Integrated Multi-Omics Data Analysis Identifies a Novel Genetics-Risk Gene of IRF4 Associated with Prognosis of Oral Cavity Cancer

Abstract

Background: Oral cavity cancer (OCC) is one of the most common carcinoma diseases. Recent genome-wide association studies (GWAS) have reported numerous genetic variants associated with OCC susceptibility. However, the regulatory mechanisms of these genetic variants underlying OCC remain largely unclear. Objective: This study aimed to identify OCC-related genetics risk genes contributing to the prognosis of OCC. Methods: By combining GWAS summary statistics (N = 4,151) with expression quantitative trait loci (eQTL) across 49 different tissues from the GTEx database, we performed an integrative genomics analysis to uncover novel risk genes associated with OCC. By leveraging various computational methods based on multi-omics data, we prioritized some of these risk genes as promising candidate genes for drug repurposing in OCC. Results: Using two independent computational algorithms, we found that 14 risk genes whose genetics-modulated expressions showed a notable association with OCC. Among them, nine genes were newly identified, such as IRF4 (P = 2.5×10-9 and P = 1.06×10-4), TNS3 (P = 1.44×10-6 and P = 4.45×10-3), ZFP90 (P = 2.37×10-6 and P = 2.93×10-4), and DRD2 (P = 2.0×10-5 and P = 6.12×10-3), by using MAGMA and S-MultiXcan methods. These 14 genes were significantly overrepresented in several cancer-related terms (FDR < 0.05), and 10 of 14 genes were enriched in 10 potential druggable gene categories. Based on differential gene expression analysis, the majority of these genes (71.43%) showed remarkable differential expressions between OCC patients and paracancerous controls. Integration of multi-omics-based evidence from genetics, eQTL, and gene expression, we identified that the novel risk gene of IRF4 exhibited the highest ranked risk score for OCC (score = 4). Survival analysis showed that dysregulation of IRF4 expression was significantly associated with cancer patients outcomes (P = 8.1×10-5). Conclusions: Based on multiple omics data, we constructed a computational framework to pinpoint risk genes for OCC, and we prioritized 14 risk genes associated with OCC. There were nine novel risk genes, including IRF4 gene, which is significantly associated with the prognosis of OCC. These identified genes provide a drug repurposing resource to develop therapeutic drugs for treating patients, thereby contributing to the personalized prognostic management of OCC patients.