scholarly journals Identification of Altered Circular RNA Expression in Serum Exosomes from Patients with Papillary Thyroid Carcinoma by High-Throughput Sequencing

2019 ◽  
Vol 25 ◽  
pp. 2785-2791 ◽  
Author(s):  
Chunjiang Yang ◽  
Youchun Wei ◽  
Leitao Yu ◽  
Yong Xiao
Author(s):  
Ying Xin ◽  
Kexin Meng ◽  
Haiwei Guo ◽  
Bin Chen ◽  
Chuanming Zheng ◽  
...  

Background: Papillary thyroid carcinoma (PTC) is a subtype of thyroid cancer with increasing incidence over time. Objective: This study aimed to build a risk score (RS) system for PTC patients. Methods: PTC microRNA (miRNA) and messenger RNA (mRNA) expression data were extracted from The Cancer Genome Atlas (TCGA) database. The 491 PTC samples were randomly divided into training and validation sets. Using the limma software package, differentially expressed mRNAs (DEGs) and miRNAs (DEMs) between the tumor and control groups were screened. In order to construct an RS system, a survival package was used to select independent miRNAs related to prognosis. Enrichment analysis was performed, and a miRNA-mRNA co-expression network was constructed. High-throughput sequencing was also used to verify the prognostic miRNAs in exosomes. Results: We found 1363 DEGs and 171 DEMs between the tumor and control groups. After identifying 26 DEMs that were significantly related to prognosis, 6 independent prognosis-associated miRNAs were selected to build an RS system. The areas under the curves of the overall survival rates of the training, validation, and entire sets were 0.847, 0.772, and 0.819, respectively. By conducting pathway analysis using the miRNA-mRNA co-expression network, one overlapping factor and five overlapping pathways were obtained. In addition, high-throughput sequencing revealed that the hsa-miR-129-2, hsa-miR-548j, hsa-miR-6734, and hsa-miR-889 expression levels in TCGA tumor tissues and exosomes were consistent, and those of hsa-miR-129-2 and hsa-miR-889 between patients and controls were significantly different in exosomes. Conclusion: The six-miRNA RS system in exosomes may comprise independent signatures for predicting PTC patient prognosis.


2020 ◽  
Vol 44 (2) ◽  
pp. 519-532
Author(s):  
Dan Guo ◽  
Fangyuan Li ◽  
Xiaoxiao Zhao ◽  
Bo Long ◽  
Sumei Zhang ◽  
...  

2020 ◽  
Author(s):  
Mengwei Wu ◽  
Rui Liu ◽  
Hongwei Yuan ◽  
Xiequn Xu ◽  
Xiaobin Li ◽  
...  

Abstract BackgroundAccurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circRNAs (DE-circRNAs) via the ceRNA mechanism could help establish a novel assessment tool. MethodsceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. LASSO-Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC.ResultsSix hub DE-mRNAs, namely CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the ATA risk stratification system and MACIS score AJCC staging system.ConclusionsBased on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.


2018 ◽  
Vol 47 (3) ◽  
pp. 1122-1132 ◽  
Author(s):  
Xiabin Lan ◽  
Jiajie Xu ◽  
Chao Chen ◽  
Chuanming Zheng ◽  
Jiafeng Wang ◽  
...  

Background/Aims: Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. However, the molecular mechanisms responsible for its tumorigenesis and progression remain largely unknown. Circular RNA (circRNA) is a novel type of noncoding RNA that can serve as an ideal biomarker due to its stability. Recent evidence suggests that circRNAs play important roles in tumorigenesis. This study aims to investigate circRNA expression profiles and their potential biological functions in PTC. Methods: High-throughput RNA sequencing was used to assess circRNA expression profiles in PTC, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate dysregulated circRNAs. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic value of circRNAs for PTC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were employed to determine the biological functions of differentially expressed circRNAs. Bioinformatic analyses were applied to predict interactions between circRNAs and microRNAs (miRNAs), and a circRNA-miRNA-mRNA network was constructed using Cytoscape software. Results: We identified a number of differentially expressed circRNAs in PTC tissues compared with paired normal thyroid tissues, with chr5: 160757890-160763776–, chr12: 40696591-40697936+, chr7: 22330794-22357656-, and chr21: 16386665-16415895– being upregulated, and chr7: 91924203-91957214+, chr2: 179514891-179516047–, chr9: 16435553-16437522–, and chr22: 36006931-36007153– being downregulated. These findings were confirmed by qRT-PCR, and ROC curves indicated that they can serve as potential biomarkers for PTC. GO and KEGG pathway analyses showed that some of these circRNAs are related to cancers. Additionally, bioinformatic analyses revealed a potential competing-endogenous-RNA-regulating network among circRNAs, miRNAs, and mRNAs. Conclusions: Our study results depict the landscape of circRNA expression profiles in PTC and also provide potential biomarkers for PTC. Further functional and mechanistic studies of these circRNAs may improve our understanding of PTC tumorigenesis.


Thyroid ◽  
2004 ◽  
Vol 14 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Manju L. Prasad ◽  
Natalia S. Pellegata ◽  
Richard T. Kloos ◽  
Catalin Barbacioru ◽  
Ying Huang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document