Risk Score Prediction Model of Prognosis in GC Patients by Age and Gender Combined With m6A Modification Genes FTO and RBM15.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35433701", "kind": "science-record", "revision": 4, "content_hash": "sha256:91b6fb188d8eca3e390a714e2701f41390ca5b0d21fd1a2d518deb24082d2496", "created_at": "2026-11-01T18:14:16Z", "updated_at": "2026-11-01T22:06:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Risk Score Prediction Model of Prognosis in GC Patients by Age and Gender Combined With m6A Modification Genes FTO and RBM15.", "doi": "10.3389/fcell.2022.710708", "pmid": "35433701", "authors": ["Limin Yue", "Rongguang Zhang", "Shuaiyin Chen", "Guangcai Duan"], "journal": "Frontiers in cell and developmental biology", "year": 2022, "abstract": "Background: Gastric cancer (GC) has a high mortality rate. N6-methyladenosine (m6A) is involved in the development of GC. Age and gender are associated with GC incidence and survival. This study aimed to explore the risk score prediction model of prognosis in GC patients by age and gender combined with m6A modification genes. Methods: Data on m6A modification gene expression and clinical information downloaded from the Cancer Genome Atlas (TCGA) database were used to construct the risk score prediction model. Cox and least absolute shrinkage and selection operator (LASSO) regression were performed to identify clinical characteristics and m6A modification genes associated with prognosis. A risk score prediction model was established based on multivariate Cox regression analysis. The Gene Expression Omnibus (GEO) database was used to validate this model. Results: Most of the m6A modification genes were upregulated in GC tumor tissues compared with that in normal tissues and were correlated with clinical characteristics including grade, stage status, and T status. The risk score prediction model was established based on age, gender, FTO, and RBM15. GC patients were divided into high- or low-risk groups based on the median risk score. Patients with a high risk score had poor prognosis. Multivariate Cox regression indicated that risk score was an independent prognostic factor for GC patients. The data from GSE84437 verified the predictive value of this model. Conclusion: The risk score prediction model based on age and gender combined with m6A modification genes FTO and RBM15 was an independent prognostic factor for GC patients.", "claims": [{"id": "claim-method", "statement": "Multivariate Cox regression was used to identify factors related to GC prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35433701", "locator": "sections[4] \"Bioinformatic Analysis\" paragraph 2", "quote": "Multivariate Cox regression was used to identify factors related to GC prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found at: https://portal.gdc.cancer.gov/ and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84437.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35433701", "locator": "sections[13] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data can be found at: https://portal.gdc.cancer.gov/ and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84437."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk score prediction model based on age and gender combined with m6A modification genes FTO and RBM15 was an independent prognostic factor for GC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35433701", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The risk score prediction model based on age and gender combined with m6A modification genes FTO and RBM15 was an independent prognostic factor for GC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk score prediction model based on age and gender combined with m6A modification genes FTO and RBM15 was an independent prognostic factor for GC patients.", "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:35433701"], "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:35433701", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2022.710708", "sha256": "sha256:ae9a3716b85b1985ab2f06ef04ecf00d531a1d034f6ea165bbaee6948db70830", "bytes": 33461, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2022.710708", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9008303", "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": "Publicly available datasets were analyzed in this study. 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