Long non‐coding RNAs (lncRNAs) are one of the ncRNAs that transcript with length more than 200 nt that are not translated into protein. Studies have shown that lncRNAs have regulatory function in human disease especially cancers. lncRNA dysfunction causes altered cellular behavior including proliferation, invasion, and migration, and also it can inhibit apoptosis. Long non‐coding zinc finger E‐box binding homeobox 2 antisense RNA1 (lnZEB2‐AS1) is one of the lncRNAs that plays the oncogenic role in different cancers. Dysregulation of lncZEB2‐AS1 can lead to tumorigenesis and cancer progression. lncZEB2‐AS1 may be introduced as a diagnostic marker or therapeutic target for human cancers. In this review, we describe briefly the mechanism of ZEB2‐AS1 in cells and its function in cancer progression.
CircRNAs are a superabundant and highly conserved group of noncoding RNAs (ncRNAs) that are characterized by their high stability and integrity compared with linear forms of ncRNAs. Recently, their critical role in gene expression regulation has been shown; thus, it is not far-fetched to believe that their abnormal expression can be a cause of different kinds of diseases such as cancer, neurodegenerative, and autoimmune diseases. They can have a function in variety of biological processes such as microRNA (miRNA) sponging, interacting with RNA-binding proteins, or even an ability to translate to proteins. A huge challenge in finding diagnostic biomarkers is finding noninvasive biomarkers that can be detected in human fluids, especially blood samples. CircRNAs are becoming candidate biomarkers for diagnosis and prognosis of these diseases through their ability to transverse from the blood-brain barrier and their broad presence in circulating exosomes. The circRNA for miRNA-7 (ciRS-7) is newly recognized, and acknowledged to being related to human pathology and cancer progression. In this review, we first briefly summarize the latest studies about their characteristics, biogenesis, and their mechanisms of action in the regulation and development of human diseases. Finally, we provide a list of diseases that are linked to one member of this novel class of ncRNAs called ciRS-7.
Aim: The exact epigenetic mechanisms that determine the balance of T helper (Th)1 and Th2 cells and autoimmune responses in multiple sclerosis (MS) remain unclear. We aim to clarify these. Methods: A combination of bioinformatics analysis and molecular evaluations was utilized to identify master hub genes. Results: A competitive endogenous RNA network containing six long noncoding RNAs (lncRNAs), 21 miRNAs and 86 mRNAs was provided through enrichment analysis and a protein–protein interaction network. NEAT1 and MALAT1 were found as differentially expressed lncRNAs using Gene Expression Omnibus (GSE21942). Quantitative real-time PCR results demonstrate dysregulation in the RUNX3 (a regulator of Th1/Th2 balance), GATA3 and TBX21, as well as miR-544a and miR-210-3p (which directly target RUNX3). ELISA also confirmed an imbalance in IFN-γ (Th1)/IL-4 (Th2) in MS patients. Conclusion: Our findings introduce novel biomarkers leading to Th1/Th2 imbalance in MS.
Differentiation of CD4+ T cells into Th17 cells is an important factor in the onset and progression of multiple sclerosis (MS) and Th17/Treg imbalance. Little is known about the role of lncRNAs in the differentiation of CD4+ cells from Th17 cells. This study aimed to analyse the lncRNA‐miRNAs network involved in MS disease and its role in the differentiation of Th17 cells. The lncRNAs in Th17 differentiation were obtained from GSE66261 using the GEO datasets. Differential expression of lncRNAs in Th17 primary cells compared to Th17 effector cells was investigated by RNA‐seq analysis. Next, the most highlighted lncRNAs in autoimmune diseases were downloaded from the lncRNAs disease database, and the most critical miRNA was extracted by literature search. Then, the lncRNA‐miRNA interaction was achieved by the Starbase database, and the ceRNA network was designed by Cytoscape. Finally, using the CytoHubba application, two hub lncRNAs with the most interactions with miRNAs were identified by the MCODE plug‐in. The expression level of genes was measured by qPCR, and the plasma level of cytokines was analysed by ELISA kits. The results showed an increase in the expression of NEAT1, KCNQ1OT1 and RORC and a decrease in the expression of FOXP3. In plasma, an upregulation of IL17 and a downregulation of TGFB inflammatory cytokines were detected. The dysregulated expression of these genes could be attributed to relapsing‐remitting MS (RR‐MS) patients and help us understand MS pathogenesis better.
Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein–protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.
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