Prognosis Of Ovarian Cancer Patients Research Articles

Plasma exosome-derived fragile site-associated tumor suppressor as a powerful prognostic predictor for patients with ovarian cancer.

The objective of the study was to investigate the levels of plasma exosome-derived fragile site-associated tumor suppressor (FATS) and evaluate its prognostic predictive ability in ovarian cancer (OC) patients. Exosome-rich fractions were isolated from the plasma of 90 patients with OC enrolled in this study. The levels of plasma exosome-derived FATS were detected by ELISA. The levels of exosome-derived FATS in OC patients were significantly lower as compared to the healthy controls (P < 0.001). The levels of plasma exosome-derived FATS were higher in OC patients with low grade (1/2), and Federation International of Gynecology and Obstetrics (FIGO) Stages I/II than those in high grade (3/4) and Stages III/IV of the disease (p = 0.003; p < 0.001), respectively. The levels of plasma exosome-derived FATS were significantly higher in OC patients with no lymph node metastasis or no ascites as compared to those with lymph node metastasis or ascites, respectively (both p < 0.001). The levels of plasma exosome-derived FATS were higher in OC patients having CA-125 below 35 U/ml as compared to those with CA-125 greater than 35 U/ml (p < 0.001). Among all enrolled OC patients, both 5-DFS and 5-OS were shorter in patients with lower plasma exosome-derived FATS levels than those with higher levels (both p < 0.001). The area under the receiver operating characteristic curve of plasma exosome-derived FATS was 0.85 (95% CI: 0.76-0.91) for 5-DFS and 0.91 (95% CI: 0.83-0.96) for 5-OS prediction in patients with OC. Plasma exosome-derived FATS levels in OC patients were significantly downregulated. Low levels of plasma exosome-derived FATS had a significant relationship with FIGO Stages III/IV, high grade, ascites, higher levels of CA-125, lymph node metastasis, and prognosis of OC patients. Thus, our findings may provide insights for the development of a new strategy OC treatment.

LncRNA SRA mediates cell migration, invasion, and progression of ovarian cancer via NOTCH signaling and epithelial-mesenchymal transition.

Long non-coding RNA (lncRNA) is a newly identified regulator of tumor formation and tumor progression. The function and expression of lncRNAs remain to be fully elucidated, but recent studies have begun to address their importance in human health and disease. The lncRNA, SRA, known as steroid receptor activator, acts as an important modulator of gynecological cancer, and its expression may affect biological functions including proliferation, apoptosis, steroid formation, and muscle development. However, it is still not well known whether SRA is involved in the regulation of ovarian cancer. The present study investigated the molecular function and association between SRA expression and clinicopathological factors. In ovarian cancer cell lines, SRA knockdown and overexpression regulated cell migration, proliferation, and invasion. Both in vivo and in vitro experiments using knockdown and overexpression showed that SRA potently regulated epithelial–mesenchymal transition (EMT) and NOTCH pathway components.Further, clinical data confirmed that SRA was a significant predictor of overall survival (OS) and progression-free survival and patients with ovarian cancer exhibiting high expression of SRA exhibited higher recurrence rates than patients with low SRA expression. In conclusion, the present study indicates that SRA has clinical significance as its expression can predict the prognosis of ovarian cancer patients. High expression of the lncRNA SRA is strongly correlated with recurrence-free survival of ovarian cancer patients.

Expression, Clinical Significance, and Prospective Pathway Signaling of miR-501-3p in Ovarian Cancer Based on Database and Informatics Analysis.

BackgroundThe present study aims to explore the expression, clinical significance, and prospective pathway signaling of miR-501-3p in ovarian cancer (OC) based on database and informatics analysis.MethodsKruskal–Wallis test, Wilcoxon sign-rank test, and logistic regression were used to evaluate the relationship between clinical features and miR-501-3p expression. Kaplan–Meier survival curve analysis was used to explore the relationship between miR-501-3p expression and the prognosis of OC patients. The miRNA targets were obtained from databases TargetScan, miRanda, TarBase, miRTarBase, miR2Disease, miRecords, and miRWalk. GO and KEGG analyses were used to analyze the significant involvement of miR-501-3p target genes in function.ResultsThe low miR-501-3p expression in OC was significantly associated with histologic grade (P=0.015). Low miR-501-3p expression predicted a poorer overall survival (HR: 0.77; 95% CI: 0.61–0.96; P=0.02) and disease-specific survival (HR: 0.77; 95% CI: 0.61–0.99; P=0.038). GO and KEGG analyses demonstrated that miR-501-3p might participate in the development of OC by pathways including one carbon pool by folate, protein digestion and absorption, cell cycle, kaposi sarcoma-associated herpesvirus infection, and viral carcinogenesis.ConclusionsLow miR-501-3p expression is significantly associated with poor survival in OC patients. It may be a promising prognostic biomarker for OC patients.

Identification of CXCL10-Relevant Tumor Microenvironment Characterization and Clinical Outcome in Ovarian Cancer.

BackgroundChemokines are implicated in tumor microenvironment (TME) cell infiltration. Development of ovarian cancer involves heterologous cells together with the adjacent microenvironment. Nonetheless, our understanding of the chemokine-related TME characteristics in ovarian cancer remains obscure.MethodsIn this large-scale multi-platform study of 10 microarray datasets consisting of 1,673 ovarian cancer patients, we comprehensively evaluated CXCL10 and CXCL9 expression risk classifications for predicting overall survival (OS) and TME immune characteristics. The cross-validation between a standard cohort (TCGA: The Cancer Genome Atlas) and three test cohorts (GEO: Gene-Expression Omnibus) was applied. We investigated differences in the biological functions and the underlying mechanisms between high- and low-risk classifications.ResultsWe identified that evaluation of CXCL10 expression could predict the tumor development, immune cell infiltration, TME signature, genetic alteration, and patient prognosis in ovarian cancer. Low-risk classification was characterized by high CXCL10 expression and prolonged prognosis, which was positively associated with specific immune cell infiltration (i.e., T cells, DCs, aDC, and Th2 cells) and TME immune-relevant signatures. Meanwhile, the high-risk classification was defined by lower CXCL10/CXCL9 expression and relevant poor prognosis and immune infiltrations. The CXCL10-based low-risk classification was also linked to antitumor biological function of specific immune gene sets, such as IL2-STAT5 signaling. Additionally, a mutational pattern featured by enrichment of C > T transition was further identified to be associated with immune cell infiltration.ConclusionsThis work proposed a promising biomarker for evaluating TME immune characteristics and clinical outcomes in patients with ovarian cancer. Estimation of CXCL10 risk pattern sheds a novel insight on ovarian cancer TME immune characteristics and provides strategies for ovarian cancer immunotherapy.

PSMC2/CCND1 axis promotes development of ovarian cancer through regulating cell growth, apoptosis and migration

Ovarian cancer is known as one of the most common malignancies of the gynecological system, whose treatment is still not satisfactory because of the unclear understanding of molecular mechanism. PSMC2 is an essential component of 19 S regulatory granules in 26 S proteasome and its relationship with ovarian cancer is still not clear. In this study, we found that PSMC2 was upregulated in ovarian cancer tissues, associated with tumor grade and could probably predict poor prognosis. Knocking down the endogenous PSMC2 expression in ovarian cancer cells could decrease colony formation ability, cell motility and cell proliferation rate, along with increasing cell apoptosis rate. Cells models or xenografts formed by cells with relatively lower expression of PSMC2 exhibited weaker oncogenicity and slower growth rate in vivo. Moreover, gene microarray was used to analyze the alteration of gene expression profiling of ovarian cancer induced by PSMC2 knockdown and identify CCND1 as a potential downstream of PSMC2. Further study revealed the mutual regulation between PSMC2 and CCND1, and demonstrated that knockdown of CCND1 could enhance the regulatory effects induced by PSMC2 knockdown and overexpression of CCND1 reverses it. In summary, PSMC2 may promote the development of ovarian cancer through CCND1, which may predict poor prognosis of ovarian cancer patients.

The prognostic role of aberrant copy number of &lt;i&gt;DDB1, PRPF19, CDKN1B, с-Myc&lt;/i&gt; genes in ovarian cancer patients

Rationale: Ovarian cancer is the leading death cause in gynecological malignancies. More than 70% of the patients are diagnosed with progressing disease extending to outside the true pelvis. The 5-year survival of ovarian cancer patients remains low (about 47%) due to frequent relapses and drug resistance. Identification of markers for early diagnosis and relapse prediction could improve the outcomes of the disease.Aim: To assess relative copy number of cancer-associated genetic loci c-Myc, CDK12, CDKN1B, PRPF19, ERBB2, DDB1, GAB2, COL6A3 in the tumor cells of ovarian cancer, in order to identify potential prognostic oncomarkers in ovarian cancer patients.Materials and methods: The study included 50 women aged 27 to 70 years with ovarian cancer T1-3cN0-1M0-1, Gr. 2 (stages I—IV), who received their elective treatment in the National Medical Research Centre for Oncology in 2015 to 2019. The study was based on samples of genomic DNA from paraffinized blocks of tumor and “healthy” tissues. Relative copy numbers of 8 genetic loci (c-Myc, CDK12, CDKN1B, PRPF19, ERBB2, DDB1, GAB2, COL6A3) was assessed by RT-qPCR technique. Relative copy quantitation of a genetic locus was calculated as 2-ΔCt. The dose of the locus studied was considered equal to diploid set (2n) if RCQtumor/healthy was about 1. If RCQtumor/healthy was &gt; 1.5 or &lt; 0.5, then the locus dose was considered increased (≥ 3n) or decreased (≤ 1n), respectively.Results: For all genetic loci, an increase of relative copy quantitation in the ovarian tumor cells was observed compared to that in “healthy” tissues. There was a significant (р&lt;0.05) aberrant copy quantitation of 4 genes: c-Myc (р = 0.001), DDB1 (р = 0.002), PRPF19 (р = 0.0001), and CDKN1B (р = 0.001). We identified differential thresholds for these genes that made it possible to predict an unfavorable disease course in the patients (р &lt; 0.05). The strongest association with the risk of adverse outcomes was found for increased copy number of PRPF19 (odds ratio (OR) 7.3; р = 0.0001) and c-Myc (OR 6.8; р = 0.001).Conclusion: In this study, we determined the prognostic value of 4 oncogenic drivers, namely, DDB1, PRPF19, CDKN1B, and с-Myc, whose increased copy number was associated with an adverse disease prognosis in ovarian cancer patients.

Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer.

Simple SummaryThe expression of tumor microenvironment-related genes is known to be correlated with ovarian cancer patients’ prognosis. Immunotherapeutic targets are in part located in this complex cluster of cells and soluble factors. In our study, we constructed a prognostic 11-gene signature for advanced serous ovarian cancer from tumor microenvironment-related genes through lasso regression. The established risk score can quantify the prognosis of ovarian cancer patients more accurately and is able to predict the putative biological response of cancer samples to a programmed death ligand 1 blocking immunotherapy. This might empower the role of immunotherapy in ovarian cancer through its usage in future study protocols.(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.

Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis.

Epithelial ovarian cancer has a low response rate to immunotherapy and a complex immune microenvironment that regulates its treatment outcomes. Understanding the immune microenvironment and its molecular basis is of great clinical significance in the effort to improve immunotherapy response and outcomes. To determine the characteristics of the immune microenvironment in ovarian cancer, we stratified ovarian cancer patients into three immune subtypes (C1, C2, and C3) using immune-related genes based on gene expression data from The Cancer Genome Atlas and found that these three subtypes had significant differences in immune characteristics and prognosis. Methylation and copy number variant analysis showed that the immune checkpoint genes that influenced immune response were significantly hypermethylated and highly deleted in the immunosuppressive C3 subtype, suggesting that epigenetic therapy may be able to reverse the efficacy of immunotherapy. In addition, the mutation frequencies of BRCA2 and CDK12 were significantly higher in the C2 subtype than in the other two subtypes, suggesting that mutation of DNA repair-related genes significantly affects the prognosis of ovarian cancer patients. Our study further elucidated the molecular characteristics of the immune microenvironment of ovarian cancer, which providing an effective hierarchical method for the immunotherapy of ovarian cancer patients, and has clinical relevance to the design of new immunotherapies and a reasonable combination strategies.

Evaluation of Twenty Genes in Prognosis of Patients with Ovarian Cancer Using Four Different Clustering Methods.

Background:Comparison of gene expression algorithms may be beneficial for obtaining disease pattern or grouping patients based on the gene expression profile. The current study aimed to investigate whether the knowledge within these data is able to group the ovarian cancer patients with similar disease pattern. Methods:Four different clustering methods were applied on 20 genes expression data of 37 women with ovarian cancer. All selected genes in this study had prominent roles in the control of the activity of the immune system, as well as the chemotaxis, angiogenesis, apoptosis, and etc. Comparison of different clustering methods such as K-means, Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation-Maximization (EM) algorithm was the other aim of the present study. In addition, the percentage of correct prediction, Robustness-Performance Trade-off (RPT), and Silhouette criteria were used to evaluate the performance of clustering methods. Results:Six out of 20 genes (IFN-γ, Foxp3, IL-4, BCL-2, Oct4 and survivin) selected by the Laplacian score showed key roles in the development of ovarian cancer and their prognostic values were clinically and statistically confirmed. The results indicated proper capability of the expression pattern of these genes in grouping the patients with similar prognosis, i.e. patients alive after 5 years or dead (62.12%). Conclusion:The results revealed the better performance for k-means and hierarchical clustering methods, and confirmed the fact that by using the expression profile of these genes, patients with similar behavior can be grouped in the same cluster with acceptable accuracy level. Certainly, the useful information from these data may contribute to the prediction of prognosis in ovarian cancer patients along with other features of patients.

Assessment of Significant Pathway Signaling and Prognostic Value of GNG11 in Ovarian Serous Cystadenocarcinoma.

BackgroundGNG11 (G protein subunit gamma 11) is a member of guanine nucleotide-binding protein (G protein) gamma family. Few studies elucidated the role of GNG11 in human disease, especially in tumors. The present study initially analyzed the function of GNG11 in ovarian serous cystadenocarcinoma.MethodsThe differential expression of GNG11 mRNA in ovarian cancer and normal tissues was evaluated through Oncomine, CCLE, Gepia, UCSC Xena and UALCAN databases. The protein expression of GNG11 was assessed via HPA database. Prognosis analysis was performed by Kaplan–Meier Plotter. Restrict survival analysis to subtypes including tumor grade, cancer stage and TP53 mutation status was then carried out. GSEA enrichment analysis was performed to explore the significant pathways associated with GNG11 in ovarian cancer. Finally, the upstream miRNAs of GNG11 were predicted by DIANA, Target Scan, miRDB and miRWalk databases, and the potential key KEGG pathways were subsequently determined by DIANA.ResultsThe mRNA expression of GNG11 was down-regulated in ovarian cancer patients (P<0.05). The cancer stage of patients correlated with the expression of GNG11 (P<0.05). Survival analysis indicated that GNG11 high expression statistically shortened the overall survival time of patients (HR=1.26, P=0.0043) compared with low expression group, especially for the patients with earlier stage (HR=2.48, P=0.035) and lower grade (HR=1.72, P=0.0016). Subsequently, the consistent upstream miRNA of GNG11, hsa-miR-22-5p, was predicted from 4 databases. The differential expression profile of hsa-miR-22-5p in blood was observed in ovarian cancer patients. According to the GSEA analysis on GNG11 and KEGG analysis on hsa-miR-22-5p, the consistent pathway of ECM-receptor interaction was observed (all P<0.01). ECM-receptor interaction pathway and differential expression of hsa-miR-22-5p in blood suggested the migration risk of ovarian cancer.ConclusionHigh expression of GNG11 indicated the poor prognosis of ovarian cancer patients. GNG11 might play a crucial role in the biological process of ovarian serous cystadenocarcinoma by ECM-receptor interaction pathway, thus affecting the prognosis of patients.

Developing Ovarian Cancer Sensors Using Molecular Perceptron

Ovarian cancer patient prognosis and quality of life are significantly affected by the failure to accurately diagnose disease at early stage. Currently, less than 15% of ovarian cancer patients are diagnosed at stage 1, when 5-year survival rates are near 93%. Early detection may save more than 80% of women diagnosed with ovarian cancer. Our goal is to develop clinical long-term implantable carbon nanotube-based sensors for ovarian cancer biomarkers to transiently detect and monitor their levels in patients. The proposed sensors will harness the unique optical properties and sensitivity of single-walled carbon nanotubes. To improve sensitivity and specificity of such sensors, previously developed to detect ovarian cancer biomarkers in vivo, we used molecular perceptron—data analytics-based methods to detect specific spectroscopic fingerprints from the binding of protein biomarkers to the nanotube surface.

Ferroptosis-Related Gene Signature Promotes Ovarian Cancer by Influencing Immune Infiltration and Invasion.

Ovarian cancer is a kind of gynecological malignancy with high mortality. Ferroptosis is a new type of iron-dependent cell death characterized by the formation of lipid peroxides and excessive accumulation of reactive oxygen species. Studies have shown that ferroptosis modulates tumor genesis, progression, and invasion, including ovarian cancer. Based on the mRNA expression data from TCGA, we construct a scoring system using consensus clustering analysis, univariate Cox regression analysis, and least absolute selection operator. Then, we systematically evaluate the relationship between score and clinical characteristics of ovarian cancer. The result from the prediction of biofunction pathways shows that score serves as an independent prognostic marker for ovarian cancer and affects tumor progression by modulating tumor metastasis. Moreover, immunocytes such as activated CD4 T cell, activated CD8 T cell, regulatory T cells, macrophage, and stromal cells, including adipocytes, epithelial cells, and fibroblast infiltrate more in the tumor microenvironment in a high-score group, indicating ferroptosis can also affect tumor immune landscape. Critically, four potentially sensitive drugs, including staurosporine, epothilone B, DMOG, and HG6-64-1 based on the scores, are predicted, and DMOG is recognized as a novel targeted drug for ovarian cancer. In general, we construct the scoring system based on ferroptosis-related genes that can predict the prognosis of ovarian cancer patients and propose that ferroptosis may affect ovarian cancer progression by mediating tumor metastasis and immune landscape. Novel drugs to target ovarian cancer are also predicted.

Increased expression of KPNA2 predicts unfavorable prognosis in ovarian cancer patients, possibly by targeting KIF4A signaling

BackgroundKaryopherin α-2 (KPNA2) is a member of karyopherin family, which is proved to be responsible for the import or export of cargo proteins. Studies have determined that KPNA2 is associated with the development and prognosis of various cancers, yet the role of KPNA2 in ovarian carcinoma and its potential molecular mechanisms remains unclear.Materials and methodsThe expression and prognosis of KPNA2 in ovarian cancer was investigated using GEPIA and Oncomine analyses. Mutations of KPNA2 in ovarian cancer were analyzed by cBioPortal database. The prognostic value of KPNA2 expression was evaluated by our own ovarian carcinoma samples using RT-qPCR. Subsequently, the cell growth, migration and invasion of ovarian cancer cells were investigated by CCK-8 and transwell assay, respectively. The protein levels of KPNA2 and KIF4A were determined by western blot.ResultsWe obtained the following important results. (1) KPNA2 and KIF4A wereoverexpressed in ovairan cancer tissues and cells. (2) Among patients with ovarian cancer, overexpressed KPNA2 was associated with lower survival rate. (3) Mutations (R197* and S140F) in KPNA2 will have some influences on protein structure, and then may cause protein function abnormal. (4) KPNA2 konckdown inhibited proliferation, migration, invasion, as well as the expression of KIF4A.ConclusionKPNA2, as a tumorigenic gene in ovarian cancer, accelerated tumor progression by up-regulating KIF4A, suggesting that KPNA2 might be a hopeful indicator of treatment and poor prognosis.

Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System.

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

HIF-1α Regulated WTAP Overexpression Promote Warburg Effect of Ovarian Cancer by m6A-Dependent Manner

N6-methyladenosine (m6A), the methylation of the N6 nitrogen on adenosine, acts as the most common internal modification of eukaryotic mRNA. The dynamic and reversible m6A RNA modification established and erased by N6-methyltransferases and demethylases regulates gene expression and determines cell fate. In this study, we find for the first time that exposure of ovarian cancer cells to hypoxia significantly increases the expression of WT1 associated protein(WTAP), a critical m6A methylase which proved to be induced by a hypoxia factor (HIF)-1α dependent manner, and expression of WTAP in ovarian cancer cells is significantly correlated with the clinicopathological characteristics and up-regulation of WTAP predicts poor prognosis of ovarian cancer patients. Knockdown/ Overexpress of WTAP in human ovarian cancer cells substantially reduces/enhances their proliferative and invasive capacity both in vitro and in vivo. Moreover, We discover that WTAP interacts with DGCR8, a vital microprocessor protein, regulating the primary microRNA 200 process in an m6A-dependent manner. Further experiments demonstrate that microRNA200 could positively regulate HK2 and promote Warburg effect. These studies reveal that HIF-1α upregulated WTAP expression mediates primary miRNA processing, accelerates the Warburg effect and promotes the occurrence and development of tumor, thus providing a novel perspective on m6A modification in ovarian cancer progression. Funding Information: This study was supported by grants from the Natural Science Foundation of Anhui Province (2008085QH422). Declaration of Interests: The authors have declared that no competing interest exists. Ethics Approval Statement: All human ovary tissues were obtained with informed consent, and the ethical review committee approved all protocols of the World Health Organization Collaborating Center for Research in Human Production. Animal studies were approved by the Institutional Animal Care and Use Committee of Second Military Medical University, Shanghai, China.

Programmed death ligand-1 regulates angiogenesis and metastasis by participating in the c-JUN/VEGFR2 signaling axis in ovarian cancer.

BackgroundAlthough programmed cell death‐ligand 1 (PD‐L1) plays a well‐known function in immune checkpoint response by interacting with programmed cell death‐1 (PD‐1), the cell‐intrinsic role of PD‐L1 in tumors is still unclear. Here, we explored the molecular regulatory mechanism of PD‐L1 in the progression and metastasis of ovarian cancer.MethodsImmunohistochemistry of benign tissues and ovarian cancer samples was performed, followed by migration, invasion, and angiogenesis assays in PD‐L1‐knockdown ovarian cancer cells. Immunoprecipitation, mass spectrometry, and chromatin immunoprecipitation were conducted along with zebrafish and mouse experiments to explore the specific functions and mechanisms of PD‐L1 in ovarian cancer.ResultsOur results showed that PD‐L1 induced angiogenesis, which further promoted cell migration and invasion in vitro and in vivo of ovarian cancer. Mechanistically, PD‐L1 was identified to directly interact with vascular endothelial growth factor receptor‐2 (VEGFR2) and then activated the FAK/AKT pathway, which further induced angiogenesis and tumor progression, leading to poor prognosis of ovarian cancer patients. Meanwhile, PD‐L1 was found to be regulated by the oncogenic transcription factor c‐JUN at the transcriptional level, which enhanced the expression of PD‐L1 in ovarian cancer. Furthermore, we demonstrated that PD‐L1 inhibitor durvalumab, combined with the antiangiogenic drug, apatinib, could enhance the effect of anti‐angiogenesis and the inhibition of cell migration and invasion.ConclusionOur results demonstrated that PD‐L1 promoted the angiogenesis and metastasis of ovarian cancer by participating in the c‐JUN/VEGFR2 signaling axis, suggesting that the combination of PD‐L1 inhibitor and antiangiogenic drugs may be considered as a potential therapeutic approach for ovarian cancer patients.

NOL4L, a novel nuclear protein, promotes cell proliferation and metastasis by enhancing the PI3K/AKT pathway in ovarian cancer

Nucleolar protein 4-like (NOL4L) was first identified in acute myeloid leukaemia. Then, it was verified to be involved in cell progression in neuroblastoma. However, the functional role of NOL4L in tumor proliferation and metastasis and the underlying molecular mechanism(s) are not fully understood. Immunohistochemistry (IHC) assays were performed in patient tissues to reveal NOL4L expression profiles. Then, we knocked down NOL4L in two ovarian cancer cell lines (Skov3-ip1 and Hey), and cell-based in-vitro and in-vivo assays were subsequently conducted to gain insight into the underlying mechanism of NOL4L in ovarian cancer. We confirmed that the expression of NOL4L was higher in tumor tissues, especially in peritoneal metastatic tissues. Furthermore, we observed that NOL4L was related to prognosis in ovarian cancer patients. Next, we conducted CCK-8 assays, colony formation assays, migration and invasion experiments and wound healing assays and verified that NOL4L could promote proliferation and metastasis in ovarian cancer cells. In addition, NOL4L promoted tumor progression and metastasis in a nude mouse model. Mechanistically, we demonstrated that NOL4L influenced gene expression in the PI3K/AKT pathway. Overall, our study provides genetic and biochemical evidence that NOL4L is critical for tumor progression and metastasis in ovarian cancer cells. Thus, it could serve as a target for antimetastatic therapy in ovarian cancer.

Multi-omics analysis of the prognosis and therapeutic significance of circadian clock in ovarian cancer.

Ovarian cancer (OV) is one of the most common female malignancies with high morbidity and mortality, but its mechanism is not fully understood. The circadian clock is involved in the regulation of the immune system and the tumor microenvironment, regulating biological processes and behaviors in multiple ways. Circadian rhythm disorders are considered a risk factor for tumorigenesis. Multi-omics analysis was performed to comprehensively illustrate the roles of circadian clock genes in OV, we found that most of circadian clock genes undergo epigenetic alterations in OV and are strongly correlated with overall and progression-free patient survival. These clock genes are mainly involved in the inhibition of Apoptosis pathway, Cell Cycle pathway and DNA Damage Response pathway, as well as the activation of RAS/MAPK pathway and RTK pathway. Drug sensitivity model indicate that the expression of core clock genes may associate with drug resistance. Further, immune infiltrates analysis shows that different mutant forms of core genes can not only suppress immune infiltration, but also affect clinical outcome of ovarian cancer patients. Overall, our results may provide novel insights for the potential selection of immunotherapeutic targets.

Identification of Novel Biomarkers and Candidate Drug in Ovarian Cancer.

This paper investigates the expression of the CREB1 gene in ovarian cancer (OV) by deeply excavating the gene information in the multiple databases and the mechanism thereof. In short, we found that the expression of the CREB1 gene in ovarian cancer tissue was significantly higher than that of normal ovarian tissue. Kaplan–Meier survival analysis showed that the overall survival was significantly shorter in patients with high expression of the CREB1 gene than those in patients with low expression of the CREB1 gene, and the prognosis of patients with low expression of the CREB1 gene was better. The CREB1 gene may play a role in the occurrence and development of ovarian cancer by regulating the process of protein. Based on differentially expressed genes, 20 small-molecule drugs that potentially target CREB1 with abnormal expression in OV were obtained from the CMap database. Among these compounds, we found that naloxone has the greatest therapeutic value for OV. The high expression of the CREB1 gene may be an indicator of poor prognosis in ovarian cancer patients. Targeting CREB1 may be a potential tool for the diagnosis and treatment of OV.

Enhancement of tumor tropism of mPEGylated nanoparticles by anti-mPEG bispecific antibody for ovarian cancer therapy

Ovarian cancer is highly metastatic, with a high frequency of relapse, and is the most fatal gynecologic malignancy in women worldwide. It is important to elevate the drug susceptibility and cytotoxicity of ovarian cancer cells, thereby eliminating resident cancer cells for more effective therapeutic efficacy. Here, we developed a bispecific antibody (BsAb; mPEG × HER2) that can easily provide HER2+ tumor tropism to mPEGylated liposomal doxorubicin (PLD) and further increase the drug accumulation in cancer cells via receptor-mediated endocytosis, and improve the cytotoxicity and therapeutic efficacy of HER2+ ovarian tumors. The mPEG × HER2 can simultaneously bind to mPEG molecules on the surface of PLD and HER2 antigen on the surface of ovarian cancer cells. Simply mixing the mPEG × HER2 with PLD was able to confer HER2 specificity of PLD to HER2+ ovarian cancer cells and efficiently trigger endocytosis and enhance cytotoxicity by 5.4-fold as compared to non-targeted PLD. mPEG × HER2-modified PLD was able to significantly increase the targeting and accumulation of HER2+ ovarian tumor by 220% as compared with non-targeted PLD. It could also significantly improve the anti-tumor activity of PLD (P < 0.05) with minimal obvious toxicity in a tumor-bearing mouse model. We believe that the mPEG × HER2 can significantly improve the therapeutic efficacy, potentially reduce the relapse freqency and thereby achieve good prognosis in ovarian cancer patients.