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

CircRNA-mediated regulation of cardiovascular disease

Cardiovascular diseases (CVDs) encompass a range of disorders affecting the heart and blood vessels, such as coronary heart disease, cerebrovascular disease (e.g., stroke), peripheral arterial disease, congenital heart anomalies, deep vein thrombosis, and pulmonary embolism. CVDs are often referred to as the leading cause of mortality worldwide. Recent advancements in deep sequencing have unveiled a plethora of noncoding RNA transcripts, including circular RNAs (circRNAs), which play pivotal roles in the regulation of CVDs. A decade of research has differentiated various circRNAs by their vasculoprotective or deleterious functions, revealing potential therapeutic targets. This review provides an overview of circRNAs and a comprehensive examination of CVDs, the regulatory circRNAs within the vasculature, and the burgeoning research domain dedicated to these noncoding RNAs.

Identification of immune-related biomarkers for intracerebral hemorrhage diagnosis based on RNA sequencing and machine learning

Background

Intracerebral hemorrhage (ICH) is a severe stroke subtype with high morbidity, disability, and mortality rates. Currently, no biomarkers for ICH are available for use in clinical practice. We aimed to explore the roles of RNAs in ICH pathogenesis and identify potential diagnostic biomarkers.

Methods

We collected 233 individual blood samples from two independent cohorts, including 64 patients with ICH, 59 patients with ischemic stroke (IS), 60 patients with hypertension (HTN) and 50 healthy controls (CTRL) for RNA sequencing. Differentially expressed genes (DEGs) analysis, gene set enrichment analysis (GSEA), and weighted correlation network analysis (WGCNA) were performed to identify ICH-specific modules. The immune cell composition was evaluated with ImmuneCellAI. Multiple machine learning algorithms to select potential biomarkers for ICH diagnosis, and further validated by quantitative real-time polymerase chain reaction (RT-PCR). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to evaluate the diagnostic value of the signature for ICH. Finally, we generated M1 and M2 macrophages to investigate the expression of candidate genes.

Results

In both cohorts, 519 mRNAs and 131 lncRNAs were consistently significantly differentially expressed between ICH patients and HTN controls. Gene function analysis suggested that immune system processes may be involved in ICH pathology. ImmuneCellAI analysis revealed that the abundances of 11 immune cell types were altered after ICH in both cohorts. WGCNA and GSEA identified 18 immune-related DEGs. Multiple algorithms identified an RNA panel (CKAP4, BCL6, TLR8) with high diagnostic value for discriminating ICH patients from HTN controls, CTRLs and IS patients (AUCs: 0.93, 0.95 and 0.82; sensitivities: 81.3%, 84.4% and 75%; specificities: 100%, 96% and 79.7%, respectively). Additionally, CKAP4 and TLR8 mRNA and protein levels decreased in RAW264.7 M1 macrophages and increased in RAW264.7 M2 macrophages, while BCL6 expression increased in M1 macrophages but not in M2 macrophages, which may provide potential therapeutic targets for ICH.

Conclusions

This study demonstrated that the expression levels of lncRNAs and mRNAs are associated with ICH, and an RNA panel (CKAP4, BCL6, TLR8) was developed as a potential diagnostic tool for distinguishing ICH from IS and controls, which could provide useful insight into ICH diagnosis and pathogenesis.

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

Signature and function of plasma exosome-derived circular RNAs in patients with hypertensive intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is one of the major causes of death and disability, and hypertensive ICH (HICH) is the most common type of ICH. Currently, the outcomes of HICH patients remain poor after treatment, and early prognosis prediction of HICH is important. However, there are limited effective clinical treatments and biomarkers for HICH patients. Although circRNA has been widely studied in diseases, the role of plasma exosomal circRNAs in HICH remains unknown. The present study was conducted to investigate the characteristics and function of plasma exosomal circRNAs in six HICH patients using circRNA microarray and bioinformatics analysis. The results showed that there were 499 differentially expressed exosomal circRNAs between the HICH patients and control subjects. According to GO annotation and KEGG pathway analyses, the targets regulated by differentially expressed exosomal circRNAs were tightly related to the development of HICH via nerve/neuronal growth, neuroinflammation and endothelial homeostasis. And the differentially expressed exosomal circRNAs could mainly bind to four RNA-binding proteins (EIF4A3, FMRP, AGO2 and HUR). Moreover, of differentially expressed exosomal circRNAs, hsa_circ_00054843, hsa_circ_0010493 and hsa_circ_00090516 were significantly associated with bleeding volume and Glasgow Coma Scale score of the subjects. Our findings firstly revealed that the plasma exosomal circRNAs are significantly involved in the progression of HICH, and could be potent biomarkers for HICH. This provides the basis for further research to pinpoint the best biomarkers and illustrate the mechanism of exosomal circRNAs in HICH.

High hsa_circ_0081621 expression indicates a poor prognosis of laryngeal squamous cell carcinoma: A bioinformatics analysis and retrospective clinical study

Laryngeal squamous cell carcinoma (LSCC) is the second most common malignant tumour of the head and neck with a low 5-year survival rate. There is need to identify novel biomarkers for diagnosis and treatment of LSCC. The present study identified differentially expressed circular RNAs (circRNAs/circs) in LSCC and larynx adjacent non-carcinoma epithelial specimens by analysing the circRNA microarray dataset GSE117001. hsa_circ_0081621 had highest expression among three circRNAs (hsa_circ_0015211, hsa_circ_0023326 and hsa_circ_0081621) in LSCC specimens by reverse transcription-quantitative PCR. The expression levels of hsa_circ_0081621 in 67 LSCC specimens were detected by fluorescence in situ hybridization (FISH). Expression levels of hsa_circ_0081621 were analysed in relation to clinicopathological parameters and prognosis of patients with LSCC. According to FISH results, 59.7% of LSCC specimens exhibited high hsa_circ_0081621 expression. In LSCC specimens, hsa_circ_0081621 high expression was associated with lymph node metastasis and high clinical stage. High expression levels of hsa_circ_0081621 were associated with a poor 5-year overall survival rate in patients with LSCC. In addition, high hsa_circ_0081621 expression was an independent prognostic factor for patients with LSCC. hsa_circ_0081621 may participate in malignant progression of LSCC, and its high expression could be used for prognostic assessment of patients with LSCC.

Circular RNAs in ferroptosis: regulation mechanism and potential clinical application in disease

Ferroptosis, an iron-dependent non-apoptotic form of cell death, is reportedly involved in the pathogenesis of various diseases, particularly tumors, organ injury, and degenerative pathologies. Several signaling molecules and pathways have been found to be involved in the regulation of ferroptosis, including polyunsaturated fatty acid peroxidation, glutathione/glutathione peroxidase 4, the cysteine/glutamate antiporter system Xc-, ferroptosis suppressor protein 1/ubiquinone, and iron metabolism. An increasing amount of evidence suggests that circular RNAs (circRNAs), which have a stable circular structure, play important regulatory roles in the ferroptosis pathways that contribute to disease progression. Hence, ferroptosis-inhibiting and ferroptosis-stimulating circRNAs have potential as novel diagnostic markers or therapeutic targets for cancers, infarctions, organ injuries, and diabetes complications linked to ferroptosis. In this review, we summarize the roles that circRNAs play in the molecular mechanisms and regulatory networks of ferroptosis and their potential clinical applications in ferroptosis-related diseases. This review furthers our understanding of the roles of ferroptosis-related circRNAs and provides new perspectives on ferroptosis regulation and new directions for the diagnosis, treatment, and prognosis of ferroptosis-related diseases.

Targeting Non-Coding RNA for CNS Injuries: Regulation of Blood-Brain Barrier Functions

Central nervous system (CNS) injuries are the most common cause of death and disability around the world. The blood-brain barrier (BBB) is located at the interface between the CNS and the surrounding environment, which protects the CNS from exogenous molecules, harmful agents or microorganisms in the blood. The disruption of BBB is a common feature of CNS injuries and participates in the pathological processes of secondary brain damage. Recently, a growing number of studies have indicated that non-coding RNAs (ncRNAs) play an important role in brain development and are involved in CNS injuries. In this review, we summarize the mechanisms of BBB breakdown after CNS injuries. We also discuss the effects of ncRNAs including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs) and microRNAs (miRNAs) on BBB damage in CNS injuries such as ischemic stroke, traumatic brain injury (TBI), intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH). In addition, we clarify the pharmacotherapies that could regulate BBB function via ncRNAs in CNS injuries, as well as the challenges and perspectives of ncRNAs on modulation of BBB function. Hence, on the basis of these effects, ncRNAs may be developed as therapeutic agents to protect the BBB for CNS injury patients.