Prognostic implications of metabolism-related genes in acute myeloid leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39421301", "kind": "science-record", "revision": 4, "content_hash": "sha256:ff5cf016d5a3289cf4765e4723a90d7608668781e4f8d1d6951a50ed4db16e98", "created_at": "2026-11-01T20:29:39Z", "updated_at": "2026-11-01T22:55:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic implications of metabolism-related genes in acute myeloid leukemia.", "doi": "10.3389/fgene.2024.1424365", "pmid": "39421301", "authors": ["Na Ren", "Jianan Wang", "Ruibing Li", "Chengliang Yin", "Mianyang Li", "Chengbin Wang"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Introduction: Acute myeloid leukemia(AML) is a diverse malignancy with a prognosis that varies, being especially unfavorable in older patients and those with high-risk characteristics. Metabolic reprogramming has become a significant factor in AML development , presenting new opportunities for prognostic assessment and therapeutic intervention.\nMethods: Metabolism-related differentially expressed genes (mDEGs) were identified by integrating KEGG metabolic gene lists with AML gene expression data from GSE63270. Using TCGA data, we performed consensus clustering and survival analysis to investigate the prognostic significance of mDEGs. A metabolic risk model was constructed using LASSO Cox reg ression and enhanced by a nomogram incorporated clinical characteristics. The model was validated through receiver operating characteristic (ROC) curves and survival statistics. Gene network analysis was conducted to identify critical prognostic factors. The tumor immune microenvironment was evaluated using CIBERSORT and ESTIMATE algorithms, followed by correlation analysis between immune checkpoint gene expression and risk scores. Drug sensitivity predictions and in vitro assays were performed to explore the effects of mDEGs on cell proliferation and chemoresistance.\nResults: An 11-gene metabolic prognostic model was established and validated. High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05). The risk score was an independent prognostic factor. High-risk patients showed increased immune cell infiltration and potential response to checkpoint inhibitors but decreased drug sensitivity. The model correlated with sensitivity to drugs such as venetoclax. Carbonic anhydrase 13 (CA13) was identified as a key gene related to prognosis and doxorubicin resistance. Knocking down CA13 reduced proliferation and increased cell death with doxorubicin treatment.\nConclusion: A novel metabolic gene signature was developed to stratify risk and predict prognosis in AML, serving as an independent prognostic factor. CA13 was identified as a potential therapeutic target. This study provides new insights into the prognostic and therapeutic implications of metabolic genes in AML.", "claims": [{"id": "claim-method", "statement": "A LASSO Cox model was built using the glmnet package (version4.1–1) (Friedman et al., 2010), with the optimal penalty parameter determined by 1000-fold cross-validation. ROC analysis was used to evaluate the model’s performance. Survival analyses were conducted in the training, validation, and entire sets, as well as an independent TARGET-AML cohort. To assess the independence of risk score in clinical application, univariate and multivariate Cox regression analyses were performed in TCGA-AML cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39421301", "locator": "sections[6] \"2.4 Construction and validation of the metabolic risk score\" paragraph 2", "quote": "A LASSO Cox model was built using the glmnet package (version4.1–1) (Friedman et al., 2010), with the optimal penalty parameter determined by 1000-fold cross-validation."}, {"asset": "source:pmid:39421301", "locator": "sections[6] \"2.4 Construction and validation of the metabolic risk score\" paragraph 3", "quote": "ROC analysis was used to evaluate the model’s performance."}, {"asset": "source:pmid:39421301", "locator": "sections[6] \"2.4 Construction and validation of the metabolic risk score\" paragraph 3", "quote": "Survival analyses were conducted in the training, validation, and entire sets, as well as an independent TARGET-AML cohort."}, {"asset": "source:pmid:39421301", "locator": "sections[19] \"3.5 Independent prognostic value of the metabolism-related risk score for AML\" paragraph 2", "quote": "To assess the independence of risk score in clinical application, univariate and multivariate Cox regression analyses were performed in TCGA-AML cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39421301", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An 11-gene metabolic prognostic model was established and validated.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05)."}, "evidence": [{"asset": "source:pmid:39421301", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "An 11-gene metabolic prognostic model was established and validated."}, {"asset": "source:pmid:39421301", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An 11-gene metabolic prognostic model was established and validated.", "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:39421301"], "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:GSE63270"], "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:GSE63270"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39421301", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2024.1424365", "sha256": "sha256:5317735b7d92f6f1723d83cf275ec2f122e80b934be7680131690df17ba20a22", "bytes": 45239, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2024.1424365", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11484252", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE63270", "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=GSE63270", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE63270", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1544800", "GSM1544801", "GSM1544802", "GSM1544803", "GSM1544804", "GSM1544805", "GSM1544806", "GSM1544807", "GSM1544808", "GSM1544809", "GSM1544810", "GSM1544811", "GSM1544812", "GSM1544813", "GSM1544814", "GSM1544815", "GSM1544816", "GSM1544817", "GSM1544818", "GSM1544819", "GSM1544820", "GSM1544821", "GSM1544822", "GSM1544823", "GSM1544824", "GSM1544825", "GSM1544826", "GSM1544827", "GSM1544828", "GSM1544829", "GSM1544830", "GSM1544831", "GSM1544832", "GSM1544833", "GSM1544834", "GSM1544835", "GSM1544836", "GSM1544837", "GSM1544838", "GSM1544839", "GSM1544840", "GSM1544841", "GSM1544842", "GSM1544843", "GSM1544844", "GSM1544845", "GSM1544846", "GSM1544847", "GSM1544848", "GSM1544849", "GSM1544850", "GSM1544851", "GSM1544852", "GSM1544853", "GSM1544854", "GSM1544855", "GSM1544856", "GSM1544857", "GSM1544858", "GSM1544859", "GSM1544860", "GSM1544861", "GSM1544862", "GSM1544863", "GSM1544864", "GSM1544865", "GSM1544866", "GSM1544867", "GSM1544868", "GSM1544869", "GSM1544870", "GSM1544871", "GSM1544872", "GSM1544873", "GSM1544874", "GSM1544875", "GSM1544876", "GSM1544877", "GSM1544878", "GSM1544879", "GSM1544880", "GSM1544881", "GSM1544882", "GSM1544883", "GSM1544884", "GSM1544885", "GSM1544886", "GSM1544887", "GSM1544888", "GSM1544889", "GSM1544890", "GSM1544891", "GSM1544892", "GSM1544893", "GSM1544894", "GSM1544895", "GSM1544896", "GSM1544897", "GSM1544898", "GSM1544899", "GSM1544900", "GSM1544901", "GSM1544902", "GSM1544903"], "sample_count": 104, "group_sizes": null, "group_sizes_basis": null, "platform": "17810", "taxon": "Homo sapiens", "title": "Expression profiles of normal hematopoietic stem and progenitor cells and acute myeloid leukemia sub-populations", "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:GSE63270 not downloaded", "asset:data:geo:GSE63270 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "reordered", "quote": "A LASSO Cox model was built using the glmnet package (version4.1–1) (Friedman et al., 2010), with the optimal penalty parameter determined by 1000-fold cross-validation. 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