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Coexpression Pattern Analysis of NPM1-Associated Genes in Chronic Myelogenous Leukemia

机译:NPM1相关基因在慢性粒细胞性白血病中的共表达模式分析

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摘要

Background. Nucleophosmin 1 (NPM1) plays an important role in ribosomal synthesis and malignancies, but NPM1 mutations occur rarely in the blast-crisis and chronic-phase chronic myelogenous leukemia (CML) patients. The NPM1-associated gene set (GCM_NPM1), in total 116 genes including NPM1, was chosen as the candidate gene set for the coexpression analysis. We wonder if NPM1-associated genes can affect the ribosomal synthesis and translation process in CML. Results. We presented a distribution-based approach for gene pair classification by identifying a disease-specific cutoff point that classified the coexpressed gene pairs into strong and weak coexpression structures. The differences in the coexpression patterns between the normal and the CML groups were reflected from the overall structure by performing two-sample Kolmogorov-Smirnov test. Our developed method effectively identified the coexpression pattern differences from the overall structure: P  value = 1.71 × 10−22 < 0.05 for the maximum deviation D = 0.109. Moreover, we found that genes involved in the ribosomal synthesis and translation process tended to be coexpressed in the CML group. Conclusion. Our developed method can identify the coexpression difference between two different groups. Dysregulation of ribosomal synthesis and translation process may be related to the CML disease. Our significant findings may provide useful information for the novel CML mechanism exploration and cancer treatment.
机译:背景。核糖蛋白1(NPM1)在核糖体合成和恶性肿瘤中起着重要作用,但是NPM1突变很少发生于急诊危机和慢性阶段的慢性粒细胞性白血病(CML)患者中。选择包括NPM1在内的总共116个基因中与NPM1相关的基因集(GCM_NPM1)作为共表达分析的候选基因集。我们想知道NPM1相关基因是否会影响CML中的核糖体合成和翻译过程。结果。我们提出了一种基于分布的方法对基因对进行分类,方法是确定将共表达基因对分为强和弱共表达结构的疾病特异性临界点。正常组和CML组之间共表达模式的差异通过执行两个样本的Kolmogorov-Smirnov测试从总体结构中得到反映。我们开发的方法有效地识别了整个结构中的共表达模式差异:对于最大偏差D = 0.109,P value = 1.71×10 −22 <0.05。此外,我们发现参与核糖体合成和翻译过程的基因倾向于在CML组中共表达。结论。我们开发的方法可以识别两个不同组之间的共表达差异。核糖体合成和翻译过程的失调可能与CML疾病有关。我们的重要发现可能为新型CML机制探索和癌症治疗提供有用的信息。

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