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An automatic NGS feature extraction algorithm for predicting EBV-associated
nasopharyngeal cancer and high-risk mutation
#MMPMID41188930
Wang Y
; Huang H
; Xiao X
; Wei J
; Long T
; Huang C
; Ai W
; Tong Y
; Guo L
; Lu R
; Gao C
Virol J
2025[Nov]; 22
(1
): 363
PMID41188930
show ga
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 .
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