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Background Evidence indicates that the dysregulation of extracellular matrix (ECM) components can lead to cardiovascular diseases. The Talin‐1 (TLN1) gene is a major component of the ECM, and it mediates integrin adhesion to the ECM. In this study, we aimed to determine microRNAs (miRs) that regulate the expression of TLN1 and determine expression alterations in TLN1 and its targeting miRs in coronary artery disease (CAD). Methods Data sets of CAD and normal samples of blood exosomes were downloaded, and TLN1 was chosen as one of the genes with differential expressions in an in silico analysis. Next, miR‐182‐5p and miR‐9‐5p, which have a binding site on 3´‐UTR of TLN1, were selected using bioinformatics tools. Then, the miR target site was cloned in the psiCHECK‐2 vector, and direct interaction between the miR target site and the TLN1 3′‐UTR putative target site was investigated by luciferase assay. The expression of miR‐182‐5p, miR‐9‐5p, and TLN1 in the serum samples of CAD and non‐CAD individuals was assessed via a real‐time quantitative polymerase chain reaction. Results Our data revealed that miR‐182‐5p directly regulated the expression of TLN1. Moreover, miR‐182‐5p and miR‐9‐5p were significantly upregulated in the CAD group. Hence, both bioinformatics and experimental analyses determined the downregulated expression of TLN1 in the CAD samples. Conclusions Our findings demonstrated that miR‐182‐5p and miR‐9‐5p could play significant roles in TLN1 regulation and participate in CAD development by targeting TLN1. These findings introduce novel biomarkers with a potential role in CAD pathogenesis.

Background: Intervertebral disc degeneration (IDD) is a common disability in the working-age population. The underlying pathogenesis of IDD needs elucidation. This study aimed to determine differentially expressed circular RNAs (circRNAs) in IDD by bioinformatics. Additionally, the environmental and clinical factors involved in IDD pathogenesis were reviewed. Methods: The circRNA array profiling of patients with IDD and healthy individuals (GSE67566) was acquired from Gene Expression Omnibus (GEO). GEO2R was employed to analyze the expression profiles of the circRNAs. Functional in silico analysis was done on circRNAs with the highest differential expression. Environmental and clinical factors were reviewed through PubMed and Google Scholar. Results: Twenty-five circRNAs were differentially expressed in IDD. Two circRNAs (hsa_ circRNA_101645 and hsa_circRNA_101852) exhibited the most downregulated and upregulated expressions. The functional in silico analysis showed that the aforementioned circRNAs harbored target sites for AGO2 and EIF4A3 and several microRNAs. The upshots indicated that these 2 circular circRNAs might sponge hsa-miR-330-3p, hsa-miR-502-5p, hsa-miR-662, hsa-miR-874, and hsa-miR-646 and regulate PSD3, SIK2, PCYT1B, ARID5B, MTMR3, and HIPK2 expressions, which play significant roles in autophagy and cellular senescence. Temperature, heavy metal exposure, age, overweight, occupation, exercise, hypertension, and smoking were the environmental and clinical factors associated with IDD progression. Conclusion: Although the results need confirmation by experimental analysis, they reflect the possible role of particular circRNAs in IDD pathogenesis. The controversy concerning the association between IDD and environmental and clinical factors necessitates in-depth population research. Investigating novel molecular regulatory markers like circRNAs could clarify the underlying molecular mechanisms of IDD.

Background: Coronary heart disease (CHD), a major cause of death worldwide, is defined as a narrowing or blockage of the coronary arteries that supply oxygen and blood to the heart. We aimed to find potential biomarkers for coronary artery disease, by comparing the expression profile of blood exosomes of both normal and CHD samples. Methods: Datasets of 6 CHD and 6 normal samples of blood exosomes were downloaded, and differentially expressed RNAs, with adjusted P<0.01 and log2FoldChange≥1 were achieved. Moreover, gene ontology (GO) and pathway analysis were accomplished by PANTHER database for datasets. Results: Our data analysis found 119 differentially expressed genes between two datasets. By comparing transcriptome profiles, we candidate the highest downregulated gene, ACSBG1, and the highest upregulated one, DEFA4, as specific biomarkers for CHD. Furthermore, GO and pathway analysis depicted that aforementioned differentially expressed genes are mostly involved in different molecular metabolic process, inflammation, immune system process and response to stimulus pathways which all cause cardiovascular diseases. Conclusion: We have provided new potential biomarkers for CHD, though experimental validation is still needed to confirm the suitability of the candidate genes for early detection of CHD.