Expression Profiles of Circular RNAs in Human Papillary Thyroid Carcinoma Based on RNA Deep Sequencing

Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer. Herein, we purposed to explore the expression patterns of circRNAs in PTC with the overarching goal of improving early diagnosis rates for individuals with PTC.

We used RNA deep sequencing to determine the expression patterns of circRNAs in PTC. Besides, RT-qPCR was employed to confirm circRNAs. The diagnostic potential of the circRNAs was explored by constructing ROC curves. GO along with KEGG pathway analyses were utilized to elucidate the potential biological roles of differentially expressed circRNAs. Moreover, we predicted cross talks among circRNAs, miRNAs, and mRNAs, followed by establishment of a ceRNA network.

Deep sequencing of four PTC pairs and neighboring nontumor tissues identified 16569 circRNAs, of which, 301 were upregulated and 419 were downregulated. The RT-qPCR data demonstrated that the expression of chr5: 38481299-38530666-, chr2: 159932176-159945082-, chr10: 179994-249088+, chr3: 121378716-121381532+, and chr1: 237423092-237445522+ was downregulated, while the expression of chr4: 25665378-25667298+, chr5: 161330883-161336769-, chr1: 12578718-12579412-, chr7: 116695750-116700284+, and chr7: 116699071-116700284+ was upregulated. The stability test exhibited that circRNAs were more tolerant to temperature, RNase R, and time. On the other hand, ROC curves illustrated that chr4: 25665378-25667298+, chr1: 12578718-12579412-, chr7: 116699071-116700284+, chr7: 116695750-116700284+, chr5: 161330883-161336769-, and chr10: 179994-249088+ were effective as diagnostic indicators. However, a logistic regression model combining the six indicators achieved a better combined prediction index, with 97.7% sensitivity and 95.3% specificity. Moreover, GO along with KEGG pathway analyses illustrated that differentially expressed circRNAs were linked to tumorigenesis. Furthermore, bioinformatics analyses established a promising ceRNAs network among mRNAs, circRNAs, and miRNAs.

Herein, we demonstrated that several circRNAs are promising PTC diagnostic biomarkers. Further study on the functions and mechanisms of these circRNAs may contribute to the understanding of PTC.