The Diagnostic Power of Circulating miR-1246 in Screening Cancer: An Updated Meta-analysis

Huynh, Khanh Quang; Le, Anh Tuan; Phan, Thang Thanh; Ho, Toan Trong; Pho, Suong Phuoc; Nguyen, Hang Thuy; Le, Binh Thanh; Nguyen, Thuc Tri; Nguyen, Son Truong

doi:https://doi.org/10.1155/2023/8379231

Oxidative Medicine and Cellular Longevity

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Abstract Introduction Materials and Methods Results Discussion Conclusion Abbreviations Data Availability Conflicts of Interest Authors’ Contributions References Copyright Related Articles

Review Article | Open Access

Volume 2023 | Article ID 8379231 | https://doi.org/10.1155/2023/8379231

The Diagnostic Power of Circulating miR-1246 in Screening Cancer: An Updated Meta-analysis

Khanh Quang Huynh,¹Anh Tuan Le,²Thang Thanh Phan,³Toan Trong Ho,³Suong Phuoc Pho,³Hang Thuy Nguyen,⁴Binh Thanh Le,⁵Thuc Tri Nguyen,⁵and Son Truong Nguyen⁵

Academic Editor: Ihtisham Bukhari

Received29 Dec 2022

Revised28 Mar 2023

Accepted11 Apr 2023

Published20 Apr 2023

Abstract

Background. MicroRNA-1246 (miR-1246), an oncomiR that regulates the expression of multiple cancer-related genes, has been attracted and studied as a promising indicator of various tumors. However, diverse conclusions on diagnostic accuracy have been shown due to the small sample size and limited studies included. This meta-analysis is aimed at systematically assessing the performance of extracellular circulating miR-1246 in screening common cancers. Methods. We searched the PubMed/MEDLINE, Web of Science, Cochrane Library, and Google Scholar databases for relevant studies until November 28, 2022. Then, the summary receiver operating characteristic (SROC) curves were drawn and calculated area under the curve (AUC), diagnostic odds ratio (DOR), sensitivity, and specificity values of circulating miR-1246 in the cancer surveillance. Results. After selection and quality assessment, 29 eligible studies with 5914 samples (3232 cases and 2682 controls) enrolled in the final analysis. The pooled AUC, DOR, sensitivity, and specificity of circulating miR-1246 in screening cancers were 0.885 (95% confidence interval (CI): 0.827-0.892), 27.7 (95% CI: 17.1-45.0), 84.2% (95% CI: 79.4-88.1), and 85.3% (95% CI: 80.5-89.2), respectively. Among cancer types, superior performance was noted for breast cancer () compared to colorectal cancer (), esophageal squamous cell carcinoma (), hepatocellular carcinoma (), pancreatic cancer (), and others (). No significant publication bias in DOR was observed in the meta-analysis (funnel plot asymmetry test with ; , ). Conclusion. Extracellular circulating miR-1246 may serve as a reliable biomarker with good sensitivity and specificity in screening cancers, especially breast cancer.

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 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 ( resampling iterations). The heterogeneity of diagnostic test accuracy between studies was measured by Higgins and Thompson’s -statistic, which is significant if . 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. 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 ( were 82.8%, 84.5%, and 88.4%, P <0.001, respectively). That is why we applied the random-effects model for the analyses.

(a)

(b)

(c)

(d)

(e)

(f)

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 (, 95% CI: 0.872-0.958) compared to other types (, 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 (%, 95% CI: 52.5-77.7%, ). 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 , Figure 4(c)). We fitted these three predictors in a meta-regression and noted that this model could explain 8% of the heterogeneity in DOR, and ESCC cancer type (), healthy control type (), and RT-qPCR technique () are independent sources (Table 3).

(a)

(b)

(c)

The funnel plot asymmetry test with linear regression indicated a nonsignificant publication bias in the meta-analysis (, Figure 5(a)). That is comparable with the analysis of skewness of the standardized deviates () (95% CI: -0.213 to 1.254, , Figure 5(b)), suggesting a low potential of publication bias [48].

(a)

(b)

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-κB, 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 (, 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 (, 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

AUC:	Area under the receiver operating characteristic (ROC) curve
BC:	Breast cancer
CRC:	Colorectal cancer
ESCC:	Esophageal squamous cell carcinoma
GBC:	Gallbladder cancer
GC:	Gastric cancer
HC:	Healthy control
HCC:	Hepatocellular carcinoma
MM:	Multiple myeloma
Na:	Not available
NSCLC:	Non-small-cell lung cancer
OC:	Ovarian cancer
PC:	Pancreatic cancer
PCa:	Prostate cancer
PT:	Pancreatobiliary tract cancer
ddPCR:	Droplet digital polymerase chain reaction
RT-qPCR:	Reverse transcriptase quantitative polymerase chain reaction
TP:	True positive
FP:	False positive
TN:	True negative
FN:	False negative
Ref.:	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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Copyright © 2023 Khanh Quang Huynh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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