The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes

Leng Han, Yuan Yuan, Siyuan Zheng, Yang Yang, Jun Li, Mary E. Edgerton, Lixia Diao, Yanxun Xu, Roeland G. W. Verhaak and Han Liang ()
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Leng Han: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Yuan Yuan: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Siyuan Zheng: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Yang Yang: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Jun Li: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Mary E. Edgerton: The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, USA
Lixia Diao: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Yanxun Xu: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Roeland G. W. Verhaak: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA
Han Liang: The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas 77030, USA

Nature Communications, 2014, vol. 5, issue 1, 1-9

Abstract: Abstract Although individual pseudogenes have been implicated in tumour biology, the biomedical significance and clinical relevance of pseudogene expression have not been assessed in a systematic way. Here we generate pseudogene expression profiles in 2,808 patient samples of seven cancer types from The Cancer Genome Atlas RNA-seq data using a newly developed computational pipeline. Supervised analysis reveals a significant number of pseudogenes differentially expressed among established tumour subtypes and pseudogene expression alone can accurately classify the major histological subtypes of endometrial cancer. Across cancer types, the tumour subtypes revealed by pseudogene expression show extensive and strong concordance with the subtypes defined by other molecular data. Strikingly, in kidney cancer, the pseudogene expression subtypes not only significantly correlate with patient survival, but also help stratify patients in combination with clinical variables. Our study highlights the potential of pseudogene expression analysis as a new paradigm for investigating cancer mechanisms and discovering prognostic biomarkers.

Date: 2014
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DOI: 10.1038/ncomms4963

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