Tumor Biol. (2014) 35:11041–11050 DOI 10.1007/s13277-014-2381-3

RESEARCH ARTICLE

MicroRNAs as potential diagnostic biomarkers in renal cell carcinoma Yongqing Gao & Hongmei Zhao & Ying Lu & Haiyi Li & Gaobo Yan

Received: 22 June 2014 / Accepted: 23 July 2014 / Published online: 6 August 2014 # International Society of Oncology and BioMarkers (ISOBM) 2014

Abstract Emerging evidence has suggested that microRNAs (miRNAs) may be promising novel biomarkers for the diagnosis of renal cell carcinoma (RCC). However, the results of current studies are still conflicting. Hence, we undertake the current meta-analysis to comprehensively assess the diagnostic potential of miRNAs in RCC. The bivariate meta-analysis model was employed to summarize the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were used to evaluate the diagnostic accuracy. Subgroup analyses and meta-regression were used to explore the between-study heterogeneity. Deeks’ funnel plot asymmetry test was used to test the potential of publication bias. All analyses were performed using STATA software (version 12.0). The pooled sensitivity and specificity of miRNAs for the diagnosis of RCC were 0.85 (95 % confidence interval (CI), 0.77–0.90) and 0.84 (95 % CI, 0.70–0.92). The value of

Y. Gao Department of Clinical Laboratory, Tieling Central Hospital, Tieling 112000, China H. Zhao Department of Clinical Laboratory, Liaoning Province People’s Hospital, Shenyang 110001, China Y. Lu Department of Clinical Laboratory, Fushun Central Hospital, Fushun 113006, China H. Li Department of Clinical Laboratory, Qinghe District Hospital of Tieling City, Tieling 112003, China G. Yan (*) Department of Clinical Laboratory, Dandong Central Hospital, Dandong 118000, China e-mail: [email protected]

AUC was 0.91 (95 % CI, 0.88–0.93), suggesting that the diagnostic accuracy of miRNAs achieved a relatively high level. Furthermore, subgroup analyses showed that tissuebased miRNA assay is recommended to improve the diagnostic accuracy. In conclusion, the high degree of diagnostic accuracy suggests that miRNA in RCC patients may serve as next-generation biomarkers for detection of the disease. However, large-scale investigations and additional improvements are urgently needed to confirm our results and verify the feasibility of routine clinical utilization. Keywords MicroRNAs . Renal cell carcinoma . Diagnosis accuracy . Meta-analysis

Introduction Renal cell carcinoma (RCC) is the most common kidney tumor, accounting for nearly 4 % of all malignancies. Each year, there are approximately 209,000 new cases reported globally, resulting in 102,000 deaths [1, 2]. RCC is morphologically and genetically heterogeneous and can be divided into both malignant and benign variants [3]. The malignant tumors include conventional clear-cell RCC (ccRCC, 70– 80 % of cases), papillary RCC (pRCC, 10–15 % of cases), and chromophobe RCC (chRCC, 5–10 % of cases). Benign tumors are mainly comprised of oncocytoma [4]. The 5-year survival rate for stage I RCC is about 98 %, while the 5-year survival rate for stage III RCC drops to approximately 50 % [5], demonstrating the significance of early detection and treatment of RCC. Currently, methods such as imaging techniques and biopsy have been introduced to detect RCC [6]. However, the widely used imaging techniques, which rely primarily on morphological features of tumors, fail to achieve high accuracy because RCC is asymptomatic and nonpalpable at its early stage. In

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addition, it is often difficult to differentiate RCC subtypes (i.e., chRCC and oncocytoma) using current imaging modalities [6]. Although biopsy is the gold standard among RCC diagnostic tools, it is necessarily invasive and therefore not suitable for all patients [7]. In addition, studies have reported that some biomarkers, such as epithelial membrane antigen (EMA), S-100 protein, and E-cadherin, might be useful to detect the presence of advanced or recurrent RCC [8–10]. However, the sensitivity and specificity of those biomarkers are suboptimal [11]. Therefore, noninvasive biomarkers with high diagnostic accuracy are urgently needed to help differentiate the subtypes of RCC and to then select the optimal treatment. Recent studies suggest that aberrant microRNA (miRNA) expressions have been found in various human malignancies [12–14]. MiRNAs, which are a group of small, noncoding, single-stranded RNAs of ~22 nucleotides in length, may play important roles in modulating cell differentiation, growth, apoptosis, and proliferation [15, 16]. MiRNAs have also been demonstrated to be highly stable and abundant in cell-free forms in serum, urine, and tissues, as they are able to resist degradation by RNase [17]. Interestingly, miRNA signatures in these specimens are similar in both men and women, regardless of patient age [18, 19]. In addition, aberrant expressions of miRNAs, which may play crucial roles in tumorigenesis and cancer diagnosis, have been shown in various cancers, including RCC [20–22]. Thus, miRNAs have the potential of being novel biomarkers for RCC diagnosis. Emerging evidence has suggested that miRNAs may be promising novel biomarkers for the diagnosis of RCC. However, the current studies fail to reach an agreement. For instance, Wulfken et al. found that the diagnostic accuracy of miR-1233 was relatively low, demonstrating a sensitivity of 77.4 % and specificity of 37.6 % [23]. Redova et al. also suggested that the diagnostic performance of miR-1233 was not convincing [24]. Instead, they investigated the diagnostic value of the combination of miR-378 and miR-451 and revealed a better performance with sensitivity of 81 % and specificity of 83 %. In the study by Wulfken et al., the circulating levels of miR-210 did not differ significantly between RCC patients and healthy individuals [23]. On the contrary, Zhao et al. found that the average level of miR-210 was significantly higher in ccRCC patients than in controls for serum samples (sensitivity of 81.0 % and specificity of 79.4 %) [25]. Similarly, Iwamoto et al. found that the expression of miR-210 was higher in the serum of ccRCC patients than that seen in healthy individuals. However, the results reported by Iwamoto et al. were obtained using a suboptimal sensitivity and specificity of 65 and 83 %, respectively [26]. These conflicting results might have been partly due to differences in study design, ethnicity, sample characteristics, specimen type, cutoff value, and miRNA profile. Therefore, we need to undertake a systematic review and meta-analysis of

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the research literature in order to provide a comprehensive and up-to-date overview of the feasibility and accuracy of miRNAs in RCC diagnosis.

Methods Search strategy PubMed, Embase, the Cochrane Library, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) were searched prior to April 15, 2014. The search was conducted using the following keywords: “microRNA” or “miRNA,” and “renal cell carcinoma” or “renal cell tumor,” and “diagnosis” or “ROC curve” or “sensitivity” or “specificity.” In addition, reference lists of relevant reviews were independently searched to obtain additional articles.

Inclusion and exclusion criteria Eligible studies included in this meta-analysis were required to fulfill the following criteria: (1) studies regarding the diagnosis potential of miRNAs for RCC; (2) studies including reference standards for the diagnosis of RCC; and (3) studies containing sufficient data for the construction of two-by-two tables, including true positive, false positive, true negative, and false negative. Exclusion criteria were as follows: (1) publications not related to the diagnostic values of miRNAs for RCC; (2) studies with duplicate data reported in other studies; and (3) letters, editorials, case reports, or reviews.

Data extraction and quality assessment Two reviewers extracted the following data from all of the included articles independently: (1) basic characteristics of studies, including name of the first author, year of publication, country of origin, mean age, patient spectrum, source of control, methods of miRNAs detection, and sample type; and (2) diagnostic outcomes, including the number of patients with sensitivity and specificity. The included studies were evaluated by two independent reviewers according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) criteria [27]. The QUADAS-2 tool is comprised of four key domains: patient selection, index test, reference standard, and flow and timing. Further, the QUADAS-2 tool uses seven questions to evaluate the quality of diagnostic accuracy studies. Each question is answered with “yes,” “no,” or “unclear”. An answer of “yes” means that the risk of bias can be judged low while an answer of “no” or “unclear” means that the risk of bias can be judged high.

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Results

All analyses in this meta-analysis were performed using the STATA 12.0 software. The bivariate meta-analysis model was employed to summarize the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) [28, 29]. A pooled summary receiver operating characteristics (SROC) curve was constructed, and the area under the curve (AUC) was calculated. The overall accuracy of miRNA detection can be evaluated by the parameters above. The between-study heterogeneity was evaluated by Q test and I2 statistics. A P value less than 0.05 for Q test or I2 values ≥50 % indicate substantial heterogeneity, and then the random-effect model was applied [30, 31]. To explore the potential sources of heterogeneity, subgroup analyses and meta-regression were performed according to the characteristics of the included studies (number of case/control, mean age, miRNA profiling, and specimen types). Deeks’ funnel plot asymmetry test was used to test the potential of publication bias, with P

MicroRNAs as potential diagnostic biomarkers in renal cell carcinoma.

Emerging evidence has suggested that microRNAs (miRNAs) may be promising novel biomarkers for the diagnosis of renal cell carcinoma (RCC). However, th...
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