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Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis
Published on: March 30, 2019
Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways
1Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany. lijian@ibe.med.uni-muenchen.de
Abstract:
To date, it is widely recognized that Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) can exert considerable anti-tumor effects regarding many types of cancers. The prolonged use of NSAIDs is highly associated with diverse side effects. Therefore, tailoring down the NSAID application onto individual patients has become a necessary and relevant step towards personalized medicine. This study conducts the systemsbiological approach to construct a molecular model (NSAID model) containing a cyclooxygenase (COX)-pathway and its related signaling pathways. Four cancer hallmarks are integrated into the model to reflect different developmental aspects of tumorigenesis. In addition, a Flux-Comparative-Analysis (FCA) based on Petri net is developed to transfer the dynamic properties (including drug responsiveness) of individual cellular system into the model. The gene expression profiles of different tumor-types with available drug-response information are applied to validate the predictive ability of the NSAID model. Moreover, two therapeutic developmental strategies, synthetic lethality and microRNA (miRNA) biomarker discovery, are investigated based on the COX-pathway. In conclusion, the result of this study demonstrates that the NSAID model involving gene expression, gene regulation, signal transduction, protein interaction and other cellular processes, is able to predict the individual cellular responses for different therapeutic interventions (such as NS-398 and COX-2 specific siRNA inhibition). This strongly indicates that this type of model is able to reflect the physiological, developmental and pathological processes of an individual. The approach of miRNA biomarker discovery is demonstrated for identifying miRNAs with oncogenic and tumor suppressive functions for individual cell lines of breast-, colon- and lung-tumor. The achieved results are in line with different independent studies that investigated miRNA biomarker related to diagnostics of cancer treatments, therefore it might shed light on the development of biomarker discovery at individual level. Particular results of this study might contribute to step further towards personalized medicine with the systemsbiological approach.
Insights
Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) show anti-tumor effects but have side effects. This study developed a systems biology model to predict individual cancer cell responses to NSAIDs, aiding personalized medicine and biomarker discovery.
Area of Science:
- Systems biology
- Computational oncology
- Pharmacogenomics
Background:
- Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) exhibit anti-tumor properties across various cancers.
- Prolonged NSAID use is linked to significant side effects, necessitating personalized treatment approaches.
- Developing individualized NSAID therapies is crucial for advancing personalized medicine in oncology.
Purpose of the Study:
- To construct a systems biology-based molecular model (NSAID model) integrating the cyclooxygenase (COX) pathway and related signaling networks.
- To incorporate four cancer hallmarks into the model to represent tumorigenesis aspects.
- To develop a Flux-Comparative-Analysis (FCA) method for predicting individual cellular drug responsiveness.
Main Methods:
- Constructed a molecular model of the COX pathway and associated signaling networks.
- Integrated four cancer hallmarks to reflect tumorigenesis.
- Developed Flux-Comparative-Analysis (FCA) using Petri nets to model dynamic cellular properties and drug responsiveness.
- Validated the model using gene expression profiles and drug response data from various tumor types.
- Investigated synthetic lethality and microRNA (miRNA) biomarker discovery strategies based on the COX pathway.
Main Results:
- The NSAID model accurately predicts individual cellular responses to therapeutic interventions, including specific drug (NS-398) and gene inhibition (COX-2 siRNA).
- The model effectively reflects physiological, developmental, and pathological processes.
- Identified potential oncogenic and tumor-suppressive miRNAs for breast, colon, and lung cancer cell lines.
- Results align with independent studies on miRNA biomarkers for cancer diagnostics and treatment.
Conclusions:
- The developed NSAID model, utilizing a systems biology approach, demonstrates predictive power for individual cellular responses to therapeutic interventions.
- The study highlights the potential of systems biology for personalized medicine, enabling prediction of treatment outcomes.
- miRNA biomarker discovery at the individual level is feasible, potentially improving cancer diagnostics and treatment strategies.
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