Cargando…

Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity

Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of...

Descripción completa

Detalles Bibliográficos
Autores principales: Ganekal, Prashanth, Vastrad, Basavaraj, Kavatagimath, Satish, Vastrad, Chanabasayya, Kotrashetti, Shivakumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967176/
https://www.ncbi.nlm.nih.gov/pubmed/36837510
http://dx.doi.org/10.3390/medicina59020309
_version_ 1784897199487844352
author Ganekal, Prashanth
Vastrad, Basavaraj
Kavatagimath, Satish
Vastrad, Chanabasayya
Kotrashetti, Shivakumar
author_facet Ganekal, Prashanth
Vastrad, Basavaraj
Kavatagimath, Satish
Vastrad, Chanabasayya
Kotrashetti, Shivakumar
author_sort Ganekal, Prashanth
collection PubMed
description Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of GSE132831 was downloaded from Gene Expression Omnibus (GEO) database. Functional enrichment analysis of DEGs was conducted with ToppGene. The protein–protein interactions network, module analysis, target gene–miRNA regulatory network and target gene–TF regulatory network were constructed and analyzed. Furthermore, hub genes were validated by receiver operating characteristic (ROC) analysis. A total of 872 DEGs, including 439 up-regulated genes and 433 down-regulated genes were observed. Results: Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for subjects with diabetes and obesity. The hub genes were validated in subjects with diabetes and obesity. Conclusion: This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for subjects with diabetes and obesity.
format Online
Article
Text
id pubmed-9967176
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99671762023-02-26 Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity Ganekal, Prashanth Vastrad, Basavaraj Kavatagimath, Satish Vastrad, Chanabasayya Kotrashetti, Shivakumar Medicina (Kaunas) Article Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of GSE132831 was downloaded from Gene Expression Omnibus (GEO) database. Functional enrichment analysis of DEGs was conducted with ToppGene. The protein–protein interactions network, module analysis, target gene–miRNA regulatory network and target gene–TF regulatory network were constructed and analyzed. Furthermore, hub genes were validated by receiver operating characteristic (ROC) analysis. A total of 872 DEGs, including 439 up-regulated genes and 433 down-regulated genes were observed. Results: Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for subjects with diabetes and obesity. The hub genes were validated in subjects with diabetes and obesity. Conclusion: This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for subjects with diabetes and obesity. MDPI 2023-02-07 /pmc/articles/PMC9967176/ /pubmed/36837510 http://dx.doi.org/10.3390/medicina59020309 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ganekal, Prashanth
Vastrad, Basavaraj
Kavatagimath, Satish
Vastrad, Chanabasayya
Kotrashetti, Shivakumar
Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity
title Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity
title_full Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity
title_fullStr Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity
title_full_unstemmed Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity
title_short Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity
title_sort bioinformatics and next-generation data analysis for identification of genes and molecular pathways involved in subjects with diabetes and obesity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967176/
https://www.ncbi.nlm.nih.gov/pubmed/36837510
http://dx.doi.org/10.3390/medicina59020309
work_keys_str_mv AT ganekalprashanth bioinformaticsandnextgenerationdataanalysisforidentificationofgenesandmolecularpathwaysinvolvedinsubjectswithdiabetesandobesity
AT vastradbasavaraj bioinformaticsandnextgenerationdataanalysisforidentificationofgenesandmolecularpathwaysinvolvedinsubjectswithdiabetesandobesity
AT kavatagimathsatish bioinformaticsandnextgenerationdataanalysisforidentificationofgenesandmolecularpathwaysinvolvedinsubjectswithdiabetesandobesity
AT vastradchanabasayya bioinformaticsandnextgenerationdataanalysisforidentificationofgenesandmolecularpathwaysinvolvedinsubjectswithdiabetesandobesity
AT kotrashettishivakumar bioinformaticsandnextgenerationdataanalysisforidentificationofgenesandmolecularpathwaysinvolvedinsubjectswithdiabetesandobesity