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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer
Zhenshu Wen1, Zhi-Ping Liu, Zhengrong Liu
1Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Background:
Many methods have been developed to identify disease genes and further module biomarkers of complex diseases based on gene expression data. It is generally difficult to distinguish whether the variations in gene expression are causative or merely the effect of a disease. The limitation of relying on gene expression data alone highlights the need to develop new approaches that can explore various data to reflect the casual relationship between network modules and disease traits.
Methods:
In this work, we developed a novel network-based approach to identify putative causal module biomarkers of complex diseases by integrating heterogeneous information, for example, epigenomic data, gene expression data, and protein-protein interaction network. We first formulated the identification of modules as a mathematical programming problem, which can be solved efficiently and effectively in an accurate manner. Then, we applied our approach to colorectal cancer (CRC) and identified several network modules that can serve as potential module biomarkers for characterizing CRC. Further validations using three additional gene expression datasets verified their candidate biomarker properties and the effectiveness of the method. Functional enrichment analysis also revealed that the identified modules are strongly related to hallmarks of cancer, and the enriched functions, such as inflammatory response, receptor and signaling pathways, are specific to CRC.
Results:
Through constructing a transcription factor (TF)-module network, we found that aberrant DNA methylation of genes encoding TF considerably contributes to the activity change of some genes, which may function as causal genes of CRC, and that can also be exploited to develop efficient therapies or effective drugs.
Conclusion:
Our method can potentially be extended to the study of other complex diseases and the multiclassification problem.
Insights
This study introduces a new network approach to find causal biomarkers for complex diseases like colorectal cancer (CRC) by integrating multiple data types. The method identifies key gene modules linked to cancer hallmarks, offering potential therapeutic targets.
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Gene expression data alone struggles to differentiate causative disease genes from effects.
- Identifying causal relationships between biological networks and disease traits requires integrated approaches.
- Complex diseases necessitate advanced methods beyond single data types for biomarker discovery.
Purpose of the Study:
- To develop a novel network-based approach for identifying putative causal module biomarkers.
- To integrate heterogeneous data including epigenomic, gene expression, and protein-protein interaction networks.
- To characterize colorectal cancer (CRC) using identified network modules.
Main Methods:
- Formulated module identification as a mathematical programming problem for efficient and accurate solutions.
- Integrated epigenomic data, gene expression data, and protein-protein interaction networks.
- Applied the approach to CRC and validated findings using external datasets and functional enrichment analysis.
Main Results:
- Identified several network modules as potential biomarkers for CRC characterization.
- Validated the candidate biomarker properties and method effectiveness across multiple datasets.
- Found identified modules strongly associated with cancer hallmarks and CRC-specific functions like inflammatory response.
Conclusions:
- The developed network-based approach effectively identifies causal module biomarkers for complex diseases.
- Aberrant DNA methylation in transcription factors (TFs) significantly impacts gene activity in CRC.
- The method shows potential for extension to other complex diseases and multiclassification problems.
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