Selecting Hub Genes and Predicting Target Genes of microRNAs in Tuberculosis via the Bioinformatics Analysis

Abstract

Tuberculosis (TB) is the world's most prevalently infectious disease. Molecular mechanisms behind tuberculosis remain unknown. microRNA (miRNA) is involved in a wide variety of diseases. To validate the significant genes and miRNAs in the current sample, two messenger RNA (mRNA) expression profile datasets and three miRNA expression profile datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed (DE) genes (DEGs) and miRNAs (DE miRNAs) between healthy and TB patients were filtered out. Enrichment analysis was executed, and a protein-protein interaction (PPI) network was developed to understand the enrich pathways and hub genes of TB. Additionally, the target genes of miRNA were predicted and overlapping target genes were identified. We studied a total of 181 DEGs (135 downregulated and 46 upregulated genes) and two DE miRNAs (2 downregulated miRNAs) from two gene profile datasets and three miRNA profile datasets, respectively. 10 hub genes were defined based on high degree of connectivity. A PPI network's top module was constructed. The 23 DEGs identified have a significant relationship with miRNAs. 25 critically significant Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were discovered. The detailed study revealed that, in tuberculosis, the DE miRNA and DEGs form an interaction network. The identification of novel target genes and main pathways would aid with our understanding of miRNA's function in tuberculosis progression.

Genes associated with diagnosis and prognosis of Burkitt lymphoma

“…Prior studies have demonstrated that overexpression of THBS1 and THBS2 leads to poor OS in GC patients and has the potential to act as both diagnostic and prognostic biomarkers for GC management [43]. These findings were consistent with Deng et al's [44] studies in another GC cohort.…”

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs

“…We performed KEGG and Gene Ontology (GO) enrichment analyses after compiling the list of hub genes, and the PPI network was then generated. R software “org.Hs.eg.db” was utilized to convert gene symbols into gene IDs, and then “clusterProfiler” was employed to determine significant items with a cutoff criterion ( p < 0.05 and false discovery rate–adjusted p values (FDR) < 0.05) ( Deng et al, 2021 ). We sorted the GO and KEGG pathways by the maximum number of genes and then screened the top ten corresponding items.…”

“…Moreover, DEGs were imported into the STRING online tool to obtain the protein interaction information. The Cytoscape software MCODE plug-in was utilized to identify the top module with an exact cutoff condition (degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and max depth = 100) ( Deng et al, 2021 ). The overlapping genes of DEGs and hub genes were defined as candidate key genes, determined by the R package “VennDiagram.” In addition, GSE34608 is an external dataset for further expression profile analysis.…”

“…Bioinformatics tools have improved dramatically over the last several decades, allowing researchers to quickly and easily validate new biomarkers and develop major signal pathways after Mtb infection. A comprehensive bioinformatics study has validated associations between TB and genes, including IFIT1, CCR7, and GPR84 ( Li et al, 2020 ; Deng et al, 2021 ). To mine the association between hub gene modules and clinical characteristics, we adopt weighted gene co-expression network analysis (WGCNA) to construct co-expression modules ( Nangraj et al, 2020 ).…”

Tuberculosis Diagnosis: Updates and Challenges

“…These circulating plasma miRNAs can further enable the differential diagnosis of latent and active TB. Diagnostic performance of miRNA can be increased by integrating serum miRNAs with diagnostic models developed by miRNA characteristics and electronic health records (HERs) [61,62] and bioinformatics analysis [63]. Even though miRNA-based detection of TB is convenient in diagnosis in children, the method is challenging to adopt in resource-limited settings.…”