F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.179155.1 Systematic Review Articles Diagnostic Performance of Micro-RNAs as Biomarkers for Endometrial Cancer: A Systematic Review and Meta-Analysis

[version 1; peer review: 1 approved with reservations, 1 not approved]

 Putra Andi Darma Conceptualization Data Curation Formal Analysis Investigation Methodology Project Administration Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-5286-5346 a 1 2 Rahman Aldi Tamara Conceptualization Formal Analysis Investigation Methodology Software Writing – Original Draft Preparation Writing – Review & Editing 2 3 Syariatin Lasmini Conceptualization Data Curation Formal Analysis Investigation Methodology Software Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-1235-5461 2 3 Darmawan Naufal Syafiq Conceptualization Formal Analysis Investigation Writing – Original Draft Preparation Writing – Review & Editing 2 3 Cuandra Kevin Nathaniel Conceptualization Formal Analysis Investigation Methodology Software Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0007-7785-7474 4 Hamka Muhammad Farid Formal Analysis Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0006-4978-8080 5 Setiya Daivan Febri Juan Data Curation Formal Analysis Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0000-2528-8459 6 Argani Aqilah Nurahmah Data Curation Formal Analysis Validation Writing – Original Draft Preparation Writing – Review & Editing 7 Aaliyya Zaki Sidqi Aaliyya Data Curation Formal Analysis Investigation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0006-1537-2175 8 Widi Vina Sari Nugrahaning Data Curation Investigation Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0008-0443-3723 5 Rachmi Siti Nurluthfia Data Curation Investigation Validation Writing – Original Draft Preparation Writing – Review & Editing 9 Huang Phelia Klarissa Data Curation Investigation Writing – Original Draft Preparation Writing – Review & Editing 5 Silalahi Sheryl Pricilla Daniela Elizabeth Data Curation Investigation Writing – Review & Editing https://orcid.org/0009-0004-2239-7069 10 Wahyudi Dhyani Paramita Investigation Writing – Review & Editing https://orcid.org/0009-0002-6534-0164 7 Firjatullah Muhammd Fariz Investigation Writing – Review & Editing https://orcid.org/0009-0004-6374-4506 5 Amri Dhiya Ayuni Amri Investigation Writing – Review & Editing 6 Triasmaja Ilhan Chandra Writing – Review & Editing 5 Tristan Christopher Daniel Writing – Review & Editing https://orcid.org/0009-0009-4105-8739 5 Division of Gynecology Oncology, Department of Obstetrics Gynecology, Dr Cipto Mangunkusumo Hospital, Central Jakarta, Indonesia Dopamine Science Institute, Depok, Indonesia Ovarian, Tubal, and Peritoneal Malignancy Research Unit, Department of Obstetrics and Gynecology, Dr Cipto Mangunkusumo Hospital, Central Jakarta, Jakarta, Indonesia Faculty of Medicine, Universitas Andalas, Padang, West Sumatra, Indonesia Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Central Java, Indonesia Faculty of Medicine, Universitas Islam Indonesia, Sleman, Special Region of Yogyakarta, Indonesia Faculty of Medicine, Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Special Capital Region of Jakarta, Indonesia Faculty of Medicine, Universitas Jenderal Soedirman, Purwokerto, Central Java, Indonesia Faculty of Medicine, Universitas Brawijaya, Malang, East Java, Indonesia Faculty of Medicine, Universitas Cenderawasih, Jayapura, Papua, Indonesia andi.darma@ui.ac.id

No competing interests were disclosed.

 16 4 2026 2026 15 530 26 3 2026 Copyright: © 2026 Putra AD et al. 2026 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

Endometrial cancer (EC) represents one of the most common gynecologic malignancies in the world. Despite advancements in imaging and histopathology, the early detection of EC remains suboptimal. MicroRNAs (miRNAs), known for their stability in biofluids and regulatory role in tumor biology, have emerged as promising non-invasive biomarkers. This meta-analysis evaluates the diagnostic performance of miRNAs in EC.

 Methods

A systematic review and meta-analysis were conducted following PRISMA guidelines. Searches were performed in major databases up to August 2025. Studies reporting the sensitivity and specificity of miRNA-based diagnostics for EC were included. Pooled diagnostic performance, such as sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio (DOR), and area under the ROC curve (AUC), was calculated using a bivariate random-effects model. Subgroup analyses and sensitivity tests were conducted to explore heterogeneity and assess robustness.

 Results

Fifty-two studies from 12 articles involving 1,098 EC patients and 957 controls were analyzed. The pooled sensitivity and specificity were 0.86 (95% CI: 0.82–0.89) and 0.81 (95% CI: 0.75–0.85), respectively. The DOR was 20.9, and the AUC was 0.91, indicating high overall diagnostic accuracy. Subgroup analyses revealed superior performance for miRNA panels and serum-based assays. Sensitivity analyses confirmed the model’s stability. No significant publication bias was detected.

 Conclusions

Circulating miRNAs demonstrate high diagnostic accuracy for EC and may serve as effective non-invasive tools for early detection. Further prospective studies with standardized protocols are essential for clinical translation.

 endometrial cancer microRNA biomarker meta-analysis diagnostic. The author(s) declared that no grants were involved in supporting this work. 1. Introduction

Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide, with a steadily increasing incidence and disease-related mortality. 1 In 2020, the global incidence of EC reached 417,336 cases, making it the sixth most commonly diagnosed cancer among women, with the majority of cases occurring between 65 and 75 years of age. 2, 3 Most ECs are generally associated with a favorable prognosis due to early clinical presentation, most commonly abnormal uterine bleeding. 4, 5 Nevertheless, a substantial proportion of patients are still diagnosed at advanced stages, where survival outcomes remain poor despite advances in surgical and adjuvant treatments. 5

Currently, the diagnosis of EC relies on transvaginal ultrasound followed by endometrial biopsy, an invasive procedure that is often associated with patient discomfort and technical limitations. 6, 7 High rates of inadequate sampling and procedural failure have been reported, particularly in postmenopausal women, limiting diagnostic reliability. 8, 9 As a result, there is a growing clinical need for non-invasive, reliable, and patient-friendly biomarkers that could support or refine existing diagnostic pathways. Such biomarkers may facilitate earlier detection and reduce unnecessary invasive procedures.

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression at the post-transcriptional level and play a central role in numerous cellular processes, including proliferation, apoptosis, differentiation, and angiogenesis. 10 Dysregulation of miRNA expression has been widely implicated in carcinogenesis, where miRNAs may function as oncogenes or tumor suppressors depending on their target genes. 11 Importantly, miRNAs exhibit remarkable stability in various biological specimens, including serum, plasma, tissue, and extracellular vesicles, making them attractive candidates for minimally invasive cancer biomarkers. 12

In recent years, multiple studies have explored the diagnostic potential of miRNAs in EC, reporting differential expression patterns in both tissue-based and circulating samples. 5 Several miRNAs have been proposed as promising diagnostic markers, either individually or in combination panels. However, the reported diagnostic performance varies substantially across studies, likely due to differences in study design, specimen types, selected miRNAs, analytical methods, and control populations. 5, 13 Consequently, the overall diagnostic value of miRNAs in EC remains uncertain, and conclusions drawn from individual studies are often inconsistent.

Given these limitations, a comprehensive synthesis of the available evidence is needed. Therefore, this meta-analysis was conducted to systematically evaluate the diagnostic performance of miRNA-based biomarkers for detecting endometrial cancer. This study aims to provide a clearer and more robust assessment of the clinical utility of miRNAs as diagnostic biomarkers for EC and to guide future research in this field.

 2. Methods 2.1 Eligibillity criteria and PICOS strategy

This systematic review and diagnostic meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. 14 The research question was formulated using the PICOS framework, as summarized in Table 1. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under protocol number CRD420261280267.

PICOS framework.
PICOS Element Description
Participants Women diagnosed with endometrial cancer (including endometrioid and mixed histological subtypes) and non-cancer controls, comprising healthy individuals and patients with benign endometrial or gynecological conditions.
Index Test Detection of single miRNAs or miRNA-based panels measured in tissue, serum, plasma, peripheral blood, or serum-derived exosomes.
Comparator Histopathological confirmation of endometrial cancer obtained from biopsy or surgical specimens is considered the diagnostic gold standard.
Outcomes Diagnostic performance metrics, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve (AUC).
Study Design Observational diagnostic performance studies, including case–control, cross-sectional, and cohort studies.

Articles were included if they met the following criteria: (1) investigated the diagnostic role of microRNAs (miRNAs) in human subjects; (2) employed an observational study design suitable for diagnostic performance evaluation, such as case–control, cross-sectional, or cohort studies; and (3) explicitly identified endometrial cancer or endometrial carcinoma as the target condition in the title or abstract. Studies were excluded if they: (1) were not published in English; (2) were reviews, editorials, conference abstracts, or case reports; or (3) provided incomplete, insufficient, or non-extractable diagnostic data.

 2.2 Information sources

Using predefined selection criteria (eligibility criteria), a pair of blinded reviewers reviewed titles, abstracts, and complete articles retrieved from PubMed/MEDLINE, ScienceDirect, Embase, and Web of Science databases to detect eligible studies. To further enhance search comprehensiveness, a snowballing approach was used to manually screen the cited references of qualifying studies and associated review articles. The ultimate literature search was completed in August 2025.

 2.3 Database search procedure

A comprehensive literature search was conducted for research reports available through August 2025, using a blend of standardized subject headings and keywords. The following keywords were applied across databases:

Search: (“microRNA” OR “miRNA” OR “miR”)

Search: (“endometrial cancer” OR “endometrial carcinoma”)

Search: (“diagnosis” OR “diagnostic”)

Search: (“sensitivity” OR “specificity”)

Final search: #1 AND #2 AND #3 AND #4

2.4 Study of screening procedure

Two investigators separately reviewed titles and summaries to assess study inclusion. Complete articles were then assessed in detail by the same reviewers. Consensus meetings resolved any disagreements during the screening phase, and when agreement was not achieved, an additional senior reviewer was consulted to provide an independent assessment.

 2.5 Data extraction process and data elements

Data were independently extracted in duplicate by two reviewers employing a prestructured extraction form derived from previous diagnostic meta-analytic studies. Discrepancies were settled via discussion with a third independent reviewer. All collected data were double-checked for accuracy before analysis.

The following data items were collected: (1) First author; (2) year the article was published; (3) country where the study was conducted; (4) research design; (5) evaluated miRNA(s); (6) direction of miRNA expression; (7) number of endometrial cancer cases and controls; (8) type of specimen; and (9) diagnostic accuracy metrics, including sensitivity, specificity, and AUC as a global accuracy index.

 2.6 Study potential bias for quality evaluation

A pair of reviewers independently evaluated the methodological rigor and potential bias of every selected study by means of the QUADAS-2 instrument. This instrument evaluates bias risk and relevance across four areas: selection of patients, test under evaluation, reference standard, progression, and timing of testing. 15 Disagreements were resolved through mutual agreement or discussion with an independent third reviewer.

 2.7 Measures of effect and analytical approach

A diagnostic meta-analysis was conducted using Stata/BE version 18.0. Pooled estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and log diagnostic odds ratio (logDOR) with 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. When not directly reported, true positive, false positive, false negative, and true negative values were reconstructed from available sensitivity and specificity data. Forest plots and summary receiver operating characteristic (SROC) curves were generated to illustrate overall diagnostic performance, including the area under the curve (AUC).

Threshold effects were assessed by examining the correlation between logit-transformed sensitivity and specificity using Spearman’s rank correlation. A significant positive correlation (p < 0.05) indicated a potential threshold effect, in which case diagnostic accuracy was summarized using the SROC curve rather than pooled sensitivity and specificity estimates.

Heterogeneity was evaluated using Cochran’s Q and the I 2 statistic, with I 2 > 50% indicating substantial heterogeneity. Subgroup analyses were performed based on endometrial cancer subtype (EEC-only vs. mixed EC), specimen type (serum, plasma, tissue), biomarker type (single miRNA vs. panel), expression pattern (upregulated vs. downregulated), and control type (healthy vs. mixed controls).

Sensitivity analyses were conducted using deviance residuals, Cook’s distance, and standardized residuals to identify influential studies. These studies were sequentially excluded to assess the stability of pooled estimates. Publication bias was assessed using Deeks’ funnel plot, with p > 0.05 indicating no significant bias. Clinical applicability was further evaluated using Fagan’s nomogram to estimate post-test probabilities. 16

3. Results 3.1 Studies characteristics

A total of 12 eligible articles, comprising 52 studies, were included in the final analysis, encompassing a combined population of 1,098 endometrial cancer cases and 957 controls ( Figure 1, Table 2). 17– 28 The articles were published between 2012 and 2023 and were conducted across multiple regions, including China, South Korea, Italy, Poland, Serbia, and several European countries. Most articles employed a case–control design, with two articles using a multiphase case–control approach and one retrospective cohort study. The majority of cases involved endometrial carcinoma, with several articles specifically focusing on endometrioid endometrial cancer (EEC) or early-stage disease. Control groups consisted of healthy women, benign gynecological conditions, or a combination of both. Regarding biological specimens, miRNA expression was evaluated using serum, plasma, peripheral blood, serum-derived exosomes, and formalin-fixed paraffin-embedded (FFPE) tissue. Several articles have assessed circulating miRNAs, while others have investigated tissue-based miRNA expression, and one study has included both plasma and tissue samples. Overall, the included articles represented diverse populations, specimen types, and study designs, providing a broad basis for the pooled diagnostic performance and subgroup analyses.

PRISMA diagram illustrating study screening and inclusion. Data extraction results from included studies.
Author, Year Country Study design Number of cases Number of controls Disease Control Samples
Wang et al., 2014 17 China Retrospective case–control 40 EEC 30 healthy +23 benign endometrial disease Endometrial endometrioid adenocarcinoma Healthy women, endometrial polyps, atypical hyperplasia Plasma
Lee et al., 2012 18 South Korea Retrospective tissue-based diagnostic study 22 EC 53 (CAH, SH, normal) Endometrial carcinoma Complex atypical hyperplasia, simple hyperplasia, normal endometrium FFPE tissue
Montagnana et al., 2017 19 Italy Prospective case–control 46 EC 28 healthy Endometrial carcinoma Healthy women Serum
Torres et al., 2012 20 Poland/Italy Case–control, multi-cohort 77 EEC 45 controls Endometrioid endometrial carcinoma Benign gynecologic disease, healthy women Plasma + tissue
Donkers et al., 2021 21 UK/Germany/Netherlands Retrospective cohort 36 EC 40 benign hysterectomies Endometrial cancer Benign gynecological disease FFPE tissue
Zheng et al., 2019 22 China Case–control 100 EC 100 healthy Endometrial carcinoma Healthy women Serum-derived exosomes
Blagojević et al., 2023 23 Serbia Case–control 40 early-stage EC 16 normal endometrium Early-stage endometrial cancer Endometrial hyperplasia without atypia Tissue
Jia et al., 2013 24 China Case–control 26 EC 22 healthy Endometrioid endometrial cancer Healthy women Serum
Jiang et al., 2016 25 China Case–control 73 EC 73 healthy Endometrial cancer Healthy women Serum
Wang et al., 2018 26 China Case–control 356 EC 304 (benign + healthy) Endometrial cancer Benign endometrial disease, healthy Peripheral blood
Fan et al., 2021b 27 China Multiphase case–control 93 EC 79 healthy Endometrial cancer Healthy women Plasma
Fan et al., 2021a 28 China Multiphase case–control 92 EC 102 healthy Endometrial cancer Healthy women Serum
3.2 Bias risk evaluation

Overall, the potential bias was considered acceptable across articles, although variability was observed among specific domains ( Table 3). The patient selection domain was the primary source of potential bias, as several articles employed retrospective case–control designs or non-consecutive sampling, increasing the risk of bias in this area for a proportion of articles. Conversely, the index testing domain showed a consistently low risk of bias, as all articles applied standardized, validated miRNA detection methods with clearly defined thresholds. Similarly, the reference-standard domain was deemed low-risk across all included articles, as endometrial cancer was consistently confirmed by histopathological analysis. The risk of systematic error in the flow and timing domain was mostly minimal to uncertain, largely attributable to incomplete reporting of the interval between the index test and the reference standard or sparse information on excluded patients. From an applicability perspective, concerns were largely minimal across all domains, indicating that the included studies were appropriate for addressing the review question on the ability of miRNA-derived biomarkers to aid in the diagnosis of endometrial cancer. On the whole, the QUADAS-2 assessment suggests that the findings of this meta-analysis. In general, the findings are based on published articles with acceptable methodological rigor, with patient selection as the main potential source of bias.

QUADAS-2 assessment results of the included articles.
Study Patient selection Index test Reference standard Flow & timing Overall risk of bias Applicability concerns
Wang et al., 2014 17 High Low Low Unclear Moderate–High Low
Lee et al., 2012 18 High Low Low Low Moderate Low
Montagnana et al., 2017 19 Low Low Low Low Low Low
Torres et al., 2012 20 High Low Low Unclear Moderate–High Low
Donkers et al., 2021 21 Low Low Low Low Low Low
Zheng et al., 2019 22 Low Low Low Low Low Low
Blagojević et al., 2023 23 High Low Low Low Moderate Low
Jia et al., 2013 24 High Low Low Unclear Moderate–High Low
Jiang et al., 2016 25 Low Low Low Low Low Low
Wang et al., 2018 26 High Low Low Unclear Moderate–High Low
Fan et al., 2021b 27 Low Low Low Low Low Low
Fan et al., 2021a 28 Low Low Low Low Low Low
3.3 Overall diagnostic performance

The chi-square statistic and I 2 measure were implemented to quantify heterogeneity across studies. The results indicated substantial heterogeneity in the pooled sensitivity (I 2 = 77.30%) and pooled specificity (I 2 = 81.99%). Therefore, the overall estimates were derived using a random-effects model diagnostic performance calculation. The compile sensitivity of miRNA-based biomarkers for the diagnosis of endometrial cancer was 86% (95% CI: 0.82–0.89), while the aggregated specificity estimate was 0.81 (95% CI: 0.75–0.85) ( Figure 2). The combined summary positive likelihood ratio was 7.97 (95% CI: 5.83–10.11), NLR was 0.41 (95% CI: 0.30–0.52) ( Figure 3), and logDOR was 3.04 (95% CI: 2.66–3.42) ( Figure 4a and 4b). The SROC curve analysis demonstrated an AUC of 0.91 (95% CI: 0.10–1.00) ( Figure 5).

Forest plot of pooled sensitivity and specificity. Diagnostic performance of miRNAs based on negative likelihood ratio (NLR). Diagnostic performance of miRNAs based on PLR (a) and logDOR (b). Summary ROC (SROC) curve of the included studies. 3.4 Sensitivity analysis and publication bias

Influence analysis using Cook’s distance identified six studies (studies 4, 11, 15, 26, 33, and 43) as potentially influential, while outlier detection based on standardized residuals revealed five studies (studies 4, 11, 15, 33, and 43) outside the reference limits ( Figure 6). After removing outlier studies, the meta-analytic summary of sensitivity and specificity were 85% (95% CI: 0.81–0.89) and 78% (95% CI: 0.73–0.82), respectively, although substantial heterogeneity persisted for both estimates (I 2 = 74.93% and 76.43%). The pooled NLR was 0.41 (95% CI: 0.29–9.53; I 2 = 0.0%, p = 0.961), and the pooled PLR was 6.84 (95% CI: 5.60–8.90; I 2 = 99.3%, p < 0.001). The AUC was 0.79 (95% CI: 0.76–0.82), and the meta-analytic log diagnostic odds estimate was 2.88 (95% CI: 2.58–3.18), both indicating marked between-study heterogeneity. According to Deeks’ funnel ( Figure 7), asymmetry analysis did not reveal convincing evidence of publication bias (p = 0.20).

Diagnostic sensitivity analyses: (a) Model goodness of fit was inspected through deviance residuals, (b) the assumption of bivariate normality was tested using Mahalanobis distance, (c) influence analysis relied on Cook’s distance, and (d) standardized residuals served to flag potential outliers. Deeks’ funnel plot used for the evaluation of publication bias. 3.5 Clinical utility analysis

The practical utility of miRNA-based biomarkers was examined using Fagan nomograms, which were used to derive post-test probabilities at initial risk levels of 25%, 50%, and 75% ( Figure 8). When the prespecified pre-test probability was 25%, a positive miRNA value increased the estimated probability of endometrial cancer to 60%, while a negative result decreased it to 5%. Given an initial (pre-test) probability of 50%, a positive test yielded a post-test probability of 82%, whereas a negative test reduced this probability to 15%. At the higher pre-test risk level of 75%, a positive result value raised the post-test probability to 93%, whereas a negative result reduced it to 34%.

Fagan nomograms corresponding to varying pre-test probabilities: (a) 25%, (b) 50%, and (c) 75%. 3.5 Subgroup analysis

Subgroup analyses showed consistent diagnostic performance of miRNA-based biomarkers across disease subtypes, specimen types, biomarker configurations, expression directions, and control definitions ( Table 4). In the subgroup limited to EEC cases, the pooled sensitivity was 0.88 (95% CI 0.84–0.91), and the reported aggregated specificity was 0.76 (95% CI 0.68–0.83), yielding an AUC of 0.79 (95% CI 0.75–0.84). Studies including mixed histologies demonstrated comparable estimates, with a pooled sensitivity of 0.85 (95% CI: 0.78–0.90), a specificity of 0.84 (95% CI: 0.76–0.89), and an AUC of 0.80 (95% CI: 0.76–0.84). For endometrial cancer subtype, studies restricted to EEC-only reported a summary sensitivity estimate of 0.88 (95% CI: 0.84–0.91) and specificity of 0.76 (95% CI: 0.68–0.83), with an AUC of 0.79 (95% CI: 0.75–0.84). In the subgroup limited to EEC cases, the pooled sensitivity was 0.88 (95% CI: 0.84–0.91) and the pooled specificity was 0.76 (95% CI: 0.68–0.83), yielding an AUC of 0.79 (95% CI: 0.75–0.84).

Subgroup analyses of miRNA-based studies in the diagnosis of endometrial cancer.
Subgroup (n) Sensitivity (95% CI) Specificity (95% CI) NLR (95% CI), I 2 PLR (95% CI), I 2 AUC (95% CI), I 2 LogDOR (95% CI), I 2
EC subtype
EEC-only (22) 0.88 (0.84–0.91) 0.76 (0.68–0.83) 0.37 (0.21–0.54), 0.0% 6.89 (6.25–8.53), 99.3% 0.79 (0.75–0.84), 92.0% 2.86 (2.48–3.24), 90.5%
Mixed EC (30) 0.85 (0.78–0.90) 0.84 (0.76–0.89) 0.42 (0.24–0.61), 29.1% 8.74 (4.88–12.59), 99.9% 0.80 (0.76–0.84), 92.2% 3.13 (2.42–3.84), 96.2%
Specimen type
Plasma (5) 0.71 (0.63–0.78) 0.81 (0.67–0.89) 0.42 (0.15–0.68), 0.0% 4.94 (2.20–7.67), 98.8% 0.79 (0.73–0.86), 74.7% 2.30 (1.50–3.10), 93.9%
Serum (4) 0.91 (0.84–0.95) 0.92 (0.62–0.99) 0.14 (0.28–0.55), 0.0% 8.11 (0.88–15.34), 99.5% 0.87 (0.81–0.94), 77.7% 3.80 (2.93–4.68), 43.1%
Tissue (24) 0.82 (0.72–0.89) 0.80 (0.70–0.87) 0.49 (0.26–0.71), 32.8% 6.01 (4.55–7.47), 98.7% 0.78 (0.75–0.82), 71.5% 2.63 (2.30–2.96), 64.9%
Biomarker type
Panel miRNA (11) 0.90 (0.84–0.94) 0.93 (0.87–0.97) 0.31 (0.07–0.56), 0.0% 12.21 (7.15–17.27), 99.6% 0.89 (0.85–0.94), 88.5% 4.38 (2.69–6.07), 95.9%
Single miRNA (41) 0.85 (0.80–0.89) 0.75 (0.70–0.81) 0.44 (0.31–0.56), 3.4% 7.04 (4.67–9.40), 99.8% 0.77 (0.73–0.81), 92.8% 2.78 (2.39–3.17), 94.0%
Expression direction
Upregulated (47) 0.85 (0.80–0.88) 0.79 (0.73–0.83) 0.44 (0.32–0.55), 0.0% 6.58 (5.38–7.78), 99.3% 0.78 (0.76–0.80), 84.7% 2.82 (2.52–3.13), 90.5%
Downregulated (2) 0.93 (0.91–0.95) 0.93 (0.90–0.96) 0.26 (0.21–0.74), 25.8% 30.32 (21.96–82.61), 100% 0.85 (0.59–1.10), 97.4% 4.51 (0.57–9.59), 99.7%
Control type
Healthy controls (6) 0.88 (0.78–0.94) 0.91 (0.70–0.98) 0.36 (0.08–0.64), 0.0% 11.48 (3.57–19.39), 99.8% 0.85 (0.76–0.93), 93.5% 3.80 (1.78–5.83), 97.5%
Mixed controls (46) 0.86 (0.81–0.89) 0.79 (0.73–0.84) 0.42 (0.31–0.54), 0.0% 7.58 (5.31–9.85), 99.8% 0.79 (0.75–0.82), 92.4% 2.95 (2.57–3.33), 93.5%
Sensitivity analysis
Outlier excluded (47) 0.85 (0.81–0.89) 0.78 (0.73–0.82) 0.41 (0.29–0.53), 0.0% 6.84 (5.60–8.90), 99.3% 0.79 (0.76–0.82), 84.7% 2.88 (2.58–3.18), 90.7%

By specimen type, serum-based assays exihibited greater combined sensitivity and specificity (0.91 [95% CI: 0.84–0.95] and 0.92 [95% CI: 0.62–0.99]) than plasma-based assays (sensitivity 0.71 [95% CI: 0.63–0.78]; specificity 0.81 [95% CI: 0.67–0.89]), whereas tissue-based investigations showed a pooled sensitivity of 0.82 (95% CI 0.72–0.89) and a pooled specificity of 0.80 (95% CI 0.70–0.87).

When the data were stratified according to biomarker type, miRNA panels showed superior diagnostic performance compared with individual miRNA assays, with pooled sensitivity and specificity of 0.90 (95% CI 0.84–0.94) and 0.93 (95% CI 0.87–0.97), respectively, versus 0.85 (95% CI 0.80–0.89) and 0.75 (95% CI 0.70–0.81). When stratified by expression direction, downregulated miRNAs showed higher pooled sensitivity and specificity than upregulated miRNAs. Studies using healthy controls reported higher specificity than those with mixed control populations. Marked heterogeneity was observed across most subgroups, particularly for positive indices based on likelihood ratios and diagnostic odds ratios.

 4. Discussion

Cancer is a complex pathological condition involving genetic, molecular, and environmental factors. Therefore, achieving a precise understanding of endometrial cancer remains challenging due to its multifaceted biological mechanisms. Among the crucial regulatory components are microRNAs (miRNAs), small single-stranded RNA molecules that control various biological processes, including cell division, angiogenesis, migration, apoptosis, and oncogenesis. 29, 30 Recently, miRNAs have been explored as additional prognostic markers alongside conventional diagnostic methods such as histopathology and ultrasonography, which are often invasive and operator-dependent. This meta-analysis demonstrates that circulating miRNAs possess strong diagnostic performance for endometrial cancer, with a pooled sensitivity of 86%, specificity of 81%, and an area under the curve (AUC) of 0.91. These results align with findings from recent studies reporting pooled sensitivity and specificity of 0.84 and 0.87, respectively, with an AUC of 0.91. 31 Collectively, the present findings strengthen growing evidence that miRNAs could complement or triage traditional diagnostic evaluations, potentially reducing unnecessary biopsies. Nevertheless, as highlighted by Donkers et al., although miRNA panels show promising potential as diagnostic biomarkers in endometrial cancer, high-quality data remain limited. 5

The biological justification for miRNA-based diagnostics is well established. MicroRNAs are pivotal in modulating essential tumor-associated activities, including cellular proliferation, migration, and invasion; epithelial-mesenchymal transition; angiogenesis; and immune evasion. 32 In cancer, miRNAs are actively secreted by tumor cells, often packaged within exosomes or other extracellular vesicles, thereby safeguarding them from enzymatic degradation and facilitating steady circulation in peripheral blood. Extracellular vesicles serve as repositories of tumor-derived miRNAs, facilitating the systemic identification of molecular changes originating from the tumor microenvironment. 33 Thus, changes in circulating blood miRNA profiles may indicate early carcinogenic occurrences within the endometrial environment. The finding that both upregulated and downregulated miRNAs enhance diagnostic efficacy is biologically credible, as miRNAs can act as oncogenes or tumor suppressors based on their targets and cellular environment. 32, 33 These mechanisms collectively establish a robust biological basis for the diagnostic value of circulating miRNAs in endometrial carcinogenesis and the modulation of the tumor microenvironment.

Importantly, systematic patterns were identified through subgroup analyses. Panels comprising multiple miRNAs have consistently outperformed single-miRNA assays, as reflected in higher sensitivity, specificity, and overall diagnostic accuracy. For instance, a six-miRNA panel reported by Lee et al. 18 achieved an area under the curve of 0.961, with sensitivity and specificity of approximately 92% for distinguishing endometrial cancer from benign tissue. These findings indicate that combining multiple miRNAs more effectively captures the disease’s molecular heterogeneity. In addition, circulating serum-based assays generally demonstrated superior diagnostic performance compared with plasma- or tissue-based analyses, which may be attributable to the greater stability of miRNAs in serum or to larger effective sample volumes. 12 Notably, when analyses were limited to endometrioid (type I) endometrial cancer, diagnostic accuracy remained comparable to that observed in studies including mixed histological subtypes. This suggests that core miRNA signatures are shared across endometrial cancer subtypes. 5, 31 Such observations are consistent with previous reports indicating that several dysregulated miRNAs, including members of the miR-200 family, are commonly involved in both type I and type II tumors. Collectively, these findings support the notion that miRNA biomarkers reflect fundamental aspects of endometrial tumor biology rather than subtype-specific features.

Clinically, these findings have important implications. MiRNA-based assays could serve as triage tools for women with postmenopausal or unexplained bleeding, enabling low-risk patients to avoid invasive biopsy. Fagan nomogram analysis indicates that the observed likelihood ratios would meaningfully shift post-test probabilities across different pre-test risk levels. In practice, miRNA testing could complement imaging and histopathology, supporting earlier detection with reduced patient burden. Beyond diagnosis, circulating miRNAs may also aid in risk stratification and surveillance, as certain profiles correlate with tumor aggressiveness or recurrence. 34 In addition, integration of miRNA panels with molecular classifications of endometrial cancer, such as TCGA subgroups, may facilitate more personalized diagnostic and management strategies.

Despite their promise, the current evidence base has important limitations. Substantial heterogeneity was observed across studies, largely driven by methodological diversity and small sample sizes. Differences in analytical platforms, specimen types, and normalization strategies contributed to inconsistent findings, with some miRNAs showing opposing expression patterns across studies. Most included studies employed retrospective case–control designs with limited sample sizes, a design that may overestimate diagnostic accuracy by excluding diagnostically challenging cases. In addition, the available evidence is geographically concentrated in Europe and Asia, limiting generalizability to other populations. Overall, the quality of evidence remains modest, and firm conclusions cannot yet be drawn.

 5. Conclusion

In conclusion, circulating miRNAs represent promising non-invasive biomarkers for the diagnosis of endometrial cancer, with potential to enhance early detection and clinical decision-making. However, methodological heterogeneity and limited study quality temper confidence in their current clinical applicability. Future research should focus on large, prospective, multi-center studies with standardized protocols and integration of miRNA profiling with molecular classifiers to facilitate translation into routine clinical practice.

 Data availability Underlying data

Zenodo: Diagnostic Performance of Micro-RNAs as Biomarkers for Endometrial Cancer: A Systematic Review and Meta-Analysis. Doi: https://doi.org/10.5281/zenodo.19108950. 35

This project contains the following underlying data:

PRISMA Flowchart.png

PRISMA_2020_checklist.pdf

Downregulated.xlsx

EC Meta.xlsx

EEC.xlsx

Healthy control.xlsx

Mixed control.xlsx

Mixed EC.xlsx

outlier.xlsx

Panel.xlsx

Plasma.xlsx

Serum.xlsx

Single.xlsx

Tissue.xlsx

Upregulated.xlsx

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Reporting guidelines

Zenodo: Diagnostic Performance of Micro-RNAs as Biomarkers for Endometrial Cancer: A Systematic Review and Meta-Analysis. Doi: https://doi.org/10.5281/zenodo.19108950. 35

The Project contains the following reporting guidelines:

PRISMA Flowchart.png

PRISMA_2020_checklist.pdf

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Acknowledgement

The authors express their gratitude to Dr. Prestasi Research Institute and PT Dopamine Medica Indonesia for their assistance in preparing and providing facilities for processing this article.

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Cancer Biomark. 2021;31:127–138. 33896823 10.3233/CBM-200972 Smolarz B Durczyński A Romanowicz H : miRNAs in Cancer (Review of Literature). Int. J. Mol. Sci. 2022;23:2805. 35269947 10.3390/IJMS23052805 PMC8910953 Soykan Y Yar Saglam AS Inan MA : Diagnostic and prognostic potential of microRNA profiles in endometrioid endometrial cancer. Sci. Rep. 2025;15(1):34697. 10.1038/s41598-025-16125-y Gao J Fan YZ Gao SS : Circulating microRNAs as Potential Biomarkers for the Diagnosis of Endometrial Cancer: a Meta-Analysis. Reprod. Sci. 2023;30:464–472. 35764858 10.1007/S43032-022-01019-5 Bogaczyk A Zawlik I Zuzak T : The Role of miRNAs in the Development, Proliferation, and Progression of Endometrial Cancer. Int. J. Mol. Sci. 2023;24. 10.3390/IJMS241411489 Baxter E Nair S West Z : Extracellular vesicles as biomarkers for endometrial cancer – A systematic review. Transl. Oncol. 2025;62:102543. 40987065 10.1016/J.TRANON.2025.102543 PMC12493225 Putra AD Darmawan NS Rahman AT : Minimal Residual Disease Detection: Bridging Molecular and Clinical Strategies for Recurrence Prevention in Gynecologic Cancers. Int. J. Mol. Sci. 2025;26. 10.3390/IJMS262311708 Putra AD : Diagnostic performance of microRNAs as biomarkers for endometrial cancer: a systematic review and meta-analysis.[dataset]. Zenodo. 2026. 10.5281/zenodo.19108950 10.5256/f1000research.197636.r488113 Reviewer response for version 1 Melekoglu Rauf 1 Referee https://orcid.org/0000-0001-7113-6691 Inonu University, Malatya, Turkey

Competing interests: No competing interests were disclosed.

 8 6 2026 Copyright: © 2026 Melekoglu R 2026 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

This systematic review and meta-analysis addresses a clinically relevant question regarding the diagnostic utility of microRNAs as biomarkers for endometrial cancer. The topic is timely and potentially important, particularly given the ongoing search for minimally invasive biomarkers that may improve diagnostic pathways in women undergoing evaluation for endometrial malignancy. The authors have synthesized a substantial body of literature and performed several subgroup analyses. However, several methodological and interpretative concerns currently limit confidence in the pooled estimates and in the strength of the conclusions. In particular, issues related to dataset independence, specimen heterogeneity, and the clinical interpretation of the findings should be addressed before the manuscript can be considered for publication.

1. The most important methodological concern relates to the unit of analysis used in the meta-analysis. The manuscript includes 52 datasets derived from only 12 articles, suggesting that multiple miRNA-specific estimates originating from the same patient cohorts may have been treated as independent observations. If this is the case, the assumption of independence underlying the pooled estimates may have been violated, potentially leading to overly precise summary measures and inflated statistical confidence. The authors should clearly explain how datasets were constructed and consider performing a sensitivity analysis restricted to one estimate per independent cohort to evaluate the robustness of the findings.

2. The interpretation of the results is complicated by the pooling of fundamentally different specimen types. Throughout the manuscript, considerable emphasis is placed on the potential role of miRNAs as non-invasive biomarkers. However, the overall analyses combine circulating specimens, such as serum, plasma, blood, and exosomal samples, with tissue-based specimens obtained from surgical or pathological samples. Because tissue-derived biomarkers do not represent a non-invasive diagnostic approach, the combined pooled estimates should not be used to support conclusions regarding non-invasive testing. The distinction between circulating and tissue-based biomarkers should be emphasized more consistently throughout the manuscript, and separate interpretation of these groups would substantially strengthen the clinical relevance of the study.

3. The conclusions regarding early detection appear stronger than the available evidence supports. Most of the included studies seem to have used retrospective or case-control designs comparing established endometrial cancer cases with controls. Such studies are appropriate for evaluating diagnostic discrimination but cannot determine screening performance or demonstrate an ability to detect disease at an earlier stage. Accordingly, statements suggesting a role in early detection should be moderated, and the findings should instead be presented as evidence supporting the potential diagnostic value of miRNAs that requires validation in prospective clinical settings.

4. There appears to be an inconsistency between the databases described in the Methods section and those reported in the PRISMA flow diagram. This discrepancy should be clarified to ensure transparency and reproducibility of the literature search process. The authors should carefully review both sections and provide a consistent description of the databases searched and the study selection process.

5. Several reported statistical results require verification. In particular, the reported AUC confidence interval of 0.10–1.00 appears unusually wide and difficult to interpret, while the confidence interval reported for the negative likelihood ratio following outlier exclusion also seems inconsistent with the corresponding point estimate. In addition, some subgroup totals do not appear to reconcile with the overall number of included datasets. A comprehensive review of the numerical results would improve confidence in the reported analyses.

6. The Methods section states that threshold effects were evaluated using Spearman correlation analysis between logit-transformed sensitivity and specificity; however, the corresponding results are not reported. Because threshold effects can be an important source of heterogeneity in diagnostic meta-analyses, the correlation coefficient, p-value, and interpretation of these findings should be included in the Results section.

7. Considerable heterogeneity was observed across several pooled analyses, including both sensitivity and specificity estimates. Although subgroup analyses were performed, substantial between-study variability remains. Given the diversity of specimen types, study designs, biomarkers, analytical platforms, and control populations, the pooled estimates should be interpreted cautiously. This limitation deserves greater emphasis in the Discussion and should be reflected more clearly in the overall conclusions.

8. The description of the QUADAS-2 methodology should be revised for accuracy. The currently reported domains do not correspond to the standard QUADAS-2 framework, which consists of patient selection, index test, reference standard, and flow and timing. Correct terminology should be used throughout the manuscript.

9. The composition of the control populations warrants further discussion. Several included studies appear to have used healthy controls, whereas others included women with benign gynecological conditions. Because diagnostic performance estimates can vary substantially depending on the characteristics of the comparison group, the potential impact of this heterogeneity should be discussed more explicitly. Results derived from highly selected case-control populations may overestimate diagnostic performance compared with real-world clinical settings.

10. Finally, the search strategy requires further clarification. Although the manuscript refers to standardized subject headings, the reported search terms appear to rely predominantly on free-text keywords. The authors should specify whether MeSH and Emtree terms were incorporated into the database-specific search strategies and provide complete reproducible search strings for each database.

Overall, the manuscript addresses an important and clinically relevant topic. Nevertheless, the concerns outlined above, particularly those related to the independence of included datasets and the pooling of tissue-based and circulating biomarkers, have important implications for the validity and interpretation of the pooled estimates. These issues are unlikely to be resolved through additional discussion alone and may require further analytical clarification. I therefore believe that substantial revision is necessary before the manuscript can be considered for approval.

Are the rationale for, and objectives of, the Systematic Review clearly stated?

Yes

Is the statistical analysis and its interpretation appropriate?

Partly

If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.)

No

Are sufficient details of the methods and analysis provided to allow replication by others?

Yes

Are the conclusions drawn adequately supported by the results presented in the review?

Partly

Reviewer Expertise:

Gynecology and Obstetrics, Biotechnology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

 10.5256/f1000research.197636.r479449 Reviewer response for version 1 Bayram Ergul 1 Referee https://orcid.org/0000-0003-1708-3036 Nigde Omer Halisdemir University Research and Training Hospital, Nigde, Turkey

Competing interests: No competing interests were disclosed.

 2 6 2026 Copyright: © 2026 Bayram E 2026 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

Summary of the article

This systematic review and diagnostic meta-analysis evaluates the diagnostic performance of microRNAs as biomarkers for endometrial cancer. The authors report 12 eligible articles comprising 52 diagnostic study units, with pooled sensitivity of 0.86, specificity of 0.81, PLR of 7.97, NLR of 0.41, logDOR of 3.04, and SROC AUC of 0.91. They also perform QUADAS-2 quality assessment, sensitivity analysis, Deeks’ funnel plot, Fagan nomogram analysis, and subgroup analyses by EC subtype, specimen type, biomarker type, expression direction, and control type.

The topic is clinically relevant, and the manuscript addresses an important need for non-invasive diagnostic biomarkers in endometrial cancer. However, several methodological, statistical, and presentation issues need to be corrected before the conclusions can be considered scientifically robust.

Dear Authors;

The revisions are as follow;

The methods state that PubMed/MEDLINE, ScienceDirect, Embase, and Web of Science were searched, but the PRISMA diagram lists PubMed, Google Scholar, and ScienceDirect. Embase and Web of Science do not appear in the PRISMA diagram, while Google Scholar appears in the diagram but is not listed consistently in the methods. This inconsistency should be corrected.

The authors should provide database-specific search strings, search dates, filters, language restrictions, and exact search syntax. The PRISMA diagram also reports 6,023 records removed by “automation tools,” but the tool, criteria, and validation process are not described. This is essential for reproducibility.

The manuscript reports pooled sensitivity of 0.86 and specificity of 0.81, but also reports PLR = 7.97 and NLR = 0.41. These values do not align with the post-test probabilities shown in the Fagan nomograms. For example, the Fagan results for 25%, 50%, and 75% pre-test probabilities appear closer to a PLR of about 4.5 and an NLR of about 0.17, not to PLR 7.97 and NLR 0.41. This is a critical issue. The authors should re-check the extracted 2×2 data, the model outputs, and the calculations used for PLR, NLR, DOR, and Fagan nomograms.

The SROC AUC is reported as 0.91 with 95% CI 0.10–1.00, which is suspicious and inconsistent with the claim of high diagnostic accuracy. Table 4 also contains an AUC confidence interval exceeding 1.00 for the downregulated miRNA subgroup: 0.85, 95% CI 0.59–1.10. AUC values cannot exceed 1.00. There is also a clear inconsistency in the sensitivity analysis: the text reports NLR = 0.41 with 95% CI 0.29–9.53, whereas Table 4 appears to show 0.29–0.53. This likely represents a typographical or calculation error and should be corrected.

The methods state that threshold effects were assessed using Spearman correlation between logit sensitivity and specificity. However, the results do not report the correlation coefficient or p value. Since diagnostic accuracy may vary substantially by miRNA, specimen type, assay platform, and cut-off, this result is important. The authors should report the threshold-effect analysis and explain whether pooled sensitivity/specificity estimates remain appropriate.

The manuscript reports substantial heterogeneity for sensitivity and specificity and persistent heterogeneity after outlier exclusion. Despite this, the discussion repeatedly emphasizes strong diagnostic performance and potential clinical utility. The authors should temper the conclusions. The evidence supports “promising but not yet clinically validated” miRNA biomarkers rather than direct clinical implementation. Statements suggesting that miRNA testing could reduce biopsies should be presented as a future possibility, not as a current clinical implication.

The QUADAS-2 table summarizes domains but does not provide signaling-question justifications. The statement that index testing was consistently low risk because all studies used “standardized, validated miRNA detection methods with clearly defined thresholds” is not sufficiently supported. Different studies likely used different platforms, sample processing protocols, normalization strategies, and cut-offs. The authors should provide a supplementary QUADAS-2 table with domain-level judgments and reasons for each judgment. Patient selection bias should be emphasized more strongly, especially because many included studies are retrospective case-control studies.

There are several language and formatting issues: “Eligibillity” should be “Eligibility”; “exihibited” should be “exhibited”; “compile sensitivity” should be “pooled sensitivity”; and section numbering repeats “3.5” for both clinical utility and subgroup analysis. Some sentences are grammatically incomplete, for example: “On the whole, the QUADAS-2 assessment suggests that the findings of this meta-analysis.”

Are the rationale for, and objectives of, the Systematic Review clearly stated?

Yes

Is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.

If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.)

Not applicable

Are sufficient details of the methods and analysis provided to allow replication by others?

Partly

Are the conclusions drawn adequately supported by the results presented in the review?

Partly

Reviewer Expertise:

Cancer biology. Complications of diabetes.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.