{"paper_id":"c6efe322-cac5-404c-9ee5-49f6300d1170","body_text":"Ovarian cancer (OC) is one of the most common and lethal gynecological malignancies and is the fifth leading cause of cancer death in females worldwide. Indeed, OC was reported to account for 2.6% and 5.0% of the total cancer incidence and deaths in women, respectively [ 1 ]. Most OC patients are diagnosed at late stages, and the 5-year survival rate is 46.5% based on the Surveillance, Epidemiology, and End Results Program, highlighting the need for an effective screening strategy. CA125 is the current serum biomarker for detecting and monitoring OC in clinical practice, but this is usually only elevated in advanced disease and has poorer sensitivity for early-stage cancer. Additionally, CA125 has limited specificity, and elevated levels do not always reflect a diagnosis of OC but can be indicative of endometriosis, liver cirrhosis, pelvic inflammatory disease, and some benign neoplasms [ 2 ,  3 ]. Thus, there is an urgent need to explore sensitive, noninvasive biomarkers for the detection of OC.\nAccumulating evidence has shown that microRNAs (miRNAs) function as oncogenes or tumor suppressor genes. miRNAs are short (18–24 nucleotide) noncoding RNAs that regulate gene expression at the posttranscriptional level through base pairing with complementary sequences of the 3′-untranslated region of mRNA. Expression of miRNAs is aberrant in various types of cancers where they are readily detected in circulating body fluids such as serum or plasma [ 4 ]. Taylor and Gercel-Taylor previously isolated eight exosomal miRNAs ( MIR21 ,  MIR141 ,  MIR200A ,  MIR200B ,  MIR200C ,  MIR203 ,  MIR205 , and  MIR214 ) in the blood of OC patients that were upregulated in comparison with benign controls, which may reflect tumor profiles and aid diagnosis [ 5 ]. Following this investigation, a series of profiling studies were carried out to explore the diagnostic potential of extracellular miRNAs. Kan et al. identified a small marker panel combining  MIR200B  and  MIR200C  as a positive classifier of OC [ 6 ], while Langhe et al. showed that four miRNAs ( MIRLET7I ,  MIR122 ,  MIR152 , and  MIR25 ) were significantly downregulated in OC patients and could discriminate benign from malignant ovarian disease [ 7 ]. More recently, Elias et al. proposed a pattern of seven microRNAs ( MIR29A ,  MIR92A ,  MIR200C ,  MIR320C ,  MIR335 ,  MIR450B , and  MIR1307 ) in the blood that appears to predict OC [ 8 ].\nIn this study, we profiled the global expression patterns of serum miRNAs in OC utilizing next-generation sequencing and clinical data from the Gene Expression Omnibus database (dataset ID:  GSE94533 ). Considering the inconsistencies across different studies of the role of miRNAs in OC, we also performed systematic analysis combined with a validation study ( GSE106817 ) to evaluate the diagnostic efficiency of circulating miRNAs with the aim of identifying a novel class of noninvasive biomarkers for the diagnosis of OC.\n\nmiRNA profiling data were obtained from  GSE94533  [ 8 ]. The corresponding platform was the Illumina NextSeq 500 system, and sequence tags were mapped to miRBase 20. Expression levels were quantified in transcripts per million. Using raw count data, the edgeR package of R was utilized to identify differentially expressed miRNAs (DEmiRNAs). A false positive rate < 0.05 and ∣log 2 FC∣ (fold change) > 1 were set as cutoff points to screen out DEmiRNAs.\nWe conducted a literature search for all studies that evaluated the diagnostic value of candidate circulating miRNAs for OC in the PubMed database up to January 2019. The following key terms were used in the search: “circulating” or “serum” or “plasma”, “DEmiRNAs screened above”, “Ovarian” and “cancer” or “carcinoma” or “neoplasm”. References cited in each retrieved article were also manually scanned to identify additional eligible studies.\nArticles were recruited for this meta-analysis if they fitted the following criteria: (1) investigated the diagnostic value of circulating miRNAs in OC, (2) peripheral blood for miRNA analysis was collected before any treatment, (3) sufficient data to construct a 2 × 2 contingency table, and (4) published in English. The following data characteristics were collected for each included article: first author's name, publication year, country of publication, sample type, detecting method, sample size, and data for the two 2 × 2 contingency tables (sensitivity, specificity, area under the ROC curve (AUC), and corresponding 95% confidence interval (95% CI)).\nThe expression levels of candidate miRNAs were subsequently validated in an independent cohort ( GSE106817 ) [ 9 ]. In this set, a total of 3938 serum samples were analyzed by miRNA microarray, including 320 OC, 2759 noncancer controls, and 859 other solid cancers. Comprehensive miRNA expression analysis was evaluated using the 3D-Gene® Human miRNA Oligo Chip (Toray Industries Inc., Tokyo, Japan). Each miRNA signal value was standardized using the ratio of the average signal value of the three internal control miRNAs (miR-149-3p, miR-2861, and miR-4463) to the preset value.\nThe potential targets of candidate DEmiRNAs were obtained using the miRTarBase database ( http://mirtarbase.mbc.nctu.edu.tw/ ). We only included target genes that were validated by at least two of the following experimental methods: reporter assay, western blot, quantitative PCR, microarray, and next-generation sequencing experiments. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID;  https://david.ncifcrf.gov/ ) online tool. A Benjamini  P  value of <0.05 was used to indicate a statistically significant difference in the above pathway enrichment analysis.\nData analysis was performed using Stata 13 (Stata Corporation, College Station, TX) and R (version 3.1.3) software. MIDAS modules for Stata were used to estimate the pooled specificity, sensitivity, and AUC of the summary receiver operating characteristic (SROC) [ 10 ]. Heterogeneity among studies was estimated with the  Q  test and  I \n 2  statistics, and  I \n 2  > 50% indicated the existence of significant heterogeneity. The presence of publication bias was detected using the Deeks funnel plot asymmetry test. Different distributions of relative serum miRNA expression levels between OC cases and controls in  GSE94533  and  GSE106817  were compared using the Mann–Whitney  U  test. ROC curve analysis was performed to calculate the AUCs to evaluate the associations of candidate miRNAs and OC. Tests of significance were two-tailed, and a  P  value < 0.05 was considered statistically significant.\n\nGSE94533  included 98 OC patients, 21 patients with borderline tumors, 45 patients with benign ovarian diseases, and 15 healthy women. Of the OC patients, 44.9% presented at stage III or IV disease, as defined by the International Federation of Gynecology and Obstetrics (FIGO). We used a five-set Venn diagram to demonstrate the common differentially expressed miRNAs ( Figure 1(a) ). A total of 81 DEmiRNAs were identified between stage I/II cancer and healthy controls, including 50 upregulated and 31 downregulated; 240 DEmiRNAs were identified between stage III/IV cancer and healthy controls, including 172 upregulated and 68 downregulated; 77 DEmiRNAs were identified between borderline tumors and healthy controls, including 52 upregulated and 25 downregulated; 115 DEmiRNAs were identified between invasive cancers and benign lesions, including 87 upregulated and 28 downregulated; and 74 DEmiRNAs were identified between borderline tumors and benign lesions, including 43 upregulated and 31 downregulated miRNAs. A total of 42 DEmiRNAs were common to three or more sets. Of these, 32 were upregulated and 10 were downregulated in invasive cancers or borderline tumors compared with benign lesions and healthy controls ( Figure 1(b) ).\nTo further evaluate the clinical applicability of the 42 common DEmiRNAs for the diagnosis of OC, we screened all available studies based on the above search strategy and identified five miRNAs ( MIR200A ,  MIR200B ,  MIR200C ,  MIR429 , and  MIR25 ) as being reported so far. We consequently focused our attention on their expression in  GSE94533 . As shown in  Figure 2(a) , miR-200b-3p, miR-200c-3p, and miR-429 serum expression was significantly higher in OC than in healthy controls, while the serum expression of miR-25-3p was extremely low in OC patients. Additionally, the expression of miR-200a-5p, miR-200b-3p, miR-200c-3p, and miR-429 was higher in OC patients compared with those with benign lesions.\nTo reflect the diagnostic potential of the selected miRNAs, ROC curve analysis was performed. We used information on the cutoff from each study to determine an optimal cutoff by maximizing the Youden index (sensitivity + specificity − 1). The relative expression of serum miR-200a-5p, miR-200b-3p, miR-200c-3p, miR-429, and miR-25-3p could distinguish OC patients from healthy controls with AUC values of 0.649, 0.737, 0.779, 0.703, and 0.875, respectively ( Figure 2(b) ). The significant differences in the serum levels of miRNAs between OC patients and patients with benign ovarian diseases were reflected by miR-200a-5p, miR-200b-3p, miR-200c-3p, and miR-429 AUC values of 0.693, 0.783, 0.762, and 0.692, respectively ( Figure 2(c) ). To improve the discrimination, the concentrations of these miRNAs were combined and analyzed by binary regression. One panel of these miRNAs could discriminate between OC patients and healthy controls with a sensitivity of 79.6%, a specificity of 100.0%, and an AUC value of 0.916 ( Figure 2(b) ); this panel could also differentiate OC from benign lesions with a sensitivity of 79.6% and a specificity and AUC value of 0.788 ( Figure 2(c) ).\nTo explore the diagnostic efficiencies of these miRNAs in OC, we performed a diagnostic meta-analysis. According to the search criteria, five articles were included in the meta-analysis [ 6 ,  8 ,  11 – 13 ] and their detailed information is listed in  Table 1 . In total, 562 OC patients, 159 healthy women, and 65 patients with benign ovarian diseases were included in the meta-analysis.\nTo discriminate OC patients from healthy controls using the five miRNAs, we summarized sensitivity and specificity as 0.64 (95% confidence interval (CI): 0.52–0.74) and 0.88 (95% CI: 0.70–0.96), respectively; heterogeneity existed in both assessments ( P \n heterogeneity  < 0.001;  I \n 2  = 87.4 and 90.1%). The SROC plot showed the summary sensitivity and specificity and the 95% confidence and prediction regions, with an AUC of 0.78 (95% CI: 0.74–0.81,  Figure 3(a) ). To differentiate between OC and benign lesions (using  MIR200A ,  MIR200B ,  MIR200C , and  MIR429 ), the pooled sensitivity and specificity were 0.92 (95% CI: 0.57–0.99) and 0.69 (95% CI: 0.53–0.82), respectively.  I \n 2  values for pooled sensitivity and specificity were 97.2% and 88.5%, respectively, indicating the existence of statistical heterogeneity between studies. Simultaneously, diagnostic accuracy was also assessed by SROC plotting, with an AUC value of 0.81 (95% CI 0.78–0.85,  Figure 3(b) ).\nFor our diagnostic meta-analysis, the funnel plot of publication bias showed no asymmetry for discriminating OC patients from healthy women and the Deeks test  P  value was 0.121. However, substantial funnel plot asymmetry suggestive of publication bias was revealed for differentiating OC from benign lesions ( P  = 0.002), possibly because only four articles containing seven studies were analyzed.\nTo validate whether the five candidate miRNAs had potential as clinical biomarkers, we compared their expression in the independent set  GSE106817 . As shown in  Figure 4(a) , miR-200a-5p, miR-200b-3p, miR-200c-3p, and miR-429 showing significant alterations in expression were upregulated in OC with respect to the healthy control group (all  P  < 0.001). By contrast, miR-25-3p was significantly increased compared with healthy controls. When patients were stratified according to FIGO, miR-25-3p expression significantly decreased in stage I/II patients compared with controls ( Figure 4(b) ). These candidate miRNAs were also found to be aberrantly expressed in other solid tumors such as breast cancer, colorectal cancer, and gastric cancer ( Figure S1 ). The diagnostic performance of the four miRNAs was confirmed in the validation set (AUC value: miR-200a-5p, 0.745; miR-200b-3p, 0.690; miR-200c-3p, 0.670; and miR-429, 0.797). miR-25-3p was calculated to have a sensitivity of 31.5%, a specificity of 75.5%, and an AUC of 0.626 ( Figure 4(c) ).\nExperimentally validated targets from miRTarBase were extracted to elucidate the biological function of these candidate miRNAs, and 167 genes were found [ 14 ]. These genes were classified into three GO categories (biological process (BP), cellular component (CC), and molecular function (MF)) using the online analysis tool DAVID. BP genes exhibited significant enrichment in the negative/positive regulation of transcription from the RNA polymerase II promoter (GO:0000122/GO:0045944,  P  = 4.04 × 10 ‐13 /2.90 × 10 ‐11 ). Among the CC and MF genes, the most clustered GO terms were nucleoplasm and protein binding, respectively (GO:0005654,  P  = 1.14 × 10 ‐10 ; GO:0005515,  P  = 2.17 × 10 ‐15 ). Regarding KEGG pathway enrichment analysis, the following three terms were identified as the most significant: miRNAs in cancer (hsa05206,  P  = 1.14 × 10 ‐13 ), prostate cancer (hsa05215,  P  = 4.68 × 10 ‐8 ), and pathways in cancer (hsa05200, 6.88 × 10 ‐8 ). The top 20 GO terms and enriched pathways are shown in  Figure 5 .\n\nThere is emerging evidence that circulating miRNAs can be repeatedly and stably detected in the blood and serve as molecular markers in both physiological and pathological conditions for OC [ 15 ]. However, differences in measurement platforms, laboratory protocols, and small sample sizes can affect gene expression levels, so robust conclusions are rarely yielded across diverse studies. To compensate for these shortcomings, this study applied bioinformatics and meta-analysis to identify valuable circulating miRNAs in the diagnosis of OC.\nBased on rigorous evaluations, we screened a total of 42 DEmiRNAs in  GSE94533 . Of these,  MIR200A ,  MIR200B ,  MIR200C ,  MIR429 , and  MIR25  have been previously reported. In the diagnostic meta-analysis, these five circulating miRNAs demonstrated a high diagnostic accuracy and yielded a combined AUC of 0.78 with 64% pooled sensitivity and 88% pooled specificity in discriminating OC cases from healthy controls. Excluding miR-25, the remaining four miRNAs showed a combined AUC of 0.81 with 92% pooled sensitivity and 69% pooled specificity in differentiating OC cases from patients with benign disease. Similarly, we observed the same diagnostic efficiency for these four miRNAs in our independent test.\nThe  MIR200  family contains  MIR200A ,  MIR200B ,  MIR200C , and  MIR429 , which are generated from two distinct transcripts:  MIR200A / MIR200B / MIR429  is derived from chromosome 1 and  MIR200C  from chromosome 12 [ 6 ].  MIR25  is located on chromosome 7 [ 16 ]. Members of the  MIR200  family are reported to be highly expressed in OC, as validated in our study, suggesting their importance in the diagnosis of OC. The  MIR200  family is thought to play an essential role in tumor metastasis by promoting epithelial-mesenchymal transition (EMT).  MIR200  family members help maintain E-cadherin expression in OC by downregulating ZEB1 and ZEB2, which are key transcription factors in EMT mediation that act as known repressors of E-cadherin transcription [ 17 ].\nDecreased serum levels of  MIR25  were detected in  GSE94533  in the present study, which is consistent with recent findings by Langhe et al. [ 7 ] and Meng et al. [ 11 ]. However, this phenomenon was only observed in early-stage OC samples in the  GSE106817  set, which could reflect the different methods used for miRNA extraction and detection. Nevertheless, data on the effect of this miRNA in OC remain contradictory. miR-25 was previously reported to be significantly upregulated in OC compared with the healthy ovarian tissue [ 16 ,  18 ]. Additionally,  MIR25  was reported to interact with large numbers of protein-coding genes ( ITGA5 ,  FBN1 , and  CDH1 ) and noncoding genes (lncRNA  PTAF ) whose expression changes promote ovarian carcinogenesis [ 18 ,  19 ]. Interestingly, Benson et al. detected a significantly decreased circulating  MIR25  concentration (–1.82-fold) in OC patients who had undergone carboplatin chemotherapy compared to subjects before treatment, which indicated that its change was associated with clinical response [ 20 ].\nTo determine the potential target genes of these miRNAs, we conducted functional and signaling pathway analysis. The top 20 enriched GO terms and signaling pathways were shown to be involved in the development and prognosis of cancer, such as focal adhesion and the phosphoinositide 3-kinase/Akt signaling pathway. Many miRNAs have been found to influence this pathway which is considered to play an instrumental role in proliferation, migration, invasion, and chemotherapy resistance [ 21 ]. Focal adhesion is a common mechanism associated with tumor cell invasion and metastasis, including that of OC [ 22 ]. Therefore, taken together with previous findings, the varied functions of these miRNAs imply that they have the capacity to interact with several targets and effect mechanistic changes.\nThe expression of miRNAs in serum or plasma is fairly stable and can be detected using common laboratory methods. This may be because circulating miRNAs are protected by binding proteins or are chemically modified, such as by methylation, making them resistant to ribonuclease activity [ 23 ,  24 ]. Some researchers have proposed that circulating miRNAs are derived from the secretion or leakage of microvesicles, exosomes, or apoptotic bodies from healthy and tumor tissues [ 25 ]. This provides further support for OC-associated circulating miRNA expression profiles as indicators of biological function.\nOur study has a number of strengths. The first is that we performed  in silico  analysis to investigate the diagnosis value and biochemical properties of circulating miRNAs in OC. Second, we included controls of patients with benign disease and healthy individuals. Third, the  MIR200  family with its high combined AUC and specificity was demonstrated to be a better molecular marker for OC prediction than  MIR25 , especially in the validation set. Fourth, we used a combination of bioinformatics and meta-analysis to identify OC biomarkers. However, we also recognize our study limitations, including the small sample size, selection bias, and heterogeneity across studies.\n\nOur comprehensive analysis identified circulating  MIR200  family members as promising noninvasive screening tools for the early detection of OC. Further large-scale prospective studies are warranted to confirm the clinical relevance of these miRNAs.","source_license":"CC-BY-4.0","license_restricted":false}