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Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia
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 cli...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ivyspring International Publisher
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008735/ https://www.ncbi.nlm.nih.gov/pubmed/36923527 http://dx.doi.org/10.7150/thno.80054 |
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author | Xia, Lin Guo, Huanping Wu, Xiao Xu, Yinying Zhao, Pan Yan, Bingbing Zeng, Yunjing He, Yundi Chen, Dan Gale, Robert Peter Zhang, Yunfang Zhang, Xi |
author_facet | Xia, Lin Guo, Huanping Wu, Xiao Xu, Yinying Zhao, Pan Yan, Bingbing Zeng, Yunjing He, Yundi Chen, Dan Gale, Robert Peter Zhang, Yunfang Zhang, Xi |
author_sort | Xia, Lin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10008735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-100087352023-03-14 Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia Xia, Lin Guo, Huanping Wu, Xiao Xu, Yinying Zhao, Pan Yan, Bingbing Zeng, Yunjing He, Yundi Chen, Dan Gale, Robert Peter Zhang, Yunfang Zhang, Xi Theranostics Research Paper 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. Ivyspring International Publisher 2023-02-13 /pmc/articles/PMC10008735/ /pubmed/36923527 http://dx.doi.org/10.7150/thno.80054 Text en © The author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
spellingShingle | Research Paper Xia, Lin Guo, Huanping Wu, Xiao Xu, Yinying Zhao, Pan Yan, Bingbing Zeng, Yunjing He, Yundi Chen, Dan Gale, Robert Peter Zhang, Yunfang Zhang, Xi Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia |
title | Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia |
title_full | Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia |
title_fullStr | Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia |
title_full_unstemmed | Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia |
title_short | Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia |
title_sort | human circulating small non-coding rna signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008735/ https://www.ncbi.nlm.nih.gov/pubmed/36923527 http://dx.doi.org/10.7150/thno.80054 |
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