1. Introduction
Despite improvements in diagnosis and treatment, cancer is still burdened disease globally with the increased new cases and deaths over the years [ 1 , 2 ]. Annual screening and earlier detection are crucial strategies that help to reduce cancer incidence and mortality [ 3 – 7 ]. Moreover, early detection of cancers leads to the use of less-aggressive interventions that improve patients' quality of life. Many tools have been used frequently in the surveillance of cancers as low-dose computed tomography, mammography, endoscopy, ultrasound, and serum protein markers such as carbohydrate antigen 125, 15-3, 19-9, CYFRA 21-1, carcinoembryonic antigen, squamous cell carcinoma antigen, alpha-fetoprotein, and prostate-specific antigen. Nevertheless, just a few tests have been well-accepted due to their disadvantages of expensive, invasiveness, discomfort, poor sensitivity, specificity, and a certain false-positive and false-negative rate [ 3 , 7 – 9 ].
In recent years, liquid biopsy materials, including microRNAs (miR-21, miR-155, miR-486, etc.) in the blood and body fluids, have been attracted and extensively studied as potential biomarkers for cancer diagnosis and prognosis [ 10 ]. These are endogenous small noncoding RNAs (19-22 nt) dysregulated in cancer cells. After production, they regulate the translation of target mRNAs or can be released into circulation, then communicate and affect distant cells and tissues, leading to condition changes of tumorigenesis, angiogenesis, invasion, migration, and metastasis [ 10 ]. Among microRNAs, miR-1246 plays as an oncogenic molecule that modulates the expression of multiple genes and pathways in various cancers [ 11 ]. Previous studies presented an elevated level of miR-1246 in the blood of cancer patients compared to healthy individuals exploring its diagnostic role [ 12 ]. However, divergent conclusions on diagnostic accuracy have been shown due to the small sample size and limited cancer types [ 12 , 13 ]. We aim to systematically assess the performance of extracellular circulating miR-1246 in cancer screening on a larger sample.
2. Materials and Methods
This meta-analysis was conducted according to the guideline of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 14 ].
2.1. Database Searching and Selection of Study
We searched electronic databases of PubMed/MEDLINE, Web of Science, Cochrane Library, and Google Scholar for relevant studies up to 28 November 2022. The keywords used in searching were “miR-1246,” “miR1246,” “miRNA-1246,” “miRNA1246,” “microRNA-1246,” and “microRNA1246.” Also, we reviewed citation reports of potential studies to find additional articles. After searching, all relevant studies were saved as an EndNote list. By removing duplicates (2772 records), 5690 remained for later evaluations ( Figure 1 ). Subsequently, only 41 articles progressed to the detailed assessment step after screening titles and abstracts. Four reduplicated studies, seven with unavailable data, and one included patients on radiotherapy were excluded. Finally, 29 studies were included in this meta-analysis.
2.2. Quality Assessment and Data Extraction
The quality of included studies was assessed by three independent researchers using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool regarding the risk of bias and applicability ( Figure 2 ) [ 15 ]. For each signaling question, “yes,” “no,” or “unclear” are phrased answers corresponding to the “low,” “high,” or “unclear” risk of bias and applicability concerns. When all signaling questions of a domain are answered “yes,” the risk of bias was judged low. If any answer “no” exists, the risk of bias was judged high. Domains were marked unclear risk of bias if any “unclear” exist without the “no” answer. In case of no consensus on judgments, three evaluators discussed in detail and determined the final decision.
Data extracted from articles include author names and country, year of publication, cancer, and control type, sample type, sample size, techniques used in experiments, and the AUC value in diagnosis. Besides, the true-positive, false-positive, true-negative, and false-negative numbers were extracted directly from articles or calculated indirectly using sensitivity and specificity corresponding to the maximum Youden's J index extracted from the receiving operating characteristic curve.
2.3. Statistical Analysis
We used the random-effects model to estimate pooled DOR, sensitivity, specificity, positive likelihood ratios, and negative likelihood ratios of circulating miR-1246 in cancer screening. Also, we constructed SROC curves and calculated summary AUC values, then compared them between groups using the bootstrap test ( B = 2000 resampling iterations). The heterogeneity of diagnostic test accuracy between studies was measured by Higgins and Thompson's I 2 -statistic, which is significant if I 2 ≥ 50%. Subsequently, the Leave-One-Out analysis was used to detect outlier studies, while meta-regression was performed to explore heterogeneity sources. Moreover, we used the funnel plot asymmetry statistic and the skewness of the standardized deviates to assess publication bias. All data analyses were done with the guidance of Shim et al., Noma et al., and Harrer et al. [ 16 – 18 ], using R statistical software v.4.2.2 (R foundation, 1020 Vienna, Austria) and packages meta, mada, metafor, dmetar, dmetatools, and altmeta. P < 0.05 was considered statistically significant.
3. Results
3.1. Study Characteristics
Among 29 included studies [ 19 – 47 ], seven studies demonstrated the diagnostic performance of circulating miR-1246 in breast cancer [ 21 , 24 , 26 , 29 , 41 , 42 , 44 ], while four studies showed data for colorectal cancer [ 20 , 33 , 37 , 46 ], four others for hepatocellular carcinoma [ 23 , 31 , 32 , 43 ], and three for esophageal squamous cell carcinoma or pancreatic cancer [ 19 , 27 , 35 , 36 , 39 , 45 ] ( Table 1 ). Twenty-six out of 29 studies included healthy individuals as the control group, which did not avoid a case-control design and thus might introduce biases according to the QUADAS-2 revised tool ( Figure 2 ). Most studies detected miR-1246 in serum or plasma samples using the reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) method. The total samples included in the meta-analysis were 5914, including 3232 cases and 2682 controls.
3.2. Performance of Circulating miR-1246 in Screening Cancers
The analyzed results indicated that circulating miR-1246 can differentiate cancers with 84.2% sensitivity (95% CI: 79.4-88.1) and 85.3% specificity (95% CI: 80.5-89.2, Figures 3(a) and 3(b) ). Besides, the diagnostic odds ratio pooled from 29 studies was 27.7 (95% CI: 17.1-45.0, Figure 3(c) ). However, heterogeneity in these analyses was substantial ( I 2 were 82.8%, 84.5%, and 88.4%, P <0.001, respectively). That is why we applied the random-effects model for the analyses.
The SROC curve of included studies shows an AUC of 0.885 (95% CI: 0.827-0.892, Figure 3(d) ), suggesting that circulating miR-1246 has high diagnostic power. Remarkably, excellent performance was noted for breast cancer (AUC = 0.950, 95% CI: 0.872-0.958) compared to other types ( P = 0.007, Figure 3(e) ). With the assumed probability of suffering cancer of 55%, positive result increases the posttest possibility to 88%, while negative result drops that measure to 18% ( Figure 3(f) ). The positive and negative likelihood ratios were 6.35 and 0.18, respectively.
Because of significant heterogeneity, we performed the influence analysis and detected three outliers that contributed most to overall heterogeneity (Figures 4(a) and 4(b) ). However, the heterogeneity remained high after removing these three outliers (DOR = 29.6, I 2 = 67.5%, 95% CI: 52.5-77.7%, P < 0.001). We performed subgroup analyses and observed that cancer type, control type, sample type, sample size, technique used, and data extraction method could contribute to the sensitivity, specificity, and DOR differences between studies ( Table 2 ). The multimodel inference analysis showed that three predictors, including technique, control type, and cancer type, are the most important ones contributing to heterogeneity overall (Akaike's information criterion was the smallest value = 102.4, Figure 4(c) ). We fitted these three predictors in a meta-regression and noted that this model could explain R 2 = 62.8% of the heterogeneity in DOR, and ESCC cancer type (coefficient = −1.825, P = 0.002), healthy control type (coefficient = 1.523, P = 0.015), and RT-qPCR technique (coefficient = −1.528, P = 0.012) are independent sources ( Table 3 ).
The funnel plot asymmetry test with linear regression indicated a nonsignificant publication bias in the meta-analysis ( P = 0.652, Figure 5(a) ). That is comparable with the analysis of skewness of the standardized deviates (skewness value = 0.672) (95% CI: -0.213 to 1.254, P = 0.071, Figure 5(b) ), suggesting a low potential of publication bias [ 48 ].
4. Discussion
miR-1246 has been evidenced as an oncogene that regulates multiple genes ( CCNG2 , GSK3β , RORα , AXIN2 , DYRK1A , Caspase-9 , FOXA2 , PDGFRβ , p53 , NFIB , etc.) and signaling pathways (RAF/MEK/ERK, Wnt/ β -catenin, NF- κ B, STAT3, THBS2/MMP, NOTCH2, etc.) related to the cell proliferation, angiogenesis, antiapoptosis, carcinogenesis, invasion, migration, metastasis, and therapy resistance [ 11 ]. Accordingly, recent studies indicated it as a potential biomarker for malignant tumors, but a small sample size resulted in the lack of consistent conclusions [ 12 , 13 ]. The study of Wei (on 242 cases of colorectal cancer, pancreatic adenocarcinoma, and pancreatobiliary tract cancer from three original reports) exhibited an excellent efficiency of exosome miR-1246 (AUC = 0.969, 92% sensitivity, and 95.8% specificity) [ 12 ], whereas in analyses of Xie (conducted on seven individual studies, 975 cases from five cancer types including hepatocellular carcinoma, breast, colorectal, ovarian, and esophageal cancers), authors concluded that miR-1246 is a good indicator with moderate diagnostic accuracy (AUC = 0.83, 80% sensitivity, and 77% specificity) [ 13 ].
We conducted a systematic review and performed a meta-analysis on 29 individual studies from 9 countries, including 12 cancer types, over 5900 samples, and confirmed that extracellular circulating miR-1246 has good sensitivity, specificity, and robust performance in screening cancers ( Figure 3(d) ). Impressively, the diagnostic capacity of miR-1246 is excellent for breast cancer ( Figure 3(e) , Table 2 ). These results indicate a superior performance of circulating miR-1246 compared to the combined model of currently used tumor biomarkers [ 8 ]. In clinical practice, it is simple to integrate the miR-1246 test into the health examination program without additional blood tubes, thanks to using a small sample volume. Also, it is quantified easily by the RT-qPCR, which is currently the widely used method with a fast turnaround time. Moreover, it is a lower cost and less invasive compared to low-dose computed tomography and endoscopy tests.
This study highlights the diagnostic power of extracellular circulating miR-1246 for cancers. However, most included studies comprise healthy individuals as the control group ( Table 1 ), which is quite different from cancerous, which thus might affect the overall results. Therefore, further clinical trial studies with cancer/benign models and early-stage diseases should be done to confirm the diagnosis role of circulating miR-1246. Another limitation of this study is the existence of significant heterogeneity that requires a cautious use of analyzed results.
5. Conclusion
The results of this study indicated that extracellular circulating miR-1246 has good sensitivity, specificity, and robust performance, which might serve as a reliable biomarker in screening cancers, especially breast cancer.
Abbreviations
Area under the receiver operating characteristic (ROC) curve
Breast cancer
Colorectal cancer
Esophageal squamous cell carcinoma
Gallbladder cancer
Gastric cancer
Healthy control
Hepatocellular carcinoma
Multiple myeloma
Not available
Non-small-cell lung cancer
Ovarian cancer
Pancreatic cancer
Prostate cancer
Pancreatobiliary tract cancer
Droplet digital polymerase chain reaction
Reverse transcriptase quantitative polymerase chain reaction
True positive
False positive
True negative
False negative
Reference.
Data Availability
All data generated or analyzed during this study are included in this published article.
Conflicts of Interest
The authors declared that no conflicts of interest exist.
Authors' Contributions
Study design, protocol writing, and statistical guidance were done by Son Truong Nguyen and Thuc Tri Nguyen. Database searching and reviews were done by Thang Thanh Phan, Toan Trong Ho, Suong Phuoc Pho, Hang Thuy Nguyen, and Binh Thanh Le. Quality assessment and data extraction were done by Thang Thanh Phan, Khanh Quang Huynh, and Anh Tuan Le. Data analysis was done Thang Thanh Phan, Khanh Quang Huynh, and Anh Tuan Le. Manuscript writing was done Thang Thanh Phan, Khanh Quang Huynh, and Anh Tuan Le. Manuscript revision was done by Son Truong Nguyen and Thuc Tri Nguyen. Khanh Quang Huynh and Son Truong Nguyen are responsible for the resources. All authors agreed on the final approval of the manuscript. Khanh Quang Huynh, Anh Tuan Le, and Thang Thanh Phan contributed equally to this work.
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