A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36451773", "kind": "science-record", "revision": 3, "content_hash": "sha256:6eee56b788415bb814771e2ae6653ebfecf81cf0940e61064c74d155027e20b5", "created_at": "2026-11-01T19:00:55Z", "updated_at": "2026-11-01T22:08:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis.", "doi": "10.1155/2022/7727424", "pmid": "36451773", "authors": ["Shuai Zhang", "Qianqian Wang", "Haoran Xia", "Hui Liu"], "journal": "Journal of oncology", "year": 2022, "abstract": "Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 (P < 0.001). The median overall survival (mOS) of subgroups 1-3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort (P < 0.001) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) regression method was performed to further screen prognostic gene sets and obtain their corresponding coefficients (R package glmnet).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36451773", "locator": "sections[6] \"2.4. Prognostic Model Construction and Validation\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression method was performed to further screen prognostic gene sets and obtain their corresponding coefficients (R package glmnet)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/repository). The datasets generated during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36451773", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "The datasets analyzed in this study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/repository). The datasets generated during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established and validated a novel prognostic model for risk stratification in AML.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36451773", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established and validated a novel prognostic model for risk stratification in AML.", "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:36451773"], "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:GSE12417"], "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:GSE12417"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36451773", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/7727424", "sha256": "sha256:b70686b6f61affbaa94dab360307bf2ad413bcc23ec5c2b366159fe03bc6eb95", "bytes": 34279, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/7727424", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9705098", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE12417", "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=GSE12417", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE12417", "version": null, "note": "The datasets analyzed in this study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/repository). The datasets generated during the current study are available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed in this study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/repository). 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