BACKGROUND: Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA.
RESULTS: CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs.
CONCLUSIONS: This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.

Our present study utilized case-control research to explore the relationship between specific circRNAs and pediatric obesity through a literature review and bioinformatics and to predict their possible biological functions, providing ideas for epigenetic mechanism studies of pediatric obesity.
CircRNAs related to pediatric obesity were preliminarily screened by a literature review and qRT-PCR. CircRNA expression in children with obesity (n = 75) and control individuals (n = 75) was confirmed with qRT-PCR in a case-control study. This was followed by bioinformatics analyses, such as GO analysis, KEGG pathway analysis, and ceRNA network construction. Multivariate logistic regression was utilized to analyze the effects of circRNAs on obesity. A receiver operating characteristic (ROC) curve was also drawn to explore the clinical application value of circRNAs in pediatric obesity.
Has_circ_0046367 and hsa_circ_0000284 were separately validated to be statistically downregulated and upregulated, respectively, in the peripheral blood mononuclear cells of children with obesity and revealed as independent indicators of increased CHD risk [hsa_circ_0046367 (OR = 0.681, 95% CI: 0.480 ~ 0.967) and hsa_circ_0000284 (OR = 1.218, 95% CI: 1.041 ~ 1.424)]. The area under the ROC curve in the combined analysis of hsa_circ_0046367 and hsa_circ_0000284 was 0.706 (95% CI: 0.623 ~ 0.789). Enrichment analyses revealed that these circRNAs were actively involved in neural plasticity mechanisms, cell secretion and signal regulation.
The present research revealed that low expression of hsa_circ_0046367 and high expression of hsa_circ_0000284 are risk factors for pediatric obesity and that neural plasticity mechanisms are closely related to obesity.

Parkinson\'s disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

Essential hypertension (EH) is a high prevalence with multifactorial diseases. Human studies on the impact of genes on this disease are just in the initial stage, the mechanism of gene regulation is still remains unclear. Circular RNAs (circRNAs) as a continuous cycle of covalent closure, RNA molecules added to the 3\'-5\' end covalently bound by the formation of incidental event. CircRNAs may be an important biomolecule in revealing the molecule regulate mechanisms of EH.
The circRNAs were selected and validated with qRT-PCR followed. Our experiment was conducted with case-control studies among 200 EH participants. The t-test was used to evaluate the different expression of circRNAs and miRNAs, the significance of which was set as p < 0.05.
The hsa_circ_0037911 expression level in EH cases were significantly higher than healthy controls (p = 0.005). There was still important significance when adjusted by logistic regression (adjusted p = 0.026). We also found that hsa_circ_0037911 was an effective marker of EH (area under curve = 0.627; p = 0.002). The levels of hsa_circ_0037911 were significantly differences in gender, BMI, smoking and drinking among EH cases. There was a positive correlation between Serum creatinine (Scr) and hsa_circ_0037911.
Our findings suggested that higher expression hsa_circ_0037911 may be key circRNAs for EH development by changing the concentration of Scr and could be a stable biomarker for early diagnosis of EH.