Two protein-coding genes act as a novel clinical signature to predict prognosis in patients with ovarian serous cystadenocarcinoma

作者：Zhang, Jue; Xu, Meng; Gao, Han; Guo, Jin-Chen; Guo, Yu-Lin; Zou, Miao; Wu, Xu-Feng^*

来源：Oncology Letters, 2018, 15(3): 3669-3675.

DOI：10.3892/ol.2018.7778

• ovarian serous cystadenocarcinoma
• gene expression
• survival
• random survival forests variable hunting algorithm
• biomarker

Ovarian cancer is the seventh most common type of cancer and the eighth most common cause of cancer-associated mortality among women. A number of studies have hypothesized that the expression status of certain genes may be used to predict prognosis in ovarian cancer. In the present study, the RNA expression data from next-generation sequencing and the clinical information of 413 patients from The Cancer Genome Atlas dataset was downloaded to identify the association between gene-expression level and the survival time of the patients with ovarian serous cystadenocarcinoma. A five-gene model was predicted to be significantly associated with patient survival in ovarian serous cystadenocarcinoma by using random survival forests variable hunting algorithm and Cox analysis. A total of two genes, mesencephalic astrocyte-derived neurotrophic factor and dedicator of cytokinesis 11, of the predicted five genes demonstrated positive expression in the ovarian serous cystadenocarcinoma cancer tissues by polymerase chain reaction analysis. Kaplan-Meier and Receiver Operating Characteristic analysis confirmed that the model of the two genes exhibited high sensitivity and specificity to predict the prognostic survival of patients. In conclusion, the expression of the two genes in the two-gene model was associated with the prognostic outcomes of patients with ovarian serous cystadenocarcinoma; the model demonstrated potential as a novel prognostic indicator, which may have important clinical significance.

• 出版日期2018-3
• 单位湖北省妇幼保健院; 汕头大学