
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)11233-9
10.1016/j.heliyon.2024.e35202
e35202
Research Article
Prognostic significance of non-coding RNAs related to the tumorigenic epithelial-mesenchymal transition (EMT) process among ovarian cancer patients: A systematic review and meta-analysis
Soltani Khaboushan Alireza alirezasoltanykhaboshan@gmail.com
ab1
Salimian Seyedeh Nazanin s.n.salimian@gmail.com
a1
Mehraban Saghar mehraban.saghar@gmail.com
a
Bahramy Afshin bahramy_afshin@yahoo.com
a
Zafari Narges narges_zafari@yahoo.com
a
Kajbafzadeh Abdol-Mohammad kajbaf105@gmail.com
kajbafzd@tums.ac.ir
a
Johnson Joshua joshua.2.johnson@cuanschutz.edu
c⁎⁎
Majidi Zolbin Masoumeh masoumeh.majidizolbin@gmail.com
mmajidizolbin@sina.tums.ac.ir
a⁎
a Pediatric Urology and Regenerative Medicine Research Center, Gene, Cell and Tissue Research Institute, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
b Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
c Division of Reproductive Sciences, Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
⁎ Corresponding author. masoumeh.majidizolbin@gmail.commmajidizolbin@sina.tums.ac.ir
⁎⁎ Corresponding author. joshua.2.johnson@cuanschutz.edu
1 ASK and SNS contributed equally to this work as co-first authors.

08 8 2024
30 8 2024
08 8 2024
10 16 e3520213 1 2024
22 7 2024
24 7 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction

Ovarian cancer is the seventh most prevalent cancer among women. It has high mortality and morbidity and imposes a great burden on healthcare systems worldwide. Unraveling the mechanisms behind the Epithelial-Mesenchymal Transition and finding a panel for predicting the prognosis of the disease may help find the appropriate treatment approaches for the management of the disease. The overarching aim of this systematic review was to define a panel of different types of EMT-associated non-coding RNAs (ncRNAs) with significant prognostic value in all types of ovarian cancers.

Methods

We searched PubMed, Web of Science, Scopus, and Embase till Jun 2024 to retrieve relevant papers. Two independent reviewers screened papers, and discrepancies were resolved by consensus. Publications related to the dysregulation of different types of ncRNAs, including microRNAs, lncRNAs, and circRNAs, only in patients with ovarian cancer were included. The participation of ncRNAs in epithelial-mesenchymal transformation should be assessed via methods evaluating different EMT-related proteins. To assess the quality and risk of bias for the included case-control and cohort studies, refined Newcastle-Ottawa Scale (NOS) and Quadas-2 were recruited. A bivariate meta-analysis was performed to analyze extracted data.

Results

A total of 37 studies with overall 42 non-coding RNAs (15 microRNA, 24 long non-coding RNAs, and 3 circular RNAs) were entered into the analysis. Overall diagnostic odds ratio for ncRNAs in lymph node metastasis, distant metastasis, TNM stage, and clinical stage were 4.19, 3.80, 6.52, and 3.97, respectively. Also, a hazard ratio of 1.39 (P = 0.32) for overall survival was observed. Bioinformatic analyses on the Pan-cancer database demonstrated a significant correlation between low expression of miRNA and high expression of lncRNAs with poor prognosis of ovarian cancer.

Conclusion

Based on the results, the defined panel of ncRNAs can properly predict prognostic factors related to EMT in ovarian cancer without involving potentially invasive methods.

Keywords

Ovarian cancer
Non-coding RNA
Diagnosis
Bioinformatics
Endothelial mesenchymal transition
Meta-analysis
==== Body
pmc1 Introduction

Ovarian cancer (OC) is one of the most prevalent gynecological malignancies and accompanies a poor prognosis and a high mortality rate. It has a five-year survival rate of less than 45 % and is considered the leading lethal malignancy among gynecological cancers [1,2]. OC is known as a cancer with asymptomatic, inconspicuous, and hidden growth with delayed symptom onset, and it is usually diagnosed in advanced stages, which limits recruiting the possible treatment methods [3]⁠. High rates of metastasis, invasion into adjacent tissues, and resistance to conventional therapies contribute to the high mortality rate and poor prognosis of this cancer [4]⁠. Early diagnostic tests of OC (e.g., measurement of serum cancer antigen 125 (CA125) and transvaginal ultrasonography) are not specific, effective, and sensitive enough for early detection and do not significantly contribute to improving clinical outcomes [5]⁠. Therefore, finding diagnostic and prognostic approaches is one of the most important challenges and necessities. Determining mechanisms that result in these features of OC and providing prognostic panels may decrease the burden of the disease and increase overall survival.

Epithelial-mesenchymal transition (EMT) is a molecular process in which cells undergo specific changes, including losing their cell-to-cell adhesion, acquiring more mobility and stem cell-like properties, and transforming from an epithelial to a mesenchymal cell type. EMT has been shown to play an important role in cancer metastasis, invasion, and cellular resistance to chemotherapy. Blocking EMT to reduce tumorigenesis is considered a key adjuvant strategy for OC treatment [6]⁠. During the EMT process, the expression of E-cadherin as an epithelial marker decreases, while mesenchymal markers, such as N-cadherin, increase. Several studies have investigated different mediators and pathways contributing to EMT, and non-coding RNAs (ncRNAs) have been shown to play roles in the induction or suppression of EMT [[7], [8], [9]]⁠.

The ncRNAs are RNA transcripts that do not encode proteins and were assumed to be by-products with no important biological functions. NcRNAs comprise various types, including housekeeping (e.g., transfer RNAs, ribosomal RNAs, and small nucleolar/nuclear RNAs) and regulatory ncRNAs (e.g., small interfering RNAs, Long non-coding RNAs (lncRNAs), microRNAs, and circular RNAs (circRNAs)). Housekeeping ncRNAs are known to be stably expressed genes supporting cell life activity, while regulatory ncRNAs participate in biological processes. Abnormalities in ncRNA regulatory networks can interfere with normal cell functions and are closely associated with pathological changes, occurrence and, or progression of various diseases, drug resistance, and multiple malignancies, including OC, which may become more aggressive in response to these different types of ncRNAs [7,[10], [11], [12], [13], [14], [15]]. For instance, overexpression of MIR503, a tumor suppressor in cancers, suppresses the tumorigenic ability of OC, impairing the proliferation, EMT, and invasiveness and facilitating cell apoptosis [16]⁠. Furthermore, long intergenic ncRNA LINC00665 is upregulated in OC, which targets and inhibits miR-181a-5p while upregulating FHDC1 expression [17]⁠. Although the mechanisms by which ncRNAs participate in cancer progression are not fully understood, numerous reports have elucidated their role in the EMT process as a known mechanism affecting malignant cell growth or spread [18]⁠.

Moreover, multiple ncRNAs have been shown to have prognostic values for this cancer. The role of ncRNA downregulation and upregulation in the EMT process and metastasis in the OC has been investigated in several studies. Also, it has been shown that they could predict the stemness of cancer stem cell proliferation, metastasis, apoptosis, and chemotherapy resistance. Although there have been some review papers on the role of ncRNAs in OC, to the best of our knowledge, there has not been a comprehensive systematic review and meta-analysis outlining the significance of EMT-related ncRNAs in the prognosis of OC [9,[19], [20], [21], [22], [23], [24], [25], [26]]⁠. Here, we have gathered all the published data and conducted a thorough systematic review and meta-analysis of the EMT-associated ncRNAs that have prognostic and diagnostic value, as well as provided mechanistic information about OC. In this review, we focused on different types of OC, including epithelial ovarian cancer (EOC), high-grade serous ovarian cancer (HGSOC), and ovarian serous carcinomas (OSC).

2 Methods

2.1 Study protocol and search strategy

A systematic search of the literature was performed to retrieve papers discussing the prognostic value of ncRNAs related to EMT in OC patients. We developed a specific search strategy for each of the Embase, PubMed, Scopus, and Web of Science databases using the keywords ((“Ovarian” OR “Ovary”) and (“cancer*" OR “Neoplasm*” OR “Ovarian Neoplasm*")) and (“Metasta*" OR “EMT” OR “Epithelial-Mesenchymal transition” OR “Epithelial-Mesenchymal transformation” OR “Epithelial Mesenchymal*") and ((“RNA and Untranslated”) OR (“Noncoding” and “RNA”) OR (“Non-coding” and “RNA*") OR “ncRNA*" OR “MicroRNA*" OR “miRNA*” OR “miR” OR (“Long Noncoding” and “RNA*") OR (“long” and “non” and “coding” and “RNA*“) OR “lncRNA*" OR (“long non-coding RNA*“) OR “ceRNA*” OR (“competing endogenous RNA*“) OR “LINC RNA*” OR “circRNA*” OR (“Circular” and “RNA*“)), and the relevant MeSH terms. The detailed search strategy for each database is available in Supplementary Table 1.

The searches were not restricted to the title/abstract or specific languages. We applied the search terms to all fields by considering papers published from 2000 to Jun 13, 2024. The search result of each database was collected in a library. Then, retracted articles and duplicates were removed. This systematic review is based on the PRISMA statement [27]⁠. The methodology of this study was registered in PROSPERO (Registration No. CRD42022304776).

2.2 Data management, screening, and detailed review

Two reviewers (SNS and SM) independently screened the title and abstract of the studies acquired from the previous step based on the defined inclusion and exclusion criteria. Disagreements were resolved by consulting with the third reviewer (ASK). In the title/abstract screening stage, conference abstracts, duplicate papers, letters, reviews, and editorial publications were excluded. Eligible articles were necessarily related to EMT, ncRNAs, and OC. Otherwise, they were excluded and classified as “not related to the topic” publications. Furthermore, papers that had not discussed prognosis and overall survival (OS) in their abstracts were not excluded at this stage, and a decision about them was taken during the full-text review.

In the detailed review step, the full text of the included papers was screened to check if the documents meet the study's inclusion criteria and have enough data to proceed with further steps. The evaluation of ncRNAs and their correlation with EMT and the evaluation of the prognostic role of the assessed ncRNAs were the essential factors reviewers appraised. Other exclusion criteria were case series, case reports, interventional and bioinformatics papers, cell line research, and animal studies lacking human samples. To be included, intervention-free samples must have been obtained from human OC patients, regardless of their disease stage, who did not suffer from other diseases or had not received medication therapies.

2.3 Data extraction

SNS and SM extracted the following data from the eligible articles: first author, publication year, country, study design, sample type, the number of cases and controls, ncRNA detection method, cancer stage, age-related information of controls and patients (mean, minimum, maximum, and standard deviation [SD]), type and name of the ncRNAs, the number of patients with upregulated and downregulated ncRNAs, p-value, prognosis levels (poor or good), the oncogenic or tumor-suppressive role of the ncRNAs, target genes and molecular mechanism of ncRNAs, type of OC, the number of patients with or without lymph node or distant metastasis regarding expression levels of ncRNAs, the number of patients with different clinical stages and TNM stages in both high and low expression levels of target ncRNA, hazard ratio (HR) and confidence of interval (CI) of both univariate and multivariate analysis of OS and progression-free survival (PFS). In case the needed data was not available in the paper or its supplementary material, the corresponding author(s) were contacted to obtain the needed data.

2.4 Quality assessment

To assess each included article's validity, quality, and risk of bias, case-control and cohort studies were evaluated based on the Newcastle-Ottawa Scale (NOS) [28]⁠. We refined NOS questions and scores according to our study and defined the total validity score ranging from a minimum of 0 to a maximum of 8. The maximum scores of four, two, and two have been awarded to the three characteristics of each study: selection, comparability, and outcome, respectively. There was a consensus on considering papers with a total score of ⩾ 5 as high-quality studies. Moreover, The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) checklist was recruited to assess the risk of bias [29]⁠.

2.5 Statistical analysis

True positive (TP), true negative (TN), false positive (FP), and false negative (FN) rates were directly extracted from the included studies or were calculated based on the reported sensitivity, specificity, and prevalence. TP, TN, FP, and FN values were used to calculate the pooled effect size of ncRNAs expression in cancerous tissue for predicting TNM staging, clinical staging, lymph node metastasis (LNM), and distant metastasis (DM) as factors, predicting the severity and prognosis of OC. A univariate meta-analysis was conducted to compute the overall sensitivity, specificity, and diagnostic odds ratio (DOR) for the mentioned variables. The ncRNAs were only included in the meta-analysis if their effect had been reported as significantly positive. A generalized linear mixed model (GLMM) with log transformation was used for a random-effect univariate meta-analysis of sensitivity and specificity. An inverse variance model was used for a random-effect univariate meta-analysis of DOR. Continuity correction for studies containing zero cell counts was performed via the method introduced by Weber et al. [30]⁠. Cochrane's Q test and I2 were used to assess the heterogeneity between studies, and a P-value of lower than 0.1 was considered significant. Subgroup analysis was performed based on the type of ncRNAs (microRNA, lncRNA, and circRNA) if there were at least two groups with at least two studies in each group. Furthermore, to find the best ncRNAs panel for predicting the prognosis of OC, based on the primary analysis, the effects of the ncRNAs with low expression were adjusted to improve the power of the model.

Bivariate meta-analysis fitted to logit-transformed sensitivities and false positive rates (FPRs) using the Reitsma et al. approach, and variance components were calculated using the restricted maximum likelihood (REML) method [31]⁠. The summary receiver operating curve (SROC) was used to visualize the summary of the diagnostic performance of the included studies. The area under the SROC (AUSROC) and its CI were calculated by bootstrapping (2000 iterations) [32]⁠. The heterogeneity was evaluated by the visual symmetry of the SROC and the correlation between logit-transformed sensitivity and specificity. Additionally, the Holling sample size adjusted method was used for calculating the I2 estimate of heterogeneity, and I2 over 75 %, 25–75 %, and under 25 % were considered high, moderate, and low heterogeneity [33,34]⁠. Deek's funnel plot asymmetry test assessed the publication bias; a P-value <0.1 was considered significant. Meta-regression was performed for the type of ncRNAs.

Moreover, a meta-analysis was performed for the HR of OS of OC as a predictor of prognosis for ncRNAs. The natural logarithm of HR was used as the effect size, and the REML method was used to calculate the variance components. Subgroup analysis was performed for the type of ncRNAs. Cochrane's Q test and I2 were used to evaluate heterogeneity. Egger's test was used to assess the asymmetry of the funnel plot. Generally, P-values <0.05 were considered significant, and all reported CIs are 95 % CI. All analyses were performed via the R programming language, V4.2.1, using “mada,” “meta,” “metafor,” “dmetar,” and “dmetatools” packages [32,[35], [36], [37], [38]]⁠.

2.6 Bioinformatics analysis

Further analysis was conducted to find a probable correlation between the obtained data from this meta-analysis and existing datasets. We used TCGA data from the Pan-cancer database. The purpose of applying the Pan-cancer database was to validate our discoveries. We re-evaluated the OS impact of the differential expression of the microRNAs and lncRNAs in OC patients compared with healthy women using the Kaplan–Meier (K-M) plotter (https://kmplot.com/analysis/) [39]⁠. It could be noted that the K-M plot visualization was performed based on the auto-select cut-off values [40]⁠.

3 Results

3.1 Study selection

Searching four databases, collecting the results of each search to a single library, omitting duplicate publications, and screening abstracts resulted in the retrieval of 332 studies. Following that, full-text reviewing led to the selection of 37 eligible articles for meta-analysis [[41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77]]. During the detailed review step, fifty-two articles were excluded due to insufficient data and failure to receive an appropriate score in the validity assessment, despite having all the inclusion criteria required for acceptance. The selection process with the exclusion reason of the articles was briefly characterized in Fig. 1.Fig. 1 PRISMA flow diagram of the systematically reviewed papers with differential ncRNAs expression in OC patients. Initial searches in four databases resulted in 8730 articles. After removing 3473 duplicated and 95 retracted articles, 5162 papers were selected to be screened based on title/abstract and be categorized based on the defined exclusion/inclusion criteria. 332 included articles were grouped based on full-text screening. Finally, 37 studies were identified as eligible for our meta-analysis.

Fig. 1

3.2 Study characteristics and quality assessment

A total of fifteen microRNAs (miR-506 [41]⁠, miR-26b [51]⁠, miR-216a [42]⁠, miR-532 [53]⁠, miR-3064 [53]⁠, miR-616 [64]⁠, miR-219a-5p [63]⁠, miR-214 [73]⁠, miR-196-5p [62]⁠, miR-99a [56]⁠, miR-18a [69]⁠, miR-126 [70]⁠, miR-489 [57]⁠, miR-98-5p [59]⁠, and miR-488 [44]⁠), twenty-four lncRNAs (HOTAIR [50]⁠, CCAT1 [60]⁠, HOXD-AS1 [61]⁠, ADAMTS9-AS2 [72]⁠, MEG3 [63]⁠, PVT1 [73]⁠, DQ786243 [74]⁠, GAS5 [62]⁠, LncARSR [54]⁠, FLVCR1-AS1 [55]⁠, TC0101441 [58]⁠, FAM83H-AS1 [65]⁠, HOXB-AS3 [66]⁠, LINC-PINT [76]⁠, NEAT1 [52]⁠, SNHG20 [75]⁠, MAFG-AS1 [68]⁠, DSCR8 [59]⁠, LINC01094 [77]⁠, E2F4as [43]⁠, DNM3OS [45]⁠, LINC01969 [46]⁠, SRA [48]⁠, and HCG18 [49]⁠), and three circRNAs (Circ_100395 [67]⁠, Circ_0000745 [47]⁠, and CircAGFG1 [71]⁠) were evaluated in the included publications. The detection method of the target ncRNAs was quantitative real-time polymerase chain reaction (qRT-PCR) in almost all studies. Overall, the studies carried out their research on 5207 clinical samples, including 2086 control and 3121 OC patient tissue samples. The main characteristics of the included studies are described in Table 1.Table 1 Main characteristics of the included studies for meta-analysis.

Table 1First Author	Year	Country	Type of ncRNA	Name of ncRNA	Detection Method	Sample Size (case/control)	Expression Levels (Up/Down)	Target Gene(s)	Type of OC	Molecular Mechanism	Ref.	
Lin J	2015	China	MicroRNA	miR-26b	qRT-PCR	97/-	48/49	KPNA2	EOC	MiR-26b inversely correlates with the expression of KPNA2 that downregulates OCT4 and Vimentin and conversely upregulates E-cadherin.	[51]⁠	
Liu H	2017	China	MicroRNA	miR-216a	qRT-PCR	87/25	44/43	PTEN	–	MiR-216a directly targets PTEN and inhibits the PTEN/AKT pathway, which promotes EMT and metastasis of OC cells.	[42]⁠	
Bai L	2017	China	MicroRNA	miR-532
miR-3064	qRT-PCR	60/20	miR-532: 0/31
miR-3064: 0/29	hTERT	EOC	Both miR-3064 and miR-532 bind to and suppress the hTERT Leading to EMT process suppression and apoptosis induction, and the loss of these MiRs leads to OC.	[53]⁠	
Chen Z	2018	China	MicroRNA	miR-616	qRT-PCR	60/60	30/30	TIMP2	–	MiR-616 directly targets TIMP2, which is required for EMT process promotion.	[64]⁠	
Wang L	2019	China	MicroRNA	miR-219a-5p	qRT-PCR	317/317	132/185	EGFR	–	MiR‐219a increases the expression of E‐cadherin and reduces the expressions of N‐cadherin. Therefore, MiR‐219a prevents EMT by targeting EGFR.	[63]⁠	
Zhao H	2018	China	MicroRNA	miR-196-5p	qRT-PCR	195/195	114/81	HOXA5	HGS-OC	MiR-196a-5p promotes EMT by targeting HOXA5, reducing E‐cadherin, and increasing N‐cadherin in HGSOC.	[62]⁠	
Chen Y	2018	China	MicroRNA	miR-214	qRT-PCR	231/58	101/130	–	EOC	MiR-214 is downregulated by PVT1, which promotes EMT in EOC.	[73]⁠	
Zhang L	2019	China	MicroRNA	miR-99a	qRT-PCR	47/47	23/24	HOXA1	–	MiR-99a directly targets HOXA1 and suppresses cell proliferation via the AKT/mTOR pathway. This microRNA also inhibits invasion-mediated EMT.	[56]⁠	
Zhao Y	2020	China	MicroRNA	miR-18a	qRT-PCR	50/50	20/30	CBX/ERK	–	MiR-18a targets and inhibits the CBX7 and ERK protein levels Promotes ERK/MAPK signaling pathway leads to suppression of the proliferation, migration, invasion, and EMT.	[69]⁠	
Zhang Y	2020	China	MicroRNA	miR-126	qRT-PCR	54/54	20/34	EGLF7/ERK	–	MiR-126 directly targets EGFL7 and regulates the ERK/MAPK signaling pathway via suppression of ERK and participates in EMT.	[70]⁠	
Jiang HW	2020	China	MicroRNA	miR-489	qRT-PCR	51/51	20/31	XIAP/PI3K/EMT- related genes	–	MiR-489 binds to and regulates the X-linked inhibitor of apoptosis protein (XIAP), phosphatidyl-inositol 3-kinase/protein kinase B pathway (PI3K/AKT), and EMT in OC.	[57]⁠	
Dong L	2020	China	MicroRNA	miR-98- 5p	qRT-PCR	52/52	26/26	STAT3/HIF-1α	–	Downregulation of miR-98-5p in OC tissues promotes EMT and cell growth progression of OC.	[59]⁠	
Guo JY	2020	China	MicroRNA	miR-488	qRT-PCR	58/-	18/40	CCNG, P53	–	MiR-488 suppresses OC metastasis by reducing the expression of p53 and CCNG and blocking EMT.	[44]⁠	
Sun Y	2015	China	MicroRNA	miR-506	ISH	204/-	95/102	EMT- related genes (Vimentin/SNAI2/CDH2)	EOC	MiR-506, an EMT inhibitor, directly targets and inhibits Vimentin, SNAI2, and CDH2 expression while increasing the E-cadherin levels in EOC.	[41]⁠	
Chen J	2021	China	LncRNA	LINC01969	qRT-PCR	41/41	19/22	miR-144-5p	–	LINC01969 sponges miR-144-5p to upregulate LARP1 and then promotes migration, invasion, EMT, and proliferation of OC cells.	[46]⁠	
Qiu JJ	2014	China	LncRNA	HOTAIR	qRT-PCR	64/29	32/32	MMPs	EOC	HOTAIR promotes EMT in EOC via MMP.	[50]⁠	
Cao Y	2017	China	LncRNA	CCAT1	qRT-PCR	72/72	36/36	miR-152/miR-130b/ADAM17/WNT1/ZEB1/STAT3/Vimentin/N-cadherin	EOC	In EOC, CCAT1 is inversely associated with the activity of miR-152 and miR-130b, Which target ADAM17, WNT1, STAT3, ZEB1, Vimentin, and N-cadherin, then negatively regulate the EMT process.	[60]⁠	
Zhang Y	2017	China	LncRNA	HOXD- AS1 (HAGLR)	qRT-PCR	43/43	22/21	miR-133a- 3p	EOC	lncRNA HOXD-AS1 promotes cell proliferation, invasion, and EMT by targeting and sponging miR-133a-3p and activates the Wnt/β-catenin pathway in EOC.	[61]⁠	
Wang A	2018	China	LncRNA	ADAMTS9-AS2	qRT-PCR	47/-	24/23	miR-182-5p	–	ADAMTS9-AS2 sponges miR-182-5p and decreases OC progression via regulating the miR182-5p/FOXF2 pathway.
Low levels of ADAMTS9-AS2 are correlated with OC cell metastasis, proliferation, invasion, and EMT. MiR-182-5p directly targets FOXF2.	[72]⁠	
Wang L	2019	China	LncRNA	MEG3	qRT-PCR	317/317	136/171	miR-219-5p	–	MEG3 regulates miR‐219a‐5p/EGFR axis and prevents EMT.	[63]⁠	
Chen Y	2018	China	LncRNA	PVT1	qRT-PCR	231/58	115/116	miR-214/EZH2	EOC	PVT1 represses miR-214 expression through interaction with EZH2. PVT1 overexpression reduces E-cadherin while elevating the expression levels of Vimentin, β-catenin, Snail, and Slug proteins, promoting EMT.	[73]⁠	
Yong W	2018	China	LncRNA	NEAT1	qRT-PCR	75/-	37/38	miR-506	HGS-OC	NEAT1 is stabilized by LIN28B, sponges miR-506, and promotes OC progression in HGSOC. NEAT1 promotes EMT by elevating the expression of E-cadherin, whereas reducing the expression of N-cadherin, MMP9, and MMP2.	[52]⁠	
Yan H	2018	China	LncRNA	DQ786243	qRT-PCR	30/30	15/15	miR‐506	–	DQ786243 interacts with and suppresses miR‐506 and promotes OC progression through targeting CREB1. Furthermore, DQ786243 promotes EMT via downregulation of E‐cadherin protein and upregulation of the Vimentin and snai2 protein levels.	[74]⁠	
Zhao H	2018	China	LncRNA	GAS5	qRT-PCR	195/195	70/125	miR-196a- 5p	HGS-OC	GAS5 directly targets miR-196a-5p in HGSOC and prevents EMT.	[62]⁠	
Shu C	2018	China	LncRNA	LncARSR	qRT-PCR	76/76	38/38	HuR/ZEB1/ZEB2	EOC	Overexpression of lncARSR activates the WNT/B-Catenin pathway and increases ZEB1 and ZEB2 expression by competitively binding to the miR-200 family (lncARSR acts as ceRNA for miR-200 family), thus inducing EMT.	[54]⁠	
Yan H	2019	China	LncRNA	FLVCR1- AS1	qRT-PCR	50/50	27/23	miR-513	OSC	FLVCR1-AS1 directly targets and downregulates miR-513, thus upregulated FLVCR1-AS1 mediates miR-513/YAP1 axis in OSC to promote EMT, cell growth, migration, and invasion and lower apoptosis.	[55]⁠	
Qiu JJ	2019	China	LncRNA	TC0101441 (ERLNC1)	qRT-PCR	74/20	37/37	KiSS1	EOC	TC0101441 targets and negatively regulates the KiSS1 and promotes cell migration/invasion and EMT in EOC.	[58]⁠	
Dou QR	2019	China	LncRNA	FAM83H- AS1 (IQANK1)	qRT-PCR	80/80	38/42	HuR	–	FAM83H-AS1 interacts with and stabilizes HuR and facilitates EMT.	[65]⁠	
Wang D	2019	China	LncRNA	SNHG20	qRT-PCR	60/15	38/22	–	EOC	SNHG20 promotes EMT in EOC.	[75]⁠	
Zhuang XH	2019	China	LncRNA	HOXB-AS3	qRT-PCR	178/178	91/87	–	EOC	HOXB-AS3, an oncogene, activates Wnt/β-catenin signaling, promotes cell proliferation, migration, invasion, and EMT, and inhibits apoptosis in EOC.	[66]⁠	
Hao T	2020	China	LncRNA	LINC-PINT	qRT-PCR	72/72	20/52	miR-374a-5p	–	LncRNA LINC-PINT sponges miR-374a-5p, inhibits cell proliferation, migration, and EMT, and augments apoptosis in OC.	[76]⁠	
Bai Y	2021	China	LncRNA	MAFG-AS1 (MILIP)	qRT-PCR	75/75	37/38	miR-339-5p	–	MAFG-AS1 recruits and upregulates NFKB1 by binding to miR-339-5p. This leads to higher levels of IGF1 and promotes EMT and cell migration/invasion.	[68]⁠	
Dong L	2020	China	LncRNA	DSCR8	qRT-PCR	52/52	26/26	miR-98- 5p	–	Overexpression of DSCR8 in OC tissue leads to cell proliferation, invasion, and EMT while inhibiting apoptosis. Overexpressed DSCR8 positively regulates the expression of hypoxia-inducible factor 1 alpha (HIF-1α) and STAT3 and participates in miR-98-5p downregulation.	[59]⁠	
Xu J	2020	China	LncRNA	LINC01094	qRT-PCR	93/93	–	miR-577	–	LINC01094 directly targets and inhibits miR-577 and promotes cell proliferation, migration, invasion, and EMT. MiR-577 targets LRP6, Wnt2b, and β-catenin and regulates the Wnt/β-catenin pathway.	[77]⁠	
Park SA	2020	Korea	LncRNA	E2F4as	qRT-PCR	108/32	78/30	–	–	E2F4as promotes cell proliferation, invasion, and EMT migration and decreases apoptosis.	[43]⁠	
He L	2021	China	LncRNA	DNM3OS	qRT-PCR	49/18	25/24	miR-193a-3p/EMT -related genes	–	DNM3OS interacts with miR-193a-3p and increases the expression of MAPK3K3 by repressing miR-193a-3p. Overexpression of DNM3OS augments OC EMT, proliferation, cell migration, invasion, and the expression of N-cadherin protein and impedes the E-cadherin levels.	[45]⁠	
Kim LK	2021	South Korea	LncRNA	Lnc-SRA	qRT-PCR	101/63	66/35	E-cadherin/β-catenin/N-cadherin/Snail/HES1/Vimentin/NOTCH/NICD/P300		LncRNA SRA regulates OC progression through NOTCH signaling and EMT.	[48]⁠	
Zhang F	2022	China	LncRNA	HCG18	qRT-PCR	30/30	15/15	miR-29a/b	EOC	LncRNA-HCG18 stimulates the NF-κB pathway-mediated EMT, proliferation, and migration of EOC cells by acting as a ceRNA of miR-29a/b, which upregulates TRAF4/5 expression levels. Overexpression of HCG18 reduces E-cadherin while increasing the protein levels of MMP2, MMP9, and Vimentin.	[49]⁠	
Li X	2020	China	CircRNA	Circ_100395	qRT-PCR	60/60	30/30	miR-1228	–	CircRNA_100395 negatively associates with miR-1228 and exerts its inhibitory activity against cell growth, cell proliferation, and metastasis of OC cells by regulating the miR-1228/p53/EMT pathway.	[67]⁠	
Wang S	2021	China	CircRNA	Circ_0000745	qRT-PCR	50/50	24/26	miR-3187- 3p	–	Circ_0000745 targets and inhibits miR-3187-3p and promotes phosphorylation of the PI3K/AKT pathway via stabilizing ERBB4. Circ_0000745 facilitates EMT by enhancing the expression of Vimentin and Snail and reducing the expression of E-cadherin. MiR-3187-3p inhibits ERBB4 and blocks Circ_0000745.	[47]⁠	
Luo J	2024	China	CircRNA	CircAGFG1	qRT-PCR	30/30	15/13	miR-409-3 p	–	CircAGFG1 promotes EMT, proliferation, and invasion/migration of the OC by targeting miR-409-3p, elevating the zinc finger E-box binding homeobox 1 (ZEB1) expression.	[71]⁠	

Among the 37 included primary studies for meta-analysis, seventeen got a score of 5 [[41], [42], [43], [44],[47], [48], [49],52,53,64,[72], [73], [74], [75], [76], [77], [78]], ten got a score of 6 [45,50,51,[54], [55], [56], [57], [58], [59],71], eight got a score of 7 [[60], [61], [62], [63],[65], [66], [67], [68]], and two got a score of 8 [69,70] in the refined NOS quality assessment. The quality assessment results have been summarized in Table 2. Moreover, the risk of bias assessment via QUADAS-2 has been shown in Fig. 2A and B.Table 2 NOS scores for included studies.

Table 2First Author (Year)	#1	#2	#3	#4	#5	#6	#7	#8	T	First Author (Year)	#1	#2	#3	#4	#5	#6	#7	#8	T	
Zhang Y (2020)	*	*	*	*	*	*	*	*	8	Qiu JJ (2014)	*	*	–	–	*	*	*	*	6	
Zhao Y (2020)	*	*	*	*	*	*	*	*	8	Zhang F (2022)	*	*	–	–	*	*	–	*	5	
Bai Y (2021)	*	*	*	–	*	*	*	*	7	Chen J (2021)	–	*	*	–	*	*	–	*	5	
Li X (2020)	*	*	*	–	*	*	*	*	7	Wang S (2021)	–	*	*	–	–	*	*	*	5	
Zhuang XH (2019)	*	*	*	–	*	*	*	*	7	Kim LK (2021)	*	*	–	–	*	*	–	*	5	
Dou Q (2019)	*	*	*	–	*	*	*	*	7	Xu J (2020)	*	*	*	–	–	*	–	*	5	
Wang L (2019)	*	*	*	–	*	*	*	*	7	Guo JY (2020)	–	*	–	–	*	*	*	*	5	
Zhao H (2018)	*	*	*	–	*	*	*	*	7	Park SA (2020)	–	*	–	–	*	*	*	*	5	
Zhang Y (2017)	*	*	*	–	*	*	*	*	7	Hao T (2020)	–	*	*	–	*	*	–	*	5	
Cao Y (2017)	*	*	*	–	*	*	*	*	7	Wang D (2019)	–	*	*	–	*	*	–	*	5	
Luo J (2024)	*	*	–	–	*	*	*	*	6	Wang A (2018)	*	*	–	–	*	*	–	*	5	
He L (2021)	–	*	*	–	*	*	*	*	6	Yong W (2018)	*	*	*	–	–	*	–	*	5	
Jiang HW (2020)	–	*	*	–	*	*	*	*	6	Yan H (2018)	–	*	*	–	*	*	–	*	5	
Dong L (2020)	*	*	*	–	*	*	–	*	6	Chen Y (2018)	*	*	–	–	*	*	–	*	5	
Qiu JJ (2020)	*	*	–	–	*	*	*	*	6	Chen Z (2018)	*	*	–	–	*	*	–	*	5	
Yan H (2019)	*	*	*	–	*	*	–	*	6	Liu H (2017)	*	*	–	–	*	*	–	*	5	
Zhang L (2019)	–	*	*	–	*	*	*	*	6	Bai L (2017)	*	*	–	–	*	*	–	*	5	
Shu C (2018)	–	*	*	–	*	*	*	*	6	Sun Y (2015)	*	*	*	*	–	–	–	*	5	
Lin J (2015)	*	*	–	–	*	*	*	*	6	Selection: #1, #2, #3, and #4/Comparability: #5 and #6/Outcome: #7 and #8/T: total score	

Fig. 2 (A) Risk of bias assessment via QUADAS-2 for included studies, low-risk (green), high-risk (red), and unclear (Yellow) for each domain, including patient selection, index test, reference standard, and flow and timing. (B) Concerns regarding applicability.

Fig. 2

3.3 Diagnostic accuracy of ncRNAs for LNM in OC

In the univariate analysis of DOR, the overall effect for ncRNAs in LNM was 4.5 (95%CI, 3.39–5.99; P-value <0.0001). The heterogeneity was moderate, with an I2 of 24 % (Q, 31.38; P-value 0.14). The subgroup analysis demonstrated that overall, DOR in microRNA and lncRNA was 4.67 (95%CI, 2.48–8.81; P-value <0.0001) and 4.44 (CI, 3.15–6.25; P-value <0.0001), respectively. Heterogeneity was moderate in both subgroups (I2, 35 %; Q, 10.79; P-value 0.15 and I2, 26 %; Q, 20.32; P-value 0.16 for microRNA and lncRNA, respectively). There was only one study that examined the effects of circRNA, so the subgroup analysis was not applicable to this ncRNA. The subgroup difference was insignificant (X2, 0.02; P-value, 0.99). The forest plot for DOR univariate meta-analysis has been depicted in Fig. 3A. Publication bias was not significant, with a P-value of 0.71. Fig. 4D shows Deek's funnel plot for DOR. Univariate analysis for sensitivity and specificity demonstrated an overall sensitivity of 72 % and specificity of 64 %, with moderate heterogeneity for both sensitivity and specificity (Fig. 3B).Fig. 3 Univariate meta-analyses of ncRNAs in LNM. (A) Univariate forest plots for LNM show an overall DOR of 4.5 with no significant difference between subgroups (P = 0.99) (B) Univariate forest plots show sensitivity and specificity of 0.72 and 0.66, respectively.

Fig. 3

Fig. 4 Bivariate meta-analysis of ncRNAs in LNM. (A) Scatter plot of logit sensitivity and specificity shows low data dispersion (B) SROC for bivariate meta-analysis shows an overall AUC of 0.72 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows no publication bias.

Fig. 4

The bivariate scatter plot shows the data are generally concentrated around the center with low dispersion (Fig. 4A). A bivariate meta-analysis demonstrated that the overall sensitivity was 0.71 (95%CI, 0.65–0.77; intercept P-value <0.001; between-studies standard deviation [SD], 0.51) and the overall specificity was 0.63 (95%CI, 0.57–0.68; intercept P-value <0.001; between-studies SD, 0.45). The total AUC was 0.72 (95%CI, 0.66–0.75). The overall DOR, LR+, and LR− were 4.19 (95%CI, 3.13–5.47), 1.91 (95%CI, 1.67–2.19), and 0.46 (95%CI, 0.38–0.55), respectively. The bivariate meta-analysis result has been shown as SROC and bagplot in Fig. 4B and C. The SROC was symmetrical, and the correlation coefficient between logit-transformed sensitivity and specificity was negative (r, −0.70), showing low heterogeneity. Also, the Holling sample size adjusted I2 was 1.5–1.7 %. The log-likelihood for the model goodness-of-fit was 31.74.

3.4 Diagnostic accuracy of ncRNAs for DM in OC

The univariate analysis demonstrated an overall DOR of 3.86 (95%CI, 2.47–6.03 and P-value <0.0001) with a moderate heterogeneity (I2, 65 %; Q, 33.80; P-value <0.01). The subgroup analysis demonstrated no significant difference between subgroups (X2, 1.97; P-value, 0.37). The overall DOR was 4.65 (95%CI, 2.20–9.84; P-value <0.0001),2.74 (95%CI, 1.56–4.82; P-value <0.001), and 5.63 (95%CI, 1.86–17.06; P-value <0.01) in miRNA, lncRNA, and circular RNA, respectively (Fig. 5A). The heterogeneity was high in the miRNA subgroup (I2, 77 %; Q, 21.79; P-value <0.01), moderate in the lncRNA subgroup (I2, 51 %; Q, 8.18; P-value, 0.09), and low in the circular RNA subgroup (I2, 0 %; Q, 0.26; P-value, 0.61). The publication bias was high (P-value <0.001). Deek's funnel plot has been depicted in Fig. 6D. Overall sensitivity and specificity in univariate analysis were 73 % and 58 %, respectively, with moderate heterogeneity in both analyses (Fig. 5B).Fig. 5 Univariate meta-analysis of ncRNAs in DM. (A) Univariate forest plots for DM show an overall DOR of 3.86 with no significant difference between subgroups (P = 0.37) (B) Univariate forest plots show sensitivity and specificity of 0.73 and 0.58, respectively.

Fig. 5

Fig. 6 Bivariate meta-analysis of ncRNAs in DM. (A) Scatter plot of logit sensitivity and specificity shows that the data are dispersed (B) SROC for bivariate meta-analysis shows an overall AUC of 0.67 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows presence of publication bias.

Fig. 6

The scatter plot demonstrated that the data are not centered and have dispersion (Fig. 6A). The bivariate meta-analysis demonstrated total sensitivity and specificity of 0.73 (95%CI, 0.66–0.78; intercept P-value, <0.001; between-studies SD, 0.38) and 0.58 (95%CI, 0.52–0.64; intercept P-value <0.01; between-studies SD, 0.29), respectively. The overall AUC was 0.67 (95%CI, 0.56–0.74). The total DOR, LR+, and LR− in the bivariate meta-analysis were 3.80 (95%CI, 2.38–5.75), 1.75 (95%CI, 1.44–2.10), and 0.47 (95%CI, 0.36–0.61), respectively. The SROC and bag plot for the bivariate meta-analysis has been depicted in Fig. 6B and C. The SROC did not seem symmetrical, and the correlation coefficient between the logit-transformed sensitivity and specificity was positive (r, 0.29), implicating potential heterogeneity in the model. The Holling sample size adjusted I2 was 3.2–3.4 %. The log-likelihood for the goodness-of-fit of the model was 22.21.

3.5 Diagnostic accuracy of ncRNAs for TNM staging in OC

The total DOR was 5.69 in univariate analysis (95%CI, 3.26–9.94; P-value <0.0001), and the heterogeneity was low (I2, 16 %; Q, 5.95; P-value, 0.31). The subgroup analysis for the type of the ncRNAs demonstrated no significant difference between subgroups (X2, 3.18; P-value, 0.07) with a DOR of 7.74 in the miRNA subgroup (95%CI, 4.18–14.35; P-value <0.0001) and 3.11 in the lncRNA subgroup (CI, 1.41–6.86; P-value <0.01). The forest plot for univariate DOR meta-analysis has been demonstrated in Fig. 7A. The heterogeneity in both subgroups was low (I2, 0 %; Q, 2.5; P-value, 0.47 for miRNA and I2, 0 %; Q, 0.27; P-value, 0.60 for lncRNA). The publication bias was low (P-value, 0.40), and Deek's funnel plot is demonstrated in Fig. 8D. In univariate analysis, the overall sensitivity was 76 % with moderate heterogeneity, and the overall specificity was 67 % with low heterogeneity, Fig. 7B.Fig. 7 Univariate meta-analysis of ncRNAs in TNM stage. (A) Univariate forest plots for TNM show an overall DOR of 3.86 with no significant difference between subgroups (P = 0.37) (B) Univariate forest plots show sensitivity and specificity of 0.67 and 0.76, respectively.

Fig. 7

Fig. 8 Bivariate meta-analysis of ncRNAs in TNM stage. (A) Scatter plot of logit sensitivity and specificity shows that the data are dispersed (B) SROC for bivariate meta-analysis shows an overall AUC of 0.69 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows no publication bias.

Fig. 8

The scatter plot of logit-transformed sensitivity and specificity demonstrated that the data are dispersed (Fig. 8A). Pooled effects of 0.75 (95%CI, 0.65–0.84; intercept P-value <0.001; between studies SD, 0.43) and 0.67 (95%CI, 0.58–0.74; intercept P-value <0.001; between-studies SD, 0.06) were observed for sensitivity and specificity, respectively. SORC and ellipse plots for the bivariate meta-analysis have been shown in Fig. 8B and C, respectively. The overall AUC was 0.69 (95%CI, 0.63–0.83). Moreover, the overall DOR, LR+, and LR− were 6.52 (95%CI, 3.27–11.60), 2.29 (95%CI, 1.79–3.01), and 0.37 (95%CI, 0.24–0.54). The SROC plot was asymmetric, and the correlation coefficient between the logit-transformed sensitivity and the specificity was positive, which may show potential heterogeneity in the bivariate model. Also, the I2 estimate based on the Holling sample size adjusted method was 1.7–1.8 %. The log-likelihood ratio for the goodness-of-fit of the model was 12.23.

3.6 Diagnostic accuracy of ncRNAs for clinical staging in OC

Based on the primary univariate analysis, the overall univariate DOR of the ncRNA panel for clinical staging was 3.97 with 95%CI, 3.18–4.96, and P-value <0.0001. Heterogeneity was moderate, with I2 equal to 45.2 % (Q, 56.58; P < 0.01). In miRNA, lncRNA, and circular RNA subgroups, the DOR was 3.62 (95%CI, 2.77–4.73; P-value <0.0001), 3.90 (95%CI, 2.48–5.35, P-value <0.0001), and 6.04 (95%CI, 2.22–16.45; P-value <0.001), respectively. The forest plot for univariate DOR meta-analysis has been shown in Fig. 9A. The heterogeneity was moderate in miRNA (I2, 41 %; Q, 18.67; P-value, 0.07), lncRNA (I2, 49 %; Q, 31.63; P-value <0.01), and circular RNA (I2, 45 %; Q, 3.61; P-value, 0.16) subgroups. The difference between the subgroups was insignificant (X2, 0.98; P-value, 0.61). The publication bias was significant based on Deek's method (P-value <0.01). The funnel plot has been demonstrated in Fig. 10D. The univariate analysis demonstrated that the overall sensitivity and specificity were 67 % and 68 %, respectively, with moderate heterogeneity in both groups (Fig. 9B).Fig. 9 Univariate meta-analysis of ncRNAs in clinical stage. (A) Univariate forest plots for the clinical stage show an overall DOR of 3.97 with no significant difference between subgroups (P = 0.61) (B) Univariate forest plots show sensitivity and specificity of 0.67 and 0.68, respectively.

Fig. 9

Fig. 10 Bivariate meta-analysis of ncRNAs in clinical stage. (A) Scatter plot of logit sensitivity and specificity shows low data dispersion (B) SROC for bivariate meta-analysis shows an overall AUC of 0.71 (C) Ellipse plot for bivariate meta-analysis. (D) Deek's funnel plot shows the presence of publication bias.

Fig. 10

The scatter plot demonstrated relatively centered data with low dispersion (Fig. 10A). The overall sensitivity and specificity were 0.66 (95%CI, 0.62–0.70; intercept P-value <0.001; between-studies SD, 0.43), 0.67 (CI, 0.62–0.71; intercept P-value <0.001; between-studies SD, 0.40), respectively in bivariate meta-analysis. The total AUC was 0.71 (CI, 0.67–0.73). The SROC and ellipse plots are depicted in Fig. 10B and C, respectively. Overall, DOR, LR+, and LR− were 3.97 (95%CI, 3.11–4.98), 1.99 (CI, 1.75–2.27), and 0.51 (CI, 0.45–0.57), respectively. The symmetric SROC curve and negative correlation between sensitivity and specificity (r, −0.15) show small heterogeneity. Additionally, Heterogeneity was low based on the Holling sample size adjusted I2 estimate of 1.9–2.0 %. The log-likelihood for the goodness-of-fit of the model was 45.96.

3.7 Meta-analysis of HR to evaluate the value of ncRNAs in predicting OC prognosis

A total of 10 ncRNAs were included in an overall survival (OS) HR meta-analysis, of which 6 belong to the lncRNA subgroup and 4 belong to the miRNA subgroup. A random-effect meta-analysis demonstrated no significant overall effect (HR, 1.39; 95%CI, 0.67–2.84; P-value, 0.32). Overall heterogeneity was significant, with an I2 of 91.2 % (Q, 102.26; P-value <0.0001). The subgroup analysis demonstrated that overall HR in the miRNA subgroup was 0.92 (95%CI, 0.24–3.59; P-value, 0.91), and in the lncRNA, a subgroup was 1.82 (95%CI, 0.81–4.09; P-value, 0.14). The difference between subgroups was not significant (X2, 0.72; P-value, 0.40). Heterogeneity within the subgroups was significant (I2, 94 %; Q, 48.25; P-value <0.01 and I2, 90 %; Q, 48.63; P-value <0.01 for miRNA and lncRNA groups, respectively). The forest plot for HR is shown in Fig. 11A. Egger's test demonstrated no significant publication bias (P-value, 0.72). The funnel plot is depicted in Fig. 11B.Fig. 11 Meta-analysis for OS. (A) The forest plot for overall survival shows an HR of 1.39 with no subgroup difference (P = 0.40) (B) The Funnel plot for OS shows no significant publication bias.

Fig. 11

3.8 Outcomes validation based on bioinformatics analysis

The prognostic value of the obtained microRNAs and lncRNAs were re-analyzed based on the OS in OC patients, applying the Pan-cancer miRNA and Pan-cancer RNA-seq data, respectively. OS analysis of microRNAs resulted in the identification of a correlation between poor prognosis and high levels of hsa-miR-216a (HR = 1.68), hsa-miR-3064 (HR = 1.43), hsa-miR-489 (HR = 1.7), hsa-miR-488 (HR = 1.61), and hsa-miR-196a (HR = 1.44). However, low levels of hsa-miR-532 (HR = 0.75), hsa-miR-219a (HR = 0.65), hsa-miR-18a (HR = 0.78), and has-miR-98 (HR = 0.73) were related to poor OS of OC patients. Combining the results using the mean expression of the whole target microRNAs represented that the correlation between low expression levels of total microRNAs and poor OS prognosis was significant (log Rank P = 0.017). K-M plots for microRNAs have been shown in Fig. 12a-j. Furthermore, OS analysis of lncRNAs recognized the elevated expression of lncRNAs, including HOTAIR (HR = 1.81), NEAT1 (HR = 1.4), GAS5 (HR = 1.5), HOXB-AS3 (HR = 1.58), DSCR8 (HR = 1.33), and LINC01969 (HR = 1.66), and decreased expression of lncRNAs, involving CCAT1 (HR = 0.54), FLVCR1- AS1 (HR = 0.76), and HCG18 (HR = 0.67), as indicators of poor prognosis in OC patients. Total lncRNAs OS analysis revealed a close correlation (log Rank P = 0.017) between high levels of the whole selected lncRNAs with poor prognosis of OC (Fig. 13a-j). P-values for ncRNAs OS have been provided in Supplementary Table 2.Fig. 12 K-M plots for microRNAs based on Pan-cancer miRNA. High levels of miR-3064, miR-216a, miR-489, miR-196a, and miR-488 and low expression of miR-532, miR-18a, miR-219a, and has-miR-98 are shown to be correlated with poor prognosis of OC. Low levels of whole microRNAs are associated with poor OS.

Fig. 12

Fig. 13 K-M plots for lncRNAs based on Pan-cancer RNAseq data. High levels of HOTAIR, GAS5, NEAT1, HOXB-AS3, LINC01969, and DSCR8, low levels of expression of CCAT1, HCG18, and FLVCR1- AS1, and high levels of whole lncRNAs are correlated with poor OS of OC patients.

Fig. 13

4 Discussion

To our knowledge, this study is the first systematic review conducted to define the designation of ncRNAs in OC prognosis and EMT. Our results provide information on the importance and applicability of ncRNAs in predicting clinical stage, DM and LNM, and TNM stage of OCs. The current meta-analysis demonstrates that ncRNAs could be appropriate markers for predicting factors related to OC prognosis, including TNM staging, clinical staging, LNM, and DM; overall DOR for them were 6.52, 3.97, 4.19, and 3.8, respectively. The HR of the high-expression ncRNA group to the low-expression ncRNA group was insignificant.

OC, the most lethal gynecological malignancy, is predicted to increase in cases and deaths annually. Commonly used OC tumor markers, such as CA125, lack enough specificity and sensitivity and are unsatisfying for OC detection in early-stage and all subtypes. Serum and ascites levels of most kallikrein-related peptidases (KLK) family members that are noticeably overexpressed in OC tissues elevate due to protease secretion. KLK could serve as a potential biomarker and is adopted as a complementary tool along with CA125 for OC diagnosis, and its upregulation is associated with aggressive tumor phenotypes. However, DNA methylation patterns, microRNAs, and circulating tumor cells are identified as promising biomarkers with early detection possibilities and better diagnostic accuracy [[79], [80], [81]]. Small ncRNAs (sncRNAs) play critical roles in gene regulation, and a combination of different types of sncRNA indicates OC development with early diagnosis benefits [82]⁠. LncRNAs are considerably specific for each tumor origin, substantially stable in body fluids involving urine, whole blood, serum, and saliva, and easily detectable employing molecular techniques such as qRT-PCR, RNA-sequencing, and microarray hybridization [83]⁠.

Many studies have shown the dysregulation of ncRNAs in cancer. MicroRNAs target transcription factors to control the EMT process of tumor cells in different types of cancers [84]⁠. MiR-148b is upregulated in 92.21 % of OC samples and could be used as competent diagnostic biomarkers for early-stage detection [85]⁠. Distinct expression of plasma microRNAs can serve as highly sensitive and specific diagnostic markers for endometriosis-related OC [86]⁠. Significant upregulated VPS13C-has-circ-001567 is positively associated with OC stage, LNM, cell proliferation, and invasion while reducing apoptosis and E-cadherin levels [87]⁠. Overexpression of lncRNA CTBP1-AS2 and PTEN leads to a decreased proliferation of OC cells and miR-216a expression [88]⁠. Based on the study by Zhonghua Chen et al. [89]⁠, the mean serum level of miR-125b in EOC patients was significantly reduced compared to control groups. Decreased levels of miR-125b are beneficial diagnostic markers and are accompanied by a poor prognosis for EOC patients.

Several studies have investigated the roles of ncRNAs in OC. Circulating ncRNAs contribute to cell migration, invasion, metastasis, and recurrence of OC [90]⁠. MiR-146a and miR-150 stimulate cell survival and promote drug resistance [91]⁠. Higher expression levels of lncRNA SNHG3 in OC tissues are positively associated with LNM, clinical stages, poor prognosis, and higher increased levels of invasion-related protein CyclinD1, CDK1, MMP9, and MMP3 [92]⁠. LncRNA-MALAT1 negatively regulates miR-503-5p expression by increasing proliferation and decreasing OC cell apoptosis via the JAK2-STAT3 pathway [93]⁠. Overexpression of lncRNA AB073614 is significantly associated with tumor size, clinical stage, lymph node invasion, and shorter survival rate of EOC patients. Therefore, AB073614 can be a prognostic biomarker and a potential treatment target for EOC [94]⁠.

Among the different roles ncRNAs participate in, some ncRNAs play a critical role in EMT-related mechanisms. MiR-26b expression is attenuated in OC tissues. Therefore, the suppressive activity of miR-26b on its target molecule, ERα, would be diminished, which leads to enhancing EMT, invasion, and cell proliferation [95]⁠. Downregulation of miR-214-3p in HGSOC leads to upregulation of its target genes, including MUC16 and MMP7, in tumor tissue, resulting in its participation in the EMT process. MiR-214-3p expression is directly correlated with progesterone receptor protein and negatively correlated with CDK6 and MAPK1 [96]⁠. LncRNA-CCAT1 acts as an oncogene in OC by contributing to TGFβ1-induced EMT via the miR-490-3p/TGFβR1 axis [97]⁠. HOXD-AS1 is overexpressed in EOC and directly binds and targets miR-186-5p, resulting in upregulation of PIK3R3 and promoting EMT, cell invasion, and migration [98]⁠. Upregulation of NEAT1 in OC results in miR-1321 downregulation and TJP3 upregulation, thus promoting OC invasion and metastasis [99]⁠. The overexpression of lncRNA MEG3, DNM3OS, and MIAT in OC results in the regulation of the EMT-related gene pathways. Although upregulation of DNM3OS remarkably correlates with reduced OS for OC patients, MIAT or MEG3 levels lack correlation with survival [100]⁠.

There are numerous reviews about the different roles of ncRNAs in OC. Circulating ncRNAs contribute to cell migration, invasion, metastasis, and recurrence of OC [90]⁠. lncRNAs H19, XIST, and LSINCT are involved in the development of OC occurrence, cell growth and proliferation, invasion, and metastasis. These mentioned lncRNAs have diagnostic and prognostic values [101]⁠. CircRNAs are responsible for adjusting cell proliferation in OC, and their aberrant expression promotes the initiation and progression of this cancer [102]⁠. Abnormal expression of microRNAs takes part in OC initiation, proliferation, chemotherapy resistance, and survival [103]⁠. Investigating specific roles of ncRNAs, Luo et al. [104]⁠, Ning et al. [105]⁠, and Seyed Hosseini et al. [106]⁠ focused on the survival value of dysregulated lncRNAs. Ferreira et al. focused on microRNAs and their chemotherapy-related response, diagnosis, and prognosis [106]⁠ roles. Despite the existence of a review on the diagnostic and prognostic roles of circRNAs in OC, there were no systematic reviews and meta-analyses on this topic [107]⁠. Identifying both the prognostic and diagnostic roles of all existing studies on EMT-related ncRNAs in OC, we did not narrow our investigation to a specific ncRNA type (e.g., lncRNAs, microRNAs, circRNAs) or subtype. At the end of the eligibility surveying step, a total of 15 microRNAs, 24 lncRNAs, and 3 circRNAs were identified for assessing the correlation of their dysregulated expression and different characteristics, including clinical stages, TNM stages, LNM, and DM. To assess the DOR and effectiveness of the target ncRNAs in the diagnosis and prediction of OC, we initiated with univariate analysis, provided a panel of ncRNAs, and then performed a bivariate analysis.

Going beyond the primary univariate analysis, we defined an adjusted panel of ncRNAs for each of the LNM, TNM staging, DM, and clinical staging items, which are all related to the prognosis assessment of OC. Our panel for prognosis evaluation based on LNM consists of eight microRNAs, including miR-216a [42]⁠, miR-3064 [53]⁠, miR-532 [53]⁠, miR-18a [69]⁠, miR-489 [57]⁠, miR-126 [70]⁠, miR-99a [56]⁠, and miR-488 [44]⁠, sixteen lncRNAs, involving, CCAT1 [60]⁠, ADAMTS9-AS2 [72]⁠, FLVCR1-AS1 [55]⁠, TC0101441 [58]⁠, MAFG-AS1 [68]⁠, FAM83H-AS1 [65]⁠, HOTAIR [50]⁠, DSCR8 [59]⁠, DQ786243 [74]⁠, HOXD-AS1 [61]⁠, HOXB-AS3 [66]⁠, LINC-PINT [76]⁠, E2F4as [43]⁠, SNHG20 [75]⁠, LINC01969 [46]⁠, and LncARSR [54]⁠, and one circRNA called circ_100395 [67]⁠. After adjusting the ncRNA panel, the overall diagnostic and prognostic effectiveness elevated in both whole-panel and subgroup analysis of three differentiated types of ncRNAs. However, lncRNAs were identified as more robust predictors compared to microRNAs for LNM. Since circ_100395 was the only representative of circRNAs for LNM items, the subgroup analysis was not executed for this type of ncRNA and cannot separately be assumed as a proper prognostic indicator of OC. The ncRNAs panel for DM comprises six microRNAs, five lncRNAs, and two circRNAs. Meanwhile, the microRNAs subgroup, involving miR-489 [57]⁠, miR-18a [69]⁠, miR-126 [70]⁠, miR-26b [51]⁠, miR-219a-5p [63]⁠, and miR-196-5p [62]⁠ was a considerably valuable diagnostic factor compared to the lncRNAs subgroup, consisting FLVCR1-AS1 [55]⁠, DQ786243 [74]⁠, GAS5 [62]⁠, LncARSR [54]⁠, and MEG3 [63]⁠. Based on statistical outcomes, the circRNAs subgroup (circ_100395 [67]⁠ and CircAGFG1 [71]⁠) accounted for the second most effective predicting factors among two other types of ncRNAs. However, due to the limited number of this ncRNA type that inhibits precisely evaluating their effectiveness, this subgroup cannot be separately recognized as a valuable marker for our purpose. Furthermore, for a more comprehensive assessment of results accuracy, LR− and LR+ were calculated together. Totally, the current study discovered that the defined panel of ncRNAs, which are EMT-related ncRNAs, were significantly associated with both DM and LNM. According to the previous papers, the EMT process can take part in the metastasis and invasion of cancer [108,109]. With high enough overall sensitivity, specificity, and accuracy, our results demonstrated that the EMT-related ncRNA panels play significant roles as efficient predictors of DM and LNM in OC.

Recruiting the same approach to appraise the significance of ncRNAs for clinical stages led to defining a panel consist of twelve microRNAs (miR-126 [70]⁠, miR-216a [42]⁠, miR-18a [69]⁠, miR-99a [56]⁠, miR-489 [57]⁠, miR-506 [41]⁠, miR-26b [51]⁠, miR-219a-5p [63]⁠, miR-214 [73]⁠, miR-488 [44]⁠, miR-98-5p [59]⁠, and miR-196-5p [62]⁠), seventeen lncRNAs (CCAT1 [60]⁠, DNM3OS [45]⁠, ADAMTS9-AS2 [72]⁠, TC0101441 [58]⁠, GAS5 [62]⁠, FAM83H-AS1 [65]⁠, HOTAIR [50]⁠, HOXD-AS1 [61]⁠, PVT1 [73]⁠, E2F4as [43]⁠, MEG3 [63]⁠, DSCR8 [59]⁠, HOXB-AS3 [66]⁠, LINC-PINT [76]⁠, lncARSR [54]⁠, LINC01969 [46]⁠, and HCG18), and three circRNAs (circ_100395 [67]⁠, circ_0000745 [47]⁠, CircAGFG1 [71]⁠). Although the entire defined panel for clinical stages served as a significant predicting factor of OC, the lncRNAs subgroup displayed better prognostic effectiveness in comparison to the microRNAs and circRNAs subgroups. The defined panel for TNM item encompasses miR-126 [70]⁠, miR-18a [69]⁠, miR-489 [57]⁠, miR-616 [64]⁠, DQ786243 [74]⁠, and MAFG-AS1 [68]⁠. Four microRNAs included in this panel are more likely to predict the TNM stages in OC patients.

Along with the univariate analysis, the bivariate meta-analysis was performed to reduce the high percentage of heterogeneity shown in the univariate analysis, which was represented by the natural discrepancies between various studies. This type of analysis reduced data heterogeneity and data dispersion, resulting in a more symmetrical SROC curve and increasing the AUC of the SROC curve.

In addition to the mentioned analysis, a meta-analysis based on HR was implemented to assess the OS and prognostic effectiveness of the included ncRNAs. The total HR of high-expressing ncRNAs compared to low-expressing groups was not significant.

Based on the validation of significant ncRNAs in our meta-analysis using previously existing data in Pan-cancer, the acquired lncRNAs and microRNAs have differential expression in OC patients compared with healthy women. Therefore, these ncRNAs could be indicators of poor OS prognosis in OC patients.

Our study encompasses limitations. Some previous studies with similar objectives did not report the risk ratio of the effect of the ncRNAs in their evaluation of OC prognosis. Additionally, we could not gain access to the full text of some papers found relevant in title-abstract screening despite contacting their authors. Moreover, we had expected to reconstruct the survival data from K-M graphs using the “Guyot algorithm.” However, neither of the K-M curves of eligible articles reported the “number at risk” data, which is an indispensable element for retrieving HR and CI. Furthermore, initial OC diagnostic tests are not specific and sensitive enough, have particular limitations, and are ineffective in the early detection of OC. The obtained ncRNA panels from this study can predict ovarian cancer more specifically and sensitively. These ncRNAs can serve as therapeutic targets for specific clinical purposes. However, more studies are needed to discover a non-invasive panel for early detection based on serum or plasma.

5 Conclusion

In conclusion, this systematic review and meta-analysis provide gathered information on the prognostic implications of EMT-related ncRNAs in OCs of all types. Our results suggest that panels of ncRNAs could effectively predict factors related to the EMT process and prognosis of OC, including LNM, DM, clinical staging, and TNM staging.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

All data are available upon reasonable request.

Competing interests

The authors declare that they have no conflicts of interest to disclose regarding this study.

Funding

This study is supported by grant No. IR.TUMS.MEDICINE.REC.1401.023.

CRediT authorship contribution statement

Alireza Soltani Khaboushan: Writing – review & editing, Validation, Project administration, Methodology, Formal analysis, Data curation. Seyedeh Nazanin Salimian: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation. Saghar Mehraban: Writing – review & editing, Writing – original draft, Methodology. Afshin Bahramy: Visualization, Validation, Project administration, Methodology, Formal analysis. Narges Zafari: Writing – review & editing, Writing – original draft, Project administration, Methodology. Abdol-Mohammad Kajbafzadeh: Visualization, Validation, Resources. Joshua Johnson: Validation, Supervision, Investigation. Masoumeh Majidi Zolbin: Writing – review & editing, Visualization, Supervision, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

figs1 figs1

Acknowledgements

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e35202.
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References

1 Arora T. Mullangi S. Vadakekut E.S. Lekkala M.R. Epithelial ovarian cancer StatPearls 2024 StatPearls Publishing Treasure Island (FL) http://www.ncbi.nlm.nih.gov/books/NBK567760/
2 Webb P.M. Jordan S.J. Epidemiology of epithelial ovarian cancer Best Pract. Res. Clin. Obstet. Gynaecol. 41 2017 3 14 10.1016/j.bpobgyn.2016.08.006 27743768
3 Momenimovahed Z. Tiznobaik A. Taheri S. Salehiniya H. Ovarian cancer in the world: epidemiology and risk factors Int. J. Womens. Health 11 2019 287 299 10.2147/IJWH.S197604 31118829
4 Yeung T.-L. Leung C.S. Yip K.-P. Au Yeung C.L. Wong S.T.C. Mok S.C. Cellular and molecular processes in ovarian cancer metastasis. A review in the theme: cell and molecular processes in cancer metastasis Am. J. Physiol. Cell Physiol. 309 2015 C444 C456 10.1152/ajpcell.00188.2015 26224579
5 Roett M.A. Evans P. Ovarian cancer: an overview Am. Fam. Physician 80 2009 609 616
6 Mittal V. Epithelial mesenchymal transition in tumor metastasis Annu. Rev. Pathol. 13 2018 395 412 10.1146/annurev-pathol-020117-043854 29414248
7 Braga E.A. V Fridman M. Moscovtsev A.A. Filippova E.A. Dmitriev A.A. Kushlinskii N.E. Lncrnas in ovarian cancer progression, metastasis, and main pathways: cerna and alternative mechanisms Int. J. Mol. Sci. 21 2020 1 44 10.3390/ijms21228855
8 Lin X. Tang X. Zheng T. Qiu J. Hua K. Long non-coding RNA AOC4P suppresses epithelial ovarian cancer metastasis by regulating epithelial-mesenchymal transition J. Ovarian Res. 13 2020 45 10.1186/s13048-020-00644-5 32334623
9 Kuang D.F. Zhang X.P. Hua S.F. Dong W. Li Z.G. Long non-coding RNA TUG1 regulates ovarian cancer proliferation and metastasis via affecting epithelial-mesenchymal transition Exp. Mol. Pathol. 101 2016 267 273 10.1016/j.yexmp.2016.09.008 27693324
10 Morey C. Avner P. Employment opportunities for non-coding RNAs FEBS Lett. 567 2004 27 34 10.1016/j.febslet.2004.03.117 15165889
11 Panni S. Lovering R.C. Porras P. Orchard S. Non-coding RNA regulatory networks Biochim. Biophys. Acta. Gene Regul. Mech. 1863 2020 194417 10.1016/j.bbagrm.2019.194417
12 Meng Q.J. Wang N.N. Duan G.L. Long non-coding RNA XIST regulates ovarian cancer progression via modulating miR-335/BCL2L2 axis World J. Surg. Oncol. 19 2021 10.1186/s12957-021-02274-7
13 Wang K. Hu Y.-B. Zhao Y. Ye C. Long non-coding RNA ASAP1-IT1 suppresses ovarian cancer progression by regulating Hippo/YAP signaling Int. J. Mol. Med. 47 2021 44 10.3892/ijmm.2021.4877 33576454
14 Liu Y. Ding W. Wang J. Ao X. Xue J. Non-coding RNAs in lung cancer: molecular mechanisms and clinical applications Front. Oncol. 13 2023 1256537 10.3389/fonc.2023.1256537
15 Zhou X.H. Ao X. Jia Z.J. Li Y.W. Kuang S.X. Du C.C. Zhang J.Y. Wang J.X. Liu Y. Non-coding RNA in cancer drug resistance: underlying mechanisms and clinical applications Front. Oncol. 12 2022 10.3389/fonc.2022.951864
16 Tian J. Yang L. Wang Z. Yan H. MIR503HG impeded ovarian cancer progression by interacting with SPI1 and preventing TMEFF1 transcription Aging (Albany. NY) 14 2022 5390 5405 10.18632/aging.204147 35771155
17 Wang S. Wang Y. Lu J. Wang J. LncRNA LINC00665 promotes ovarian cancer cell proliferation and inhibits apoptosis via targeting miR-181a-5p/FHDC Appl. Biochem. Biotechnol. 194 2022 3819 3832 10.1007/s12010-022-03943-3 35524876
18 Liu S.J. Dang H.X. Lim D.A. Feng F.Y. Maher C.A. Long noncoding RNAs in cancer metastasis Nat. Rev. Cancer 21 2021 446 460 10.1038/s41568-021-00353-1 33953369
19 Munker R. Calin G.A. MicroRNA profiling in cancer Clin. Sci. 121 2011 141 158 10.1042/CS20110005
20 Chi Y. Wang D. Wang J. Yu W. Yang J. Long non-coding RNA in the pathogenesis of cancers Cells 8 2019 10.3390/cells8091015
21 Abba M.L. Patil N. Leupold J.H. Allgayer H. MicroRNA regulation of epithelial to mesenchymal transition J. Clin. Med. 5 2016 10.3390/jcm5010008
22 Muralidhar G.G. V Barbolina M. The miR-200 family: versatile players in epithelial ovarian cancer Int. J. Mol. Sci. 16 2015 16833 16847 10.3390/ijms160816833 26213923
23 Shi M. Mu Y.L. Zhang H. Liu M. Wan J.P. Qin X.Y. Li C.Z. MicroRNA-200 and microRNA-30 family as prognostic molecular signatures in ovarian cancer A meta-analysis, Medicine (Baltimore) 97 2018 10.1097/md.0000000000011505
24 Zhan L. Li J. Wei B. Long non-coding RNAs in ovarian cancer J. Exp. Clin. Cancer Res. 37 2018 10.1186/s13046-018-0793-4
25 Zhang H. Lu B. microRNAs as biomarkers of ovarian cancer Expert Rev. Anticancer Ther. 20 2020 373 385 10.1080/14737140.2020.1760095 32326768
26 Asri N. Fallah S. Rostami-Nejad M. Fallah Z. Khanlari-Kochaksaraei M. Jafari-Marandi S. Forouzesh F. Shahrokh S. Jahani-Sherafat S. Zali M.R. The role of mir-197-3p in regulating the tight junction permeability of celiac disease patients under gluten free diet Mol. Biol. Rep. 50 2023 2007 2014 10.1007/s11033-022-08147-w 36536183
27 Page M.J. McKenzie J.E. Bossuyt P.M. Boutron I. Hoffmann T.C. Mulrow C.D. Shamseer L. Tetzlaff J.M. Akl E.A. Brennan S.E. Chou R. Glanville J. Grimshaw J.M. Hróbjartsson A. Lalu M.M. Li T. Loder E.W. Mayo-Wilson E. McDonald S. McGuinness L.A. Stewart L.A. Thomas J. Tricco A.C. Welch V.A. Whiting P. Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews BMJ 372 2021 n71 10.1136/bmj.n71 33782057
28 Wells G. Shea B. O'Connell J. The newcastle-ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses Ottawa Heal. Res. Inst. Web Site 2019 http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
29 Whiting P.F. Rutjes A.W.S. Westwood M.E. Mallett S. Deeks J.J. Reitsma J.B. Leeflang M.M.G. Sterne J.A.C. Bossuyt P.M.M. Q.-2 Group QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies Ann. Intern. Med. 155 2011 529 536 10.7326/0003-4819-155-8-201110180-00009 22007046
30 Weber F. Knapp G. Ickstadt K. Kundt G. Glass Ä. Zero-cell corrections in random-effects meta-analyses Res. Synth. Methods 11 2020 913 919 10.1002/jrsm.1460 32991790
31 Reitsma J.B. Glas A.S. Rutjes A.W.S. Scholten R.J.P.M. Bossuyt P.M. Zwinderman A.H. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews J. Clin. Epidemiol. 58 2005 982 990 10.1016/j.jclinepi.2005.02.022 16168343
32 Noma H. Matsushima Y. Ishii R. Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies, Commun Stat. Case Stud. Data Anal. Appl. 7 2021 344 358 10.1080/23737484.2021.1894408
33 Shim S.R. Kim S.-J. Lee J. Diagnostic test accuracy: application and practice using R software Epidemiol. Health 41 2019 e2019007 10.4178/epih.e2019007
34 Holling H. Böhning W. Masoudi E. Böhning D. Sangnawakij P. Evaluation of a new version of I2 with emphasis on diagnostic problems Commun. Stat. Simulat. Comput. 49 2020 942 972 10.1080/03610918.2018.1489553
35 Doebler P. Holling H. Meta-analysis of diagnostic accuracy with mada https://cran.r-project.org/web/packages/mada/vignettes/mada.pdf 2015
36 Balduzzi S. Rücker G. Schwarzer G. How to perform a meta-analysis with R: a practical tutorial, Evid. Based. Ment Health 22 2019 153 160 10.1136/ebmental-2019-300117
37 Viechtbauer W. Conducting meta-analyses in R with the metafor package J. Stat. Software 36 2010 1 48 10.18637/jss.v036.i03
38 Harrer M. Cuijpers P. Ebert D. Doing meta-analysis in R 10.5281/zenodo.2551803 2019
39 Nagy Á. Munkácsy G. Győrffy B. Pancancer survival analysis of cancer hallmark genes Sci. Rep. 11 2021 6047 10.1038/s41598-021-84787-5 33723286
40 Lánczky A. Győrffy B. Web-based survival analysis tool tailored for medical research (KMplot): development and implementation J. Med. Internet Res. 23 2021 e27633 10.2196/27633
41 Sun Y. Hu L. Zheng H. Bagnoli M. Guo Y. Rupaimoole R. Rodriguez-Aguayo C. Lopez-Berestein G. Ji P. Chen K. Sood A.K. Mezzanzanica D. Liu J. Sun B. Zhang W. MiR-506 inhibits multiple targets in the epithelial-to- mesenchymal transition network and is associated with good prognosis in epithelial ovarian cancer J. Pathol. 235 2015 25 36 10.1002/path.4443 25230372
42 Liu H. Pan Y. Hhan X. Liu J. Li R. MicroRNA-216a promotes the metastasis and epithelial-mesenchymal transition of ovarian cancer by suppressing the PTEN/AKT pathway OncoTargets Ther. 10 2017 2701 2709 10.2147/OTT.S114318
43 Park S.A. Kim L.K. Kim Y.T. Heo T.H. Kim H.J. Long noncoding RNA E2F4as promotes progression and predicts patient prognosis in human ovarian cancer Cancers 12 2020 10.3390/cancers12123626
44 Guo J.Y. Wang X.Q. Sun L.F. MicroRNA-488 inhibits ovarian cancer cell metastasis through regulating CCNG1 and p53 expression Eur. Rev. Med. Pharmacol. Sci. 24 2020 2902 2910 32271408
45 He L. He G.L. DNM3OS facilitates ovarian cancer progression by regulating miR-193a-3p/MAP3K3 Axis Yonsei Med. J. 62 2021 535 544 10.3349/ymj.2021.62.6.535 34027641
46 Chen J. Li X. Yang L. Zhang J. Long non-coding RNA LINC01969 promotes ovarian cancer by regulating the miR-144-5p/LARP1 Axis as a competing endogenous RNA Front. Cell Dev. Biol. 8 2021 10.3389/fcell.2020.625730
47 Wang S. Li Z. Zhu G. Hong L. Hu C. Wang K. Cui K. Hao C. RNA-binding protein IGF2BP2 enhances circ_0000745 abundancy and promotes aggressiveness and stemness of ovarian cancer cells via the microRNA-3187-3p/ERBB4/PI3K/AKT axis J. Ovarian Res. 14 2021 10.1186/s13048-021-00917-7
48 Kim L.K. Park S.A. Yang Y. Kim Y.T. Heo T.H. Kim H.J. LncRNA SRA mediates cell migration, invasion, and progression of ovarian cancer via NOTCH signaling and epithelial-mesenchymal transition Biosci. Rep. 41 2021 10.1042/bsr20210565
49 Zhang F. Luo B.-H. Wu Q.-H. Li Q.-L. Yang K.-D. LncRNA HCG18 upregulates TRAF4/TRAF5 to facilitate proliferation, migration and EMT of epithelial ovarian cancer by targeting miR-29a/b Mol. Med. 28 2022 10.1186/s10020-021-00415-y
50 Qiu J.J. Lin Y.Y. Ye L.C. Ding J.X. Feng W.W. Jin H.Y. Zhang Y. Li Q. Hua K.Q. Overexpression of long non-coding RNA HOTAIR predicts poor patient prognosis and promotes tumor metastasis in epithelial ovarian cancer Gynecol. Oncol. 134 2014 121 128 10.1016/j.ygyno.2014.03.556 24662839
51 Lin J. Zhang L. Huang H. Huang Y. Huang L. Wang J. Huang S. He L. Zhou Y. Jia W. Yun J. Luo R. Zheng M. MiR-26b/KPNA2 axis inhibits epithelial ovarian carcinoma proliferation and metastasis through downregulating OCT4 Oncotarget 6 2015 23793 23806 10.18632/oncotarget.4363 26204489
52 Yong W. Yu D. Jun Z. Yachen D. Weiwei W. Midie X. Xingzhu J. Xiaohua W. Long noncoding RNA NEAT1, regulated by LIN28B, promotes cell proliferation and migration through sponging miR-506 in high-grade serous ovarian cancer Cell Death Dis. 9 2018 10.1038/s41419-018-0908-z
53 Bai L. Wang H. Wang A.H. Zhang L.Y. Bai J. MicroRNA-532 and microRNA-3064 inhibit cell proliferation and invasion by acting as direct regulators of human telomerase reverse transcriptase in ovarian cancer PLoS One 12 2017 10.1371/journal.pone.0173912
54 Shu C. Yang D.M. Mo Y.X. Gu J.S. Shah N. He J. Long noncoding RNA lncARSR promotes epithelial ovarian cancer cell proliferation and invasion by association with HuR and miR-200 family Am. J. Cancer Res. 8 2018 981 992 30034936
55 Yan H. Li H. Silva M.A. Guan Y. Yang L. Zhu L. Zhang Z. Li G. Ren C. LncRNA FLVCR1-AS1 mediates miR-513/YAP1 signaling to promote cell progression, migration, invasion and EMT process in ovarian cancer J. Exp. Clin. Cancer Res. 38 2019 10.1186/s13046-019-1356-z
56 Zhang L. Liu X.L. Yuan Z. Cui J. Zhang H. MiR-99a suppressed cell proliferation and invasion by directly targeting HOXA1 through regulation of the AKT/mTOR signaling pathway and EMT in ovarian cancer Eur. Rev. Med. Pharmacol. Sci. 23 2019 4663 4672 10.26355/eurrev_201906_18046 31210292
57 Jiang H.W. Li L. Jiang P. Wang Y.F. MicroRNA-489 targets XIAP to inhibit the biological progression of ovarian cancer via regulating PI3K/Akt signaling pathway and epithelial-to-mesenchymal transition Eur. Rev. Med. Pharmacol. Sci. 24 2020 4113 4122 10.26355/eurrev_202004_20990 32373947
58 Qiu J.J. Lin X.J. Tang X.Y. Zheng T.T. Zhang X.Y. Hua K.Q. Long noncoding RNA TC0101441 induces epithelial–mesenchymal transition in epithelial ovarian cancer metastasis by downregulating KiSS1 Int. J. Cancer 146 2020 2588 2598 10.1002/ijc.32692 31577838
59 Dong L. Cao X. Luo Y. Zhang G. Zhang D. A positive feedback loop of lncRNA DSCR8/miR-98-5p/STAT3/HIF-1α plays a role in the progression of ovarian cancer Front. Oncol. 10 2020 10.3389/fonc.2020.01713
60 Cao Y. Shi H. Ren F. Jia Y. Zhang R. Long non-coding RNA CCAT1 promotes metastasis and poor prognosis in epithelial ovarian cancer Exp. Cell Res. 359 2017 185 194 10.1016/j.yexcr.2017.07.030 28754469
61 Zhang Y. Dun Y. Zhou S. Huang X.H. LncRNA HOXD-AS1 promotes epithelial ovarian cancer cells proliferation and invasion by targeting miR-133a-3p and activating Wnt/β-catenin signaling pathway Biomed. Pharmacother. 96 2017 1216 1221 10.1016/j.biopha.2017.11.096 29239819
62 Zhao H. Yu H. Zheng J. Ning N. Tang F. Yang Y. Wang Y. Lowly-expressed lncRNA GAS5 facilitates progression of ovarian cancer through targeting miR-196-5p and thereby regulating HOXA5 Gynecol. Oncol. 151 2018 345 355 10.1016/j.ygyno.2018.08.032 30201235
63 Wang L. Yu M.X. Zhao S.S. lncRNA MEG3 modified epithelial-mesenchymal transition of ovarian cancer cells by sponging miR-219a-5p and regulating EGFR J. Cell. Biochem. 120 2019 17709 17722 10.1002/jcb.29037 31161607
64 Chen Z. Zhu J. Zhu Y. Wang J. MicroRNA-616 promotes the progression of ovarian cancer by targeting TIMP2 Oncol. Rep. 39 2018 2960 2968 10.3892/or.2018.6368 29658596
65 Dou Q. Xu Y. Zhu Y. Hu Y. Yan Y. Yan H. LncRNA FAM83H-AS1 contributes to the radioresistance, proliferation, and metastasis in ovarian cancer through stabilizing HuR protein Eur. J. Pharmacol. 852 2019 134 141 10.1016/j.ejphar.2019.03.002 30831080
66 Zhuang X.H. Liu Y. Li J.L. Overexpression of long noncoding RNA HOXB-AS3 indicates an unfavorable prognosis and promotes tumorigenesis in epithelial ovarian cancer via Wnt/β-catenin signaling pathway Biosci. Rep. 39 2019 10.1042/BSR20190906
67 Li X. Lin S.H. Mo Z.F. Jiang J.X. Tang H.F. Wu C.L. Song J. CircRNA_100395 inhibits cell proliferation and metastasis in ovarian cancer via regulating miR-1228/p53/epithelial-mesenchymal transition (EMT) axis (vol 11, pg 599, 2020) J. Cancer 15 2024 1731 1732 10.7150/jca.93299 38370366
68 Bai Y. Ren C. Wang B. Xue J. Li F. Liu J. Yang L. LncRNA MAFG-AS1 promotes the malignant phenotype of ovarian cancer by upregulating NFKB1-dependent IGF1 Cancer Gene Ther. 2021 10.1038/s41417-021-00306-8
69 Zhao Y. Liu X.L. Huang J.H. Yin A.J. Zhang H. MicroRNA-18a suppresses ovarian carcinoma progression by targeting CBX7 and regulating ERK/MAPK signaling pathway and epithelial-to-mesenchymal transition Eur. Rev. Med. Pharmacol. Sci. 24 2020 5292 5302 10.26355/eurrev_202005_21311 32495862
70 Zhang Y. Qin X. Jiang J. Zhao W. MicroRNA-126 exerts antitumor functions in ovarian cancer by targeting EGFL7 and affecting epithelial-to-mesenchymal transition and ERK/MAPK signaling pathway Oncol. Lett. 20 2020 1327 1335 10.3892/ol.2020.11687 32724375
71 Luo J. Zhong H. Guo M. Xiao P. Cao R. Zhao M. Jing Y. CircAGFG1 promotes ovarian cancer progression through the miR-409-3 p/ZEB1 Axis Technol. Cancer Res. Treat. 23 2024 10.1177/15330338241252423
72 Wang A. Jin C. Li H. Qin Q. Li L. LncRNA ADAMTS9-AS2 regulates ovarian cancer progression by targeting miR-182-5p/FOXF2 signaling pathway Int. J. Biol. Macromol. 120 2018 1705 1713 10.1016/j.ijbiomac.2018.09.179 30268751
73 Chen Y. Du H. Bao L.W. Liu W.X. LncRNA PVT1 promotes ovarian cancer progression by silencing miR-214 Cancer Biol. Med. 15 2018 238 250 10.20892/j.issn.2095-3941.2017.0174 30197791
74 Yan H. Silva M.A. Li H. Zhu L. Li P. Li X. Wang X. Gao J. Wang P. Zhang Z. Long noncoding RNA DQ786243 interacts with miR-506 and promotes progression of ovarian cancer through targeting cAMP responsive element binding protein 1 J. Cell. Biochem. 119 2018 9764 9780 10.1002/jcb.27295 30105876
75 Wang D. Dai J. Hou S. Qian Y. LncRNA SNHG20 predicts a poor prognosis and promotes cell progression in epithelial ovarian cancer Biosci. Rep. 29 2019 10.1042/BSR20182186
76 Hao T. Huang S. Han F. LINC-PINT suppresses tumour cell proliferation, migration and invasion through targeting miR-374a-5p in ovarian cancer Cell Biochem. Funct. 38 2020 1089 1099 10.1002/cbf.3565 32638404
77 Xu J. Zhang P. Sun H. Liu Y. LINC01094/miR-577 axis regulates the progression of ovarian cancer J. Ovarian Res. 13 2020 10.1186/s13048-020-00721-9
78 Wang F. Yang Q. Retraction: long non-coding RNA LINC01089 enhances the development of gastric cancer by sponging miR-145-5p to mediate SOX9 expression OncoTargets Ther. 13 9213–9224 2020 10.2147/OTT.S249392 10.2147/OTT.S249392), Onco. Targets. Ther. 17 (2024) 35
79 Matsas A. Stefanoudakis D. Troupis T. Kontzoglou K. Eleftheriades M. Christopoulos P. Panoskaltsis T. Stamoula E. Iliopoulos D.C. Tumor markers and their diagnostic significance in ovarian cancer Life 13 2023 10.3390/life13081689
80 Riedel M. Bronger H. Magdolen V. Dreyer T. The prognostic and diagnostic potential of kallikrein-related peptidases in ovarian cancer Expert Rev. Mol. Diagn. 21 2021 535 545 10.1080/14737159.2021.1924680 33947310
81 Zhang R. Siu M.K.Y. Ngan H.Y.S. Chan K.K.L. Molecular biomarkers for the early detection of ovarian cancer Int. J. Mol. Sci. 23 2022 10.3390/ijms231912041
82 Beg A. Parveen R. Fouad H. Yahia M.E. Hassanein A.S. Role of different non-coding RNAs as ovarian cancer biomarkers J. Ovarian Res. 15 2022 10.1186/s13048-022-01002-3
83 Oncul S. Amero P. Rodriguez-Aguayo C. Calin G.A. Sood A.K. Lopez-Berestein G. Long non-coding RNAs in ovarian cancer: expression profile and functional spectrum RNA Biol. 17 2020 1523 1534 10.1080/15476286.2019.1702283 31847695
84 Feng J. Hu S. Liu K. Sun G. Zhang Y. The role of MicroRNA in the regulation of tumor epithelial–mesenchymal transition Cells 11 2022 10.3390/cells11131981
85 Chang H. Zhou X. Wang Z.-N. Song Y.-X. Zhao F. Gao P. Chiang Y. Xu H.-M. Increased expression of miR-148b in ovarian carcinoma and its clinical significance Mol. Med. Rep. 5 2012 1277 1280 10.3892/mmr.2012.794 22344713
86 Suryawanshi S. Vlad A.M. Lin H.-M. Mantia-Smaldone G. Laskey R. Lee M. Lin Y. Donnellan N. Klein-Patel M. Lee T. Mansuria S. Elishaev E. Budiu R. Edwards R.P. Huang X. Plasma microRNAs as novel biomarkers for endometriosis and endometriosis-associated ovarian cancer Clin. Cancer Res. an Off. J. Am. Assoc. Cancer Res. 19 2013 1213 1224 10.1158/1078-0432.CCR-12-2726
87 Bao L. Zhong J. Pang L. Upregulation of circular RNA VPS13C-has-circ-001567 promotes ovarian cancer cell proliferation and invasion Cancer Biother. Radiopharm. 34 2019 110 118 10.1089/cbr.2018.2641 30376358
88 Cui K.Y. Zhu G.H. LncRNA CTBP1-AS2 regulates miR-216a/PTEN to suppress ovarian cancer cell proliferation J. Ovarian Res. 13 2020 10.1186/s13048-020-00689-6
89 Chen Z.H. Guo X.L. Sun S.K. Lu C.X. Wang L.M. Serum miR-125b levels associated with epithelial ovarian cancer (EOC) development and treatment responses Bioengineered 11 2020 311 317 10.1080/21655979.2020.1736755 32107956
90 Schwarzenbach H. Gahan P.B. Circulating non-coding RNAs in recurrent and metastatic ovarian cancer Cancer Drug Resist 2 2019 399 418 10.20517/cdr.2019.51 35582568
91 Vang S. Wu H.T. Fischer A. Miller D.H. MacLaughlan S. Douglass E. Steinhoff M. Collins C. Smith P.J.S. Brard L. Brodsky A.S. Identification of ovarian cancer metastatic miRNAs PLoS One 8 2013 10.1371/journal.pone.0058226
92 Hong L. Chen W. Wu D. Wang Y. Upregulation of SNHG3 expression associated with poor prognosis and enhances malignant progression of ovarian cancer Cancer Biomarkers 22 2018 367 374 10.3233/CBM-170710 29758922
93 Sun Q. Li Q. Xie F.F. LncRNA-MALAT1 regulates proliferation and apoptosis of ovarian cancer cells by targeting miR-503-5p OncoTargets Ther. 12 2019 6297 6307 10.2147/ott.S214689
94 Zeng S.T. Liu S.K. Feng J. Gao J.F. Xue F.X. Upregulation of lncRNA AB073614 functions as a predictor of epithelial ovarian cancer prognosis and promotes tumor growth in vitro and in vivo Cancer Biomarkers 24 2019 421 428 10.3233/cbm-182160 30909184
95 He G.N. Liu X.M. Liu L.Y. microRNA-26b inhibits growth and cellular invasion of ovarian cancer cells by targeting estrogen receptor alpha 3 BIOTECH 12 2022 10.1007/s13205-022-03222-2
96 V Timofeeva A. Fedorov I.S. V Asaturova A. V Sannikova M. V Tregubova A. Mayboroda O.A. Khabas G.N. Frankevich V.E. Sukhikh G.T. Blood plasma small non-coding RNAs as diagnostic molecules for the progesterone-receptor-negative phenotype of serous ovarian tumors Int. J. Mol. Sci. 24 2023 10.3390/ijms241512214
97 Mu Y. Li N. Cui Y.L. The lncRNA CCAT1 upregulates TGFβR1 via sponging miR-490-3p to promote TGFβ1-induced EMT of ovarian cancer cells Cancer Cell Int. 18 2018 10.1186/s12935-018-0604-1
98 Dong S.S. Wang R.R. Wang H. Ding Q. Zhou X. Wang J. Zhang K.Q. Long Y. Lu S. Hong T. Ren H.Y. Wong K. Sheng X.W. Wang Y. Zeng Y. HOXD-AS1 promotes the epithelial to mesenchymal transition of ovarian cancer cells by regulating miR-186-5p and PIK3R3 J. Exp. Clin. Cancer Res. 38 2019 10.1186/s13046-019-1103-5
99 Luo M. Zhang L. Yang H. Luo K. Qing C. Long non-coding RNA NEAT1 promotes ovarian cancer cell invasion and migration by interacting with miR-1321 and regulating tight junction protein 3 expression Mol. Med. Rep. 22 2020 3429 3439 10.3892/mmr.2020.11428 32945443
100 Mitra R. Chen X. Greenawalt E.J. Maulik U. Jiang W. Zhao Z. Eischen C.M. Decoding critical long non-coding RNA in ovarian cancer epithelial-to-mesenchymal transition Nat. Commun. 8 2017 1604 10.1038/s41467-017-01781-0 29150601
101 Ren C. Li X. Wang T. Wang G. Zhao C. Liang T. Zhu Y. Li M. Yang C. Zhao Y. Zhang G.M. Functions and mechanisms of long noncoding RNAs in ovarian cancer Int. J. Gynecol. Cancer 25 2015 566 569 10.1097/IGC.0000000000000413 25756403
102 Shabaninejad Z. Vafadar A. Movahedpour A. Ghasemi Y. Namdar A. Fathizadeh H. Pourhanifeh M.H. Savardashtaki A. Mirzaei H. Circular RNAs in cancer: new insights into functions and implications in ovarian cancer J. Ovarian Res. 12 2019 10.1186/s13048-019-0558-5
103 Alshamrani A.A. Roles of microRNAs in ovarian cancer tumorigenesis: two decades later, what have we learned? Front. Oncol. 10 2020 10.3389/fonc.2020.01084
104 Luo P. Liu X.F. Wang Y.C. Li N.D. Liao S.J. Yu M.X. Liang C.Z. Tu J.C. Prognostic value of abnormally expressed lncRNAs in ovarian carcinoma: a systematic review and meta-analysis Oncotarget 8 2017 23927 23936 10.18632/oncotarget.14760 28118613
105 Ning L. Hu Y.C. Wang S. Lang J.H. Altered long noncoding RNAs and survival outcomes in ovarian cancer: a systematic review and meta-analysis (PRISMA Compliant) Medicine (Baltim.) 97 2018 10.1097/md.0000000000011481
106 Seyed Hosseini E. Alizadeh Zarei M. Haddad Kashani H. Milajerdi A. Zare Dehghanani Z. Hassani Bafrani H. Nikzad H. The role of altered long noncoding RNAs in overall survival of ovarian cancer: a systematic review and meta-analysis Pathol. Res. Pract. 219 2021 153363 10.1016/j.prp.2021.153363
107 Sheng R. Li X. Wang Z. Wang X. Circular RNAs and their emerging roles as diagnostic and prognostic biomarkers in ovarian cancer Cancer Lett. 473 2020 139 147 10.1016/j.canlet.2019.12.043 31904484
108 Iwatsuki M. Mimori K. Yokobori T. Ishi H. Beppu T. Nakamori S. Baba H. Mori M. Epithelial–mesenchymal transition in cancer development and its clinical significance Cancer Sci. 101 2010 293 299 10.1111/j.1349-7006.2009.01419.x 19961486
109 Thiery J.P. Epithelial–mesenchymal transitions in tumour progression Nat. Rev. Cancer 2 2002 442 454 10.1038/nrc822 12189386
