Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach

Basar, Md. Abul; Hosen, Md. Faruk; Paul, Bikash Kumar; Hasan, Md. Rakibul; Shamim, S. M.; Bhuyian, Touhid

doi:10.1016/j.imu.2023.101174

Cited by 17 publications

(8 citation statements)

References 41 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: Tf-gene Interactionsmentioning

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: Tf-gene Interactionsmentioning

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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“…The construction of the PPI network, gene-microRNA (miR-NA) interaction, and gene-transcriptional factors (TFs) interaction can help the development of new therapeutic strategies in the levels of genes and proteins. [11][12][13] Thus, we predicted links between TFs and miRNAs, identifying a total of 28 TFs and 82 miRNAs associated with the four hub genes that may influence the development of COVID-19 and OA. Additionally, effective candidate drugs were screened that may be helpful for patients.…”

Section: Introductionmentioning

confidence: 99%

“…Matrix metalloproteinases (MMP) 9, ATF3, CCL4, and RELA were identified as hub genes. The construction of the PPI network, gene–microRNA (miRNA) interaction, and gene–transcriptional factors (TFs) interaction can help the development of new therapeutic strategies in the levels of genes and proteins 11–13 . Thus, we predicted links between TFs and miRNAs, identifying a total of 28 TFs and 82 miRNAs associated with the four hub genes that may influence the development of COVID‐19 and OA.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics and system biology analysis revealed the crosstalk between COVID‐19 and osteoarthritis

Lai,

Jiang,

Liao

et al. 2023

Immunity Inflam & Disease

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BackgroundThe global coronavirus disease 2019 (COVID‐19) outbreak has significantly impacted public health. Moreover, there has been an association between the incidence and severity of osteoarthritis (OA) and the onset of COVID‐19. However, the optimal diagnosis and treatment strategies for patients with both diseases remain uncertain. Bioinformatics is a novel approach that may help find the common pathology between COVID‐19 and OA.MethodsDifferentially expressed genes (DEGs) were screened by R package “limma.” Functional enrichment analyses were performed to find key biological functions. Protein–protein interaction (PPI) network was constructed by STRING database and then Cytoscape was used to select hub genes. External data sets and OA mouse model validated and identified the hub genes in both mRNA and protein levels. Related transcriptional factors (TF) and microRNAs (miRNAs) were predicted with miRTarBase and JASPR database. Candidate drugs were obtained from Drug Signatures database. The immune infiltration levels of COVID‐19 and OA were evaluated by CIBERSORT and scRNA‐seq.ResultsA total of 74 common DEGs were identified between COVID‐19 and OA. Receiver operating characteristic curves validated the effective diagnostic values (area under curve > 0.7) of four hub genes (matrix metalloproteinases 9, ATF3, CCL4, and RELA) in both the training and validation data sets of COVID‐19 and OA. Quantitative polymerase chain reaction and Western Blot showed significantly higher hub gene expression in OA mice than in healthy controls. A total of 84 miRNAs and 28 TFs were identified to regulate the process of hub gene expression. The top 10 potential drugs were screened including “Simvastatin,” “Hydrocortisone,” and “Troglitazone” which have been proven by Food and Drug Administration. Correlated with hub gene expression, Macrophage M0 was highly expressed while Natural killer cells and Mast cells were low in both COVID‐19 and OA.ConclusionFour hub genes, disease‐related miRNAs, TFs, drugs, and immune infiltration help to understand the pathogenesis and perform further studies, providing a potential therapy target for COVID‐19 and OA.

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“…[13] Bioinformatics is a hybrid science that stores biological information by means of a combination of biological and technical data and plays a massive role in gaining extremely significant information on disorders that is useful in treatment development. [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.…”

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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Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach

Cited by 17 publications

References 41 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

Bioinformatics and system biology analysis revealed the crosstalk between COVID‐19 and osteoarthritis

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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