Background Ovarian cancer is the leading cause of death among gynecological malignancies. Immunotherapy has demonstrated potential effects in ovarian cancer. However, few studies on immune-related prognostic signatures in ovarian cancer have been reported. This study aimed to identify hub genes associated with immune infiltrates to provide insight into the immune regulatory mechanisms in ovarian cancer. Methods Raw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and University of California, Santa Cruz (UCSC) Xena websites. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were used to identify hub genes. Kaplan-Meier analysis and differential expression analysis were applied to explore the real hub genes. Results Through ssGSEA and WGCNA, 7 hub genes (LY9, CD5, CXCL9, IL2RG, SLAMF1, SLAMF6, and SLAMF7) were identified. Finally, LY9 and SLAMF1 were recognized as the real hub genes in immune infiltrates of ovarian cancer. LY9 and SLAMF1 are classified as SLAM family receptors involved in the activation of hematopoietic cells and the pathogenesis of multiple malignancies. Furthermore, 12 lncRNAs and 43 miRNAs significantly related to the 2 hub genes were applied to construct a lncRNA-miRNA-mRNA ceRNA network. The lncRNA-miRNA-mRNA ceRNA network shows upstream regulatory sites of the 2 hub genes. Conclusions These findings improve our understanding of the regulatory mechanism of and reveal potential immune checkpoints for immunotherapy for ovarian cancer.

Background:The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).Methods: Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.Results: A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024). Conclusions:We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this Frontiers in Oncology frontiersin.org 01

Background: The homologous recombination (HR) pathway defects in cancers induced abrogation of cell cycle checkpoints, resulting in the accumulation of DNA damage, mitotic catastrophe, and cell death. Cancers with BRCA1/2 loss and other accumulation of similar genomic scars resulting in HRD displayed increased sensitivity to chemotherapy. Our study aimed to explore HRD score genetic mechanisms and subsequent clinical outcomes in human cancers, especially ovarian cancer.Methods: We analyzed TCGA data of HRD score in 33 cancer types and evaluated HRD score distribution and difference among tumor stages and between primary and recurrent tumor tissues. A weighted gene co-expression network analysis (WGCNA) was performed to identify highly correlated genes representing essential modules contributing to the HRD score and distinguish the hub genes and significant pathways. We verified HRD status predicting roles in patients’ overall survival (OS) with univariate and multivariate Cox regression analyses and built the predicting model for patient survival.Results: We found that the HRD score increased with the rise in tumor stage, except for stage IV. The HRD score tended to grow up higher in recurrent tumor tissue than in their primary counterparts (p = 0.083). We constructed 15 co-expression modules with WGCNA, identified co-expressed genes and pathways impacting the HRD score, and concluded that the HRD score was tightly associated with tumor cells replication and proliferation. A combined HRD score ≥42 was associated with shorter OS in 33 cancer types (HR = 1.010, 95% CI: 1.008–1.011, p &lt; 0.001). However, in ovarian cancer, which ranked the highest HRD score among other cancers, HRD ≥42 cohort was significantly associated with longer OS (HR = 0.99, 95% CI: 0.98–0.99, p &lt; 0.0001). We also built a predicting model for 3 and 5 years survival in HGSC patients.Conclusion: A quantitative HRD score representing the accumulated genomic scars was dynamically increasing in proliferating tumor cells since the HRD score was tightly correlated to tumor cell division and replication. We highlighted HRD score biomarker role in prognosis prediction of ovarian cancer.

BackgroundThe overall cumulative live birth rate (CLBR) of poor ovarian responders (POR) is extremely low. Minimal ovarian stimulation (MOS) provides a relatively realistic solution for ovarian stimulation in POR. Our study aimed to investigate whether multiple MOS strategies resulted in higher CLBR compared to conventional gonadotropin releasing hormone (GnRH) antagonists in POR.MethodsThis retrospective study included 699 patients (1,058 cycles) from one center, who fulfilled the Bologna criteria between 2010 and 2018. Overall, 325 women (325 cycles) were treated with one-time conventional GnRH antagonist ovarian stimulation (GnRH-antagonist). Another 374 patients (733 cycles) were treated with multiple MOS including natural cycles. CLBR and time-and-cost-benefit analyses were compared between these two groups of women.ResultsGnRH antagonists provided more retrieved oocytes, meiosis II oocytes, fertilized oocytes, and more viable embryos compared to both the first MOS (p &lt; 0.001) and the cumulative corresponding numbers in multiple MOSs (p &lt; 0.001). For the first in vitro fertilization (IVF) cycle, GnRH antagonists resulted in higher CLBR than MOS [12.92 versus 4.54%, adjusted OR (odds ratio) 2.606; 95% CI (confidence interval) 1.386, 4.899, p = 0.003]. The one-time GnRH-antagonist induced comparable CLBR (12.92 versus 7.92%, adjusted OR 1.702; 95% CI 0.971, 2.982, p = 0.063), but a shorter time to live birth [9 (8, 10.75) months versus 11 (9, 14) months, p = 0.014] and similar financial expenditure compared to repeated MOS [20,838 (17,953, 23,422) ¥ versus 21,261.5 (15,892.5, 35,140.25) ¥, p = 0.13].ConclusionBoth minimal ovarian stimulation (MOS) and GnRH-antagonists provide low chances of live birth in poor responders. The GnRH antagonist protocol is considered a suitable choice for PORs with comparable CLBR, shorter times to live birth, and similar financial expenditure compared to repeated MOS.

This study was conducted to evaluate the prognostic value of receptor-interacting protein kinase 4 (RIPK4) in ovarian cancer (OC) and its role in tumorigenesis. RNA expression and the corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The relationship between clinical-pathological characteristics and RIPK4 expression was analyzed using the Wilcoxon signed-rank test and logistic regression. The Cox regression and the Kaplan-Meier method were used to evaluate the relationship between clinicopathological features and overall survival (OS). Gene set enrichment analysis (GSEA) was performed using Molecular Signatures Database. Scratch assay, transwell assay, and cell transfection were used to verify the function of RIPK4. Overexpression of RIPK4 was associated with the stage of OC and distant metastasis. Survival analysis revealed that patients with OC and higher expression of RIPK4 had a poorer prognosis. Univariate and multivariate analyses indicated that high expression of RIPK4 was associated with poor OS, as well as age and stage of OC. The areas under the curve (AUC) at 1, 4, and 8 years were 0.737, 0.634, and 0.669, respectively, according to the established OS prediction model. GSEA revealed that adherens junction, cadherin binding, and Wnt signaling pathway were enriched in the high RIPK4 expression group. Cell transfection confirmed RIPK4 was involved in the Wnt signaling pathway. RIPK4 can act as a potential prognostic molecular marker for poor survival in OC. Moreover, RIPK4 is associated with tumor metastasis and implicated in the regulation of the Wnt signaling pathway.