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Updated: Aug 28, 2025
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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
Published on: September 18, 2020
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iCancer-Pred: A tool for identifying cancer and its type using DNA methylation
Weizhong Lin1, Siqin Hu1, Zhicheng Wu2
1School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China.
Genomics
|September 20, 2022
Summary
This study introduces iCancer-Pred, a novel model for identifying and classifying cancer types using DNA methylation data. The model achieved over 97% accuracy in predicting seven cancer types, aiding in early tumor detection.
Area of Science:
- Epigenetics and Cancer Genomics
- Computational Biology and Bioinformatics
Background:
- DNA methylation plays a crucial role in early tumor formation.
- Understanding the link between DNA methylation and cancer is vital for diagnosis and treatment.
Purpose of the Study:
- To develop a novel computational model, iCancer-Pred, for accurate cancer identification and classification.
- To leverage DNA methylation data for improved cancer prediction.
Main Methods:
- Feature selection using coefficient of variation and elastic network on The Cancer Genome Atlas (TCGA) DNA methylation datasets.
- Construction of a fully connected neural network for cancer prediction.
- Utilized DNA methylation data from seven cancer types.
Main Results:
- iCancer-Pred achieved over 97% accuracy in predicting seven cancer types using 5-fold cross-validation.
- The model effectively identifies and classifies cancer types based on DNA methylation patterns.
Conclusions:
- iCancer-Pred demonstrates high efficacy in cancer type prediction using DNA methylation profiles.
- The developed model and accessible web server facilitate practical applications in cancer research and diagnostics.
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