Abstract
Ovarian cancer is prevalent in women which is usually diagnosed at an advanced stage with a high mortality rate. The aim of this study is to investigate protein-coding gene, long non-coding RNA, and microRNA associated with the prognosis of patients with ovarian serous carcinoma by mining data from TCGA (The Cancer Genome Atlas) public database. The clinical data of ovarian serous carcinoma patients was downloaded from TCGA database in September, 2016. The mean age and survival time of 407 patients with ovarian serous carcinoma were 59.71 ± 11.54 years and 32.98 ± 26.66 months. Cox's proportional hazards regression analysis was conducted to analyze genes that were significantly associated with the survival of ovarian serous carcinoma patients in the training group. Using the random survival forest algorithm, Kaplan–Meier and ROC analysis, we kept prognostic genes to construct the multi-dimensional transcriptome signature with max area under ROC curve (AUC) (0.69 in the training group and 0.62 in the test group). The selected signature composed by VAT1L, CALR, LINC01456, RP11-484L8.1, MIR196A1 and MIR148A, separated the training group patients into high-risk or low-risk subgroup with significantly different survival time (median survival: 35.3 months vs. 64.9 months, P < 0.001). The signature was validated in the test group showing similar prognostic values (median survival: 41.6 months in high-risk vs. 57.4 months in low-risk group, P=0.018). Chi-square test and multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with ovarian serous carcinoma. Finally, we validated the expression of the genes experimentally.
Highlights
Ovarian cancer (OC) is a deadly female reproductive cancer, accounting for 5% of female cancer deaths [1]
Accumulating evidence suggests that protein-coding gene (PCG), long non-coding RNAs (lncRNAs) and microRNAs are involved in oncogenic and tumor suppressive pathways and they may serve as biomarkers [14, 38,39,40,41,42,43]
We used different statistics and machine learning methods to identify a PCGs-lncRNAsmicroRNAs expression signature that was associated with survival of ovarian serous carcinoma patients
Summary
Ovarian cancer (OC) is a deadly female reproductive cancer, accounting for 5% of female cancer deaths [1]. The majority of women with ovarian cancer are always diagnosed in an advanced stage, which substantially increases the risk of early death [2, 3]. Despite advances in imaging diagnosis, preoperative and postoperative care and chemotherapy, there has been little improvement in overall survival [4,5,6]. Identification of clinical markers in OC is of significance to early diagnosis, select appropriate treatment and improve prognosis of patients with OC. As the development of high-throughput sequencing technology, attempts have been made to identify molecular markers from sequencing data that affect clinical outcomes by integrating multiple profiles and clinical data [7,8,9,10]. A number of studies have shown that proteincoding genes (PCGs) involved in the many important www.impactjournals.com/oncotarget
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