Human epidermal growth factor receptor-2 expression and subsequent dynamic changes in patients with ovarian cancer.

Human epidermal growth factor receptor-2 (HER2)-targeting drugs are increasingly being incorporated into therapeutic paradigms for non-breast cancers, yet studies on HER2 expression in ovarian cancer (OC) are inadequate. Here, we studied the HER2 status and dynamic changes in OC by reviewing the records of patients who underwent HER2 testing at a single institution. Clinical parameters, including histology, BRCA status, and immunohistochemistry (IHC), were evaluated alongside HER2 expression, timing, and anatomical location. Among 200 patients, 28% and 6% exhibited expression scores of 2+ and 3+, respectively. HER2 3+ scores were observed in 23%, 11%, 9%, and 5% of mucinous, endometrioid, clear cell, and high-grade serous tumors, respectively, and were exclusively identified in BRCA-wildtype, mismatch repair-proficient, or PD-L1-low-expressing tumors. The TP53 mutation rate was low, whereas ARID1A, KRAS, and PIK3CA mutations were relatively more prevalent with HER2 scores of 2+ or 3+ than with 0 or 1+. Four of the five tumors with an HER2 3+ score exhibited ERBB2 amplification. Among 19 patients who underwent multiple time-lagged biopsies, 11 showed increased HER2 expression in subsequent biopsies. Patients with HER2-overexpressing OC exhibited distinct histological, IHC, and genomic profiles. HER2-targeting agents are potential options for BRCA-wildtype patients, particularly as later lines of treatment.

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study.

Endometriosis is a prevalent and chronic inflammatory gynecologic disorder affecting approximately 6-10% of women globally, and has been associated with an increased risk of cancer. Nevertheless, previous studies have been hindered by methodological limitations that compromise the validity and robustness of their findings. In this study we conducted a comprehensive two-sample Mendelian randomization analysis to explore the genetically driven causal relationship between endometriosis and the risk of cancer. We conducted the analysis via the inverse variance weighted method, MR Egger method, and weighted median method utilizing publicly available genome-wide association study summary statistics. Furthermore, we implemented additional sensitivity analyses to assess the robustness and validity of the causal associations identified. We found strong evidence of a significant causal effect of endometriosis on a higher risk of ovarian cancer via inverse-variance weighted method (OR = 1.19, 95% CI 1.11-1.29, p < 0.0001), MR-Egger regression, and weighted median methodologies. Remarkably, our findings revealed a significant association between endometriosis and an increased risk of clear cell ovarian cancer (OR = 2.04, 95% CI 1.66-2.51, p < 0.0001) and endometrioid ovarian cancer (OR = 1.45, 95% CI 1.27-1.65, p < 0.0001). No association between endometriosis and other types of cancer was observed. We uncovered a causal relationship between endometriosis and an elevated risk of ovarian cancer, particularly clear cell ovarian cancer and endometrioid ovarian cancer. No significant associations between endometriosis and other types of cancer could be identified.

MicroRNA Expression Profiles in Human Samples and Cell Lines Revealed Nine miRNAs Associated with Cisplatin Resistance in High-Grade Serous Ovarian Cancer.

Metastasis and drug resistance are major contributors to cancer-related fatalities worldwide. In ovarian cancer (OC), a staggering 70% develop resistance to the front-line therapy, cisplatin. Despite proposed mechanisms, the molecular events driving cisplatin resistance remain unclear. Dysregulated microRNAs (miRNAs) play a role in OC initiation, progression, and chemoresistance, yet few studies have compared miRNA expression in OC samples and cell lines. This study aimed to identify key miRNAs involved in the cisplatin resistance of high-grade-serous-ovarian-cancer (HGSOC), the most common gynecological malignancy. MiRNA expression profiles were conducted on RNA isolated from formalin-fixed-paraffin-embedded human ovarian tumor samples and HGSOC cell lines. Nine miRNAs were identified in both sample types. Targeting these with oligonucleotide miRNA inhibitors (OMIs) reduced proliferation by more than 50% for miR-203a, miR-96-5p, miR-10a-5p, miR-141-3p, miR-200c-3p, miR-182-5p, miR-183-5p, and miR-1206. OMIs significantly reduced migration for miR-183-5p, miR-203a, miR-296-5p, and miR-1206. Molecular pathway analysis revealed that the nine miRNAs regulate pathways associated with proliferation, invasion, and chemoresistance through PTEN, ZEB1, FOXO1, and SNAI2. High expression of miR-1206, miR-10a-5p, miR-141-3p, and miR-96-5p correlated with poor prognosis in OC patients according to the KM plotter database. These nine miRNAs could be used as targets for therapy and as markers of cisplatin response.

A Stem-like Patient-Derived Ovarian Cancer Model of Platinum Resistance Reveals Dissociation of Stemness and Resistance.

To understand chemoresistance in the context of cancer stem cells (CSC), a cisplatin resistance model was developed using a high-grade serous ovarian cancer patient-derived, cisplatin-sensitive sample, PDX4. As a molecular subtype-specific stem-like cell line, PDX4 was selected for its representative features, including its histopathological and BRCA2 mutation status, and exposed to cisplatin in vitro. In the cisplatin-resistant cells, transcriptomics were carried out, and cell morphology, protein expression, and functional status were characterized. Additionally, potential signaling pathways involved in cisplatin resistance were explored. Our findings reveal the presence of distinct molecular signatures and phenotypic changes in cisplatin-resistant PDX4 compared to their sensitive counterparts. Surprisingly, we observed that chemoresistance was not inherently linked with increased stemness. In fact, although resistant cells expressed a combination of EMT and stemness markers, functional assays revealed that they were less proliferative, migratory, and clonogenic-features indicative of an underlying complex mechanism for cell survival. Furthermore, DNA damage tolerance and cellular stress management pathways were enriched. This novel, syngeneic model provides a valuable platform for investigating the underlying mechanisms of cisplatin resistance in a clinically relevant context, contributing to the development of targeted therapies tailored to combat resistance in stem-like ovarian cancer.

The Clinicopathological Significance of the Cyclin D1/E1-Cyclin-Dependent Kinase (CDK2/4/6)-Retinoblastoma (RB1/pRB1) Pathway in Epithelial Ovarian Cancers.

Cyclin-dependent kinases (CDK2, CDK4, CDK6), cyclin D1, cyclin E1 and phosphorylated retinoblastoma (pRB1) are key regulators of the G1/S cell cycle checkpoint and may influence platinum response in ovarian cancers. CDK2/4/6 inhibitors are emerging targets in ovarian cancer therapeutics. In the current study, we evaluated the prognostic and predictive significance of the CDK2/4/6-cyclin D1/E1-pRB1 axis in clinical ovarian cancers (OC). The CDK2/4/6, cyclin D1/E1 and RB1/pRB1 protein expression were investigated in 300 ovarian cancers and correlated with clinicopathological parameters and patient outcomes. CDK2/4/6, cyclin D1/E1 and RB1 mRNA expression were evaluated in the publicly available ovarian TCGA dataset. We observed nuclear and cytoplasmic staining for CDK2/4/6, cyclins D1/E1 and RB1/pRB1 in OCs with varying percentages. Increased nuclear CDK2 and nuclear cyclin E1 expression was linked with poor progression-free survival (PFS) and a shorter overall survival (OS). Nuclear CDK6 was associated with poor OS. The cytoplasmic expression of CDK4, cyclin D1 and cyclin E1 also has predictive and/or prognostic significance in OCs. In the multivariate analysis, nuclear cyclin E1 was an independent predictor of poor PFS. Tumours with high nuclear cyclin E1/high nuclear CDK2 have a worse PFS and OS. Detailed bioinformatics in the TCGA cohort showed a positive correlation between cyclin E1 and CDK2. We also showed that cyclin-E1-overexpressing tumours are enriched for genes involved in insulin signalling and release. Our data not only identified the prognostic/predictive significance of these key cell cycle regulators but also demonstrate the importance of sub-cellular localisation. CDK2 targeting in cyclin-E1-amplified OCs could be a rational approach.

Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer.

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

Spotlight on New Hallmarks of Drug-Resistance towards Personalized Care for Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy worldwide. Despite the latest advances, a major clinical issue in EOC is the disappointing prognosis related to chemoresistance in almost one-third of cases. Drug resistance relies on heterogeneous cancer stem cells (CSCs), endowed with tumor-initiating potential, leading to relapse. No biomarkers of chemoresistance have been validated yet. Recently, major signaling pathways, micro ribonucleic acids (miRNAs), and circulating tumor cells (CTCs) have been advocated as putative biomarkers and potential therapeutic targets for drug resistance. However, further investigation is mandatory before their routine implementation. In accordance with the increasing rate of therapeutic efforts in EOC, the need for biomarker-driven personalized therapies is growing. This review aims to discuss the emerging hallmarks of drug resistance with an in-depth insight into the underlying molecular mechanisms lacking so far. Finally, a glimpse of novel therapeutic avenues and future challenges will be provided.

Functional analysis and validation of oncodrive gene AP3S1 in ovarian cancer through filtering of mutation data from whole-exome sequencing.

BACKGROUND: High-grade serous ovarian carcinoma (HGSOC) is the most aggressive and prevalent subtype of ovarian cancer and accounts for a significant portion of ovarian cancer-related deaths worldwide. Despite advancements in cancer treatment, the overall survival rate for HGSOC patients remains low, thus highlighting the urgent need for a deeper understanding of the molecular mechanisms driving tumorigenesis and for identifying potential therapeutic targets. Whole-exome sequencing (WES) has emerged as a powerful tool for identifying somatic mutations and alterations across the entire exome, thus providing valuable insights into the genetic drivers and molecular pathways underlying cancer development and progression. METHODS: Via the analysis of whole-exome sequencing results of tumor samples from 90 ovarian cancer patients, we compared the mutational landscape of ovarian cancer patients with that of TCGA patients to identify similarities and differences. The sequencing data were subjected to bioinformatics analysis to explore tumor driver genes and their functional roles. Furthermore, we conducted basic medical experiments to validate the results obtained from the bioinformatics analysis. RESULTS: Whole-exome sequencing revealed the mutational profile of HGSOC, including BRCA1, BRCA2 and TP53 mutations. AP3S1 emerged as the most weighted tumor driver gene. Further analysis of AP3S1 mutations and expression demonstrated their associations with patient survival and the tumor immune response. AP3S1 knockdown experiments in ovarian cancer cells demonstrated its regulatory role in tumor cell migration and invasion through the TGF-ß/SMAD pathway. CONCLUSION: This comprehensive analysis of somatic mutations in HGSOC provides insight into potential therapeutic targets and molecular pathways for targeted interventions. AP3S1 was identified as being a key player in tumor immunity and prognosis, thus providing new perspectives for personalized treatment strategies. The findings of this study contribute to the understanding of HGSOC pathogenesis and provide a foundation for improved outcomes in patients with this aggressive disease.

MATN2 overexpression suppresses tumor growth in ovarian cancer via PTEN/PI3K/AKT pathway.

The incidence rate of developing ovarian cancer decreases over the years; however, mortality ranks top among malignancies of women, mainly metastasis through local invasion. Matrilin-2 (MATN2) is a member of the matrilin family that plays an important role in many cancers. However, its relationship with ovarian cancer remains unknown. Our study aimed to explore the function and possible mechanism of MATN2 in ovarian cancer. Human ovarian cancer tissue microarrays were used to detect the MATN2 expression in different types of ovarian cancer using immunohistochemistry (IHC). CCK-8, wound scratch healing assay, transwell assay, and flow cytometry were used to detect cell mobility. Gene and protein expression were detected using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. MATN2 interacts with phosphatase, and the tensin homolog (PTEN) deleted on chromosome 10 was analyzed using TCGA database and co-immunoprecipitation (Co-IP). In vivo experiments were conducted using BALB/c nude mice, and tumor volume and weight were recorded. Tumor growth was determined using hematoxylin and eosin (H&E) and IHC staining. MATN2 was significantly downregulated in ovarian cancer cells. The SKOV3 and A2780 cell mobility was significantly inhibited by MATN2 overexpression, while the cell apoptosis rate was significantly increased. MATN2 overexpression decreased transplanted tumor size in vivo. These results were reversed by inhibiting MATN2. Furthermore, we found that PTEN closely interacted with MATN2 using bioinformatics and Co-IP. MATN2 overexpression significantly inhibited the PI3K/AKT pathway, however, PTEN suppression reversed this effect of MATN2 overexpression. These results indicated that MATN2 may play a critical role in ovarian cancer development by inhibiting cells proliferation and migration. The mechanism was related to interacting with PTEN, thus inhibiting downstream effectors in the PI3K/AKT pathway, which may be a novel target for treating ovarian cancer.

Evaluating homologous recombination activity in tissues to predict the risk of hereditary breast and ovarian cancer and olaparib sensitivity.

Homologous recombination (HR) repairs DNA damage including DNA double-stranded breaks and alterations in HR-related genes results in HR deficiency. Germline alteration of HR-related genes, such as BRCA1 and BRCA2, causes hereditary breast and ovarian cancer (HBOC). Cancer cells with HR deficiency are sensitive to poly (ADP-ribose) polymerase (PARP) inhibitors and DNA-damaging agents. Thus, accurately evaluating HR activity is useful for diagnosing HBOC and predicting the therapeutic effects of anti-cancer agents. Previously, we developed an assay for site-specific HR activity (ASHRA) that can quantitatively evaluate HR activity and detect moderate HR deficiency. HR activity in cells measured by ASHRA correlates with sensitivity to the PARP inhibitor, olaparib. In this study, we applied ASHRA to lymphoblastoid cells and xenograft tumor tissues, which simulate peripheral blood lymphocytes and tumor tissues, respectively, as clinically available samples. We showed that ASHRA could be used to detect HR deficiency in lymphoblastoid cells derived from a BRCA1 pathogenic variant carrier. Furthermore, ASHRA could quantitatively measure the HR activity in xenograft tumor tissues with HR activity that was gradually suppressed by inducible BRCA1 knockdown. The HR activity of xenograft tumor tissues quantitatively correlated with the effect of olaparib. Our data suggest that ASHRA could be a useful assay for diagnosing HBOC and predicting the efficacy of PARP inhibitors.

Long non-coding RNA RAD51-AS1 promotes the tumorigenesis of ovarian cancer by elevating EIF5A2 expression.

PURPOSE: The present study aims to determine the molecular mechanism mediated by RAD51 antisense RNA 1 (RAD51-AS1) in ovarian cancer (OvCA). METHODS: The data associated with RAD51-AS1 in OvCA were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Relative expression of RAD51-AS1 was detected. Determination of cell proliferation, metastasis, and invasion was performed by cell counting, colony formation, would-healing, and transwell invasion assays. Protein levels were detected by western blotting. The molecular mechanism mediated by RAD51-AS1 was predicted by bioinformatics analysis and verified by dual-luciferase reporter assays. Subcutaneous tumorigenesis models were used to confirm the function of RAD51-AS1 in vivo. RESULTS: Data from TCGA and GEO showed that RAD51-AS1 was associated with poor prognosis in OvCA patients and DNA repair, cell cycle, focal adhesion, and apoptosis in SKOV3.ip cells. High levels of RAD51-AS1 were detected in OvCA cells. Overexpressing RAD51-AS1 enhanced the proliferative, invading, and migratory capabilities of OvCA cells in vitro while silencing RAD51-AS1 exhibited the opposite effects. Mechanically, RAD51-AS1 elevated eukaryotic initiation factor 5A2 (EIF5A2) expression as a sponge for microRNA (miR)-140-3p. Finally, the role of RAD51-AS1 was verified by subcutaneous tumorigenesis models. CONCLUSION: RAD51-AS1 promoted OvCA progression by the regulation of the miR-140-3p/EIF5A2 axis, which illustrated the potential therapeutic target for OvCA.

Olaparib Addition to Maintenance Bevacizumab Therapy in Ovarian Carcinoma With BRCA-Like Genomic Aberrations.

Importance: Testing for homologous recombination deficiency is required for the optimal treatment of high-grade epithelial ovarian cancer. The search for accurate biomarkers is ongoing. Objective: To investigate whether progression-free survival (PFS) and overall survival (OS) of patients with high-grade epithelial ovarian cancer treated with maintenance olaparib or placebo differed between patients with a tumor BRCA-like genomic profile and patients without a tumor BRCA-like profile. Design, Setting, and Participants: This cohort study was a secondary analysis of the PAOLA-1 randomized clinical trial that compared olaparib plus bevacizumab with placebo plus bevacizumab as maintenance treatment in patients with advanced high-grade ovarian cancer after a good response to first-line platinum with taxane chemotherapy plus bevacizumab, irrespective of germline or tumor BRCA1/2 mutation status. All patients with available tumor DNA were included in the analysis. The current analysis tested for an interaction between BRCA-like status and olaparib treatment on survival outcomes. The original trial was conducted between July 2015 and September 2017; at the time of data extraction for analysis in March 2022, a median follow-up of 54.1 months (IQR, 28.5-62.2 months) and a total follow-up time of 21 711 months was available, with 336 PFS and 245 OS events. Exposures: Tumor homologous recombination deficiency was assessed using the BRCA-like copy number aberration profile classifier. Myriad MyChoice CDx was previously measured. The trial was randomized between the olaparib and bevacizumab and placebo plus bevacizumab groups. Main Outcomes and Measures: This secondary analysis assessed hazard ratios (HRs) of olaparib vs placebo among biomarker strata and tested for interaction between BRCA-like status and olaparib treatment on PFS and OS, using Cox proportional hazards regression. Results: A total of 469 patients (median age, 60 [range 26-80] years) were included in this study. The patient cohort consisted of women with International Federation of Gynaecology and Obstetrics stage III (76%) high-grade serous (95%) ovarian cancer who had no evaluable disease or complete remission at initial or interval debulking surgery (76%). Thirty-one percent of the tumor samples (n = 138) harbored a pathogenic BRCA mutation, and BRCA-like classification was performed for 442 patients. Patients with a BRCA-like tumor had a longer PFS after olaparib treatment than after placebo (36.4 vs 18.6 months; HR, 0.49; 95% CI, 0.37-0.65; P < .001). No association of olaparib with PFS was found in patients with a non-BRCA-like tumor (17.6 vs 16.6 months; HR, 1.02; 95% CI, 0.68-1.51; P = .93). The interaction was significant (P = .004), and HRs and P values (for interaction) were similar in the relevant subgroups, OS, and multivariable analyses. Conclusions and Relevance: In this secondary analysis of the PAOLA-1 randomized clinical trial, patients with a BRCA-like tumor, but not those with a non-BRCA-like tumor, had a significantly longer survival after olaparib plus bevacizumab treatment than placebo plus bevacizumab treatment. Thus, the BRCA1-like classifier could be used as a biomarker for olaparib plus bevacizumab as a maintenance treatment.

ALKBH5 modulates macrophages polarization in tumor microenvironment of ovarian cancer.

BACKGROUND: Macrophages play an essential role in regulating ovarian cancer immune microenvironment. Studies have shown that m6A methylation could influence immune microenvironment in cancer. In this study, we investigated the roles of m6A demethylase ALKBH5 and m6A recognition protein IGF2BP2 played in regulating macrophages polarization in ovarian cancer. METHODS: In this study, we first explored the differentially expressed m6A methylation enzymes in M0 and M2 macrophages according to two independent GEO datasets. TIMER2.0 and GSCA database were used to explore the immune analysis of ALKBH5 and IGF2BP2 in ovarian cancer. K-M plotter and TIMER2.0 databases were used to evaluate the prognostic role of ALKBH5 and IGF2BP2 in ovarian cancer. For CNV mutation analysis of ALKBH5 and IGF2BP2, cBioPortal and GSCA databases were used. For single-cell analysis, sc-TIME and HPA softwares were used to analyze the roles of ALKBH5 and IGF2BP2 played in immune cells in ovarian cancer. To identify the role of ALKBH5 played in macrophage polarization, RT-PCR was used to verify the macrophage polarization related markers in vitro study. The function of ALKBH5 played in ovarian cancer was further analyzed through GO and KEGG analysis. FINDINGS: In this study, we found that ALKBH5 and IGF2BP2 were up-regulated in M2 macrophages, which showed closely correlation with immune cells expressions in ovarian cancer, especially with macrophages. Ovarian cancer patients with higher expression of ALKBH5 and IGF2BP2 showed worse prognosis, possibly because of their close correlation with immune response. ALKBH5 also correlated with macrophage phenotypes in single-cell levels analysis. However, the expression level of IGF2BP2 in ovarian cancer immune microenvironment was very low. The results of RT-PCR indicated the potential role of ALKBH5 in M2 polarization of macrophages. INTERPRETATION: ALKBH5 participated in regulating macrophage M2 polarization in ovarian cancer immune microenvironment.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer.

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Germline mutations in BRCA1 and BRCA2 among Brazilian women with ovarian cancer treated in the Public Health System.

BACKGROUND: Germline mutations in BRCA1 and BRCA2 genes are among the main causes of hereditary ovarian cancer. Identifying these mutations may reduce cancer risk, facilitate early detection, and enable personalized treatment. However, genetic testing is limited in the Brazilian Public Health System, and data regarding germline mutations in many regions are scarce. Therefore, the study aimed to investigate the prevalence of germline mutations in BRCA1 and BRCA2 in women with ovarian cancer treated in the Public Health System in Pernambuco, Brazil. METHODS: A cross-sectional study was conducted in the Hereditary Cancer Program from two reference oncological centers in Pernambuco. Women (n = 45) with high-grade serous ovarian cancer underwent genetic counseling and DNA sequencing for BRCA1 and BRCA2 genes. RESULTS: The prevalence of deleterious mutations in the BRCA1 and BRCA2 genes was 33%. Of the 15 germline mutations found, 13 were in BRCA1 and 2 in BRCA2; two mutations of unknown clinical significance were also found in BRCA2. Mutations c.5266dupC and c.2215 A > T were the most frequent; each was mutation observed in three patients. Additionally, the mutations c.7645dupT and c.921dupT were reported for the first time. CONCLUSION: One in three women showed a pathogenic mutation, demonstrating a significant prevalence of germline mutations in this sample. Additionally, the small sample revealed an interesting number of mutations, indicating the need to explore more regions of the country.

Integration of single-cell RNA-seq and bulk RNA-seq data to construct and validate a cancer-associated fibroblast-related prognostic signature for patients with ovarian cancer.

BACKGROUND: To establish a prognostic risk profile for ovarian cancer (OC) patients based on cancer-associated fibroblasts (CAFs) and gain a comprehensive understanding of their role in OC progression, prognosis, and therapeutic efficacy. METHODS: Data on OC single-cell RNA sequencing (scRNA-seq) and total RNA-seq were collected from the GEO and TCGA databases. Seurat R program was used to analyze scRNA-seq data and identify CAFs clusters corresponding to CAFs markers. Differential expression analysis was performed on the TCGA dataset to identify prognostic genes. A CAF-associated risk signature was designed using Lasso regression and combined with clinicopathological variables to develop a nomogram. Functional enrichment and the immune landscape were also analyzed. RESULTS: Five CAFs clusters were identified in OC using scRNA-seq data, and 2 were significantly associated with OC prognosis. Seven genes were selected to develop a CAF-based risk signature, primarily associated with 28 pathways. The signature was a key independent predictor of OC prognosis and relevant in predicting the results of immunotherapy interventions. A novel nomogram combining CAF-based risk and disease stage was developed to predict OC prognosis. CONCLUSION: The study highlights the importance of CAFs in OC progression and suggests potential for innovative treatment strategies. A CAF-based risk signature provides a highly accurate prediction of the prognosis of OC patients, and the developed nomogram shows promising results in predicting the OC prognosis.

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer.

Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.

Effects of silencing hsa_circ_0015326 on proliferation, migration, invasion, and apoptosis of epithelial ovarian cancer cells.

Although the treatment of ovarian cancer has made great progress, there are still many patients who are not timely detected and given targeted therapy due to unknown pathogenesis. Recent studies have found that hsa_circ_0015326 is upregulated in ovarian cancer and is involved in the proliferation, invasion, and migration of ovarian cancer cells. However, whether hsa_circ_0015326 can be used as a new target of ovarian cancer needs further investigation. Therefore, the effect of hsa_circ_0015326 on epithelial ovarian cancer was investigated in this study. At first, si-hsa_circ_0015326 lentivirus was transfected into epithelial ovarian cancer cells. Then real-time fluorescence quantitative PCR (qRT-PCR) was used to detect hsa_circ_0015326 level. The proliferation of ovarian cancer cells was detected by CCK-8 assay. The horizontal and vertical migration abilities of the cells were detected by wound-healing assay and Transwell assay, respectively. Transwell assay was also used to determine the invasion rate. As for the apoptosis rate, it was assessed by flow cytometry. As a result, the expression level of hsa_circ_0015326 in A2780 and SKOV3 was found to be higher than that in IOSE-80. However, after transfecting si-hsa_circ_0015326 and si-NC into the cells, the proliferation, migration, and invasion abilities of A2780 and SKOV3 cells in the si-hsa_circ_0015326 group were significantly reduced in comparison to those in the si-NC and mock groups, while their apoptosis rates were elevated. Collectively, silencing hsa_circ_0015326 bears the capability of inhibiting the proliferation, migration, and invasion of ovarian cancer cells while increasing apoptosis rate. It can be concluded that hsa_circ_0015326 promotes the malignant biological activities of epithelial ovarian cancer cells.

Construction and analysis of competitive endogenous RNA networks and prognostic models associated with ovarian cancer based on the exoRBase database.

OBJECTIVE: To construct a competitive endogenous RNA (ceRNA) regulatory network in blood exosomes of patients with ovarian cancer (OC) using bioinformatics and explore its pathogenesis. METHODS: The exoRbase2.0 database was used to download blood exosome gene sequencing data from patients OC and normal controls and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were detected independently using R language for differential expression analysis. TargetScan and miRanda databases were combined for the prediction and differential expression of mRNA-binding microRNAs (miRNA). The miRcode and starBase databases were used to predict miRNAs that bind to differentially expressed lncRNAs and circRNAs repectively. The relevant mRNA, circRNA, lncRNA and their corresponding miRNA prediction data were imported into Cytoscape software for visualization of the ceRNA network. The R language and KEGG Orthology-based Annotation System (KOBAS) were used to execute and illustrate the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using The CytoHubba plugin. RESULTS: Thirty-one differentially expressed mRNAs, 17 differentially expressed lncRNAs, and 24 differentially expressed circRNAs were screened. Cytoscape software was used to construct the ceRNA network with nine mRNA nodes, two lncRNA nodes, eight circRNA nodes, and 51 miRNA nodes. Both GO and KEGG were focused on the Spliceosome pathway, indicating that spliceosomes are closely linked with the development of OC, while heterogenous nuclear ribonucleoprotein K and RNA binding motif protein X-linked genes were the top 10 score Hub genes screened by Cytoscape software, including two lncRNAs, four mRNAs, and four circRNAs. In patients with OC, the expression of eukaryotic translation initiation factor 4 gamma 2 (EIF4G2), SERPINE 1 mRNA binding protein 1 (SERBP1), ribosomal protein L15 (RPL15) and human leukocyte antigen complex P5 (HCP5) was significantly higher whereas that of testis expressed transcript, Y-linked 15 and DEAD-box helicase 3 Y-linked genes was lower compared to normal controls Immunocorrelation scores revealed that SERBP1 was significantly and negatively correlated with endothelial cells and CD4+ T cells and positively correlated with natural killer (NK) cells and macrophages, respectively; RPL15 was significantly positively correlated with macrophages and endothelial cells and negatively correlated with CD8+ T cells and uncharacterized cells, respectively. EIF4G2 was significantly and negatively correlated with endothelial cells and CD4+ T cells, and positively correlated with uncharacterized cells, respectively. Based on the survival data and the significant correlation characteristics derived from the multifactorial Cox analysis (P < 0.05), the survival prediction curves demonstrated that the prognostic factors associated with 3-year survival in patients with OC were The prognostic factors associated with survival were Macrophage, RPL15. CONCLUSION: This study successfully constructs a ceRNA regulatory network in blood exosomes of OV patients, which provides the specific targets for diagnosis and treatment of OC.