Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). However, a comprehensive understanding of disease progression profiles comparing HPV+ and HPV- HNSCC cases is still lacking. The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV- HNSCC and to provide insights into potential regulatory mechanisms/effects (such as methylation and mutation) within the analyzed networks. For conducting this, we selected 276 samples from The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information (HPV status and tumor staging). We further added external information such as the identification of transcription factors to the networks. Genes were selected as differentially expressed and differentially methylated based on HPV status, of which 12 genes were doubly selected, including SYCP2, GJB6, FLRT3, PITX2 and CCNA1. Weight correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three main modules were associated with distinct co-expression patterns in HPV+ versus HPV- HNSCC. The molecular signatures found were mainly related to cell fate specification, keratinocyte differentiation, focal adhesion and regulation of protein oligomerization. This study provides comprehensive insights into complex genetic and epigenetic particularities in the development and progression of HNSCC in patients according to HPV status, identifying unseen gene interactions, and may contribute to unveiling specific genes/pathways as novel therapeutic targets for HNSCC.