Abstract
The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.
Highlights
Head and neck squamous cell carcinoma (HNSCC) is the most prevalent of the mucosal head and neck cancers and represents a significant health burden in the United States with approximately 40,000 new cases and almost 8,000 deaths per year
We constructed a co-expression network based on the progressor samples, identifying 21 modules ranging in size from 45 to 1,127 genes
Weighted networks analysis can provide a holistic view on disease dynamics, and enables us to reduce the complexity into organized and measurable relations
Summary
Head and neck squamous cell carcinoma (HNSCC) is the most prevalent of the mucosal head and neck cancers and represents a significant health burden in the United States with approximately 40,000 new cases and almost 8,000 deaths per year. Due to its non-specific presenting symptoms, patients often go undiagnosed until the cancer has progressed beyond local involvement leading to poorer treatment outcomes. Many HNSCCs will become progressive which is associated with a 40–50% 5year survival rate (Bonner et al, 2010). Reliable stratification of patients with HNSCC given the current tumornode-metastasis (TNM) staging system can be quite challenging with both social and biological factors at play (Cancer Staging— National Cancer Institute; Patel and Shah, 2005). Understanding tumor progression mechanisms is a critical step toward achieving better clinical outcomes. To this end, here we identify features predictive of tumor progression based on gene expression data
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