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基于 RNA-Seq 数据的前列腺癌中激酶基因表达和剪接谱的特征分析。

Characterization of kinase gene expression and splicing profile in prostate cancer with RNA-Seq data.

机构信息

MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China.

Present address: Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, NY, 10032, USA.

出版信息

BMC Genomics. 2018 Aug 13;19(Suppl 6):564. doi: 10.1186/s12864-018-4925-1.

DOI:10.1186/s12864-018-4925-1
PMID:30367578
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6101066/
Abstract

BACKGROUND

Alternative splicing is a ubiquitous post-transcriptional regulation mechanism in most eukaryotic genes. Aberrant splicing isoforms and abnormal isoform ratios can contribute to cancer development. Kinase genes are key regulators of multiple cellular processes. Many kinases are found to be oncogenic and have been intensively investigated in the study of cancer and drugs. RNA-Seq provides a powerful technology for genome-wide study of alternative splicing in cancer besides the conventional gene expression profiling. But this potential has not been fully demonstrated yet.

METHODS

We characterized the transcriptome profile of prostate cancer using RNA-Seq data from viewpoints of both differential expression and differential splicing, with an emphasis on kinase genes and their splicing variations. We built a pipeline to conduct differential expression and differential splicing analysis, followed by functional enrichment analysis. We performed kinase domain analysis to identify the functionally important candidate kinase gene in prostate cancer, and calculated the expression levels of isoforms to explore the function of isoform switching of kinase genes in prostate cancer.

RESULTS

We identified distinct gene groups from differential expression and splicing analyses, which suggested that alternative splicing adds another level to gene expression regulation. Enriched GO terms of differentially expressed and spliced kinase genes were found to play different roles in regulation of cellular metabolism. Function analysis on differentially spliced kinase genes showed that differentially spliced exons of these genes are significantly enriched in protein kinase domains. Among them, we found that gene CDK5 has isoform switching between prostate cancer and benign tissues, which may affect cancer development by changing androgen receptor (AR) phosphorylation. The observation was validated in another RNA-Seq dataset of prostate cancer cell lines.

CONCLUSIONS

Our work characterized the expression and splicing profiles of kinase genes in prostate cancer and proposed a hypothetical model on isoform switching of CDK5 and AR phosphorylation in prostate cancer. These findings bring new understanding to the role of alternatively spliced kinases in prostate cancer and also demonstrate the use of RNA-Seq data in studying alternative splicing in cancer.

摘要

背景

可变剪接是大多数真核基因中普遍存在的转录后调控机制。异常剪接异构体和异常异构体比例可能导致癌症的发生。激酶基因是多种细胞过程的关键调节剂。许多激酶被发现是致癌的,并在癌症和药物的研究中得到了深入研究。RNA-Seq 除了常规的基因表达谱分析外,还为癌症中可变剪接的全基因组研究提供了一种强大的技术。但这一潜力尚未得到充分证明。

方法

我们从差异表达和差异剪接的角度,利用 RNA-Seq 数据对前列腺癌的转录组图谱进行了特征描述,重点是激酶基因及其剪接变化。我们构建了一个管道来进行差异表达和差异剪接分析,然后进行功能富集分析。我们进行了激酶结构域分析,以确定前列腺癌中功能重要的候选激酶基因,并计算了异构体的表达水平,以探索激酶基因异构体转换在前列腺癌中的功能。

结果

我们从差异表达和剪接分析中确定了不同的基因群,这表明可变剪接为基因表达调控增加了另一个层次。差异表达和剪接激酶基因的富集 GO 术语被发现在细胞代谢的调节中发挥不同的作用。对差异剪接激酶基因的功能分析表明,这些基因的差异剪接外显子显著富集在蛋白激酶结构域中。其中,我们发现基因 CDK5 在前列腺癌和良性组织之间存在异构体转换,这可能通过改变雄激素受体(AR)磷酸化来影响癌症的发展。这一观察结果在另一个前列腺癌细胞系的 RNA-Seq 数据集得到了验证。

结论

我们的工作描述了前列腺癌中激酶基因的表达和剪接谱,并提出了 CDK5 和 AR 磷酸化在前列腺癌中异构体转换的假设模型。这些发现为可变剪接激酶在前列腺癌中的作用提供了新的认识,也展示了 RNA-Seq 数据在癌症中可变剪接研究中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/a75bc862ef14/12864_2018_4925_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/c37089980aa3/12864_2018_4925_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/66018877d5e2/12864_2018_4925_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/d34168b2685f/12864_2018_4925_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/19e4b414d9a0/12864_2018_4925_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/a75bc862ef14/12864_2018_4925_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/c37089980aa3/12864_2018_4925_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/66018877d5e2/12864_2018_4925_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/d34168b2685f/12864_2018_4925_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/19e4b414d9a0/12864_2018_4925_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cd9/6101066/a75bc862ef14/12864_2018_4925_Fig5_HTML.jpg

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