Background: Although the incidence of cervical cancer has decreased in decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related deaths worldwide. Identify the potential biomarker for treatment and prognosis of cervical cancer is necessary.Methods: Samples with mRNA-seq, copy number variant, single single nucleotide polymorphism data and clinical follow-up information were download from TCGA database, which were randomly divided into training datasets (N=146) and test datasets (N=147). We selected and identified prognostic gene set and genomic mutated gene set and then integrated the two set of data with random survival forest algorithm and constructed a prognostic signature. External validation datasets and immunohistochemical staining were also evaluated.Results: We obtained 1,416 different expression prognostic-related genes, 624 genes with copy number amplification, 1,038 genes with copy number deletion, and 163 significantly mutated gene. A total of 75 candidate genes were obtained after overlap of the different expression genes and genomic variations. Subsequently we obtained six characteristic genes through random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, DPAGT1 and low expression of CCL17, DES were associated with poor prognosis in cervical cancer patients. We constructed a six-gene signature which can separate cervical cancer samples associated with different overall survival and showed robust performance for predicting survival (Training set: p ˂ 0.001, AUC = 0.82; Testing set: p ˂ 0.01, AUC = 0.59). Conclusions: Our study identified a novel six-gene signature and nomogram to predict overall survival of cervical cancer, which may be beneficial to clinical decision making for individual treatment.