Serum microRNA expression signatures identified from genome-wide microRNA profiling serve as novel noninvasive biomarkers for diagnosis and recurrence of bladder cancer.

Serum microRNA expression signatures identified from genome-wide miRNA profiling serve as novel non-invasive biomarkers for diagnosis and recurrence of bladder cancer

Xiumei Jiang1†, Lutao Du1†, Lili Wang1, Juan Li1, Yimin Liu1, Guixi Zheng1, Ailin Qu 1

, Xin Zhang1, Hongwei Pan1, Yongmei Yang1, Chuanxin Wang1*

Authors′ Affiliations: 1Department of Clinical Laboratory, Qilu Hospital, Shandong University, Jinan, 250012, Shandong Province, China

†

contributed equally to this work

*

Correspondence to: Chuanxin Wang, MD, PhD,

Department of Clinical Laboratory, Qilu Hospital, Shandong University, 107 Wenhua Xi Road, Jinan 250012, Shandong Province, China, Tel: +86-0531-82166801; Fax: +86-0531-86927544, E-mail: [email protected]

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1002/ijc.29041

International Journal of Cancer

Abbreviations: ROC, receiver operating characteristic; AUC, area under the ROC curve; miRNA, microRNA; RFS, recurrence-free survival; RT-qPCR, reverse transcription quantitative real-time PCR; CI, confidence interval; BC, bladder cancer; NMIBC, non-muscle-invasive bladder cancer; MIBC, muscle-invasive bladder cancer.

Appropriate article category: Research article. Early detection and diagnosis.

A brief description of the novelty and impact: We performed genome-wide serum miRNA analysis by Miseq sequencing in BC patients and finally defined a six-miRNA panel (miR-152, miR-148b-3p, miR-3187-3p, miR-15b-5p, miR-27a-3p, and miR-30a-5p) based on the multivariate logistic regression model as a novel non-invasive biomarker for BC detection. This panel showed higher sensitivity in BC diagnosis than urine cytology, especially for patients with early stage (Ta and T1). Moreover, of the six miRNAs, miR-152 was identified as an independent predictor of NMIBC recurrence.

Figures and tables count: Figures=4; Tables=2; Supplementary Figures=2; Supplementary Tables=6.

John Wiley & Sons, Inc.

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Abstract Recent advantages of serum microRNAs (miRNAs) open a new realm of possibilities for non-invasive diagnosis and prognosis of bladder cancer (BC). The aim of this study was to identify serum miRNA expression signatures in BC patients and establish new models for BC diagnosis and recurrence prediction. We performed genome-wide serum miRNA analysis by Miseq sequencing followed by evaluations in the training and validation sets with reverse transcription quantitative real-time PCR assays from serum samples of 250 BC patients and 240 controls. A six-miRNA panel (miR-152, miR-148b-3p, miR-3187-3p, miR-15b-5p, miR-27a-3p, and miR-30a-5p) for the diagnosis of BC was finally developed by multivariate logistic regression model with an area under the receiver operating characteristic curve (AUC) of 0.899. The corresponding sensitivities of this panel for Ta, T1 and T2-T4 were 90.00%, 84.85%, 89.36%, significantly higher than those of urine cytology, which were 13.33%, 30.30%, 44.68%, respectively (all at p < 0.001). Moreover, Kaplan-Meier analysis showed that non-muscle-invasive BC (NMIBC) patients with high miR-152 level and low miR-3187-3p level had worse recurrence-free survival (RFS) (p = 0.023, and p = 0.043, respectively). In multivariate Cox regression analysis, miR-152 was independently associated with tumor recurrence of NMIBC (p = 0.028). Our results suggested that a serum miRNA signature may have considerable clinical value in diagnosing BC. Furthermore, expression level of serum miR-152 could provide information on the recurrence risk of NMIBC.

Key words: serum microRNAs, bladder cancer, diagnosis, recurrence



Introduction Bladder cancer (BC) is one of the leading causes of cancer-related death worldwide, with an estimated 72,570 new cases and 15,210 deaths in 2013 in the United States.1 About 70% of patients are initially diagnosed with non-muscle-invasive BC (NMIBC), but as many as 50-70% of these tumors will recur and roughly 10-20% will progress to muscle-invasive BC (MIBC).2 Cancer screening and early diagnosis have major importance in improving survival of patients with BC. Currently, urine cytology is most commonly used as the non-invasive test for BC detection. However, this test is of limited value due to its poor sensitivity, especially for low-grade lesions.3-7 Cystoscopy-guided biopsy for histological evaluation can offer high diagnostic accuracy, but it is invasive and inconvenient, which limit its use for general cancer screening. Therefore, non-invasive and more sensitive molecular biomarkers are still needed to complete and improve on current strategies for BC detection. MicroRNAs (miRNAs) are an abundant class of non-coding small RNAs (~22 necleotides in length) that regulate gene expression by binding to the 3’ un-translated region (UTR) of target mRNAs.8,9 miRNAs participate in almost all of the known hallmarks of tumorigenesis, including cell proliferation, differentiation, apoptosis, invasion and metastasis.10-15 Previous studies have shown that miRNAs exist stably in human serum and have potential role in the diagnosis and prognosis of various cancers, such as breast cancer, prostate cancer, gastric cancer, ovarian cancer, esophageal squamous cell cancer and non-small cell lung cancer.16-22 Recently, a study by Adam et al. has demonstrated that expression of plasma miRNAs in BC patients and healthy individuals is significantly different.23 However, unique serum miRNA signatures for the diagnosis and recurrence of BC have not been determined. In the present study, we conducted high-throughput Miseq sequencing followed


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by two phases of reverse transcription quantitative real-time PCR (RT-qPCR) assays to systematically and extensively evaluate serum miRNA expression from BC patients and control individuals. We determined that a six-miRNA panel using logistic regression model may prove useful as serum biomarker for early diagnosis of BC. Moreover, the correlation between the six miRNAs and BC recurrence was further assessed.

Materials and Methods

Study design, patients and control subjects A multiphase, case-control study was designed to identify serum miRNAs as potential biomarkers for BC (Supporting Information Fig. S1). Briefly, 250 patients of diagnosed BC without other diseases and 240 control individuals without history of BC were recruited from Qilu Hospital, Shandong University between January 2005 and February 2008. All these participants were allocated to three phases. In the discovery phase, serum samples pooled from 10 NMIBC patients, 10 MIBC patients and 10 healthy donors were subjected to Miseq sequencing to identify miRNAs that were significantly differentially expressed. Demographic and clinical features of the BC patients and controls are summarized in Supporting Information Table S1. In the training phase, the candidate miRNAs were tested with RT-qPCR in an independent cohort from 120 BC patients and 120 controls to construct the diagnostic miRNA panel based on the logistic regression model for the differentiation between the BC group and the control group. In the validation phase, serum samples from another cohort of 110 BC patients and 110 controls were entered into the discriminatory model to validate the diagnostic accuracy of the constructed algorithm, while urine



samples from the same cohort were obtained for traditional urine cytology. All samples were collected prior to any therapies. BC patients were diagnosed by histopathology or histobiopsy. Tumor stage was defined according to the 2002 UICC TNM classification of BC and tumor grade was designated according to the WHO 2004 grading scheme. This study was approved by Clinical Research Ethics Committee of Qilu Hospital, Shandong University and informed consent was obtained from each participant. BC patients in the validation phase have been followed up at intervals of 3 months during the first 2 years and 6 months up to the fifth year, and the date of latest record retrieved was March 31, 2013. Of these follow-up cases, 7 were excluded because of incomplete follow-up data. The median follow-up time was 62 months (range 4-76 months).

Serum and preparation 5ml venous blood from each participant were centrifuged at 4000 rpm for 10 min at 4°C within 2 h of collection, followed by a second centrifugation at 12000 rpm for 15 min at 4°C to eliminate any residual cells debris. The supernatant serum was then stored at -80°C until use.

Miseq sequencing For Miseq sequencing, equal volumes of serum from 10 NMIBC, 10 MIBC patients and 10 healthy controls with similar age and sex distributions were pooled, respectively. Total RNA was extracted and purified using the miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. Briefly, a pair of adaptors was ligated sequentially to the 3' and 5' ends of the miRNA, and the


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ligated miRNA molecules were amplified by RT-qPCR to establish a cDNA library. The quality of library was measured using the KAPA RT-qPCR kit following the two criteria: (a) higher cDNA concentration than 1 nM, (b) no dimmer contamination. The purified cDNA was then used directly for sequencing analysis by the Miseq sequencer (Illumina, San Diego, CA, USA) following the protocol provided by the manufacturer. For Miseq sequencing, the final reads of each miRNA were determined by normalization with the total reads of all called miRNAs in the sample. Bioinformatics analysis was performed by searching against the miRBase 17.0 to identify known mature miRNAs.

Quantification of miRNAs by RT-qPCR analysis RT-qPCR was performed in the ABI PRISM 7500 Sequence Detection System (Applied Biosystems, Foster City, CA) using the SYBR PrimeScript miRNA RT-qPCR Kit (Takara Bio Inc). In brief, the 20µl reverse transcription (RT) reaction system contained 10µl of 2× miRNA Reaction Buffer Mix, 2µl of miRNA Primescript RT Enzyme Mix, 2µl of 0.1% BSA and 3µl serum that mixed with 3µl serum buffer (2.5% Tween 20, 50 mmol/L Tris and 1mmol/L EDTA24). The reactions were incubated at 37°C for 60min, followed by 85°C for 5s and 4°C for 60min. The generated cDNAs were centrifugated at 10000 rpm for 10min. The 25µl RT-qPCR reaction system contained 12.5µl of SYBR Premix Ex TaqⅡ, 0.5µl of DyeⅡ, 2µl of 5µM of forward primer, 1µl of 10µM of Uni-miR RT-qPCR Primer, 7µl of ddH2O and 2µl of template cDNA. The reactions were incubated at 95°C for 30s, followed by 45 cycles of 95°C for 5s and 57°C for 34s. The specificity of the RT-qPCR product was assessed by performing melting curve analysis. All reactions were performed in triplicate and the Cq values were determined using the default threshold setting. To



date, no general established housekeeping miRNA has been defined. According to our previous research, the combination of miR-16-5p and miR-193a-5p were used as the reference gene.

Urine cytology determination Midstream urine samples (15ml) were centrifuged at 1,300 × g for 10 min and sediments were immediately processed for cytologic examination in a blinded fashion by two cytopathologists. The cytology was defined as positive if cancer cells or cells with atypical changes suggesting malignancy were detected, and it was categorized as negative in cases with mild to moderate atypical changes.

Statistical analysis Kolmogorov-Smirnov test was used to determine the distribution of the samples of each group. Data were presented as median (interquartile range). Nonparametric Mann-Whitney U tests were performed to compare differences in concentrations of serum miRNAs between the BC group and the control group. Receiver operating characteristic (ROC) curves were established to discriminate BC patients from controls. Area under the ROC curve (AUC) was employed as an accuracy index for evaluating the diagnostic performance of the selected miRNA panel.25 In NMIBC group and MIBC group, survival curves were respectively estimated with the Kaplan-Meier method and comparisons were made using the log-rank test. The Cox proportional hazards regression model was used to identify the independent prognostic factors. MedCalc 9.3.9.0 (MedCalc, Mariakerke, Belgium) was employed for ROC analysis, Matlab software (Matlab, R2013a) was used for logistic regression analysis, and others were calculated using SPSS version 17.0 software (SPSS Inc.,


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Chicago, IL). A p value < 0.05 was considered statistically significant. RESULTS

Genome-wide expression profiling of serum miRNAs in BC patients Miseq sequencing was initially used to identify the miRNAs with significantly altered expression. Among the 529 serum miRNAs that were scanned by Miseq sequencing (≥ 1 copy), 180, 259 and 206 miRNAs were detectable (≥ 10 copies) in controls, NMIBC patients and MIBC patients, respectively. The expression of a miRNA was considered altered only if at least 50 copies were detected by Miseq sequencing, together with a larger than two-fold change in its expression level between the BC and control groups. Based on these criteria, 26 miRNAs were found differentially expressed in BC, in which 8 miRNAs were up-regulated and 18 miRNAs were down-regulated (Supporting Information Table S2).

Evaluation of miRNA expression by RT-qPCR Subsequently, the 26 candidate miRNAs were first tested by RT-qPCR using serum samples from 120 BC patients and 120 controls in the training phase. There was no significant difference in the distribution of age, sex and tumor characteristics between the training and validation sets for the BC and control groups (Supporting Information Table S3). Only miRNAs that were statistically significant (p value < 0.001) were selected from the training set for further validation. miRNAs with a Cq value > 35 and detection rate < 75% in either BC group or control group were excluded from further analysis. Six miRNAs (miR-152, miR-148b-3p, miR-3187-3p, miR-15b-5p, miR-27a-3p, and miR-30a-5p) were finally identified to have differential expression patterns between BC group and control group (Table 1). Two miRNAs (miR-152,



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miR-148b-3p) were up-regulated and four (miR-3187-3p, miR-15b-5p, miR-27a-3p, miR-30a-5p) were down-regulated in BC patients compared with the control group (Fig. 1). The corresponding AUCs of the six miRNAs were 0.738, 0.729, 0.814, 0.715, 0.703 and 0.645, respectively (Fig. 2). The concentrations of these six miRNAs were further measured using the validation cohort consisting of 110 BC patients and 110 matched controls. The alterations in the miRNA expression pattern of the validation set were consistent with those of the training set (Table 1 and Supporting Information Fig. S2).

Establishing the predictive miRNAs panel A stepwise logistic regression model to estimate the risk of being diagnosed with BC was applied on the training data set (120 BC patients and 120 controls). The predicted probability of being diagnosed with BC from the logit model based on the six-miRNA panel, logit (p = BC) = - 0.5568 - 0.1685×miR-152 - 0.1667×miR-148b-3p + 0.5417×miR-3187-3p

+

0.2421×miR-15b-5p

+

0.2962×miR-27a-3p

+

0.1797×miR-30a-5p was used to construct the ROC curve. The diagnostic performance for the established miRNA panel was evaluated by the ROC analysis. The AUC for the six-miRNA panel was 0.956 (95% confidence interval [CI], 0.922 to 0.978, sensitivity = 90.00%, specificity = 90.00%) (Fig. 3a).

Validating the miRNA panel The parameters estimated from the training set were used to predict the probability of being diagnosed with BC for the independent validation data set (110 BC patients and 110 controls). Similarly, the predicted probability was used to construct the ROC curve. The AUC of the six-miRNA panel was 0.899 (95% CI, 0.851 to 0.936,


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sensitivity = 80.00%, specificity = 89.09%) (Fig. 3b). The AUCs of the panel for BC patients with Ta, T1 and T2-T4 were 0.841 (95%CI, 0.770 to 0.897, sensitivity = 90.00%, specificity = 66.36%), 0.886 (95%CI, 0.822 to 0.933, sensitivity = 84.85%, specificity = 85.45%) and 0.945 (95%CI, 0.897 to 0.975, sensitivity = 89.36%, specificity = 89.09%), respectively (Fig. 3c-3e). The diagnostic performance of the six-miRNA panel and urine cytology in distinguishing BC patients from control individuals was further evaluated on the validation set. The AUC of urine cytology was 0.645 (95% CI, 0.578 to 0.709, sensitivity = 31.82%, specificity = 97.27%) (Fig. 3f). The corresponding AUCs of the panel for Ta, T1 and T2-T4 were significantly higher than those of urine cytology, which were 0.550 (95%CI, 0.416 to 0.679, sensitivity = 13.33%, specificity = 96.67%), 0.636 (95%CI, 0.509 to 0.751, sensitivity = 30.30%, specificity = 96.97%), 0.713 (95%CI, 0.610 to 0.801, sensitivity = 44.68%, specificity = 98.87%), respectively (Fig. 3g-3i). In terms of sensitivity, the six-miRNA panel was statistically superior to urine cytology in discriminating patients with different tumor stages (all at p < 0.001, Supporting Information Table S4).

Correlation between the six miRNAs and clinicopathological characteristics The data presented in Supporting Information Table S5 showed the relationship between the six miRNAs and clinicopathological characteristics of BC patients in the validation set. Higher levels of serum miR-152 and lower levels of miR-3187-3p significantly correlated with advanced tumor stage (p < 0.05, respectively). Lower levels of miR-27a-3p correlated with higher tumor grade and lower levels of miR-3187-3p correlated with positive lymph nodes metastasis (p = 0.04, and p = 0.03, respectively). However, no significant associations were found between the six



miRNAs with age or sex (all at p > 0.05).

Correlation between the six miRNAs and tumor recurrence In the validation cohort (n=110), 7 patients were lost to follow up: 4 NMIBC and 3 MIBC. Survival analysis was carried on 59 NMIBC and 44 MIBC, respectively. In the NMIBC group, Kaplan-Meier survival analysis revealed that patients with high miR-152 levels and low miR-3187-3p levels had a dramatically lower recurrence-free survival (RFS) rather than those with low miR-152 levels and high miR-3187-3p levels (p = 0.023, and p = 0.043, respectively) (Fig. 4). Univariate Cox proportional hazards regression model analysis revealed a statistically significant correlation between RFS of NMIBC and miR-152 level (p = 0.028), miR-3187-3p level (p = 0.049) and tumor stage (p = 0.034). Parameters significantly related to recurrence in the univariate analysis were put into the multivariate analysis to identify the independent factors for prognoses. The results showed that only miR-152 level and tumor stage maintained their significance as independent prognostic factors for RFS of NMIBC (p = 0.028, and p = 0.026, respectively) (Table 2). In the MIBC group, however, none of the six deregulated miRNAs influenced patient predicted recurrence (all at p > 0.05, Supporting Information Table S6).

Discussion To our knowledge, this is the first study to identify a serum miRNA-based BC signature by genome-wide miRNA expression profiling. We found that a six-miRNA panel (miR-152, miR-148b-3p, miR-3187-3p, miR-15b-5p, miR-27a-3p, and miR-30a-5p) from the multivariate logistic regression model demonstrated high


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accuracy for diagnosing BC. Compared with traditional urine cytology, this panel was significantly superior based on its higher sensitivity to detect patients with early stage diseases (Ta and T1). Moreover, of the six miRNAs, miR-152 was identified as an independent factor for tumor recurrence in NMIBC patients. Previous studies on searching for serum miRNA-based cancer biomarkers generally focused on individual cancer-specific miRNAs.26,27 However, a single miRNA may not be a reliable tumor biomarker because of the complex pathogenesis during the initiation and development of a severe malignancy.28,29 Simultaneous assessment of a panel of tumor-specific miRNAs in serum may improve the sensitivity and specificity for cancer diagnosis and prognosis.30,31 In this study, we screened the whole miRNA profile in both BC and control serum samples via Miseq sequencing, which enabled us to have better chance to identify potential diagnostic biomarkers. Miseq sequencing is a high-throughput assay to initially screen miRNAs and could exclude possible contamination by other small RNA and DNA fragments. However, the Miseq results from pooled serum samples might include inaccurate information due to individual variation. For this reason, candidate miRNAs revealed by Miseq sequencing were evaluated by two phases of RT-qPCR assays using a large number of individual samples. Finally, a six-miRNA panel from the logistic model was identified for the diagnosis of BC. The high diagnostic accuracy in the training and validation set indicated that the expression profile of the six miRNAs could serve as an accurate biomarker for BC detection. In addition, we performed a direct comparison of our results with urine cytology in the same cohort. Our data clearly demonstrated that the panel can more effectively discriminate BC patients from controls with better sensitivity than urine cytology, especially in early stage tumors. Based on these findings, the serum miRNA panel provides a much more sensitive



detection of BC. Furthermore, technically speaking, serum test is more convenient and non-invasive, thus being an ideal for the investigation of a panel containing a small number of miRNAs. Functional studies of miRNAs in tumor tissue may be helpful for evaluating serum miRNAs as biomarkers for various types of cancer. Among the six miRNAs identified in BC in our study, several are involved in tumorigenesis. For instance, miR-148/152 family (miR-148a, miR-148b, and miR-152) is differentially expressed in tumor and non-tumor tissues, which suggests that it might be involved in the genesis and development of various tumor types.32-35 Kohler and colleagues showed significant down-regulation of miR-152 in BC cells and suggested that hypermethylation of the regulative DNA sequences of miR-152 might serve as epigenetic BC biomarkers.36 Wang et al. identified miR-27a as a target of mutant p53-273H and uncovered a novel mutant p53-273H/miR-27a/EGFR pathway which played an important role in tumorigenesis.37 Further studies are necessary to identify the target genes of these six serum miRNAs and elucidate the underlying mechanism that regulates the biogenesis of these miRNAs. It is found that two out of six miRNAs in our panel have been previously identified as differentially expressed in BC urine samples. Dudziec et al. showed that the urinary expression of epigenetically silenced miR-152 was associated with the presence of BC.38 Reduced expression of miR-15b was reported in BC patients and a characteristic panel of 3 miRNAs (miR-15b, miR-135b, and miR-1224-3p) in urine sediments was suggested to be used for BC detection.39 The fact that some of the miRNAs revealed in our study overlap with those found in prior studies using urine samples may reinforce our findings and further support the use of serum miRNAs as potential diagnostic indicators. The other four serum miRNAs, however, have not


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been reported in urine for BC diagnosis. These findings may be explained by recent researches suggesting that serum and urine display different miRNA abundance profiles as might be expected for two dissimilar biological fluids.40,41 To fully understand the reasons for this distinction, more studies will be needed. Although mounting evidence highlighted the important roles of serum miRNA-based biomarkers in various tumors, significant efforts to identify circulating miRNAs for BC diagnosis have met with limited success. In agreement with our findings, upregulation of miR-148b in plasma of BC patients has already been reported by Adam and colleagues.23 However, other deregulated miRNAs such as miR-1290 and miR-200b demonstrated in their study were not present in the six-miRNA profile. Their relatively small sample size and the need for either internal or external independent validation do raise concerns about the robustness of these biomarkers. Scheffer et al. performed RT-qPCR to measure the serum levels of 22 miRNAs which were up-regulated in BC tissues and yet failed to find any significant diagnostic miRNAs for BC.42 Their results may be supported by the study suggesting that miRNAs found in cancer tissue/cell are different from those secreted in the biofluids.43 In comparison, our study identified a six-miRNA panel with relatively higher diagnostic accuracy for BC. It is likely that the specific selection of differently expressed miRNAs in serum samples, as well as the large number of samples analyzed, the combination of six miRNAs in a multivariate logistic model, and the confirmation with independent validation could account for this increased performance. Nevertheless, several factors such as patients’ ethnicities, sample subtypes, study design, sample numbers, RNA isolation, and technologies platforms may affect the results of miRNA studies in biofluids.44 Thus, standardization of isolation, normalization, and data analysis methods may be required for the clinical



utility of these putative biomarkers. Previously, several clinical studies showed that the deregulation of miRNA expression in BC tissues significantly correlated with tumor recurrence and progression.45,46 However, good serum biomarkers for the prediction of BC recurrence have not yet been identified. To explore this, we further investigated whether the serum miRNAs identified in this study could be used as potential factors for BC recurrence. Interestingly, several miRNAs in the panel were associated with characteristics of poor prognosis in BC, including advanced clinical stage, higher tumor grade and positive lymph nodes metastasis. Moreover, high miR-152 expression and low miR-3187-3p expression correlated with shorter RFS of NMIBC, respectively. Cox proportional hazards regression model analysis revealed that serum miR-152 level was an independent factor influencing RFS of NMIBC. Thus, pretreatment serum miR-152 level may serve to identify NMIBC patients at higher or lower risk for tumor recurrence. However, none of the deregulated miRNAs influenced MIBC patient outcome in terms of RFS. In conclusion, we have defined a distinctive serum miRNA signature for BC detection and identified miR-152 as an independent predictor of NMIBC recurrence. Although further multi-center studies are needed to confirm results of this study, our findings may serve as the basis for future researches about the clinical value of serum miRNAs in monitoring treatment efficacy and forecasting prognosis of BC.

Acknowledgements This study was supported by National Natural Science Foundation of China (No.


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81271916). The authors thank Dr. Junhui Zhen (Department of Pathology, Qilu Hospital, Shandong University) and Dr. Chengjun Zhou (Department of Pathology, Second Hospital of Shandong University) for assistance in evaluation of cytology and histology.

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FIGURE LEGENDS Fig. 1. The concentrations of six selected serum miRNAs in BC patients (n = 120) and control individuals (n = 120) using RT-qPCR assay in training set (a-f), *p< 0.001. Fig. 2. Roc curves analysis for the detection of BC using miR-152 (a), miR-148b-3p (b), miR-3187-3p (c), miR-15b-5p (d), miR-27a-3p (e), miR-30a-5p (f) in BC patients (n = 120) and control individuals (n = 120) in training set. Fig. 3. (a-b) Roc curves for the detection of BC using six- miRNA panel in training set (a) and validation set (b); (c-e) Roc curves using the six-miRNA panel for the detection of Ta (c), T1 (d) and T2-T4 (e) in validation set; (f-i) Roc curves analysis using urine cytology for the detection of BC with all stages (f), Ta (g), T1 (h) and T2-T4 (i) in validation set. Fig. 4. Kaplan-Meier curves for recurrence-free survival according to the serum levels of miR-152 (a) and miR-3187-3p (b) in NMIBC patients in validation set. Fig. S1. Study outline. Serum samples were obtained from a total of 250 BC patients and 240 controls. Samples were divided into discovery phase (30 samples), training phase (240 samples) and validation phase (220 samples). Miseq sequencing technology was first used to screen 529 miRNAs in BC and control serum followed by evaluation of 26 differentially expressed miRNAs using RT-qPCR assay. A six-miRNA panel based on the logistic regression model was constructed for the diagnosis of BC in the training phase and then confirmed in the validation phase. Abbreviations: NMIBC, non-muscle-invasive bladder cancer; MIBC, muscle-invasive bladder cancer; ROC, receiver operating characteristic; RT-qPCR, reverse transcription quantitative real-time PCR. Fig. S2. The concentrations of six selected serum miRNAs in BC patients (n = 110) and control individuals (n = 110) using RT-qPCR assay in validation set (a-f), *p

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