Medical Center of Hematology, Xinqiao Hospital, State Key Laboratory of Trauma, Burns and Combined Injury, Army Medical University, Chongqing, China.
Haematology Centre, Department of Immunology and Inflammation, Imperial College London, London, UK.
Theranostics. 2023 Feb 13;13(4):1289-1301. doi: 10.7150/thno.80054. eCollection 2023.
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. 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. 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. 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.
急性髓系白血病(AML)是成人中最常见的急性白血病;AML 高度异质,涉及多个组学水平的异常。体液中的小非编码 RNA(sncRNA)是重要的调节分子,被认为是疾病有前途的非侵入性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。我们检查了 80 例连续新诊断 AML 患者和 12 例健康对照者的血液和骨髓样本的高通量小 RNA 测序数据。分析差异表达的 sncRNA 以揭示 AML 患者与对照组之间的不同模式。使用机器学习方法评估特定 sncRNA 区分 AML 患者和对照组个体的效率。通过 RT-PCR、Q-PCR 和 northern blot 评估单个 sncRNA 的表达水平变化。采用相关分析评估血清和骨髓上清液之间的 sncRNA 模式。我们在血清和骨髓上清液中发现了超过 20 种除 miRNA 以外的 sncRNA 类别,它们之间具有高度协调的表达模式。非经典 sncRNA,包括 rsRNA(62.86%)、ysRNA(14.97%)和 tsRNA(4.22%),在血清 sncRNA 中占主导地位,在 AML 患者中表现出敏感的改变模式。根据基于机器学习的算法,基于 tsRNA 的特征可以稳健地区分 AML 患者和对照组,并且比包含 miRNA 的特征更可靠。我们的数据还表明,血清 tsRNA 与 AML 预后密切相关,表明血清 tsRNA 作为生物标志物辅助 AML 诊断的潜在应用。我们全面描述了血液和骨髓中循环 sncRNA 的表达模式及其在健康对照者和 AML 患者之间的改变特征。这项研究丰富了 sncRNA 在 AML 调控中的研究,为 sncRNA 在 AML 中的作用提供了新的见解。