An automatic NGS feature extraction algorithm for predicting EBV-associated nasopharyngeal cancer and high-risk...

Area of Science:

• Oncology
• Virology
• Bioinformatics

Background:

• Epstein-Barr virus (EBV) infection is a significant risk factor for nasopharyngeal carcinoma (NPC).
• The latent membrane protein 1 (LMP1) gene's heterogeneity is crucial for EBV-associated NPC (EBVaNPC) oncogenesis.
• Early detection and risk stratification of EBVaNPC are critical for patient outcomes.

Purpose of the Study:

• To develop and validate an algorithmic approach for identifying key mutations in the EBV LMP1 gene.
• To investigate the functional impact of identified LMP1 mutations on EBVaNPC development.
• To establish a novel strategy for high-risk EBVaNPC identification.

Main Methods:

• Sequencing of the LMP1 gene in clinical EBV-positive samples.
• Development of an automated viral sequence feature extraction (ViSFE) approach.
• Experimental validation including qRT-PCR, EdU assays, transwell invasion, RNA sequencing, and Gene Ontology Fingerprinting (GOF).

Main Results:

• The ViSFE approach demonstrated feasibility for analyzing nucleotide data.
• A significant H101R mutation in LMP1 was identified as a key feature, validated by proliferation and invasion assays.
• Differentially expressed genes associated with the H101R mutation were primarily involved in immune regulation, specifically FOXP3-T cell anergy and WNT7A-stem cell population maintenance.

Conclusions:

• The study presents a novel ViSFE strategy for identifying high-risk EBV-infected individuals for NPC.
• The H101R mutation in LMP1 is functionally significant in EBVaNPC, impacting immune regulation and stem cell populations.
• This research provides a foundation for improved early detection and risk assessment of EBVaNPC.