ArticlePloS one2015
ICan: an integrated co-alteration network to identify ovarian cancer-related genes.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis.MethodsX · 2023Article
- Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis.Computational and structural biotechnology journal · 2021Review
- Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.Frontiers in oncology · 2020Review
- Integration of Online Omics-Data Resources for Cancer Research.Frontiers in genetics · 2020Review
- LPRP: A Gene-Gene Interaction Network Construction Algorithm and Its Application in Breast Cancer Data Analysis.Interdisciplinary sciences, computational life sciences · 2018Article
- Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer.Scientific reports · 2017Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver 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.
resultsWe 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).
conclusionIn 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.
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