biomarkers for predicting treatment response and streamlining metabolic therapy in CRC.", "claims": [{"id": "claim-method", "statement": "The two-sided log-rank p<0.05 was used to determine statistical significance for survival analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34398900", "locator": "sections[8] \"Survival analysis of the prognostic model\" paragraph 2", "quote": "The two-sided log-rank p<0.05 was used to determine statistical significance for survival analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression and clinical data were fetched from the Cancer Genome Atlas-Colorectal Cancer data set (TCGA-CRC), which contained 44 normal colorectal and 568 CRC samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34398900", "locator": "sections[3] \"Data acquisition\" paragraph 3", "quote": "The gene expression and clinical data were fetched from the Cancer Genome Atlas-Colorectal Cancer data set (TCGA-CRC), which contained 44 normal colorectal and 568 CRC samples."}], "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:34398900"], "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:GSE39582"], "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:GSE39582"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34398900", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0251323", "sha256": 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