Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology

Immune, RNA, and Neurocognitive Genetic Networks in Bipolar Disorder Subtypes: A Transcriptomic Meta-Analysis

Background

Little is known about the pathogenesis of Bipolar Disorder, and even less is known about the genetic differences between its subtypes. Bipolar Disorder is classified into different subtypes, which present different symptoms and lifetime courses. While genetic studies have been conducted in Bipolar Disorder, most examined the gene expression of only Bipolar Disorder Type 1. Studies that include Bipolar Disorder Type 1 and Bipolar Disorder Type 2 often fail to differentiate them into separate conditions. Few large transcriptomic meta-analyses in Bipolar Disorder have been conducted to identify genetic pathways. Thus, using publicly available data sets we aim here to uncover significant differential gene expression that allows distinguishing Type 1 and Type 2 Bipolar Disorders, as well as find patterns in Bipolar Disorder as a whole.

Methods

We analyze 17 different gene expression data sets from different tissue in Bipolar Disorder using GEO2R and manual analysis, of which 15 contained significant differential gene expression results. We use STRING and Cytoscape to examine Gene Ontology to find significantly affected genetic pathways. We identify hub genes using cytoHubba, a plugin in Cytoscape. We find genes common to data sets of the same material or subtype.

Results

12 out of 15 data sets are enriched for immune system and RNA related pathways. 9 out of 15 data sets are enriched for neurocognitive and metal ion related GO terms. Analysis of Bipolar Disorder Type 1 vs Bipolar Disorder Type 2 revealed most differentially expressed genes were related to immune function, especially cytokines. Terms related to synaptic signaling and neurotransmitter secretion were found in down-regulated GO terms while terms related to neuron apoptosis and death were up-regulated. We identify the gene SNCA as a potential biomarker for overall Bipolar Disorder diagnosis due to its prevalence in our data sets.

Conclusions

The immune system and RNA related pathways are significantly enriched across the Bipolar Disorder data sets. The role of these pathways is likely more critically important to the function of Bipolar Disorder than currently understood. Further studies should clearly label the subtype of Bipolar Disorder used in their research and more effort needs to be undertaken to collect samples from Cyclothymic Disorder and Bipolar Disorder Type 2.

Gene expression insights: Chronic stress and bipolar disorder: A bioinformatics investigation

Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD.

Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.

Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis

To perform bioinformatics analysis on the papillary thyroid carcinoma (PTC) gene chip dataset to explore new biological markers for PTC. The gene expression profiles of GSE3467 and GSE6004 chip data were collected by GEO2R, and the differentially expressed genes (DEGs) were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction (PPI) relationship analysis was achieved using STRING, and the hub genes were obtained using the Cytoscape software. GEPIA was used to validate the expressions of the hub genes in the normal and tumor tissues and to conduct survival analyses. Pertinent genetic pathology results were fetched using the HPA database. Finally, the key genes were clinically verified by reverse transcription-polymerase chain reaction. 97 genes were jointly up-regulated and 107 genes were jointly down-regulated in GSE3467 and GSE6004. GO function enrichment analysis revealed that the DEGs were involved in the regulation of calcium ion transport into cytosol, integrin binding, and cell adhesion molecule binding. KEGG pathway enrichment analysis indicated that the DEGs were chiefly associated with thyroid cancer and non-small cell lung cancer. According to the PPI network, 30 key target genes were identified. Only the expressions of ANK2, TLE1, and TCF4 matched between the normal and tumor tissues, and were associated with disease prognosis. When compared with the normal thyroid tissues, the protein and mRNA expressions of ANK2, TLE1, and TCF4 were down-regulated in PTC. Significant differences exist in overall gene expression between the thyroid tissues of patients with PTC and those of healthy people. Furthermore, the differential genes ANK2, TLE1, and TCF4 are expected to be reliable molecular markers for the mechanism study and diagnosis of PTC.

Endo-Lysosomal and Autophagy Pathway and Ubiquitin-Proteasome System in Mood Disorders: A Review Article

Mood disorders are disabling conditions that cause significant functional impairment. Due to the clinical heterogeneity and complex nature of these disorders, diagnostic and treatment strategies face challenges. The etiology of mood disorders is multifactorial, involving genetic and environmental aspects that are associated with specific biological pathways including inflammation, oxidative stress, and neuroprotection. Alterations in these pathways may reduce the cell's ability to recover from stress conditions occurring during mood episodes. The endo-lysosomal and autophagy pathway (ELAP) and the ubiquitin-proteasome system (UPS) play critical roles in protein homeostasis, impacting neuroplasticity and neurodevelopment. Thus, emerging evidence has suggested a role for these pathways in mental disorders. In the case of neurodegenerative diseases (NDDs), a deeper understanding in the role of ELAP and UPS has been critical to discover new treatment targets. Since it is suggested that NDDs and mood disorders share clinical symptomatology and risk factors, it has been hypothesized that there might be common underlying molecular pathways. Here, we review the importance of the ELAP and UPS for the central nervous system and for mood disorders. Finally, we discuss potential translational strategies for the diagnosis and treatment of major depressive disorder and bipolar disorder associated with these pathways.