scholarly journals Identification of Hub Genes Associated With Tuberculous Pleurisy by Integrated Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Lei Shi ◽  
Zilu Wen ◽  
Hongwei Li ◽  
Yanzheng Song
Keyword(s):  
Gene Expression Analysis ◽  
Bioinformatics Analysis ◽  
Biological Process ◽  
Cell Component ◽  
Hub Genes ◽  
Nucleotide Sugar ◽  
Protein Protein Interaction ◽  
Differential Gene ◽  
Tuberculous Pleurisy ◽  
Therapy Strategies

Improving the understanding of the molecular mechanism of tuberculous pleurisy is required to develop diagnosis and new therapy strategies of targeted genes. The purpose of this study is to identify important genes related to tuberculous pleurisy. In this study, the expression profile obtained by sequencing the surgically resected pleural tissue was used to explore the differentially co-expressed genes between tuberculous pleurisy tissue and normal tissue. 29 differentially co-expressed genes were screened by weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis methods. According to the functional annotation analysis of R clusterProfiler software package, these genes are mainly enriched in nucleotide−sugar biosynthetic process (biological process), ficolin−1−rich granule lumen (cell component), and electron transfer activity (molecular function). In addition, in the protein-protein interaction (PPI) network, 20 hub genes of DEGs and WCGNA genes were identified using the CytoHubba plug-in of Cytoscape. In the end, RPL17 was identified as a gene that can be the biomarker of tuberculous pleurisy. At the same time, there are seven genes that may have relationship with the disease (UBA7, NDUFB8, UQCRFS1, JUNB, PSMC4, PHPT1, and MAPK11).

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

scholarly journals Spica Prunellae Extract Enhances Fluorouracil Sensitivity of 5-Fluorouracil-Resistant Human Colon Carcinoma HCT-8/5-FU Cells via TOP2α and miR-494

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yi Fang ◽  
Chi Yang ◽  
Ling Zhang ◽  
Lihui Wei ◽  
Jiumao Lin ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Ethanol Extract ◽  
Human Colon ◽  
Protein Interaction Networks ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Beneficial Effects ◽  
Human Colon Carcinoma ◽  
Public Dataset ◽  
Protein Protein Interaction Networks

The use of 5-fluorouracil (5-FU) has been proven benefits, but it also has adverse events in colorectal cancer (CRC) chemotherapy. In this study, we explored the mechanism of 5-FU resistance by bioinformatics analysis of the NCBI public dataset series GSE81005. Fifteen hub genes were screened out of 582 different expressed genes. Modules of the hub genes in protein-protein interaction networks gathered to TOP2α showed a decrease in HCT-8 cells but an increase in 5-FU-resistant HCT-8/5-FU cells with 5-FU exposure. Downregulation of TOP2α with siRNA or miR-494 transfection resulted in an increase of cytotoxicity and decrease of cell colonies to 5-FU for HCT-8/5-FU cells. Moreover, we found that an ethanol extract of Spica Prunellae (EESP), which is a traditional Chinese medicine with clinically beneficial effects in various cancers, was able to enhance the sensitivity of 5-FU in HCT-8/5-FU cells and partly reverse the 5-FU resistance effect. It significantly helped suppress cell growth and induced cell apoptosis in HCT-8/5-FU cells with the expression of TOP2α being significantly suppressed, which increased by 5-FU. Consistently, miR-494, which reportedly regulates TOP2α, exhibited reverse trends in EESP/5-FU combination treatment. These results suggested that Spica Prunellae may be beneficial in the treatment of 5-FU-resistant CRC patients.

scholarly journals Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

2021 ◽  
Author(s):  
Congcong Wang ◽  
Jianping Guo ◽  
Xiaoyang Zhao ◽  
Jia Jia ◽  
Wenting Xu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Analysis Tool ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Prognostic Assessment ◽  
Scheme Selection

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

scholarly journals Bioinformatics Analysis of Genes and Mechanisms in Postherpetic Neuralgia

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yong Qiu ◽  
Meng-Lei Hao ◽  
Xu-Tao Cheng ◽  
Zhen Hua
Keyword(s):  
Neuropathic Pain ◽  
Postherpetic Neuralgia ◽  
Bioinformatics Analysis ◽  
Life Quality ◽  
Hub Genes ◽  
Comparative Toxicogenomics Database ◽  
Protein Protein Interaction ◽  
Receptor Interactions ◽  
Anxiety Depression

Objective. Elderly patients are prone to postherpetic neuralgia (PHN), which may cause anxiety, depression, and sleep disorders and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of PHN has not been fully elucidated, and current treatments remain inadequate. Therefore, it is important to explore the molecular mechanism of PHN. Methods. We analyzed the GSE64345 dataset, which includes gene expression from the ipsilateral dorsal root ganglia (DRG) of PHN model rats. Differentially expressed genes (DEGs) were identified and analyzed by Gene Ontology. Protein-protein interaction (PPI) network was constructed. The miRNA associated with neuropathic pain and inflammation was found in miRNet. Hub genes were identified and analyzed in Comparative Toxicogenomics Database (CTD). miRNA-mRNA networks associated with PHN were constructed. Results. A total of 116 genes were up-regulated in the DRG of PHN rats, and 135 genes were down-regulated. Functional analysis revealed that variations were predominantly enriched for genes involved in neuroactive ligand-receptor interactions, the Jak-STAT signaling pathway, and calcium channel activity. Eleven and thirty-one miRNAs associated with neuropathic pain and inflammation, respectively, were found. Eight hub genes (S1PR1, OPRM1, PDYN, CXCL3, S1PR5, TBX5, TNNI3, MYL7, PTGDR2, and FBXW2) associated with PHN were identified. Conclusions. Bioinformatics analysis is a useful tool to explore the mechanism and pathogenesis of PHN. The identified hub genes may participate in the onset and development of PHN and serve as therapeutic targets.

scholarly journals Identification of key genes in allergic rhinitis by bioinformatics analysis

2021 ◽  
Vol 49 (7) ◽  
pp. 030006052110295
Author(s):  
Yunfei Zhang ◽  
Yue Huang ◽  
Wen-xia Chen ◽  
Zheng-min Xu
Keyword(s):  
Allergic Rhinitis ◽  
Antigen Processing ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Validation Dataset ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Pathway Analyses ◽  
Antigen Processing And Presentation

Objective This study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods. Methods The dataset GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein–protein interaction (PPI) network was constructed to identify hub genes. GSE44037 and GSE43523 datasets were screened to validate critical genes. Results A total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six ( CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset. Conclusions Our findings suggest that hub genes play important roles in the development of AR.

scholarly journals Analysis of Potential Hub Genes and Related Transcription Factors in Rosacea Patients using Bioinformatics Analysis

2021 ◽  
Author(s):  
Xin Wang ◽  
Wenfang Dong ◽  
Huan Wang ◽  
Jianjun You ◽  
Ruobing Zheng ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Biological Functions ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
String Databases ◽  
Cell Signaling Pathways

Abstract Objective The aim of this study is to discover the adipocyte genes and pathways involved in rosacea using bioinformatics analysis.Methods The GSE65914 gene expression profile was obtained. The GEO2R tool was used to screen out differentially expressed genes (DEGs). It was further analyzed with Gene Ontology (GO) to explore functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore cell signaling pathways. Protein-protein interaction (PPI) networks among the DEGs were found by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs were also constructed.Results A total of 254 DEGs, including 72 up-regulated genes and 182 down-regulated genes, were obtained in rosacea samples. The biological functions of DEGs are mainly involved in the inflammatory response and chemokine activity. A PPI network consisting of 217 nodes and 710 edges was constructed using STRING, and ten hub genes were identified with Cytoscape software. Some transcriptional factors were also found to interact with these hub DEGs.Conclusion In this study, we obtained ten hub genes, including CXCL8, CCR5, CXCR4, CXCL10, MMP9, CD2, CCL19, CXCL9, CCL5, CD3D, which play an essential role in the pathology of rosacea, and these genes may provide a basis for the screening of treatment biomarkers for rosacea in the future.

scholarly journals Integrated Analysis of Immunocyte Infiltration and Differential Gene Expression in Hypertrophic Obstructive Cardiomyopathy

2020 ◽  
Author(s):  
Xianyu Qin ◽  
Shaoxian Chen ◽  
Min Wu ◽  
Yueheng Wu ◽  
Jian Zhuang
Keyword(s):  
Linear Models ◽  
Nk Cell ◽  
Hypertrophic Obstructive Cardiomyopathy ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Differential Gene ◽  
Immune Related Genes ◽  
Core Genes

Abstract Objective: Hypertrophic obstructive cardiomyopathy (HOCM) is one of the main reasons for sudden cardiac death (SCD) of young people. Researches have revealed that immune-related genes are closely relevant with HOCM. Therefore, it is important to explore the key immune regulatory mechanisms and biomarkers of HOCM.Methods: We used many bioinformatics methods, including linear models for microarray analysis (LIMMA), protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), and CIBERSORT to assess the key pathway and hub genes involved in HOCM. Furthermore, expression levels of hub genes were validated in human tissue.Results: Our results showed that the degree of infiltration of five immune cells were linked to HOCM, including monocytes, macrophages M2, NK cell resting, B cells native, and T cells regulatory (Tregs). A total of 7 hub genes (CCL2, CXCL8, FOS, MAP2K1, NFKBIA, STAT3, and TNFRSF1A) were identified and validated by qt-PCR. The core genes including CCL2, MAP2K1, NFKBIA, STAT3, and TNFRSF1A are closely related to monocytes infiltration in HOCM.Conclusion: Taken together, our research will provide useful information to explore the immune mechanisms underlying HOCM and the potential targets for therapy. The candidate genes CCL2, MAP2K1, NFKBIA, STAT3, and TNFRSF1A were involved in the regulation of monocytes tissue infiltration, which is closely related to the HOCM.

scholarly journals Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis

2021 ◽  
Author(s):  
Ji-Chun Chen ◽  
Tian-Ao Xie ◽  
Zhen-Zong Lin ◽  
Yi-Qing Li ◽  
Yu-Fei Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Digestive Tract ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Data Set ◽  
Alcoholic Fatty Liver ◽  
Protein Protein Interaction ◽  
Human Digestive Tract ◽  
Gene Expression Profile Data

AbstractCOVID-19 is a serious infectious disease that has recently swept the world, and research on its causative virus, SARS-CoV-2, remains insufficient. Therefore, this study uses bioinformatics analysis techniques to explore the human digestive tract diseases that may be caused by SARS-CoV-2 infection. The gene expression profile data set, numbered GSE149312, is from the Gene Expression Omnibus (GEO) database and is divided into a 24-h group and a 60-h group. R software is used to analyze and screen out differentially expressed genes (DEGs) and then gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses are performed. In KEGG, the pathway of non-alcoholic fatty liver disease exists in both the 24-h group and 60-h group. STRING is used to establish a protein–protein interaction (PPI) network, and Cytoscape is then used to visualize the PPI and define the top 12 genes of the node as the hub genes. Through verification, nine statistically significant hub genes are identified: AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. In conclusion, the results of this study can provide a certain direction and basis for follow-up studies of SARS-CoV-2 infection of the human digestive tract and provide new insights for the prevention and treatment of diseases caused by SARS-CoV-2.

scholarly journals Bioinformatics Analysis and Identification of Potential Biomarkers in Gastric Cancer

2020 ◽  
Author(s):  
Jingdi Yang ◽  
Bo Peng ◽  
Xianzheng Qin ◽  
Tian Zhou
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.

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