Comprehensive Analysis of Long Non-coding RNA-Associated Competing Endogenous RNA Network in Duchenne Muscular Dystrophy

The Role of MicroRNA in the Pathogenesis of Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is an X-linked progressive disorder associated with muscle wasting and degeneration. The disease is caused by mutations in the gene that encodes dystrophin, a protein that links the cytoskeleton with cell membrane proteins. The current treatment methods aim to relieve the symptoms of the disease or partially rescue muscle functionality. However, they are insufficient to suppress disease progression. In recent years, studies have uncovered an important role for non-coding RNAs (ncRNAs) in regulating the progression of numerous diseases. ncRNAs, such as micro-RNAs (miRNAs), bind to their target messenger RNAs (mRNAs) to suppress translation. Understanding the mechanisms involving dysregulated miRNAs can improve diagnosis and suggest novel treatment methods for patients with DMD. This review presents the available evidence on the role of altered expression of miRNAs in the pathogenesis of DMD. We discuss the involvement of these molecules in the processes associated with muscle physiology and DMD-associated cardiomyopathy.

Analysis of Long Noncoding RNAs-Related Regulatory Mechanisms in Duchenne Muscular Dystrophy Using a Disease-Related lncRNA-mRNA Pathway Network

Objective

This study aimed to investigate the molecular regulatory mechanisms underpinning Duchenne muscular dystrophy (DMD).

Methods

Using microarray data, differentially expressed long noncoding RNAs (DELs) and DMD-related differentially expressed mRNAs (DEMs) were screened based on the comparative toxicogenomics database, using a cutoff of |log₂ fold change| > 1 and false discovery rate (FDR) < 0.05. Then, protein-protein interaction (PPI), coexpression network of lncRNA-mRNA, and DMD-related lncRNA-mRNA pathway networks were constructed, and functional analyses of the genes in the network were performed. Finally, the proportions of immune cells infiltrating the muscle tissues in DMD were analyzed, and the correlation between the immune cells and expression of the DELs/DEMs was studied.

Results

A total of 46 DELs and 313 DMD-related DEMs were identified. The PPI network revealed STAT1, VEGFA, and CCL2 to be the top three hub genes. The DMD-related lncRNA-mRNA pathway network comprising two pathways, nine DELs, and nine DMD-related DEMs showed that PYCARD, RIPK2, and CASP1 were significantly enriched in the NOD-like receptor signaling pathway, whereas MAP2K2, LUM, RPS6, PDCD4, TWIST1, and HIF1A were significantly enriched with proteoglycans in cancers. The nine DELs in this network were DBET, MBNL1-AS1, MIR29B2CHG, CCDC18-AS1, FAM111A-DT, GAS5, LINC01290, ATP2B1-AS1, and PSMB8-AS1.

Conclusion

The nine DMD-related DEMs and DELs identified in this study may play important roles in the occurrence and progression of DMD through the two pathways of the NOD-like receptor signaling pathway and proteoglycans in cancers.

Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Objective

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system characterized by chronic inflammation and demyelination. This study is aimed at identifying crucial genes and molecular pathways involved in MS pathogenesis.

Methods

Raw data in GSE52139 were collected from the Gene Expression Omnibus. The top 50% expression variants were subjected to weighted gene coexpression network analysis (WGCNA), and the key module associated with MS occurrence was identified. A long noncoding RNA- (lncRNA-) associated competing endogenous RNA (ceRNA) network was constructed in the key module. The hub gene candidates were subsequently verified in an individual database.

Results

Of the 18 modules obtained, the cyan module was designated as the key module. The established ceRNA network was composed of seven lncRNAs, 45 mRNAs, and 21 microRNAs (miRNAs), and the FAM13A-AS1 was the lncRNA with the highest centrality. Functional assessments indicated that the genes in the cyan module primarily gathered in ribosome-related functional terms. Interestingly, the targeted mRNAs of the ceRNA network enriched in diverse categories. Moreover, highly expressed CYBRD1, GNG12, and SMAD1, which were identified as hub genes, may be associated with “valine leucine and isoleucine degradation,” “base excision repair,” and “fatty acid metabolism,” respectively, according to the results of single gene-based genomes and gene set enrichment analysis (GSEA).

Conclusions

Combined with the WGCNA and ceRNA network, our findings provide novel insights into the pathogenesis of MS. The hub genes discovered herein might also serve as novel biomarkers that correlate with the development and management of MS.

LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification

Background

Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate.

Results

Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717.

Conclusions

Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04485-x.

Identification of novel biomarkers for intracerebral hemorrhage via long noncoding RNA-associated competing endogenous RNA network

Intracerebral hemorrhage (ICH) is a leading cause of death and disability worldwide. This study aimed to examine the involvement of long non-coding RNAs (lncRNAs), a group of non-coding transcripts, in ICH as potential biomarkers. An expression profile of patients with ICH using four contralateral grey matter controls (GM) and four contralateral white matter controls (WM) was downloaded from the Gene Expression Omnibus (GEO) database. Co-expressed lncRNAs and mRNAs were selected to create competing endogenous RNA (ceRNA) networks. Key lncRNAs were identified in ceRNA networks, which were validated through Real-time qPCR (RT-qPCR) with peripheral blood samples from patients with ICH. A total of 49 differentially expressed lncRNAs were discovered in different brain regions. The ceRNA network in GM included 9 lncRNAs, 40 mRNAs, and 20 microRNAs (miRNAs), while the one in WM covered 6 lncRNAs, 25 mRNAs, and 14 miRNAs. Six hub lncRNAs were observed and RT-qPCR results showed that LY86-AS1, DLX6-AS1, RRN3P2, and CRNDE were down-regulated, while HCP5 and MIAT were up-regulated in patients with ICH. Receiver Operating Characteristic (ROC) assessments demonstrated the diagnostic value of these lncRNAs. Our findings highlight the potential roles of lncRNA in ICH pathogenesis. Moreover, the hub lncRNAs discovered here might become novel biomarkers and promising targets for ICH drug development.

Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.

Assessment of Weighted Gene Co-Expression Network Analysis to Explore Key Pathways and Novel Biomarkers in Muscular Dystrophy

Purpose

This study aimed to explore the key molecular pathways involved in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) and thereby identify hub genes to be potentially used as novel biomarkers using a bioinformatics approach.

Methods

Raw GSE109178 data were collected from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted on the top 50% of altered genes. The key modules associated with the clinical features of DMD and BMD were identified. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID website. A protein-protein interaction (PPI) network was constructed using the STRING website. MCODE, together with the Cytohubba plug-ins of Cytoscape, screened out the potential hub genes, which were subsequently verified via receiver operating characteristic (ROC) curves in other datasets.

Results

Among the 11 modules obtained, the black module was predominantly associated with pathology and DMD, whereas the light-green module was primarily related to age and BMD. Functional enrichment assessments indicated that the genes in the black module were primarily clustered in “immune response” and “phagosome,” whereas the ones in the light-green module were chiefly enriched in “protein polyubiquitination”. Eleven essential genes were eventually identified, including VCAM1, TYROBP, CD44, ITGB2, CSF1R, LCP2, C3AR1, CCL2, and ITGAM for DMD, along with UBA5 and UBR2 for BMD.

Conclusion

Overall, our findings may be useful for investigating the mechanisms underlying DMD and BMD. In addition, the hub genes discovered might serve as novel molecular markers correlated with dystrophinopathies.

Long noncoding RNA LINC01291 promotes the aggressive properties of melanoma by functioning as a competing endogenous RNA for microRNA-625-5p and subsequently increasing IGF-1R expression

Studies have confirmed the relationship between dysregulated long noncoding RNAs and melanoma pathogenesis. However, the regulatory functions of long intergenic non-protein coding RNA 1291 (LINC01291) in melanoma remain unknown. Therefore, we evaluated LINC01291 expression in melanoma and explored its roles in regulating tumor behaviors. Further, the molecular events via which LINC01291 affects melanoma cells were investigated. LINC01291 expression in melanoma cells was analyzed using The Cancer Genome Atlas database and quantitative real-time polymerase chain reaction. Functional assays, including the Cell Counting Kit-8 assay, colony formation assay, flow cytometry, cell migration and invasion assays, and tumor xenograft models, were used to examine LINC01291’s role in melanoma cells. Additionally, bioinformatics analysis, RNA immunoprecipitation, luciferase reporter assay, and western blotting were conducted to determine the tumor-promoting mechanism of LINC01291. LINC01291 was upregulated in melanoma tissues and cell lines. Following LINC01291 knockdown, cell proliferation, colony formation, migration, and invasion were diminished, whereas apoptosis was enhanced and the cell cycle was arrested at G0/G1. In addition, loss of LINC01291 decreased the chemoresistance of melanoma cells to cisplatin. Furthermore, LINC01291 interference inhibited melanoma tumor growth in vivo. Mechanistically, LINC01291 functions as a competing endogenous RNA by sponging microRNA-625-5p (miR-625-5p) in melanoma cells and maintaining insulin-like growth factor 1 receptor (IGF-1R) expression. Rescue experiments revealed that the roles induced by LINC01291 depletion in melanoma cells could be reversed by suppressing miR-625-5p or overexpressing IGF-1R. Our study identified the LINC01291/miR-625-5p/IGF-1R competing endogenous RNA pathway in melanoma cells, which may represent a novel diagnostic biomarker and an effective therapeutic target for melanoma.