Dataset Information

Transcriptome network of the papillary thyroid carcinoma radiation marker CLIP2.

ABSTRACT: BACKGROUND:We present a functional gene association network of the CLIP2 gene, generated by de-novo reconstruction from transcriptomic microarray data. CLIP2 was previously identified as a potential marker for radiation induced papillary thyroid carcinoma (PTC) of young patients in the aftermath of the Chernobyl reactor accident. Considering the rising thyroid cancer incidence rates in western societies, potentially related to medical radiation exposure, the functional characterization of CLIP2 is of relevance and contributes to the knowledge about radiation-induced thyroid malignancies. METHODS:We generated a transcriptomic mRNA expression data set from a CLIP2-perturbed thyroid cancer cell line (TPC-1) with induced CLIP2 mRNA overexpression and siRNA knockdown, respectively, followed by gene-association network reconstruction using the partial correlation-based approach GeneNet. Furthermore, we investigated different approaches for prioritizing differentially expressed genes for network reconstruction and compared the resulting networks with existing functional interaction networks from the Reactome, Biogrid and STRING databases. The derived CLIP2 interaction partners were validated on transcript and protein level. RESULTS:The best reconstructed network with regard to selection parameters contained a set of 20 genes in the 1st neighborhood of CLIP2 and suggests involvement of CLIP2 in the biological processes DNA repair/maintenance, chromosomal instability, promotion of proliferation and metastasis. Peptidylprolyl Isomerase Like 3 (PPIL3), previously identified as a potential direct interaction partner of CLIP2, was confirmed in this study by co-expression at the transcript and protein level. CONCLUSION:In our study we present an optimized preselection approach for genes subjected to gene-association network reconstruction, which was applied to CLIP2 perturbation transcriptome data of a thyroid cancer cell culture model. Our data support the potential carcinogenic role of CLIP2 overexpression in radiation-induced PTC and further suggest potential interaction partners of the gene.

SUBMITTER: Selmansberger M

PROVIDER: S-EPMC7392692 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Transcriptome network of the papillary thyroid carcinoma radiation marker CLIP2.

Selmansberger Martin M Michna Agata A Braselmann Herbert H Höfig Ines I Schorpp Kenji K Weber Peter P Anastasov Natasa N Zitzelsberger Horst H Hess Julia J Unger Kristian K

Radiation oncology (London, England) 20200729 1

<h4>Background</h4>We present a functional gene association network of the CLIP2 gene, generated by de-novo reconstruction from transcriptomic microarray data. CLIP2 was previously identified as a potential marker for radiation induced papillary thyroid carcinoma (PTC) of young patients in the aftermath of the Chernobyl reactor accident. Considering the rising thyroid cancer incidence rates in western societies, potentially related to medical radiation exposure, the functional characterization o ...[more]

PMID: 32727620

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Project description:The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA?sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE?miRNAs), long non?coding RNAs (DE?lncRNAs) and genes (DEGs). Subsequently, lncRNA?miRNA, miRNA?gene (that is, miRNA?mRNA) and gene?gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA?mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE?lncRNAs, 917 DEGs and 30 DE?miRNAs were screened. The miRNA?mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand?receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine?cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA?206 and kallikrein?related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

Project description:The incidence rate of papillary thyroid carcinoma (PTC) has rapidly increased in the recent decades, and the microRNA (miRNA) is one of the potential biomarkers in this cancer. Despite good prognosis, certain features such as lymph node metastasis (LNM) and BRAF V600E mutation are associated with a poor outcome. More than 50% of PTC patients present with LNM and BRAF V600E is the most common mutation identified in this cancer. The molecular mechanisms underlying these features are yet to be elucidated. This study aims to elucidate miRNA-genes interaction networks in PTC with or without LNM and to determine the association of BRAF V600E mutation with miRNAs and genes expression profiles. Next generation sequencing was performed to characterize miRNA and gene expression profiles in 20 fresh frozen tumor and the normal adjacent tissues of PTC with LNM positive (PTC LNM-P) and PTC without LNM (PTC LNN). BRAF V600E was genotyped using Sanger sequencing. Bioinformatics integration and pathway analysis were performed to determine the regulatory networks involved. Based on network analysis, we then investigated the association between miRNA and gene biomarkers, and pathway enrichment analysis was performed to study the role of candidate biomarkers. We identified 138 and 43 significantly deregulated miRNAs (adjusted p value?<?0.05; log2 fold change???-1.0 or ?1.0) in PTC LNM-P and PTC LNN compared to adjacent normal tissues, respectively. Ninety-six miRNAs had significant expression ratios of 3p-to-5p in PTC LNM-P as compared to PTC LNN. In addition, ribosomal RNA-reduced RNA sequencing analysis revealed 699 significantly deregulated genes in PTC LNM-P versus normal adjacent tissues, 1,362 genes in PTC LNN versus normal adjacent tissue, and 1,576 genes in PTC LNM-P versus PTC LNN. We provide the evidence of miRNA and gene interactions, which are involved in LNM of papillary thyroid cancer. These findings may lead to better understanding of carcinogenesis and metastasis processes. This study also complements the existing knowledge about deregulated miRNAs in papillary thyroid carcinoma development.

Project description:Background:The primary goal of papillary thyroid cancer (PTC) management was to stratify patients at pre- and post-surgical level to identify the small proportion of cases with potentially aggressive disease. Purpose:The aim of our study is to evaluate the possible role of programmed cell death 4 (PDCD4) and BRAF status as prognostic markers in PTC. Patients and methods:We investigate programmed cell death 4 (PDCD4) immunohistochemical expression in 125 consecutive PTCs with median follow-up of 75.3 months (range, 15-98 months) to verify the possible correlation between BRAF status and correlate the classical clinicopathological prognostic factors and PTC outcome with PDCD4 expression. To further support the data, miR-21 expression was tested (by quantitative real-time PCR and in situ hybridization) in a different series of 30 cases (15 PTCs BRAFwt and 15 PTCs BRAFV600E). Moreover, we validated our results using TGCA thyroid carcinoma dataset. Results:We found that 59.8% of the patients showed low-grade PDCD4 nuclear expression and low-grade expression correlated with BRAF V600E. Compared with BRAF 15 wild-type tissue samples, a significant miR-21 up-regulation was associated with BRAF V600E mutations. Low-grade PDCD4 resulted, and was associated with aggressive histological variants, higher cancer size, extra-thyroidal extension, multifocality, lymph-node metastasis and lymph nodal ratio at the diagnosis. Concerning the outcome, the low-grade PDCD4 expression correlated at univariate and multivariate analysis, with lower levels of recurrence-free survival rate (RFS) and with poor outcome. Moreover, there was significant association between BRAF V600E patients with PDCD4 nuclear loss and lower RFS, whilet here was significant association between BRAF wild-type patients with PDCD4 nuclear expression and better outcome. Conclusion:These results showed that PDCD4 could predict PTC outcome and that the sum of PDCD4 and BRAF alterations increases the prognostic power of BRAF mutation alone.

Dataset Information

Transcriptome network of the papillary thyroid carcinoma radiation marker CLIP2.

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Transcriptome network of the papillary thyroid carcinoma radiation marker CLIP2.

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