Clinicopathological Significance of AKT1 and PLK1 Expression in Oral Squamous Cell Carcinoma.

Purpose: Oral squamous cell carcinoma (OSCC) is the sixth leading cause of cancer-related death worldwide and is characterized by metastasis and recurrence. We aimed to evaluate the expression of AKT1 and PLK1 in OSCC and identify their correlation with the clinical and histological features and prognosis of patients with OSCC. Methods: Tissue samples were collected from 70 patients with OSCC and 50 patients with normal oral mucosa. The expression levels of AKT1 and PLK1 in OSCC tissues and normal oral mucosa were detected by immunohistochemistry. The chi-square test was used to identify correlations between the expression levels of AKT1 and PLK1 with patients' clinicopathologic characteristics. Survival analysis was assessed by the Kaplan-Meier method. Spearman's rank correlation test was used to determine the relationships between AKT1 and PLK1 expressions. The bioinformatics database GEPIA was used to verify the experimental results. Results: The chi-square test and Fisher's exact test showed that the positive expression rate of AKT1 and PLK1 in OSCC tissue was significantly higher than that in the normal oral mucosa (P < 0.05). PLK1 expression levels were significantly correlated with tumor stage and size (P < 0.05). Kaplan-Meier analysis showed that the survival time of AKT1 and PLK1 with high expression was significantly shorter than that of patients with low expression (P < 0.05). Spearman's rank correlation test showed a strong correlation between AKT1 and PLK1 expression in OSCC tissue (R = 0.53; P < 0.05). GEPIA bioinformatics database analysis results show that the expression and overall survival of AKT1 and PLK1 analysis and the correlation analysis of AKT1 and PLK1 were consistent with experimental results. Conclusion: AKT1 and PLK1 expressions are associated with the occurrence and progression of OSCC and may be used as diagnostic and prognostic indicators of OSCC. There may be a correlation between AKT1 and PLK1 in OSCC tissue.

[MicroRNA model that can predict the prognosis of oral squamous cell carcinoma based on bioinformatics analysis].

OBJECTIVE: The microRNA (miRNA) prognostic model can predict the prognosis of patients with oral squamous cell carcinoma (OSCC) on the basis of bioinformatics. Moreover, it can accurately group OSCC patients to improve targeted treatment. METHODS: We downloaded the miRNA and mRNA expression profile and clinical data of OSCC from The Cancer Genome Atlas (TCGA). The risk score model of miRNA was screened and established by univariate and multivariate Cox regression models. The performance of this prognostic model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). The target genes of six miRNAs were predicted and intersected with differential mRNA for enrichment analysis by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and gene ontology (GO) enrichment analysis. A protein protein interaction network (PPI) was constructed to screen hub genes. RESULTS: By using univariate and multivariate Cox regression analyses, the prognostic risk model was obtained. The AUC of the ROC curve for predicting 5-year survival in the training group, test group, and whole cohort were 0.757, 0.673, and 0.724, respectively. Furthermore, univariate Cox regression and multivariate Cox regression considering other clinical factors showed that the six-miRNAs signature could serve as an independent prognostic factor (P<0.001). The top 10 hub genes in the PPI network screened by intersecting target genes include CCNB1, EGF, KIF23, MCM10, ITGAV, MELK, PLK4, ADCY2, CENPF, and TRIP13. EGF and ADCY2 were associated with survival prognosis (P<0.05). CONCLUSIONS: The six-miRNAs signature could efficiently function as a novel and independent prognostic model for OSCC patients, which may be a new method to guide the accurate targeting treatment of OSCC.