Dataset Information

Bioinformatics analysis of key genes and latent pathway interactions based on the anaplastic thyroid carcinoma gene expression profile.

ABSTRACT: Anaplastic thyroid carcinoma (ATC) is an aggressive malignant disease in older adults with a high mortality rate. The present study aimed to examine several key genes and pathways, which are associated with ATC. The GSE33630 gene expression profile was downloaded from the Gene Expression Omnibus database, which included 11 ATC and 45 normal thyroid samples. The differentially expressed genes (DEGs) in ATC were identified using the Limma package in R. The Gene Ontology functions and Kyoto Encyclopedia of Genes and Genomes pathways of the selected DEGs were enriched using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the DEGs was constructed to select significant modules. Furthermore, a latent pathway interactive network was constructed to select the significant pathways associated with ATC. A total of 665 DEGs in the ATC samples were screened, and four significant modules were selected from the PPI network. The DEGs in the four modules were enriched in several functions and pathways. In addition, 29 significant pathways associated with ATC were selected, and he Toll-like receptor (TLR) signaling pathway, extracellular matrix (ECM)-receptor interaction and cytokine-cytokine interaction pathway were identified as important pathways. FBJ murine osteosarcoma viral oncogene homolog (FOS), chemokine C-X-C motif ligand 10 (CXCL10), collagen type V ?1 (COL5A1) and chemokine (C-C motif) ligand 28 (CCL28) were the key DEGs involved in these significant pathways. The data obtained in the present study revealed that the TLR signaling pathway, ECM-receptor interaction and cytokine-cytokine receptor interaction pathway, and the FOS, CXCL10, COL5A1, COL11A1 and CCL28 genes have different roles in the progression of ATC, and these may be used as therapeutic targets for ATC.

SUBMITTER: Huang Y

PROVIDER: S-EPMC5396846 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioinformatics analysis of key genes and latent pathway interactions based on the anaplastic thyroid carcinoma gene expression profile.

Huang Yun Y Tao Yiming Y Li Xinying X Chang Shi S Jiang Bo B Li Feng F Wang Zhi-Ming ZM

Oncology letters 20161130 1

Anaplastic thyroid carcinoma (ATC) is an aggressive malignant disease in older adults with a high mortality rate. The present study aimed to examine several key genes and pathways, which are associated with ATC. The GSE33630 gene expression profile was downloaded from the Gene Expression Omnibus database, which included 11 ATC and 45 normal thyroid samples. The differentially expressed genes (DEGs) in ATC were identified using the Limma package in R. The Gene Ontology functions and Kyoto Encyclo ...[more]

PMID: 28428828

Similar Datasets

Project description:BACKGROUND To provide a better understanding of anaplastic thyroid carcinoma (ATC) at the molecular level, this study aimed to identify the genes and key pathways associated with ATC by using integrated bioinformatics analysis. MATERIAL AND METHODS Based on the microarray data GSE9115, GSE65144, and GSE53072 derived from the Gene Expression Omnibus, the differentially expressed genes (DEGs) between ATC samples and normal controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein-protein interaction (PPI) network was constructed and visualized, with which the hub gene nodes were screened out. Finally, modules analysis for the PPI network was performed to further investigate the potential relationships between DEGs and ATC. RESULTS A total of 537 common DEGs were screened out from all 3 datasets, among which 247 genes were upregulated and 275 genes were downregulated. GO analysis indicated that upregulated DEGs were mainly involved in cell division and mitotic nuclear division and the downregulated DEGs were significantly enriched in ventricular cardiac muscle cell action potential. KEGG pathway analysis showed that the upregulated DEGs were mainly enriched in cell cycle and ECM-receptor interaction and the downregulated DEGs were mainly enriched in thyroid hormone synthesis, insulin resistance, and pathways in cancer. The top 10 hub genes in the constructed PPI network were CDK1, CCNB1, TOP2A, AURKB, CCNA2, BUB1, AURKA, CDC20, MAD2L1, and BUB1B. The modules analysis showed that genes in the top 2 significant modules of PPI network were mainly associated with mitotic cell cycle and positive regulation of mitosis, respectively. CONCLUSIONS We identified a series of key genes along with the pathways that were most closely related with ATC initiation and progression. Our results provide a more detailed molecular mechanism for the development of ATC, shedding light on the potential biomarkers and therapeutic targets.

Project description:Background Since anaplastic thyroid carcinoma (ATC) has rapid progression and a poor outcome, identification of the key genes and underlying mechanisms of ATC is required. Methods Gene expression profiles of GSE29265 and GSE33630 were available from the Gene Expression Omnibus database. The two profile datasets included 19 ATC tissues, 55 normal thyroid tissues and 59 papillary thyroid cancer (PTC) tissues. Differentially expressed genes (DEGs) between ATC tissues and normal thyroid tissues as well as ATC tissues and PTC tissues were identified using the GEO2R tool. Common DEGs between the two datasets were selected via Venn software online. Then, we applied the Database for Annotation, Visualization and Integrated Discovery for Kyoto Encyclopedia of Gene and Genome pathway and gene ontology (GO) analyses. Additionally, protein-protein interactions (PPIs) of these DEGs were visualized via Cytoscape with Search Tool for the Retrieval of Interacting Genes. In the PPI networks analyzed by the Molecular Complex Detection plug-in, all 54 upregulated core genes were selected. Furthermore, Kaplan-Meier analysis was applied to analyze overall survival based on these 54 genes. Then, we used the DrugBank database to identify drug relationships for the 54 genes. Additionally, we validated the correlations between genes enriched in pathways and genes identified as prognosis biomarkers of THCA by Gene Expression Profiling Interactive Analysis. Results Four genes (CCNB1, CCNB2, CDK1 and CHEK1) involved cell cycle arrest and DNA repair were significantly enriched in the G2/M phase of the cell cycle pathway and before G2 phase arrest of the P53 pathway. Inhibitors of CHEK1, CDK1 and TOP2A were identified in the DrugBank database. ANLN, DEPDC1, KIF2C, CENPN, TACC3 CCNB2 and CDC6 were hypothesized to be prognostic biomarkers of ATC. Furthermore, CCNB1, CCNB2, CDK1 and CHEK1 were significantly positively associated with these prognosis genes. Conclusions CCNB1, CCNB2, CDK1 and CHEK1 may be key genes involved cell cycle arrest and DNA damage repair in ATC. Further studies are required to confirm the contributions of the identified genes to ATC progression and survival.

Project description:Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and rapidly lethal tumors. However, limited advances have been made to prolong the survival and to reduce the mortality over the last decades. Therefore, identifying the master regulators underlying ATC progression is desperately needed. In our present study, three datasets including GSE33630, GSE29265, and GSE65144 were retrieved from Gene Expression Omnibus with a total of 32 ATC samples and 78 normal thyroid tissues. A total of 1804 consistently changed differentially expressed genes (DEGs) were identified from three datasets. KEGG pathways enrichment suggested that upregulated DEGs were mainly enriched in ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, key gene modules in PPI network were identified by Cytoscape plugin MCODE and they were mainly associated with DNA replication, cell cycle process, collagen fibril organization, and regulation of leukocyte migration. Additionally, TOP2A, CDK1, CCNB1, VEGFA, BIRC5, MAPK1, CCNA2, MAD2L1, CDC20, and BUB1 were identified as hub genes of the PPI network. Interestingly, module analysis showed that 8 out of 10 hub genes participated in Module 1 network and more than 70% genes of Module 2 consisted of collagen family members. Notably, transcription factors (TFs) regulatory network analysis indicated that E2F7, FOXM1, and NFYB were master regulators of Module 1, while CREB3L1 was the master regulator of Module 2. Experimental validation showed that CREB3L1, E2F7, and FOXM1 were significantly upregulated in ATC tissue and cell line when compared with normal thyroid group. In conclusion, the TFs regulatory network provided a more detail molecular mechanism underlying ATC occurrence and progression. TFs including E2F7, FOXM1, CREB3L1, and NFYB were likely to be master regulators of ATC progression, suggesting their potential role as molecular therapeutic targets in ATC treatment.

Project description:Thyroid carcinoma (TC) is one of the most common types of endocrine neoplasm with poor prognosis due to its aggressive behavior. Biomarkers for early diagnosis and prevention of TC are in urgent demand. By using a bioinformatics analysis, the present study aimed to identify essential genes and pathways associated with TC. First, the GSE27155 and GSE50901 expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were obtained using the two microarray datasets and further subjected to integrated analysis. A gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed 45 common DEGs in the two datasets. GO and KEGG pathway analysis indicated that the biological functions of the DEGs included protein binding, cardiac muscle cell potential involved in contraction, aldehyde dehydrogenase activity, the TGF-β receptor signaling pathway and the canonical Wnt signaling pathway. A protein-protein interaction network was also constructed and visualized to display the nodes of the top 9 up- and 36 downregulated common DEGs. The integrated bioinformatics analysis indicated that potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) was the most significantly upregulated DEG. The transcriptional levels of KCNJ2 were confirmed to be elevated in TC tissues compared with those in normal tissues using reverse transcription-quantitative PCR analysis. Furthermore, the expression level of KCNJ2 was significantly associated with the 5-year survival rate of patients with TC, which was determined using the Kaplan-Meier method. In TC cell lines, KCNJ2 was also upregulated as compared with that in a normal control cell line. Finally, small interfering RNA was used to knock down the expression of KCNJ2, which was demonstrated to inhibit cell proliferation, migration and invasion, while increasing apoptosis in TC cells. In conclusion, in the present study, KCNJ2 was screened as an oncogene with a crucial role in TC development and progression and may represent a promising candidate biomarker and therapeutic target for TC.

Project description:BackgroundAnaplastic thyroid carcinoma (ATC) accounts for only 3% of thyroid cancers, yet strikingly, it accounts for almost 40% of thyroid cancer deaths. Currently, no effective therapies exist. In an effort to identify ATC-specific therapeutic targets, we analyzed global gene expression data from multiple studies to identify ATC-specific dysregulated genes.MethodsThe National Center for Biotechnology Information Gene Expression Omnibus database was searched for high-throughput gene expression microarray studies from human ATC tissue along with normal thyroid and/or papillary thyroid cancer (PTC) tissue. Gene expression levels in ATC were compared with normal thyroid or PTC using seven separate comparisons, and an ATC-specific gene set common in all seven comparisons was identified. We investigated these genes for their biological functions and pathways.ResultsThere were three studies meeting inclusion criteria, (including 32 ATC patients, 69 PTC, and 75 normal). There were 259 upregulated genes and 286 downregulated genes in ATC with at least two-fold change in all seven comparisons. Using a five-fold filter, 36 genes were upregulated in ATC, while 40 genes were downregulated. Of the 10 top globally upregulated genes in ATC, 4/10 (MMP1, ANLN, CEP55, and TFPI2) are known to play a role in ATC progression; however, 6/10 genes (TMEM158, CXCL5, E2F7, DLGAP5, MME, and ASPM) had not been specifically implicated in ATC. Similarly, 3/10 (SFTA3, LMO3, and C2orf40) of the most globally downregulated genes were novel in this context, while 7/10 genes (SLC26A7, TG, TSHR, DUOX2, CDH1, PDE8B, and FOXE1) have been previously identified in ATC. We experimentally validated a significant correlation for seven transcription factors (KLF16, SP3, ETV6, FOXC1, SP1, EGFR1, and MAFK) with the ATC-specific genes using microarray analysis of ATC cell lines. Ontology clustering of globally altered genes revealed that "mitotic cell cycle" is highly enriched in the globally upregulated gene set (44% of top upregulated genes, p-value <10-30).ConclusionsBy focusing on globally altered genes, we have identified a set of consistently altered biological processes and pathways in ATC. Our data are consistent with an important role for M-phase cell cycle genes in ATC, and may provide direction for future studies to identify novel therapeutic targets for this disease.

Dataset Information

Bioinformatics analysis of key genes and latent pathway interactions based on the anaplastic thyroid carcinoma gene expression profile.

Publications

Bioinformatics analysis of key genes and latent pathway interactions based on the anaplastic thyroid carcinoma gene expression profile.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets