Gene network in pulmonary tuberculosis based on bioinformatic analysis

Li, Lili; Lv, Jian; He, Yuan; Wang, Zhihua

doi:10.1186/s12879-020-05335-6

Cited by 10 publications

(9 citation statements)

References 30 publications

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“…Recently, the biomarkers of Mtb that regulate immune response have been identified to potentially develop drugs for TB. It has been previously described that the functionality of cellular components was associated with infection and verified the regulation of these cellular components as relevant regulators of the immune response in the host ( Li et al, 2020 ). Thus, describing the genes involved in cellular components is crucial for understanding the interactions of bacteria with host molecules that regulate immune response.…”

Section: Resultsmentioning

confidence: 69%

An Epistatic Network Describes oppA and glgB as Relevant Genes for Mycobacterium tuberculosis

Posada-Reyes

Balderas-Martínez

Ávila‐Ríos

et al. 2022

Front. Mol. Biosci.

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Mycobacterium tuberculosis is an acid-fast bacterium that causes tuberculosis worldwide. The role of epistatic interactions among different loci of the M. tuberculosis genome under selective pressure may be crucial for understanding the disease and the molecular basis of antibiotic resistance acquisition. Here, we analyzed polymorphic loci interactions by applying a model-free method for epistasis detection, SpydrPick, on a pan–genome-wide alignment created from a set of 254 complete reference genomes. By means of the analysis of an epistatic network created with the detected epistatic interactions, we found that glgB (α-1,4-glucan branching enzyme) and oppA (oligopeptide-binding protein) are putative targets of co-selection in M. tuberculosis as they were associated in the network with M. tuberculosis genes related to virulence, pathogenesis, transport system modulators of the immune response, and antibiotic resistance. In addition, our work unveiled potential pharmacological applications for genotypic antibiotic resistance inherent to the mutations of glgB and oppA as they epistatically interact with fprA and embC, two genes recently included as antibiotic-resistant genes in the catalog of the World Health Organization. Our findings showed that this approach allows the identification of relevant epistatic interactions that may lead to a better understanding of M. tuberculosis by deciphering the complex interactions of molecules involved in its metabolism, virulence, and pathogenesis and that may be applied to different bacterial populations.

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Section: Resultsmentioning

confidence: 69%

An Epistatic Network Describes oppA and glgB as Relevant Genes for Mycobacterium tuberculosis

Posada-Reyes

Balderas-Martínez

Ávila‐Ríos

et al. 2022

Front. Mol. Biosci.

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“…Biological processes (BP), molecular functions (MF), and cellular components (CC) were included in the GO analysis. Subsequently, the PPI network was constructed based on common up-regulated genes using Metascape and the MCODE plug-in was used to screen out seven important subnetworks in the PPI network, namely MCODE 1, MCODE 2, MCODE 3, MCODE 4, MCODE 5, MCODE 6, and MCODE 7 [ 20 ]. MCODE 1 contained the largest number of key genes, including AURKB, CDC20, CDCA5, CDCA8, CENPF, KNTC1, and CCNB2.…”

Section: Discussionmentioning

confidence: 99%

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

et al. 2022

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Objective Lung adenocarcinoma (LUAD) is one of the major subtypes of lung cancer that is associated with poor prognosis. The aim of this study was to identify useful biomarkers to enhance the treatment and diagnosis of LUAD. Methods GEO2R was used to identify common up-regulated differentially expressed genes (DEGs) in the GSE32863, GSE40791, and GSE75037 datasets. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis as well as construction of the protein-protein interaction (PPI) network, while the molecular complex detection (MCODE) plug-in was employed to filter important subnetworks. The expression levels of the hub genes and their prognostic values were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The timer algorithm was utilized to determine the correlation between immune cell infiltration and the expression levels of hub genes in LUAD tissues. In addition, the hub gene mutation landscape and the correlation analysis with tumor mutational burden (TMB) score were evaluated using maftools package and ggstatsplot package in R software, respectively. Results We identified 156 common up-regulated DEGs, with gene ontology and pathway enrichment analysis indicating that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1. The seven hub genes were highly expressed in LUAD tissues and were associated with poor prognosis. These hub genes were negatively correlated with most immune cells. The somatic mutation landscape showed that AURKB, CDC20, CENPF, and KNTC1 had mutations and were positively correlated with TMB scores. Conclusions Our findings demonstrate that increased expression of seven hub genes is associated with poor prognosis for LUAD patients. Additionally, the TMB score indicates that the high expression of hub gene increases immune cell infiltration in patients with lung adenocarcinoma which may significantly improve response to immunotherapy.

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“…A bioinformatics platform called TIMER2 ( Li et al, 2020d ) enables systematic analysis of immune infiltrates in various tumor types. It is implemented for analyzing the relationship between immune infiltration and hub gene expression ( Li et al, 2020c ). The expression of hub genes in various TCGA tumors and the relationship between hub genes related to immune infiltrating cells and CC prognosis were also examined.…”

Section: Methodsmentioning

confidence: 99%

“…Molecular mechanism explanation and tumor-correlated diagnostic markers have been facilitated in part by a bioinformatics approach combining biology, mathematics, and computer science ( Wang et al, 2017a ). In molecular biology research, gene expression analysis plays an important role ( Ye et al, 2018 ) and has become a popular method for detecting differentially expressed genes in human diseases ( Li et al, 2020c ). Weighted gene co-expression network (WGCNA) is a new tool used to identify co-expressed modules and hub genes by analyzing gene co-expression networks ( Xing et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Prognostic value of SOX9 in cervical cancer: Bioinformatics and experimental approaches

Chen

Zeng

et al. 2022

Front. Genet.

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Among gynecological cancers, cervical cancer is a common malignancy and remains the leading cause of cancer-related death for women. However, the exact molecular pathogenesis of cervical cancer is not known. Hence, understanding the molecular mechanisms underlying cervical cancer pathogenesis will aid in the development of effective treatment modalities. In this research, we attempted to discern candidate biomarkers for cervical cancer by using multiple bioinformatics approaches. First, we performed differential expression analysis based on cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas database, then used differentially expressed genes for weighted gene co-expression network construction to find the most relevant gene module for cervical cancer. Next, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the module genes, followed by using protein–protein interaction network analysis and Cytoscape to find the key gene. Finally, we validated the key gene by using multiple online sites and experimental methods. Through weighted gene co-expression network analysis, we found the turquoise module was the highest correlated module with cervical cancer diagnosis. The biological process of the module genes focused on cell proliferation, cell adhesion, and protein binding processes, while the Kyoto Encyclopedia of Genes and Genomes pathway of the module significantly enriched pathways related to cancer and cell circle. Among the module genes, SOX9 was identified as the hub gene, and its expression was associated with cervical cancer prognosis. We found the expression of SOX9 correlates with cancer-associated fibroblast immune infiltration in immune cells by Timer2.0. Furthermore, cancer-associated fibroblast infiltration is linked to cervical cancer patients’ prognosis. Compared to those in normal adjacent, immunohistochemical and real-time quantitative polymerase chain reaction (qPCR) showed that the protein and mRNA expression of SOX9 in cervical cancer were higher. Therefore, the SOX9 gene acts as an oncogene in cervical cancer, interactive with immune infiltration of cancer-associated fibroblasts, thereby affecting the prognosis of patients with cervical cancer.

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Gene network in pulmonary tuberculosis based on bioinformatic analysis

Cited by 10 publications

References 30 publications

An Epistatic Network Describes oppA and glgB as Relevant Genes for Mycobacterium tuberculosis

An Epistatic Network Describes oppA and glgB as Relevant Genes for Mycobacterium tuberculosis

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

Prognostic value of SOX9 in cervical cancer: Bioinformatics and experimental approaches

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