ArticleBlood cancer journal2026
Prognostic implications of genetic and transcriptomic abnormalities in MDS according to IPSS-R, IPSS-M, and the International Consensus Classification.
Article in Blood cancer journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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19 authors.
Funding
Abstract
Cytogenetic abnormalities and recurrent gene mutations are central to the biology and prognosis of myelodysplastic syndromes/neoplasms (MDS); however, the significance of patients without detectable genomic lesions remains unclear. In this retrospective analysis of 758 patients with primary MDS, the participants were stratified according to the presence of gene mutations and/or cytogenetic abnormalities. Approximately twenty percent lacked both abnormalities and were characterized by younger age, female predominance, lower IPSS-R/IPSS-M risk, and the most favorable survival outcomes. Bulk RNA sequencing further revealed that mutation-negative/normal-karyotype MDS was characterized by immune-metabolic transcriptional programs, whereas genomically altered disease exhibited inflammatory, proliferative, and stress-response signatures, indicating a biologically distinct low-risk state. Therefore, patients without genomic abnormalities appear to constitute a biologically distinct subgroup of MDS. To refine risk assessment beyond IPSS-M systems, a multivariable Cox-based nomogram was developed incorporating age, ferritin level >800 ng/mL, lactate dehydrogenase level >200 U/L, bone marrow fibrosis, and IPSS-M. The proposed model outperformed IPSS-M alone and remained robust following bootstrap validation, allowing individualized 12- and 36-month survival estimation. These findings underscore the limitations of relying solely on molecular assays for risk stratification and support the integration of clinical, genomic, and model-based predictors to improve classification and guide individualized MDS management.
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