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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.

Background: We used a targeted metabolomics approach to identify fatty acid (FA) metabolites that distinguished patients with coronary artery ectasia (CAE) from healthy Controls and patients with coronary artery disease (CAD).Materials and methods: Two hundred fifty-two human subjects were enrolled in our study, such as patients with CAE, patients with CAD, and Controls. All the subjects were diagnosed by coronary angiography. Plasma metabolomic profiles of FAs were determined by an ultra-high-performance liquid chromatography coupled to triple quadrupole mass spectrometric (UPLC-QqQ-MS/MS).Results: Ninety-nine plasma metabolites were profiled in the discovery sets (n = 72), such as 35 metabolites of arachidonic acid (AA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), 10 FAs, and 54 phospholipids. Among these metabolites, 36 metabolites of AA, EPA, and DHA showed the largest difference between CAE and Controls or CAD. 12-hydroxyeicosatetraenoic acid (12-HETE), 17(S)-hydroxydocosahexaenoic acid (17-HDoHE), EPA, AA, and 5-HETE were defined as a biomarker panel in peripheral blood to distinguish CAE from CAD and Controls in a discovery set (n = 72) and a validation set (n = 180). This biomarker panel had a better diagnostic performance than metabolite alone in differentiating CAE from Controls and CAD. The areas under the ROC curve of the biomarker panel were 0.991 and 0.836 for CAE versus Controls and 1.00 and 0.904 for CAE versus CAD in the discovery and validation sets, respectively.Conclusions: Our findings revealed that the metabolic profiles of FAs in the plasma from patients with CAE can be distinguished from those of Controls and CAD. Differences in FAs metabolites may help to interpret pathological mechanisms of CAE.