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Theranostics 2017; 7(11):2888-2899. doi:10.7150/thno.19425 This issue Cite

Research Paper

A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers

Qi-Gang Li^{1, 3*}, Yong-Han He^{1, 3*}, Huan Wu^{1, 3, 9*}, Cui-Ping Yang^2*, Shao-Yan Pu^{1, 3}, Song-Qing Fan⁴, Li-Ping Jiang^{2, 9}, Qiu-Shuo Shen^{2, 9}, Xiao-Xiong Wang^{1, 3, 9}, Xiao-Qiong Chen^{1, 3}, Qin Yu^{1, 3, 9}, Ying Li⁵, Chang Sun⁶, Xiangting Wang⁷, Jumin Zhou², Hai-Peng Li⁸, Yong-Bin Chen^2✉, Qing-Peng Kong^{1, 3✉}

1. State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China;
2. Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China;
3. KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming 650223, China;
4. Department of Pathology, the Second Xiangya Hospital, Central South University, Changsha 410013, China;
5. Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China;
6. Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming 650091, China;
7. School of Life Sciences, University of Science and Technology of China, Hefei 230027, China;
8. Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
9. Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China.
* These authors contributed equally to this work.

✉ Corresponding authors: Dr. Qing-Peng Kong, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China. Tel/Fax: +86-871-68125403; Email: kongqpkiz.ac.cn Dr. Yong-Bin Chen, Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China. Tel/Fax: +86-871-65176312; Email: ybchenkiz.ac.cn More

Abstract

Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption—both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo, to be crucial in tumorigenesis, e.g., alcohol metabolism (ADH1B), chromosome remodeling (NCAPH) and complement system (Adipsin). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.

Keywords: Cross-Value Association Analysis, normalization-free, pan-cancer, transcriptome, heterogeneity.

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