A novel prognostic model of methylation-associated genes in acute myeloid leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36715873", "kind": "science-record", "revision": 4, "content_hash": "sha256:7383d6966a7be9b8f9d4ac2c7e3d09a3c3239e4958b48c861f9c9ae73f0e2e8f", "created_at": "2026-11-01T19:01:17Z", "updated_at": "2026-11-01T22:51:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel prognostic model of methylation-associated genes in acute myeloid leukemia.", "doi": "10.1007/s12094-022-03069-2", "pmid": "36715873", "authors": ["Meiyu Chen", "Zhao Zeng", "Wei Qin", "Xiaohui Cai", "Xuzhang Lu", "Suning Chen"], "journal": "Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico", "year": 2023, "abstract": "BACKGROUND: There is growing evidence that methylation-associated genes (MAGs) play an important role in the prognosis of acute myeloid leukemia (AML) patients. Thus, the aim of this research was to investigate the impact of MAGs in predicting the outcomes of AML patients.\nMETHODS: The expression profile and clinical information of patients were downloaded from public databases. A novel prognostic model based on 7 MAGs was established in the TCGA training cohort and validated in the GSE71014 dataset. To validate the clinical implications, the correlation between MAGs signature and drug sensitivity was further investigated.\nRESULTS: 76 genes were screened out by the univariate Cox regression and significantly enriched in multiple methylation-related pathways. After filtering variables using LASSO regression analysis, 7 MAGs were introduced to construct the predictive model. The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01). Furthermore, the risk score system as a prognostic factor also worked in the intermediate-risk patients based on ELN-2017 classification. Importantly, the risk score was demonstrated to be an independent prognostic factor for AML in the univariate and multivariate Cox regression analysis. Interestingly, GSEA analysis revealed that multiple metabolism-related pathways were significantly enriched in the high-risk group. Drug sensitivity analysis showed there was a significant difference in sensitivity of some drugs between the two groups.\nCONCLUSION: We developed a robust and accurate prognostic model with 7 MAGs. Our findings might provide a reference for the clinical prognosis and management of AML.", "claims": [{"id": "claim-method", "statement": "We performed the univariate Cox regression analysis of 362 MAGs using the ‘survival’ package in R software to obtain the prognostic genes (p < 0.05). Further analysis to filter variables was performed by the least absolute shrinkage and selection operator (Lasso) regression analysis, which utilized Lasso regularization with 1000-fold cross-validation to prevent overfitting [12].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36715873", "locator": "sections[5] \"Establishment and evaluation of prognostic model\" paragraph 2", "quote": "We performed the univariate Cox regression analysis of 362 MAGs using the ‘survival’ package in R software to obtain the prognostic genes (p < 0.05). Further analysis to filter variables was performed by the least absolute shrinkage and selection operator (Lasso) regression analysis, which utilized Lasso regularization with 1000-fold cross-validation to prevent overfitting [12]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data analyzed during the study can be obtained from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36715873", "locator": "sections[21] \"Data availability\" paragraph 2", "quote": "The data analyzed during the study can be obtained from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01).", "kind": "quantitative", "target": {"value": 0.01, "unit": "p-value", "tolerance": null, "source": "The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01)."}, "evidence": [{"asset": "source:pmid:36715873", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01).", "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:36715873"], "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:GSE71014"], "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:GSE71014"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36715873", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12094-022-03069-2", "sha256": "sha256:00b9e23a9fe41e82c2f0e00dfce0d591cca42208e1f74cc780dd0bbea53b1118", "bytes": 37121, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12094-022-03069-2", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10203004", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE71014", "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=GSE71014", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE71014", "version": null, "note": "The data analyzed during the study can be obtained from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data analyzed during the study can be obtained from the corresponding author upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1825455", "GSM1825456", "GSM1825457", "GSM1825458", "GSM1825459", "GSM1825460", "GSM1825461", "GSM1825462", "GSM1825463", "GSM1825464", "GSM1825465", "GSM1825466", "GSM1825467", "GSM1825468", "GSM1825469", "GSM1825470", "GSM1825471", "GSM1825472", "GSM1825473", "GSM1825474", "GSM1825475", "GSM1825476", "GSM1825477", "GSM1825478", "GSM1825479", "GSM1825480", "GSM1825481", "GSM1825482", "GSM1825483", "GSM1825484", "GSM1825485", "GSM1825486", "GSM1825487", "GSM1825488", "GSM1825489", "GSM1825490", "GSM1825491", "GSM1825492", "GSM1825493", "GSM1825494", "GSM1825495", "GSM1825496", "GSM1825497", "GSM1825498", "GSM1825499", "GSM1825500", "GSM1825501", "GSM1825502", "GSM1825503", "GSM1825504", "GSM1825505", "GSM1825506", "GSM1825507", "GSM1825508", "GSM1825509", "GSM1825510", "GSM1825511", "GSM1825512", "GSM1825513", "GSM1825514", "GSM1825515", "GSM1825516", "GSM1825517", "GSM1825518", "GSM1825519", "GSM1825520", "GSM1825521", "GSM1825522", "GSM1825523", "GSM1825524", "GSM1825525", "GSM1825526", "GSM1825527", "GSM1825528", "GSM1825529", "GSM1825530", "GSM1825531", "GSM1825532", "GSM1825533", "GSM1825534", "GSM1825535", "GSM1825536", "GSM1825537", "GSM1825538", "GSM1825539", "GSM1825540", "GSM1825541", "GSM1825542", "GSM1825543", "GSM1825544", "GSM1825545", "GSM1825546", "GSM1825547", "GSM1825548", "GSM1825549", "GSM1825550", "GSM1825551", "GSM1825552", "GSM1825553", "GSM1825554", "GSM1825555", "GSM1825556", "GSM1825557", "GSM1825558"], "sample_count": 104, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "An mRNA Expression Signature for Prognostication in De Novo Acute Myeloid Leukemia Patients with Normal Karyotype", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE71014 not downloaded", "asset:data:geo:GSE71014 licence not read"]}, "analysis": {"state": "complete", "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": "We performed the univariate Cox regression analysis of 362 MAGs using the ‘survival’ package in R software to obtain the prognostic genes (p < 0.05). 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