A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered pathways

Abstract

Pathway analysis allows us to gain insights into a comprehensive understanding of the molecular mechanisms underlying cancers. Currently, high-throughput multi-omics data and various types of large-scale biological networks enable us to identify cancer-related pathways by comprehensively analyzing these data. Combining information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify cancer-related pathways. Here we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend original pathways. The results show that the extension of original pathways can provide a basis for discovering new components of the original pathway and understanding the crosstalk between pathways in a large-scale biological network. By inputting the gene lists of the extended pathways into the classical gene set analysis (ORA and FCS), we effectively identified the altered pathways which are correlated well with the corresponding cancer. The method is evaluated on three datasets retrieved from TCGA (BRCA, LUAD and COAD). The results show that the integration of DNA methylation and gene expression data through a network of known gene interactions is effective in identifying altered pathways.

Conserved Control Path in Multilayer Networks

“…For example, CCP "NUPL2→HDAC4→RUNX3→LEF1→CCND1→E2F2→EZH2→CDKN2A→CDK6→TFDP2 →CDT1" in BRCA and CCP "NEK9→NUP205→HDAC4→RUNX3→LEF1→CCND1→E2F2 →EZH2→H2AFV" in COAD. Both paths had common parts and specific parts, and they were enriched for breast cancer pathways and colorectal cancer pathways related to BRCA and COAD, respectively, which was reported previously [37]. They were also enriched for pathways in cancer, which was a common core pathway related to the carcinogenesis of various cancers.…”

“…Additionally, from the research of Li. et al [ 37 ], we obtained some pathways related to BRCA and COAD, which helped us to understand the similarity between BRCA and COAD. For example, CCP “NUPL2→HDAC4→RUNX3→LEF1→CCND1→E2F2→EZH2→CDKN2A→CDK6→TFDP2→CDT1” in BRCA and CCP “NEK9→NUP205→HDAC4→RUNX3→LEF1→CCND1→E2F2→EZH2→H2AFV” in COAD.…”

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

“…Li et al used principal component analysis for feature reduction and sparse canonical correlation analysis to infer edge weights for gene pairs. A network-based pathway-extending approach using DNA methylation and gene expression data identified altered pathways [ 77 ]. Rather than relying on a PPI network as prior information, disease-specific regulatory networks can be inferred directly from epigenomic data.…”

Analyzing Integrated Network of Methylation and Gene Expression Profiles in Lung Squamous Cell Carcinoma

“…For example, the network study in leukemia found that both of the gene expression and methylation consistently affected the Ras, PI3K-Akt, and Rap1 signaling pathway 7 . Another study has identified novel cancer related pathway by integrating the methylation data and protein-protein interaction networks 8 . These studies used the networks that were obtained from open source databases.…”