Bioinformatics analysis of long non-coding RNA-associated competing endogenous RNA network in schizophrenia

Inhibition of ADORA3 promotes microglial phagocytosis and alleviates chronic ischemic white matter injury

BACKGROUND

Adenosine A3 receptor (ADORA3) belongs to the adenosine receptor families and the role of ADORA3 in vascular dementia (VaD) is largely unexplored. The present study sought to determine the therapeutic role of ADORA3 antagonist in a mouse model of VaD.

METHODS

The GSE122063 dataset was selected to screen the differential expression genes and pathways between VaD patients and controls. A mouse model of bilateral carotid artery stenosis (BCAS) was established. The cognitive functions were examined by the novel object recognition test, Y maze test, and fear of conditioning test. The white matter injury (WMI) was examined by 9.4 T MRI, western blot, and immunofluorescence staining. The mechanisms of ADORA3-regulated phagocytosis by microglia were examined using qPCR, western blot, dual immunofluorescence staining, and flow cytometry.

RESULTS

The expression of ADORA3 was elevated in brain tissues of VaD patients and ADORA3 was indicated as a key gene for VaD in the GSE122063. In BCAS mice, the expression of ADORA3 was predominantly elevated in microglia in the corpus callosum. ADORA3 antagonist promotes microglial phagocytosis to myelin debris by facilitating cAMP/PKA/p-CREB pathway and thereby ameliorates WMI and cognitive impairment in BCAS mice. The therapeutic effect of ADORA3 antagonist was partially reversed by the inhibition of the cAMP/PKA pathway.

CONCLUSIONS

ADORA3 antagonist alleviates chronic ischemic WMI by modulating myelin clearance of microglia, which may be a potential therapeutic target for the treatment of VaD.

Unravelling the complex interplay of cuproptosis, lncRNAs, and immune infiltration in Alzheimer's disease: a step towards novel therapeutic targets

BACKGROUND

Cuproptosis, a type of cell death involving copper ion accumulation and oxidative stress, has been implicated in the development of Alzheimer's disease (AD).

AIM

This study aimed to explore the potential mechanisms and roles of cuproptosis-related genes (CRGs), long non-coding RNAs (lncRNAs), and immune cells in the development of cuproptosis in AD.

SUBJECTS AND METHODS

Gene expression profiles of AD were acquired from the Gene Expression Omnibus (GEO) database, and differential analysis was conducted to identify CRGs. Random Forest (RF) modelling was employed to select the most crucial CRGs, which were subsequently validated in the test set. A nomogram model was created to predict AD risk and categorise AD subtypes based on the identified CRGs. A lncRNA-related ceRNA network was built, and immune cell infiltration analysis was conducted.

RESULTS

Twelve differentially expressed CRGs were identified in the AD dataset. The RF model pinpointed the five most critical CRGs, which were validated in the test set with an AUC of 0.90. A lncRNA-related ceRNA network was developed, and immune cell infiltration analysis revealed high levels of M1 macrophages and mast cells, along with low levels of memory B cells in AD samples. Correlation analysis unveiled associations between CRGs, lncRNAs, and differentially infiltrating immune cells.

CONCLUSION

This research offers insights into the potential mechanisms and roles of CRGs, lncRNAs, and immune cells in the development of cuproptosis in AD. The identified CRGs and lncRNAs may serve as potential therapeutic targets for AD, and the nomogram model may assist in early AD diagnosis and subtyping.

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.

Plasma exosomes lncRNA-miRNA-mRNA network construction and its diagnostic efficacy identification in first-episode schizophrenia

BACKGROUND

The exosomal lncRNA-miRNA-mRNA networks in first episode schizophrenia (FOS) have not reported yet. This study examined the lncRNA, miRNA and mRNA expression level in exosome derived from first episode schizophrenia (FOS) patients, and explored the the potential of exosomes as biomarkers for schizophrenia.

METHODS

We recruited 10 FOS patients and healthy controls (HCs) respectively, examined the lncRNA, miRNA and mRNA expression level of plasma exosome by high throughput sequencing, constructed lncRNA-miRNA-mRNA network, and performed correlation analysis, GO and KEGG pathway analysis, PPI network construction and ROC analysis.

RESULTS

There were 746 differently expressed lncRNA, 22 differently expressed miRNA, and 2637 differently expressed mRNA in plasma exosome in FOS compared with HCs. Then we constructed ceRNA network consisting of 8 down-regulated lncRNA, 7 up-regulated miRNA and 65 down-regulated mRNA, and 1 up-regulated lncRNA, 1 down-regulated miRNA and 4 up-regulated mRNA. The expression level of 1 lncRNA and 7 mRNA in exosomal network were correlated with PANSS score. GO and KEGG pathway analysis showed that 4 up-regulated mRNAs were enriched in neuropsychiatric system function. Down-regulated mRNA EZH2 and SIRT1 were identified as hub gene. Finally, we detected the ROC curve of ENSG00000251562, miR-26a-5p, EZH2, miR-22-3p, SIRT1, ENSG00000251562-miR-26a-5p-EZH2, ENSG00000251562-miR-22-3p-SIRT1, and found that the AUC of ceRNA network was higher than lncRNA, miRNA and mRNA alone.

CONCLUSION

We constructed the lncRNA-miRNA-mRNA network in exosome derived from FOS plasma, and found that lncRNA-miRNA-mRNA network has potential as biomarkers for FOS.

A review on the role of MYC-induced long non-coding RNA in human disorders

MINCR (MYC-Induced Long Non-Coding RNA) is classified as an lncRNA. It has a significant correlation with MYC gene. MINCR has important roles in the carcinogenesis. It has been approved that this lncRNA can act as molecular sponge for miR-28-5p, miR-708-5p, miR-876-5p and miR-146a-5p. Dysregulated levels of MINCR has been observed in different types of cancer, especially hepatocellular carcinoma. In addition to malignant conditions, schizophrenia and neurodegenerative diseases such as Alzheimer's disease and amyotrophic lateral sclerosis are associated with dysregulation of expression patterns of MINCR. This review outlines MINCR molecular mechanisms of action in different disorders.

Oncogenic roles and related mechanisms of the long non-coding RNA MINCR in human cancers

Long non-coding RNAs (lncRNAs) play vital roles in regulating epigenetic mechanisms and gene expression levels, and their dysregulation is closely associated with a variety of diseases such as cancer. Several studies have demonstrated that lncRNAs are dysregulated during tumor progression. Recently, the MYC-induced long non-coding RNA MINCR, a newly identified lncRNA, has been demonstrated to act as an oncogene in different cancers, including gallbladder cancer, hepatocellular cancer, colorectal cancer, non-small cell lung cancer, oral squamous cell carcinoma, nasopharyngeal cancer, and glioma. Moreover, MINCR has been reported to act as a biomarker in the prognosis of patients with different cancers. In this review, we summarize and analyze the oncogenic roles of MINCR in a variety of human cancers in terms of its clinical significance, biological functions, cellular activities, and regulatory mechanism. Our analysis of the literature suggests that MINCR has potential as a novel biomarker and therapeutic target in human cancers.

A review on the role of NCK1 Antisense RNA 1 (NCK1-AS1) in diverse disorders

NCK1 Antisense RNA 1 (NCK1-AS1), alternatively named as NCK1-DT, is a long non-coding RNA (lncRNA) with important roles in the carcinogenesis. Multiple studies verified its oncogenic role in different types of cancer, including gastric cancer, non-small cell lung cancer, glioma, prostate cancer and cervical cancer. NCK1-AS1 functions as a sponge for several microRNAs, including miR-137, miR-22-3p, miR-526b-5p, miR-512-5p, miR-138-2-3p and miR-6857. In this review we present an outline of NCK1-AS1 function in malignant conditions as well as atherosclerosis.

lncRNA-associated ceRNA network revealing the potential regulatory roles of ferroptosis and immune infiltration in Alzheimer's disease

Background

Alzheimer's disease (AD) is the most common form of dementia characterized by a prominent cognitive deterioration of sufficient magnitude to impair daily living. Increasing studies indicate that non-coding RNAs (ncRNAs) are involved in ferroptosis and AD progression. However, the role of ferroptosis-related ncRNAs in AD remains unexplored.

Methods

We obtained the intersection of differentially expressed genes in GSE5281 (brain tissue expression profile of patients with AD) from the GEO database and ferroptosis-related genes (FRGs) from the ferrDb database. Least absolute shrinkage and selection operator model along with weighted gene co-expression network analysis screened for FRGs highly associated with AD.

Results

A total of five FRGs were identified and further validated in GSE29378 (area under the curve = 0.877, 95% confidence interval = 0.794-0.960). A competing endogenous RNA (ceRNA) network of ferroptosis-related hub genes (EPT1, KLHL24, LRRFIP1, CXCL2 and CD44) was subsequently constructed to explore the regulatory mechanism between hub genes, lncRNAs and miRNAs. Finally, CIBERSORT algorithms were used to unravel the immune cell infiltration landscape in AD and normal samples. M1 macrophages and mast cells were more infiltrated whereas memory B cells were less infiltrated in AD samples than in normal samples. Spearman's correlation analysis revealed that LRRFIP1 was positively correlated with M1 macrophages (r = -0.340, P < 0.001) whereas ferroptosis-related lncRNAs were negatively correlated with immune cells, wherein miR7-3HG correlated with M1 macrophages and NIFK-AS1, EMX2OS and VAC14-AS1 correlated with memory B cells (|r| > 0.3, P < 0.001).

Conclusion

We constructed a novel ferroptosis-related signature model including mRNAs, miRNAs and lncRNAs, and characterized its association with immune infiltration in AD. The model provides novel ideas for the pathologic mechanism elucidation and targeted therapy development of AD.

Mapping miRNA Research in Schizophrenia: A Scientometric Review

Micro RNA (miRNA) research has great implications in uncovering the aetiology of neuropsychiatric conditions due to the role of miRNA in brain development and function. Schizophrenia, a complex yet devastating neuropsychiatric disorder, is one such condition that had been extensively studied in the realm of miRNA. Although a relatively new field of research, this area of study has progressed sufficiently to warrant dozens of reviews summarising findings from past to present. However, as a majority of reviews cannot encapsulate the full body of research, there is still a need to synthesise the diversity of publications made in this area in a systematic but easy-to-understand manner. Therefore, this study adopted bibliometrics and scientometrics, specifically document co-citation analysis (DCA), to review the literature on miRNAs in the context of schizophrenia over the course of history. From a literature search on Scopus, 992 papers were found and analysed with CiteSpace. DCA analysis generated a network of 13 major clusters with different thematic focuses within the subject area. Finally, these clusters are qualitatively discussed. miRNA research has branched into schizophrenia, among other medical and psychiatric conditions, due to previous findings in other forms of non-coding RNA. With the rise of big data, bioinformatics analyses are increasingly common in this field of research. The future of research is projected to rely more heavily on interdisciplinary collaboration. Additionally, it can be expected that there will be more translational studies focusing on the application of these findings to the development of effective treatments.

Assessing the expression of two post-transcriptional BDNF regulators, TTP and miR-16 in the peripheral blood of patients with Schizophrenia

Schizophrenia (SCZ) is a severe mental disorder with an unknown pathophysiology. Brain-Derived Neurotrophic Factor (BDNF) is a neurotrophin that has been associated with synapse plasticity, learning, and memory, as well as neurodevelopment and neuroprotection. The importance of neurodevelopmental and neurotoxicity-related components in the pathophysiology of SCZ has been highlighted in research on the neurobiology of this disease. The purpose of this research is to investigate the significant expression of two variables, tristetraprolin (TTP) and miR-16, which are known to be regulators of BDNF expression. Fifty Iranian Azeri SCZ patients were enrolled, and fifty healthy volunteers were age- and gender-matched as controls. A quantitative polymerase chain reaction measured the expression levels of the TTP and miR-16 in the peripheral blood (PB) of SCZ patients and healthy people. TTP expression levels in patients were higher than in controls, regardless of gender or age (posterior beta = 1.532, adjusted P-value = 0.012). TTP and miR-16 expression levels were found to be significantly correlated in both SCZ patients and healthy controls (r = 0.701, P < 0.001 and r = 0.777, P < 0.001, respectively). Due to the increased expression of TTP in SCZ and the existence of a significant correlation between TTP and miR-16, which helps to act on target mRNAs with AU-rich elements, this mechanism can be considered an influencing factor in SCZ.

Inferring miRNA sponge modules across major neuropsychiatric disorders

The role of non-coding RNAs in neuropsychiatric disorders (NPDs) is an emerging field of study. The long non-coding RNAs (lncRNAs) are shown to sponge the microRNAs (miRNAs) from interacting with their target mRNAs. Investigating the sponge activity of lncRNAs in NPDs will provide further insights into biological mechanisms and help identify disease biomarkers. In this study, a large-scale inference of the lncRNA-related miRNA sponge network of pan-neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), was carried out using brain transcriptomic (RNA-Seq) data. The candidate miRNA sponge modules were identified based on the co-expression pattern of non-coding RNAs, sharing of miRNA binding sites, and sensitivity canonical correlation. miRNA sponge modules are associated with chemical synaptic transmission, nervous system development, metabolism, immune system response, ribosomes, and pathways in cancer. The identified modules showed similar and distinct gene expression patterns depending on the neuropsychiatric condition. The preservation of miRNA sponge modules was shown in the independent brain and blood-transcriptomic datasets of NPDs. We also identified miRNA sponging lncRNAs that may be potential diagnostic biomarkers for NPDs. Our study provides a comprehensive resource on miRNA sponging in NPDs.

Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Slow-burning inflammation at the lesion rim is connected to the expansion of chronic multiple sclerosis (MS) lesions. However, the underlying processes causing expansion are not clearly realized. In this context, the current study used a bioinformatics approach to identify the expression profiles and related lncRNA-associated ceRNA regulatory axes in the periplaque region in MS patients. Expression data (GSE52139) from periplaque regions in the secondary progressive MS spinal cord and controls were downloaded from the Gene Expression Omnibus database (GEO), which has details on mRNAs and lncRNAs. Using the R software’s limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also found using the DIANA-LncBase, miRTarBase, and HMDD databases. The Pearson correlation coefficient was used to determine whether there were any positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Finally, lncRNA-associated ceRNA axes were created based on co-expression and connections between DElncRNA, miRNA, and DEmRNA. We used the Enrichr tool to enrich the biological process, molecular function, and pathways for DEmRNAs and DElncRNAs. A network of DEmRNAs’ protein-protein interactions was developed, and the top five hub genes were found using Cytoscape and STRING. The current study indicates that 15 DEmRNAs, including FOS, GJA1, NTRK2, CTNND1, and SP3, are connected to the MS ceRNA network. Additionally, four DElncRNAs (such as TUG1, ASB16-AS1, and LINC01094) that regulated the aforementioned mRNAs by sponging 14 MS-related miRNAs (e.g., hsa-miR-145-5p, hsa-miR-200a-3p, hsa-miR-20a-5p, hsa-miR-22-3p, hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p, hsa-miR-34a-5p) were found. In addition, the analysis of pathway enrichment revealed that DEmRNAs were enriched in the pathways for the “MAPK signaling pathway”, “Kaposi sarcoma-associated herpesvirus infection”, “Human immunodeficiency virus one infection”, “Lipid and atherosclerosis”, and “Amphetamine addiction”. Even though the function of these ceRNA axes needs to be investigated further, this study provides research targets for studying ceRNA-mediated molecular mechanisms related to periplaque demyelination in MS.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

Identification of key long non-coding RNA-associated competing endogenous RNA axes in Brodmann Area 10 brain region of schizophrenia patients

Schizophrenia (SCZ) is a serious mental condition with an unknown cause. According to the reports, Brodmann Area 10 (BA10) is linked to the pathology and cortical dysfunction of SCZ, which demonstrates a number of replicated findings related to research on SCZ and the dysfunction in tasks requiring cognitive control in particular. Genetics' role in the pathophysiology of SCZ is still unclear. Therefore, it may be helpful to understand the effects of these changes on the onset and progression of SCZ to find novel mechanisms involved in the regulation of gene transcription. In order to determine the molecular regulatory mechanisms affecting the SCZ, the long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) axes in the BA10 area were determined using a bioinformatics approach in the present work. A microarray dataset (GSE17612) consisted of brain post-mortem tissues of the BA10 area from SCZ patients and matched healthy subjects was downloaded from the Gene Expression Omnibus (GEO) database. This dataset included probes for both lncRNAs and mRNAs. Using the R software's limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also discovered using the DIANA-LncBase and miRTarBase databases. In the ceRNA network, positive correlations between DEmRNAs and DElncRNAs were evaluated using the Pearson correlation coefficient. Finally, lncRNA-associated ceRNA axes were built by using the co-expression and DElncRNA-miRNA-DEmRNA connections. We identified the DElncRNA-miRNA-DEmRNA axes, which included two key lncRNAs (PEG3-AS1, MIR570HG), seven key miRNAs (hsa-miR-124-3p, hsa-miR-17-5p, hsa-miR-181a-5p, hsa-miR-191-5p, hsa-miR-26a-5p, hsa-miR-29a-3p, hsa-miR-29b-3p), and eight key mRNAs (EGR1, ETV1, DUSP6, PLOD2, CD93, SERPINB9, ANGPTL4, TGFB2). Furthermore, DEmRNAs were found to be enriched in the "AGE-RAGE signaling pathway in diabetic complications", "Amoebiasis", "Transcriptional misregulation in cancer", "Human T-cell leukemia virus 1 infection", and "MAPK signaling pathway". This study offers research targets for examining significant molecular pathways connected to the pathogenesis of SCZ, even though the function of these ceRNA axes still needs to be investigated.