非编码 RNA 及其在阿扎胞苷治疗骨髓增生异常综合征和伴有骨髓发育异常相关变化的急性髓系白血病患者中的反应预测价值。
Noncoding RNAs and Their Response Predictive Value in Azacitidine-treated Patients With Myelodysplastic Syndrome and Acute Myeloid Leukemia With Myelodysplasia-related Changes.
机构信息
Department of Genomics, Institute of Hematology and Blood Transfusion, Prague, Czech Republic;
Department of Computer Sciences, Czech Technical University, Prague, Czech Republic.
出版信息
Cancer Genomics Proteomics. 2022 Mar-Apr;19(2):205-228. doi: 10.21873/cgp.20315.
BACKGROUND/AIM: Prediction of response to azacitidine (AZA) treatment is an important challenge in hematooncology. In addition to protein coding genes (PCGs), AZA efficiency is influenced by various noncoding RNAs (ncRNAs), including long ncRNAs (lncRNAs), circular RNAs (circRNAs), and transposable elements (TEs).
MATERIALS AND METHODS
RNA sequencing was performed in patients with myelodysplastic syndromes or acute myeloid leukemia before AZA treatment to assess contribution of ncRNAs to AZA mechanisms and propose novel disease prediction biomarkers.
RESULTS
Our analyses showed that lncRNAs had the strongest predictive potential. The combined set of the best predictors included 14 lncRNAs, and only four PCGs, one circRNA, and no TEs. Epigenetic regulation and recombinational repair were suggested as crucial for AZA response, and network modeling defined three deregulated lncRNAs (CTC-482H14.5, RP11-419K12.2, and RP11-736I24.4) associated with these processes.
CONCLUSION
The expression of various ncRNAs can influence the effect of AZA and new ncRNA-based predictive biomarkers can be defined.
背景/目的：预测阿扎胞苷（AZA）治疗的反应是血液肿瘤学的一个重要挑战。除了蛋白质编码基因（PCGs），AZA 的效率还受到各种非编码 RNA（ncRNA）的影响，包括长 ncRNA（lncRNA）、环状 RNA（circRNA）和转座元件（TE）。
材料与方法
在 AZA 治疗前对骨髓增生异常综合征或急性髓系白血病患者进行 RNA 测序，以评估 ncRNA 对 AZA 机制的贡献，并提出新的疾病预测生物标志物。
结果
我们的分析表明，lncRNA 具有最强的预测潜力。最佳预测因子的组合包括 14 个 lncRNA，以及仅 4 个 PCG、1 个 circRNA 和没有 TE。表观遗传调控和重组修复被认为对 AZA 反应至关重要，网络建模定义了三个失调的 lncRNA（CTC-482H14.5、RP11-419K12.2 和 RP11-736I24.4）与这些过程相关。
结论
各种 ncRNA 的表达可以影响 AZA 的效果，可以定义新的基于 ncRNA 的预测生物标志物。
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