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Genome composition-based deep learning predicts oncogenic potential of HPVs.

Lin Hao1, Yu Jiang2, Can Zhang2

  • 1Department of Pharmacy, Linfen Central Hospital, Linfen, China.

Frontiers in Cellular and Infection Microbiology
|August 6, 2024
PubMed
Summary

This study reveals distinct genomic compositional traits in human papillomavirus (HPV) E6 and E7 genes, enabling accurate prediction of oncogenic potential using deep learning. This advances HPV genotype classification and risk assessment.

Keywords:
E6E7deep learninghuman papilloma viruses (HPVs)oncogenicity

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Area of Science:

  • Virology
  • Genomics
  • Bioinformatics

Background:

  • Human papillomaviruses (HPVs) are linked to over 30% of cancers, with E6 and E7 genes playing a key oncogenic role.
  • Current methods for identifying high-risk HPV genotypes are slow, relying on biological and clinical observations, leaving many types unclassified with unknown oncogenicity.

Purpose of the Study:

  • To analyze genomic compositional traits of HPV E6 and E7 genes.
  • To develop a deep learning model for predicting HPV oncogenic potential.
  • To improve the identification of oncogenic HPV types, especially unclassified ones.

Main Methods:

  • Retrieved and cleaned high-quality HPV sequence records.
  • Analyzed dinucleotide (DNT) and DNT representation (DCR) traits of E6 and E7 genes.
  • Built and trained a convolutional neural network (CNN) model using DCR data to predict oncogenicity.

Main Results:

  • Distinct DCR traits were observed for E6 and E7 coding sequences, clearly separating Alpha, Beta, and Gamma HPV groups.
  • CNN models effectively learned DCR data from E6 and E7 genes.
  • The CNN classifier accurately predicted the oncogenicity of both high and low-risk HPVs.

Conclusions:

  • Genomic compositional traits of HPV E6 and E7 genes differ significantly between high and low oncogenic types.
  • A DCR-based deep learning classifier accurately predicts HPV oncogenic phenotype.
  • This predictor aids in identifying HPV oncogenicity, particularly for unclassified or phenotypically unclear HPV types.