Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34745226", "kind": "science-record", "revision": 5, "content_hash": "sha256:297ceb7424f954c6662fc0574b4407cb940403f9a94295774041c0f699bb5a76", "created_at": "2026-11-01T17:36:26Z", "updated_at": "2026-11-01T22:47:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer.", "doi": "10.3389/fgene.2021.758926", "pmid": "34745226", "authors": ["Donghui Liu", "Long Li", "Liru Wang", "Chao Wang", "Xiaowei Hu", "Qingxin Jiang", "Xuyao Wang", "Guiqin Xue", "Yu Liu", "Dongbo Xue"], "journal": "Frontiers in genetics", "year": 2021, "abstract": "Background: The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms. Methods: In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes. Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways. Conclusion: Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.", "claims": [{"id": "claim-method", "statement": "In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34745226", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://xena.ucsc.edu/. https://www.ncbi.nlm.nih.gov/geo/. http://ualcan.path.uab.edu/analysis.html.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34745226", "locator": "sections[26] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://xena.ucsc.edu/. https://www.ncbi.nlm.nih.gov/geo/. http://ualcan.path.uab.edu/analysis.html."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34745226", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment.", "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:34745226"], "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:GSE30601"], "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:GSE30601"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34745226", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2021.758926", "sha256": "sha256:ff9e545f3120427cbc952c85127d8606868b4a40d452220fd0b2bbdead22b7f9", "bytes": 65755, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2021.758926", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8566671", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE30601", "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=GSE30601", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE30601", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: https://xena.ucsc.edu/. https://www.ncbi.nlm.nih.gov/geo/. http://ualcan.path.uab.edu/analysis.html.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. This data can be found here: https://xena.ucsc.edu/. https://www.ncbi.nlm.nih.gov/geo/. http://ualcan.path.uab.edu/analysis.html.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM759072", "GSM759073", "GSM759074", "GSM759075", "GSM759076", "GSM759077", "GSM759078", "GSM759079", "GSM759080", "GSM759081", "GSM759082", "GSM759083", "GSM759084", "GSM759085", "GSM759086", "GSM759087", "GSM759088", "GSM759089", "GSM759090", "GSM759091", "GSM759092", "GSM759093", "GSM759094", "GSM759095", "GSM759096", "GSM759097", "GSM759098", "GSM759099", "GSM759100", "GSM759101", "GSM759102", "GSM759103", "GSM759104", "GSM759105", "GSM759106", "GSM759107", "GSM759108", "GSM759109", "GSM759110", "GSM759111", "GSM759112", "GSM759113", "GSM759114", "GSM759115", "GSM759116", "GSM759117", "GSM759118", "GSM759119", "GSM759120", "GSM759121", "GSM759122", "GSM759123", "GSM759124", "GSM759125", "GSM759126", "GSM759127", "GSM759128", "GSM759129", "GSM759130", "GSM759131", "GSM759132", "GSM759133", "GSM759134", "GSM759135", "GSM759136", "GSM759137", "GSM759138", "GSM759139", "GSM759140", "GSM759141", "GSM759142", "GSM759143", "GSM759144", "GSM759145", "GSM759146", "GSM759147", "GSM759148", "GSM759149", "GSM759150", "GSM759151", "GSM759152", "GSM759153", "GSM759154", "GSM759155", "GSM759156", "GSM759157", "GSM759158", "GSM759159", "GSM759160", "GSM759161", "GSM759162", "GSM759163", "GSM759164", "GSM759165", "GSM759166", "GSM759167", "GSM759168", "GSM759169", "GSM759170", "GSM759171", "GSM759172", "GSM759173", "GSM759174", "GSM759175", "GSM759176", "GSM759177", "GSM759178", "GSM759179", "GSM759180", "GSM759181", "GSM759182", "GSM759183", "GSM759184", "GSM759185", "GSM759186", "GSM759187", "GSM759188", "GSM759189", "GSM759190", "GSM759191", "GSM759192", "GSM759193", "GSM759194", "GSM759195", "GSM759196", "GSM759197", "GSM759198", "GSM759199", "GSM759200", "GSM759201", "GSM759202", "GSM759203", "GSM759204", "GSM759205", "GSM759206", "GSM759207", "GSM759208", "GSM759209", "GSM759210", "GSM759211", "GSM759212", "GSM759213", "GSM759214", "GSM759215", "GSM759216", "GSM759217", "GSM759218", "GSM759219", "GSM759220", "GSM759221", "GSM759222", "GSM759223", "GSM759224", "GSM759225", "GSM759226", "GSM759227", "GSM759228", "GSM759229", "GSM759230", "GSM759231", "GSM759232", "GSM759233", "GSM759234", "GSM759235", "GSM759236", "GSM759237", "GSM759238", "GSM759239", "GSM759240", "GSM759241", "GSM759242", "GSM759243", "GSM759244", "GSM759245", "GSM759246", "GSM759247", "GSM759248", "GSM759249", "GSM759250", "GSM759251", "GSM759252", "GSM759253", "GSM759254", "GSM759255", "GSM759256", "GSM759257", "GSM759258", "GSM759259", "GSM759260", "GSM759261", "GSM759262", "GSM759263", "GSM759264", "GSM759265", "GSM759266", "GSM759267", "GSM759268", "GSM759269", "GSM759270", "GSM759271", "GSM759272", "GSM759273", "GSM759274", "GSM759275", "GSM759276", "GSM759277", "GSM759278", "GSM759279", "GSM759280", "GSM759281", "GSM759282", "GSM759283", "GSM759284", "GSM759285", "GSM759286", "GSM759287", "GSM759288", "GSM759289", "GSM759290", "GSM759291", "GSM759292", "GSM759293", "GSM759294", "GSM759295", "GSM759296", "GSM759297", "GSM759298", "GSM759299", "GSM759300", "GSM759301", "GSM759302", "GSM759303", "GSM759304", "GSM759305", "GSM759306", "GSM759307", "GSM759308", "GSM759309", "GSM759310", "GSM759311", "GSM759312", "GSM759313", "GSM759314", "GSM759315", "GSM759316", "GSM759317", "GSM759318", "GSM759319", "GSM759320", "GSM759321", "GSM759322", "GSM759323", "GSM759324", "GSM759325", "GSM759326", "GSM759327", "GSM759328", "GSM759329", "GSM759330", "GSM759331", "GSM759332", "GSM759333", "GSM759334", "GSM759335", "GSM759336", "GSM759337", "GSM759338", "GSM759339", "GSM759340", "GSM759341", "GSM759342", "GSM759343", "GSM759344", "GSM759345", "GSM759346", "GSM759347", "GSM759348", "GSM759349", "GSM759350", "GSM759351", "GSM759352", "GSM759353", "GSM759354", "GSM759355", "GSM759356", "GSM759357", "GSM759358", "GSM759359", "GSM759360", "GSM759361", "GSM759362", "GSM759363", "GSM759364", "GSM759365", "GSM759366", "GSM759367", "GSM759368"], "sample_count": 297, "group_sizes": null, "group_sizes_basis": null, "platform": "8490", "taxon": "Homo sapiens", "title": "Epigenetic analysis of gastric cancer", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE30601 not downloaded", "asset:data:geo:GSE30601 licence not read"]}, "analysis": {"state": "partial", "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": "In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {"p_value": "ith gastric cancer (OS: p value = 0"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {"p_value": "ith gastric cancer (OS: p value = 0"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-5", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-6", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "PPI"}}, {"id": "method-7", "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"}}, {"id": "method-8", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["GSE30601"], "resolved_accessions": ["GSE30601"], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here: https://xena.ucsc.edu/. https://www.ncbi.nlm.nih.gov/geo/. http://ualcan.path.uab.edu/analysis.html."], "basis": "The paper names GSE30601 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:34745226", "data:geo:GSE30601"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE30601"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:06:11Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 18743, "completion_tokens": 1673, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:34745226", "type": "paper", "label": "pmid:34745226"}, {"id": "source:pmid:34745226", "type": "manuscript", "label": "PMID 34745226 full text"}, {"id": "data:geo:GSE30601", "type": "dataset", "label": "GEO GSE30601"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34745226", "type": "paper", "label": "pmid:34745226"}, {"id": "source:pmid:34745226", "type": "manuscript", "label": "PMID 34745226 full text"}, {"id": "data:geo:GSE30601", "type": "dataset", "label": "GEO GSE30601"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34745226", "type": "extraction", "used": ["source:pmid:34745226"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE30601", "type": "accession_resolution", "used": ["data:geo:GSE30601"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:34745226", "parent_hash": "sha256:c3c6b70de0620095383676b42133eea875c6c8b9e75e01627ad200cf7a9d413f", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE30601 licence not read", "asset:data:geo:GSE30601 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis."], "order_basis": "analysis_only", "id": "assay:1"}]}