Pan-organ transcriptome variation across 21 cancer types
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Wangxiong Hu1,*, Yanmei Yang2,*, Xiaofen Li1, Shu Zheng1,3
1Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
2Key Laboratory of Reproductive and Genetics, Ministry of Education, Women’s Hospital, Zhejiang University, Hangzhou, Zhejiang 310006, China
3Research Center for Air Pollution and Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310009, China
*These authors have contributed equally to this work
Shu Zheng, email: firstname.lastname@example.org
Keywords: gene expression, organ-specific genes, pan-cancer, weighted correlation network analysis
Received: June 08, 2016 Accepted: December 05, 2016 Published: December 27, 2016
It is widely accepted that some messenger RNAs are evolutionarily conserved across species, both in sequence and tissue-expression specificity. To date, however, little effort has been made to exploit the transcriptome divergence between cancer and adjacent normal tissue at the pan-organ level. In this work, a transcriptome sequencing dataset from 675 normal-tumor pairs, representing 21 solid organs in The Cancer Genome Atlas, is used to evaluate expression evolution. The results show that in most cancer types, gene expression divergence and organ-specificity are reduced in cancer tissue compared to adjacent normal tissue. Furthermore, we observe that all cancers share cell cycle dysregulation through interrogating differentially expressed protein coding genes. Meanwhile, weighted correlation network analysis is used to detect of the gene module structure variation between cancer and adjacent normal tissue. And modules consisting of tightly co-regulated genes in cancer change substantially compared with those in adjacent normal tissue. We thus assume that the destruction of a coordinated regulatory network might result in tumorigenesis and tumor progression. Our results provide new insights into the complex cancer biology and shed light on the mysterious regulation mode for cancer.
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