Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38577477", "kind": "science-record", "revision": 3, "content_hash": "sha256:5670158070aa74832340867e23b56f7947ea3d82a14a01e7c65e3c8aba2844d9", "created_at": "2026-11-01T19:20:32Z", "updated_at": "2026-11-01T22:11:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients.", "doi": "10.4251/wjgo.v16.i3.945", "pmid": "38577477", "authors": ["Jia-Li Hu", "Mei-Jin Huang", "Halike Halina", "Kun Qiao", "Zhi-Yuan Wang", "Jia-Jie Lu", "Cheng-Liang Yin", "Feng Gao"], "journal": "World journal of gastrointestinal oncology", "year": 2024, "abstract": "BACKGROUND: Gastric cancer (GC) is a highly aggressive malignancy with a heterogeneous nature, which makes prognosis prediction and treatment determination difficult. Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours. Inflammation also affects the prognosis of GC patients. Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis. However, the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.\nAIM: To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.\nMETHODS: In this study, the mRNA expression profiles and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) database (GSE66229). An inflammatory-related gene prognostic signature model was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database. GC patients from the GSE26253 cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent prognostic factors, and a prognostic nomogram was established. The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram. The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram. Gene set enrichment analysis was performed to explore the potential regulatory pathways involved. The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT. Finally, we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.\nRESULTS: A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed. Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients. According to the risk score, GC patients were stratified into high- and low-risk groups, and patients in the high-risk group had significantly worse prognoses according to age, sex, TNM stage and Lauren type. Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging. Finally, the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group, indicating a link between inflammation-related genes and drug sensitivity.\nCONCLUSION: In conclusion, we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.", "claims": [{"id": "claim-method", "statement": "To reduce the number of prognostically significant candidate inflammatory-related genes, we employed least absolute shrinkage and selection operator (LASSO) Cox regression analysis for OS, which utilized the above mentioned screened genes as input.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38577477", "locator": "sections[5] \"Establishing and verifying a prognostic model of inflammatory-related genes\" paragraph 2", "quote": "To reduce the number of prognostically significant candidate inflammatory-related genes, we employed least absolute shrinkage and selection operator (LASSO) Cox regression analysis for OS, which utilized the above mentioned screened genes as input."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data were downloaded from the Gene Expression Omnibus (GEO) cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38577477", "locator": "sections[36] \"Data sharing statement\" paragraph 2", "quote": "The data were downloaded from the Gene Expression Omnibus (GEO) cohort."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38577477", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed.", "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:38577477"], "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:GSE26253"], "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:GSE26253"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38577477", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.4251/wjgo.v16.i3.945", "sha256": "sha256:e99b09c1bfcd435598fe4cd6bea2d119f1d4625825dcf4ad7a8c7301a02139b1", "bytes": 54902, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.4251/wjgo.v16.i3.945", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10989359", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE26253", "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=GSE26253", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE26253", "version": null, "note": "The data were downloaded from the Gene Expression Omnibus (GEO) cohort.", "structure": {"repository": "GEO", 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