Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia.
Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia.
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DOI：
10.7150/thno.80054
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发表时间：
2023
期刊：
影响因子：
12.4
通讯作者：
Zhang X
中科院分区：
文献类型：
--
作者：
Xia L;Guo H;Wu X;Xu Y;Zhao P;Yan B;Zeng Y;He Y;Chen D;Gale RP;Zhang Y;Zhang X
背景资料：急性髓性白血病（AML）是成人中最常见的急性白血病; AML具有高度异质性，涉及多个组学水平的异常。存在于体液中的小的非编码RNA（sncRNA）是重要的调节分子，并且被认为是有前途的非侵入性疾病临床诊断生物标志物。然而，AML患者血清和骨髓上清液中sncRNA谱改变的特征仍在探索中。研究方法：我们检查了来自80名连续的新诊断的AML患者和12名健康对照的血液和骨髓样本的数据，以进行高通量小RNA测序。分析差异表达的sncRNA以揭示AML患者和对照之间的不同模式。机器学习方法用于评估特异性sncRNA在区分AML个体与对照中的效率。通过RT-PCR、Q-PCR和北方印迹来评估单个sncRNA的改变的表达水平。采用相关分析评估血清和骨髓上清液之间的sncRNA模式。结果如下：我们在血清和骨髓上清液中鉴定了超过20种类型的sncRNA类别，它们之间具有高度协调的表达模式。非经典sncRNA在AML患者血清sncRNA中占主导地位，包括rsRNA（62.86%）、ysRNA（14.97%）和tsRNA（4.22%），并显示出敏感的改变模式。根据基于机器学习的算法，基于tsRNA的签名可以将AML受试者与对照区分开来，并且比包含miRNA的签名更可靠。我们的数据还显示，血清tsRNA与AML预后密切相关，提示血清tsRNA作为生物标志物辅助AML诊断的潜在应用。结论：我们全面描述了健康对照和AML患者血液和骨髓中循环sncRNA的表达模式及其变化特征。本研究丰富了sncRNA在AML调控中的研究，为进一步了解sncRNA在AML中的作用提供了新的思路。
Background: Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults; AML is highly heterogeneous and involves abnormalities at multiple omics levels. Small non-coding RNAs (sncRNAs) present in body fluids are important regulatory molecules and considered promising non-invasive clinical diagnostic biomarkers for disease. However, the signature of sncRNA profile alteration in AML patient serum and bone marrow supernatant is still under exploration. Methods: We examined data for blood and bone marrow samples from 80 consecutive, newly-diagnosed patients with AML and 12 healthy controls for high throughput small RNA-sequencing. Differentially expressed sncRNAs were analysed to reveal distinct patterns between AML patients and controls. Machine learning methods were used to evaluate the efficiency of specific sncRNAs in discriminating individuals with AML from controls. The altered expression level of individual sncRNAs was evaluated by RT-PCR, Q-PCR, and northern blot. Correlation analysis was employed to assess sncRNA patterns between serum and bone marrow supernatant. Results: We identified over 20 types of sncRNA categories beyond miRNAs in both serum and bone marrow supernatant, with highly coordinated expression patterns between them. Non-classical sncRNAs, including rsRNA (62.86%), ysRNA (14.97%), and tsRNA (4.22%), dominated among serum sncRNAs and showed sensitive alteration patterns in AML patients. According to machine learning-based algorithms, the tsRNA-based signature robustly discriminated subjects with AML from controls and was more reliable than that comprising miRNAs. Our data also showed that serum tsRNAs to be closely associated with AML prognosis, suggesting the potential application of serum tsRNAs as biomarkers to assist in AML diagnosis. Conclusions: We comprehensively characterized the expression pattern of circulating sncRNAs in blood and bone marrow and their alteration signature between healthy controls and AML patients. This study enriches research of sncRNAs in the regulation of AML, and provides insights into the role of sncRNAs in AML.
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