Identification of key genes in allergic rhinitis by bioinformatics analysis

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.

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Weighted gene co-expression network analysis to define pivotal modules and genes in diabetic heart failure

Bioscience Reports ◽

10.1042/bsr20200507 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Weiwei Liang ◽

FangFang Sun

Keyword(s):

Heart Failure ◽

Kegg Pathway ◽

Diabetic Heart ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Light Yellow ◽

Go Enrichment ◽

Key Genes ◽

Functional Analyses

Abstract This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

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Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Data ◽

10.3390/data5020038 ◽

2020 ◽

Vol 5 (2) ◽

pp. 38

Author(s):

Chuan Zhang ◽

Mandy Berndt-Paetz ◽

Jochen Neuhaus

Keyword(s):

Bladder Cancer ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Tumor Stage ◽

Hub Genes ◽

Go Enrichment ◽

Comprehensive Information ◽

Pathway Analyses ◽

Potential Biomarkers

Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa.

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Construction of a Novel Ferroptosis-Related Gene Signature of Atherosclerosis

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.800833 ◽

2022 ◽

Vol 9 ◽

Author(s):

Tucheng Huang ◽

Kangjie Wang ◽

Yuewei Li ◽

Yanchen Ye ◽

Yangxin Chen ◽

...

Keyword(s):

Regulatory Mechanism ◽

Kegg Pathway ◽

Gene Signature ◽

Chronic Inflammatory Disease ◽

Ppi Network ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Causes Of Mortality

Atheroclerosis refers to a chronic inflammatory disease featured by the accumulation of fibrofatty lesions in the intima of arteries. Cardiovasular events associated with atherosclerosis remain the major causes of mortality worldwide. Recent studies have indicated that ferroptosis, a novel programmed cell death, might participate in the process of atherosclerosis. However, the ferroptosis landscape is still not clear. In this study, 59 genes associated with ferroptosis were ultimately identified in atherosclerosis in the intima. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for functional annotation. Through the construction of protein–protein interaction (PPI) network, five hub genes (TP53, MAPK1, STAT3, HMOX1, and PTGS2) were then validated histologically. The competing endogenous RNA (ceRNA) network of hub genes was ultimately constructed to explore the regulatory mechanism between lncRNAs, miRNAs, and hub genes. The findings provide more insights into the ferroptosis landscape and, potentially, the therapeutic targets of atherosclerosis.

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Gene network in pulmonary tuberculosis based on bioinformatic analysis

10.21203/rs.2.22571/v1 ◽

2020 ◽

Author(s):

Lili Li ◽

Jian Lv ◽

Yuan He ◽

Zhihua Wang

Keyword(s):

Pulmonary Tuberculosis ◽

Chemokine Receptors ◽

Gene Network ◽

Kegg Pathway ◽

Bioinformatic Analysis ◽

Core Gene ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Causal Genes

Abstract Background: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. Methods: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. Results: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. Conclusions: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

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Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma

Frontiers in Oncology ◽

10.3389/fonc.2021.591507 ◽

2021 ◽

Vol 11 ◽

Author(s):

Rui Sun ◽

Simin Li ◽

Ke Zhao ◽

Mei Diao ◽

Long Li

Keyword(s):

Random Forest ◽

Kegg Pathway ◽

Random Forest Classifier ◽

Ppi Network ◽

Hub Genes ◽

Forest Model ◽

Go Enrichment ◽

Pathway Analyses ◽

Core Genes ◽

Potential Biomarkers

BackgroundThis study aimed to systematically investigate gene signatures for hepatoblastoma (HB) and identify potential biomarkers for its diagnosis and treatment.Materials and MethodsGSE131329 and GSE81928 were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between hepatoblastoma and normal samples were identified using the Limma package in R. Then, the similarity of network traits between two sets of genes was analyzed by weighted gene correlation network analysis (WGCNA). Cytoscape was used to visualize and select hub genes. PPI network of hub genes was construed by Cytoscape. GO enrichment and KEGG pathway analyses of hub genes were carried out using ClueGO. The random forest classifier was constructed based on the hub genes using the GSE131329 dataset as the training set, and its reliability was validated using the GSE81928 dataset. The resulting core hub genes were combined with the InnateDB database to identify the innate core genes.ResultsA total of 4244 DEGs in HB were identified. WGCNA identified four modules that were significantly correlated with the disease status. A total of 114 hub genes were obtained within the top 20 genes of each node rank. 6982 relation pairs and 3700 nodes were contained in the PPI network of 114 hub genes. GO enrichment and KEGG pathway analyses of hub genes were focused on MAPK, cell cycle, p53, and other crucial pathways involved in HB. A random forest classifier was constructed using the 114 hub genes as feature genes, resulting in a 95.5% true positive rate when classifying HB and normal samples. A total of 35 core hub genes were obtained through the mean decrease in accuracy and mean decrease Gini of the random forest model. The classification efficiency of the random forest model was 81.4%. Finally, CDK1, TOP2A, ADRA1A, FANCI, XRCC1, TPX2, CCNB2, CDK4, GLYATL1, and CFHR3 were identified by cross-comparison with the InnateDB database.ConclusionOur study established a random forest classifier that identified 10 core genes in HB. These findings may be beneficial for the diagnosis, prediction, and targeted therapy of HB.

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Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis

PeerJ ◽

10.7717/peerj.10419 ◽

2020 ◽

Vol 8 ◽

pp. e10419

Author(s):

Jingyi Ding ◽

Yanxi Liu ◽

Yu Lai

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Prognostic Biomarkers ◽

Ductal Adenocarcinoma ◽

Receptor Interaction ◽

Analysis Tool ◽

Ppi Network ◽

Hub Genes

Background Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype–Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM–receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05). Conclusions The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

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Cell Adhesion-Related Molecules Play a Key Role in Renal Cancer Progression by Multinetwork Analysis

BioMed Research International ◽

10.1155/2019/2325765 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Anbang Wang ◽

Ming Chen ◽

Hui Wang ◽

Jinming Huang ◽

Yi Bao ◽

...

Keyword(s):

Cell Adhesion ◽

Cancer Progression ◽

Kegg Pathway ◽

Ppi Network ◽

Hub Genes ◽

Genetic Characteristics ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Underlying Mechanisms ◽

Upregulated Genes

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The study aimed to identify genetic characteristics and reveal the underlying mechanisms in RCC. GSE53757, GSE46699, and TCGA KIRC database (n = 897) were analyzed to screen differentially expressed genes (DEGs) in RCC. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by the analysis of the protein-protein interaction (PPI) network of the DEGs by Cytoscape software. In all, 834 DEGs were identified in RCC, including 416 upregulated genes and 418 downregulated genes. The top 10 hub genes, VEGFA, EGFR, EGF, CD44, CD86, FN1, ITGAM, ITGB2, TLR2, and PTPRC, were identified from the PPI network according to the core degree. The following subnetwork revealed that these significant modules were enriched in positive regulation of response to external stimulus, regulation of leukocyte-mediated immunity, and regulation of exocytosis. The expressions of these hub genes were also validated using qRT-PCR and IHC in Changzheng RCC database (n = 160). We especially found that half of the top ten hub genes were cell adhesion-related molecules, which were associated with RCC progression and poor prognosis. In conclusion, these hub genes, particularly cell adhesion-related molecules, could be used as prognostic biomarkers and potential therapeutic targets for RCC.

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

10.21203/rs.3.rs-824062/v1 ◽

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.

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Gene network in pulmonary tuberculosis based on bioinformatic analysis

10.21203/rs.2.22571/v2 ◽

2020 ◽

Author(s):

Lili Li ◽

Jian Lv ◽

Yuan He ◽

Zhihua Wang

Keyword(s):

Pulmonary Tuberculosis ◽

Chemokine Receptors ◽

Gene Network ◽

Kegg Pathway ◽

Bioinformatic Analysis ◽

Core Gene ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Causal Genes

Abstract Background: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. Methods: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. Results: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. Conclusions: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

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