Dataset Information

Identification of potential agents for thymoma by integrated analyses of differentially expressed tumour-associated genes and molecular docking experiments.

ABSTRACT: Thymoma, derived from the epithelial cells of the thymus, is a rare malignant tumour type. Following diagnosis with thymoma, patients generally undergo surgical treatment. However, patients with advanced-stage disease are only candidates for chemotherapy and have poor survival. Therefore, it is urgently required to explore effective chemotherapeutic agents for the treatment of thymoma. In the present study, a Bioinformatics analysis was performed to identify novel drugs for thymoma. Differentially expressed genes (DEGs) in thymoma were obtained by Gene Expression Profiling Interactive Analysis. Subsequently, these genes were processed by Connectivity Map analysis to identify suitable compounds. In addition, Metascape software was used to verify drug and target binding. Molecular docking technology was used to verify drug and target binding. Finally, absorption, distribution, metabolism and excretion parameters in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database were used for drug screening and for evaluation of the potential clinical value. In total, 2,447 DEGs, including 2,204 upregulated and 243 downregulated genes, were identified from 118 thymoma patients and 339 normal samples. The top 10 drugs displaying the most significant negative correlations were fulvestrant, hesperetin, zidovudine, hydrocortisone, rolitetracycline, ellipticine, sirolimus, quinisocaine, oestradiol (estradiol) and harmine. The predicted targets of these drugs were then confirmed. The score for the association between estrogen receptor 1 (ESR1) and fulvestrant was 0.99. According to the molecular docking analysis, the total scores for the interaction between ESR1 were 10.26, and those for the interaction between tamoxifen and ESR1 were 6.60. The oral bioavailability (%), drug-likeness and drug half-life for hesperetin were 70.31, 0.27 and 15.78, respectively; those for oestradiol were 53.56, 0.32 and 3.50, respectively; and those for harmine were 56.80, 0.13 and 5.04, respectively. In conclusion, several potential therapeutic drugs for thymoma were identified in the present study. The results suggested that the compounds, including fulvestrant, estradiol, hesperetin and ellipticine, represent the most likely drugs for the treatment of thymoma. Future studies should focus on testing these novel compounds in vitro and in vivo.

SUBMITTER: Wang XD

PROVIDER: S-EPMC6704584 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of potential agents for thymoma by integrated analyses of differentially expressed tumour-associated genes and molecular docking experiments.

Wang Xiao-Dong XD Lin Peng P Li Yu-Xin YX Chen Gang G Yang Hong H He Yun Y Li Qing Q Liu Ruo-Chuan RC

Experimental and therapeutic medicine 20190726 3

Thymoma, derived from the epithelial cells of the thymus, is a rare malignant tumour type. Following diagnosis with thymoma, patients generally undergo surgical treatment. However, patients with advanced-stage disease are only candidates for chemotherapy and have poor survival. Therefore, it is urgently required to explore effective chemotherapeutic agents for the treatment of thymoma. In the present study, a Bioinformatics analysis was performed to identify novel drugs for thymoma. Differential ...[more]

PMID: 31452699

Similar Datasets

Project description:BACKGROUND:One of the most striking features of the childhood malignancy neuroblastoma (NB) is its clinical heterogeneity. Although there is a great need for better clinical and biological markers to distinguish between tumours with different severity and to improve treatment, no clear-cut prognostic factors have been found. Also, no major NB tumour suppressor genes have been identified. METHODS:In this study we performed expression analysis by quantitative real-time PCR (QPCR) on primary NB tumours divided into two groups, of favourable and unfavourable outcome respectively. Candidate genes were selected on basis of lower expression in unfavourable tumour types compared to favourables in our microarray expression analysis. Selected genes were studied in two steps: (1) using TaqMan Low Density Arrays (TLDA) targeting 89 genes on a set of 12 NB tumour samples, and (2) 12 genes were selected from the TLDA analysis for verification using individual TaqMan assays in a new set of 13 NB tumour samples. RESULTS:By TLDA analysis, 81 out of 87 genes were found to be significantly differentially expressed between groups, of which 14 have previously been reported as having an altered gene expression in NB. In the second verification round, seven out of 12 transcripts showed significantly lower expression in unfavourable NB tumours, ATBF1, CACNA2D3, CNTNAP2, FUSIP1, GNB1, SLC35E2, and TFAP2B. The gene that showed the highest fold change in the TLDA analysis, POU4F2, was investigated for epigenetic changes (CpG methylation) and mutations in order to explore the cause of the differential expression. Moreover, the fragile site gene CNTNAP2 that showed the largest fold change in verification group 2 was investigated for structural aberrations by copy number analysis. However, the analyses of POU4F2 and CNTNAP2 showed no genetic alterations that could explain a lower expression in unfavourable NB tumours. CONCLUSION:Through two steps of verification, seven transcripts were found to significantly discriminate between favourable and unfavourable NB tumours. Four of the transcripts, CACNA2D3, GNB1, SLC35E2, and TFAP2B, have been observed in previous microarray studies, and are in this study independently verified. Our results suggest these transcripts to be markers of malignancy, which could have a potential usefulness in the clinic.

Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Project description:BACKGROUND: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. RESULTS: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. CONCLUSION: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

Dataset Information

Identification of potential agents for thymoma by integrated analyses of differentially expressed tumour-associated genes and molecular docking experiments.

Publications

Identification of potential agents for thymoma by integrated analyses of differentially expressed tumour-associated genes and molecular docking experiments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets