Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy

Kirk, Jon; Shah, Nirav R.; Noll, Braxton; Stevens, Craig B.; Lawler, Marshall; Mougeot, Farah Bahrani; Mougeot, Jean-Luc

doi:10.1007/s00520-018-4096-2

Cited by 22 publications

(19 citation statements)

References 44 publications

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“…This database contains drug-gene interaction data from 30 disparate sources including ChEMBL, DrugBank, Ensembl, NCBI Entrez, PharmGKB, PubChem, clinical trial databases, and literature in NCBI PubMed. The results of this process were arranged such that each entry was a specific drug-gene interaction associated with its source link [ 37 ]. The identified target network was visualized using the Cytoscape software.…”

Section: Methodsmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis

Luo

Chang

et al. 2020

Hereditas

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Background: The aim of this study was to identify the candidate biomarkers and pathways associated with psoriasis. GSE13355 and GSE14905 were extracted from the Gene Expression Omnibus (GEO) database. Then the differentially expressed genes (DEGs) with |logFC| > 2 and adjusted P < 0.05 were chosen. In addition, the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEGs were performed. Then, the GO terms with P < 0.05 and overlap coefficient greater than 0.5 were integrated by EnrichmentMap. Additionally, risk subpathways analysis for DEGs was also conducted by using the iSubpathwayMiner package to obtain more psoriasis-related DEGs and pathways. Finally, protein-protein interaction (PPI) network analysis was performed to identify the hub genes, and the DGIdb database was utilized to search for the candidate drugs for psoriasis. Results: A total of 127 DEGs which were mostly associated with keratinization, keratinocyte differentiation, and epidermal cell differentiation biological processes were identified. Based on these GO terms, 3 modules (human skin, epidermis and cuticle differentiation, and enzyme activity) were constructed. Moreover, 9 risk subpathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism were screened. Finally, PPI network analysis demonstrated that CXCL10 was the hub gene with the highest degree, and CXCR2, CXCL10, IVL, OASL, and ISG15 were the potential gene targets of the drugs for treating psoriasis. Conclusion: Psoriasis may be mostly caused by keratinization, keratinocyte differentiation, and epidermal cell differentiation; the pathogeneses were more related with pathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism. Besides, some psoriasis-related genes such as SPRR genes, HSD11B1, GGH, CXCR2, IVL, OASL, ISG15, and CXCL10 may be important targets in psoriatic therapy.

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Section: Methodsmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis

Luo

Chang

et al. 2020

Hereditas

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“…This database contains drug-gene interaction data from 30 disparate sources including ChEMBL, DrugBank, Ensembl, NCBI Entrez, PharmGKB, PubChem, clinical trial databases, and literature in NCBI PubMed. The results of this process were arranged so that each entry was a specific drug-gene interaction associated with its source link [27]. Drugs supported by more than one databases or PubMed references were selected as the potential drugs.…”

Section: Methodsmentioning

confidence: 99%

“…The final list only involved the drugs that have been approved by the Food and Drug Administration (FDA). The identified target network was visualized using STITCH (http://stitch.embl.de/; version: 5.0), a program similar to STRING which also incorporated drug-gene relationships [27, 28].…”

Section: Methodsmentioning

confidence: 99%

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy

et al. 2019

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Background Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC. Methods mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein–protein interaction (PPI) network, and miRNA–gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC. Results Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC. Conclusions In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future. Electronic supplementary material The online version of this article (10.1186/s12935-019-0854-6) contains supplementary material, which is available to authorized users.

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“…Additionally,the identi ed target gene network was constructed through STITCH database (http://stitch.embl.de/), a software which also incorporated drug-gene relationships [26,27].…”

Section: Drug-hub Gene Interactionmentioning

confidence: 99%

Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

Chen

Xia

Wan

et al. 2020

Preprint

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BackgroundHepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven-genes and potential drugs in HCC. MethodsThree mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, GO terms analysis and KEGG enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on TCGA, GEPIA and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of the hub genes were further conducted by Kaplan-Meier plotter and the GEPIA. DGIdb database was performed to search the candidate drugs for HCC. ResultsFinally, 197 DEGs were identified. The PPI network was constructed using STRING software. Then ten genes were selected and considered as the hub genes. The ten genes were all closely related to the survival of HCC patients. DGIdb database predicted 39 small molecules as the possible drugs for treating HCC. ConclusionsOur study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in future.

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Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy

Abstract: Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Cited by 22 publications

References 44 publications

Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis

Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy

Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

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