Prognostic biomarkers in ovarian cancer

The prognostic role of systemic inflammatory markers in apparent early-stage ovarian cancer

BACKGROUND

Few studies analyzed the prognostic role of systemic inflammatory markers in early-stage ovarian cancer. The primary endpoint of the present study was to assess the prognostic impact of baseline inflammatory markers in early-stage ovarian cancer. The secondary endpoints were to compare the disease-free survival (DFS) of inflammatory markers with standard risk factors and to correlate these with BRCA mutational status.

METHODS

Retrospective, single-center, observational study. Patients with FIGO-stage I-II and IIIA1 epithelial ovarian cancer undergoing primary surgery between 10/2012 and 12/2019 were included. Inflammatory markers were evaluated on the results of the complete blood count and coagulation tests, performed before ovarian cancer surgery. The Receiver Operating Characteristic curve was used to determine the optimal cut-off value of different baseline inflammatory biomarkers for the DFS analysis.

RESULTS

Three hundred fifty-nine patients were included in the study period. Baseline neutrophil-lymphocyte ratio (NLR) ≥ 3 and systemic immune inflammation index (SII, defined as platelet x neutrophil-lymphocyte ratio) ≥ 1000 were associated with worse 3 year DFS and baseline SII ≥ 1000 was associated with worse 3 year OS. BRCA-mutated patients with SII ≥ 1000 and with NLR ≥ 3 had significantly worse DFS compared to SII < 1000 and with NLR < 3. FIGO stage > I was the only independent risk factor for higher risk of recurrence.

CONCLUSION

SII ≥ 1000 and NLR ≥ 3 were associated with worse 3 year DFS and SII ≥ 1000 was associated with worse 3 year OS. The subgroups of BRCA-mutated patients with higher inflammation markers (SII ≥ 1000 and NLR ≥ 3) were associated with worse DFS. These findings might be helpful to design personalized treatment and more intensive surveillance.

Combined BRCA2 and MAGEC3 Expression Predict Outcome in Advanced Ovarian Cancers

Like BRCA2, MAGEC3 is an ovarian cancer predisposition gene that has been shown to have prognostic significance in ovarian cancer patients. Despite the clinical significance of each gene, no studies have been conducted to assess the clinical significance of their combined expression. We therefore sought to determine the relationship between MAGEC3 and BRCA2 expression in ovarian cancer and their association with patient characteristics and outcomes. Immunohistochemical staining was quantitated on tumor microarrays of human tumor samples obtained from 357 patients with epithelial ovarian cancer to ascertain BRCA2 expression levels. In conjunction with our previously published MAGEC3 expression data, we observed a weak inverse correlation of MAGEC3 with BRCA2 expression (r = −0.15; p < 0.05) in cases with full-length BRCA2. Patients with optimal cytoreduction, loss of MAGEC3, and detectable BRCA2 expression had better overall (median OS: 127.9 vs. 65.3 months, p = 0.035) and progression-free (median PFS: 85.3 vs. 18.8 months, p = 0.002) survival compared to patients that were BRCA2 expressors with MAGEC3 normal levels. Our results suggest that combined expression of MAGEC3 and BRCA2 serves as a better predictor of prognosis than each marker alone.

DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer

BACKGROUND

Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements.

RESULTS

In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges.

CONCLUSIONS

Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM .

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

Beyond the snapshot: optimizing prognostication and prediction by moving from fixed to functional multidimensional cancer pathology

The role of pathology in patient management has evolved over time from the retrospective review of cells, tissue, and disease (‘what happened’) to a prospective outlook (‘what will happen’). Examination of a static, two‐dimensional hematoxylin and eosin (H&E)‐stained tissue slide has traditionally been the pathologist's primary task, but novel ancillary techniques enabled by technological breakthroughs have supported pathologists in their increasing ability to predict disease status and behaviour. Nevertheless, the informational limits of 2D, fixed tissue are now being reached and technological innovation is urgently needed to ensure that our understanding of disease entities continues to support improved individualized treatment options. Here we review pioneering work currently underway in the field of cancer pathology that has the potential to capture information beyond the current basic snapshot. A selection of exciting new technologies is discussed that promise to facilitate integration of the functional and multidimensional (space and time) information needed to optimize the prognostic and predictive value of cancer pathology. Learning how to analyse, interpret, and apply the wealth of data acquired by these new approaches will challenge the knowledge and skills of the pathology community. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Prognostic utility of the ovarian cancer secretome: a systematic investigation

BACKGROUND

Ovarian cancer is usually detected at an advanced stage with frequent recurrence. The recurrence-free survival and overall survival is influenced by the age at diagnosis, tumor stage and histological subtype. Nonetheless, quantifiable prognostic biomarkers are needed for early identification of the high-risk patients and for personalized medicine. Several studies link tumor-specific dysregulated expression of certain proteins with ovarian cancer prognosis. However, careful investigation of presence of these prognostically relevant proteins in ovarian cancer secretome is lacking.

OBJECTIVE

To critically analyze the recent published data on prognostically relevant proteins for ovarian cancer and to carefully search how many of them are reported in the published ovarian cancer secretome datasets.

DESIGN

A search for relevant studies in the past 2 years was conducted in PubMed and a comprehensive list of proteins associated with the ovarian cancer prognosis was prepared. These were cross-referred to the published ovarian cancer secretome profiles. The proteins identified in the secretome were further shortlisted based on a scoring strategy employing stringent criteria.

RESULTS

A panel of seven promising secretory biomarkers associated with ovarian cancer prognosis is proposed.

CONCLUSION

Scanning the ovarian cancer secretome datasets provides the opportunity to identify if tumor-specific biomarkers could be tested as secretory biomarkers. Detecting their levels in the body fluid would be more advantageous than evaluating the expression in the tissue, since it could be monitored multiple times over the course of the disease to have a better judgment of the prognosis and response to therapy.

Development of a novel transcription factors-related prognostic signature for serous ovarian cancer

Growing evidence suggest that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. A 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score and cluster models were effective at accurately distinguishing the overall survival of SOC. Analysis of genomic alterations were used to elaborate on the association between the 17-TFs related prognostic signature and genomic aberrations. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. The potential immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups. The results indicated that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients. In conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.

Subtype specific biomarkers associated with chemoresistance in epithelial ovarian cancer

In spite of the advent of many high throughput technologies, tumor tissue biomarkers are still the gold standard for diagnosis and prognosis of different malignancies including epithelial ovarian cancer (EOC). EOC is a heterogeneous disease comprised of five major subtypes which show distinct clinicopathological features and therapy response. Acquirement of chemoresistance toward therapy is a major challenge for successful treatment outcome in EOC patients. Several markers have been tested by immunohistochemical method to evaluate their prognostic merit to predict clinical outcome. However, a vast majority of such markers have been assessed for high-grade serous and clear cell ovarian cancer, among all subtypes of EOC. The current review elaborates upon those biomarkers that can potentially predict chemoresistance with subtype specificity.

Oligopeptides for Immunotherapy Approaches in Ovarian Cancer Treatment

BACKGROUND

Anti-ovarian cancer vaccines based on minimal immune determinants uniquely expressed in ovarian cancer biomarkers appear to promise a high level of sensitivity and specificity for ovarian cancer immunodiagnostics, immunoprevention, and immunotherapy.

METHODS

Using the Pir Peptide Match program, three ovarian cancer biomarkers - namely, sperm surface protein Sp17, WAP four-disulfide core domain protein 2, and müllerian-inhibiting substance - were searched for unique peptide segments not shared with other human proteins. Then, the unique peptide segments were assembled to define oligopeptides potentially usable as synthetic ovarian cancer antigens.

RESULTS AND CONCLUSION

This study describes a methodology for constructing ovarian cancer biomarkerderived oligopeptide constructs that might induce powerful, specific, and non-crossreactive immune responses against ovarian cancer.

Discovery of potential serum and urine-based microRNA as minimally-invasive biomarkers for breast and gynecological cancer

BACKGROUND

Deregulated microRNAs (miRNAs) in breast and gynecological cancer might contribute to improve early detection of female malignancies.

OBJECTIVE

Specification of miRNA types in serum and urine as minimally-invasive biomarkers for breast (BC), endometrial (EC) and ovarian cancer (OC).

METHODS

In a discovery phase, serum and urine samples from 17 BC, five EC and five OC patients vs. ten healthy controls (CTRL) were analyzed with Agilent human miRNA microarray chip. Selected miRNA types were further investigated by RT-qPCR in serum (31 BC, 13 EC, 15 OC patients, 32 CTRL) and urine (25 BC, 10 EC, 10 OC patients, 30 CTRL) applying two-sample t-tests.

RESULTS

Several miRNA biomarker candidates exhibited diagnostic features due to distinctive expression levels (serum: 26; urine: 22). Among these, miR-518b, -4719 and -6757-3p were found specifically deregulated in BC serum. Four, non-entity-specific, novel biomarker candidates with unknown functional roles were identified in urine (miR-3973; -4426; -5089-5p and -6841). RT-qPCR identified miR-484/-23a (all p⩽ 0.001) in serum as potential diagnostic markers for EC and OC while miR-23a may also serve as an endogenous control in BC diagnosis.

CONCLUSIONS

Promising miRNAs as liquid biopsy-based tools in the detection of BC, EC and OC qualified for external validation in larger cohorts.

CircRNA FGFR3 induces epithelial-mesenchymal transition of ovarian cancer by regulating miR-29a-3p/E2F1 axis

Circular RNAs (circRNAs) are a class of non-coding RNAs that regulate gene expression after transcription. However, the specific function of circRNAs in ovarian cancer remains undetermined. Previous studies have demonstrated abnormal expression of circFGFR3 in several cancers. The present study was designed to reveal the roles of circFGFR3 in ovarian cancer (OC). CircFGFR3 expression in OC tissues and cells was detected by RT-qPCR. The effects of CircFGFR3 on OC cells were evaluated by transwell assay and CCK-8 assay. Finally, the underlying mechanism was further revealed by luciferase reporter assay and western blotting. Our results showed that circFGFR3 expression was higher in OC cells and tissues than in normal ovarian cells and adjacent normal tissues; in addition, in OC patients, a high level of CircFGFR3 was related to lower survival rates and higher recurrence rates than a low level of circFGFR3. CircFGFR3 overexpression promotes OC progression by inducing epithelial-mesenchymal transition (EMT) in vitro. Mechanistically, circFGFR3 upregulates E2F1 expression by sponging miR-29a-3p, and the overexpression of E2F1 or the suppression of miR-29a-3p induces OC cell EMT. Therefore, circFGFR3 serves as a promoter of OC by inducing OC cell EMT via the miR-29a-3p/E2F1 axis and circFGFR3 may be a prognostic biomarker for OC patients.

Rho GTPases in Gynecologic Cancers: In-Depth Analysis toward the Paradigm Change from Reactive to Predictive, Preventive, and Personalized Medical Approach Benefiting the Patient and Healthcare

Rho guanosine triphospatases (GTPases) resemble a conserved family of GTP-binding proteins regulating actin cytoskeleton dynamics and several signaling pathways central for the cell. Rho GTPases create a so-called Ras-superfamily of GTPases subdivided into subgroups comprising at least 20 members. Rho GTPases play a key regulatory role in gene expression, cell cycle control and proliferation, epithelial cell polarity, cell migration, survival, and apoptosis, among others. They also have tissue-related functions including angiogenesis being involved in inflammatory and wound healing processes. Contextually, any abnormality in the Rho GTPase function may result in severe consequences at molecular, cellular, and tissue levels. Rho GTPases also play a key role in tumorigenesis and metastatic disease. Corresponding mechanisms include a number of targets such as kinases and scaffold/adaptor-like proteins initiating GTPases-related signaling cascades. The accumulated evidence demonstrates the oncogenic relevance of Rho GTPases for several solid malignancies including breast, liver, bladder, melanoma, testicular, lung, central nervous system (CNS), head and neck, cervical, and ovarian cancers. Furthermore, Rho GTPases play a crucial role in the development of radio- and chemoresistance e.g. under cisplatin-based cancer treatment. This article provides an in-depth overview on the role of Rho GTPases in gynecological cancers, highlights relevant signaling pathways and pathomechanisms, and sheds light on their involvement in tumor progression, metastatic spread, and radio/chemo resistance. In addition, insights into a spectrum of novel biomarkers and innovative approaches based on the paradigm shift from reactive to predictive, preventive, and personalized medicine are provided.

Hormonal regulation and function of an RNA helicase, Ddx5 in corpus luteum of adult Wistar rats

Corpus luteum (CL) is an endocrine tissue involved in regulation of reproductive cycle and early pregnancy establishment. In the present study DEAD-box helicase-5 (Ddx5), a member of the DEAD box family of RNA helicases was investigated for its expression, regulation and function in CL of Wistar rats. Ddx5 was expressed in adult rat CL. Primary cell culture from supra-ovulated ovaries were established for in vitro studies. Addition of luteinizing hormone (LH; 100 ng/ml), a luteotrophic factor in primary cell culture, decreased Ddx5 RNA expression (foldchange:0.6 ± 0.075) while prostaglandin alpha (PGF_2α; 1μM), a luteolytic factor caused an increase (foldchange:2.4 ± 0.4) compared to control group. Under in vivo conditions, the administration of PGF_2α or gonadotropin-releasing hormone antagonist; cetrorelix (CET) caused luteolysis as well as an increase in the protein level of Ddx5 (foldchange:1.9 ± 0.27 and 1.4 ± 0.09 viz.; p < 0.05) in CL of adult rats. LH was administered post CET treatment which suppressed Ddx5 protein expression (foldchange:0.8 ± 0.16; p < 0.05) compared to CET treated group. Further, it was observed that the expression of Ddx5 was upregulated (foldchange:1.5 ± 0.23; p < 0.05) in CL during late pregnancy compared to mid pregnancy concomitant to luteolysis in adult rats. Overall, the results suggest for the first time that Ddx5 is expressed in rat CL and regulated by luteolytic and luteotrophic factors in an inverse fashion. Further, the data significantly correlates ddx5 expression to CL regression suggesting involvement of ddx5 in luteolysis. These results suggest a significant role of Ddx5 in female reproduction biology and warrant in depth examination of the function of Ddx5 in CL.

IGF1R predicts better survival in high-grade serous epithelial ovarian cancer patients and correlates with hCtr1 levels

Aim: To evaluate the potential of IGF1R as a prognostic marker for high-grade serous ovarian cancer (HGSOC) patients. Patients & methods: The expression levels of IGF1R and drug transporters (ABCB1, hCtr1) were measured longitudinally in chemo-naive and chemo-treated tumor samples from 19 HGSOC patients, and their correlation with the clinical outcome was examined. Results: IGF1R expression was significantly upregulated in treated tumor samples, which positively correlated with hCtr1 levels. Patients with metastatic tumors with IGF1R expression higher than median showed better overall survival (median not reached) and disease-free survival (26.7 months) than those with less than median expression (overall survival: 27.5 months [p = 0.029]; disease-free survival: 11.9 months [p = 0.014]). Conclusion: IGF1R prognosticates prolonged survival in HGSOC patients, possibly due to its positive correlation with hCtr1.

Integrated multiomic predictors for ovarian cancer survival

Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.

Clinical significance of growth factor receptor EGFR and angiogenesis regulator VEGF‑R2 in patients with ovarian cancer at FIGO stages I-II

The aim of the present retrospective cohort study was to investigate the prognostic effect of epidermal growth factor receptor (EGFR) and the angiogenesis regulator vascular endothelial growth factor receptor 2 (VEGF‑R2) on disease-free survival (DFS) rate and recurrent disease, and their association with clinicopathological characteristics in 131 patients with International Federation of Gynecology and Obstetrics (FIGO) stages I-II epithelial ovarian cancer. The techniques of tissue microar-rays and immunohistochemistry were used for the positive detection of the markers. The frequency of positive staining in tumors for EGFR was 24% and for VEGF‑R2 was 77%. Across the cohort, there was a total of 34/131 recurrences (26%) and the 5‑year DFS rate was 68%. In a multivariate logistic regression analysis with recurrent disease as the endpoint, FIGO stage (OR=9.7), type (I/II) of tumor (OR=3.0) and VEGF‑R2 status (OR=0.2) were all found to be independent predictive factors in the cohort of patients (n=131). For patients with non‑serous tumors (n=78), the FIGO stage (OR=76), type (I/II) of tumor (OR=44), EGFR status (OR=0.05) and VEGF‑R2 status (OR=0.008) were all significant and independent predictive factors. On comparing the four subgroups, in terms of concomitant EGFR and VEGF‑R2 status, in a survival analysis, the subgroup of patients (n=21) with concomitant positive expression of EGFR and VEGF‑R2 had a 5‑year DFS rate of 100%. Therefore, the prognostic effect of EGFR and VEGF‑R2 for recurrent disease and survival rates was confirmed by the above findings. Certain results in the present study were not in line with results from previous studies on the prognostic effect of EGFR and VEGF‑R2. An increasing number of preclinical and clinical observations have shown that the process of angiogenesis remains to be fully elucidated. Therefore, one of the challenges for future ovarian cancer investigations is to identify which biomarkers may be used as predictive and prognostic markers.

Construction of diagnosis system and gene regulatory networks based on microarray analysis

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.

Systematic review: Tumor-associated antigen autoantibodies and ovarian cancer early detection

OBJECTIVES

Tumor-associated autoantibodies (AAbs), produced as an immune response to tumor-associated antigens (TAAs), are a novel pathway of early detection markers.

METHODS

We conducted a systematic review on AAbs and ovarian cancer to summarize the diagnostic performance of individual AAbs and AAb panels. A total of 29 studies including 85 AAbs were included; 27 of the studies were conducted in prevalent cases and cancer-free controls and 2 investigations included pre-diagnosis samples. The majority of studies were hypothesis-driven, evaluating AAbs to target TAAs; 10 studies used screening approaches such as serological expression cloning (SEREX) and nucleic acid-programmable protein arrays (NAPPA).

RESULTS

The highest sensitivities for individual AAbs were reported for RhoGDI-AAbs (89.5%) and TUBA1C-AAbs (89%); however, specificity levels were relatively low (80% and 75%, respectively). High sensitivities at high specificities were reported for HOXA7-AAbs for detection of moderately differentiated ovarian tumors (66.7% sensitivity at 100% specificity) and IL8-AAbs in stage I-II ovarian cancer (65.5% sensitivity at 98% specificity). A panel of 11 AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) provided 45% sensitivity at 98% specificity for serous ovarian cancer, when at least 2 AAbs were above a threshold of 95% specificity. Twelve of the AAbs identified in this review were investigated in more than one study. Data on diagnostic discrimination by tumor histology and stage at diagnosis are sparse. Limited data suggest select AAb markers improve diagnostic discrimination when combined with markers such as CA125 and HE4.

CONCLUSIONS

AAbs for ovarian cancer early detection is an emerging area, and large-scale, prospective investigations considering histology and stage are required for discovery and validation. However, data to date suggests panels of AAbs may eventually reach sufficient diagnostic discrimination to allow earlier detection of disease as a complement to existing markers and transvaginal ultrasound.

Expression of miR-146a in patients with ovarian cancer and its clinical significance

The aim of the present retrospective study was to compare microRNA (miR)-146a expression levels in primary tumors and omental metastases of 48 patients, who had undergone surgery for advanced ovarian serous cancer. Possible correlations between miR-146a expression level and clinicopathological features were investigated, including chemosensitivity and survival. miR-146a was evaluated in formalin-fixed, paraffin-embedded samples. miR-146a expression level in primary tumors was demonstrated to be increased in comparison with normal ovary tissues (P=0.02) and metastases (P=0.01). A negative correlation was demonstrated between miR-146a expression in primary tumors and serum levels of cancer antigen 125 (R=-0.37; P=0.03) and Risk of Malignancy Algorithm index (R=-0.79; P=0.0007). Overall survival positively correlated with miR-146a expression in primary tumor tissue samples (R=0.38; P=0.01). Probability of survival was decreased in patients with low miR-146a expression levels in primary tumor tissues (hazard ratio=0.21; P=0.003). Lower levels of miR-146a in primary tumor tissue samples were correlated with a shorter progression-free survival (P=0.04) and platinum-resistance of metastases (P=0.006). In conclusion, miR-146a may be a prognostic marker for serous ovarian cancer.

DNA methylation and Transcriptome Changes Associated with Cisplatin Resistance in Ovarian Cancer

High-grade serous ovarian cancer is the most common ovarian cancer type. Although the combination of surgery and platinum-taxane chemotherapy provide an effective treatment, drug resistance frequently occurs leading to poor outcome. In order to clarify the molecular mechanisms of drug resistance, the DNA methylation and transcriptomic changes, associated with the development of drug resistance in high-grade serous ovarian cancer, were examined from patient derived malignant ascites cells. In parallel with large-scale transcriptome changes, cisplatin resistance was associated with loss of hypermethylation at several CpG sites primarily localized in the intergenic regions of the genome. The transcriptome and CpG methylome changes in response to cisplatin treatment of both sensitive and resistant cells were minimal, indicating the importance of post-translational mechanisms in regulating death or survival of the cells. The response of resistant cells to high concentrations of cisplatin revealed transcriptomic changes in potential key drivers of drug resistance, such as KLF4. Among the strongest changes was also induction of IL6 in resistant cells and the expression was further increased in response to cisplatin. Also, several other components of IL6 signaling were affected, further supporting previous observations on its importance in malignant transformation and development of drug resistance in ovarian cancer.