A literature mining-based approach for identification of cellular pathways associated with chemoresistance in cancer
Abstract:
Chemoresistance is a major obstacle to the successful treatment of many human cancer types. Increasing evidence has revealed that chemoresistance involves many genes and multiple complex biological mechanisms including cancer stem cells, drug efflux mechanism, autophagy and epithelial-mesenchymal transition. Many studies have been conducted to investigate the possible molecular mechanisms of chemoresistance. However, understanding of the biological mechanisms in chemoresistance still remains limited. We surveyed the literature on chemoresistance-related genes and pathways of multiple cancer types. We then used a curated pathway database to investigate significant chemoresistance-related biological pathways. In addition, to investigate the importance of chemoresistance-related markers in protein-protein interaction networks identified using the curated database, we used a gene-ranking algorithm designed based on a graph-based scoring function in our previous study. Our comprehensive survey and analysis provide a systems biology-based overview of the underlying mechanisms of chemoresistance.
Insights
Chemoresistance, a major cancer treatment obstacle, involves complex mechanisms like cancer stem cells and drug efflux. This study provides a systems biology overview of chemoresistance mechanisms using pathway analysis and network algorithms.
Area of Science:
- Oncology
- Systems Biology
- Genetics
Background:
- Chemoresistance is a significant challenge in treating various human cancers.
- Mechanisms include cancer stem cells, drug efflux, autophagy, and epithelial-mesenchymal transition.
- Current understanding of chemoresistance biological mechanisms remains limited.
Purpose of the Study:
- To provide a systems biology-based overview of chemoresistance mechanisms.
- To identify significant chemoresistance-related biological pathways.
- To investigate the importance of chemoresistance markers in protein-protein interaction networks.
Main Methods:
- Literature survey of chemoresistance-related genes and pathways across multiple cancer types.
- Analysis using a curated pathway database to identify significant pathways.
- Application of a graph-based gene-ranking algorithm to protein-protein interaction networks.
Main Results:
- Identification of key biological pathways implicated in chemoresistance.
- Evaluation of chemoresistance marker importance within biological networks.
- A comprehensive systems biology perspective on chemoresistance mechanisms.
Conclusions:
- The study offers a systems biology framework for understanding chemoresistance.
- Highlights the complexity of chemoresistance involving multiple genes and pathways.
- Provides insights into potential therapeutic targets by analyzing molecular interactions.
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