Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage

Congxia Bai; Xiaoyan Hao; Lei Zhou; Yingying Sun; Li Song; Fengjuan Wang; Liu Yang; Jiayun Liu; Jingzhou Chen

doi:10.3389/fnins.2022.1002590

Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage

Front Neurosci. 2022 Nov 29:16:1002590. doi: 10.3389/fnins.2022.1002590. eCollection 2022.

Authors

Congxia Bai¹, Xiaoyan Hao¹, Lei Zhou¹, Yingying Sun², Li Song², Fengjuan Wang¹, Liu Yang¹, Jiayun Liu¹, Jingzhou Chen^{2

3}

Affiliations

¹ Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
² State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
³ National Health Commission Key Laboratory of Cardiovascular Regenerative Medicine, Fuwai Central-China Hospital, Central-China Branch of National Center for Cardiovascular Diseases, Zhengzhou, China.

Abstract

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.

Keywords: RNA sequencing; biomarkers; circular RNA; intracerebral hemorrhage; machine learning algorithms.