Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36406121", "kind": "science-record", "revision": 3, "content_hash": "sha256:c5d207c3eb74839fb97bd00c9961e73100ec9d7dd5b17265117e5f010178be3e", "created_at": "2026-11-01T19:00:52Z", "updated_at": "2026-11-01T22:08:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses.", "doi": "10.3389/fgene.2022.959170", "pmid": "36406121", "authors": ["Jiawei Zeng", "Honglin Tan", "Bin Huang", "Qian Zhou", "Qi Ke", "Yan Dai", "Jie Tang", "Bei Xu", "Jiafu Feng", "Lin Yu"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting therapeutic efficacy. Obviously, a systematic analysis of lipid metabolism characterizations of gastric cancer (GC) needs to be reported. Experimental design: Based on two proposed computational algorithms (TCGA-STAD and GSE84437), the lipid metabolism characterization of 367 GC patients and its systematic relationship with genomic characteristics, clinicopathologic features, and clinical outcomes of GC were analyzed in our study. Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore for GC. To verify the specific changes in lipid metabolism, a total of 90 serum, 30 tumor, and non-tumor adjacent tissues from GC patients, were included for pseudotargeted metabolomics analysis via SCIEX triple quad 5500 LC-MS/MS system. Results: Five lipid metabolism signature genes were identified from a total of 3,104 DEGs. The LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic drugs. Additionally, significant changes in sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue from GC patients by pseudotargeted metabolomics. Conclusion: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the characteristics of lipid metabolism may help to provide insights into the pathogenesis of GC, interpret the responses of gastric tumors to therapies, and achieve a better outcome in the treatment of GC. In addition, significant alterations of sphingolipid metabolism and increased levels of sphingolipids, in particular, sphingosine (d16:1) and ceramide, were discovered in GC tissue by lipidome pseudotargeted metabolomics, and most of the sphingolipid molecules have the potential to be diagnostic biomarkers for GC.", "claims": [{"id": "claim-method", "statement": "A multivariate Cox regression model was used to determine independent prognostic factors using the survminer package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36406121", "locator": "sections[11] \"2.3 Statistical analysis\" paragraph 2", "quote": "A multivariate Cox regression model was used to determine independent prognostic factors using the survminer package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The bioinformatics analysis data are available from the corresponding author upon reasonable request. At the same time, the pseudotargeted metabolomics data presented in the study are deposited in the iProX partner repository, accession number PXD034233, which can be found below: http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD034233.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36406121", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "The bioinformatics analysis data are available from the corresponding author upon reasonable request. At the same time, the pseudotargeted metabolomics data presented in the study are deposited in the iProX partner repository, accession number PXD034233, which can be found below: http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD034233."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five lipid metabolism signature genes were identified from a total of 3,104 DEGs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36406121", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Five lipid metabolism signature genes were identified from a total of 3,104 DEGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Five lipid metabolism signature genes were identified from a total of 3,104 DEGs.", "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:36406121"], "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:GSE84437"], "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:GSE84437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36406121", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.959170", "sha256": "sha256:b5dfc96fc9b6e3df7cfd4f85c29925654ce2df30c72cbde4152482fe29e7b97a", "bytes": 40270, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.959170", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9669965", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE84437", "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=GSE84437", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE84437", "version": null, "note": "The bioinformatics analysis data are available from the corresponding author upon reasonable request. At the same time, the pseudotargeted metabolomics data presented in the study are deposited in the iProX partner repository, accession number PXD034233, which can be found below: http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD034233.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The bioinformatics analysis data are available from the corresponding author upon reasonable request. 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At the same time, the pseudotargeted metabolomics data presented in the study are deposited in the iProX partner repository, accession number PXD034233, which can be found below: http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD034233.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The bioinformatics analysis data are available from the corresponding author upon reasonable request. 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