Abstract: Abstract: Objective To analyze the expression of miR-1301 and clinical significance in patients of ovarian cancer using public database, and predict the possible target genes of miR- 1301 related to the prognosis of ovarian cancer.Methods Data of ovarian cancer in the TCGA database were collated. Ovarian cancer patients were divided into two groups(low expression group and high expression group) according to the median of miR- 1301 expression. Patients were divided into two groups according to age (＜60 years vs. ≥60 years), tumor location (unilateral vs. bilateral), clinical stage (Ⅰ+Ⅱ vs.Ⅲ+Ⅳ), longest dimension of tumor (＜2 cm vs. ≥2 cm), tumor grade (G1+G2 vs. G3+G4) and residual tumor (no residue vs.residual). Chi-square test was used to analyze the difference of miR-1301 expression in different patients of ovarian cancer.Kaplan- Meier (K- M) was used to analyze the survival difference of different factors, and multivariate Cox proportional hazards regression model was used to analyze the prognostic factors of patients with ovarian cancer. The target genes of miR-1301 were predicted by bioinformatics method, and survival analysis was performed by K-M. The correlation between the expression of target gene and miR-1301 expression was analyzed by Pearson and Spearman correlation methods. Results Chi-square test showed that there was no significant difference in the expression of miR-1301 in different clinical features of ovarian cancer. K-M survival analysis and Cox regression analysis showed that miR-1301 was an independent risk factor for the prognosis of ovarian cancer. Low expression of miR-1301 was associated with poor prognosis in patients with ovarian cancer (P＜0.05). The results of bioinformatics analysis displayed that high expressions of miR-1301 target genes including PAQR5, ATP2B1, KPNA3, TSC22D3, PTRF and HS6ST3 were associated with poor prognosis. Among them, PTRF and HS6ST3 expressions were negatively correlated with expression of miR- 1301 (P＜0.05). Conclusion Low expression of miR-1301 is significantly associated with poor prognosis of patients with ovarian cancer, which may provide a new target for the prediction of prognosis of ovarian cancer.

Key words: microRNAs, ovarian neoplasms, prognosis, computational biology, miR-1301, TCGA database