Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia

Abstract

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.

METTL1 mediated tRNA m 7 G modification promotes leukaemogenesis of AML via tRNA regulated translational control

“…Moreover, the present study con rms that METTL1 is involved in the maintenance of tRNA stability in AML cells, and that METTL1 knockdown results in a reduction of various tRNAs abundance and elevated the levels of tsRNAs. The emerging evidence shows that abnormal expression of individual tsRNAs is involved in cancer biology [20,38,39,41,52]. However, we did not observe signi cant alteration of cellular translation e ciency when transfected in vivo isolated tsRNAs from METTL1 knockdown cells and synthesized 3' tsRNA Leu into AML cells, suggesting that the molecular regulatory mechanisms of tsRNAs in AML might beyond regulating cellular nascent protein synthesis.…”

METTL1 mediated tRNA m7G modification promotes leukaemogenesis of AML via tRNA regulated translational control

“…Moreover, the present study confirms that METTL1 is involved in the maintenance of tRNA stability in AML cells and that METTL1 knockdown results in a reduction of various tRNA abundance and elevated the levels of tsRNAs. The emerging evidence shows that abnormal expression of individual tsRNAs is involved in cancer biology [ 25 , 46 , 47 , 49 , 59 , 60 ]. However, we did not observe significant alteration of cellular translation efficiency when transfected in vivo isolated tsRNAs from METTL1 knockdown cells into AML cells, suggesting that the molecular regulatory mechanisms of tsRNAs in AML might beyond regulating cellular nascent protein synthesis.…”

Untacking small RNA profiling and RNA fragment footprinting: Approaches and challenges in library construction

“…Not surprisingly, more recent technological advances have unveiled novel, but previously unnoticed, types of sRNAs, including transfer RNA‐derived small RNA (tsRNA, ~15–40 nt) (Q. Chen et al, 2021; Park et al, 2020; Weng et al, 2022; D. Xu et al, 2022), ribosomal RNA derived small RNA (rsRNA, ~21–44 nt) (C. Chu et al, 2017; Lambert et al, 2019), Y RNA derived small RNA (ysRNA, ~22–36 nt) (Guglas et al, 2020; Nicolas et al, 2012; Xia et al, 2023), vtRNA derived small RNA (vtsRNA, ~23–32 nt) (Hussain et al, 2013; Meng et al, 2016; Persson et al, 2009), and so forth. These newly discovered sRNA categories likely constitute a hidden layer of gene expression regulation underlying a broad range of biological processes, although their functional mechanisms remain by far largely elusive (Gupta et al, 2022; Xia et al, 2023; D. Xu et al, 2022).…”