https://orcid.org/0000-0002-8567-6338
Figures
Abstract
The purpose of the current study was to clarify differences in microRNA expression according to clinicopathological characteristics, and to investigate if miRNA profiles could predict cytoreductive outcome in patients with FIGO stage IIIC and IV ovarian cancer. Patients enrolled in the Pelvic Mass study between 2004 and 2010, diagnosed and surgically treated for epithelial ovarian cancer, were used for investigation. MicroRNA was profiled from tumour tissue with global microRNA microarray analysis. Differences in miRNA expression profiles were analysed according to histologic subtype, FIGO stage, tumour grade, type I or II tumours and result of primary cytoreductive surgery. One microRNA, miR-130a, which was found to be associated with serous histology and advanced FIGO stage, was also validated using data from external cohorts. Another seven microRNAs (miR-34a, miR-455-3p, miR-595, miR-1301, miR-146-5p, 193a-5p, miR-939) were found to be significantly associated with the clinicopathological characteristics (p ≤ 0.001), in our data, but mere not similarly significant when tested against external cohorts. Further validation in comparable cohorts, with microRNA profiled using newest and similar methods are warranted.
Citation: Prahm KP, Høgdall CK, Karlsen MA, Christensen IJ, Novotny GW, Høgdall E (2021) MicroRNA characteristics in epithelial ovarian cancer. PLoS ONE 16(6): e0252401. https://doi.org/10.1371/journal.pone.0252401
Editor: Shannon M. Hawkins, Indiana University School of Medicine, UNITED STATES
Received: July 24, 2020; Accepted: May 14, 2021; Published: June 4, 2021
Copyright: © 2021 Prahm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data availability The full microarray data are available from the NCBI Expression Omnibus Database with the accession number: GSE94320. Clinical data are provided in Supporting Information files.
Funding: Funding the study was supported by: The Mermaid Project, awarded to CH and EH, https://www.mermaidprojektet.dk/en/frontpage/. Danish Cancer Research Foundation, awarded to KPP, https://www.dansk-kraeftforsknings-fond.dk/. Herlev Hospital Research Council, awarded to EH, https://www.herlevhospital.dk/forskning. The funders had no role in study design, data collection and analysis, decisions to publish, or preparation of the manuscript. Medical Prognosis Institute A/S (MPI), https://medical-prognosis.com/, performed the microarray analyses and provided study reagents for the analyses. MPI was not engaged in any other part of the scientific content of this paper, or in the decision to submit the manuscript for publication. Their participation does not alter our adherence to PLOS ONE policies on sharing data and materials.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Ovarian cancer (OC) is the fifth most common type of cancer death for women, and the most lethal gynaecologic cancer in the Western world [1]. The majority of patients (70%) are diagnosed in advanced stages (International Federation of Gynaecologic Oncology (FIGO) stage III-IV) having a 5-year survival rate less than 30% [2].
OC is known to be a heterogenic disease, consisting of four major histologic subtypes; serous, endometrioid, mucinous and clear cell tumours, with serous subtypes being the most common (approx. 70%) [2, 3]. Current data indicates that all of the histologic subtypes possess different morphologic and genetic alterations [4]. Previously most OCs were assumed to arise from cells lining the surface of the ovary; however, more recent studies suggest that many OCs may originate from cells of the fallopian tube [5, 6]. Additionally, with improved knowledge on morphologic and molecular genetic features of the subtypes, a two-tier grading system, dividing the OC patients in type I and type II tumours has been more and more accepted within the last decade [7–9]. Type I tumours are usually diagnosed in earlier stages, are less aggressive, and rarely harbour TP53 mutations. By contrast, type II tumours usually present in advanced stages, are more aggressive, unstable, often contain TP53 mutations and usually have a much worse prognosis [8, 9]. However, a recent, large, study have clarified that tumor grade is still of great importance, and high grade type I tumors shouldn’t be mistaken as indolent tumors [10]. Nonetheless, a better understanding of both histologic subtypes and other bio-pathological features that characterize the cancer is still needed.
Standard treatment of epithelial OC (EOC) patients is primary cytoreductive surgery followed by adjuvant platinum-based chemotherapy [11]. However, as the patients’ prognosis greatly depends on the cytoreductive result, with residual disease as the most important independent prognostic factor, the goal of primary treatment is to achieve complete cytoreductive surgery (no macroscopic visual tumour left) [12, 13]. For patients where cytoreductive surgery is considered impossible due to extensive disease, advanced age or a poor medical condition, neoadjuvant chemotherapy (NACT) followed by interval debulking surgery and adjuvant chemotherapy is a viable alternative [14]. However, despite several attempts to identify predictors for the cytoreductive outcome, controversy about their predictive ability remains, and still none have been implemented in a clinical setting [15–17]. Some researchers have addressed the possibility that the cytoreductive result is a matter of tumour biology, rather than the surgical effort [18, 19]. Identifying a test that could predict the cytoreductive result, would be of great value for treatment decisions, and could potentially decrease the risk of unsuccessful primary cytoreductive surgeries.
MicroRNAs (miRNAs) are a group of small, non-coding RNA molecules of 19–25 nucleotides, that within the last decade have shown important regulatory functions in cancer [20]. They exert their function at the post-transcriptional level, by complementary binding with targets on messenger-RNAs (mRNA) resulting in mRNA translational inhibition, degradation or gene activation [21]. A single miRNA can have multiple target genes, and numerous studies have indicated that miRNAs are involved in cancer development [20–25], and can act as either oncogenes or tumour suppressor genes, depending on their targets [26]. Some studies have described differences in miRNA signatures according to OC histologic subtypes, however, most studies have a small study cohort or have used qRT-PCR for miRNA profiling, limiting the number of miRNAs analysed [27–29].
In our previous work, the same cohort that provides the basis of this study, was used to identify miRNAs associated with survival and progression in EOC, several miRNAs were identified and two miRNAs, associated with time to progression, were validated in external cohorts [30]. This study continues our work to advance the knowledge of miRNA as a biomarker for clinical outcomes in OC. And here the aim was to elucidate if EOC would reflect differences in miRNA signatures according to histologic subtypes, FIGO stages, tumour grade, and the dualistic tumour classification. Furthermore, we aimed to clarify if miRNAs are associated with the results of primary cytoreductive surgery.
Material and methods
Patients
Patients were diagnosed and surgically treated for EOC between October 2004 and January 2010. All patients were part of the prospective ongoing cohort study; Pelvic Mass, initiated at Department of Gynecology, Rigshospitalet, Copenhagen, Denmark, and the cohort has been used and are described in further details previously [31, 32]. The miRNAs were profiled and have been used for investigation of their predictive performance for sensitivity to chemotherapy and survival outcomes, in previous studies [30, 31]. Clinical information was retrieved from the Danish Gynecologic Cancer Database having a coverage rate of 97% according to the latest annual report [33–35]. Inclusion criteria for the current study were: a histologic verified diagnose of EOC and primary cytoreductive surgery. Exclusion criteria were: histology other than EOC, neoadjuvant chemotherapy, insufficient tumour tissue for analysis, a previous or an active cancer other than EOC. Investigated endpoints and characteristics of interest were FIGO stage (early stages (I-II) vs. advanced stages (III-IV)), tumour grade (low grade 1 vs. high grade 2–3) [36], histologic subtype (serous vs. others) and type I vs. type II tumours. All low-grade serous carcinomas, endometrioid carcinomas, clear cell neoplasms, and mucinous carcinomas were defined as type I tumours, and all high-grade serous carcinomas were defined as type II tumours (Table 1). Patients with FIGO stage IIIC and IV were furthermore used in the analysis of the residual disease after cytoreductive surgery (complete cytoreduction (no macroscopic visual tumour) vs. residual tumour) (Table 1).
All patients were followed until death of any cause, emigration or until January 17th, 2015, which ever came first.
Tissue
Tumour tissue from the primary operation, was stored as formalin fixed and paraffin embedded (FFPE) tissue in the Danish CancerBiobank [37]. A pathologist, specialized in gynaecologic pathology, has revised histologic diagnoses for all tissue samples. From Haematoxylin and Eosin staining, all tissue samples showed to have a tumour percent above 50.
miRNA microarray analyses
The miRNA microarray analyses was profiled for a previous research project [31] and performed using Affymetrix GeneChip miRNA Array, according to manufactures instruction [38, 39]. In short, total RNA was extracted from 20 μm thick FFPE tumour sections using the RecoverAll Total Nuclei Acid Isolation Kit for FFPE samples (Ambion, Inc 2130 Woodward St. Austin, TX9).
After RNA isolation the RNA concentration was measured for all samples using a nanodrop procedure (NanoDrop 1000 Spectrophotometer) to ensure a sufficient yield. If a sample had a low RNA concentration, the sample was concentrated using Eppendorf Concentrator 5301 (SpeedVac). The miRNAs were then labelled using FlashTag HSR Biotin RNA Labelling Kit (Genishere, PA) and run on Affymetrix GeneChip 1.0 miRNA microarrays.
Validation cohorts
For validation of our results we retrieved three publicly available datasets (GSE25204, GSE73582 and GSE73581) from the NCBI Gene Expression Omnibus database [40]. The datasets contain clinical information and miRNA array expression profiles from patients with EOC and have been described in detail in previous reports where they were used for investigation and validation [41–43]. The GSE25204 and GSE73582 datasets were combined and treated as one dataset. An overview of the cohorts is presented in S1 Table, and further details are described in S1 File.
Data processing and statistical analyses
The miRNA array data was background adjusted, normalized and log2 transformed in R (R Development Core Team, Vienna, Austria, http://www.R-project.org) using the justRMA function in the Bioconductor package (the Quantile-Quantile method) [44]. MiRNA array data for the external cohorts were already normalized and log2 transformed. Due to the log2 transformation, a rise of one in OR represents a twofold change in the miRNA expression level.
The complete miRNA data files, used in the current study, are available from the NCBI’s Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE94320 [40].
Preliminary, univariate logistic regression modelling of the expression level of 847 human miRNAs were performed for all outcomes. Multivariate logistic regression analyses were then performed with the identified miRNAs form the univariate analyses, adjusted for all significant miRNAs. As only one miRNA was identified with a significant association to residual disease, no adjustment for other miRNAs were performed in multivariate analysis here, but the result was verified by 10-fold cross validation (Table 2).
To reduce potential overfitting the multivariate results were verified by ten-fold cross validation.
Due to small number of patients in some subgroups, histologic subtype, FIGO stage, tumour grade and result of cytoreductive surgery were treated as binary outcomes, as defined previously. Receiver-operating characteristics (ROC) were performed to assess the predictive value of identified miRNAs from the multivariate analyses.
For validation of our results, significant miRNAs identified in the multivariate results (Table 2) were tested in the external datasets (GSE25204+GSE73582 and GSE73581).
Statistical significance was for the preliminary univariate logistic regression analyses defined by a P-value ≤ 0.001. In subsequent analyses a p-value < 0.05 was accepted as statistically significant. The statistical analyses were performed using SAS statistical software packaged (version 9.4, Cary N.C. USA), R (v 3.1.0 R Development Core team, Vienna, Austria, http://www.R-project.org) (package RMS), and IBM SPSS statistical software version 19.
Ethics statement
The Pelvic Mass study was approved by the Danish Ethical Committee (KF01-227/03 and KF01-143/04). Methods were carried out in accordance with relevant guidelines and regulations. All patients included in the study were informed both verbally and in writing, and written consent was given before enrolment in the Pelvic Mass study, including use of their tissue for the purposes of research.
Results
Clinical and pathological findings
From the Pelvic Mass Study, the first 246 consecutively included patients with EOC were identified. 49 patients were excluded due to insufficient tumour material for analysis (n = 24), NACT or palliative care (n = 15), carcinosarcoma (n = 5), other active cancer (n = 3), questionable diagnosis at pathologic revision (n = 2). A total of 197 patients with EOC were eligible for inclusion in the study. Baseline characteristics and clinical features of the patients are summarized in Table 1. Histological subtypes were represented as expected, with 162 (82.2%) serous carcinomas, 15 (7.6%) endometrioid carcinomas, 11 (5.6%) mucinous carcinomas and nine (4.6%) clear cell carcinomas. Patients diagnosed in early stages amounted 52 (26.4%) and 145 (73.6%) were diagnosed in advanced stages. Low-grade tumours were found in 20 (10.2%) patients and 177 (89.8%) were found to be high-grade. 39 (19.8%) patients were categorized with type I tumours and 158 (80.2%) with type II tumours. Complete cytoreductive surgery was obtained in 94 (47.7%) of the patients.
miRNA and clinicopathologic characteristics
In the preliminary analyses of miRNAs associated with clinicopathologic characteristics 64 miRNAs were found to correlate with histologic subtype, 9 with FIGO stage, 21 with tumour grade, and 62 with type I or II OC (p ≤ 0.001) (S2 Table). After adjustment for all significant miRNAs, three miRNAs (miR-130a, miR-34a, miR-455-3p) maintained a significant association with histologic subtype (odds ratio (OR) = 4.72, p<0.0001; OR = 0.15, p<0.0001 and OR = 0.14, p = 0.0003). Two miRNAs (miR-130a and miR-595) were found to be associated with advanced FIGO stage (OR = 1.93, p = 0.0003 and OR = 6.99, p = 0.0002). Three miRNAs (miR-1301, miR-146b-5p, miR-34a) maintained a significant association with tumour grade (OR = 5.38, p≤0.0008; OR = 3.65 p<0.0001; OR = 0.10, p≤0.0007). And three miRNAs (miR-34a, miR-193a-5p, miR-455-3p) maintained a significant association with type I or II tumours (OR = 0.09, p<0.0001; OR = 7.81, p<0.0001; OR = 0.05, p<0.0001) (Table 2). The combined performance of the identified miRNAs for the different characteristics, measured by the AUC of the ROC curve was 0.94 for histologic subtype, 0.75 for FIGO stage, 0.96 for tumour grade, and 0.94 for type I or type II tumours (Table 2 and Fig 1). Mean expression levels and fold changes of investigated outcomes are presented in Table 3.
In validation of the miRNAs associated with histologic subtypes, FIGO stage and tumour grade, miR-130a was the only miRNA that resisted validation. MiR-130a was significantly validated for histology in both the GSE25204+GSE73581 cohort (OR = 1.76, 95% CI = 1.31–2.37, p = 0.0002) (Table 4) and the GSE73582 cohort (OR = 1.53, 95% CI = 1.15–2.05, p = 0.0039) (Table 5). MiR-130a was also significantly validated for FIGO stage in the GSE25204+GSE73581 cohort (OR = 1.25, 95% CI = 1.00–1.56, p = 0.049) (Table 4), but not in the GSE73582 cohort (Table 5). Furthermore, miR-34a was found with an opposite association for histologic subtype in the GSE25204+GSE73581 cohort (Table 4). Neither of the remaining miRNAs were significantly validated (Tables 4 and 5). MiR-595 and miR-146b-5p were not analysed in the microarray profiling from the GSE73781 cohort, and therefore not available for validation in this cohort.
miRNAs and surgical outcome
A subgroup of 132 patients with FIGO stage IIIC (n = 106) or IV (n = 26) were used in evaluation of result after cytoreductive surgery. Of these, complete cytoreductive surgery were obtained in 40 (30.3%) patients, whereas 92 (69.6%) had residual tumour.
In the preliminary univariate analysis, one miRNA (miR-939) was differentially expressed between patients who obtained complete cytoreductive surgery and patients who did not (OR = 0.32, p = 0.0003) (S2 Table). The area under the curve (AUC) of the ROC for miR-939 corresponding to cytoreductive outcome was 0.72 (Table 2 and Fig 1). However, miR-939 did not remain significant in validation in the external cohorts (Tables 4 and 5).
Discussion
In a prospective included cohort of Danish patients with EOC we investigated the association of miRNA profiling with clinicopathologic characteristics and result of cytoreductive surgery.
One miRNA, miR-130a, resisted validation, and was found to be significantly associated with a serous histologic subtype in both of the external cohorts, and with advanced FIGO stage in one of the external cohorts. Another seven miRNAs (miR-34a, miR-455-3p, miR-595, miR-1301, miR-146-5p, 193a-5p, miR-939) were identified as independently and significantly associated with clinicopathological characteristics and result of cytoreductive surgery in patients with EOC, but failed validation in external cohorts.
Substantial studies have shown that miRNAs are aberrantly expressed in cancer, including OC, and that they could act as either tumour suppressors or as oncogenes [45, 46]. Other studies have previously demonstrated associations between miRNAs and clinicopathological features of OC, however, most studies were performed on smaller cohorts using qRT-PCR for the miRNA profiling [28, 29, 47], where only a few miRNAs are investigated, and few studies have used global miRNA microarray technology taking all known miRNAs in consideration [27, 48, 49].
MiR-130a which resisted validation in external cohorts and was found to be significantly associated with serous histology and advanced FIGO stage has also been described previously with aberrant expression in cancers [50, 51]. Several studies have demonstrated miR-130a to be associated with cisplatin resistance in OC, although with conflicting results, which could be due to different roles in different OC cell lines [52–54]. In vascular epithelial cell lines, miR-130a has shown to exert oncogenic functions, by interfering with genes that regulate angiogenesis [55]. The oncogenic features of miR-130a is in line with our results with higher expression in advanced FIGO stages.
MiR-34a, miR-130a and miR-455-3p that demonstrated significant associations with several outcomes in our primary analyses have all previously shown to exert oncogenic and tumour suppressing functions [51, 55–61]. In the present study miR-34a showed a significant association with histologic subtype, tumour grade and the dualistic tumour classification (type I or type II tumours). MiR-34a is directly regulated by the tumour-suppressor and transcription factor p53 and has shown to exert tumour-suppressing functions when it is introduced into cancer cells [56, 62, 63]. Consistent with previous studies we found miR-34a to be associated with non-serous histology, grade 1 and type I tumours, underlining the tumour-suppressive characteristics of miR-34a [64]. Abnormal expression of miR-455 has been reported for several cancers [58, 60, 65, 66], and one study found miR-455 to be downregulated in EOC tumour samples with low expression associated with advanced FIGO stages [59]. This corresponds to our findings, where miR-455-3p was lower expressed in the aggressive Type II tumours. The study was not able to demonstrate any association with histological subtypes; however, this was likely due to a small sample size (n = 45), with only 7 tumours of non-serous histology.
Complete cytoreductive surgery is an important prognostic factor for patients with advanced EOC [12, 67–69]. Similar survival rates have been observed for patients treated with primary cytoreductive surgery and chemotherapy compared to NACT and interval debulking surgery, in advanced FIGO stages [69–72]. However, observational studies have shown that patients who achieved complete cytoreductive surgery at primary surgery has improved survival compared to patients treated with NACT and interval debulking surgery [73, 74]. But today, no common uniform selection of patients for NACT exist. At multidisciplinary team conferences, usually a combination of the patients’ performance status and evaluation of ultrasound, computed tomography (CT) or positron emission-CT (PET-CT) are used in assessment of the optimal treatment choice. However, a recent review showed that CT has a poor predictive performance for residual disease [17]. FDG-PET/CT and MRI may have a better predictive performance, even so, only a limited number of studies have investigated the use on a routine basis [75–77]. Also, diagnostic laparoscopy has been suggested as a method for prediction of cytoreductive outcome. A recent randomized clinical trial, of 201 advanced stage OC randomized to either initial diagnostic laparoscopy or directly to primary cytoreductive surgery, demonstrated that initial diagnostic laparoscopy could reduce the number of futile primary cytoreductive surgeries with up to 29% [78]. However, the study has several limitations. First, radical surgery was defined as tumour < 1 cm, although the definition of complete cytoreductive surgery today is defined by no visible macroscopic tumour. Additionally, they included patient with suspected FIGO stage IIB or higher, although, complete cytoreductive surgery should be achievable in FIGO stage IIB-IIIB for surgeons with expertise in gynaecologic cancers. In contrast, a Cochrane review of laparoscopy for prediction of cytoreductive outcome, including seven studies with OC patients having FIGO stage IIB-IV, were unable to draw any firm conclusions, and included studies possessed similar limitation as the randomized trial [79].
In the current study miR-939 was found to be significantly associated with residual disease after primary surgery. Previously miR-939 has also been found as a promotor of cell proliferation in OC cells [80]. With a preoperative biopsy or maybe using ascites, miRNA profiling could be performed, and the expression level of miR-939 could potentially guide the choice of primary treatment. However, the same significant performance of miR-939 was not demonstrated in the validation cohorts, therefore our results must be confirmed in another study before any clinical use can be considered. Several other studies have investigated the tumour biology for surgical outcome with different genetic and molecular biomarkers. Studies of cancer antigen 125 (CA 125) have yielded conflicting results [81], while human epididymis protein 4 (HE4) have shown better performance but needs further validation [82, 83]. Also, gene expression has been explored as a possible predictor, but has mostly shown to be un-successful [18, 84]. Even so, one recent metanalysis including gene expression analysis from 13 studies, with a total of 1525 OC patient, where results were validated in independent samples, have shown more promising results. Here a gene signature was able to classify patients correctly in high- and low-risk of residual disease 92.8% of the time, with an AUC of 0.89 (95% CI = 0.84–0.93). However, this study also included all advanced stages of OC, and only optimal debulked (residual disease < 1cm) [85]. However, to our knowledge, no previous studies have investigated the association between miRNAs and surgical result in OC.
Our study has several strengths. First, the cohort consisted of a consecutive inclusion of patients, where all patients were operated and treated at the same tertiary centre by gynaecologic oncologists with high expertise. All clinical data were continuously updated, none of the patients were lost to follow-up, and missing data is limited. The representation of tumors according to histologic subtype, grade and FIGO stage matched the expected clinical representation of the tumors [34], and we have a mature study, where the shortest follow-up of a patient still alive is 61 months. Furthermore, the size of our cohort was similar to the validation cohorts and miRNA was profiled by global microarray, investigating a broad spectrum of miRNAs. Additionally, several of the miRNAs identified as significant in our preliminary and multivariate analyses are miRNAs previously described with oncogenic or tumour suppressing functions (S2 Table). All, except one, of the miRNAs from the well described Oncomir-1 family, also known as the miR-17-92 cluster, were identified with significant association to our outcomes [86]. Increasing expression of the miRNAs (miR-17, miR-18a, miR-20a, miR-19b, miR-92a), from the Oncomir-1 family, demonstrated significant associations with the more aggressive type II tumours (S2 Table). This strengthens the validity of our data. However, an important limitation is, that the validation was performed in external cohorts where miRNA profiling had been analysed by different microarray platforms, complicating the comparison between the studies. This limitation may explain the lack of accordance between our results and results from validation. No other publicly available data with miRNA microarray profiling from OC patients, profiled on Affymetrix miRNA microarrays was found. It should also be mentioned that the miRNAs in the multivariate model were identified by choosing the best cross validated candidates, however, the association between miRNAs is substantial for some (21 combinations showed a correlation coefficient greater than 0.9 (S3 Table) suggesting that the chosen signature is not necessarily unique.
Other reasons for the unsuccessful validation could be addressed to possible batch effects, caused by laboratory differences, variations in analyses methods, reagent lots and personnel differences [87]. Additionally, our study and the used validation cohorts have significant differences in the study populations; patients with complete cytoreductive surgery ranged from 48% in our cohort to 29% and 41% in the validation cohorts. Also, tumour grade varied significantly, with only 38% having grade 3 in our cohort compared with 67% and 70% in the validation cohorts. Furthermore, our cohort comprised a higher number of patients with serous adenocarcinomas (82% in our cohort vs. 69% and 72% in the validation cohorts). Additionally, patients in our cohort were included between 2004–2010, whereas today more expertise gathered at tertiary centres has increased the use of NACT and more aggressive surgery, which has improved treatment results with a larger number of patients obtaining complete cytoreductive surgery today compared to the amount in our cohort. However, this could not explain the reason for variation in the number of patients who achieved complete cytoreductive surgery between the cohorts, as our cohort had the highest degree of complete debulked patients.
The results were not validated with qRT-PCR, which could be addressed as a limitation. However, validation in external cohorts must be considered as a more reliable form of validation limiting the risk of repeating false results. Further, miRNA microarray expression has shown to be highly concordant when re-analyzed with qRT-PCR [88, 89], with correlation coefficients measuring from r = 0.986 to 0.994, depending on the normalization method [90].
As previously mentioned, gene expression has also been investigated as promising biomarkers for OC. And in a recent large metanalysis, including 15 studies and a total of 3769 women with high-grade serous OC, gene expressions were used to develop a prognostic gene expression signature, that could predict patients at high- and low risk of achieving 5-year survival. A future interesting study would be to look at gene expressions signatures for the investigated clinicopathologic outcomes in the current study [91].
Conclusions
In conclusion, miR-130a was found to be associated with serous histology and advanced FIGO stage in patients with EOC, and additionally validated in in two external cohorts. Another seven miRNAs were found to be significantly associated with clinicopathological characteristics and residual disease after cytoreductive surgery in patients with EOC, but only in our own cohort.
MiR-130a may be of prognostic importance for patients with EOC. Inconsistent results in external validation of the remaining miRNAs may be due to differences in study population characteristics, the use of different microarray platforms for miRNA profiling, differences in analyses methods or interlaboratory variation. Further validation of the results is warranted, preferably in a cohort of EOC patients where miRNA profiling has been performed on similar microarray platforms.
Supporting information
S1 Table. Baseline characteristics of the validation cohorts.
https://doi.org/10.1371/journal.pone.0252401.s002
(DOCX)
S2 Table. Univariate logistic regression analysis of miRNAs associated with clinicopathologic characteristics.
https://doi.org/10.1371/journal.pone.0252401.s003
(DOCX)
S3 Table. Combinations of miRNAs demonstrating a correlation greater than 0.9.
https://doi.org/10.1371/journal.pone.0252401.s004
(DOCX)
Acknowledgments
We thank pathologist Lotte Nedergaard for her confirmation of all included patients’ histologic diagnoses. We are grateful to the Danish CancerBiobank and the Danish Gynecologic Cancer Database for making tissue and data available for use in the present study. We thank Medical Prognosis Institute for providing study reagents, and for the conduct of the microarray analyses.
References
- 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA: a cancer journal for clinicians. 2020;70(1):7–30. Epub 2020/01/09. pmid:31912902.
- 2.
DGCG. Annual Report 2014–2015. Danish Gynecological Cancer Database. Nationwide clinical database for cancer of the ovaries, uterus and cervix. http://dgcg.dk/images/rsrapport_DGCD_2014-15.pdf: Lægeforeningens forlag, København., 2015.
- 3.
Danish Gynecologic Cancer Database. A nation wide clinical database for cancer in ovaries, uterus and cervix. [Internet]. Dansk Gynecologic Cancer Database. http://www.dgcg.dk. 2011.
- 4. Bell DA. Origins and molecular pathology of ovarian cancer. Modern pathology: an official journal of the United States and Canadian Academy of Pathology, Inc. 2005;18 Suppl 2:S19–32. Epub 2005/03/12. pmid:15761464.
- 5. Piek JM, Kenemans P, Verheijen RH. Intraperitoneal serous adenocarcinoma: a critical appraisal of three hypotheses on its cause. American journal of obstetrics and gynecology. 2004;191(3):718–32. Epub 2004/10/07. pmid:15467531.
- 6. Kindelberger DW, Lee Y, Miron A, Hirsch MS, Feltmate C, Medeiros F, et al. Intraepithelial carcinoma of the fimbria and pelvic serous carcinoma: Evidence for a causal relationship. The American journal of surgical pathology. 2007;31(2):161–9. Epub 2007/01/27. pmid:17255760.
- 7. Kurman RJ, Shih Ie M. The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. The American journal of surgical pathology. 2010;34(3):433–43. Epub 2010/02/16. pmid:20154587; PubMed Central PMCID: PMC2841791.
- 8. Kurman RJ, Shih Ie M. The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. The American journal of pathology. 2016;186(4):733–47. Epub 2016/03/26. pmid:27012190; PubMed Central PMCID: PMC5808151.
- 9. Prahm KP, Karlsen MA, Hogdall E, Scheller NM, Lundvall L, Nedergaard L, et al. The prognostic value of dividing epithelial ovarian cancer into type I and type II tumors based on pathologic characteristics. Gynecologic oncology. 2015;136(2):205–11. Epub 2014/12/30. pmid:25546113.
- 10. Pavlik EJ, Smith C, Dennis TS, Harvey E, Huang B, Chen Q, et al. Disease-Specific Survival of Type I and Type II Epithelial Ovarian Cancers-Stage Challenges Categorical Assignments of Indolence & Aggressiveness. Diagnostics (Basel). 2020;10(2). Epub 2020/01/25. pmid:31973035; PubMed Central PMCID: PMC7168156.
- 11. Ledermann JA, Raja FA, Fotopoulou C, Gonzalez-Martin A, Colombo N, Sessa C. Newly diagnosed and relapsed epithelial ovarian carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of oncology: official journal of the European Society for Medical Oncology / ESMO. 2013;24 Suppl 6:vi24–32. Epub 2013/10/23. pmid:24078660.
- 12. du Bois A, Reuss A, Pujade-Lauraine E, Harter P, Ray-Coquard I, Pfisterer J. Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: a combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials: by the Arbeitsgemeinschaft Gynaekologische Onkologie Studiengruppe Ovarialkarzinom (AGO-OVAR) and the Groupe d’Investigateurs Nationaux Pour les Etudes des Cancers de l’Ovaire (GINECO). Cancer. 2009;115(6):1234–44. Epub 2009/02/04. pmid:19189349.
- 13. Bristow RE, Tomacruz RS, Armstrong DK, Trimble EL, Montz FJ. Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2002;20(5):1248–59. Epub 2002/03/01. pmid:11870167.
- 14. Wright AA, Bohlke K, Armstrong DK, Bookman MA, Cliby WA, Coleman RL, et al. Neoadjuvant chemotherapy for newly diagnosed, advanced ovarian cancer: Society of Gynecologic Oncology and American Society of Clinical Oncology Clinical Practice Guideline. Gynecologic oncology. 2016;143(1):3–15. Epub 2016/09/22. pmid:27650684; PubMed Central PMCID: PMC5413203.
- 15. Bristow RE, Duska LR, Lambrou NC, Fishman EK, O’Neill MJ, Trimble EL, et al. A model for predicting surgical outcome in patients with advanced ovarian carcinoma using computed tomography. Cancer. 2000;89(7):1532–40. Epub 2000/10/03. pmid:11013368.
- 16. Fagotti A, Ferrandina G, Fanfani F, Ercoli A, Lorusso D, Rossi M, et al. A laparoscopy-based score to predict surgical outcome in patients with advanced ovarian carcinoma: a pilot study. Annals of surgical oncology. 2006;13(8):1156–61. Epub 2006/06/23. pmid:16791447.
- 17. Rutten MJ, van de Vrie R, Bruining A, Spijkerboer AM, Mol BW, Kenter GG, et al. Predicting surgical outcome in patients with International Federation of Gynecology and Obstetrics stage III or IV ovarian cancer using computed tomography: a systematic review of prediction models. International journal of gynecological cancer: official journal of the International Gynecological Cancer Society. 2015;25(3):407–15. Epub 2015/02/20. pmid:25695545.
- 18. Abdallah R, Chon HS, Bou Zgheib N, Marchion DC, Wenham RM, Lancaster JM, et al. Prediction of Optimal Cytoreductive Surgery of Serous Ovarian Cancer With Gene Expression Data. International journal of gynecological cancer: official journal of the International Gynecological Cancer Society. 2015;25(6):1000–9. Epub 2015/06/23. pmid:26098088.
- 19. Berchuck A, Iversen ES, Lancaster JM, Dressman HK, West M, Nevins JR, et al. Prediction of optimal versus suboptimal cytoreduction of advanced-stage serous ovarian cancer with the use of microarrays. American journal of obstetrics and gynecology. 2004;190(4):910–25. Epub 2004/05/01. pmid:15118612.
- 20. Iorio MV, Croce CM. MicroRNAs in cancer: small molecules with a huge impact. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2009;27(34):5848–56. Epub 2009/11/04. pmid:19884536; PubMed Central PMCID: PMC2793003.
- 21. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136(2):215–33. Epub 2009/01/27. pmid:19167326; PubMed Central PMCID: PMC3794896.
- 22. Zhang W, Dahlberg JE, Tam W. MicroRNAs in tumorigenesis: a primer. The American journal of pathology. 2007;171(3):728–38. Epub 2007/08/29. pmid:17724137; PubMed Central PMCID: PMC1959494.
- 23. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, et al. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences of the United States of America. 2002;99(24):15524–9. Epub 2002/11/16. pmid:12434020; PubMed Central PMCID: PMC137750.
- 24. Ma L, Teruya-Feldstein J, Weinberg RA. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature. 2007;449(7163):682–8. Epub 2007/09/28. pmid:17898713.
- 25. Nagel R, le Sage C, Diosdado B, van der Waal M, Oude Vrielink JA, Bolijn A, et al. Regulation of the adenomatous polyposis coli gene by the miR-135 family in colorectal cancer. Cancer research. 2008;68(14):5795–802. Epub 2008/07/18. pmid:18632633.
- 26. Calin GA, Croce CM. MicroRNA-cancer connection: the beginning of a new tale. Cancer research. 2006;66(15):7390–4. Epub 2006/08/04. pmid:16885332.
- 27. Iorio MV, Visone R, Di Leva G, Donati V, Petrocca F, Casalini P, et al. MicroRNA signatures in human ovarian cancer. Cancer research. 2007;67(18):8699–707. Epub 2007/09/19. pmid:17875710.
- 28. Lee H, Park CS, Deftereos G, Morihara J, Stern JE, Hawes SE, et al. MicroRNA expression in ovarian carcinoma and its correlation with clinicopathological features. World journal of surgical oncology. 2012;10:174. Epub 2012/08/29. pmid:22925189; PubMed Central PMCID: PMC3449188.
- 29. Zuberi M, Mir R, Das J, Ahmad I, Javid J, Yadav P, et al. Expression of serum miR-200a, miR-200b, and miR-200c as candidate biomarkers in epithelial ovarian cancer and their association with clinicopathological features. Clinical & translational oncology: official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. 2015;17(10):779–87. Epub 2015/06/13. pmid:26063644.
- 30. Prahm KP, Hogdall C, Karlsen MA, Christensen IJ, Novotny GW, Hogdall E. Identification and validation of potential prognostic and predictive miRNAs of epithelial ovarian cancer. PloS one. 2018;13(11):e0207319. Epub 2018/11/27. pmid:30475821.
- 31. Prahm KP, Hogdall C, Karlsen MA, Christensen IJ, Novotny GW, Knudsen S, et al. Clinical validation of chemotherapy predictors developed on global microRNA expression in the NCI60 cell line panel tested in ovarian cancer. PloS one. 2017;12(3):e0174300. Epub 2017/03/24. pmid:28334047.
- 32. Hentze JL, Hogdall C, Kjaer SK, Blaakaer J, Hogdall E. Searching for new biomarkers in ovarian cancer patients: Rationale and design of a retrospective study under the Mermaid III project. Contemporary clinical trials communications. 2017;8:167–74. Epub 2018/04/27. pmid:29696206; PubMed Central PMCID: PMC5898550.
- 33.
Annual Report 2016/2017. Danish Gynecological Cancer Database. Nationwide clinical database for cancer of the ovaries, uterus and cervix. Regionernes Kliniske Kvalitetsudviklingsprogram ∙ www.rkkp.dk, http://dgcg.dk/images/Grupper/Databasegruppen/rsrapport_DGCD_2016-17_endelig_anonymiseret.pdf 2017.
- 34.
Danish Gynecologic Cancer Database. A nation wide clinical database for cancer in ovaries, uterus and cervix. Available from: http://dgcg.dk/index.php/database.
- 35. Sorensen SM, Bjorn SF, Jochumsen KM, Jensen PT, Thranov IR, Hare-Bruun H, et al. Danish Gynecological Cancer Database. Clinical epidemiology. 2016;8:485–90. Epub 2016/11/09. pmid:27822089; PubMed Central PMCID: PMC5094526.
- 36. Malpica A, Deavers MT, Lu K, Bodurka DC, Atkinson EN, Gershenson DM, et al. Grading ovarian serous carcinoma using a two-tier system. The American journal of surgical pathology. 2004;28(4):496–504. Epub 2004/04/17. pmid:15087669.
- 37.
Danish Cancer Biobank, www.cancerbiobank.dk [cited 2015].
- 38.
RecoverAll Total Nucleic Acid Isolation Kit 2011. Available from: https://www.thermofisher.com/order/catalog/product/AM1975.
- 39. FlashTag Biotin HSR RNA Labeling Kit for GeneChip miRNA Arrays 2013. Available from: https://www.thermofisher.com/search/results?persona=DocSupport&refinementSearch=true&query=Manuals+%26+Protocols&navId=4294959596&refinementQuery=FlashTag%E2%84%A2+Biotin+HSR+RNA+Labeling+Kit+for+GeneChip%E2%84%A2+miRNA+Arrays+Version%3A+JAN.2017.
- 40. Clough E, Barrett T. The Gene Expression Omnibus Database. Methods in molecular biology (Clifton, NJ). 2016;1418:93–110. Epub 2016/03/24. pmid:27008011; PubMed Central PMCID: PMC4944384.
- 41. Bagnoli M, Canevari S, Califano D, Losito S, Maio MD, Raspagliesi F, et al. Development and validation of a microRNA-based signature (MiROvaR) to predict early relapse or progression of epithelial ovarian cancer: a cohort study. The Lancet Oncology. 2016;17(8):1137–46. Epub 2016/07/13. pmid:27402147.
- 42. Bagnoli M, De Cecco L, Granata A, Nicoletti R, Marchesi E, Alberti P, et al. Identification of a chrXq27.3 microRNA cluster associated with early relapse in advanced stage ovarian cancer patients. Oncotarget. 2011;2(12):1265–78. Epub 2012/01/17. pmid:22246208; PubMed Central PMCID: PMC3282083.
- 43. Pignata S, Scambia G, Ferrandina G, Savarese A, Sorio R, Breda E, et al. Carboplatin plus paclitaxel versus carboplatin plus pegylated liposomal doxorubicin as first-line treatment for patients with ovarian cancer: the MITO-2 randomized phase III trial. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2011;29(27):3628–35. Epub 2011/08/17. pmid:21844495.
- 44. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nature methods. 2015;12(2):115–21. Epub 2015/01/31. pmid:25633503; PubMed Central PMCID: PMC4509590.
- 45. Bartels CL, Tsongalis GJ. MicroRNAs: novel biomarkers for human cancer. Clinical chemistry. 2009;55(4):623–31. Epub 2009/02/28. pmid:19246618.
- 46. Hayes J, Peruzzi PP, Lawler S. MicroRNAs in cancer: biomarkers, functions and therapy. Trends in molecular medicine. 2014;20(8):460–9. Epub 2014/07/17. pmid:25027972.
- 47. Wang S, Zhao X, Wang J, Wen Y, Zhang L, Wang D, et al. Upregulation of microRNA-203 is associated with advanced tumor progression and poor prognosis in epithelial ovarian cancer. Medical oncology (Northwood, London, England). 2013;30(3):681. Epub 2013/08/07. pmid:23918241.
- 48. Yang D, Sun Y, Hu L, Zheng H, Ji P, Pecot CV, et al. Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. Cancer cell. 2013;23(2):186–99. Epub 2013/02/16. pmid:23410973; PubMed Central PMCID: PMC3603369.
- 49. Kim TH, Kim YK, Kwon Y, Heo JH, Kang H, Kim G, et al. Deregulation of miR-519a, 153, and 485-5p and its clinicopathological relevance in ovarian epithelial tumours. Histopathology. 2010;57(5):734–43. Epub 2010/11/19. pmid:21083603.
- 50. Wang XC, Tian LL, Wu HL, Jiang XY, Du LQ, Zhang H, et al. Expression of miRNA-130a in nonsmall cell lung cancer. The American journal of the medical sciences. 2010;340(5):385–8. Epub 2010/07/14. pmid:20625274.
- 51. Zhang HD, Jiang LH, Sun DW, Li J, Ji ZL. The role of miR-130a in cancer. Breast cancer (Tokyo, Japan). 2017;24(4):521–7. Epub 2017/05/10. pmid:28477068.
- 52. Yang L, Li N, Wang H, Jia X, Wang X, Luo J. Altered microRNA expression in cisplatin-resistant ovarian cancer cells and upregulation of miR-130a associated with MDR1/P-glycoprotein-mediated drug resistance. Oncology reports. 2012;28(2):592–600. Epub 2012/05/23. pmid:22614869.
- 53. Zhang X, Huang L, Zhao Y, Tan W. Downregulation of miR-130a contributes to cisplatin resistance in ovarian cancer cells by targeting X-linked inhibitor of apoptosis (XIAP) directly. Acta biochimica et biophysica Sinica. 2013;45(12):995–1001. Epub 2013/10/23. pmid:24145606.
- 54. Sorrentino A, Liu CG, Addario A, Peschle C, Scambia G, Ferlini C. Role of microRNAs in drug-resistant ovarian cancer cells. Gynecologic oncology. 2008;111(3):478–86. Epub 2008/10/01. pmid:18823650.
- 55. Chen Y, Gorski DH. Regulation of angiogenesis through a microRNA (miR-130a) that down-regulates antiangiogenic homeobox genes GAX and HOXA5. Blood. 2008;111(3):1217–26. Epub 2007/10/25. pmid:17957028; PubMed Central PMCID: PMC2214763.
- 56. Bommer GT, Gerin I, Feng Y, Kaczorowski AJ, Kuick R, Love RE, et al. p53-mediated activation of miRNA34 candidate tumor-suppressor genes. Current biology: CB. 2007;17(15):1298–307. Epub 2007/07/28. pmid:17656095.
- 57. Liu C, Kelnar K, Liu B, Chen X, Calhoun-Davis T, Li H, et al. The microRNA miR-34a inhibits prostate cancer stem cells and metastasis by directly repressing CD44. Nature medicine. 2011;17(2):211–5. Epub 2011/01/18. pmid:21240262; PubMed Central PMCID: PMC3076220.
- 58. Liu J, Zhang J, Li Y, Wang L, Sui B, Dai D. MiR-455-5p acts as a novel tumor suppressor in gastric cancer by down-regulating RAB18. Gene. 2016;592(2):308–15. Epub 2016/07/28. pmid:27451075.
- 59. Xu L, Li H, Su L, Lu Q, Liu Z. MicroRNA455 inhibits cell proliferation and invasion of epithelial ovarian cancer by directly targeting Notch1. Molecular medicine reports. 2017;16(6):9777–85. Epub 2017/10/19. pmid:29039517.
- 60. Li YJ, Ping C, Tang J, Zhang W. MicroRNA-455 suppresses non-small cell lung cancer through targeting ZEB1. Cell biology international. 2016;40(6):621–8. Epub 2016/01/24. pmid:26801503.
- 61. Lin M, Chen W, Huang J, Gao H, Ye Y, Song Z, et al. MicroRNA expression profiles in human colorectal cancers with liver metastases. Oncology reports. 2011;25(3):739–47. Epub 2010/12/22. pmid:21174058.
- 62. Tarasov V, Jung P, Verdoodt B, Lodygin D, Epanchintsev A, Menssen A, et al. Differential regulation of microRNAs by p53 revealed by massively parallel sequencing: miR-34a is a p53 target that induces apoptosis and G1-arrest. Cell cycle (Georgetown, Tex). 2007;6(13):1586–93. Epub 2007/06/08. pmid:17554199.
- 63. Hermeking H. The miR-34 family in cancer and apoptosis. Cell death and differentiation. 2010;17(2):193–9. Epub 2009/05/23. pmid:19461653.
- 64. Schmid G, Notaro S, Reimer D, Abdel-Azim S, Duggan-Peer M, Holly J, et al. Expression and promotor hypermethylation of miR-34a in the various histological subtypes of ovarian cancer. BMC cancer. 2016;16:102. Epub 2016/02/18. pmid:26879132; PubMed Central PMCID: PMC4754861.
- 65. Guo J, Liu C, Wang W, Liu Y, He H, Chen C, et al. Identification of serum miR-1915-3p and miR-455-3p as biomarkers for breast cancer. 2018;13(7):e0200716. pmid:30048472.
- 66. Qin L, Zhang Y, Lin J, Shentu Y, Xie X. MicroRNA-455 regulates migration and invasion of human hepatocellular carcinoma by targeting Runx2. Oncology reports. 2016;36(6):3325–32. Epub 2016/10/18. pmid:27748890.
- 67. Hoskins WJ, McGuire WP, Brady MF, Homesley HD, Creasman WT, Berman M, et al. The effect of diameter of largest residual disease on survival after primary cytoreductive surgery in patients with suboptimal residual epithelial ovarian carcinoma. American journal of obstetrics and gynecology. 1994;170(4):974–9; discussion 9–80. Epub 1994/04/01. pmid:8166218.
- 68. Elattar A, Bryant A, Winter-Roach BA, Hatem M, Naik R. Optimal primary surgical treatment for advanced epithelial ovarian cancer. The Cochrane database of systematic reviews. 2011;(8):Cd007565. Epub 2011/08/13. pmid:21833960.
- 69. Vergote I, Trope CG, Amant F, Kristensen GB, Ehlen T, Johnson N, et al. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. The New England journal of medicine. 2010;363(10):943–53. Epub 2010/09/08. pmid:20818904.
- 70. Fago-Olsen CL, Ottesen B, Kehlet H, Antonsen SL, Christensen IJ, Markauskas A, et al. Does neoadjuvant chemotherapy impair long-term survival for ovarian cancer patients? A nationwide Danish study. Gynecologic oncology. 2014;132(2):292–8. Epub 2013/12/11. pmid:24321400.
- 71. Glasgow MA, Yu H, Rutherford TJ, Azodi M, Silasi DA, Santin AD, et al. Neoadjuvant chemotherapy (NACT) is an effective way of managing elderly women with advanced stage ovarian cancer (FIGO Stage IIIC and IV). Journal of surgical oncology. 2013;107(2):195–200. Epub 2012/06/01. pmid:22648987.
- 72. Kehoe S, Hook J, Nankivell M, Jayson GC, Kitchener H, Lopes T, et al. Primary chemotherapy versus primary surgery for newly diagnosed advanced ovarian cancer (CHORUS): an open-label, randomised, controlled, non-inferiority trial. Lancet (London, England). 2015;386(9990):249–57. Epub 2015/05/24. pmid:26002111.
- 73. Fagö-Olsen CL, Ottesen B, Kehlet H, Antonsen SL, Christensen IJ, Markauskas A, et al. Does neoadjuvant chemotherapy impair long-term survival for ovarian cancer patients? A nationwide Danish study. Gynecologic oncology. 2014;132(2):292–8. Epub 2013/12/11. pmid:24321400.
- 74. Fanfani F, Ferrandina G, Corrado G, Fagotti A, Zakut HV, Mancuso S, et al. Impact of interval debulking surgery on clinical outcome in primary unresectable FIGO stage IIIc ovarian cancer patients. Oncology. 2003;65(4):316–22. Epub 2004/01/07. pmid:14707451.
- 75. Alessi A, Martinelli F, Padovano B, Serafini G, Lorusso D, Lorenzoni A, et al. FDG-PET/CT to predict optimal primary cytoreductive surgery in patients with advanced ovarian cancer: preliminary results. Tumori. 2016;102(1):103–7. Epub 2015/09/10. pmid:26350201.
- 76. Hynninen J, Kemppainen J, Lavonius M, Virtanen J, Matomaki J, Oksa S, et al. A prospective comparison of integrated FDG-PET/contrast-enhanced CT and contrast-enhanced CT for pretreatment imaging of advanced epithelial ovarian cancer. Gynecologic oncology. 2013;131(2):389–94. Epub 2013/09/03. pmid:23994535.
- 77. Michielsen K, Vergote I, Op de Beeck K, Amant F, Leunen K, Moerman P, et al. Whole-body MRI with diffusion-weighted sequence for staging of patients with suspected ovarian cancer: a clinical feasibility study in comparison to CT and FDG-PET/CT. European radiology. 2014;24(4):889–901. Epub 2013/12/11. pmid:24322510.
- 78. Rutten MJ, van Meurs HS, van de Vrie R, Gaarenstroom KN, Naaktgeboren CA, van Gorp T, et al. Laparoscopy to Predict the Result of Primary Cytoreductive Surgery in Patients With Advanced Ovarian Cancer: A Randomized Controlled Trial. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2017;35(6):613–21. Epub 2016/12/29. pmid:28029317.
- 79. Rutten MJ, Leeflang MM, Kenter GG, Mol BW, Buist M. Laparoscopy for diagnosing resectability of disease in patients with advanced ovarian cancer. The Cochrane database of systematic reviews. 2014;(2):Cd009786. Epub 2014/02/25. pmid:24563459.
- 80. Ying X, Li-ya Q, Feng Z, Yin W, Ji-hong L. MiR-939 promotes the proliferation of human ovarian cancer cells by repressing APC2 expression. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie. 2015;71:64–9. Epub 2015/05/12. pmid:25960217.
- 81. Ibeanu OA, Bristow RE. Predicting the outcome of cytoreductive surgery for advanced ovarian cancer: a review. International journal of gynecological cancer: official journal of the International Gynecological Cancer Society. 2010;20 Suppl 1:S1–11. Epub 2010/01/28. pmid:20065732.
- 82. Shen Y, Li L. Serum HE4 superior to CA125 in predicting poorer surgical outcome of epithelial ovarian cancer. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine. 2016;37(11):14765–72. Epub 2016/09/16. pmid:27629144.
- 83. Karlsen MA, Fago-Olsen C, Hogdall E, Schnack TH, Christensen IJ, Nedergaard L, et al. A novel index for preoperative, non-invasive prediction of macro-radical primary surgery in patients with stage IIIC-IV ovarian cancer-a part of the Danish prospective pelvic mass study. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine. 2016;37(9):12619–26. Epub 2016/10/27. pmid:27440204.
- 84. Bonome T, Levine DA, Shih J, Randonovich M, Pise-Masison CA, Bogomolniy F, et al. A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer. Cancer research. 2008;68(13):5478–86. Epub 2008/07/03. pmid:18593951.
- 85. Riester M, Wei W, Waldron L, Culhane AC, Trippa L, Oliva E, et al. Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples. Journal of the National Cancer Institute. 2014;106(5). Epub 2014/04/05. pmid:24700803; PubMed Central PMCID: PMC4580556.
- 86. Olive V, Jiang I, He L. mir-17-92, a cluster of miRNAs in the midst of the cancer network. The international journal of biochemistry & cell biology. 2010;42(8):1348–54. Epub 2010/03/17. pmid:20227518; PubMed Central PMCID: PMC3681296.
- 87. Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature reviews Genetics. 2010;11(10):733–9. Epub 2010/09/15. pmid:20838408; PubMed Central PMCID: PMC3880143.
- 88. Szafranska AE, Davison TS, Shingara J, Doleshal M, Riggenbach JA, Morrison CD, et al. Accurate molecular characterization of formalin-fixed, paraffin-embedded tissues by microRNA expression profiling. The Journal of molecular diagnostics: JMD. 2008;10(5):415–23. Epub 2008/08/09. pmid:18687792; PubMed Central PMCID: PMC2518736.
- 89. Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: approaches and considerations. Nature reviews Genetics. 2012;13(5):358–69. Epub 2012/04/19. pmid:22510765; PubMed Central PMCID: PMC4517822.
- 90. Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K, et al. Impact of normalization on miRNA microarray expression profiling. RNA (New York, NY). 2009;15(3):493–501. Epub 2009/01/30. pmid:19176604; PubMed Central PMCID: PMC2657010.
- 91. Millstein J, Budden T, Goode EL, Anglesio MS, Talhouk A, Intermaggio MP, et al. Prognostic gene expression signature for high-grade serous ovarian cancer. Annals of oncology: official journal of the European Society for Medical Oncology / ESMO. 2020;31(9):1240–50. Epub 2020/05/31. pmid:32473302; PubMed Central PMCID: PMC7484370.