Identification of a 5-Nutrient Stress-Sensitive Gene Signature to Predict Survival for Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35496037", "kind": "science-record", "revision": 5, "content_hash": "sha256:629a33a99f741b235f093e8c04a7cc4acd8f4072c8cc937a5e2885e10069f12e", "created_at": "2026-11-01T18:14:22Z", "updated_at": "2026-11-01T22:48:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a 5-Nutrient Stress-Sensitive Gene Signature to Predict Survival for Colorectal Cancer.", "doi": "10.1155/2022/2587120", "pmid": "35496037", "authors": ["Jingjie Xiong", "Subing Xiong", "Xi Feng", "Huaming Zhang", "Qisheng Su"], "journal": "BioMed research international", "year": 2022, "abstract": "Background: The high heterogeneity and the complexity of the tumor microenvironment of colorectal cancer (CRC) have enhanced the difficulty of prognosis prediction based on conventional clinical indicators. Recent studies revealed that tumor cells could overcome various nutritional deficiencies by gene regulation and metabolic remodeling. However, whether differentially expressed genes (DEGs) in CRC cells under kinds of nutrient deficiency could be used to predict prognosis remained unveiled.\nMethods: Three datasets (GSE70976, GSE13548, and GSE116087), in which colon cancer cells were, respectively, cultured in serum-free, glucose-free, or glutamine-free medium, were included to delineate the profiles of gene expression by nutrient stress. DEGs were figured out in three datasets, and gene functional analysis was performed. Survival analyses and Cox proportional hazards model were then used to identify nutrient stress sensitive genes in CRC datasets (GSE39582 and TCGA COAD). Then, a 5-gene signature was constructed and the risk scores were also calculated. Survival analyses, cox analyses, and nomogram were applied to predict the prognosis of patients with colorectal cancer. The effectiveness of the risk model was also tested.\nResults: A total of 48 genes were found to be dysregulated in serum, glucose, or glutamine-deprived CRC cells, which were mainly enriched in cell cycle and endoplasmic reticulum stress pathways. After further analyses, 5 genes, MCM5, MCM6, CDCA2, GINS2, and SPC25, were identified to be differentially expressed in CRC and be related to prognosis of in CRC datasets. We used the above nutrient stress-sensitive genes to construct a risk scoring model. CRC samples in the datasets were divided into low-risk and high-risk groups. Data showed that higher risk scores were associated with better outcomes and risk scores decreased significantly with tumor procession. Moreover, the risk score could be used to predict the probability of survival based on nomogram.\nConclusions: The 5-nutrient stress-sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate cox proportional hazards model and survival analyses were performed by the “survival” R package in combination with log-rank test to assess the differences.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35496037", "locator": "sections[6] \"2.4. Survival Analysis and Statistical Analysis\" paragraph 2", "quote": "Univariate and multivariate cox proportional hazards model and survival analyses were performed by the “survival” R package in combination with log-rank test to assess the differences."}], "state": "complete"}, {"id": "claim-data-access", "statement": "TCGA data were extracted from GDC data portal (https://portal.gdc.cancer.gov/). 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The GSE39582 gene expression profiles were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 5-nutrient stress-sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35496037", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The 5-nutrient stress-sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The 5-nutrient stress-sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients.", "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:35496037"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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