Transcriptional Biomarkers in Oral Cancer: An Integrative Analysis and the Cancer Genome Atlas Validation

OBJECTIVE: An impervious mortality rate in oral cancer (OC) to a certain extent explains the exigencies of precise biomarkers. Therefore, the study was intended to identify OC candidate biomarkers using samples of healthy normal tissues (N=335), adjacent normal tissues (N=93) and OC tissues (N=533) from online microarray data. METHODS: Differentially expressed genes (DEGs) were recognised through GeneSpring software (Fold change >4.0 and ‘p’ value <0.001 with Benjamini Hochberg false discovery rate). The DEGs were analysed for their functional annotation and network using GeneCodis 4.0 and STRING databases. The DEGs were further cross-examined in the cancer genome atlas (TCGA) database. RESULTS: 197, 229 and 104 DEGs were spotted from three comparisons: i) OC tissues against healthy normal tissues, ii) OC tissues against adjacent normal tissues and iii) adjacent normal tissues against healthy normal tissues, respectively. Functional gene set enrichment unveiled significance of focal adhesion pathway in OC initiation and progression. Advanced analysis of TCGA cohort (n=345) recognised 85 genes that were altered in ≥10% OC patients for mutations, copy number variations, m-RNA expression and protein expression. Strikingly, the elected 5-gene panel (YWHAZ, RHOA, DLG1, LY6E and PLEC) showed the location on chromosome 3 and 8 and each gene was found to be altered in ≥20% OC patients for m-RNA expression. Further, TCGA validation demonstrated significant association of EIF4A2, CTNNA1 and PMEPA1 expression with overall and disease-free survival. CONCLUSION: Using this integrative approach, our study identified prominent transcriptional biomarkers which may be significant targets for OC therapy. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.