Comprehensive Analysis of mRNA Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis

Abstract EN

Background.

Head and neck squamous cell cancer (HNSCC) is the sixth most common cancer in the world; its pathogenic mechanism remains to be further clarified.

Methods.

Robust rank aggregation (RRA) analysis was utilized to identify the metasignature dysregulated genes, which were then used for potential drug prediction.

Weighted gene coexpression network analysis (WGCNA) was performed on all metasignature genes to find hub genes.

DNA methylation analysis, GSEA, functional annotation, and immunocyte infiltration analysis were then performed on hub genes to investigate their potential role in HNSCC.

Result.

A total of 862 metasignature genes were identified, and 6 potential drugs were selected based on these genes.

Based on the result of WGCNA, six hub genes (ITM2A, GALNTL1, FAM107A, MFAP4, PGM5, and OGN) were selected (GS>0.1, MM>0.75, GS p value < 0.05, and MM p value < 0.05).

All six genes were downregulated in tumor tissue (FDR<0.01) and were related to the clinical stage and prognosis of HNSCC in different degrees.

Methylation analysis showed that the dysregulation of ITM2A, GALNTL1, FAM107A, and MFAP4 may be caused by hypermethylation.

Moreover, the expression level of all 6 hub genes was positively associated with immune cell infiltration, and the result of GSEA showed that all hub genes may be involved in the process of immunoregulation.

Conclusion.

All identified hub genes could be potential biomarkers for HNSCC and provide a new insight into the diagnosis and treatment of head and neck tumors.