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pmid:38845982
A telomere-related gene risk model for predicting prognosis and treatment response in acute myeloid leukemia.
Hui-Zhong Shi · Ming-Wei Wang · Yu-Song Huang · Zhong Liu · Ling Li · Li-Ping Wan
Heliyon · 2024 doi:10.1016/j.heliyon.2024.e31705
Paper
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Experiment
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Data
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Analysis
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Execution
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Complete file
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Despite the limited focus on the prognostic value of telomere-related genes (TRGs) in AML, our study aims at addressing this gap by compiling a list of TRGs from TelNet, as well as collecting clinical information and TRGs expression data through the Gene Expression Omnibus (GEO) database. The GSE37642 dataset, sourced from GEO and based on the GPL96 platform, was divided into training and validation sets at a 6:4 ratio. Additionally, the GSE71014 dataset (based on the GPL10558 platform), GSE12417 dataset (based on the GPL96 and GPL570 platforms), and another portion of the GSE37642 dataset (based on the GPL570 platform) were designated as external testing sets. Univariate Cox regression analysis identified 96 TRGs significantly associated with OS. Subsequent Lasso-Cox stepwise regression analysis pinpointed eight TRGs (MCPH1, SLC25A6, STK19, PSAT1, KCTD15, DNMT3B, PSMD5, and TAF2) exhibiting robust predictive potential for patient survival. Both univariate and multivariate survival analyses unveiled TRG risk scores and age as independent prognostic variables. To refine the accuracy of survival prognosis, we developed both a nomogram integrating clinical parameters and a predictive risk score model based on TRGs. In subsequent investigations, associations were emphasized not solely regarding the TRG risk score and immune infiltration patterns but also concerning the response to immune-checkpoint inhibitor (ICI) therapy. 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null, "group_sizes_basis": null, "platform": "570;96;97", "taxon": "Homo sapiens", "title": "Prognostic gene signature for AML", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"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 datasets generated or analyzed during the current study are publicly available. The GSE datasets are accessible in the database of GEO (https://www.ncbi.nlm.nih.gov/geo/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated or analyzed during the current study are publicly available. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE37642", "GSE12417", "GSE71014"], "resolved_accessions": ["GSE37642", "GSE12417", "GSE71014"], "statements": ["The datasets generated or analyzed during the current study are publicly available. The GSE datasets are accessible in the database of GEO (https://www.ncbi.nlm.nih.gov/geo/)."], "basis": "The paper names GSE37642, GSE12417, GSE71014 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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