Profiling and Bioinformatics Analyses of Differential Circular RNA Expression in Prostate Cancer Cells

Zhang, Chunlei; Xiong, Jun; Yang, Qi; Wang, Ye; Shi, Haoqing; Tian, Quan; Huang, Hai; Kong, De-Pei; Lv, Jian-Min; Liú, Dan; Gao, Xu; Zi, Xiaoyuan; Sun, Yinghao

doi:10.4155/fsoa-2018-0046

Cited by 20 publications

(21 citation statements)

References 59 publications

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“…CircRNA-miRNA mapping has revealed that studying the interaction between circRNAs and miRNAs may further help to characterize the role of certain circRNAs in PCa development. In vitro investigations of interactions such as CDR1as-miR-7[ 66 ], circRNA-MYLK- miR-29a[ 64 ], and circBAGE2-miR-103a[ 66 ] have implicated tumor suppressive and oncogenic roles of circRNAs, which could imply their utility as biomarkers as well as therapeutic targets[ 64 ]. Other studies have shown that some circRNAs may play roles in contributing to therapy-resistance PCa.…”

Section: Circular Rna In Prostate Cancermentioning

confidence: 99%

Circular RNA and its potential as prostate cancer biomarkers

Tucker¹,

Zheng²,

Zhang³

et al. 2020

WJCO

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Advancing knowledge of the transcriptome has revealed that circular RNAs (circRNAs) are widely expressed and evolutionarily conserved molecules that may serve relevant biological roles. More interesting is the accumulating evidence which demonstrates the implication of circRNAs in diseases, especially cancers. This revelation has helped to form the rationale for many studies exploring their utility as clinical biomarkers. CircRNAs are highly stable due to their unique structures, exhibit some tissue specificity, and are enriched in exosomes, which facilitate their detection in a range of body fluids. These properties make circRNAs ideal candidates for biomarker development in many diseases. This review will outline the discovery, biogenesis, and proposed functions of circRNAs.

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Section: Circular Rna In Prostate Cancermentioning

confidence: 99%

Circular RNA and its potential as prostate cancer biomarkers

Tucker¹,

Zheng²,

Zhang³

et al. 2020

WJCO

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show abstract

“…Thus, the relationship between the circRNA and the lncRNA transcript was of particular interest. In this circRNA panel, two additional circRNAs from the genes CSNK1G3 and GUCY1A2 were included as these circRNAs were recently identified in PCa tissue samples and PCa cell lines as mentioned in the introduction [ 44 , 45 ]. Collectively, the microarray analysis of the six paired samples in the discovery phase ( Figure 1 ) must be considered an exploratory study for ranking deregulated circRNAs using the unadjusted p -values supported by the mentioned additional selection criteria.…”

Section: Resultsmentioning

confidence: 99%

“…Circ CSNK1G3 , for example, seems to promote cell proliferation in PCa [ 44 ]. Furthermore, Zhang et al [ 45 ] applied a bioinformatics approach using various PCa cells to identify numerous circRNAs, including circ GUCY1A2 , as potential candidates for PCa progression. Other working groups using microarray platforms have reported lists of the top up- and downregulated circRNAs in PCa tissue compared with adjacent normal tissue [ 46 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

Circular RNAs and Their Linear Transcripts as Diagnostic and Prognostic Tissue Biomarkers in Prostate Cancer after Prostatectomy in Combination with Clinicopathological Factors

Rochow

Jung

Weickmann

et al. 2020

IJMS

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As new biomarkers, circular RNAs (circRNAs) have been largely unexplored in prostate cancer (PCa). Using an integrative approach, we aimed to evaluate the potential of circRNAs and their linear transcripts (linRNAs) to act as (i) diagnostic biomarkers for differentiation between normal and tumor tissue and (ii) prognostic biomarkers for the prediction of biochemical recurrence (BCR) after radical prostatectomy. In a first step, eight circRNAs (circATXN10, circCRIM1, circCSNK1G3, circGUCY1A2, circLPP, circNEAT1, circRHOBTB3, and circSTIL) were identified as differentially expressed via a genome-wide circRNA-based microarray analysis of six PCa samples. Additional bioinformatics and literature data were applied for this selection process. In total, 115 malignant PCa and 79 adjacent normal tissue samples were examined using robust RT-qPCR assays specifically established for the circRNAs and their linear counterparts. Their diagnostic and prognostic potential was evaluated using receiver operating characteristic curves, Cox regressions, decision curve analyses, and C-statistic calculations of prognostic indices. The combination of circATXN10 and linSTIL showed a high discriminative ability between malignant and adjacent normal tissue PCa. The combination of linGUCY1A2, linNEAT1, and linSTIL proved to be the best predictive RNA-signature for BCR. The combination of this RNA signature with five established reference models based on only clinicopathological factors resulted in an improved predictive accuracy for BCR in these models. This is an encouraging study for PCa to evaluate circRNAs and their linRNAs in an integrative approach, and the results showed their clinical potential in combination with standard clinicopathological variables.

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“…They ranked in the top 3% and were associated with liver cancer. For prostate cancer (DOID: 10283), hsa_circ_0001946 (Zhang et al, 2018) and hsa_circ_0001649 (Yi et al, 2016) ranked 3rd and 5th in the ranking, respectively. They were documented to be related to prostate cancer.…”

Section: Resultsmentioning

confidence: 99%

Predicting circRNA-Disease Associations Based on circRNA Expression Similarity and Functional Similarity

et al. 2019

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Circular RNAs (circRNAs) are a novel class of endogenous noncoding RNAs that have well-conserved sequences. Emerging evidence has shown that circRNAs can be novel biomarkers or therapeutic targets for many diseases and play an important role in the development of various pathological conditions. Therefore, identifying potential disease-related circRNAs is helpful in improving the efficiency of finding therapeutic targets for diseases. Here, we propose a computational model (PreCDA) to predict potential circRNA–disease associations. First, we calculated the circRNA expression similarity based on circRNA expression profiles. The circRNA functional similarity is calculated based on cosine similarity, and the disease similarity is used as the dimension of each circRNA vector. The associations between circRNAs and diseases are defined based on the circRNA functional similarity and expression similarity. We constructed a disease-related circRNA association network and used a graph-based recommendation algorithm (PersonalRank) to sort candidate disease-related circRNAs. As a result, PreCDA has an average area under the receiver operating characteristic curve value of 78.15% in predicting candidate disease-related circRNAs. In addition, we discuss the factors that affect the performance of this method and find some unknown circRNAs related to diseases, with several common diseases used as case studies. These results show that PreCDA has good performance in predicting potential circRNA–disease associations and is helpful for the diagnosis and treatment of human diseases.

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Profiling and Bioinformatics Analyses of Differential Circular RNA Expression in Prostate Cancer Cells

Cited by 20 publications

References 59 publications

Circular RNA and its potential as prostate cancer biomarkers

Circular RNA and its potential as prostate cancer biomarkers

Circular RNAs and Their Linear Transcripts as Diagnostic and Prognostic Tissue Biomarkers in Prostate Cancer after Prostatectomy in Combination with Clinicopathological Factors

Predicting circRNA-Disease Associations Based on circRNA Expression Similarity and Functional Similarity

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