Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing...

Area of Science:

• Genomics
• Oncology
• Bioinformatics

Background:

• Oropharyngeal squamous cell carcinoma (OPSCC) incidence is alarmingly high worldwide.
• Understanding OPSCC etiology is crucial for effective treatment strategies.

Purpose of the Study:

• To identify key oncogenic drivers in OPSCC pathogenesis using an integrative genomics approach.
• To develop predictive models for OPSCC using machine learning algorithms.

Main Methods:

• RNA-Sequencing (RNA-Seq) data from 46 HPV-positive OPSCC and 25 normal samples were analyzed.
• Differential gene expression analysis identified 714 candidate genes, reduced to 73 using Particle Swarm Optimization (PSO).
• Machine learning algorithms were trained on gene subsets to identify key drivers.

Main Results:

• Seven genes, including ECT2, LAMC2, DSG2, FAT1, PLOD2, COL1A1, and PLAU, were identified as significant contributors to OPSCC.
• Machine learning models achieved perfect validation scores using PSO-selected features.
• Gene set enrichment, protein-protein interaction, and disease ontology analyses confirmed the association of these genes with OPSCC.

Conclusions:

• The study elucidates critical oncogenic drivers in OPSCC.
• These findings provide vital insights for developing targeted therapies and understanding OPSCC pathogenesis.