Ovarian Cancer Tissue Samples Research Articles

Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies

Information about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.

Contribution of sphingomyelin phosphodiesterase acid-like 3B to the proliferation, migration, and invasion of ovarian cancer cells.

Cancer has the highest mortality rate among gynecological cancers and poses a serious threat to women's lives. However, the treatment options for ovarian cancer are still limited, and exploring effective targeted biomarkers is particularly important for predicting and treating ovarian cancer. Therefore, it is necessary to explore the molecular mechanisms of the occurrence and development of ovarian cancer. This investigation encompassed the analysis of gene expression profiles, measurement of transcription levels of potential target genes in peripheral blood samples from ovarian cancer patients and characterization of the ovarian cancer-related secretory protein sphingomyelin phosphodiesterase acid-like 3B (SMPDL3B). Through bioinformatics analysis, potential target genes were identified, and their association with overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients was assessed utilizing relevant databases. Subsequently, differences in target gene expression in ovarian cancer tissue samples were validated through protein blotting and quantitative real-time PCR (qRT-qPCR). Cell proliferation assays using the cell count kit-8 (CCK-8) method, as well as transwell chamber assay and pre coated matrix gel chamber assay were employed to elucidate the role of SMPDL3B in ovarian cancer cell migration and invasion. This study revealed a substantial upregulation of SMPDL3B in the serum of ovarian cancer patients, correlating with an unfavorable prognosis. High SMPDL3B expression was linked not only to increased proliferation of ovarian cancer cells, but also enhanced migration and invasion. Remarkably, the knockdown the human alkaline ceramidase 2 (ACER2) gene in cancer cells with heightened SMPDL3B expression significantly inhibited cell proliferation, migration, and invasion induced by SMPDL3B activation (P<0.05), highlighting the functional interplay between ACER2 and SMPDL3B in ovarian cancer. In summary, this study proposes SMPDL3B as a prognostic marker for ovarian cancer, with implications for potential therapeutic intervention targeting the ACER2-SMPDL3B axis.

The Role of Urothelial Cancer-Associated 1 in Gynecological Cancers.

Gynecological cancers (GC) represent some of the most frequently diagnosed malignancies in women worldwide. Long-non-coding RNAs (lncRNAs) are regulatory RNAs increasingly being recognized for their role in tumor progression and metastasis in various cancers. Urothelial cancer-associated 1 (UCA1) is a lncRNA, first found deregulated in bladder cancer, and many studies have exposed its oncogenic effects in more tumors since. However, the role of UCA1 in gynecological malignancies is still unclear. This review aims to analyze and define the role of UCA1 in GC, in order to identify its potential use as a diagnostic, prognostic, or therapeutic biomarker of GC. By employing the search terms "UCA1", "breast cancer", "endometrial cancer", "ovarian cancer", "cervical cancer", "vaginal cancer", and "vulvar cancer" in the PubMed database for the literature review, we identified a total of sixty-three relevant research articles published between 2014 and 2024. Although there were some opposing results, UCA1 was predominantly found to be upregulated in most of the breast, endometrial, ovarian, cervical, and vulvar cancer cells, tissue samples, and mouse xenograft models. UCA1 overexpression mainly accounts for enhanced tumor proliferation and increased drug resistance, while also being associated with some clinicopathological features, such as a high histological grade or poor prognosis. Nonetheless, no reviews were identified about the involvement of UCA1 in vaginal carcinogenesis. Therefore, further clinical trials are required to explore the role of UCA1 in these malignancies and, additionally, examine its possible application as a target for upcoming treatments, or as a novel biomarker for GC diagnosis and prognosis.

FBXO28 promotes cell proliferation, migration and invasion via upregulation of the TGF-beta1/SMAD2/3 signaling pathway in ovarian cancer

BackgroundOvarian cancer is one of the most common gynecological malignancies due to the lack of early symptoms, early diagnosis and limited screening. Therefore, it is necessary to understand the molecular mechanism underlying the occurrence and progression of ovarian cancer and to identify a basic biomarker for the early diagnosis and clinical treatment of ovarian cancer.MethodsThe association between FBXO28 and ovarian cancer prognosis was analyzed using Kaplan‒Meier survival analysis. The difference in FBXO28 mRNA expression between normal ovarian tissues and ovarian tumor tissues was obtained from The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) cohorts. The expression levels of the FBXO28 protein in ovarian cancer tissues and normal ovarian tissues were measured via immunohistochemical staining. Western blotting was used to determine the level of FBXO28 expression in ovarian cancer cells. The CCK-8, the colony formation, Transwell migration and invasion assays were performed to evaluate cell proliferation and motility.ResultsWe found that a higher expression level of FBXO28 was associated with poor prognosis in ovarian cancer patients. Analysis of the TCGA and GTEx cohorts showed that the FBXO28 mRNA level was lower in normal ovarian tissue samples than in ovarian cancer tissue samples. Compared with that in normal ovarian tissues or cell lines, the expression of FBXO28 was greater in ovarian tumor tissues or tumor cells. The upregulation of FBXO28 promoted the viability, proliferation, migration and invasion of ovarian cancer cells. Finally, we demonstrated that FBXO28 activated the TGF-beta1/Smad2/3 signaling pathway in ovarian cancer.ConclusionsIn conclusion, FBXO28 enhanced oncogenic function via upregulation of the TGF-beta1/Smad2/3 signaling pathway in ovarian cancer.

Unveiling the Significance of FGF8 Overexpression in Orchestrating the Progression of Ovarian Cancer.

The asymptomatic nature, high rate of disease recurrence, and resistance to platinum-based chemotherapy highlight the need to identify and characterize novel target molecules for ovarian cancer. Fibroblast growth factor 8 (FGF8) aids in the development and metastasis of ovarian cancer; however, its definite role is not clear. We employed ELISA and IHC to examine the expression of FGF8 in the saliva and tissue samples of epithelial ovarian cancer (EOC) patients and controls. Furthermore, various cell assays were conducted to determine how FGF8 silencing influences ovarian cancer cell survival, adhesion, migration, and invasion to learn more about the functions of FGF8. In saliva samples, from controls through low-grade to high-grade EOC, a stepped overexpression of FGF8 was observed. Similar expression trends were seen in tissue samples, both at protein and mRNA levels. FGF8 gene silencing in SKOV3 cells adversely affected various cell properties essential for cancer cell survival and metastasis. A substantial reduction was observed in the cell survival, cell adhesion to the extracellular matrix, migration, and adhesion properties of SKOV3 cells, suggesting that FGF8 plays a crucial role in the development of EOC. Conclusively, this study suggests a pro-metastatic function of FGF8 in EOC.

Establishing Molecular Subgroups of CD8+ T Cell-Associated Genes in the Ovarian Cancer Tumour Microenvironment and Predicting the Immunotherapy Response.

The mechanism by which infiltrating CD8+ T lymphocytes in the tumour microenvironment influence the survival of patients with ovarian cancer (OC) remains unclear. To identify biomarkers to optimise OC treatment, 13 immune-cell-line-associated datasets, RNA sequencing data, and clinical data from the GEO, TCGA, and the ICGC were collected. Gene expression in OC was assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining. We identified 520 genes and three immunological clusters (IC1, IC2, and IC3) associated with CD8+ T cells. Higher IFN scores, immune T cell lytic activity, and immune cell infiltration and upregulated expression of immune-checkpoint-related genes indicated that IC3 is more responsive to immunotherapy, whereas IC1 and IC2 have a poorer prognosis. A 10-gene signature, including SEMA4F, CX3CR1, STX7, PASK, AKIRIN2, HEMGN, GBP5, NSG1, and CXorf65, was constructed, and a multivariate Cox regression analysis revealed a significant association between the 10-gene signature-based risk model and overall survival (p < 0.001). A nomogram was constructed with age and the 10-gene signature. Consistent with the bioinformatics analysis, IHC and qRT-PCR confirmed the accuracy of the signatures in OC tissue samples. The predictive ability of the risk model was demonstrated using the Imvigor210 immunotherapy dataset. The development of a novel gene signature associated with CD8+ T cells could facilitate more accurate prognostics and prediction of the immunotherapeutic response of patients with OC.

DNA methylation patterns between tissue and blood samples in ovarian cancer.

5558 Background: Altered DNA methylation pattern, with functional consequences in the activity of key carcinogenic pathways, has been shown to be involved in the carcinogenesis of ovarian cancer. Tumor DNA is released into the blood via apoptosis or necrosis. Therefore, cell-free DNA (cfDNA) methylation alteration can also be detected in blood, which has shown promising performance in ovarian cancer early detection and prognostication. However, the difference of DNA methylation patterns between the tissue and blood samples of ovarian cancer is still unclear. Methods: PERCEIVE-I study (NCT04903665) is a prospective study aimed at the early detection of gynecological malignancies. Blood and paired tissue samples from this study were collected at the Fudan University Shanghai Cancer Center. Blood samples from age-matched non-cancer controls were also collected. All the blood and paired tissue samples were sequenced by a target methylation panel covering ~490,000 CpG sites. Finally, The ovarian cancer methylation patterns were analyzed based on 11 paired cancer-adjacent tissue samples and 170 blood samples (cancer, n=48; non-cancer control, n=122). Results: In total, 7,225 differentially methylated blocks (DMBs) were identified by comparing cancer and adjacent tissues, with 5,093 hypomethylated and 2,132 hypermethylated. By contrast, there were 5,853 hypomethylated and 8,685 hypermethylated DMBs in cancer blood samples compared with non-cancer controls. Among the 7,225 DMBs in tissues, 3,962 (54.8%) blocks were also observed to be altered in blood samples, including 2,354 hypomethylated blocks and 1,114 hypermethylated blocks. GO analysis revealed that these hypomethylated blocks were mainly associated with functions such as regulation of synaptic membrane and DNA-binding transcription activator activity, while the hypermethylated blocks were mainly enriched in the pattern specification process, transcription factor complex, and the DNA-binding transcription activator activity. The alteration of the above 3,962 DMBs in tissue were highly correlated with the alteration in blood (correlation coefficient of 0.71, 0.50, 0.73 and 0.73 for stage I-IV, respectively). Furthermore, in patients carrying germline BRCA1/2 mutations (n=11), most of the above DMBs were not significantly different from those without germline BRCA1/2 mutations (n=37). Conclusions: In this study, we demonstrated that blood DNA methylation alteration was highly consistent with tissue DNA methylation alteration in ovarian cancer, indicating the sustained release of DNA methylation signals from the primary cancer sites. These results suggest that cfDNA methylation can be used as reliable biomarkers for the early detection of ovarian cancer. Clinical trial information: NCT04903665 .

LncRNA gadd7 promotes mitochondrial membrane potential decrease and apoptosis of alveolar type II epithelial cells by positively regulating MFN1 in an in vitro model of hyperoxia-induced acute lung injury.

The mortality and morbidity rates of ovarian cancer (OC) are high, but the underlying mechanisms of OC have not been characterized. In this study, we determined the role of Rho GTPase Activating Protein 30 (ARHGAP30) in OC progression. We measured ARHGAP30 abundance in OC tissue samples and cells using immunohistochemistry (IHC) and RT-qPCR. EdU, transwell, and annexin V/PI apoptosis assays were used to evaluate proliferation, invasiveness, and apoptosis of OC cells, respectively. The results showed that ARHGAP30 was overexpressed in OC tissue samples and cells. Inhibition of ARHGAP30 suppressed growth and metastasis of OC cells, and enhanced apoptosis. Knockdown of ARHGAP30 in OC cells significantly inhibited the PI3K/AKT/mTOR pathway. Treatment with the PI3K/AKT/mTOR pathway inhibitor buparlisib simulated the effects of ARHGAP30 knockdown on growth, invasiveness, and apoptosis of OC cells. Following buparlisib treatment, the expression levels of p-PI3K, p-AKT, and p-mTOR were significantly decreased. Furthermore, buparlisib inhibited the effects of ARHGAP30 upregulation on OC cell growth and invasiveness. In conclusion, ARHGAP30 regulated the PI3K/AKT/mTOR pathway to promote progression of OC.

Knockdown of ARHGAP30 inhibits ovarian cancer cell proliferation, migration, and invasiveness by suppressing the PI3K/AKT/mTOR signaling pathway

The mortality and morbidity rates of ovarian cancer (OC) are high, but the underlying mechanisms of OC have not been characterized. In this study, we determined the role of Rho GTPase Activating Protein 30 (ARHGAP30) in OC progression. We measured ARHGAP30 abundance in OC tissue samples and cells using immunohistochemistry (IHC) and RT-qPCR. EdU, transwell, and annexin V/PI apoptosis assays were used to evaluate proliferation, invasiveness, and apoptosis of OC cells, respectively. The results showed that ARHGAP30 was overexpressed in OC tissue samples and cells. Inhibition of ARHGAP30 suppressed growth and metastasis of OC cells, and enhanced apoptosis. Knockdown of ARHGAP30 in OC cells significantly inhibited the PI3K/AKT/mTOR pathway. Treatment with the PI3K/AKT/mTOR pathway inhibitor buparlisib simulated the effects of ARHGAP30 knockdown on growth, invasiveness, and apoptosis of OC cells. Following buparlisib treatment, the expression levels of p-PI3K, p-AKT, and p-mTOR were significantly decreased. Furthermore, buparlisib inhibited the effects of ARHGAP30 upregulation on OC cell growth and invasiveness. In conclusion, ARHGAP30 regulated the PI3K/AKT/mTOR pathway to promote progression of OC.

Ovarian cancer-associated immune exhaustion involves SPP1+ T cell and NKT cell, symbolizing more malignant progression.

Ovarian cancer (OC) is highly heterogeneous and has a poor prognosis. A better understanding of OC biology could provide more effective therapeutic paradigms for different OC subtypes. To reveal the heterogeneity of T cell-associated subclusters in OC, we performed an in-depth analysis of single-cell transcriptional profiles and clinical information of patients with OC. Then, the above analysis results were verified by qPCR and flow cytometry examine. After screening by threshold, a total of 85,699 cells in 16 ovarian cancer tissue samples were clustered into 25 major cell groups. By performing further clustering of T cell-associated clusters, we annotated a total of 14 T cell subclusters. Then, four distinct single-cell landscapes of exhausted T (Tex) cells were screened, and SPP1 + Tex significantly correlated with NKT cell strength. A large amount of RNA sequencing expression data combining the CIBERSORTx tool were labeled with cell types from our single-cell data. Calculating the relative abundance of cell types revealed that a greater proportion of SPP1 + Tex cells was associated with poor prognosis in a cohort of 371 patients with OC. In addition, we showed that the poor prognosis of patients in the high SPP1 + Tex expression group might be related to the suppression of immune checkpoints. Finally, we verified in vitro that SPP1 expression was significantly higher in ovarian cancer cells than in normal ovarian cells. By flow cytometry, knockdown of SPP1 in ovarian cancer cells could promote tumorigenic apoptosis. This is the first study to provide a more comprehensive understanding of the heterogeneity and clinical significance of Tex cells in OC, which will contribute to the development of more precise and effective therapies.

SPOCK1 Overexpression Suggests Poor Prognosis of Ovarian Cancer

Sparc/osteonectin, cwcv, and kazal-like domains proteoglycan 1 (SPOCK1) has been found in a variety of malignant tumors and is associated with a poor prognosis. We aimed to explore the role of SPOCK1 in ovarian cancer. Ovarian cancer cell lines SKOV3 and SW626 were transfected with SPOCK1 overexpressing or empty vector using electroporation. Cells were studied by immunostaining and an automated Western blotting system. BrdU uptake and wound healing assays assessed cell proliferation and migration. SPOCK1 expression in human ovarian cancer tissues and in blood samples were studied by immunostaining and ELISA. Survival of patients with tumors exhibiting low and high SPOCK1 expression was analyzed using online tools. Both transfected cell lines synthesized different SPOCK1 variants; SKOV3 cells also secreted the proteoglycan. SPOCK1 overexpression stimulated DNA synthesis and cell migration involving p21CIP1. Ovarian cancer patients had increased SPOCK1 serum levels compared to healthy controls. Tumor cells of tissues also displayed abundant SPOCK1. Moreover, SPOCK1 levels were higher in untreated ovarian cancer serum and tissue samples and lower in recipients of chemotherapy. According to in silico analyses, high SPOCK1 expression was correlated with shorter survival. Our findings suggest SPOCK1 may be a viable anti-tumor therapeutic target and could be used for monitoring ovarian cancer.

Next Generation of Ovarian Cancer Detection Using Aptamers

Ovarian cancer is among the seven most common types of cancer in women, being the most fatal gynecological tumor, due to the difficulty of detection in early stages. Aptamers are important tools to improve tumor diagnosis through the recognition of specific molecules produced by tumors. Here, aptamers and their potential targets in ovarian cancer cells were analyzed by in silico approaches. Specific aptamers were selected by the Cell-SELEX method using Caov-3 and OvCar-3 cells. The five most frequent aptamers obtained from the last round of selection were computationally modeled. The potential targets for those aptamers in cells were proposed by analyzing proteomic data available for the Caov-3 and OvCar-3 cell lines. Overexpressed proteins for each cell were characterized as to their three-dimensional model, cell location, and electrostatic potential. As a result, four specific aptamers for ovarian tumors were selected: AptaC2, AptaC4, AptaO1, and AptaO2. Potential targets were identified for each aptamer through Molecular Docking, and the best complexes were AptaC2-FXYD3, AptaC4-ALPP, AptaO1-TSPAN15, and AptaO2-TSPAN15. In addition, AptaC2 and AptaO1 could detect different stages and subtypes of ovarian cancer tissue samples. The application of this technology makes it possible to propose new molecular biomarkers for the differential diagnosis of epithelial ovarian cancer.

Proteomic analysis reveals CAAP1 negatively correlates with platinum resistance in ovarian cancer

The present study sought to investigate the correlation between CAAP1 and platinum resistance in ovarian cancer and to preliminarily explore the potential biological function of CAAP1. Proteomic analysis was used to analyze differentially expressed proteins in platinum-sensitive and -resistant tissue samples of ovarian cancer. The Kaplan-Meier plotter was used for prognostic analysis. Immunohistochemistry assay and chi-square test were employed to explore the relationship between CAAP1 and platinum resistance in tissue samples. Lentivirus transfection, immunoprecipitation-mass spectrometry, and bioinformatics analysis were used to determine the potential biological function of CAAP1. Based on results, the expression level of CAAP1 was significantly higher in platinum-sensitive tissues compared to that in resistant tissues. Chi-square test demonstrated that there is a negative correlation between high expression of CAAP1 and platinum resistance. Overexpression of CAAP1 increased cis‑platinum sensitivity of the A2780/DDP cell line likely via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. In summary, there is a negative correlation between high expression of CAAP1 and platinum resistance. CAAP1 might be a potential biomarker for platinum resistance in ovarian cancer. SignificancePlatinum resistance is a key factor affecting the survival of ovarian cancer patients. Understanding the mechanisms of platinum resistance is highly important for ovarian cancer management. Here, we performed the DIA- and DDA-based proteomics to analyze differentially expressed proteins in tissue and cell samples of ovarian cancer. We found that the protein identified as CAAP1, which was first reported to be involved in the regulation of apoptosis, may be negatively correlates with platinum resistance in ovarian cancer. In addition, we also found that CAAP1 enhanced the sensitivity of platinum-resistant cells to cis‑platinum via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. Our data would be useful to reveal novel molecular mechanisms of platinum resistance in ovarian cancer.

132P Genomic profiling of primary and recurrent ovarian cancers: A retrospective single-center experience

The oncology landscape is rapidly evolving towards more personalized treatment with the use of molecular-driven therapies. Thus, genomic information has become an integral component of clinical decision-making. This study aims to analyze tumor tissue samples of high-grade serous ovarian cancer (HGSOC) to understand if genomic changes in gene mutation frequencies between primary and relapsing tumors occur and can influence the detection of actionable gene alterations.

A comparison of four technologies for detecting p53 aggregates in ovarian cancer.

The tumor suppressor protein p53 is mutated in half of all cancers and has been described to form amyloid-like structures, commonly known from key proteins in neurodegenerative diseases. Still, the clinical relevance of p53 aggregates remains largely unknown, which may be due to the lack of sensitive and specific detection methods. The aim of the present study was to compare the suitability of four different methodologies to specifically detect p53 aggregates: co-immunofluorescence (co-IF), proximity ligation assay (PLA), co-immunoprecipitation (co-IP), and the p53-Seprion-ELISA in cancer cell lines and epithelial ovarian cancer tissue samples. In 7 out of 10 (70%) cell lines, all applied techniques showed concordance. For the analysis of the tissue samples co-IF, co-IP, and p53-Seprion-ELISA were compared, resulting in 100% concordance in 23 out of 30 (76.7%) tissue samples. However, Co-IF lacked specificity as there were samples, which did not show p53 staining but abundant staining of amyloid proteins, highlighting that this method demonstrates that proteins share the same subcellular space, but does not specifically detect p53 aggregates. Overall, the PLA and the p53-Seprion-ELISA are the only two methods that allow the quantitative measurement of p53 aggregates. On the one hand, the PLA represents the ideal method for p53 aggregate detection in FFPE tissue, which is the gold-standard preservation method of clinical samples. On the other hand, when fresh-frozen tissue is available the p53-Seprion-ELISA should be preferred because of the shorter turnaround time and the possibility for high-throughput analysis. These methods may add to the understanding of amyloid-like p53 in cancer and could help stratify patients in future clinical trials targeting p53 aggregation.

Assessing homologous recombination deficiency (HRD) in ovarian cancer: Optimizing concordance of the regulatory-approved companion diagnostic and a next-generation sequencing (NGS) assay kit.

e17571 Background: Olaparib + bevacizumab is approved as first-line maintenance treatment of ovarian cancer that is HRD positive, defined as the presence of a deleterious/suspected deleterious BRCA mutation (BRCAm) and/or genomic instability. HRD genomic instability score (GIS) has been assessed in clinical trials using a regulatory-approved companion diagnostic, Myriad myChoice® CDx. We evaluated the performance of an in-development NGS assay kit that identifies BRCA variants based on genomic content from Illumina’s TruSight™ Oncology 500 research assay, and, with additional genomic content, measures HRD GIS in tumor tissue (Illumina test) in parallel. Analytic concordance of the Illumina test versus the Myriad myChoice® PLUS assay (Myriad test) under improved algorithms for calculating GIS scores is reported. Methods: Ovarian cancer tissue samples were analyzed with the Illumina (40 ng DNA; N = 227) and Myriad (200 ng DNA; N = 254) tests. Agreement rates for BRCA analysis, HRD GIS, and overall HRD status (includes BRCAm and HRD GIS [cutoff, 42]) were analyzed. For the overall and the non-BRCAm analysis cohorts, Pearson correlation between the continuous HRD GIS of the Illumina and Myriad tests was determined. Analytic sensitivity and specificity of the Illumina-derived HRD GIS to classify HRD GIS (cutoff, 42) were evaluated using AUROC. After initial concordance calculations, modifications were made to increase the quality of the Illumina preprocessing data (ie, reducing allele dosage noise and allele bias) upstream of the GIS calling algorithm. Reprocessing of the existing raw data led to further improvement in the point estimates of positive and negative percent agreement. Results: Prevalence using the Illumina and Myriad tests was 51.2% and 49.2% (overall HRD positive) and 27.6% and 25.5% (BRCAm), respectively. Agreement rates are reported in the Table. Correlation of the Illumina HRD GIS with Myriad HRD GIS was 0.980 (all samples) and 0.976 (non-BRCAm cohort); AUROCs were 0.995 and 0.992, respectively. Conclusions: Comparison between the Illumina and Myriad tests showed that overall HRD status, BRCA analysis, and HRD GIS detection results were &gt; 90% concordant; HRD GIS was highly correlated (r &gt; 0.98); and prevalence estimates were similar. These data suggest that a distributable kit solution, such as the Illumina test under development, will approach the performance of the Myriad myChoice HRD assay.[Table: see text]

The enhancer RNA ADCY10P1 is associated with the progression of ovarian cancer

BackgroundEmerging evidence identifies enhancer RNAs (eRNAs) as a class of regulatory ncRNAs that can contribute to the transcription of target genes. In this study, we used an integrated data analysis method to identify the important role of eRNAs in ovarian cancer (OC).MethodsGene expression profiles and clinical information from The Cancer Genome Atlas (TCGA) database were used for this study. Based on expression analysis using GEPIA2 gene and Kaplan–Meier survival was performed to ensure the significance of the selected enhancer RNA ADCY10P1 in OC. Next, we explored the correlation and clinical significance between ADCY10P1 and target gene NFYA. Furthermore, we evaluated the effects of overexpression of ADCY10P1 on the proliferation, migration, invasion and epithelial-mesenchymal transformation (EMT) of OC cell lines. We also investigated the biological function enrichment score of ADCY10P1 and verified it with OC cell lines. Finally, external validation was conducted, and the prognostic value of the ADCY10P1 in different tumors was demonstrated.ResultsWe selected the eRNA ADCY10P1 associated with OC prognosis, with NFYA as its predicted target gene. Low ADCY10P1 expression was found to be associated with poor overall survival, high histological grade, and advanced stage of OC. Additionally, overexpression of ADCY10P1 inhibited the proliferation, migration, invasion and EMT phenotype of OC cell lines. Furthermore, ADCY10P1 was observed to inhibit glycolysis and fatty acid metabolism, thereby affecting OC progression. Meanwhile, OC tissue samples were externally validated. In addition, the pan-cancer analysis revealed that ADCY10P1 had prognostic value in other cancers.ConclusionsThis study showed that ADCY10P1 plays a key role in OC progression and may facilitate prognosis prediction.

Biological Differences Between Ovarian Cancer-associated Fibroblasts and Contralateral Normal Ovary-derived Mesenchymal Stem Cells

The aim of this study was to clarify the biological differences between ovarian cancer-associated fibroblasts (OCa-CAFs) and normal ovary-derived mesenchymal stem cells (NO-MSCs). Surgically resected ovarian cancer and contralateral normal ovarian tissue samples were cut into small pieces for culture as "explants". The number of outgrown cells, their proliferative kinetics, and expression levels of cell surface markers of CAFs, as well as three miRNAs in OCa-CAFs and NO-MSCs were compared directly. Differentially expressed genes between both groups were also investigated. Comparable numbers of outgrown cells were harvested from both groups. Significantly higher expression of α-smooth muscle actin and miR-142 was found in OCa-CAFs, which decreased significantly during ex vivo cell expansion. A total of 21 differentially expressed genes were identified between both groups. OCa-CAFs showed different biological properties in direct comparison with NO-MSCs, which might play major roles in the pathogenesis of ovarian cancer.

Dubious effects of methadone as an “anticancer” drug on ovarian cancer cell-lines and patient-derived tumor-spheroids

BackgroundThe opioid agonist D,L-methadone exerts analgesic effects via the mu opioid receptor, encoded by OPRM1 and therefore plays a role in chronic pain management. In preclinical tumor-models D,L-methadone shows apoptotic and chemo-sensitizing effects and was therefore hyped as an off-label “anticancer” drug without substantiation from clinical trials. Its effects in ovarian cancer (OC) are completely unexplored. MethodsWe analyzed OPRM1-mRNA expression in six cisplatin-sensitive, two cisplatin-resistant OC cell-lines, 170 OC tissue samples and 12 non-neoplastic control tissues. Pro-angiogenetic, cytotoxic and apoptotic effects of D,L-methadone were evaluated in OC cell-lines and four patient-derived tumor-spheroid models. ResultsOPRM1 was transcriptionally expressed in 69% of OC-tissues and in three of eight OC cell-lines. D,L-methadone exposure significantly reduced cell-viability in five OC cell-lines irrespective of OPRM1 expression. D,L-methadone, applied alone or combined with cisplatin, showed no significant effects on apoptosis or VEGF secretion in cell-lines. Notably, in two of the four spheroid models, treatment with D,L-methadone significantly enhanced cell growth (by up to 121%), especially after long-term exposure. This is consistent with the observed attenuation of the inhibitory effects of cisplatin in three spheroid models when adding D,L-methadone. The effect of methadone treatment on VEGF secretion in tumor-spheroids was inconclusive. ConclusionsOur study demonstrates that certain OC samples express OPRM1, which, however, is not a prerequisite for D,L-methadone function. As such, D,L-methadone may exert also detrimental effects by stimulating the growth of certain OC-cells and abrogating cisplatin's therapeutic effect.

Diagnostic performance of microRNA-34a, let-7f and microRNA-31 in epithelial ovarian cancer prediction.

ObjectiveTo correlate the genome-wide methylation signature of microRNA genes with dysregulated expression of selected candidate microRNA in tissue and serum samples of epithelial ovarian cancer (EOC) and control using quantitative reverse transcription polymerase chain reaction (qRT-PCR), and evaluation of EOC predictive value of candidate microRNA at an early stage.MethodsWe performed Methylated DNA Immunoprecipitation coupled with NGS (MeDIP-NGS) sequencing of 6 EOC and 2 normal tissue samples of the ovary. Expression of selected microRNA from tissue (EOC=85, normal=30) and serum (EOC=50, normal=15) samples was evaluated using qRT-PCR. We conducted bioinformatics analysis to identify the candidate miRNA’s potential target and functional role.ResultsMeDIP-NGS sequencing revealed hypermethylation of several microRNAs gene promoters. Three candidate microRNAs were selected (microRNA-34a, let-7f, and microRNA-31) from MeDIP-NGS data analysis based on log2FC and P-value. The relative expression level of microRNA-34a, let-7f, and microRNA-31 was found to be significantly reduced in early-stage EOC tissues and serum samples (p<0.0001). The receiver operating characteristic analysis of microRNA-34a, let-7f and miR-31 showed improved diagnostic value with area under curve(AUC) of 92.0 (p<0.0001), 87.9 (p<0.0001), and 85.6 (p<0.0001) and AUC of 82.7 (p<0.0001), 82.0 (p<0.0001), and 81.0 (p<0.0001) in stage III-IV and stage I-II EOC serum samples respectively. The integrated diagnostic performance of microRNA panel (microRNA-34a+let-7f+microRNA-31) in late-stage and early-stage serum samples was 95.5 and 96.9 respectively.ConclusionOur data correlated hypermethylation-associated downregulation of microRNA in EOC. In addition, a combined microRNA panel from serum could predict the risk of EOC with greater AUC, sensitivity, and specificity.