Identification of a novel prognostic model for gastric cancer utilizing glutamine-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39386842", "kind": "science-record", "revision": 3, "content_hash": "sha256:682aad14a95647c4f1fdcc1f423157d9be620a16943800e170643faca35da345", "created_at": "2026-11-01T20:29:36Z", "updated_at": "2026-11-01T22:12:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a novel prognostic model for gastric cancer utilizing glutamine-related genes.", "doi": "10.1016/j.heliyon.2024.e37985", "pmid": "39386842", "authors": ["Weidong Li", "Qixing Zhong", "Naisheng Deng", "Haitao Wang", "Jun Ouyang", "Zhifen Guan", "Xinhao Zhou", "Kai Li", "Xueying Sun", "Yao Wang"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Glutamine metabolism presents a promising avenue for cancer prevention and treatment, but the underlying mechanisms in gastric cancer (GC) progression remain elusive.\nMethods: The TCGA-STAD and GEO GSE62254 datasets, containing gene expression, clinical information, and survival outcomes of GC, were meticulously examined. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to excavate a key module (MEturquoise), which was used to intersect with glutamine metabolism-related genes (GMRGs) and differentially expressed genes (DEGs) to identify differentially expressed GMRGs (DE-GMRGs). LASSO and Cox Univariate analyses were implemented to determine risk model genes. Correlation of the risk model with clinical parameters, pathways, and tumor immune microenvironments, was analyzed, and its prognostic independence was validated by Cox analyses. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to validate the expression levels of MYB, LRFN4, LMNB2, and SLC1A5 in GC and para-carcinoma tissue.\nResults: The excavation of 4521 DEGs led to the discovery of the key MEturquoise module, which exhibited robust correlations with GC traits. The intersection analysis identified 42 DE-GMRGs, among which six genes showed consistency. Further LASSO analysis established MYB, LRFN4, LMNB2, and SLC1A5 as pivotal risk model genes. The risk model demonstrated associations with oncogenic and metabolism-related pathways, inversely correlating with responses to immune checkpoint blockade therapies. This risk model, together with \"age\", was validated to be an independent prognostic factor for GC. RT-qPCR result indicated that MYB, LRFN4, LMNB2, and SLC1A5 expressions were remarkably up-regulated in GC tissues comparison with para-carcinoma tissue.\nConclusion: The present study has generated a novel risk module containing four DE-GMRGs for predicting the prognosis and the response to immune checkpoint blockade treatments for GC. This risk model provides new insights into the involvement of glutamine metabolism in GC, warranting further investigation.", "claims": [{"id": "claim-method", "statement": "In the TCGA-STAD dataset, the risk score and clinical parameters were analyzed using univariate Cox analysis with the R package survival (version 3.2–13) [22], factors with P < 0.05 were then subjected to multivariate Cox analysis to identify independent prognostic factors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39386842", "locator": "sections[9] \"Independent prognostic analysis\" paragraph 2", "quote": "In the TCGA-STAD dataset, the risk score and clinical parameters were analyzed using univariate Cox analysis with the R package survival (version 3.2–13) [22], factors with P < 0.05 were then subjected to multivariate Cox analysis to identify independent prognostic factors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data analyzed in this study were obtained from public databases. In the TCGA database (https://cancergenome.nih.gov/abouttcga/overview), GEO database (https://www.ncbi.nlm.nih.gov/gds), MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) and GeneCard database (href=\"https://genecards.weizmann.ac.il/v3/\"title = \"https://genecards.weizmann.ac.il/v3/\">https://genecards.weizmann.ac.il/v3/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39386842", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The data analyzed in this study were obtained from public databases. In the TCGA database (https://cancergenome.nih.gov/abouttcga/overview), GEO database (https://www.ncbi.nlm.nih.gov/gds), MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) and GeneCard database (href=\"https://genecards.weizmann.ac.il/v3/\"title = \"https://genecards.weizmann.ac.il/v3/\">https://genecards.weizmann.ac.il/v3/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This risk model, together with “age”, was validated to be an independent prognostic factor for GC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39386842", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "This risk model, together with “age”, was validated to be an independent prognostic factor for GC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "This risk model, together with “age”, was validated to be an independent prognostic factor for GC.", "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:39386842"], "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:GSE62254"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE62254"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39386842", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e37985", "sha256": "sha256:b6f4b4e41d9304098b734ec315fdd714451c787780f6d2cbed63383b640d0099", "bytes": 47042, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e37985", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11462029", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE62254", "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=GSE62254", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62254", "version": null, "note": "The data analyzed in this study were obtained from public databases. In the TCGA database (https://cancergenome.nih.gov/abouttcga/overview), GEO database (https://www.ncbi.nlm.nih.gov/gds), MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) and GeneCard database (href=\"https://genecards.weizmann.ac.il/v3/\"title = \"https://genecards.weizmann.ac.il/v3/\">https://genecards.weizmann.ac.il/v3/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data analyzed in this study were obtained from public databases. 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RT-qPCR result indicated that MYB, LRFN4, LMNB2, and", "n-quantitative polymerase chain reaction (RT-qPCR) was performed to validate the expression le", "an independent prognostic factor for GC. RT-qPCR result indicated that MYB, LRFN4, LMNB2, and"], "order_basis": "paper_text_order:1037", "id": "assay:1"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Wilcoxon"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}