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Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma.

ABSTRACT: Aim:Anaplastic thyroid carcinoma (ATC) is the most lethal thyroid malignancy. Identification of novel drug targets is urgently needed. Materials & Methods:We re-analyzed several GEO datasets by systematic retrieval and data merging. Differentially expressed genes (DEGs) were filtered out. We also performed pathway enrichment analysis to interpret the data. We predicted key genes based on protein-protein interaction networks, weighted gene co-expression network analysis and genes' cancer/testis expression pattern. We also further characterized these genes using data from the Cancer Genome Atlas (TCGA) project and gene ontology annotation. Results:Cell cycle-related pathways were significantly enriched in upregulated genes in ATC. We identified TRIP13, DLGAP5, HJURP, CDKN3, NEK2, KIF15, TTK, KIF2C, AURKA and TPX2 as cell cycle-related key genes with cancer/testis expression pattern. We further uncovered that most of these putative key genes were critical components during chromosome segregation. Conclusion:We predicted several key genes harboring potential therapeutic value in ATC. Cell cycle-related processes, especially chromosome segregation, may be the key to tumorigenesis and treatment of ATC.

SUBMITTER: Liu M

PROVIDER: S-EPMC6203939 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma.

Liu Mu M Qiu Yu-Lu YL Jin Tong T Zhou Yin Y Mao Zhi-Yuan ZY Zhang Yong-Jie YJ

PeerJ 20181024

<h4>Aim</h4>Anaplastic thyroid carcinoma (ATC) is the most lethal thyroid malignancy. Identification of novel drug targets is urgently needed.<h4>Materials & methods</h4>We re-analyzed several GEO datasets by systematic retrieval and data merging. Differentially expressed genes (DEGs) were filtered out. We also performed pathway enrichment analysis to interpret the data. We predicted key genes based on protein-protein interaction networks, weighted gene co-expression network analysis and genes' ...[more]

PMID: 30386706

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Project description:The current outbreak of coronavirus disease (COVID-19) has been affecting millions of people and has caused devastating mortality worldwide. Moreover, it is to be noted that cytokine storm has become an important cause for the rising mortality. However, the efforts for the development of drugs, vaccines and treatment has also been intervened due to poor understanding of host's defense mechanism and also due to the development of cytokine storm against this viral infection. Thus, a deeper understanding of the mechanism behind the immune dysregulation and cytokine storm development might give us clues for the clinical management of the severe cases. Hence, we have implemented differential gene expression analysis together with protein-protein interaction and Gene Ontology (GO) studies with the help of Severe Acute respiratory syndrome coronavirus (SARS-CoV) data sets such as GSE1739 and GSE33267 to give us more knowledge on the host immune response for the pathogenic coronavirus which in turn reduces the mortality. A total of 79 differentially-expressed genes (DEGs) were identified in our data set using the filters such as P-value and log2 fold change values of less than 0.05 and 1.5 respectively. Further, network analysis and GO studies showed that differential expression of two hub genes namely ELANE and LTF which could induce higher levels of pro-inflammatory cytokines in the lungs. We are certain that differential expression of ELANE and LTF results in an excessive inflammatory reaction known as the cytokine storm and ultimately leading to death. Therefore, targeting these key drivers of cytokine storm genes appears to be the potential therapeutic targets for combating the Severe Acute respiratory syndrome coronavirus - 2 (SARS-CoV-2) infection ultimately resulting in reduced mortality. Indeed, this predictive view may open new insights for designing an immune intervention for COVID-19 in the near future resulting in the mitigation of mortality rate.

Dataset Information

Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma.

Publications

Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma.

Similar Datasets

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