A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36241983", "kind": "science-record", "revision": 4, "content_hash": "sha256:95aff5b742f604803db95489f073ad6353dc219c7f9874753a0b464dbe035245", "created_at": "2026-11-01T18:15:37Z", "updated_at": "2026-11-01T22:07:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma.", "doi": "10.1186/s12876-022-02510-8", "pmid": "36241983", "authors": ["Zining Liu", "Hua Liu", "Yinkui Wang", "Ziyu Li"], "journal": "BMC gastroenterology", "year": 2022, "abstract": "BACKGROUND: Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided.\nMETHODS: The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially.\nRESULTS: 22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08-1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11-1.32, P < 0.001).\nCONCLUSION: Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.", "claims": [{"id": "claim-method", "statement": "The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to reduce the dimensions of the data and select the DEGs that best distinguished the data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36241983", "locator": "sections[11] \"Establishment of outcome signature with LASSO logistic regression model\" paragraph 2", "quote": "The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to reduce the dimensions of the data and select the DEGs that best distinguished the data."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in TCGA (https://portal.gdc.cancer.gov/), and GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15459). The authors ensure the availability of supporting data and materials which can be obtained from the supplementary materials.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36241983", "locator": "sections[27] \"notes\" paragraph 1", "quote": "The datasets analyzed during the current study are available in TCGA (https://portal.gdc.cancer.gov/), and GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15459). The authors ensure the availability of supporting data and materials which can be obtained from the supplementary materials."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36241983", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression."}, {"asset": "source:pmid:36241983", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression.", "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:36241983"], "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:36241983", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12876-022-02510-8", "sha256": "sha256:dcfe72a84edb2407b16b3ecff11ed5d3e28e83cbe128d123a9716f1b32d40bc8", "bytes": 53549, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12876-022-02510-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9564244", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15459", "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=GSE15459", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15459", "version": null, "note": "The datasets analyzed during the current study are available in TCGA (https://portal.gdc.cancer.gov/), and GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15459). 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