Yi Wu, Wanjia Chen, Siqi Gong, Jiajia Wang, Zhimin Zhai
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引用次数: 0
Abstract
Acute myeloid leukemia (AML) remains challenging to treat due to clinical heterogeneity and a lack of prognostic biomarkers. To address this, we developed a prognostic signature based on platelet-related genes (PRGs). By analyzing transcriptomic data from TCGA-LAML, GSE146173, and Beat AML 2.0 cohorts, we identified and validated an 11-gene signature (PSME2, PPIF, SYTL4, S100A4, CCND3, SMIM15, PARVB, STXBP5, KCNMB1, GABRE, SLC50A1) using LASSO-Cox regression. This model effectively stratified patients into high- and low-risk groups with distinct survival outcomes (p < 0.001) and demonstrated high predictive accuracy (1-/3-/5-year AUC: 0.832/0.782/0.880). High-risk patients exhibited immunosuppressive features, including upregulated immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2, PDCD1), prominent monocyte infiltration, and reduced dendritic`11 cell activity. Drug sensitivity analysis suggested gefitinib, zebularine, and simvastatin as potential therapies for high-risk AML (p < 0.05). We further validated the signature's prognostic value using qPCR and clinical grouping. Notably, in vitro studies indicated that KCNMB1 facilitates AML progression. In conclusion, our robust PRG-based model elucidates the link between platelet biology, immune dysregulation, and therapeutic vulnerability in AML, offering clinical utility for risk stratification and treatment decisions.
由于临床异质性和缺乏预后生物标志物,急性髓性白血病(AML)的治疗仍然具有挑战性。为了解决这个问题,我们开发了一种基于血小板相关基因(PRGs)的预后标记。通过分析来自TCGA-LAML、GSE146173和Beat AML 2.0队列的转录组学数据,我们使用LASSO-Cox回归识别并验证了11个基因特征(PSME2、PPIF、SYTL4、S100A4、CCND3、SMIM15、PARVB、STXBP5、KCNMB1、GABRE、SLC50A1)。该模型有效地将患者分为高危组和低危组,具有不同的生存结果
期刊介绍:
BioFactors, a journal of the International Union of Biochemistry and Molecular Biology, is devoted to the rapid publication of highly significant original research articles and reviews in experimental biology in health and disease.
The word “biofactors” refers to the many compounds that regulate biological functions. Biological factors comprise many molecules produced or modified by living organisms, and present in many essential systems like the blood, the nervous or immunological systems. A non-exhaustive list of biological factors includes neurotransmitters, cytokines, chemokines, hormones, coagulation factors, transcription factors, signaling molecules, receptor ligands and many more. In the group of biofactors we can accommodate several classical molecules not synthetized in the body such as vitamins, micronutrients or essential trace elements.
In keeping with this unified view of biochemistry, BioFactors publishes research dealing with the identification of new substances and the elucidation of their functions at the biophysical, biochemical, cellular and human level as well as studies revealing novel functions of already known biofactors. The journal encourages the submission of studies that use biochemistry, biophysics, cell and molecular biology and/or cell signaling approaches.