Membrane protein-regulated networks across human cancers

Chun-Yu Lin; Chia-Hwa Lee; Yi-Hsuan Chuang; Jung-Yu Lee; Yi-Yuan Chiu; Yan-Hwa Wu Lee; Yuh-Jyh Jong; Jenn-Kang Hwang; Sing-Han Huang; Li-Ching Chen; Chih-Hsiung Wu; Shih-Hsin Tu; Yuan-Soon Ho; Jinn-Moon Yang

doi:10.1038/s41467-019-10920-8

Membrane protein-regulated networks across human cancers

Nat Commun. 2019 Jul 16;10(1):3131. doi: 10.1038/s41467-019-10920-8.

Authors

Chun-Yu Lin^{1

2}, Chia-Hwa Lee^{3

4

5}, Yi-Hsuan Chuang¹, Jung-Yu Lee¹, Yi-Yuan Chiu¹, Yan-Hwa Wu Lee^{6

7

8}, Yuh-Jyh Jong^{6

8

9}, Jenn-Kang Hwang¹⁰, Sing-Han Huang¹, Li-Ching Chen^{4

11

12}, Chih-Hsiung Wu^{13

14}, Shih-Hsin Tu^{4

11

12

13}, Yuan-Soon Ho^{15

16

17

18}, Jinn-Moon Yang^{19

20

21}

Affiliations

¹ Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
² Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, 611-0011, Japan.
³ School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, 110, Taiwan.
⁴ TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, 110, Taiwan.
⁵ Ph.D. Program in Medicine Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, 110, Taiwan.
⁶ Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.
⁷ Institute of Biochemistry and Molecular Biology, School of Life Sciences, National Yang-Ming University, Taipei, 112, Taiwan.
⁸ Center for Intelligent Drug Systems and Smart Bio-devices, National Chiao Tung University, Hsinchu, 300, Taiwan.
⁹ Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
¹⁰ Warshel Institute for Computational Biology, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China.
¹¹ Division of Breast Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, 110, Taiwan.
¹² Taipei Cancer Center, Taipei Medical University, Taipei, 110, Taiwan.
¹³ Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan.
¹⁴ Department of Surgery, En Chu Kong Hospital, New Taipei City, 237, Taiwan.
¹⁵ School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, 110, Taiwan. hoyuansn@tmu.edu.tw.
¹⁶ TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, 110, Taiwan. hoyuansn@tmu.edu.tw.
¹⁷ Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan. hoyuansn@tmu.edu.tw.
¹⁸ Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei, 110, Taiwan. hoyuansn@tmu.edu.tw.
¹⁹ Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. moon@faculty.nctu.edu.tw.
²⁰ Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan. moon@faculty.nctu.edu.tw.
²¹ Center for Intelligent Drug Systems and Smart Bio-devices, National Chiao Tung University, Hsinchu, 300, Taiwan. moon@faculty.nctu.edu.tw.

Abstract

Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging. Here, we present a systematically integrated method to generate a resource of cancer membrane protein-regulated networks (CaMPNets), containing 63,746 high-confidence protein-protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MP-based gene sets for identifying prognostic biomarkers and druggable targets. For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its anti-metastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/heterogeneity.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Antineoplastic Agents / pharmacology
Antineoplastic Agents / therapeutic use
Biomarkers, Tumor / antagonists & inhibitors
Biomarkers, Tumor / genetics*
Biomarkers, Tumor / metabolism
Bupropion / pharmacology
Bupropion / therapeutic use
Cell Line, Tumor
Datasets as Topic
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic / drug effects
Gene Regulatory Networks*
Humans
Kaplan-Meier Estimate
Mice
Neoplasms / drug therapy
Neoplasms / genetics*
Neoplasms / mortality
Nicotinic Antagonists / pharmacology
Nicotinic Antagonists / therapeutic use
Prognosis
Protein Interaction Mapping / methods
Protein Interaction Maps / drug effects
Protein Interaction Maps / genetics
Receptors, Nicotinic / genetics*
Receptors, Nicotinic / metabolism
Xenograft Model Antitumor Assays

Substances

Antineoplastic Agents
Biomarkers, Tumor
CHRNA9 protein, human
Nicotinic Antagonists
Receptors, Nicotinic
Bupropion