Potential utility of pretreatment serum miRNAs for optimal treatment selection in advanced high-grade serous ovarian cancer.

OBJECTIVE: The primary treatment of patients with advanced ovarian cancer is selected from whether primary debulking surgery or neoadjuvant chemotherapy. We investigated whether pretreatment serum microRNA profiles are useful for selecting patients with advanced high-grade serous ovarian cancer who obtain better outcomes from undergoing primary debulking surgery or neoadjuvant chemotherapy. METHODS: Consecutive patients with clinical stage IIIB-IVB and serum microRNA data were selected. Patients who underwent primary debulking surgery or neoadjuvant chemotherapy were subjected to 1:1 propensity score matching before comparing their progression-free survival using Cox modelling. Progression-free probabilities for the selected microRNA profiles were calculated, and the estimated progression-free survival with the recommended primary treatment was determined and compared with the actual progression-free survival of the patients. RESULTS: Of the 108 patients with stage IIIB-IVB disease, the data of 24 who underwent primary debulking surgery or neoadjuvant chemotherapy were compared. Eleven and three microRNAs were independent predictors of progression-free survival in patients who underwent primary debulking surgery and neoadjuvant chemotherapy, respectively. Two microRNAs correlated significantly with complete resection of the tumours in primary debulking surgery. No differences were found between the actual and estimated progression-free survival in the primary debulking surgery and neoadjuvant chemotherapy groups (P > 0.05). The recommended and actual primary treatments were identical in 27 (56.3%) of the 48 patients. The median improved survival times between recommended and actual treatment were 11.7 and 32.6 months for patients with actual primary debulking surgery and neoadjuvant chemotherapy, respectively. CONCLUSIONS: Pretreatment microRNA profiles could be used to select subgroups of patients who benefited more from primary debulking surgery or neoadjuvant chemotherapy and might contribute to selecting the optimal primary treatment modality in advanced high-grade serous ovarian cancer patients.

Prediction of tissue-of-origin of early stage cancers using serum miRNomes.

BACKGROUND: Noninvasive detection of early stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively. METHODS: A serum miRNA profile (miRNomes)-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7931 serum samples from patients with 13 types of solid cancers and 5013 noncancer samples. The validation set consisted of 1990 cancer and 1256 noncancer samples. The contribution of each miRNA to the cancer-type classification was evaluated, and those with a high contribution were identified. RESULTS: Cancer type was predicted with an accuracy of 0.88 (95% confidence interval [CI] = 0.87 to 0.90) in all stages and an accuracy of 0.90 (95% CI = 0.88 to 0.91) in resectable stages (stages 0-II). The F1 score for the discrimination of the 13 cancer types was 0.93. Optimal classification performance was achieved with at least 100 miRNAs that contributed the strongest to accurate prediction of cancer type. Assessment of tissue expression patterns of these miRNAs suggested that miRNAs secreted from the tumor environment could be used to establish cancer type-specific serum miRNomes. CONCLUSIONS: This study demonstrates that large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system. Further investigations of the regulating mechanisms of the miRNAs that contributed strongly to accurate prediction of cancer type could pave the way for the clinical use of circulating miRNA diagnostics.

Serum microRNAs as new criteria for referral to early palliative care services in treatment-naïve advanced cancer patients.

A major obstacle to the implementation of early palliative care (EPC) is the lack of objective criteria for referral to EPC. Circulating microRNAs (miRNAs) have been recognized as promising biomarkers. The present study investigated objective definitions for referral to EPC using microRNA. A total of 178 serum samples were obtained from patients with lung, gastrointestinal, colorectal, bile duct, pancreas and bladder cancers who were treatment-naïve and received chemotherapy between January 2011 and December 2013 at National Cancer Center Hospital East. We investigated expression levels of miRNAs using microarrays. The primary outcome was prediction of admission to a palliative care unit ≤6 months after first visit. Diagnostic models using clinical characteristics, miRNAs and combinations of both were constructed. The miRNA models were constructed using 6 miRNA levels. The best areas under the receiver operating characteristic curve (AUCs) of the clinical model was 0.741, while the average AUCs of miRNA-based models and combination models were 0.769 and 0.806, respectively. Combination models showed higher AUCs than the clinical model (p < 0.023). The present combination models might offer new objective definitions for referral to EPC and thus contribute to real-world implementation of EPC.

Circulating microRNAs: Challenges with their use as liquid biopsy biomarkers.

Circulating microRNA (miRNA) is a major focus in liquid biopsy studies. The circulating levels of certain miRNAs have been suggested to reflect specific physiological conditions, and several studies have reported their potential use as biomarkers for the detection and prognosis of cancer, as well as for predicting responses to chemotherapy or radiotherapy. Alongside these biomarker studies, research into the effects of specific background factors on circulating miRNA levels is progressing. Indeed, several studies have shown that a number of factors, including blood sample collection and processing methods, as well as subject-specific factors such as age, sex, and other physiological conditions, can affect the normal levels of circulating miRNAs. Unfortunately, the evidence supporting these effects is not yet strong enough to support a definite conclusion and further research is warranted. Here, we summarize the findings of several studies that have addressed these concerns and identify important topics that should be considered when analyzing circulating miRNA levels in liquid biopsy studies.

Identification of circulating microRNAs as potential biomarkers for hepatic necroinflammation in patients with autoimmune hepatitis.

OBJECTIVE: MicroRNAs (miRNAs) are implicated in the pathogenesis of autoimmune diseases and could be biomarkers of disease activity. This study aimed to identify highly expressed circulating miRNAs in patients with autoimmune hepatitis (AIH) and to evaluate their association with clinical characteristics. METHODS: Microarray analyses were performed, and miRNA expression profiling for AIH, primary biliary cholangitis (PBC) and overlap syndrome (OS) using the serum of patients and healthy individuals was done. Samples were divided into discovery and test sets to identify candidate miRNAs that could discriminate AIH from PBC; the former included 21 AIH and 23 PBC samples, while the latter included five AIH and eight PBC samples. RESULTS: Among 11 candidate miRNAs extracted in the discovery set, 4 (miR-3196, miR-6125, miR-4725-3 p and miR-4634) were specifically and highly expressed in patients with AIH in the test set. These four miRNAs discriminated AIH from PBC with high sensitivity (0.80-1.00) and specificity (0.88-1.00). In situ hybridisation analysis revealed that these miRNAs were expressed in the cytoplasm of hepatocytes in patients with AIH. Their expression levels were highest in untreated patients with AIH, followed by those in untreated patients with OS. They drastically or moderately decreased after prednisolone treatment. Histological analysis demonstrated that the expression levels of miR-3196, miR-6125 and miR-4634 in patients with AIH and OS were correlated with severe hepatic necroinflammatory activity. CONCLUSION: These circulating miRNAs are suggested to reflect hepatic necroinflammatory activity and serve as AIH-related and treatment-responsive biomarkers. These miRNAs could be beneficial in developing new therapeutic strategies for AIH.

Comprehensive serum and tissue microRNA profiling in dedifferentiated liposarcoma.

Sarcoma is a rare cancer with several subtypes; therefore, our understanding of the pathogenesis of sarcoma is limited, and designing effective treatments is difficult. Circulating microRNAs (miRNAs), including exosomal miRNAs, have attracted attention as biomarkers in cancer. However, the roles of miRNAs and exosomes in sarcoma remain unclear. The present analysis of tissue and serum miRNA expression in osteosarcoma, Ewing's sarcoma and dedifferentiated liposarcoma (DDLPS) identified miR-1246, -4532, -4454, -619-5p and -6126 as biomarkers for DDLPS. These miRNAs were highly expressed in human DDLPS cell lines and exosomes, suggesting that they are secreted from DDLPS tissues. The present results suggested that specific miRNAs may be used as biomarkers for early diagnosis or treatment targets in DDLPS.

A novel combination of serum microRNAs for the detection of early gastric cancer.

BACKGROUND: The aim of this study was to identify serum miRNAs that discriminate early gastric cancer (EGC) samples from non-cancer controls using a large cohort. METHODS: This retrospective case-control study included 1417 serum samples from patients with EGC (seen at the National Cancer Center Hospital in Tokyo between 2008 and 2012) and 1417 age- and gender-matched non-cancer controls. The samples were randomly assigned to discovery and validation sets and the miRNA expression profiles of whole serum samples were comprehensively evaluated using a highly sensitive DNA chip (3D-Gene®) designed to detect 2565 miRNA sequences. Diagnostic models were constructed using the levels of several miRNAs in the discovery set, and the diagnostic performance of the model was evaluated in the validation set. RESULTS: The discovery set consisted of 708 samples from EGC patients and 709 samples from non-cancer controls, and the validation set consisted of 709 samples from EGC patients and 708 samples from non-cancer controls. The diagnostic EGC index was constructed using four miRNAs (miR-4257, miR-6785-5p, miR-187-5p, and miR-5739). In the discovery set, a receiver operating characteristic curve analysis of the EGC index revealed that the area under the curve (AUC) was 0.996 with a sensitivity of 0.983 and a specificity of 0.977. In the validation set, the AUC for the EGC index was 0.998 with a sensitivity of 0.996 and a specificity of 0.953. CONCLUSIONS: A novel combination of four serum miRNAs could be a useful non-invasive diagnostic biomarker to detect EGC with high accuracy. A multicenter prospective study is ongoing to confirm the present observations.

A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy.

Lung cancer, the leading cause of cancer death worldwide, is most frequently detected through imaging tests. In this study, we investigated serum microRNAs (miRNAs) as a possible early screening tool for resectable lung cancer. First, we used serum samples from participants with and without lung cancer to comprehensively create 2588 miRNAs profiles; next, we established a diagnostic model based on the combined expression levels of two miRNAs (miR-1268b and miR-6075) in the discovery set (208 lung cancer patients and 208 non-cancer participants). The model displayed a sensitivity of 99% and specificity of 99% in the validation set (1358 patients and 1970 non-cancer participants) and exhibited high sensitivity regardless of histological type and pathological TNM stage of the cancer. Moreover, the diagnostic index markedly decreased after lung cancer resection. Thus, the model we developed has the potential to markedly improve screening for resectable lung cancer.

Highly Sensitive Circulating MicroRNA Panel for Accurate Detection of Hepatocellular Carcinoma in Patients With Liver Disease.

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer deaths worldwide. The high mortality rate in HCC is largely due to the difficulty of early detection. In this study, to improve patient outcomes, serum samples from 345 patients with HCC, 46 patients with chronic hepatitis (CH), 93 patients with liver cirrhosis (LC), and 1,033 healthy individuals were analyzed with microRNA (miRNA) microarrays. We investigated the diagnostic potential of circulating miRNAs in serum and developed a detection model of HCC, including early stage. A diagnostic model was constructed based on the expression levels of a combination of miRNAs in a discovery set. We selected 52 miRNAs that had altered expressions according to disease progression status, established the diagnostic model with a combination of eight miRNAs in the discovery set, and tested the model in a validation set. The diagnostic values for discriminating cancer from HCC at-risk control samples were as follows: area under the curve, 0.99; sensitivity, 97.7%; specificity, 94.7%. With this model, 98% of stage I HCC cases were detected; these results were much better than those observed from conventional methods. Conclusion: Circulating miRNAs could serve as biomarkers for the accurate detection of HCC. Because the diagnostic accuracy was maintained even in stage I, this may represent an accurate detection method even for early stage HCC.

Peripheral neuropathy from paclitaxel: risk prediction by serum microRNAs.

OBJECTIVES: MicroRNAs (miRNAs) have recently been reported as useful diagnostic markers in cancer; however, relationships of miRNAs with adverse events during chemotherapy have yet to be fully described. In this study, we examined the relationship between serum miRNA and the risk of peripheral neuropathy (PN), a common and persistent adverse event induced by paclitaxel, in patients with breast cancer. METHODS: A total of 84 serum samples from patients with breast cancer, who received paclitaxel as neoadjuvant or adjuvant chemotherapy, were obtained between January 2011 and September 2013 at National Cancer Center Hospital. Samples were divided, 2:1, into a training cohort and a test cohort, respectively; both cohorts included specimens from patients with severe PN (≥grade 2, PN group) and non-severe PN controls (non-PN group). The training cohort was used to identify miRNAs, and combinations thereof, that could predict PN, which then were validated in the test cohort. RESULTS: Eighty-four patients received paclitaxel: 38 and 46 patients in the PN and non-PN groups, respectively. We identified 15 discriminatory miRNAs with |fold change|>0.5, and 14 combinations of three miRNAs showed the ability to discriminate, with sensitivity, specificity and accuracy of >50%. The most discriminatory miRNA, with the highest |fold change|, was miR-451a, which regulates the expression of the drug-transporter protein P-glycoprotein, potentially promoting paclitaxel resistance. CONCLUSION: MiR-451a could be a predictive marker for PN caused by paclitaxel-containing chemotherapy; however, further investigation of the underlying mechanism is required to determine the role of miR-451a.

Identification of serum microRNAs predicting the response of esophageal squamous-cell carcinoma to nivolumab.

BACKGROUND: Nivolumab, a programmed cell death protein 1 (PD-1) inhibitor, showed promising activity for the treatment of advanced esophageal squamous-cell carcinoma in a phase II study (ONO-4538-07; JapicCTI-No.142422). We explored serum microRNA (miRNA) candidate predictive markers of the response to nivolumab. METHODS: In the phase II study, 19 patients received nivolumab (3 mg/kg IV Q2W) at National Cancer Center Hospital. The expression of 2565 serum miRNAs before and during treatment was analyzed using a 3D-Gene Human miRNA Oligo Chip (Toray Industries, Inc.). Immune-related response evaluation criteria used to evaluate response and miRNA expression were compared between responders and non-responders. The top 20 miRNAs by accuracy in receiver operating characteristic curve analysis were identified by leave-one-out cross-validation, and those with the area under curve values > 0.8, cross-validated accuracy > 0.8, and a 0.5 difference in the average log2 expression level between responders and non-responders were further analyzed. RESULTS: Of the 19 patients, five responded to nivolumab. We identified miRNAs related to the response to nivolumab, including one detected in the serum before treatment (miR-1233-5p; AUC = 0.895) and three present after treatment (miR-6885-5p, miR-4698 and miR-128-2-5p; AUC = 0.93, 0.97 and 0.93, respectively). CONCLUSIONS: Candidate miRNAs capable of predicting the response to nivolumab were identified in the serum of patients with advanced esophageal squamous-cell carcinoma in ONO-4538-07.

Assessment of the Diagnostic Utility of Serum MicroRNA Classification in Patients With Diffuse Glioma.

Importance: A blood-based screening tool for detecting diffuse glioma is necessary to improve clinical outcomes. Objectives: To establish models using serum microRNAs to distinguish patients with diffuse glioma from control individuals without cancer (the Glioma Index) and to differentiate glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic brain tumors (the 3-Tumor Index). Design, Setting, and Participants: This retrospective, case-control diagnostic study included 157 patients with diffuse glioma and 109 patients with central nervous system (CNS) diseases other than diffuse glioma diagnosed from August 1, 2008, through May 1, 2016, and 314 sex- and age-matched controls without cancer. Samples of patients with diffuse glioma and controls were randomly divided into training and validation set 1, and those of patients with CNS diseases other than diffuse glioma were allocated to an exploratory set. Samples of patients with GBM, PCNSL, and metastatic brain tumors were randomly divided into training and validation set 2. Data were analyzed from April 1, 2018, to March 31, 2019. Main Outcomes and Measures: The expression of 2565 microRNAs was assessed, and the diagnostic performance was evaluated by calculating the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. Results: A total of 580 patients were included in the analysis (309 [53.3%] male; median age, 57 years [range, 10-87 years]). In training set 1, 100 patients with diffuse glioma (median age, 56 years [range, 14-87 years]; 55 male [55.0%]) were compared with 200 control patients (median age, 56 years [range, 14-87 years]; 105 male [52.5%]), and the Glioma Index was constructed using 3 microRNAs (miR-4763-3p, miR-1915-3p, and miR-3679-5p). In validation set 1, the AUC was 0.99 (95% CI, 0.99-1.00); sensitivity, 0.95 (95% CI, 0.89-1.00); and specificity, 0.97 (95% CI, 0.93-1.00). The Glioma Index classified 39 of 42 PCNSL samples (92.9%) and 25 of 28 metastatic brain tumor samples (89.3%) as positive and 2 of 2 spinal tumors (100%) as negative in the exploratory set. In training set 2, 68 patients with GBM, 34 with PCNSL, and 23 with metastatic brain tumor were compared, and the 3-Tumor Index was constructed using 48 microRNAs. The 3-Tumor Index had an accuracy of 0.80, positively detecting 16 of 17 GBM samples (94.1%), 4 of 5 metastatic brain tumor samples (80.0%), and 4 of 8 PCNSL samples (50.0%) in validation set 2. Conclusions and Relevance: This study appears to have identified promising serum microRNA combinations for detecting diffuse glioma and for assessing histologic features of brain tumors.

Brain metastasis-related microRNAs in patients with advanced breast cancer.

Brain metastasis is a major distant metastasis occurring in patients with advanced breast cancer, and is associated with poor prognosis. MicroRNAs (miRNAs) have a strong influence on various oncological functions and have been reported as potential biomarkers for detecting distant metastasis. Specific biomarkers and unique miRNAs for brain metastasis have yet to be reported. The aim of this study was to identify novel miRNAs in serum, to assist in the diagnosis of brain metastasis in patients with advanced breast cancer. We retrospectively analyzed the medical records of patients with breast cancer and collected clinical data. In addition, we evaluated serum miRNA profiles in patients with breast cancer, with and without brain metastasis, using high-sensitivity microarrays. All patients underwent computed tomography or magnetic resonance imaging brain imaging tests. A total of 51 serum samples from patients with breast cancer and brain metastasis, stored in the National Cancer Center Biobank, were used, and 28 serum samples were obtained from controls without brain metastasis. Two miRNAs, miR-4428 and miR-4480, could significantly distinguish patients with brain metastasis, with area under the receiver operating characteristic curve (AUC) values of 0.779 and 0.781, respectively, while a combination of miR-4428 and progesterone receptor had an AUC value of 0.884. No significant correlations were identified between the expression levels of these two miRNAs in serum and clinical data. We conclude that serum miR-4428 and miR-4480 may be useful as biomarkers for predicting brain metastasis in patients with breast cancer.

Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma.

Uterine leiomyosarcoma (ULMS) is the major subtype of uterine sarcoma (US) and contributes to uterine cancer deaths. Although preoperative diagnosis of US remains challenging, frequent application of laparoscopic surgery for benign uterine leiomyomas (ULM) requires precise exclusion of US. MicroRNAs are stably present in the bloodstream, and the application of circulating miRNAs as disease biomarkers has been recognized. In the present study, we aimed to identify diagnostic biomarkers for distinguishing US from ULM by focusing on circulating miRNAs. All serum samples were collected preoperatively between 2009 and 2017, and all cases were histopathologically diagnosed. Whole miRNA profiles were obtained using a miRNA microarray. By analyzing expression levels of the miRNAs, candidate miRNAs were selected based on diagnostic performance in discriminating US from ULM, and a diagnostic model was then constructed. A total of 90 serum samples were analyzed, and clustering analyses revealed that the profiles of ULMS were distinct from those of controls. Based on leave-one-out cross-validation, seven miRNAs were selected as biomarker candidates. Based on model construction, the optimal model consisted of two miRNAs (miR-1246 and miR-191-5p), with an area under the receiver operating characteristic curve (AUC) for identifying ULMS of 0.97 (95% confidence interval [CI], 0.91-1.00). In contrast, serum lactate dehydrogenase had an AUC of only 0.64 (95% CI, 0.34-0.94). Seven serum miRNAs with high diagnostic performance for preoperative US screening were detected, and a promising diagnostic model for ULMS was generated.

Serum microRNA-based prediction of responsiveness to eribulin in metastatic breast cancer.

The identification of biomarkers for predicting the responsiveness to eribulin in patients with metastatic breast cancer pretreated with an anthracycline and a taxane remains an unmet need. Here, we established a serum microRNA (miRNA)-based prediction model for the emergence of new distant metastases after eribulin treatment. Serum samples were collected from metastatic breast cancer patients prior to eribulin treatment and comprehensively evaluated by miRNA microarray. The prediction model for estimating eribulin efficacy was established using the logistic LASSO regression model. Serum samples were collected from 147 patients, of which 52 developed at least one new distant metastasis after eribulin monotherapy and 95 did not develop new distant metastases. A combination of eight serum miRNAs (miR-4483, miR-8089, miR-4755-3p, miR-296-3p, miR-575, miR-4710, miR-5698 and miR-3160-5p) predicted the appearance of new distant metastases with an area under the curve of 0.79, sensitivity of 0.69 and specificity of 0.82. The serum levels of miR-8089 and miR-5698 were significantly associated with overall survival after the initiation of eribulin treatment. The present study provides evidence that serum miRNA profiling may serve as a biomarker for the responsiveness to eribulin and for predicting the development of new distant metastases in metastatic breast cancer.

Development and Validation of an Esophageal Squamous Cell Carcinoma Detection Model by Large-Scale MicroRNA Profiling.

Importance: Patients with late-stage esophageal squamous cell carcinoma (ESCC) have a poor prognosis. Noninvasive screening tests using serum microRNAs (miRNAs) to accurately detect early-stage ESCC are needed to improve mortality. Objective: To establish a model using serum miRNAs to distinguish patients with ESCC from noncancer controls. Design, Setting, and Participants: In this case-control study, serum miRNA expression profiles of patients with ESCC (n = 566) and control patients without cancer (n = 4965) were retrospectively analyzed to establish a diagnostic model, which was tested in a training set and confirmed in a validation set. Patients histologically diagnosed as having ESCC who did not receive prior therapy or have a past or concurrent cancer other than ESCC were enrolled from the National Cancer Center Hospital in Tokyo, Japan. Between October 2010 and November 2015, control samples were collected from the National Cancer Center Biobank, the Biobank of the National Center for Geriatrics and Gerontology, and the general population undergoing routine health examination. Data analysis was performed between August 2015 and October 2018. Serum samples were randomly divided into discovery and validation sets. Main Outcomes and Measures: The expression of 2565 miRNAs was assessed in each sample. The discriminant model (named the EC index) was evaluated in the training set using Fisher linear discriminant analysis with a greedy algorithm. Receiver operating characteristic curve analysis evaluated the diagnostic ability of the model in the validation set. Results: In the training set, 283 patients with esophageal cancer (median age, 67 years [range, 37-90 years]; 83.4% male) were compared with 283 control patients (median age, 54 years [range, 22-100 years]; 43.1% male), and the EC index was constructed using 6 miRNAs (miR-8073, miR-6820-5p, miR-6794-5p, miR-3196, miR-744-5p, and miR-6799-5p). The area under the receiver operating characteristic curve was 1.00, with sensitivity of 1.00 and specificity of 0.98. The validation set included 283 patients (median age, 66 years [range, 42-87 years]; 83.0% male) and 4682 control patients (median age, 68 years [range, 20-98 years]; 44.7% male), and the area under the receiver operating characteristic curve for the EC index was 1.00, with sensitivity of 0.96 and specificity of 0.98. Conclusions and Relevance: What appears to be novel serum miRNA discriminant model was developed for the diagnosis of ESCC. A multicenter prospective study is ongoing to confirm the present observations.

Serum MicroRNA-Based Risk Prediction for Stroke.

Background and Purpose- Numerous studies have shown that circulating microRNAs (miRNAs) can be used as noninvasive biomarkers of various diseases. This study aimed to identify serum miRNAs that predict the risk of stroke. Methods- The cases were individuals who had been diagnosed with cerebrovascular disorder by brain imaging. The controls were individuals with no history of stroke who had undergone a medical checkup. Serum miRNA profiling was performed for all participants using microarray analysis. Samples were divided into discovery, training, and validation sets. In the discovery set, which consisted of control samples only, serum miRNAs that correlated with the predicted risk of stroke, as calculated using 7 clinical risk factors, were identified by Pearson correlation analysis. In the training set, a discriminant model between cases and controls was constructed using the identified miRNAs, Fisher linear discrimination model with leave-one-out cross-validation and DeLong test. In the validation set, the predictive accuracy of the constructed model was calculated. Results- First, in 1523 control samples (discovery set), we identified 10 miRNAs that correlated with a predicted risk of stroke. Second, in 45 controls and 87 cases (training set), we identified 7 of 10 miRNAs that significantly associated with cerebrovascular disorder (miR-1228-5p, miR-1268a, miR-1268b, miR-4433b-3p, miR-6090, miR-6752-5p, and miR-6803-5p). Third, a 3-miRNA combination model (miR-1268b, miR-4433b-3p, and miR-6803-5p) was constructed in the training set with a sensitivity of 84%, a specificity of 98%, and an area under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.98). Finally, in 45 controls and 86 cases (validation set), the 3-miRNA model achieved a sensitivity of 80%, a specificity of 82%, and an area under the receiver operating characteristic of 0.89 (95% CI, 0.83-0.95) for cerebrovascular disorder. Conclusions- We identified 7 serum miRNAs that could predict the risk of cerebrovascular disorder before the onset of stroke.

A serum microRNA classifier for the diagnosis of sarcomas of various histological subtypes.

Due to their rarity and diversity, sarcomas are difficult to diagnose. Consequently, there is an urgent demand for a novel diagnostic test for these cancers. In this study, we investigated serum miRNA profiles from 1002 patients with bone and soft tissue tumors representing more than 43 histological subtypes, including sarcomas, intermediate tumors, and benign tumors, to determine whether serum miRNA profiles could be used to specifically detect sarcomas. Circulating serum miRNA profiles in sarcoma patients were clearly distinct from those in patients with other types of tumors. Using the serum levels of seven miRNAs, we developed a molecular detector, Index VI, that could distinguish sarcoma patients from benign and healthy controls with remarkably high sensitivity (90%) and specificity (95%), regardless of histological subtype. Index VI provides an approach to the early and precise detection of sarcomas, potentially leading to curative treatment and longer survival.

Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data.

Alzheimer's disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia. The final risk prediction model achieved a high accuracy of 0.873 on a validation cohort in AD, when using 78 miRNAs: Accuracy = 0.836 with 86 miRNAs in VaD; Accuracy = 0.825 with 110 miRNAs in DLB. To our knowledge, this is the first report applying miRNA-based risk prediction models to a dementia prospective cohort. Our study demonstrates our models to be effective in prospective disease risk prediction, and with further improvement may contribute to practical clinical use in dementia.

Large-scale Circulating microRNA Profiling for the Liquid Biopsy of Prostate Cancer.

PURPOSE: The high false-positive rate of prostate-specific antigen (PSA) may lead to unnecessary prostate biopsies. Therefore, the United States Preventive Services Task Force recommends that decisions regarding PSA-based screening of prostate cancer should be made with caution in men ages 55-69 years, and that men ≥70 years should not undergo PSA screening. Here, we investigated the potential of serum miRNAs as an accurate diagnostic method in patients with suspected prostate cancer. EXPERIMENTAL DESIGN: Serum samples of 809 patients with prostate cancer, 241 negative prostate biopsies, and 500 patients with other cancer types were obtained from the National Cancer Center, Japan. Forty-one healthy control samples were obtained from two other hospitals in Japan. Comprehensive microarray analysis was performed for all samples. Samples were divided into three sets. Candidate miRNAs for prostate cancer detection were identified in the discovery set (n = 123). A diagnostic model was constructed using combinations of candidate miRNAs in the training set (n = 484). The performance of the diagnostic model was evaluated in the validation set (n = 484). RESULTS: In the discovery set, 18 candidate miRNAs were identified. A robust diagnostic model was constructed using the combination of two miRNAs (miR-17-3p and miR-1185-2-3p) in the training set. High diagnostic performance with a sensitivity of 90% and a specificity of 90% was achieved in the validation set regardless of the Gleason score and clinical tumor-node-metastasis stage. CONCLUSIONS: The model developed in this study may help improve the diagnosis of prostate cancer and reduce the number of unnecessary prostate biopsies.