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Updated: Oct 13, 2025

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells
Published on: August 25, 2021
Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin
Jayaprakash Chinnappan1, Akilandeswari Ramu2, Vidhya Rajalakshmi V2
1Anthropology and Health Informatics Lab, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India. japee.bioinfo@gmail.com.
Abstract:
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.
Insights
This study used integrative bioinformatics to identify Nitroglycerin
Area of Science:
- Bioinformatics
- Genomics
- Cancer Research
Background:
- Nitroglycerin's anticancer mechanisms are not fully understood.
- Identifying target genes and pathways is crucial for understanding its effects on cancer.
Purpose of the Study:
- To explore Nitroglycerin's mechanisms in various cancers using integrative bioinformatics.
- To predict target genes, identify mutated and co-expressed genes, and discover biomarkers.
Main Methods:
- Utilized public databases (PubChem, SwissTargetPrediction, GEO) and tools (STRING, CluePedia, Survexpress).
- Performed direct target gene identification, PPI network construction, mutation analysis, DEG analysis, and survival analysis.
Main Results:
- Identified 12 direct target genes of Nitroglycerin.
- Discovered interconnected, mutated, upregulated, downregulated, and co-expressed genes.
- Pinpointed four cancer types: bladder, endometrial, melanoma, and non-small cell lung cancer.
- Identified novel biomarkers for Nitroglycerin in these cancers.
Conclusions:
- Integrative bioinformatics effectively elucidated Nitroglycerin's complex mechanisms in cancer.
- The identified biomarkers hold potential for targeted cancer therapies.
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