Prognosis Of Ovarian Cancer Patients Research Articles

M6A-modified circASXL1 promotes proliferation and migration of ovarian cancer through the miR-320d/RACGAP1 axis.

Ovarian cancer (OC) is one of the most common malignant tumors in women. Circular RNAs (circRNAs) can potentially regulate the development of OC. Therefore, this study investigated the role of circASXL1 in OC progression. Cell functions were assessed by MTT, colony formation, wound healing, and transwell assays. RIP and dual luciferase reporter assays confirmed the relationship between miR-320d and circASXL1 or RACGAP1. MeRIP was utilized to detect m6A levels. Xenograft tumor was established for in vivo experiments. CircASXL1 and RACGAP1 levels were increased in OC tissues and cells, whereas miR-320d expression was decreased. Upregulation of circASXL1 was associated with poor prognosis in OC patients. CircASXL1 silencing suppressed OC cell proliferation, migration and invasion in vitro and in vivo. Mechanistically, METTL3/IGF2BP1-mediated m6A modification maintained circASXL1 stability and upregulated its expression. CircASXL1 was a ceRNA that sequestrated miR-320d from RACGAP1, leading to increased RACGAP1 expression. CircASXL1 promoted OC cell proliferation, migration and invasion via the miR-320d/RACGAP1 axis. Therefore, m6A-modified circASXL1 acts as an oncogene in OC by targeting miR-320d and activating RACGAP1/PI3K/Akt pathway, which provides novel promising biomarkers for OC diagnosis.

LncRNA GAS5-hnRNPK axis inhibited ovarian cancer progression via inhibition of AKT signaling in ovarian cancer cells

BackgroundThe incidence of ovarian cancer ranks third among gynecologic malignancies, but the mortality rate ranks first.MethodsThe expression of GAS5 is low in ovarian cancer and is associated with the low survival of ovarian cancer patients according to public ovarian cancer databases. GAS5 overexpression inhibited ovarian malignancy by affecting the proliferation and migratory abilities in OVCAR3 and A2780 cells. GAS5 overexpression increased the rate of cell apoptosis, and the cells were blocked in the G1 phase as assessed by flow cytometry.ResultsWe found that hnRNPK was a potential target gene, which was regulated negatively by GAS5 based on RNA-pulldown and mass spectrometry analysis. Mechanistically, GAS5 affected the inhibition of the PI3K/AKT/mTOR pathways and bound the protein of hnRNPK, which influenced hnRNPK stability. Furthermore, rescue assays demonstrated hnRNPK was significantly involved in the progression of ovarian cancer.ConclusionsOur study showed one of the mechanisms that GAS5 inhibited ovarian cancer metastasis by down-regulating hnRNPK expression, and GAS5 can be used to predict the prognosis of ovarian cancer patients.Graphical

Liquid biopsy in ovarian cancer: advantages and limitations for prognosis and diagnosis.

Ovarian cancer (OC) is a highly fatal gynecologic malignancy, often diagnosed at an advanced stage which presents significant challenges for disease management. The clinical application of conventional tissue biopsy methods and serological biomarkers has limitations for the diagnosis and prognosis of OC patients. Liquid biopsy is a novel sampling method that involves analyzing distinctive tumor elements secreted into the peripheral blood. Growing evidence suggests that liquid biopsy methods such as circulating tumor cells, cell-free RNA, circulating tumor DNA, exosomes, and tumor-educated platelets may improve early prognosis and diagnosis of OC, leading to enhanced therapeutic management of the disease. This study reviewed the evidence demonstrating the utility of liquid biopsy components in OC prognosis and diagnosis, and evaluated the current advantages and limitations of these methods. Additionally, the existing obstacles and crucial topics for future studies utilizing liquid biopsy in OC patients were discussed.

Diabetes and ovarian cancer: risk factors, molecular mechanisms and impact on prognosis.

Diabetes mellitus has been linked to a lower rate of cancer survival and an increase in the incidence of most malignancies. Investigations showed that diabetes might affect ovarian cancer (OC) prognosis and survival. Based on the current information, this study intends to review the risk factors, molecular pathways, and impact of diabetes on OC. The data was derived from online databases, including Web of Science, PubMed, and Scopus. The inclusion criteria were original studies, which included the risk factors, molecular mechanisms, and impact of diabetes on OC. The effect of different antidiabetic drugs was also discussed in this manuscript. All of the clinical, in vivo, and in vitro studies were included in the present study. The diagnosis of diabetes mellitus negatively affects the survival and prognosis in OC cases. The epidemiologic data shows that the risk of OC increases in patients with diabetes mellitus compared to the healthy population. Insulin-like growth factors family was raised in diabetic patients, which target several mechanisms, including targeting oxidative stress, angiogenesis, and tumor markers. Antidiabetic drugs such as metformin, sitagliptin, and rosiglitazone have a promising effect on elongation of survival and enhancement of prognosis in OC patients. Diabetes mellitus is a significant risk factor for OC in women, and it negatively impacts survival and prognosis. Molecular mechanisms such as IGF family, oxidative stress, and inflammatory cytokines have been identified to explain this relationship. Antidiabetic drugs like metformin, sitagliptin, and rosiglitazone have shown promise in improving survival and prognosis of OC patients.

Poly(ADP-ribose) Polymerase Inhibitor Olaparib in the Treatment of Ovarian Cancer: A Comprehensive Review of Current Literature

Purpose of the review: This comprehensive review aims to provide a summary of current research on the utilization of olaparib, a poly(ADP-ribose) polymerase (PARP) inhibitor, in the treatment of ovarian cancer. The review aims to highlight the key findings from recent clinical trials and assess the potential of olaparib as a targeted therapy for improving the prognosis of ovarian cancer patients.
 Recent findings: Ovarian cancer remains a significant global health concern with high mortality rates. While optimal debulking surgery and platinum-based chemotherapy are the standard treatments, the recurrence rates remain substantial. The emergence of PARP inhibitors, particularly olaparib, has introduced a novel therapeutic approach that targets the genomic instability and DNA repair mechanisms in cancer cells. Notable clinical trials, such as SOLO1, SOLO2, and PAOLA-1, have demonstrated the effectiveness of olaparib in significantly improving progression-free survival, particularly in patients with BRCA mutations or homologous recombination deficiency. Additionally, combination therapies involving olaparib, such as those with bevacizumab or entinostat, have shown promising results.
 Summary: The utilization of olaparib has brought about a paradigm shift in the treatment of ovarian cancer. Notably, it has shown significant improvements in progression-free survival and overall survival, particularly in patients with BRCA mutations or homologous recombination deficiency. The exploration of olaparib through various clinical trials and combination therapies continues to provide valuable insights and offer new prospects for ovarian cancer patients. Moreover, the growing understanding of PARP inhibitors holds the potential for further advancements in the prognosis of patients with this formidable condition.

Advances in application of circulating tumor DNA in ovarian cancer.

Ovarian cancer is the third most common gynecologic cancer worldwide and has the highest mortality rate among gynecologic cancers. Identifying timely and effective biomarkers at different stages of the disease is the key to improve the prognosis of ovarian cancer patients. Circulating tumor DNA (ctDNA) is a fragment of free DNA produced by tumor cells in the blood. Current techniques for detecting ctDNA mainly include quantitative polymerase chain reaction (PCR), targeted next-generation sequencing (NGS), and non-targeted NGS (such as whole exon or whole genome sequencing). As a non-invasive liquid biopsy technique, ctDNA has a good application prospect in the ovarian cancer diagnosis, monitoring of treatment response and efficacy evaluation, detection of reverse mutation and related medication guidance, and prognosis evaluation. This article reviews the advances in application of ctDNA in ovarian cancer.

Ascites-derived CDCP1+ extracellular vesicles subcluster as a novel biomarker and therapeutic target for ovarian cancer.

Ovarian cancer (OVCA) is one of the most prevalent malignant tumors of the female reproductive system, and its diagnosis is typically accompanied by the production of ascites. Although liquid biopsy has been widely implemented recently, the diagnosis or prognosis of OVCA based on liquid biopsy remains the primary emphasis. In this study, using proximity barcoding assay, a technique for analyzing the surface proteins on single extracellular vesicles (EVs). For validation, serum and ascites samples from patients with epithelial ovarian cancer (EOC) were collected, and their levels of CDCP1 was determined by enzyme-linked immunosorbent assay. Tissue chips were prepared to analyze the relationship between different expression levels of CDCP1 and the prognosis of ovarian cancer patients. We discovered that the CUB domain-containing protein 1+ (CDCP1+) EVs subcluster was higher in the ascites of OVCA patients compared to benign ascites. At the same time, the level of CDCP1 was considerably elevated in the ascites of OVCA patients. The overall survival and disease-free survival of the group with high CDCP1 expression in EOC were significantly lower than those of the group with low expression. In addition, the receiver operating characteristic curve demonstrates that EVs-derived CDCP1 was a biomarker of early response in OVCA ascites. Our findings identified a CDCP1+ EVs subcluster in the ascites of OVCA patients as a possible biomarker for EOC prevention.

Identification and validation of shared genes and key pathways in endometriosis and endometriosis-associated ovarian cancer by weighted gene co-expression network analysis and machine learning algorithms.

Epidemiological studies reported that patients with endometriosis had an increased risk of developing endometriosis-associated ovarian cancer (EAOC). The present study aimed to identify shared genes and key pathways that commonly interacted between EAOC and endometriosis. The expression matrix of ovarian cancer and endometriosis were collected from the Gene Expression Omnibus database. The weighted gene co-expression network analysis (WGCNA) was used to construct co-expression gene network. Machine learning algorithms were applied to identify characteristic genes. CIBERSORT deconvolution algorithm was used to explore the difference in tumor immune microenvironment. Furthermore, diagnostic nomogram was constructed and evaluated for supporting clinical practicality. We identified 262 shared genes between EAOC and endometriosis via WGCNA analysis. They were mainly enriched in cytokine-cytokine receptor interaction. After protein-protein interaction network and machine learning algorithms, we recognized two characteristic genes (EDNRA, OCLN) and established a nomogram that presented an outstanding predictive performance. The hub genes demonstrated remarkable associations with immunological functions. Survival analysis indicated that dysregulated expressions of EDNRA and OCLN were closely correlated with prognosis of ovarian cancer patients. gene set enrichment analyses revealed that the two characteristic genes were mainly enriched in the cancer- and immune-related pathways. Our findings pave the way for further investigation of potential candidate genes and will aid in improving the diagnosis and treatment of EAOC in endometriosis patients. More research is required to determine the exact mechanisms by which these two hub genes affecting the development and progression of EAOC from endometriosis.

Different disease progression patterns based on RECIST and CA-125 criteria and the impact on prognosis in patients with ovarian cancer treated with PARP inhibitors.

e17536 Background: We aimed to assess the clinical features of disease progression (PD) based on Response Evaluation Criteria in Solid Tumors (RECIST) and CA-125 criteria, and analyse the impact on prognosis in ovarian cancer patients treated with poly (ADP-ribose) polymerase (PARP) inhibitors. Methods: All epithelial ovarian cancer patients included in this retrospective analysis were admitted to the Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University between January 2011 and July 2021. All enrolled patients were treated with PARP inhibitor monotherapy or combination therapy with antiangiogenic agents and regularly assessed with RECIST and CA-125 criteria until disease progression confirmed by RECIST criteria. Patients were divided to RECIST PD group and CA-125 PD group based on regular assessment: RECIST PD group comprised patients who were first confirmed with RECIST-assessed PD and did not achieve PD according to CA-125 criteria during the subsequent 28 days; CA-125 PD group comprised as follows: (i) patients who were first diagnosed with RECIST-assessed PD and defined as consistent if CA-125 PD occurred within the following 28 days; (ii) patients who had doubled CA-125 levels for the first time, confirmed after at least 1 week, and defined as consistent if subsequent RECIST progression occurred within 28 days of the initial doubling of CA-125; (iii) patients in whom the first occurrence of CA-125 was doubled and confirmed after at least 1 week, defined as priority PD of CA-125 provided there was no progression in RECIST assessment during the subsequent 28 days. Results: A total of 124 patients were included, with 64 (51.6%) in RECIST PD group and 60 (48.4%) in CA-125 PD group. Forty-three patients (34.7%) had disease progression consistent with RECIST and CA-125 criteria, 64 patients (51.6%) had priority PD with RECIST criteria, and 17 patients (13.7%) had priority PD with CA-125 criteria. The median interval between disease progression as determined by priority CA-125 criteria and subsequent RECIST criteria was 55 days (range 29–192 days). Patients in CA-125 PD group had a higher proportion of lymph node progression (40.0% vs. 17.2%, p = 0.005), multi-site disease progression (41.7% vs. 21.9%, p=0.018), and a worse prognosis with a median overall survival (OS) of 70.5 vs. 87.2 months (hazard ratio (HR)=2.01, p=0.015). In multivariate analysis, residual lesions after primary surgery (no vs. yes, HR=0.493, p=0.015), duration of PARP inhibitor treatment (≤ 6 vs. > 6 months, HR=2.262, p=0.006), priority criteria for PD (RECIST vs. CA-125, HR=0.441, p=0.006), and BRCA1/2 mutation status (wild-type vs. mutation, HR=2.150, p=0.028) were significantly associated with OS. Conclusions: Priority disease progression based on CA-125 criteria in ovarian cancer patients treated with PARP inhibitors might suggest a poor prognosis.

Signal transducer and activator of transcription 3 and cancer associated fibroblasts jointly generate chemo-resistance and affect prognosis in epithelial ovarian cancer

Objective: To investigate the mechanism of signal transducer and activator of transcription 3 (STAT3) and cancer associated fibroblasts (CAF) jointly generate chemo-resistance in epithelial-ovarian cancer and their effect on prognosis. Methods: A total of 119 patients with high-grade ovarian serous cancer who received surgery in Cancer Hospital of Chinese Academy of Medical Sciences from September 2009 to October 2017 were collected. The clinico-pathological data and follow-up data were complete. Multivariate Cox regression model was used to analyze the prognostic factors. Ovarian cancer tissue chips of patients in our hospital were prepared. EnVision two-step method immunohistochemistry was used to detect the protein expression levels of STAT3, the specific markers of CAF activation, fibroblast activating protein (FAP), and type Ⅰ collagen (COL1A1) secreted by CAF. The relationship between the expression of STAT3, FAP, COL1A1 protein and drug resistance and prognosis of ovarian cancer patients was analyzed, and the correlation between the expression of three proteins was analyzed. These results were verified through the gene expression and prognostic information of human ovarian cancer tissues collected in the GSE26712 dataset of gene expression omnibus (GEO) database. Results: (1) Multivariate Cox regression model analysis showed that chemotherapy resistance was an independent risk factor for overall survival (OS) of ovarian cancer (P<0.001). (2) The expression levels of STAT3, FAP, and COL1A1 proteins in chemotherapy resistant patients were significantly higher than those in chemotherapy sensitive patients (all P<0.05). Patients with high expression of STAT3, FAP, and COL1A1 had significantly shorter OS than those with low expression (all P<0.05). According to the human ovarian cancer GSE26712 dataset of GEO database, patients with high expression of STAT3, FAP, and COL1A1 also showed shorter OS than patients with low expression (all P<0.05), the verification results were consistent with the detection results of ovarian cancer patients in our hospital. (3) Correlation analysis showed that the protein level of STAT3 was positively correlated with FAP and COL1A1 in our hospital's ovarian cancer tissue chips (r=0.47, P<0.001; r=0.30, P=0.006), the analysis of GEO database GSE26712 dataset showed that the expression of STAT3 gene and FAP, COL1A1 gene were also significantly positively correlated (r=0.31, P<0.001; r=0.52, P<0.001). Conclusion: STAT3 and CAF could promote chemotherapy resistance of ovarian cancer and lead to poor prognosis.

Differences of survival benefits brought by various treatments in ovarian cancer patients with different tumor stages

PurposeThe current study aimed to explore the prognosis of ovarian cancer patients in different subgroup using three prognostic research indexes. The current study aimed to build a prognostic model for ovarian cancer patients.MethodsThe study dataset was downloaded from Surveillance Epidemiology and End Results database. Accelerated Failure Time algorithm was used to construct a prognostic model for ovary cancer.ResultsThe mortality rate in the model group was 51.6% (9,314/18,056), while the mortality rate in the validation group was 52.1% (6,358/12,199). The current study constructed a prognostic model for ovarian cancer patients. The C indexes were 0.741 (95% confidence interval: 0.731–0.751) in model dataset and 0.738 (95% confidence interval: 0.726–0.750) in validation dataset. Brier score was 0.179 for model dataset and validation dataset. The C indexes were 0.741 (95% confidence interval: 0.733–0.749) in bootstrap internal validation dataset. Brier score was 0.178 for bootstrap internal validation dataset.ConclusionThe current research indicated that there were significant differences in the survival benefits of treatments among ovarian cancer patients with different stages. The current research developed an individual mortality risk predictive system that could provide valuable predictive information for ovarian cancer patients.Graphical

LINC01119 encapsulated by cancer-associated adipocytes-derived exosomes promotes M2 polarization of macrophages to induce immune escape in ovarian cancer in a 3D co-culture cell-based model.

In the present study, we sought to clarify the role of LINC01119 delivered by cancer-associated adipocytes (CAAs)-derived exosomes (CAA-Exo) and its mechanistic actions in ovarian cancer (OC). The expression of LINC01119 was determined in OC, and the relationship between LINC01119 expression and the prognosis of OC patients was analyzed. Besides, 3D co-culture cell models were constructed using green fluorescent protein-labeled OC cells and red fluorescent protein-labeled mature adipocytes. Mature adipocytes were co-cultured with OC cells to induce CAA. Macrophages treated with CAA-Exo were co-cultured with SKOV3 cells following ectopic expression and depletion experiments of LINC01119 and SOCS5 to detect M2 polarization of macrophages, PD-L1 level, proliferation of CD3+ T cells, and cytotoxicity of T cells to SKOV3 cells. LINC01119 was elevated in the plasma Exo of OC patients, which was related to shorter overall survival in OC patients. LINC01119 expression was increased in CAA-Exo, which could upregulate SOCS5 in OC. Finally, CAA-Exo carrying LINC01119 induced M2 polarization of macrophages to promote immune escape in OC, as evidenced by inhibited CD3+ T cell proliferation, increased PD-L1 level, and attenuated T cell toxicity to SKOV3 cells. In conclusion, the key findings of the current study demonstrated the promoting effects of CAA-Exo containing LINC01119 mediating SOCS5 on M2 polarization of macrophages and immune escape in OC.

The myo-inositol biosynthesis rate-limiting enzyme ISYNA1 suppresses the stemness of ovarian cancer via Notch1 pathway

Cancer stem cells (CSCs) play a central role in ovarian cancer (OC), understanding regulatory mechanisms governing their stemness is critical. Here, we report ISYNA1, the rate-limiting enzyme in myo-inositol biosynthesis, as a suppressor of OC regulating cancer stemness. We identified ISYNA1 as a differentially expressed gene in normal ovary and ovarian cancer tissues, as well as OC cells and OCSCs. Low ISYNA1 expression correlated with poor prognosis in OC patients. In addition, ISYNA1 was negatively correlated with cancer stem cell (CSC) markers, and ISYNA1-related pathways were enriched in Wnt, Notch, and other critical cancer pathways. ISYNA1 deficiency promoted OC cell growth, migration, and invasion ability in vitro and in vivo. Knockdown of ISYNA1 increased stemness of OC cells, including self-renewal, CSC markers expression, ALDH activity, and proportion of CD44+/CD117+ CSCs. Conversely, ectopic overexpression of ISYNA1 suppresses cell proliferation, migration, invasion and stemness of OC cells. Mechanistically, ISYNA1 inhibits OC stemness by regulating myo-inositol to suppress Notch1 signaling. In summary, these data provide evidence that ISYNA1 act as a tumor suppressor in OC and a regulator of stemness, providing insight into potentially targetable pathways for ovarian cancer therapy.

Knockdown of heat shock protein family D member 1 (HSPD1) promotes proliferation and migration of ovarian cancer cells via disrupting the stability of mitochondrial 3-oxoacyl-ACP synthase (OXSM)

BackgroundHeat shock protein 60 (HSP60) is essential for the folding and assembly of newly imported proteins to the mitochondria. HSP60 is overexpressed in most types of cancer, but its association with ovarian cancer is still in dispute. SKOV3 and OVCAR3 were used as experimental models after comparing the expression level of mitochondrial HSP60 in a normal human ovarian epithelial cell line and four ovarian cancer cell lines.ResultsLow HSPD1 (Heat Shock Protein Family D (HSP60) Member 1) expression was associated with unfavorable prognosis in ovarian cancer patients. Knockdown of HSPD1 significantly promoted the proliferation and migration of ovarian cancer cells. The differentially expressed proteins after HSPD1 knockdown were enriched in the lipoic acid (LA) biosynthesis and metabolism pathway, in which mitochondrial 3-oxoacyl-ACP synthase (OXSM) was the most downregulated protein and responsible for lipoic acid synthesis. HSP60 interacted with OXSM and overexpression of OXSM or LA treatment could reverse proliferation promotion mediated by HSPD1 knockdown.ConclusionsHSP60 interacted with OXSM and maintained its stability. Knockdown of HSPD1 could promote the proliferation and migration of SKOV3 and OVCAR3 via lowering the protein level of OXSM and LA synthesis.

Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies.

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecological cancer that requires accurate prognostic models and personalized treatment strategies. The tumor microenvironment (TME) is crucial for disease progression and treatment. Machine learning-based integration is a powerful tool for identifying predictive biomarkers and developing prognostic models. Hence, an immune-related risk model developed using machine learning-based integration could improve prognostic prediction and guide personalized treatment for HGSOC. During the bioinformatic study in HGSOC, we performed (i) consensus clustering to identify immune subtypes based on signatures of immune and stromal cells, (ii) differentially expressed genes and univariate Cox regression analysis to derive TME- and prognosis-related genes, (iii) machine learning-based procedures constructed by ten independent machine learning algorithms to screen and construct a TME-related risk score (TMErisk), and (iv) evaluation of the effect of TMErisk on the deconstruction of TME, indication of genomic instability, and guidance of immunotherapy and chemotherapy. We identified two different immune microenvironment phenotypes and a robust and clinically practicable prognostic scoring system. TMErisk demonstrated superior performance over most clinical features and other published signatures in predicting HGSOC prognosis across cohorts. The low TMErisk group with a notably favorable prognosis was characterized by BRCA1 mutation, activation of immunity, and a better immune response. Conversely, the high TMErisk group was significantly associated with C-X-C motif chemokine ligands deletion and carcinogenic activation pathways. Additionally, low TMErisk group patients were more responsive to eleven candidate agents. Our study developed a novel immune-related risk model that predicts the prognosis of ovarian cancer patients using machine learning-based integration. Additionally, the study not only depicts the diversity of cell components in the TME of HGSOC but also guides the development of potential therapeutic techniques for addressing tumor immunosuppression and enhancing the response to cancer therapy.

Abstract 654: Ovarian cancer-cell glucocorticoid receptor activity modulates cytokine secretion promoting infiltration of immunosuppressive cells into the tumor microenvironment

Abstract Elevated levels of immunosuppressive myeloid-derived suppressor cells (MDSCs) circulate in the peripheral blood or infiltrate the tumors as well as ascites in ovarian cancer patients, however the cause for this observed phenotype is unknown. MDSCs contribute to tumor immune tolerance primarily via inhibition of cytotoxic T-cells or by activation of immunosuppressive T-regulatory cells. We previously showed that high tumor cell glucocorticoid receptor (GR) expression is associated with a relatively poor prognosis in ovarian cancer patients. Additionally, using primary tumor data retrieved from the ovarian cancer TCGA dataset, we show high tumor GR expression is associated with significantly higher expression of Foxp3, a marker of immunosuppressive T-regulatory cells, as well as CD33, a marker of immunosuppressive myeloid cells, further suggesting an immunosuppressive phenotype in ovarian cancer patients. We thus hypothesize that tumor-intrinsic GR activation in ovarian cancer is associated with increased MDSC infiltration into the tumor microenvironment in part via modulating tumor cell cytokine secretion. To determine whether tumor-cell GR activation modulates the cytokine secretome, we previously showed that GR activation in ovarian cancer cells upregulated secretion of immunomodulatory factors including G-CSF, TGFb and CXCL5. Moreover, co-treatment of cells with a selective GR modulator (SGRM), reverses this secretome effect. Considering that elevated levels of these cytokines are necessary for the production and recruitment of MDSCs, these data highlight the relevance of this evident glucocorticoid receptor-cytokine circuit in ovarian cancer tumor cells. Furthermore, we show that this tumor-cell cytokine secretome has the capacity to promote differentiation of MDSCs from precursor myeloid cells in healthy PBMCs. Importantly, measuring expression of activation markers such as Arginase-1 and NOS2 illustrated that these MDSCs are in their functional state. To our knowledge this is the first evidence that a nuclear receptor in ovarian cancer tumor cells is actively driving MDSC differentiation. Finally, employing ovarian cancer xenograft models as well as a syngeneic ID8 tumor model, treatment with SGRMs demonstrated reduced levels of circulating MDSCs in the peripheral blood as well as tumor infiltrated MDSCs. Using neutralizing antibodies for cytokines, we will now identify key mechanisms, specifically, identify which targetable immunomodulatory factors are contributing to immune suppression. Based on the insight provided by these data, we propose to determine whether targeting ovarian tumor cell-intrinsic GR activation and resulting MDSC infiltration contributes to improved anti-tumor immune response. Citation Format: Manisha Taya, Eleanore LeBlanc, Lynda Bennett, Suzanne D. Conzen. Ovarian cancer-cell glucocorticoid receptor activity modulates cytokine secretion promoting infiltration of immunosuppressive cells into the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 654.

Quality of therapy in early ovarian cancer: results of the quality assurance program of the AGO Study Group

ObjectiveThe quality assurance program for ovarian cancer (QS-OVAR) evaluates the implementation of treatment standards and impact on survival for International Federation of Gynecology and Obstetrics (FIGO) stage I ovarian cancer.MethodsPatients...

Matrix Gla Protein drives stemness and tumor initiation in ovarian cancer

Ovarian cancer (OC) displays the highest mortality among gynecological tumors, mainly due to early peritoneal dissemination, the high frequency of tumor relapse following primary debulking, and the development of chemoresistance. All these events are thought to be initiated and sustained by a subpopulation of neoplastic cells, termed ovarian cancer stem cells (OCSC), that are endowed with self-renewing and tumor-initiating properties. This implies that interfering with OCSC function should offer novel therapeutic perspectives to defeat OC progression. To this aim, a better understanding of the molecular and functional makeup of OCSC in clinically relevant model systems is essential. We have profiled the transcriptome of OCSC vs. their bulk cell counterpart from a panel of patient-derived OC cell cultures. This revealed that Matrix Gla Protein (MGP), classically known as a calcification-preventing factor in cartilage and blood vessels, is markedly enriched in OCSC. Functional assays showed that MGP confers several stemness-associated traits to OC cells, including a transcriptional reprogramming. Patient-derived organotypic cultures pointed to the peritoneal microenvironment as a major inducer of MGP expression in OC cells. Furthermore, MGP was found to be necessary and sufficient for tumor initiation in OC mouse models, by shortening tumor latency and increasing dramatically the frequency of tumor-initiating cells. Mechanistically, MGP-driven OC stemness was mediated by the stimulation of Hedgehog signaling, in particular through the induction of the Hedgehog effector GLI1, thus highlighting a novel MGP/Hedgehog pathway axis in OCSC. Finally, MGP expression was found to correlate with poor prognosis in OC patients, and was increased in tumor tissue after chemotherapy, supporting the clinical relevance of our findings. Thus, MGP is a novel driver in OCSC pathophysiology, with a major role in stemness and in tumor initiation.

Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer.

Epithelial ovarian cancer is by far the most lethal gynecological malignancy. The exploration of promising immunomarkers to predict prognosis in ovarian cancer patients remains challenging. In our research, we carried out an integrated bioinformatic analysis of genome expressions and their immune characteristics in the ovarian cancer microenvironment with validation in different experiments. We filtrated 332 differentially expressed genes with 10 upregulated hub genes from the Gene Expression Omnibus database. These genes were closely related to ovarian tumorigenesis. Subsequently, the survival and immune infiltration analysis demonstrated that the upregulation of five candidate genes, ITGB2, VEGFA, CLDN4, OCLN, and SPP1, were correlated with an unfavorable clinical outcome and increased immune cell infiltration in ovarian cancer. Of these genes, ITGB2 tended to be the gene most correlated with various immune cell infiltrations and had a strong correlation with significant M2 macrophages infiltration (r = 0.707, p = 4.71 × 10-39), while it had a moderate correlation with CD4+/CD8+ T cells and B cells. This characteristic explains why the high expression of ITGB2 was accompanied by immune activation but did not reverse carcinogenesis. Additionally, we confirmed that ITGB2 was over-expressed in ovarian cancer tissues and was mainly located in cytoplasm, detected by Western blotting and the immunohistochemical method. In summary, ITGB2 may serve as a prognostic immunomarker for ovarian cancer patients.

MiR-5195-3p functions as a tumor suppressor by targeting RHBDD1 in ovarian cancer.

Recent studies have reported the tumor suppressive role of miR-5195-3p in the progression of several cancers, but the potential roles of miR-5195-3p in ovarian cancer (OC) remain largely unknown. We first analyzed the expression levels of miR-5195-3p in 83 pairs of human OC tissues and adjacent specimens by reverse transcription-quantitative PCR. The correlation of miR-5195-3p/rhomboid domain containing 1 (RHBDD1) and clinicopathological parameters was analyzed by chi-square test. The prognostic value of miR-5195-3p was evaluated by Kaplan-Meier method Cox proportional hazards models. The effects of miR-5195-3p on cell proliferation, cell cycle distribution, migration and invasion were examined by CCK-8 assay, colony formation assay, flow cytometry and transwell assay. Tumor forming was evaluated by nude mice model in vivo. The association between miR-5195-3p and RHBDD1 was verified by luciferase reporter assay. We observed that miR-5195-3p level was remarkably reduced in OC tissues as compared to adjacent tissues. The expression of miR-5195-3p was associated with FIGO stage, depth of invasion and poor survival prognosis in OC patients. Overexpression of miR-5195-3p significantly suppressed cell proliferation, cell cycle G1/S transition, migration and invasion in OC cell lines (SKOV-3 and OVCAR3), while knockdown of miR-5195-3p obtained the opposite results. We further confirmed miR-5195-3p as a negative post-transcriptional modulator of RHBDD1. RHBDD1 expression was upregulated in OC tissues compared with adjacent tissues, which was inversely correlated with miR-5195-3p expression. The expression of RHBDD1 was associated with FIGO stage and distant metastasis. RHBDD1 overexpression reversed the suppressive role of miR-5195-3p on OC cell proliferation, migration and invasion. Consistent with the in vitro results, miR-5195-3p overexpression decreased the growth of subcutaneously inoculated tumors in nude mice. Taken together, the present results indicated that miR-5195-3p acts a tumor suppressor by targeting RHBDD1 in OC.