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Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage
BACKGROUND: The roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive. METHODS: This study aims to investigate the expression profiles of circRNAs by RNA sequencing and RT–PCR in a discovery cohort and an independent validation cohort. Bioinformatics analy...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745062/ https://www.ncbi.nlm.nih.gov/pubmed/36523430 http://dx.doi.org/10.3389/fnins.2022.1002590 |
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author | Bai, Congxia Hao, Xiaoyan Zhou, Lei Sun, Yingying Song, Li Wang, Fengjuan Yang, Liu Liu, Jiayun Chen, Jingzhou |
author_facet | Bai, Congxia Hao, Xiaoyan Zhou, Lei Sun, Yingying Song, Li Wang, Fengjuan Yang, Liu Liu, Jiayun Chen, Jingzhou |
author_sort | Bai, Congxia |
collection | PubMed |
description | BACKGROUND: The roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive. METHODS: This study aims to investigate the expression profiles of circRNAs by RNA sequencing and RT–PCR in a discovery cohort and an independent validation cohort. Bioinformatics analysis was performed to identify the potential functions of circRNA host genes. Machine learning classification models were used to assess circRNAs as potential biomarkers of ICH. RESULTS: A total of 125 and 284 differentially expressed circRNAs (fold change > 1.5 and FDR < 0.05) were found between ICH patients and healthy controls in the discovery and validation cohorts, respectively. Nine circRNAs were consistently altered in ICH patients compared to healthy controls. The combination of the novel circERBB2 and circCHST12 in ICH patients and healthy controls showed an area under the curve of 0.917 (95% CI: 0.869–0.965), with a sensitivity of 87.5% and a specificity of 82%. In combination with ICH risk factors, circRNAs improved the performance in discriminating ICH patients from healthy controls. Together with hsa_circ_0005505, two novel circRNAs for differentiating between patients with ICH and healthy controls showed an AUC of 0.946 (95% CI: 0.910–0.982), with a sensitivity of 89.1% and a specificity of 86%. CONCLUSION: We provided a transcriptome-wide overview of aberrantly expressed circRNAs in ICH patients and identified hsa_circ_0005505 and novel circERBB2 and circCHST12 as potential biomarkers for diagnosing ICH. |
format | Online Article Text |
id | pubmed-9745062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97450622022-12-14 Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage Bai, Congxia Hao, Xiaoyan Zhou, Lei Sun, Yingying Song, Li Wang, Fengjuan Yang, Liu Liu, Jiayun Chen, Jingzhou Front Neurosci Neuroscience BACKGROUND: The roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive. METHODS: This study aims to investigate the expression profiles of circRNAs by RNA sequencing and RT–PCR in a discovery cohort and an independent validation cohort. Bioinformatics analysis was performed to identify the potential functions of circRNA host genes. Machine learning classification models were used to assess circRNAs as potential biomarkers of ICH. RESULTS: A total of 125 and 284 differentially expressed circRNAs (fold change > 1.5 and FDR < 0.05) were found between ICH patients and healthy controls in the discovery and validation cohorts, respectively. Nine circRNAs were consistently altered in ICH patients compared to healthy controls. The combination of the novel circERBB2 and circCHST12 in ICH patients and healthy controls showed an area under the curve of 0.917 (95% CI: 0.869–0.965), with a sensitivity of 87.5% and a specificity of 82%. In combination with ICH risk factors, circRNAs improved the performance in discriminating ICH patients from healthy controls. Together with hsa_circ_0005505, two novel circRNAs for differentiating between patients with ICH and healthy controls showed an AUC of 0.946 (95% CI: 0.910–0.982), with a sensitivity of 89.1% and a specificity of 86%. CONCLUSION: We provided a transcriptome-wide overview of aberrantly expressed circRNAs in ICH patients and identified hsa_circ_0005505 and novel circERBB2 and circCHST12 as potential biomarkers for diagnosing ICH. Frontiers Media S.A. 2022-11-29 /pmc/articles/PMC9745062/ /pubmed/36523430 http://dx.doi.org/10.3389/fnins.2022.1002590 Text en Copyright © 2022 Bai, Hao, Zhou, Sun, Song, Wang, Yang, Liu and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Bai, Congxia Hao, Xiaoyan Zhou, Lei Sun, Yingying Song, Li Wang, Fengjuan Yang, Liu Liu, Jiayun Chen, Jingzhou Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage |
title | Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage |
title_full | Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage |
title_fullStr | Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage |
title_full_unstemmed | Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage |
title_short | Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage |
title_sort | machine learning-based identification of the novel circrnas circerbb2 and circchst12 as potential biomarkers of intracerebral hemorrhage |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745062/ https://www.ncbi.nlm.nih.gov/pubmed/36523430 http://dx.doi.org/10.3389/fnins.2022.1002590 |
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