An Automatic NGS Feature Extraction Algorithm for Predicting EBV-Associated Nasopharyngeal Cancer and High-risk Mutation

Epstein-Barr virus (EBV) infection is closely associated with the occurrence of nasopharyngeal carcinoma (NPC). The latent membrane protein 1 (LMP1) gene, known for its high heterogeneity, plays a crucial role in the oncogenic potential of EBV associated NPC (EBVaNPC). This study aimed to integrate algorithm with experimental validation to contribute valuable insights into the early detection and risk assessment of EBVaNPC, and investigate the functional significance of LMP1 key mutation. The LMP1 region in clinical EBV-positive subjects was sequenced with amplicon-based next-generation sequencing. An automatic viral sequence feature extraction (ViSFE) approach was developed. Biological implications of predicted key mutation on tumor cell biological behaviors were investigated through qRT-PCR, EdU analysis, transwell invasion assay, RNA sequencing and gene ontology fingerprint (GOF) method. Validation results demonstrate the feasibility of ViSFE applied to nucleotide data of varying lengths. Our study identified H101R mutation in LMP1 as a top feature, confirmed by proliferation and invasion experiments. By integrating EBVaNPC GOF and RNA sequencing data, the differentially expressed genes linked to the H101R mutation were primarily involved in immune regulation processes. Both approaches indicated a notable association between FOXP3-T cell anergy and WNT7A-stem cell population maintenance in HNE-1 ^MUT-LMP1 . This study offers a new strategy for high-risk NPC identification in EBV infected subjects. A tool for ViSFE is available at: http://www.biomedinfo.cn/ViSFE/index.html .