MicroRNA model that can predict the prognosis of oral squamous cell carcinoma based on bioinformatics analysis.
- Author:
Ge ZHAO
1
;
Chang-Xue LI
1
;
Chao GUO
1
;
Hui ZHU
1
Author Information
- Publication Type:Journal Article
- Keywords: Cox regression analysis; The Cancer Genome Atlas; microRNA; oral squamous cell carcinoma; prognostic model
- MeSH: ATPases Associated with Diverse Cellular Activities; Biomarkers, Tumor; Carcinoma, Squamous Cell/genetics*; Cell Cycle Proteins; Computational Biology; Head and Neck Neoplasms; Humans; MicroRNAs; Mouth Neoplasms/genetics*; Prognosis; Squamous Cell Carcinoma of Head and Neck
- From: West China Journal of Stomatology 2020;38(6):622-627
- CountryChina
- Language:Chinese
-
Abstract:
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.
