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Identifying disease genes and module biomarkers by differential interactions
Xiaoping Liu1, Zhi-Ping Liu, Xing-Ming Zhao
1Institute of Systems Biology, Shanghai University, Shanghai, China.
Objective:
A complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs.
Materials And Methods:
In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods.
Results And Discussion:
As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method.
Conclusion:
We proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.
Insights
This study introduces a new method to identify disease-related gene modules using differential interactions, proving effective for gastric cancer diagnosis and offering insights into complex diseases.
Area of Science:
- Bioinformatics
- Systems Biology
- Genomics
Background:
- Complex diseases arise from multiple genetic mutations or biological process dysfunctions.
- Identifying causal genes and biomarkers is crucial for understanding disease mechanisms and developing therapies.
Purpose of the Study:
- To present a novel approach for predicting disease genes and identifying dysfunctional molecular networks or modules.
- To utilize differential interaction analysis for disease gene and module biomarker discovery.
Main Methods:
- Developed a method analyzing differential interactions between disease and control samples.
- Contrasted this approach with traditional differential gene or protein expression analyses.
- Applied the method to three-stage microarray data for gastric cancer.
Main Results:
- Identified network modules and module biomarkers associated with gastric cancer.
- Demonstrated the predictive capability of the identified modules.
- Validated the module's effectiveness as a biomarker for accurate gastric cancer detection using holdout data.
Conclusions:
- Proposed a novel approach for detecting disease module biomarkers.
- Differential interactions are effective for identifying dysfunctional modules in molecular networks.
- Identified modules serve as robust biomarkers for disease detection and diagnosis.
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