Dataset Information

Genome-wide identification of cancer-specific alternative splicing in circRNA.

ABSTRACT: Circular RNA (circRNA) is a group of RNA families generated by RNA circularization, which was discovered ubiquitously across different cancers. However, the internal structure of circRNA is difficult to determine due to alternative splicing that occurs in its exons and introns. Furthermore, cancer-specific alternative splicing of circRNA is less likely to be identified. Here, we proposed a de novo algorithm, CircSplice, that could identify internal alternative splicing in circRNA and compare differential circRNA splicing events between different conditions ( http://gb.whu.edu.cn/CircSplice or https://github.com/GeneFeng/CircSplice ). By applying CircSplice in clear cell renal cell carcinoma and bladder cancer, we detected 4498 and 2977 circRNA alternative splicing (circ-AS) events in the two datasets respectively and confirmed the expression of circ-AS events by RT-PCR. We further inspected the distributions and patterns of circ-AS in cancer and adjacent normal tissues. To further understand the potential functions of cancer-specific circ-AS, we classified those events into tumor suppressors and oncogenes and performed pathway enrichment analysis. This study is the first comprehensive view of cancer-specific circRNA alternative splicing, which could contribute significantly to regulation and functional research of circRNAs in cancers.

SUBMITTER: Feng J

PROVIDER: S-EPMC6408762 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Genome-wide identification of cancer-specific alternative splicing in circRNA.

Feng Jing J Chen Ke K Dong Xin X Xu Xiaolong X Jin Yuxuan Y Zhang Xinyang X Chen Wenbo W Han Yujing Y Shao Lin L Gao Yang Y He Chunjiang C

Molecular cancer 20190308 1

Circular RNA (circRNA) is a group of RNA families generated by RNA circularization, which was discovered ubiquitously across different cancers. However, the internal structure of circRNA is difficult to determine due to alternative splicing that occurs in its exons and introns. Furthermore, cancer-specific alternative splicing of circRNA is less likely to be identified. Here, we proposed a de novo algorithm, CircSplice, that could identify internal alternative splicing in circRNA and compare dif ...[more]

PMID: 30849979

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Project description:The molecular mechanism of AAA formation is still poorly understood and has not been fully elucidated. The study was designed to identify the immune-related genes, immune-RAS in AAA using bioinformatics methods. The GSE175683 datasets were downloaded from the GEO database. The DEseq2 software was used to identify differentially expressed genes (DEGs). SUVA pipeline was used to quantify AS events and RAS events. KOBAS 2.0 server was used to identify GO terms and KEGG pathways to sort out functional categories of DEGs. The CIBERSORT algorithm was used with the default parameter for estimating immune cell fractions. Nine samples from GSE175683 were used to construct the co-disturbed network between expression of SFs and splicing ratio of RAS events. PCA analysis was performed by R package factoextra to show the clustering of samples, and the pheatmap package in R was used to perform the clustering based on Euclidean distance. The results showed that there were 3,541 genes significantly differentially expressed, of which 177 immune-related genes were upregulated and 48 immune-related genes were downregulated between the WT and WTA group. Immune-RAS events were mainly alt5P and IR events, and about 60% of it was complex splicing events in AAA. The WT group and the WTA group can be clearly distinguished in the first principal component by using the splicing ratio of immune-RAS events. Two downregulated genes, Nr4a1 and Nr4a2, and eight upregulated genes, Adipor2, Akt2, Bcl3, Dhx58, Pparg, Ptgds, Sytl1, and Vegfa were identified among the immune-related genes with RAS and DEGs. Eighteen differentially expressed SFs were identified and displayed by heatmap. The proportion of different types of cells and ratio of the average ratio of different cells were quite different. Both M1 and M2 types of macrophages and plasma cells were upregulated, while M0 type was downregulated in AAA. The proportion of plasma cells in the WTA group had sharply increased. There is a correlation between SF expression and immune cells/immune-RAS. Sf3b1, a splicing factor with significantly different expression, was selected to bind on a mass of immune-related genes. In conclusion, our results showed that immune-related genes, immune-RAS, and SFs by genome-wide identification were involved in AAA.

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Dataset Information

Genome-wide identification of cancer-specific alternative splicing in circRNA.

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Genome-wide identification of cancer-specific alternative splicing in circRNA.

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