scholarly journals Identification of Hub Genes and Key Pathways of Paraquat Induced Human Embryonic Pulmonary Fibrosis by Bioinformatics Analysis and Vitro Experiments

2020 ◽  
Author(s):  
Yue Fu ◽  
Xiang Xia Zeng ◽  
Jin Lun Hu ◽  
Mei Yan ◽  
CHun Ming Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pulmonary Fibrosis ◽  
Lung Fibrosis ◽  
Bioinformatics Analysis ◽  
Core Promoter ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Hub Genes

Abstract Background: Paraquat is highly toxic pesticide, which usually led to acute lung injury and subsequently develop pulmonary fibrosis, the exact mechanisms of PQ-induced lung fibrosis remain largely unclear and no specific drugs for this disease have been approved. Methods: Our study aimed to identify its potential mechanism though modeling study in vitro and bioinformatics analysis. Gene expression datasets associated with PQ-induced lung fibrosis were obtained from the Gene Expression Omnibus and differentially expressed genes (DEGs) were identified using GEO2R. Functional enrichment analyses were performed using the Database for Annotation. Results: The DEGs in the two datasets, of which 92 overlapping genes were found in two microarray datasets. Functional analysis demonstrated that the 92 DEGs were enriched in the ‘TNF signaling pathway’, ‘CXCR chemokine receptor binding’, and ‘core promoter binding’. Moreover, nine hub genes were identified from a protein‑protein interaction network. Conclusions: This integrative analysis firstly identified candidate genes and pathways in PQ-induced lung fibrosis, as well as benefit to research novel approaches for treating for control of PQ-induced pulmonary fibrosis.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

scholarly journals 975. Why Sex Make a Difference in HIV Clinical Course? Bioinformatics Analysis of Differential Expressed Gene in Females and Males with HIV Disease

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S516-S517
Author(s):  
Kulachanya Suwanwongse ◽  
Nehad Shabarek
Keyword(s):  
Gene Expression ◽  
Bioinformatics Analysis ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Gender Inequalities ◽  
Hiv Disease ◽  
Go Analysis ◽  
Hiv Progression ◽  
Pathway Analyses

Abstract Background Human immunodeficiency virus (HIV) disease progression are different among genders, in which women usually progress to acquired immunodeficiency syndrome (AIDS) faster than men. The mechanisms resulting in the gender biases of HIV progression are unclear. We conducted a bioinformatics analysis of differentially expressed genes (DEGs) in women and men with HIV disease to understand the sex-based differences in HIV pathogenesis. Methods We obtained microarray data from the Gene Expression Omnibus (GEO) database using our pre-defined search strategy and analyzed data using the GEO2R platform. The t-test was done to compare DEGs between females and males with HIV diseases. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was implemented to systematically extract biological features and processes of retrieving DEGs via gene ontology (GO) analysis. A Systemic search was performed to evaluate each DEG function and its possible association with HIV. Results One gene expression profiling data were retrieved: GSE 140713, composed of 40 males and 10 females with HIV1 infected samples. A GEO2R analysis yielded 19 DEGs (Table 1). The GO analysis result was demonstrated in Tables 2 and 3. Following a systemic search, we found two DEGs, which have previous studies reported an association with HIV: DDX3X (20 studies) and PDS5 (1 study). We proposed DDX3X (t 5.3, p 0.0037) is responsible for gender inequalities of HIV progression because of: 1. DDX3X is needed in the HIV1 life cycle. 2. Several studies confirmed a positive correlation between DDX3X expression and HIV1 replication. 3. Our study found an up-regulated DDX3X expression in women corresponded to the fact that women progress to AIDS faster than men. 4. Our GO analysis showed female up-regulated genes were enriched in positive regulation of the gene expression pathway, which can be explained by DDX3X and its underlying mechanism. Table 1: DEGs in women and men with HIV1 disease Table 2: GO functional enrichment pathway analyses of overall retrieving DEGs Table 3: GO functional enrichment pathway analyses of down- and up-regulated clusters of DEGs Conclusion Aberrant DDX3X expression may contribute to sex-based differences in HIV disease. Drugs modifying DDX3X gene expression will be beneficial in the treatment of HIV especially resolving the HIV drug resistance problem because current anti-HIV drugs target viral components posed the risk of viral mutation. Disclosures All Authors: No reported disclosures

scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

scholarly journals Identification of novel potential biomarkers in hepatocarcinoma cancer; a transcriptome analysis

2021 ◽  
Author(s):  
Mohib kakar ◽  
Muhammad Mehboob ◽  
Muhammad Akram ◽  
Imran Iqbal ◽  
Hafza Ijaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Predictive Biomarkers ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Molecular Therapy ◽  
Hub Genes ◽  
Healthy Control

Abstract Objective The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Methods GEO contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC samples and 10 healthy samples as control. GSE62232 was analyzed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting genes interacting. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analyzed using GEPIA to estimate the effect of their differential expression on cancer progression. Results We identified the top 10 hub genes through Cytohubba plugin. These genes include Cell Cycle Regulatory Cyclins and Cyclin-dependent proteins CCNA2, CCNB1 and CDK1. The pathogenesis and prognosis of HCC may be directly linked with the aforementioned genes. Conclusion In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

scholarly journals Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Md. Rakibul Islam ◽  
Lway Faisal Abdulrazak ◽  
Mohammad Khursheed Alam ◽  
Bikash Kumar Paul ◽  
Kawsar Ahmed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log   fold   change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

scholarly journals Identification of Novel Potential Biomarkers in Hepatocarcinoma Cancer; A Transcriptome Analysis

2021 ◽  
Author(s):  
Mohib kakar ◽  
Muhammad Mehboob ◽  
Muhammad Akram ◽  
Imran Iqbal ◽  
Hafza Ijaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Predictive Biomarker ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Molecular Therapy ◽  
Hub Genes ◽  
Healthy Control

Abstract The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Gene Expression Omnibus (GEO) contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server GEO. A total of 91 samples were collected, including 81 HCC samples and 10 healthy samples as control. GSE62232 was analyzed through GEO2R, and functional enrichment analysis was performed to extract rational information from a set of DEGs. The protein-protein relationship networking search method was used for extracting interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analyzed using GEPIA to estimate the effect of their differential expression on cancer progression. We identified the top 10 hub genes through Cytohubba plugin. These genes include cell cycle regulatory cyclins and cyclin-dependent proteins CCNA2, CCNB1 and CDK1. The pathogenesis and prognosis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarker studies that could promote selective molecular therapy for HCC.

scholarly journals Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

2020 ◽  
Author(s):  
Huatian Luo ◽  
Da-qiu Chen ◽  
Jing-jing Pan ◽  
Zhang-wei Wu ◽  
Can Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Hub Genes ◽  
Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival. Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis. Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

scholarly journals Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma: A Bioinformatics Analysis

2021 ◽  
Author(s):  
Shuo Xu ◽  
Dingsheng Liu ◽  
Mingming Cui ◽  
Yao Zhang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Early Diagnosis ◽  
Bioinformatics Analysis ◽  
Nuclear Division ◽  
Colon Adenocarcinoma ◽  
Functional Enrichment ◽  
Data Sets ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide. Although some molecular analyses of colon cancer were previously performed, the pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Methods: Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis of the hub genes was performed. To explore the early diagnostic value of the hub genes, UALCAN was used to analyse expression characteristics. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Results: Altogether 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (p<0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis. Conclusions: In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.

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