Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38042786", "kind": "science-record", "revision": 2, "content_hash": "sha256:938b2ef9067c9b8082924f57e1aefc0d105a8722056330e38e6e5180059e6a7c", "created_at": "2026-11-01T19:02:59Z", "updated_at": "2026-11-01T21:32:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing.", "doi": "10.1186/s12944-023-01977-y", "pmid": "38042786", "authors": ["Jinze Zhang", "He Wang", "Yu Tian", "Tianfeng Li", "Wei Zhang", "Li Ma", "Xiangjuan Chen", "Yushan Wei"], "journal": "Lipids in health and disease", "year": 2023, "abstract": "Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lipid metabolism-related genes (LMRGs) and examine its correlation with the tumor immune microenvironment (TIME). Transcriptome data and clinical information from patients with GC were collected from the TCGA and GEO databases. Data from GC samples were analyzed using both bulk RNA-seq and single-cell sequencing of RNA (scRNA-seq). To identify survival-related differentially expressed LMRGs (DE-LMRGs), differential expression and prognosis studies were carried out. We built a predictive signature using LASSO regression and tested it on the TCGA and GSE84437 datasets. In addition, the correlation of the prognostic signature with the TIME was comprehensively analyzed. In this study, we identified 258 DE-LMRGs in GC and further screened seven survival-related DE-LMRGs. The results of scRNA-seq identified 688 differentially expressed genes (DEGs) between the three branches. Two critical genes (GPX3 and NNMT) were identified using the above two gene groups. In addition, a predictive risk score that relies on GPX3 and NNMT was developed. Survival studies in both the TCGA and GEO datasets revealed that patients categorized to be at low danger had a significantly greater prognosis than those identified to be at high danger. Additionally, by employing calibration plots based on TCGA data, the study demonstrated the substantial predictive capacity of a prognostic nomogram, which incorporated a risk score along with various clinical factors. Within the high-risk group, there was a noticeable abundance of active natural killer (NK) cells, quiescent monocytes, macrophages, mast cells, and activated CD4 + T cells. In summary, a two-gene signature and a predictive nomogram have been developed, offering accurate prognostic predictions for general survival in GC patients. These findings have the potential to assist healthcare professionals in making informed medical decisions and providing personalized treatment approaches.", "claims": [{"id": "claim-method", "statement": "A predictive gene signature was developed using a least absolute shrinkage and selection operator (LASSO) regression model, which helped to select optimal parameters and establish a sequence of genes with predictive capabilities.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38042786", "locator": "sections[6] \"Creation and assessment of the LMRG signature\" paragraph 2", "quote": "A predictive gene signature was developed using a least absolute shrinkage and selection operator (LASSO) regression model, which helped to select optimal parameters and establish a sequence of genes with predictive capabilities."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38042786", "locator": "sections[24] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The GPX3/NNMT-based two-gene prognostic signature achieved 1-, 3-, and 5-year overall-survival AUCs of 0.599, 0.610, and 0.640, respectively, while the prognostic nomogram achieved an AUC of 0.722.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38042786", "locator": "sections[15] \"Construction and assessment of a prognostic model based on LMRGs\" paragraph 2", "quote": "Figure 6K demonstrates the area under the curve (AUC) values for predicting OS at 1-year, 3-year, and 5-year intervals, which were measured to be 0.599, 0.610, and 0.640, respectively."}, {"asset": "source:pmid:38042786", "locator": "sections[16] \"Construction of a nomogram to forecast survival\" paragraph 2", "quote": "Furthermore, the results of AUC testing demonstrated that the nomogram (AUC = 0.722) outperformed individual indicators such as age (AUC = 0.602), clinical stage (AUC = 0.617), and the predictive risk assessment model (AUC = 0.638) (Fig. 9C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The GPX3/NNMT-based two-gene prognostic signature achieved 1-, 3-, and 5-year overall-survival AUCs of 0.599, 0.610, and 0.640, respectively, while the prognostic nomogram achieved an AUC of 0.722.", "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:38042786"], "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. 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