ICan: an integrated co-alteration network to identify ovarian cancer-related genes

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of...

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Published in:PloS one 2015-03, Vol.10 (3), p.e0116095
Main Authors: Zhou, Yuanshuai, Liu, Yongjing, Li, Kening, Zhang, Rui, Qiu, Fujun, Zhao, Ning, Xu, Yan
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Angiogenesis
Apoptosis
Bioinformatics
BRCA1 protein
Breast cancer
Cancer
Cancer genetics
Carcinogenesis
Carcinogens
Computational Biology - methods
Copy number
Data analysis
Data processing
Databases, Genetic
DNA Copy Number Variations
DNA Methylation
EphA2 protein
Female
Fibroblasts
Gene expression
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Genes
Genetic aspects
Genomes
Gynecology
Humans
Kaplan-Meier Estimate
Medical prognosis
Medical research
Metastasis
Methods
Methylation
Mutation
Oncogenes
Ovarian cancer
Ovarian carcinoma
Ovarian Neoplasms - genetics
Ovarian Neoplasms - mortality
p53 Protein
Pathology
Prognosis
Proteins
PTEN protein
Science
Survival analysis
Tumor proteins
Tumor suppressor genes
Tumorigenesis
Workflow
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container_issue 3
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creator Zhou, Yuanshuai
Liu, Yongjing
Li, Kening
Zhang, Rui
Qiu, Fujun
Zhao, Ning
Xu, Yan
description Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.
doi_str_mv 10.1371/journal.pone.0116095
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Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. 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subjects Algorithms
Analysis
Angiogenesis
Apoptosis
Bioinformatics
BRCA1 protein
Breast cancer
Cancer
Cancer genetics
Carcinogenesis
Carcinogens
Computational Biology - methods
Copy number
Data analysis
Data processing
Databases, Genetic
DNA Copy Number Variations
DNA Methylation
EphA2 protein
Female
Fibroblasts
Gene expression
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Genes
Genetic aspects
Genomes
Gynecology
Humans
Kaplan-Meier Estimate
Medical prognosis
Medical research
Metastasis
Methods
Methylation
Mutation
Oncogenes
Ovarian cancer
Ovarian carcinoma
Ovarian Neoplasms - genetics
Ovarian Neoplasms - mortality
p53 Protein
Pathology
Prognosis
Proteins
PTEN protein
Science
Survival analysis
Tumor proteins
Tumor suppressor genes
Tumorigenesis
Workflow
title ICan: an integrated co-alteration network to identify ovarian cancer-related genes
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