Metabolism reprogramming signature associated with stromal cells abundance in tumor microenvironment improve prognostic risk classification for gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35907819", "kind": "science-record", "revision": 3, "content_hash": "sha256:b137e57f6eb5a967b3e22385f1efb52b611e0d01c15fdfb3aadb8bfda9810376", "created_at": "2026-11-01T18:14:59Z", "updated_at": "2026-11-01T21:28:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Metabolism reprogramming signature associated with stromal cells abundance in tumor microenvironment improve prognostic risk classification for gastric cancer.", "doi": "10.1186/s12876-022-02451-2", "pmid": "35907819", "authors": ["Junyu Huo", "Jing Guan", "Yankun Li"], "journal": "BMC gastroenterology", "year": 2022, "abstract": "BACKGROUND: Stromal cells play an important role in the process of tumor progression, but the relationship between stromal cells and metabolic reprogramming is not very clear in gastric cancer (GC).\nMETHODS: Metabolism-related genes associated with stromal cells were identified in The Cancer Genome Atlas (TCGA) and GSE84437 datasets, and the two datasets with 804 GC patients were integrated into a training cohort to establish the prognostic signature. Univariate Cox regression analysis was used to screen for prognosis-related genes. A risk score was constructed by LASSO regression analysis combined with multivariate Cox regression analysis. The patients were classified into groups with high and low risk according to the median value. Two independent cohorts, GSE62254 (n = 300) and GSE15459 (n = 191), were used to externally verify the risk score performance. The CIBERSORT method was applied to quantify the immune cell infiltration of all included samples.\nRESULTS: A risk score consisting of 24 metabolic genes showed good performance in predicting the overall survival (OS) of GC patients in both the training (TCGA and GSE84437) and testing cohorts (GSE62254 and GSE15459). As the risk score increased, the patients' risk of death increased. The risk score was an independent prognostic indicator in both the training and testing cohorts suggested by the univariate and multivariate Cox regression analyses. The patients were clustered into four subtypes according to the quantification of 22 kinds of immune cell infiltration (ICI). The proportion of ICI Cluster C with the best prognosis in the low-risk group was approximately twice as high as that in the high-risk group, and the risk score of ICI Cluster C was significantly lower than that of the other three subtypes.\nCONCLUSION: Our study proposed the first scheme for prognostic risk classification of GC from the perspective of tumor stromal cells and metabolic reprogramming, which may contribute to the development of therapeutic strategies for GC.", "claims": [{"id": "claim-method", "statement": "First, univariate Cox regression analysis was applied to screen DEMRGs associated with prognosis (p value < 0.05 was considered significant). Next, the least absolute shrinkage and selection operator (LASSO) algorithm with penalty parameter tuning performed via tenfold cross‐validation was used to remove overfitting between the prognosis-related genes. Finally, genes with nonzero regression coefficients were included in multivariate Cox regression analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35907819", "locator": "sections[6] \"Development and validation of a metabolic-related gene prognostic signature for GC\" paragraph 2", "quote": "First, univariate Cox regression analysis was applied to screen DEMRGs associated with prognosis (p value < 0.05 was considered significant). Next, the least absolute shrinkage and selection operator (LASSO) algorithm with penalty parameter tuning performed via tenfold cross‐validation was used to remove overfitting between the prognosis-related genes. Finally, genes with nonzero regression coefficients were included in multivariate Cox regression analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed for this study were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35907819", "locator": "sections[25] \"notes\" paragraph 1", "quote": "The datasets analysed for this study were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A risk score consisting of 24 metabolic genes performed well in predicting overall survival in the training and testing cohorts and was an independent prognostic indicator.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35907819", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A risk score consisting of 24 metabolic genes showed good performance in predicting the overall survival (OS) of GC patients in both the training (TCGA and GSE84437) and testing cohorts (GSE62254 and GSE15459)."}, {"asset": "source:pmid:35907819", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The risk score was an independent prognostic indicator in both the training and testing cohorts suggested by the univariate and multivariate Cox regression analyses."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A risk score consisting of 24 metabolic genes performed well in predicting overall survival in the training and testing cohorts and was an independent prognostic indicator.", "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:35907819"], "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:GSE15459"], "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:GSE15459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35907819", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12876-022-02451-2", "sha256": 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Next, the least absolute shrinkage and selection operator (LASSO) algorithm with penalty parameter tuning performed via tenfold cross‐validation was used to remove overfitting between the prognosis-related genes. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE84437", "GSE62254", "GSE15459"], "resolved_accessions": ["GSE84437", "GSE62254", "GSE15459"], "statements": ["The datasets analysed for this study were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)."], "basis": "The paper names GSE84437, GSE62254, GSE15459 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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