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Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and
Siyao Liu1, Baotong Zheng1, Yuqi Sheng1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Abstract:
A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS).
Insights
We developed a new method, Identification of Cancer Dysfunctional Subpathways (ICDS), to find key cancer-driving subpathways by integrating multi-omics data. ICDS effectively identifies cancer-associated subpathways, outperforming methods using single data types.
Area of Science:
- Bioinformatics
- Computational Biology
- Cancer Genomics
Background:
- Cancer development involves complex molecular alterations affecting biological pathways.
- Investigating dysfunctional subpathways requires integrating multi-omics data (DNA methylation, copy number variation, gene expression).
Purpose of the Study:
- To propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), for identifying oncogenic dysfunctional subpathways.
- To integrate multi-omics data and pathway topology for enhanced subpathway identification.
Main Methods:
- Gene-risk scores were calculated by integrating DNA methylation, copy number variation (CNV), and gene expression data.
- A greedy search algorithm identified key dysfunctional subpathways with locally maximal discriminative scores.
- Permutation tests assessed the statistical significance of identified subpathways.
Main Results:
- ICDS effectively identified cancer-associated subpathways in liver hepatocellular carcinoma, head-neck squamous cell carcinoma, and cervical cancer datasets.
- ICDS demonstrated superior performance compared to methods using only DNA methylation, CNV, or gene expression data.
- The ICDS method was implemented as an R-based tool for public use.
Conclusions:
- The ICDS method provides a robust approach for identifying dysfunctional subpathways in cancer.
- Integrating multi-omics data significantly improves the identification of cancer-associated subpathways.
- The freely available ICDS tool facilitates cancer research by enabling the discovery of key dysfunctional subpathways.
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