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A pan-cancer analysis of alternative splicing of splicing factors in 6904 patients

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Abstract

Great progress has been made in the investigation on mutation and expression of splicing factor. However, little is known on the role of alternative splicing of splicing factors across cancers. Here, we reported a pan-cancer analysis of alternative splicing of splicing factors spanning 6904 patients across 16 cancer types, and identified 167 splicing factors with implications regulating cancer-specific splicing patterns through alternative splicing. Furthermore, we found that abnormal splicing events of splicing factors could serve as potential common regulators for alternative splicing in different cancers. In addition, we developed a splicing-derived neoepitopes database (ASPNs), which provided the corresponding putative alternative splicing-derived neoepitopes of 16 cancer types. Our results suggested that alternative splicing of splicing factors involved in the pre-RNA splicing process was common across cancer types and may represent an underestimated hallmark of tumorigenesis.

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Fig. 1: Splicing Landscape and detection of tumor alternative splicing events.
Fig. 2: Alternative splicing of splicing factors.
Fig. 3: Alternative splicing of splicing factors and cancer-specific splicing patterns.
Fig. 4: Common splicing patterns of cancer pairs.
Fig. 5: Alternative Splicing-derived Neoepitopes.

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Code availability

The code for the analyses described in this study is available at https://github.com/asd77088/Jiang-lab.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61822108 and 62032007 to QJ).

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  1. These authors contributed equally: Rui Cheng, Lixing Xiao, Wenyang Zhou.

Authors and Affiliations

  1. School of Life Science and Technology, Harbin Institute of Technology, Harbin, China

    Rui Cheng, Lixing Xiao, Wenyang Zhou, Xiyun Jin, Zhaochun Xu, Chang Xu, Pingping Wang, Meng Luo, Mengyun Wang, Kexin Ma, Huimin Cao, Yan Huang, Xiaoyu Lin, Fenglan Pang, Yiqun Li & Qinghua Jiang

  2. Key Laboratory of Biological Big Data (Harbin Institute of Technology), Ministry of Education, Harbin, China

    Qinghua Jiang

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  1. Rui Cheng
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Contributions

Conception and design: QJ. Methodology: QJ, RC, LX, and WZ. Analysis and interpretation of data: QJ, RC, WZ, PW, KM, HC, FP, YH, XL, and YL. Writing-original draft: RC, LX, CX, ZX, XJ, ML, MW, and QJ.

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Correspondence to Qinghua Jiang.

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Cheng, R., Xiao, L., Zhou, W. et al. A pan-cancer analysis of alternative splicing of splicing factors in 6904 patients. Oncogene 40, 5441–5450 (2021). https://doi.org/10.1038/s41388-021-01947-7

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