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Network-based method for mining novel HPV infection related genes using random walk with restart algorithm.

Liucun Zhu1, Fangchu Su1, YaoChen Xu2

  • 1School of Life Sciences, Shanghai University, Shanghai 200444, China.

Biochimica Et Biophysica Acta. Molecular Basis of Disease
|December 4, 2017
PubMed
Summary

Researchers identified 104 novel genes linked to human papillomavirus (HPV) infection using a computational approach. This discovery aids understanding of HPV-related cancers and potential new therapies.

Keywords:
GO termsHuman papillomavirusKEGG pathwaysProtein-protein interactionRandom walk with restart algorithm

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Area of Science:

  • Genomics and Bioinformatics
  • Oncology
  • Virology

Background:

  • Human papillomavirus (HPV) is a common infection linked to various cancers, including cervical, anal, and vaginal.
  • Understanding genes involved in HPV infection is crucial for developing targeted therapies.

Purpose of the Study:

  • To identify novel genes associated with HPV infection using a computational prediction method.
  • To enhance understanding of cellular pathways and processes implicated in HPV infection.

Main Methods:

  • Employed a random walk with restart (RWR) algorithm on a protein-protein interaction (PPI) network using known HPV genes from HPVbase.
  • Filtered candidate genes using permutation and association tests to select key genes based on interaction confidence and functional similarity.
  • Utilized databases like STRING, Gene Ontology (GO) terms, and KEGG pathways for validation.

Main Results:

  • Identified 104 novel genes potentially related to HPV infection.
  • Confirmed literature-based associations for a subset of these genes with HPV infection processes and complications.
  • Demonstrated the reliability of the computational method for identifying HPV-related genes.

Conclusions:

  • The study successfully identified novel HPV infection-related genes, contributing valuable data for precision medicine approaches.
  • The findings provide a foundation for further research into HPV pathogenesis and therapeutic strategies.
  • The computational method proves effective for discovering disease-associated genes within complex biological networks.