Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage
BackgroundThe roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive.MethodsThis 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 w...
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| Format: | Article |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1002590/full |
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| author | Congxia Bai Xiaoyan Hao Lei Zhou Yingying Sun Li Song Fengjuan Wang Liu Yang Jiayun Liu Jingzhou Chen Jingzhou Chen |
| author_facet | Congxia Bai Xiaoyan Hao Lei Zhou Yingying Sun Li Song Fengjuan Wang Liu Yang Jiayun Liu Jingzhou Chen Jingzhou Chen |
| author_sort | Congxia Bai |
| collection | DOAJ |
| description | BackgroundThe roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive.MethodsThis 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.ResultsA 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%.ConclusionWe 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. |
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| last_indexed | 2024-04-11T15:08:33Z |
| publishDate | 2022-11-01 |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-72ad098432f742378bf5a6912f74282d2022-12-22T04:16:44ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-11-011610.3389/fnins.2022.10025901002590Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhageCongxia Bai0Xiaoyan Hao1Lei Zhou2Yingying Sun3Li Song4Fengjuan Wang5Liu Yang6Jiayun Liu7Jingzhou Chen8Jingzhou Chen9Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, ChinaDepartment of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, ChinaDepartment of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, ChinaState Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaState Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, ChinaDepartment of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, ChinaDepartment of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, ChinaState Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaNational Health Commission Key Laboratory of Cardiovascular Regenerative Medicine, Fuwai Central-China Hospital, Central-China Branch of National Center for Cardiovascular Diseases, Zhengzhou, ChinaBackgroundThe roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive.MethodsThis 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.ResultsA 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%.ConclusionWe 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.https://www.frontiersin.org/articles/10.3389/fnins.2022.1002590/fullintracerebral hemorrhageRNA sequencingcircular RNAbiomarkersmachine learning algorithms |
| spellingShingle | Congxia Bai Xiaoyan Hao Lei Zhou Yingying Sun Li Song Fengjuan Wang Liu Yang Jiayun Liu Jingzhou Chen Jingzhou Chen Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage Frontiers in Neuroscience intracerebral hemorrhage RNA sequencing circular RNA biomarkers machine learning algorithms |
| 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 | intracerebral hemorrhage RNA sequencing circular RNA biomarkers machine learning algorithms |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1002590/full |
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