Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

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Identification of key pathways and genes in obesity using bioinformatics analysis and molecular docking studies

10.21203/rs.3.rs-95029/v2 ◽

2020 ◽

Author(s):

Harish Joshi ◽

Basavaraj Mallikarjunayya Vastrad ◽

Nidhi Joshi ◽

Anandkumar Tengli ◽

Chanabasayya Mallikarjunayya Vastrad ◽

...

Keyword(s):

Molecular Docking ◽

Regulatory Network ◽

Target Genes ◽

Bioinformatics Analysis ◽

Therapeutic Targets ◽

Rank Aggregation ◽

Receiver Operating Curve ◽

Progression Rate ◽

Hub Genes ◽

Molecular Docking Study

Abstract Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network. We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. Finally molecular docking study was performed. The robust DEGs linked with the development of obesity was screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

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Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

10.21203/rs.3.rs-95029/v1 ◽

2020 ◽

Author(s):

Harish Joshi ◽

Basavaraj Mallikarjunayya Vastrad ◽

Nidhi Joshi ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Expression Profiles ◽

Therapeutic Targets ◽

Rank Aggregation ◽

Receiver Operating Curve ◽

Progression Rate ◽

Hub Genes

Abstract Background Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. ObjectivesTherefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. MethodsThe gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network.Results We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. The robust DEGs linked with the development of obesity were screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. ConclusionsOur study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

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Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis

10.1101/2021.04.18.440371 ◽

2021 ◽

Author(s):

Basavaraj Mallikarjunayya Vastrad ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Hub Gene ◽

Network Modules ◽

Gene Regulatory ◽

Key Pathways

To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein protein interaction (PPI) network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis and RT PCR. A total of 948 DEGs were screened out, among which 475 genes were up regulated; while 473 were down regulated. Functional enrichment analyses indicated that DEGs were mainly involved in defense response, ion transport, neutrophil degranulation and neuronal system. The hub genes (CDK1, TOP2A, MAD2L1, RSL24D1, CDKN1A, NOTCH3, MYB, PWP2, WNT7B and HSPA12B) were identified from PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network. We identified a series of key genes along with the pathways that were most closely related with dementia initiation and progression. Our results provide a more detailed molecular mechanism for the advancement of dementia, shedding light on the potential biomarkers and therapeutic targets.

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Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Integrated Bioinformatics Analysis and Molecular Docking Experiments

10.1101/2020.12.20.20248601 ◽

2020 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Anandkumar Tengli

Keyword(s):

Molecular Docking ◽

Regulatory Network ◽

Molecular Targets ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Hub Genes ◽

Docking Experiment ◽

Gene Regulatory

AbstractThe current investigation aimed to mine therapeutic molecular targets that play an key part in the advancement of pancreatic ductal adenocarcinoma (PDAC). The expression profiling by high throughput sequencing dataset profile GSE133684 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Limma package of R was used to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs were performed. Protein-protein interaction (PPI) networks of the DEGs were constructed. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Furthermore, consistent hub genes were further validated. Molecular docking experiment was conducted. A total of 463 DEGs (232 up regulated and 231 down regulated genes) were identified between very early PDAC and normal control samples. The results of Functional enrichment analysis revealed that the DEGs were significantly enriched in vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis and adaptive immune system. The PPI network and gene regulatory network of up regulated genes and down regulated genes were established, and hub genes were identified. The expression of hub genes (CCNB1, FHL2, HLA-DPA1 and TUBB1) were also validated to be differentially expressed among PDAC and normal control samples. Molecular docking experiment predicted the novel inhibitory molecules for CCNB1 and FHL2. The identification of hub genes in PDAC enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with PDAC.

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Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

10.1101/2021.03.28.437386 ◽

2021 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad

Keyword(s):

Regulatory Network ◽

Roc Analysis ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Rt Pcr ◽

Hub Genes ◽

Hub Gene ◽

Gene Regulatory

AbstractType 2 diabetes mellitus (T2DM) is etiologically related to metabolic disorder. The aim of our study was to screen out candidate genes of T2DM and to elucidate the underlying molecular mechanisms by bioinformatics methods. Expression profiling by high throughput sequencing data of GSE154126 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between T2DM and normal control were identified. And then, functional enrichment analyses of gene ontology (GO) and REACTOME pathway analysis was performed. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. Additionally, potential miRNAs of hub genes were predicted by miRNet database . Transcription factors (TFs) of hub genes were detected by NetworkAnalyst database. Further, validations were performed by receiver operating characteristic curve (ROC) analysis and real-time polymerase chain reaction (RT-PCR). In total, 925 DEGs were identified in T2DM, including 447 up regulated genes and 478 down-regulated genes. Functional enrichment analysis results showed that up regulated DEGs were significantly enriched in defense response, neutrophil degranulation, cell adhesion and extracellular matrix organization. The top 10 hub genes, JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 were identified from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis and RT-PCR revealed that JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 might serve as biomarkers in T2DM. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of T2DM. The identified hub genes may participate in the onset and advancement of T2DM and serve as therapeutic targets.

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Screening Driving Transcription Factors in the Processing of Gastric Cancer

Gastroenterology Research and Practice ◽

10.1155/2016/8431480 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Guangzhong Xu ◽

Kai Li ◽

Nengwei Zhang ◽

Bin Zhu ◽

Guosheng Feng

Keyword(s):

Gastric Cancer ◽

Transcription Factors ◽

Regulatory Network ◽

Target Genes ◽

Transcriptional Regulatory Network ◽

Expression Profiles ◽

Functional Enrichment ◽

Regulatory Mechanisms ◽

Integrated Analysis ◽

Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

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Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2020.631775 ◽

2021 ◽

Vol 7 ◽

Author(s):

Tao Yan ◽

Shijie Zhu ◽

Miao Zhu ◽

Chunsheng Wang ◽

Changfa Guo

Keyword(s):

Atrial Fibrillation ◽

Network Analysis ◽

Molecular Mechanism ◽

Immune Cells ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Correlation Network ◽

Hub Genes ◽

Qrt Pcr ◽

Gene Correlation

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF.Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR).Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis.Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

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Screening and Functional Analysis of Hub MicroRNAs Related to Tumor Development in Colon Cancer

BioMed Research International ◽

10.1155/2020/3981931 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Dong-Hu Yu ◽

Wei Li ◽

Jing-Yu Huang ◽

Xiao-Ping Liu ◽

Chi Zhang ◽

...

Keyword(s):

Colon Cancer ◽

Functional Analysis ◽

Gene Networks ◽

Target Genes ◽

Bioinformatics Analysis ◽

Tumor Development ◽

Therapeutic Targets ◽

Cancer Development ◽

Candidate Mirnas ◽

The Future

Various microRNAs (miRNAs) are of importance in the development of colon cancer, but most of the mechanisms of the miRNAs are still unclear. In order to clarify the hub miRNAs and their roles in colon cancer development, GSE98406 was used to screen hub miRNAs by bioinformatics analysis. 46 DE-miRNAs (14 were upregulated and 32 were downregulated) and 1738 target genes of DE-miRNAs were ascertained. miRNAs-gene-networks and miRNAs-GO-networks were built to get more knowledge about the function of candidate miRNAs. After validation, three miRNAs (miR-17-5p, miR-182-5p and miR-200a-3p) were recognized to be hub miRNAs associated with the progression of colon cancer. More importantly, the hub miRNAs and the putative targets genes might be new diagnostic and therapeutic targets for colon cancer in the future.

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Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

European Journal of Medical Research ◽

10.1186/s40001-020-00441-2 ◽

2020 ◽

Vol 25 (1) ◽

Author(s):

Xue Jiang ◽

Zhijie Xu ◽

Yuanyuan Du ◽

Hongyu Chen

Keyword(s):

Gene Ontology ◽

Inflammatory Response ◽

Molecular Mechanism ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Control Group ◽

Integrin Subunit ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

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Identification of key genes, pathways and potential therapeutic agents for IgA nephropathy using an integrated bioinformatics analysis

Journal of the Renin-Angiotensin-Aldosterone System ◽

10.1177/1470320320919635 ◽

2020 ◽

Vol 21 (2) ◽

pp. 147032032091963

Author(s):

Xiaoxue Chen ◽

Mindan Sun

Keyword(s):

Differentially Expressed Genes ◽

Immunoglobulin A ◽

Therapeutic Targets ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Signal Pathways ◽

Immunoglobulin A Nephropathy ◽

Hub Genes ◽

Limma Package

Purpose: This study aims to identify immunoglobulin-A-nephropathy-related genes based on microarray data and to investigate novel potential gene targets for immunoglobulin-A-nephropathy treatment. Methods: Immunoglobulin-A-nephropathy chip data was obtained from the Gene Expression Omnibus database, which included 10 immunoglobulin-A-nephropathy and 22 normal samples. We used the limma package of R software to screen differentially expressed genes in immunoglobulin-A-nephropathy and normal glomerular compartment tissues. Functional enrichment (including cellular components, molecular functions, biological processes) and signal pathways were performed for the differentially expressed genes. The online analysis database (STRING) was used to construct the protein-protein interaction networks of differentially expressed genes, and Cytoscape software was used to identify the hub genes of the signal pathway. In addition, we used the Connectivity Map database to predict possible drugs for the treatment of immunoglobulin-A-nephropathy. Results: A total of 348 differentially expressed genes were screened including 107 up-regulated and 241 down-regulated genes. Functional analysis showed that up-regulated differentially expressed genes were mainly concentrated on leukocyte migration, and the down-regulated differentially expressed genes were significantly enriched in alpha-amino acid metabolic process. A total of six hub genes were obtained: JUN, C3AR1, FN1, AGT, FOS, and SUCNR1. The small-molecule drugs thapsigargin, ciclopirox and ikarugamycin were predicted therapeutic targets against immunoglobulin-A-nephropathy. Conclusion: Differentially expressed genes and hub genes can contribute to understanding the molecular mechanism of immunoglobulin-A-nephropathy and providing potential therapeutic targets and drugs for the diagnosis and treatment of immunoglobulin-A-nephropathy.

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