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 target...
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2021.628907/full |
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| author | Harish Joshi Basavaraj Vastrad Nidhi Joshi Chanabasayya Vastrad Anandkumar Tengli Iranna Kotturshetti |
| author_facet | Harish Joshi Basavaraj Vastrad Nidhi Joshi Chanabasayya Vastrad Anandkumar Tengli Iranna Kotturshetti |
| author_sort | Harish Joshi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T10:11:18Z |
| format | Article |
| id | doaj.art-5ba79dbba099469daab322be70401b7a |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-12-19T10:11:18Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-5ba79dbba099469daab322be70401b7a2022-12-21T20:26:21ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922021-06-011210.3389/fendo.2021.628907628907Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking StudiesHarish Joshi0Basavaraj Vastrad1Nidhi Joshi2Chanabasayya Vastrad3Anandkumar Tengli4Iranna Kotturshetti5Department of Endocrinology, Endocrine and Diabetes Care Center, Hubbali, IndiaDepartment of Biochemistry, Basaveshwar College of Pharmacy, Gadag, IndiaDepartment of Medicine, Dr. D. Y. Patil Medical College, Kolhapur, IndiaBiostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, IndiaDepartment of Pharmaceutical Chemistry, JSS College of Pharmacy, Mysuru and JSS Academy of Higher Education & Research, Mysuru, IndiaDepartment of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, IndiaObesity 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.https://www.frontiersin.org/articles/10.3389/fendo.2021.628907/fulladipositiesobesitydifferentially expressed genesmodulesprotein–protein interaction network |
| spellingShingle | Harish Joshi Basavaraj Vastrad Nidhi Joshi Chanabasayya Vastrad Anandkumar Tengli Iranna Kotturshetti Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies Frontiers in Endocrinology adiposities obesity differentially expressed genes modules protein–protein interaction network |
| title | Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies |
| title_full | Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies |
| title_fullStr | Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies |
| title_full_unstemmed | Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies |
| title_short | Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies |
| title_sort | identification of key pathways and genes in obesity using bioinformatics analysis and molecular docking studies |
| topic | adiposities obesity differentially expressed genes modules protein–protein interaction network |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2021.628907/full |
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