Identification and verification of a glycolysis-related gene signature for gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36267782", "kind": "science-record", "revision": 4, "content_hash": "sha256:08db762fdd97ac6d408223affb4c4fe36a8d644985f29c62a0f922b152b34189", "created_at": "2026-11-01T18:15:40Z", "updated_at": "2026-11-01T22:50:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and verification of a glycolysis-related gene signature for gastric cancer.", "doi": "10.21037/atm-22-3980", "pmid": "36267782", "authors": ["Yi Liu", "Min Wu", "Jian Cao", "Yaning Zhu", "Yu Ma", "Yansong Pu", "Xueping Huo", "Jianhua Wang"], "journal": "Annals of translational medicine", "year": 2022, "abstract": "Background: Glycolysis is a central metabolic pathway for tumor cells. However, the relationship between glycolysis and the prognosis of gastric cancer (GC) patients is not well established. In this study, we sought to construct a glycolysis-related gene signature for GC.\nMethods: The messenger ribonucleic acid (mRNA) expression profiles were analyzed using data from The Cancer Genome Atlas (TCGA) database. Glycolysis-related gene sets and pathways were obtained from the Molecular Signatures Database (MSigDB). Subsequently, a prognosis prediction model of the glycolysis-related genes was constructed using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. An external validation was conducted using data from the Gene Expression Omnibus (GEO) database. Risk scores were also calculated based on the signature. Finally, the correlations between the risk score and overall survival (OS), mutation, immune cell infiltration, immune score, and stromal score were examined in 22 types of infiltrating immune cells.\nResults: Fifty-five glycolysis-related genes were identified from TCGA database and MSigDB. Using the LASSO and Cox models, 4 novel genes (i.e., VCAN, EFNA3, ADH4, and CLDN9) were identified to construct a gene signature for GC prognosis prediction. The GC patients with low-risk scores had significantly better OS than those with high-risk scores in the training set. Similar results were also found in the independent GEO GSE84437 testing set. Additionally, the degree of cell infiltration in the low-risk group was significantly higher than that in the high-risk group in terms of naive B cells, plasma cells, and T follicular helper cells. In monocytes, M2 macrophages, resting dendritic cells, and resting Mast cells, the degree of infiltration in the high-risk group was significantly higher than that in the low-risk group. The immune score and stromal score of the high-risk group were also significantly higher than those of the low-risk group. Finally, the univariate and multivariate Cox regression analyses showed that 4 glycolysis-related genes were independent prognostic factors for GC.\nConclusions: The established 4 glycolysis-related gene signature may serve as a reliable tool for the prognosis of GC patients and provide a potential glycolysis therapeutic target for GC.", "claims": [{"id": "claim-method", "statement": "A univariate Cox regression analysis was performed to screen the glycolysis-related DEGs whose expression levels were closely related to the overall survival (OS) of the GC patients using the “survival” R package. Subsequently, we further used least absolute shrinkage and selection operator (LASSO) regression to identify glycolysis-related genes for the prognostic signature through the R package “glmnet” (22,23) according to the results of the univariate Cox regression analysis (P<0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36267782", "locator": "sections[8] \"Construction and validation of the prognostic model of GC\" paragraph 2", "quote": "A univariate Cox regression analysis was performed to screen the glycolysis-related DEGs whose expression levels were closely related to the overall survival (OS) of the GC patients using the “survival” R package. Subsequently, we further used least absolute shrinkage and selection operator (LASSO) regression to identify glycolysis-related genes for the prognostic signature through the R package “glmnet” (22,23) according to the results of the univariate Cox regression analysis (P<0.05)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The clinical data and messenger ribonucleic acid (mRNA) expression profiles of GC were downloaded from The Cancer Genome Atlas (TCGA) database (https://xena.ucsc.edu/). In total, 350 GC samples and 31 normal control samples were obtained from TCGA database. The somatic mutation data of the GC samples were also downloaded from TCGA database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36267782", "locator": "sections[4] \"Tumor and clinical data collection\" paragraph 2", "quote": "The clinical data and messenger ribonucleic acid (mRNA) expression profiles of GC were downloaded from The Cancer Genome Atlas (TCGA) database (https://xena.ucsc.edu/). In total, 350 GC samples and 31 normal control samples were obtained from TCGA database. The somatic mutation data of the GC samples were also downloaded from TCGA database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using LASSO and Cox models, the study identified 4 glycolysis-related genes and constructed a gastric cancer prognostic signature.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36267782", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Using the LASSO and Cox models, 4 novel genes (i.e., VCAN, EFNA3, ADH4, and CLDN9) were identified to construct a gene signature for GC prognosis prediction."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using LASSO and Cox models, the study identified 4 glycolysis-related genes and constructed a gastric cancer prognostic signature.", "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:36267782"], "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:36267782", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-3980", "sha256": "sha256:415ee406e012843b02ce81520ae5e21c13dd40e56ba0ae11d0b14740abe85fa8", "bytes": 39321, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/atm-22-3980", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9577805", "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 clinical data and messenger ribonucleic acid (mRNA) expression profiles of GC were downloaded from The Cancer Genome Atlas (TCGA) database (https://xena.ucsc.edu/). In total, 350 GC samples and 31 normal control samples were obtained from TCGA database. The somatic mutation data of the GC samples were also downloaded from TCGA database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The clinical data and messenger ribonucleic acid (mRNA) expression profiles of GC were downloaded from The Cancer Genome Atlas (TCGA) database (https://xena.ucsc.edu/). In total, 350 GC samples and 31 normal control samples were obtained from TCGA database. 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"group_sizes": null, "group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Molecular subtypes in gastric cancer.", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE84437 not downloaded", "asset:data:geo:GSE84437 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "A univariate Cox regression analysis was performed to screen the glycolysis-related DEGs whose expression levels were closely related to the overall survival (OS) of the GC patients using the “survival” R package. Subsequently, we further used least absolute shrinkage and selection operator (LASSO) regression to identify glycolysis-related genes for the prognostic signature through the R package “glmnet” (22,23) according to the results of the univariate Cox regression analysis (P<0.05)."}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE84437"], "resolved_accessions": ["GSE84437"], "statements": ["The clinical data and messenger ribonucleic acid (mRNA) expression profiles of GC were downloaded from The Cancer Genome Atlas (TCGA) database (https://xena.ucsc.edu/). In total, 350 GC samples and 31 normal control samples were obtained from TCGA database. The somatic mutation data of the GC samples were also downloaded from TCGA database."], "basis": "The paper names GSE84437 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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Subsequently, we further used least absolute shrinkage and selection operator (LASSO) regression to identify glycolysis-related genes for the prognostic signature through the R package “glmnet” (22,23) according to the results of the univariate Cox regression analysis (P<0.05)."], "order_basis": "analysis_only", "id": "assay:1"}]}