A System Biology and Bioinformatics Approach to Determine the Molecular Signature, Core Ontologies, Functional Pathways, Drug Compounds in Between Stress and Type 2 Diabetes

Basar, Md. Abul; Hasan, Md. Rakibul; Paul, Bikash Kumar; Shadhin, Khairul Alam; Mollah, Md. Sarwar Hossain

doi:10.1007/978-3-031-34953-9_25

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…The control of gene transcription as well as the establishment of cellular identity and activity are assumed to depend on transcriptional factors (TFs), the TF (transferrin) gene‐producing proteins [ 76 ]. The TF‐gene interaction investigates how TF affects functional pathways and levels of gene expression [ 77 ]. Finding the important TF‐gene interactions is crucial for comprehending the roles of pleiotropic global regulators [ 78 ].…”

Section: Methodsmentioning

confidence: 99%

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR‐mutated lung adenocarcinoma

Bhattacharjya,

Islam,

Uddin

et al. 2024

FEBS Open Bio

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Hypopharyngeal cancer gene regulatory networks and therapeutic molecules are a disease that is associated with EGFR‐mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

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Section: Methodsmentioning

confidence: 99%

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR‐mutated lung adenocarcinoma

Bhattacharjya,

Islam,

Uddin

et al. 2024

FEBS Open Bio

Self Cite

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show abstract

“…[14] Bioinformatics is used in a variety of fields, including genomics, proteomics, and drug discovery. [15] In this present investigation, a microarray data analysis-based gene chip was downloaded for HF and used. Bioinformatics analysis to explore the molecular mechanism and key targets in the progress of HF.…”

Section: Introductionmentioning

confidence: 99%

Identification of key genes associated with heart failure based on bioinformatics analysis and screening of traditional Chinese medicines for the prevention and treatment of heart failure

Luo,

Wang,

Zhang

et al. 2023

Medicine

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Heart failure (HF) is the final stage of heart disease. An increasing number of experiments and clinical reports have shown that traditional Chinese medicine (TCM) has many therapeutic effects and advantages in treating HF. In this study, we used bioinformatics methods to screen key genes and predict the components of Chinese herbal medicines with preventive and therapeutic effects on HF. GSE120895 and GSE21610 HF chips were downloaded from the Gene Expression Omnibus database. We screened differentially expressed genes (DEGs). Weighted gene coexpression network analysis was performed to determine key modules. Genes in key modules were used for Gene Ontology and Kyoto Encyclopedia of Genes Genomes analysis to determine the biological functions. Finally, receiver operating characteristic curve analysis was used to screen out key genes, and single-sample GSEA was conducted to screen TCM compounds and effective ingredients of TCM compounds related to HF. We have selected a key module (MeTerquoise) and identified 489 DEGs, of which 357 are up regulated and 132 are down regulated. Gene Ontology and Kyoto Encyclopedia of Genes Genomes analyses indicated that the DEGs were associated with the extracellular matrix, fat metabolism and inflammatory response. We identified IL2, CXCR4, CCL5, THY1, CCN2, and IL7R as key genes. Single-sample GSEA showed that key genes were mainly related to energy metabolism, mitochondrial oxidative phosphorylation, extracellular matrix, and immunity. Finally, a total of 70 TCM compounds and 30 active ingredients of TCM compounds were identified. Bioinformatics methods were applied to preliminarily predict the key genes and TCM compounds involved in HF. These results provide theoretical support for the treatment of HF with TCM compounds and provide targets and research strategies for the development of related new Chinese medicines.

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A System Biology and Bioinformatics Approach to Determine the Molecular Signature, Core Ontologies, Functional Pathways, Drug Compounds in Between Stress and Type 2 Diabetes

Cited by 2 publications

References 24 publications

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR‐mutated lung adenocarcinoma

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR‐mutated lung adenocarcinoma

Identification of key genes associated with heart failure based on bioinformatics analysis and screening of traditional Chinese medicines for the prevention and treatment of heart failure

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