Competing endogenous RNA network analysis explores the key lncRNAs, miRNAs, and mRNAs in type 1 diabetes

Long non-coding RNAs as emerging regulators of miRNAs and epigenetics in diabetes-related chronic kidney disease

Diabetes is one of the major cause of chronic kidney disease (CKD), including "diabetic nephropathy," and is an increasingly prevalent accelerator of the progression of non-diabetic forms of CKD. The long non-coding RNAs (lncRNAs) have come into the limelight in the past few years as one of the emerging weapons against CKD in diabetes. Available data over the past few years demonstrate the interaction of lncRNAs with miRNAs and epigenetic machinery. Interestingly, the evolving data suggest that lncRNAs play a vital role in diabetes-associated CKD by regulation of epigenetic enzymes such as DNA methyltransferase, histone deacetylases, and histone methyltransferases. LncRNAs are also engaged in the regulation of several miRNAs in diabetic nephropathy. Hence this review will elaborate on the association between lncRNAs and their interaction with epigenetic regulators involved in different aspects and thus the progression of CKD in diabetes.

GAS5: A pivotal lncRNA in diabetes mellitus pathogenesis and management

The long non-coding RNA (lncRNA), GAS5, has garnered significant attention recently for its multifaceted involvement in cellular processes, particularly within the context of diabetes. This comprehensive review delves into the intricate molecular interactions associated with GAS5 and their profound implications for understanding, diagnosing, and effectively managing diabetes mellitus. The article begins by highlighting the global prevalence of diabetes and the urgent need for innovative insights into its underlying mechanisms and therapeutic approaches. It introduces GAS5 as a crucial regulator of gene expression, with emerging significance in the context of diabetes-related processes. The core of this review unravels the regulatory network of GAS5 in diabetes, elucidating its impact on various aspects of the disease. It explores how GAS5 influences insulin signaling pathways, glucose metabolism, and the function of β-cells, shedding light on its role in hyperglycemia and insulin resistance. Moreover, the article underscores the clinical relevance of GAS5's interactions by discussing their associations with different diabetes subtypes, predictive value, and potential applications as both diagnostic tools and therapeutic targets. It provides insights into ongoing research endeavours aimed at harnessing the potential of GAS5 for innovative disease management strategies, including the development of RNA-based therapeutics. Concluding with a forward-looking perspective, the abstract highlights the broader implications of GAS5 in the field of diabetes, such as its connection to diabetic complications and its potential for personalized approaches in disease management.

Identification of key regulatory genes and their working mechanisms in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells in pancreatic islets. Identification of the key genes involved in T1D progression and their mechanisms of action may contribute to a better understanding of T1D.

METHODS

The microarray profile of T1D-related gene expression was searched using the Gene Expression Omnibus (GEO) database. Then, the expression data of two messenger RNAs (mRNAs) were integrated for Weighted Gene Co-Expression Network Analysis (WGCNA) to generate candidate genes related to T1D. In parallel, T1D microRNA (miRNA) data were analyzed to screen for possible regulatory miRNAs and their target genes. An miRNA-mRNA regulatory network was then established to predict the key regulatory genes and their mechanisms.

RESULTS

A total of 24 modules (i.e., clusters/communities) were selected using WGCNA analysis, in which three modules were significantly associated with T1D. Further correlation analysis of the gene module revealed 926 differentially expressed genes (DEGs), of which 327 genes were correlated with T1D. Analysis of the miRNA microarray showed that 13 miRNAs had significant expression differences in T1D. An miRNA-mRNA network was established based on the prediction of miRNA target genes and the combined analysis of mRNA, in which the target genes of two miRNAs were found in T1D correlated genes.

CONCLUSION

An miRNA-mRNA network for T1D was established, based on which 2 miRNAs and 12 mRNAs were screened, suggesting that they may play key regulatory roles in the initiation and development of T1D.

Comprehensive analysis of a TPX2-related TRHDE-AS1/PKIA ceRNA network involving prognostic signatures in Hepatitis B virus-infected hepatocellular carcinoma

Hepatitis B virus (HBV) infection is a main carcinogenic factor of hepatocellular carcinoma (HCC). TPX2 microtubule nucleation factor is recently recommended as a novel prognostic biomarker in HBV-infected HCC tissues. This study aimed to explore a TPX2-related ceRNA regulatory network in HBV-infected HCC and the potential impact on HCC prognosis. We comprehensively identified 541 differential expressed lncRNAs (DElncRNAs), 37 DEmiRNAs and 439 DEmRNAs from HBV-related TCGA-HCC cohorts in TPX2low and TPX2high groups. Based on their RNA-RNA interaction and expression analysis, four DElncRNAs (TRHDE-AS1, DLX6-AS1, SNHG14, HOXA11-AS), four DEmiRNAs (miR-23b, miR-320a, miR-589, miR-126) and five DEmRNAs (PKIA, PCDHA2, SHCBP1, PRSS16, KIF18A) in HCC tumor vs normal groups were subjected to the hub regulatory networks analysis and further prognostic value analysis. Importantly, the TRHDE-AS1/miR-23b/PKIA ceRNA network was associated with HCC prognosis. Furthermore, cellular location analysis and base-base interaction analysis indicated that the cytoplasmic lncRNA TRHDE-AS1 was regarded as a ceRNA to sponging miR-23b and then regulating PKIA. Interestingly, correlation analysis suggested the expression correlation between TRHDE-AS1 and PKIA in HCC. Finally, we further performed the methylation and immune infiltration analysis to explore the functional process of PKIA in HCC. We proposed a ceRNA regulatory network may help elucidate the mechanism by which TPX2 contributes to the prognosis of HBV-related HCC.

Hub Genes Associated with the Diagnosis of Diabetic Retinopathy

Purpose

This study aimed to identify genes that may be effective in diagnosing or treating diabetic retinopathy (DR), the most common complication of diabetes mellitus (DM).

Methods

Differentially expressed genes (DEGs) were identified between DR and DM in GSE146615 dataset. DEGs that were consistently up- or down-regulated under both standard glucose and high glucose conditions were identified as common genes and used to generate a protein-protein interaction network and modules. The module genes were assessed for the area under the receiver operating characteristic curve (AUC), leading to the identification of hub genes. Differentially methylated probes in GSE76169 were also compared with common DEGs to identify specific methylation markers of DR. Enrichment analysis was used to explore the biological characteristics. The Short Time-series Expression Miner algorithm was used to identify genes that were progressively dysregulated in the sequence: healthy controls < DM < DR.

Results

A total of 1917 common genes were identified for seven modules. The eight genes with AUC > 0.8 under high glucose and standard glucose conditions were considered as hub genes. The module genes were significantly enriched during vascular smooth muscle cell development and regulation of oxygen metabolism, while 92 methylation markers were involved in the similar terms. Among the progressively dysregulated genes, three intersection genes under both standard glucose and high glucose conditions were found to be module genes and were considered as key genes.

Conclusion

We identified eight potential DR-specific diagnostic and therapeutic genes, whose abnormal expression can cause oxidative stress, thus favoring the course of the disease.

Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches

BACKGROUND

Type 1 diabetes mellitus (T1DM), an autoimmune disease with a genetic tendency, has an increasing prevalence. Long non-coding RNA (lncRNA) and circular RNA (circRNA) are receiving increasing attention in disease pathogenesis. However, their roles in T1DM are poorly understood. The present study aimed at identifying signature lncRNAs and circRNAs and investigating their roles in T1DM using the competing endogenous RNA (ceRNA) network analysis.

METHODS

The T1DM expression profile was downloaded from Gene Expression Omnibus (GEO) database to identify the differentially expressed circRNAs, lncRNAs, and mRNAs. The biological functions of these differentially expressed circRNAs, lncRNAs, and mRNAs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Targeting relationships of circRNA-miRNA, lncRNA-miRNA, and miRNA-mRNA were predicted, and the circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network was established. Finally, qRT-PCR was applied to identify the effect of hsa_circ_0002202 inhibition on the IFN-I induced macrophage inflammation.

RESULTS

A total of 178 circRNAs, 404 lncRNAs, and 73 mRNAs were identified to be abnormally expressed in T1DM samples. Functional enrichment analysis results indicated that the differentially expressed genes were mainly enriched in extracellular matrix components and macrophage activation. CeRNA regulatory network showed that circRNAs and lncRNAs regulate mRNAs through integrate multiple miRNAs. In addition, in vitro experiments showed that hsa_circ_0002202 inhibition suppressed the type I interferon (IFN-I)-induced macrophage inflammation.

CONCLUSION

In the present study, the circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network in T1DM was established for the first time. We also found that hsa_circ_0002202 inhibition suppressed the IFN-I-induced macrophage inflammation. Our study may lay a foundation for future studies on the ceRNA regulatory network in T1DM.