Data Mining for Drug Repurposing and New Targets Identification for Radioprotection

2017 ◽  
Vol 2 (3) ◽  
pp. 343 ◽  
Author(s):  
Yogesh Kumar Verma ◽  
Gurudutta Gangenahalli
Keyword(s):  
Data Mining ◽  
Target Genes ◽  
Tissue Injury ◽  
Drug Repurposing ◽  
Gene Interaction ◽  
Pharmacological Agents ◽  
New Targets ◽  
Cancer Pathways ◽  
Interaction Database ◽  
Us Fda

<p>Ionising radiation (IR) is responsible for various types of tissue injury leading to morbidity at low doses and mortality at high radiation exposure. Although many radioprotective and pharmacological agents are being tested for decreasing radiation injury, however, the availability of Amifostine as the only clinically used radioprotector with limited indication has prompted us to find out new potential molecules through drugs repurposing for protecting or decreasing radiation damage by data mining. In this work we have used text-mining based network generation approach to find out the gene targets of radioprotectors under evaluation by Agilent Literature Search app in Cytoscape. Extracted genes were evaluated for their association with radiation in Radiation Genes database. These genes were searched against therapeutic drugs and molecules under clinical trial in the Drug Gene Interaction database. We found that most of the radiation target genes were involved in cell death, proliferation, homeostasis, cell cycle and cancer pathways. Many of these genes were druggable and could be targeted by the drugs under clinical research, whereas there were few genes (new targets), which were never considered for radioprotective drug development. This study would likely help in repurposing of identified drugs for use in the event of radiation fallout, keeping in mind that no radiation medical countermeasure for acute radiation syndrome has been approved by the US FDA for use in humans. Results also revealed new target genes for drug targeting and indicates use of similar pipeline in other pathologies for drug repurposing and development.<br /><br /></p>

Discovery of Biomarkers in Hepatocellular Carcinoma Metastasis Using Bioinformatic Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Jinrui Wei ◽  
Haroon ur Rashid ◽  
Lichuan Wu
Keyword(s):  
Data Mining ◽  
Liver Cancer ◽  
Mirna Target ◽  
Cancer Metastasis ◽  
Target Genes ◽  
Interaction Analysis ◽  
Gene Interaction ◽  
Bioinformatic Analysis ◽  
Mirna Microarray ◽  
Carcinoma Metastasis

Background: Liver cancer is one of the most deadly malignancies worldwide. Tumor metastasis is the main cause of liver cancer related death. So far the mechanism of liver cancer metastasis are far away from fully elucidated. In this study, we aim to discover key regulators involved in liver cancer metastasis by data mining. Methods: Two different types of data including mRNA microarray (GSE6222 and GSE6764) and miRNA microarray (GSE67138) were analyzed. A total of 83 intersectant differently expressed genes (DEGs) with same expression pattern in GSE6222 and GSE6764 were identified. One hundred and thirty one differently expressed miRNAs (DEMs) were identified in GSE 67138. Further, a total of 26 pairs of miRNAtarget including 18 DEMs and 13 DEGs were identified as critical miRNA-target axis via miRNA-target gene interaction analysis. Result and Conclusion: Among the 18 DEMs and 13 DEGs, 10 miRNAs and 10 target genes are significantly correlated with patients’ survival (p < 0.05). Our results and methods might be interesting for data mining and helpful for further experimental functional validation.

scholarly journals MCGA: a multi-strategy conditional gene-based association framework integrating with isoform-level expression profiles reveals new susceptible and druggable candidate genes of schizophrenia

2021 ◽  
Author(s):  
Xiangyi Li ◽  
Lin Jiang ◽  
Chao Xue ◽  
Mulin Jun Li ◽  
Miaoxin Li
Keyword(s):  
Target Genes ◽  
Type I Error ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Gene Interaction ◽  
Susceptibility Genes ◽  
Error Rates ◽  
Type I ◽  
Interaction Database ◽  
Gene Level

Linkage disequilibrium and disease-associated variants in non-coding regions make it difficult to distinguish truly associated genes from redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called MCGA that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. MCGA initially integrated two conventional strategies to map genetic variants to genes, i.e., mapping a variant to its physically nearby gene and mapping a variant to a gene if the variant is a gene-level expression quantitative trait locus (eQTL) of the gene. We further performed a simulation study and demonstrated that the isoform-level eQTL was more powerful than the gene-level eQTL in the association analysis. Then the third strategy, i.e., mapping a variant to a gene if the variant is an isoform-level eQTL of the gene, was also integrated with MCGA. We applied MCGA to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes identified by MCGA were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the drug-gene interaction database (DGIdb). More importantly, nine susceptibility genes were the target genes of multiple approved antipsychotics in DrugBank. Comparing the susceptibility genes identified by the above three strategies implied that strategy based on isoform-level eQTL could be an important supplement for the other two strategies and help predict more candidate susceptibility isoforms and genes for complex diseases in a multi-tissue context.

scholarly journals A circular RNAs dataset landscape reveals potential signatures for the detection and prognosis of early-stage lung adenocarcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhiying Chen ◽  
Jiahui Wei ◽  
Min Li ◽  
Yongjuan Zhao
Keyword(s):  
Functional Analysis ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Target Genes ◽  
Interaction Analysis ◽  
Early Stage ◽  
Gene Interaction ◽  
Gene Expression Omnibus ◽  
Circular Rnas ◽  
Cerna Network

Abstract Background This study aimed to identify potential circular ribonucleic acid (circRNA) signatures involved in the pathogenesis of early-stage lung adenocarcinoma (LAC). Methods The circRNA sequencing dataset of early-stage LAC was downloaded from the Gene Expression Omnibus database. First, the differentially expressed circRNAs (DEcircRNAs) between tumour and non-tumour tissues were screened. Then, the corresponding miRNAs and their target genes were predicted. In addition, prognosis-related genes were identified using survival analysis and further used to build a network of competitive endogenous RNAs (ceRNAs; DEcircRNA–miRNA–mRNA). Finally, the functional analysis and drug–gene interaction analysis of mRNAs in the ceRNA network was performed. Results A total of 35 DEcircRNAs (30 up-regulated and 5 down-regulated circRNAs) were identified. Moreover, 135 DEcircRNA–miRNA and 674 miRNA–mRNA pairs were predicted. The survival analysis of these target mRNAs revealed that 60 genes were significantly associated with survival outcomes in early-stage LAC. Of these, high levels of PSMA 5 and low levels of NAMPT, CPT 2 and TNFSF11 exhibited favourable prognoses. In addition, the DEcircRNA–miRNA–mRNA network was constructed, containing 5 miRNA–circRNA (hsa_circ_0092283/hsa-miR-762/hsa-miR-4685-5p; hsa_circ_0070610/hsa-let-7a-2-3p/hsa-miR-3622a-3p; hsa_circ_0062682/hsa-miR-4268) and 60 miRNA–mRNA pairs. Functional analysis of the genes in the ceRNA network showed that they were primarily enriched in the Wnt signalling pathway. Moreover, PSMA 5, NAMPT, CPT 2 and TNFSF11 had strong correlations with different drugs. Conclusion Three circRNAs (hsa_circ_0062682, hsa_circ_0092283 and hsa_circ_0070610) might be potential novel targets for the diagnosis of early-stage LAC.

scholarly journals Research for Expression and Prognostic Value of GABRD in Colon Cancer and Coexpressed Gene Network Construction Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tao Liu ◽  
Yuejun Fang
Keyword(s):  
Data Mining ◽  
Colon Cancer ◽  
Normal Tissue ◽  
Predictive Ability ◽  
Gene Interaction ◽  
Notch Signaling Pathway ◽  
Cancer Tissue ◽  
Gamma Aminobutyric Acid ◽  
Colon Cancer Tissue ◽  
Go Annotation

Colon cancer is one of the top five cancers with the highest incidence rate in the world. In order to better understand the pathogenesis and progression of colon cancer, it is still necessary to investigate the abnormally expressed genes in cancer tissue. In this study, the Oncomine database was used for expression analysis, and it was found that the expression level of gamma-aminobutyric acid type A receptor subunit delta (GABRD) gene was upregulated in colon cancer tissue compared with that in normal tissue. UALCAN was used to analyze the expression of GABRD in different groups of age, gender, cancer stage, N stage, and histological subtype. Then, it was also found that the expression of GABRD in each subgroup of colon cancer tissue was all high compared with that in normal tissue. LinkedOmics was used to screen out the differentially expressed genes related to GABRD expression in colon cancer. GO annotation and KEGG pathway enrichment analyses found that the correlated genes may be related to breast cancer, human papillomavirus infection, Notch signaling pathway, and other pathways. Thereafter, GSEA was performed to obtain GABRD-related kinases, miRNAs, and transcription factors, and gene interaction networks were constructed. It was found that GABRD may be involved in cell cycle regulation. Finally, websites like GEPIA were used to detect the predictive ability of GABRD on the prognosis of patients with colon cancer. Kaplan-Meier analysis suggested that the upregulation of GABRD expression was related to the poor prognosis of patients with colon cancer. Overall, in this study, the potential role and prognostic ability of GABRD in colon cancer were explored through data mining, which can be a clue for further research on GABRD.

Cancer Pathway Network Analysis Using Cellular Automata

Biotechnology ◽  
2019 ◽  
pp. 2039-2053
Author(s):  
Kalyan Mahata ◽  
Anasua Sarkar
Keyword(s):  
Data Mining ◽  
Cellular Automata ◽  
High Speed ◽  
Discrete Dynamical System ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Pathway Network ◽  
Cancer Pathways ◽  
Huge Data ◽  
Functional Involvement

Identification of cancer pathways is the central goal in the cancer gene expression data analysis. Data mining refers to the process analyzing huge data in order to find useful pattern. Data classification is the process of identifying common properties among a set of objects and grouping them into different classes. A cellular automaton is a discrete, dynamical system with simple uniformly interconnected cells. Cellular automata are used in data mining for reasons such as all decisions are made locally depend on the state of the cell and the states of neighboring cells. A high-speed, low-cost pattern-classifier, built around a sparse network referred to as cellular automata (ca) is implemented. Lif-stimulated gene regulatory network involved in breast cancer has been simulated using cellular automata to obtain biomarker genes. Our model outputs the desired genes among inputs with highest priority, which are analysed for their functional involvement in relevant oncological functional enrichment analysis. This approach is a novel one to discover cancer biomarkers in cellular spaces.

scholarly journals Genomic variant annotation workflow for clinical applications

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1963 ◽  
Author(s):  
Thomas Thurnherr ◽  
Franziska Singer ◽  
Daniel J. Stekhoven ◽  
Niko Beerenwinkel
Keyword(s):  
Gene Interaction ◽  
Clinical Applications ◽  
Interaction Data ◽  
Bioconductor Package ◽  
Variant Annotation ◽  
Interaction Database ◽  
Genomic Aberrations ◽  
Genomic Variant ◽  
Different Levels ◽  
Generation Sequencing

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different resources and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, rDGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

scholarly journals Systematic Elucidation of the Potential Mechanisms of Core Chinese Materia Medicas in Treating Liver Cancer Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhulin Wu ◽  
Lina Yang ◽  
Li He ◽  
Lianan Wang ◽  
Lisheng Peng
Keyword(s):  
Data Mining ◽  
Liver Cancer ◽  
Signaling Pathways ◽  
Target Genes ◽  
Primary Liver Cancer ◽  
Network Pharmacology ◽  
Data Mining Technique ◽  
Antitumor Effects ◽  
Bioactive Ingredients ◽  
Common Target

Objective. In this study, the data mining method was used to screen the core Chinese materia medicas (CCMMs) against primary liver cancer (PLC), and the potential mechanisms of CCMMs in treating PLC were analyzed based on network pharmacology. Methods. Traditional Chinese medicine (TCM) prescriptions for treating PLC were obtained from a famous TCM doctor in Shenzhen, China. According to the data mining technique, the TCM Inheritance Support System (TCMISS) was applied to excavate the CCMMs in the prescriptions. Then, bioactive ingredients and corresponding targets of CCMMs were collected using three different TCM online databases, and target genes of PLC were obtained from GeneCards and OMIM. Afterwards, common targets of CCMMs and PLC were screened. Furthermore, a network of CCMMs bioactive ingredients and common target gene was constructed by Cytoscape 3.7.1, and gene ontology (GO) and signaling pathways analyses were performed to explain the mechanism of CCMMs in treating PLC. Besides, protein-protein interaction (PPI) analysis was used to identify key target genes of CCMMs, and the prognostic value of key target genes was verified using survival analysis. Results. A total of 15 high-frequency Chinese materia medica combinations were found, and CCMMs (including Paeoniae Radix Alba, Radix Bupleuri, Macrocephalae Rhizoma, Coicis Semen, Poria, and Curcumae Radix) were identified by TCMISS. A total of 40 bioactive ingredients (e.g., quercetin, kaempferol, and naringenin) of CCMMs were obtained, and 202 common target genes of CCMMs and PLC were screened. GO analysis indicated that biological processes of CCMMs were mainly involved in response to drug, response to ethanol, etc. Pathway analysis demonstrated that CCMMs exerted its antitumor effects by acting on multiple signaling pathways, including PI3K-Akt, TNF, and MAPK pathways. Also, some key target genes of CCMMs were determined by PPI analysis, and four genes (MAPK3, VEGFA, EGF, and EGFR) were found to be correlated with survival in PLC patients. Conclusion. Based on data mining and network pharmacology methods, our results showed that the therapeutic effect of CCMMs on PLC may be realized by acting on multitargets and multipathways related to the occurrence and development of PLC.

scholarly journals DGIdb 3.0: a redesign and expansion of the drug–gene interaction database

2017 ◽  
Vol 46 (D1) ◽  
pp. D1068-D1073 ◽  
Author(s):  
Kelsy C Cotto ◽  
Alex H Wagner ◽  
Yang-Yang Feng ◽  
Susanna Kiwala ◽  
Adam C Coffman ◽  
...  
Keyword(s):  
Gene Interaction ◽  
Interaction Database

scholarly journals Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhijie Cui ◽  
Hong Kang ◽  
Kailin Tang ◽  
Qi Liu ◽  
Zhiwei Cao ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Target Genes ◽  
Pharmacophore Model ◽  
Pregnane X Receptor ◽  
Rank Aggregation ◽  
Pharmacophore Modelling ◽  
Interaction Database ◽  
Drug Metabolizing Enzyme ◽  
Metabolizing Enzyme

The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR’s agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

Sign in / Sign up

Export Citation Format

Share Document