Abstract
Cervical cancer is the second most commonly diagnosed cancer in women. Novel prognostic biomarkers are required to predict the progression of cervical cancer. Cervical cancer expression data were obtained from The Cancer Genome Atlas (TCGA) database. MicroRNAs (miRNAs) significantly differentially expressed between early‑ and advanced‑stage samples were identified by expression analysis. An optimal subset of signature miRNAs for pathologic stage prediction was delineated using the random forest algorithm and was used for the construction of a cervical cancer‑specific support vector machine (SVM) classifier. The roles of signature miRNAs in cervical cancer were analyzed by functional annotation. In total, 44 significantly differentially expressed miRNAs were identified. An optimal subset of 7 signature miRNAs was identified, including hsa‑miR‑144, hsa‑miR‑147b, hsa‑miR‑218‑2, hsa‑miR‑425, hsa‑miR‑451, hsa‑miR‑483 and hsa‑miR‑486. The signature miRNAs were used to construct an SVM classifier and exhibited a good performance in predicting pathologic stages of samples. SVM classification was found to be an independent prognostic factor. Functional enrichment analysis indicated that these signature miRNAs are involved in tumorigenesis. In conclusion, the subset of signature miRNAs could potentially serve as a novel diagnostic and prognostic predictor for cervical cancer.