@article{RN6157,
   author = {Han, L. and Yuan, Y. and Zheng, S. and Yang, Y. and Li, J. and Edgerton, M. E. and Diao, L. and Xu, Y. and Verhaak, R. G. W. and Liang, H.},
   title = {The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes},
   journal = {Nat Commun},
   volume = {5},
   pages = {3963},
   note = {2041-1723
Han, Leng
Yuan, Yuan
Zheng, Siyuan
Yang, Yang
Li, Jun
Edgerton, Mary E
Diao, Lixia
Xu, Yanxun
Verhaak, Roeland G W
Liang, Han
P30 CA016672/CA/NCI NIH HHS/United States
U24 CA143883/CA/NCI NIH HHS/United States
CA016672/CA/NCI NIH HHS/United States
CA143883/CA/NCI NIH HHS/United States
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
England
2014/07/08
Nat Commun. 2014 Jul 7;5:3963. doi: 10.1038/ncomms4963.},
   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.},
   keywords = {Biomarkers, Tumor/genetics/*metabolism
Gene Expression
Genes, Neoplasm
Humans
Neoplasms/*classification/genetics/*metabolism
*Pseudogenes},
   ISSN = {2041-1723},
   DOI = {10.1038/ncomms4963},
   year = {2014},
   type = {Journal Article}
}

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W.","Liang, H."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Han"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Yuan"],"firstnames":["Y."],"suffixes":[]},{"propositions":[],"lastnames":["Zheng"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Yang"],"firstnames":["Y."],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Edgerton"],"firstnames":["M.","E."],"suffixes":[]},{"propositions":[],"lastnames":["Diao"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Xu"],"firstnames":["Y."],"suffixes":[]},{"propositions":[],"lastnames":["Verhaak"],"firstnames":["R.","G.","W."],"suffixes":[]},{"propositions":[],"lastnames":["Liang"],"firstnames":["H."],"suffixes":[]}],"title":"The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes","journal":"Nat Commun","volume":"5","pages":"3963","note":"2041-1723 Han, Leng Yuan, Yuan Zheng, Siyuan Yang, Yang Li, Jun Edgerton, Mary E Diao, Lixia Xu, Yanxun Verhaak, Roeland G W Liang, Han P30 CA016672/CA/NCI NIH HHS/United States U24 CA143883/CA/NCI NIH HHS/United States CA016672/CA/NCI NIH HHS/United States CA143883/CA/NCI NIH HHS/United States Comparative Study Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't England 2014/07/08 Nat Commun. 2014 Jul 7;5:3963. doi: 10.1038/ncomms4963.","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. 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W. and Liang, H.},\n title = {The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes},\n journal = {Nat Commun},\n volume = {5},\n pages = {3963},\n note = {2041-1723\nHan, Leng\nYuan, Yuan\nZheng, Siyuan\nYang, Yang\nLi, Jun\nEdgerton, Mary E\nDiao, Lixia\nXu, Yanxun\nVerhaak, Roeland G W\nLiang, Han\nP30 CA016672/CA/NCI NIH HHS/United States\nU24 CA143883/CA/NCI NIH HHS/United States\nCA016672/CA/NCI NIH HHS/United States\nCA143883/CA/NCI NIH HHS/United States\nComparative Study\nJournal Article\nResearch Support, N.I.H., Extramural\nResearch Support, Non-U.S. Gov't\nEngland\n2014/07/08\nNat Commun. 2014 Jul 7;5:3963. doi: 10.1038/ncomms4963.},\n 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. 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