Dataset Information

Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids.

ABSTRACT: It is hypothesized that there may be common characteristics between the genetic regulatory networks of different diseases. To identify these potential similarities, analysis of overlapping differentially expressed genes (DEGs) in several diseases, which are believed to be associated in traditional Chinese medicine (TCM) was performed in the present study. The gene expression profiles associated with depression, papillary thyroid carcinoma (PTC) and uterine fibroids (UF) were preliminarily analyzed using Gene Expression Omnibus 2R tools. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and protein-protein interaction network analysis of the overlapping DEGs in depression, PTC and UF was performed. The results indicated that multiple genes, including activating transcription factor 3 and WSC domain containing 2 and the phosphoinositide 3 kinase/protein kinase b signaling pathway and its downstream effectors may be common factors associated with depression, PTC and/or UF. The neuroendocrine functions of the hypothalamic-pituitary-ovarian axis and hypothalamic-pituitary-thyroid axis were also identified as being mutually associated with depression, PTC and/or UF. However, due to the limitations of DNA microassays, it is recommended that future studies take epigenetics into consideration. Further transcriptomic, methylomic and metabolomic analyses of depression, PTC and UF are also required to identify and elucidate the key associated biomarkers. In conclusion, the results of the current study shed light on the potential genetic interconnections between depression, PTC and UF, which may be beneficial for understanding their underlying coregulatory mechanisms and contributing to the development of homeotherapy based on bioinformatics prediction.

SUBMITTER: Tang H

PROVIDER: S-EPMC5952074 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids.

Tang Hanxiao H Zhang Yongsheng Y

Experimental and therapeutic medicine 20180402 6

It is hypothesized that there may be common characteristics between the genetic regulatory networks of different diseases. To identify these potential similarities, analysis of overlapping differentially expressed genes (DEGs) in several diseases, which are believed to be associated in traditional Chinese medicine (TCM) was performed in the present study. The gene expression profiles associated with depression, papillary thyroid carcinoma (PTC) and uterine fibroids (UF) were preliminarily analyz ...[more]

PMID: 29805500

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Project description:BackgroundThe techniques of DNA microarray and bioinformatic analysis have exhibited efficiency in identifying dysregulated gene expression in human cancers. In this study, we used integrated bioinformatics analysis to improve our understanding of the pathogenesis of papillary thyroid cancer (PTC).MethodsIn this study, we integrated four Gene Expression Omnibus (GEO) datasets, GSE33630, GSE35570, GSE60542 and GSE29265, including 136 normal samples and 157 PTC specimens. The contents of the four datasets are based on GPL570, an Affymetrix Human Genome U133 Plus 2.0 array. Gene ontology (GO) analysis was used to identify characteristic the biological attributes of differentially expressed genes (DEGs) between PTC and normal samples. GO annotation was performed on the DEGs obtained, and the process relied on the DAVID online tool. Kyoto Encyclopedia of Genes and Genomes (KEGG) approach enrichment analyses were adopted to obtain the basic functions of the DEGs. The KOBAS online analysis database was used to complete DEG KEGG pathway comparison and analysis. The search tool (STRING) database was mainly used to search for interacting genes and complete the construction of protein-protein interaction (PPI) networks.ResultsFive hundred-ninety DEGs were consistently expressed in the four datasets; 327 of them were upregulated, while 263 were downregulated. Ten DEGs, including five upregulated (ENTPD1, THRSP, KLK10, ADAMTS9, MIR31HG) and five downregulated (SCARA5, EPHB1, CHRDL1, LOC440934, FOXP2) genes, were randomly selected for q-PCR in our own tissue samples to validate the integrated data. The most highly enriched GO terms were extracellular exosome (GO:0070062), cell adhesion (GO:0070062), positive regulation of gene expression (GO:0010628), and extracellular matrix (ECM) organization (GO:0030198). KEGG pathway analysis was performed, and it was found that abnormally expressed genes effectively participated in pathways such as tyrosine metabolism, complement and coagulation cascades, cell adhesion molecules (CAMs), transcriptional misregulation and ECM-receptor interaction pathways.ConclusionsFive hundred-ninety DEGs were identified in PTC by integrated microarray analysis. The GO and KEGG analyses presented here suggest that the DEGs were enriched in extracellular exosome, tyrosine metabolism, CAMs, complement and coagulation cascades, transcriptional misregulation and ECM-receptor interaction pathways. Functional studies of PTC should focus on these pathways.

Project description:Stroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine - cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

Dataset Information

Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids.

Publications

Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids.

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