Splicing junction-based classifier for the detection of abnormal constitutive activation of the KEAP1-NRF2 system

Area of Science:

• Molecular Biology
• Genomics
• Cancer Research

Background:

• The KEAP1-NRF2 system regulates cellular responses to oxidative and electrophilic stress.
• Dysregulation of this system is linked to cancer development through downstream gene overexpression.
• Detecting aberrant KEAP1-NRF2 activity is crucial for disease mechanism elucidation and therapeutic target identification.

Purpose of the Study:

• To develop and validate a novel classifier for identifying constitutive activation of the KEAP1-NRF2 system.
• To utilize splicing pattern analysis as a biomarker for KEAP1-NRF2 pathway dysregulation.
• To assess the classifier's performance across large-scale genomic datasets.

Main Methods:

• Development of a naive Bayes-based classifier to analyze gene splicing patterns.
• Focus on identifying abnormal splicing junctions indicative of downstream gene overexpression.
• Validation of the classifier using datasets from The Cancer Genome Atlas and Sequence Read Archive.

Main Results:

• The splicing-based classifier demonstrated robust performance in identifying KEAP1-NRF2 pathway activation.
• The classifier successfully analyzed extensive transcriptomic datasets, confirming its reliability.
• Abnormal splicing junctions were identified as reliable indicators of KEAP1-NRF2 system activation.

Conclusions:

• The developed classifier offers a powerful tool for analyzing hundreds of thousands of transcriptomes.
• Splicing aberrations due to gene overexpression can serve as valuable diagnostic markers.
• This approach provides new insights for improving cancer diagnosis and treatment strategies.