Cargando…

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is i...

Descripción completa

Detalles Bibliográficos
Autores principales: Chinnappan, Jayaprakash, Ramu, Akilandeswari, V., Vidhya Rajalakshmi, S., Akil Kavya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586365/
https://www.ncbi.nlm.nih.gov/pubmed/34764329
http://dx.doi.org/10.1038/s41598-021-01508-8
_version_ 1784597876700086272
author Chinnappan, Jayaprakash
Ramu, Akilandeswari
V., Vidhya Rajalakshmi
S., Akil Kavya
author_facet Chinnappan, Jayaprakash
Ramu, Akilandeswari
V., Vidhya Rajalakshmi
S., Akil Kavya
author_sort Chinnappan, Jayaprakash
collection PubMed
description Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.
format Online
Article
Text
id pubmed-8586365
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-85863652021-11-16 Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin Chinnappan, Jayaprakash Ramu, Akilandeswari V., Vidhya Rajalakshmi S., Akil Kavya Sci Rep Article Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer. Nature Publishing Group UK 2021-11-11 /pmc/articles/PMC8586365/ /pubmed/34764329 http://dx.doi.org/10.1038/s41598-021-01508-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chinnappan, Jayaprakash
Ramu, Akilandeswari
V., Vidhya Rajalakshmi
S., Akil Kavya
Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin
title Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin
title_full Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin
title_fullStr Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin
title_full_unstemmed Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin
title_short Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin
title_sort integrative bioinformatics approaches to therapeutic gene target selection in various cancers for nitroglycerin
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586365/
https://www.ncbi.nlm.nih.gov/pubmed/34764329
http://dx.doi.org/10.1038/s41598-021-01508-8
work_keys_str_mv AT chinnappanjayaprakash integrativebioinformaticsapproachestotherapeuticgenetargetselectioninvariouscancersfornitroglycerin
AT ramuakilandeswari integrativebioinformaticsapproachestotherapeuticgenetargetselectioninvariouscancersfornitroglycerin
AT vvidhyarajalakshmi integrativebioinformaticsapproachestotherapeuticgenetargetselectioninvariouscancersfornitroglycerin
AT sakilkavya integrativebioinformaticsapproachestotherapeuticgenetargetselectioninvariouscancersfornitroglycerin