About RFE45Cancer Project

Project Overview

RFE45Cancer is a comprehensive research initiative focused on analyzing DNA methylation patterns across multiple cancer types to identify epigenetic biomarkers with diagnostic and prognostic potential. Our project integrates data from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to provide robust, cross-validated findings.

Using advanced bioinformatics approaches and machine learning algorithms, particularly Recursive Feature Elimination (RFE), we identify gene signatures that can differentiate between cancer and normal tissues with high accuracy.

Our Methodology

Data Integration

Combining multiple datasets from TCGA and GEO databases to ensure robust findings across diverse patient populations.

Methylation Analysis

Comprehensive preprocessing of methylation data using ChAMP package, including quality control, normalization, and batch effect correction.

Feature Selection

Application of Recursive Feature Elimination (RFE) to identify the most predictive DNA methylation markers.

Machine Learning

Ensemble-based voting classifiers combining Random Forest, SVM, and Gradient Boosting to improve classification stability and accuracy.

Validation

Rigorous cross-validation techniques to ensure the generalizability of our findings.

Project Process

Preprocessing Workflow

Analysis Workflow

Project Significance

DNA methylation alterations are crucial epigenetic changes in cancer development and progression. Our research aims to identify methylation signatures that can:

• Serve as early diagnostic biomarkers for cancer detection
• Predict patient outcomes and treatment responses
• Provide insights into cancer biology and potential therapeutic targets
• Contribute to the development of non-invasive liquid biopsy approaches

By focusing on consistent methylation patterns across multiple datasets, we enhance the reliability and clinical applicability of our findings.