Dissecting the critical pathway crosstalk mechanisms of thyroid cancer based on drug-target genes and disease genes

Biologia ◽  
2021 ◽  
Author(s):  
Weishan Han ◽  
Yanyan Wu ◽  
Xiaxia Wang ◽  
Li Liu ◽  
Yanrui Ding
Keyword(s):  
Thyroid Cancer ◽  
Drug Target ◽  
Target Genes ◽  
Disease Genes ◽  
Critical Pathway ◽  
Pathway Crosstalk

scholarly journals Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pusheng Quan ◽  
Kai Wang ◽  
Shi Yan ◽  
Shirong Wen ◽  
Chengqun Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Target ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Drug Candidates ◽  
Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

scholarly journals An exploratory assessment of the applicability of direct-to-consumer genetic testing to translational research in Japan

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Masahiro Inoue ◽  
Shota Arichi ◽  
Tsuyoshi Hachiya ◽  
Anna Ohtera ◽  
Seok-Won Kim ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Translational Research ◽  
Drug Target ◽  
Target Genes ◽  
Healthy Individuals ◽  
Alcohol Sensitivity ◽  
Direct To Consumer ◽  
Disease States ◽  
Multiple Phenotypes ◽  
Related Phenotype

Abstract Objective In order to assess the applicability of a direct-to-consumer (DTC) genetic testing to translational research for obtaining new knowledge on relationships between drug target genes and diseases, we examined possibility of these data by associating SNPs and disease related phenotype information collected from healthy individuals. Results A total of 12,598 saliva samples were collected from the customers of commercial service for SNPs analysis and web survey were conducted to collect phenotype information. The collected dataset revealed similarity to the Japanese data but distinguished differences to other populations of all dataset of the 1000 Genomes Project. After confirmation of a well-known relationship between ALDH2 and alcohol-sensitivity, Phenome-Wide Association Study (PheWAS) was performed to find association between pre-selected drug target genes and all the phenotypes. Association was found between GRIN2B and multiple phenotypes related to depression, which is considered reliable based on previous reports on the biological function of GRIN2B protein and its relationship with depression. These results suggest possibility of using SNPs and phenotype information collected from healthy individuals as a translational research tool for drug discovery to find relationship between a gene and a disease if it is possible to extract individuals in pre-disease states by properly designed questionnaire.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

scholarly journals Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tien-Dzung Tran ◽  
Duc-Tinh Pham
Keyword(s):  
Anticancer Drug ◽  
Drug Target ◽  
Target Genes ◽  
Signaling Network ◽  
Competitive Dynamics ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Dynamics Model ◽  
Cancer Signaling ◽  
The Impact

AbstractEach cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

scholarly journals Identification of key genes and associated pathways in neuroendocrine tumors through bioinformatics analysis and predictions of small drug molecules

2020 ◽  
Author(s):  
Praveenkumar Devarbhavi ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Drug Target ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Regulatory Elements ◽  
Future Research ◽  
High Morbidity ◽  
Hub Genes ◽  
Go Enrichment ◽  
Significant Difference

AbstractNeuroendocrine tumor (NET) is one of malignant cancer and is identified with high morbidity and mortality rates around the world. With indigent clinical outcomes, potential biomarkers for diagnosis, prognosis and drug target are crucial to explore. The aim of this study is to examine the gene expression module of NET and to identify potential diagnostic and prognostic biomarkers as well as to find out new drug target. The differentially expressed genes (DEGs) identified from GSE65286 dataset was used for pathway enrichment analyses and gene ontology (GO) enrichment analyses and protein - protein interaction (PPI) analysis and module analysis. Moreover, miRNAs and transcription factors (TFs) that regulated the up and down regulated genes were predicted. Furthermore, validation of hub genes was performed. Finally, molecular docking studies were performed. DEGs were identified, including 453 down regulated and 459 up regulated genes. Pathway and GO enrichment analysis revealed that DEGs were enriched in sucrose degradation, creatine biosynthesis, anion transport and modulation of chemical synaptic transmission. Important hub genes and target genes were identified through PPI network, modules, target gene - miRNA network and target gene - TF network. Finally, survival analyses, receiver operating characteristic (ROC) curve and RT-PCR validated the significant difference of ATP1A1, LGALS3, LDHA, SYK, VDR, OBSL1, KRT40, WWOX, NINL and PPP2R2B between metastatic NET and normal controls. In conclusion, the DEGs and hub genes with their regulatory elements identified in this study will help us understand the molecular mechanisms underlying NET and provide candidate targets for future research.

scholarly journals Anti-drug Pattern of Drug-resistant Mycobacterium tuberculosis and Analysis of Mutation in Drug-target Genes

2005 ◽  
Vol 79 (6) ◽  
pp. 388-396 ◽  
Author(s):  
Jun MUKAIGAWA ◽  
Miyoko ENDOH ◽  
Yoshitoki YANAGAWA ◽  
Satoshi MOROZUMI
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Target Genes ◽  
Drug Resistant

Identification of potential key genes in anaplastic thyroid cancer using bioinformatics analysis

2021 ◽  
Author(s):  
Zhenzhen Li ◽  
Xiong Chaoliang ◽  
Jin Wei ◽  
Ping Chen ◽  
Yanping Zhang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Thyroid Cancer ◽  
Target Genes ◽  
Collagen Type ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Anaplastic Thyroid Cancer ◽  
Type I ◽  
Tissue Samples ◽  
Km Plotter

Abstract Background Anaplastic thyroid cancer (ATC) has a high degree of malignancy and a poor prognosis. Its incidence accounts for approximately 10–15% of all thyroid cancers. The purpose of this study was to determine the differentially expressed genes (DEGs) of ATC through biometric analysis technology, clarify the potential interactions between them, and screen genes related to the prognosis of ATC. Methods The GSE29265, GSE65144, GSE33630, and GSE85457 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 117 tissue samples (81 normal thyroid tissue samples and 36 ATC samples). The four datasets were integrated and analyzed by the limma packages to obtain DEGs. With these DEGs, we performed gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway analyses using the Database for Annotation, Visualization and Integrated Discovery, protein-protein interaction (PPI) analysis using Cytoscape, and survival analysis using the Kaplan-Meier (KM) plotter. Results. After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated and 450 downregulated genes. Among the hub DEGs obtained in the PPI network, the expression levels of thymidylate synthase (TYMS), fibronectin 1, chordin-like 1, syndecan 2, integrin alpha 2, collagen type I alpha 1 chain, collagen type IX alpha 3 chain (COL9A3), and collagen type XXIII alpha 1 chain (COL23A1) were associated with ATC prognosis. These results showed that the overall survival and recurrence-free survival of TYMS, COL9A3, and COL23A1 were statistically significant in our KM plotter survival analysis; thus, these DEGs may be used as potential biomarkers of ATC. Conclusion This study identified several potential target genes and pathways that may affect the development of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.

Association of Antihypertensive Drug Target Genes With Psychiatric Disorders

2021 ◽  
Author(s):  
Solal Chauquet ◽  
Zhihong Zhu ◽  
Michael C. O’Donovan ◽  
James T. R. Walters ◽  
Naomi R. Wray ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Antihypertensive Drug ◽  
Drug Target ◽  
Target Genes
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