Higher circular RNA_0015278 correlates with absence of extrathyroidal invasion, lower pathological tumor stages, and prolonged disease-free survival in papillary thyroid carcinoma patients

CircWalk: a novel approach to predict CircRNA-disease association based on heterogeneous network representation learning

Background

Several types of RNA in the cell are usually involved in biological processes with multiple functions. Coding RNAs code for proteins while non-coding RNAs regulate gene expression. Some single-strand RNAs can create a circular shape via the back splicing process and convert into a new type called circular RNA (circRNA). circRNAs are among the essential non-coding RNAs in the cell that involve multiple disorders. One of the critical functions of circRNAs is to regulate the expression of other genes through sponging micro RNAs (miRNAs) in diseases. This mechanism, known as the competing endogenous RNA (ceRNA) hypothesis, and additional information obtained from biological datasets can be used by computational approaches to predict novel associations between disease and circRNAs.

Results

We applied multiple classifiers to validate the extracted features from the heterogeneous network and selected the most appropriate one based on some evaluation criteria. Then, the XGBoost is utilized in our pipeline to generate a novel approach, called CircWalk, to predict CircRNA-Disease associations. Our results demonstrate that CircWalk has reasonable accuracy and AUC compared with other state-of-the-art algorithms. We also use CircWalk to predict novel circRNAs associated with lung, gastric, and colorectal cancers as a case study. The results show that our approach can accurately detect novel circRNAs related to these diseases.

Conclusions

Considering the ceRNA hypothesis, we integrate multiple resources to construct a heterogeneous network from circRNAs, mRNAs, miRNAs, and diseases. Next, the DeepWalk algorithm is applied to the network to extract feature vectors for circRNAs and diseases. The extracted features are used to learn a classifier and generate a model to predict novel CircRNA-Disease associations. Our approach uses the concept of the ceRNA hypothesis and the miRNA sponge effect of circRNAs to predict their associations with diseases. Our results show that this outlook could help identify CircRNA-Disease associations more accurately.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04883-9.

Function and Clinical Significance of Circular RNAs in Thyroid Cancer

Thyroid cancer (TC) is the leading cause and mortality of endocrine malignancies worldwide. Tumourigenesis involves multiple molecules including circular RNAs (circRNAs). circRNAs with covalently closed single-stranded structures have been identified as a type of regulatory RNA because of their high stability, abundance, and tissue/developmental stage-specific expression. Accumulating evidence has demonstrated that various circRNAs are aberrantly expressed in thyroid tissues, cells, exosomes, and body fluids in patients with TC. CircRNAs have been identified as either oncogenic or tumour suppressor roles in regulating tumourigenesis, tumour metabolism, metastasis, ferroptosis, and chemoradiation resistance in TC. Importantly, circRNAs exert pivotal effects on TC through various mechanisms, including acting as miRNA sponges or decoys, interacting with RNA-binding proteins, and translating functional peptides. Recent studies have suggested that many different circRNAs are associated with certain clinicopathological features, implying that the altered expression of circRNAs may be characteristic of TC. The purpose of this review is to provide an overview of recent advances on the dysregulation, functions, molecular mechanisms and potential clinical applications of circRNAs in TC. This review also aimes to improve our understanding of the functions of circRNAs in the initiation and progression of cancer, and to discuss the future perspectives on strategies targeting circRNAs in TC.

Hsa_circ_0058129 regulates papillary thyroid cancer development via miR‐873‐5p/follistatin‐like 1 axis

Background

Papillary thyroid cancer (PTC) is an endocrine malignancy with a high incidence. Circular RNAs (circRNAs) participate in regulating PTC. Here, we analyzed the role of hsa_circ_0058129 (circ_0058129) in PTC.

Methods

The expression of circ_0058129, fibronectin 1 (FN1) mRNA, microRNA‐873‐5p (miR‐873‐5p), and follistatin‐like 1 (FSTL1) was detected by qRT‐PCR and western blot. Cell proliferation was analyzed by CCK‐8, EdU, and flow cytometry analysis assays. Cell migration and invasion were evaluated by Transwell assay. The targeting relationship of miR‐873‐5p and circ_0058129 or FSTL1 was identified through dual‐luciferase reporter assay, RIP assay, and RNA pull‐down assay. Xenograft mouse model assay was implemented to determine the effect of circ_0058129 on tumor formation in vivo.

Results

The circ_0058129 and FSTL1 abundances were increased, while the miR‐873‐5p content was decreased in PTC tissues and cells compared with control groups. Circ_0058129 shortage inhibited PTC cell proliferation, migration, and invasion. Moreover, miR‐873‐5p repressed PTC cell malignancy by binding to FSTL1. Circ_0058129 targeted miR‐873‐5p to regulate FSTL1.

Conclusion

Circ_0058129 expedited PTC progression through the miR‐873‐5p/FSTL1 pathway.