Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI

TL;DR

This paper introduces a pipeline that combines dimensionality reduction and explainable AI to accurately classify 33 cancer types from mRNA gene expression data and identify relevant biomarkers.

Contribution

It presents a novel approach that reduces high-dimensional gene expression data to 500 features while maintaining high classification accuracy and providing biological insights.

Findings

Achieved 96.61% classification accuracy.

Identified cancer-specific genes using only 500 features.

Demonstrated biological relevance through DGE analysis.

Abstract

Gene expression analysis is a critical method for cancer classification, enabling precise diagnoses through the identification of unique molecular signatures associated with various tumors. Identifying cancer-specific genes from gene expression values enables a more tailored and personalized treatment approach. However, the high dimensionality of mRNA gene expression data poses challenges for analysis and data extraction. This research presents a comprehensive pipeline designed to accurately identify 33 distinct cancer types and their corresponding gene sets. It incorporates a combination of normalization and feature selection techniques to reduce dataset dimensionality effectively while ensuring high performance. Notably, our pipeline successfully identifies a substantial number of cancer-specific genes using a reduced feature set of just 500, in contrast to using the full dataset comprising 19,238 features. By employing an ensemble approach that combines three top-performing classifiers, a classification accuracy of 96.61% was achieved. Furthermore, we leverage Explainable AI to elucidate the biological significance of the identified cancer-specific genes, employing Differential Gene Expression (DGE) analysis.