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Published on: April 5, 2018
Methodology for Exploring Patterns of Epigenetic Information in Cancer Cells Using Data Mining Technique
Hanan Aljuaid1, Hanan A Hosni Mahmoud1,2
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11047, Saudi Arabia.
Abstract:
Epigenetic changes are a necessary characteristic of all cancer types. Tumor cells usually target genetic changes and epigenetic alterations as well. It is most beneficial to identify epigenetic similar features among cancer various types to be able to discover the appropriate treatments. The existence of epigenetic alteration profiles can aid in targeting this goal. In this paper, we propose a new technique applying data mining and clustering methodologies for cancer epigenetic changes analysis. The proposed technique aims to detect common patterns of epigenetic changes in various cancer types. We demonstrated the validation of the new technique by detecting epigenetic patterns across seven cancer types and by determining epigenetic similarities among various cancer types. The experimental results demonstrate that common epigenetic patterns do exist across these cancer types. Additionally, epigenetic gene analysis performed on the associated genes found a strong relationship with the development of various types of cancer and proved high risk across the studied cancer types. We utilized the frequent pattern data mining approach to represent cancer types compactly in the promoters for some epigenetic marks. Utilizing the built frequent pattern item set, the most frequent items are identified and yield the group of the bi-clusters of these patterns. Experimental results of the proposed method are shown to have a success rate of 88% in detecting cancer types according to specific epigenetic pattern.
Insights
Researchers identified common epigenetic patterns across seven cancer types using data mining and clustering. This discovery aids in understanding cancer development and finding targeted treatments, achieving an 88% success rate in cancer detection.
Area of Science:
- Oncology
- Bioinformatics
- Genetics
Background:
- Epigenetic alterations are fundamental to all cancer types, alongside genetic mutations.
- Identifying shared epigenetic features across diverse cancers is crucial for developing effective treatments.
- Epigenetic alteration profiles offer a pathway to targeted cancer therapies.
Purpose of the Study:
- To introduce a novel data mining and clustering technique for analyzing cancer epigenetic changes.
- To detect common epigenetic alteration patterns across various cancer types.
- To validate the technique by identifying epigenetic similarities among seven distinct cancer types.
Main Methods:
- Application of data mining and clustering methodologies for epigenetic analysis.
- Utilizing frequent pattern data mining to represent cancer types based on epigenetic marks in promoters.
- Identification of frequent item sets and bi-clusters of epigenetic patterns.
Main Results:
- Common epigenetic patterns were successfully detected across seven studied cancer types.
- Epigenetic gene analysis revealed a strong association with cancer development and high risk.
- The proposed method achieved an 88% success rate in classifying cancer types based on epigenetic patterns.
Conclusions:
- Common epigenetic patterns exist across different cancer types, offering therapeutic targets.
- The developed data mining technique effectively identifies and analyzes these shared epigenetic features.
- This approach holds promise for advancing precision oncology and treatment strategies.
