Clinical and pathologic features of patients with non-epithelial ovarian cancer: retrospective analysis of a single institution 15-year experience

The potential of programmed death ligand-1 expression in ovarian malignant germ cell tumors as a prognostic factor

Background: Ovarian malignant germ cell tumors (OMGCT) that fail to recover with conventional management have a poor prognosis. Several recurrent events after chemotherapy have been found. Programmed death ligand-1 (PD-L1) is expressed in various malignancies and tumor infiltrating lymphocytes (TILs) with a known role as a prognostic factor.

Objective: To determine the role of PD-L1 expression in OMGCT in determining overall survival (OS) and progression-free survival (PFS).

Methods: Expression of PD-L1 was assessed from PD-L1 immunohisto-chemistry in paraffin block preparations from 40 patients diagnosed with OMGCT who met the inclusion criteria. The relationship between clinicopathological characteristics and OS and PFS was analyzed using the Kaplan-Meier method and multivariate analysis using the Cox regression model.

Results: No significant relationship was found between PD-L1 expression in tumor cells with 2-year OS (p=0.275) and PFS (p=0.421) in OMGCT. A significant association was found between histopathologic types with 2-year OS (p=0.002), and cancer stage with 2- year OS (p=0.028) and PFS (p=0.014).

Conclusion: PD-L1 expression in tumor cells was not related to OS and PFS in OMGCT patients. There is a tendency for death and recurrence in patients OMGCTs with low PD-L1 expression in tumor cells.

Recurrence-Associated Multi-RNA Signature to Predict Disease-Free Survival for Ovarian Cancer Patients

Ovarian cancer (OvCa) is an intractable gynecological malignancy due to the high recurrence rate. Several molecular biomarkers have been previously screened for early identifying patients with a high recurrence risk and poor prognosis. However, all the known studies focused on a single type of RNAs, not integrating various types. This study was to construct a new multi-RNA-based model to predict the recurrence and prognosis for OvCa patients by using the messenger RNA (mRNA, including long noncoding RNA (lncRNA)) and microRNA (miRNA) sequencing data of The Cancer Genome Atlas database. After univariate Cox regression and least absolute shrinkage and selection operator analyses, a multi-RNA-based signature (2 miRNAs: hsa-miR-508, hsa-miR-506; 1 lncRNA: TM4SF1-AS1; 11 mRNAs: MAGI3, SLAMF7, GLI2, PDK1, ARID3A, PLEKHG4B, TNFAIP8L3, C1QTNF3, NDUFAF1, CH25H, TMEM129) was generated and used to establish a risk score model. The high- and low-risk patients classified by the median risk score exhibited significantly different recurrence risks (89% versus 61%, p < 0.001) and survival time (the area under the receiver operating characteristic curve (AUC) = 0.901 for 5-year disease-free survival (DFS)). This risk model was independent of other clinical features and superior to pathologic staging for DFS prediction (AUC, 0.906 versus 0.524; C-index, 0.633 versus 0.510). Furthermore, some new interaction axes were revealed to explain the possible functions of these RNAs (competing endogenous RNA: TM4SF1-AS1-miR-186-STEAP2, LINC00536-miR-508-STEAP2, LINC00475-miR-506-TMEM129; coexpression: LINC00598-PLEKHG4B). In conclusion, this multi-RNA-based risk model may be clinically useful to stratify OvCa patients with different recurrence risks and survival outcomes and included RNAs may be potential therapeutic targets.