Multi-Omics Characterization of a Glycerolipid Metabolism-Related Gene Enrichment Score in Colon Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35600382", "kind": "science-record", "revision": 5, "content_hash": "sha256:b8845e2efcbb2fe7f7d0b30f80cc6e32069a5b45fb2dca5cbbb642c3a7db2554", "created_at": "2026-11-01T18:14:30Z", "updated_at": "2026-11-01T22:49:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Multi-Omics Characterization of a Glycerolipid Metabolism-Related Gene Enrichment Score in Colon Cancer.", "doi": "10.3389/fonc.2022.881953", "pmid": "35600382", "authors": ["Zhiyu Wang", "Zhuoqi Zhang", "Ke Zhang", "Qiaoxia Zhou", "Sidong Chen", "Hao Zheng", "Guoqiang Wang", "Shangli Cai", "Fujing Wang", "Shenglong Li"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: Glycerolipid metabolism is involved in the genesis and progression of colon cancer. The current study aims at exploring the prognostic value and potential molecular mechanism of glycerolipid metabolism-related genes in colon cancer from the perspective of multi-omics.\nMethods: Clinical information and mRNA expression data of patients with colon cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Single-sample gene set enrichment analysis (ssGSEA) was applied to calculate the glycerolipid metabolism-related gene enrichment score (GLMS). Univariable and multivariable Cox regression analyses were used to study the prognostic value of GLMS in TCGA-COAD and GSE39582 cohorts. The molecular mechanism of the prognostic factor was investigated via immune cell infiltration estimation and correlation analysis of cancer hallmark pathways. Single-cell transcriptomic dataset GSE146771 was used to identify the cell populations which glycerolipid metabolism targeted on.\nResults: The GLMS was found to be associated with tumor location and consensus molecular types (CMSs) of colon cancer in TCGA-COAD cohort (P < 0.05). Patients in the low-GLMS group exhibited poorer overall survival (OS) in TCGA cohort (P = 0.03; HR, 0.63; 95% CI, 0.42-0.94), which was further validated in the GSE39582 dataset (P < 0.001; HR, 0.57; 95% CI, 0.43-0.76). The association between the GLMS and OS remained significant in the multivariable analysis (TCGA cohort: P = 0.04; HR, 0.64; 95% CI, 0.42-0.98; GSE39582 cohort: P < 0.001; HR, 0.60; 95% CI, 0.45-0.80). The GLMS was positively correlated with cancer hallmark pathways including bile acid metabolism, xenobiotic metabolism, and peroxisome and negatively correlated with pathways such as interferon gamma response, allograft rejection, apoptosis, and inflammatory response (P < 0.05). Increased immune infiltration and upregulated expression of immune checkpoints were observed in patients with lower GLMS (P < 0.05). Single-cell datasets verified the different distribution of GLMS in cell subsets, with significant enrichment of GLMS in malignant cells and Tprolif cells.\nConclusion: We demonstrated that GLMS was a potential independent prognostic factor for colon cancer. The GLMS was also correlated with several cancer hallmark pathways, as well as immune microenvironment.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier analysis was performed to estimate the overall survival (OS) rates, and the difference of survival curves between high- and low-GLMS groups was compared by the log-rank test through R package “survival.” Univariable and multivariable Cox proportional hazards regression analyses were conducted via R package “survival” coxph function to determine whether the GLMS could be an independent prognostic factor for colon cancer.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35600382", "locator": "sections[12] \"Statistical Analysis\" paragraph 2", "quote": "Kaplan–Meier analysis was performed to estimate the overall survival (OS) rates, and the difference of survival curves between high- and low-GLMS groups was compared by the log-rank test through R package “survival.” Univariable and multivariable Cox proportional hazards regression analyses were conducted via R package “survival” coxph function to determine whether the GLMS could be an independent prognostic factor for colon cancer."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed in the present study are available in the public data repository, TCGA-COAD: https://portal.gdc.cancer.gov/repository, and GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35600382", "locator": "sections[21] \"Data Availability Statement\" paragraph 2", "quote": "The datasets generated and analyzed in the present study are available in the public data repository, TCGA-COAD: https://portal.gdc.cancer.gov/repository, and GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We demonstrated that GLMS was a potential independent prognostic factor for colon cancer.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Patients in the low-GLMS group exhibited poorer overall survival (OS) in TCGA cohort (P = 0.03; HR, 0.63; 95% CI, 0.42–0.94), which was further validated in the GSE39582 dataset (P < 0.001; HR, 0.57; 95% CI, 0.43–0.76)."}, "evidence": [{"asset": "source:pmid:35600382", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We demonstrated that GLMS was a potential independent prognostic factor for colon cancer."}, {"asset": "source:pmid:35600382", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Patients in the low-GLMS group exhibited poorer overall survival (OS) in TCGA cohort (P = 0.03; HR, 0.63; 95% CI, 0.42–0.94), which was further validated in the GSE39582 dataset (P < 0.001; HR, 0.57; 95% CI, 0.43–0.76)."}, {"asset": "source:pmid:35600382", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The association between the GLMS and OS remained significant in the multivariable analysis (TCGA cohort: P = 0.04; HR, 0.64; 95% CI, 0.42–0.98; GSE39582 cohort: P < 0.001; HR, 0.60; 95% CI, 0.45–0.80)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We demonstrated that GLMS was a potential independent prognostic factor for colon cancer.", "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:35600382"], "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:GSE146771"], "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:GSE146771"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35600382", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2022.881953", "sha256": 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