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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Asakura, Kadota et al. demonstrate the diagnostic potential of serum microRNAs for resectable lung cancer. Their diagnostic model based on the combined expression levels of two miRNAs predicts resectable lung cancer with 99% sensitivity, regardless of histological types and pathological stages of cancer, suggesting its promising, diagnostic utility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Bladder cancer is the 9th leading cause of cancer death worldwide. The major problem in bladder cancer is primarily the high recurrence rate after drug treatment and resection. Although conventional screening methods, such as cystoscopy, urinary cytology and ultrasound sonography, have become widely used in clinical settings, the diagnostic performance of these modalities is unsatisfactory due to low accuracy or high invasiveness. Because circulating micro RNA (miRNA) profiles have recently been reported as an attractive tool for liquid biopsy in cancer screening, here, we performed global miRNA profiling of 392 serum samples of bladder cancer patients with 100 non‐cancer samples and 480 samples of other types of cancer as controls. We randomly classified the bladder cancer and control samples into 2 cohorts, a training set (N = 486) and a validation set (N = 486). By comparing both controls, we identified specific miRNA, such as miR‐6087, for diagnosing bladder cancer in the training and validation sets. Furthermore, we found that a combination of 7 miRNA (7‐miRNA panel: miR‐6087, miR‐6724‐5p, miR‐3960, miR‐1343‐5p, miR‐1185‐1‐3p, miR‐6831‐5p and miR‐4695‐5p) could discriminate bladder cancer from non‐cancer and other types of tumors with the highest accuracy (AUC:.97; sensitivity: 95%; specificity: 87%). The diagnostic accuracy was high, regardless of the stage and grade of bladder cancer. Our data demonstrated that the 7‐miRNA panel could be a biomarker for the specific and early detection of bladder cancer. The present study selected 7 miRNA expression levels among bladder cancer, non‐cancer and other cancer samples. A diagnostic index was calculated and plotted in the dot plot among 12 different cancers. An index score ≥0 indicated the presence of bladder cancer and an index score <0 indicated the absence of bladder cancer. [ABSTRACT FROM AUTHOR]

MicroRNA (miRNA), which are stably present in serum, have been reported to be potentially useful for detecting cancer. In the present study, we examined the expression profiles of serum miRNA in several large cohorts to identify novel miRNA that can be used to detect early stage breast cancer. We comprehensively evaluated the serum miRNA expression profiles using highly sensitive microarray analysis. A total of 1280 serum samples of breast cancer patients stored in the National Cancer Center Biobank were used. In addition, 2836 serum samples were obtained from non-cancer controls, 451 from patients with other types of cancers, and 63 from patients with non-breast benign diseases. The samples were divided into a training cohort including non-cancer controls, other cancers and breast cancer, and a test cohort including non-cancer controls and breast cancer. The training cohort was used to identify a combination of miRNA that could detect breast cancer, and the test cohort was used to validate that combination. miRNA expressions were compared between patients with breast cancer and non-breast cancer, and a combination of five miRNA (miR-1246, miR-1307-3p, miR-4634, miR-6861-5p and miR-6875-5p) was found to be able to detect breast cancer. This combination had a sensitivity of 97.3%, specificity of 82.9% and accuracy of 89.7% for breast cancer in the test cohort. In addition, this combination could detect early stage breast cancer (sensitivity of 98.0% for Tis). [ABSTRACT FROM AUTHOR]

It is difficult to detect pancreatic cancer or biliary-tract cancer at an early stage using current diagnostic technology. Utilizing microRNA (miRNA) markers that are stably present in peripheral blood, we aimed to identify pancreatic and biliary-tract cancers in patients. With “3D-Gene”, a highly sensitive microarray, we examined comprehensive miRNA expression profiles in 571 serum samples obtained from healthy patients, patients with pancreatic, biliary-tract, or other digestive cancers, and patients with non-malignant abnormalities in the pancreas or biliary tract. The samples were randomly divided into training and test cohorts, and candidate miRNA markers were independently evaluated. We found 81 miRNAs for pancreatic cancer and 66 miRNAs for biliary-tract cancer that showed statistically different expression compared with healthy controls. Among those markers, 55 miRNAs were common in both the pancreatic and biliary-tract cancer samples. The previously reported miR-125a-3p was one of the common markers; however, it was also expressed in other types of digestive-tract cancers, suggesting that it is not specific to cancer types. In order to discriminate the pancreato-biliary cancers from all other clinical conditions including the healthy controls, non-malignant abnormalities, and other types of cancers, we developed a diagnostic index using expression profiles of the 10 most significant miRNAs. A combination of eight miRNAs (miR-6075, miR-4294, miR-6880-5p, miR-6799-5p, miR-125a-3p, miR-4530, miR-6836-3p, and miR-4476) achieved a sensitivity, specificity, accuracy and AUC of 80.3%, 97.6%, 91.6% and 0.953, respectively. In contrast, CA19-9 and CEA gave sensitivities of 65.6% and 40.0%, specificities of 92.9% and 88.6%, and accuracies of 82.1% and 71.8%, respectively, in the same test cohort. This diagnostic index identified 18/21 operable pancreatic cancers and 38/48 operable biliary-tract cancers in the entire cohort. Our results suggest that the assessment of these miRNA markers is clinically valuable to identify patients with pancreato-biliary cancers who could benefit from surgical intervention. [ABSTRACT FROM AUTHOR]

Demand for high quality gene expression data has driven the development of revolutionary microarray technologies. The quality of the data is affected by the performance of the microarray platform as well as how the nucleic acid targets are prepared. The most common method for target nucleic acid preparation includes in vitro transcription amplification of the sample RNA. Although this method requires a small amount of starting material and is reported to have high reproducibility, there are also technical disadvantages such as amplification bias and the long, laborious protocol. Using RNA derived from human brain, breast and colon, we demonstrate that a non-amplification method, which was previously shown to be inferior, could be transformed to a highly quantitative method with a dynamic range of five orders of magnitude. Furthermore, the correlation coefficient calculated by comparing microarray assays using non-amplified samples with qRTPCR assays was approximately 0.9, a value much higher than when samples were prepared using amplification methods. Our results were also compared with data from various microarray platforms studied in the MicroArray Quality Control (MAQC) project. In combination with micro-columnar 3D-Gene™ microarray, this non-amplification method is applicable to a variety of genetic analyses, including biomarker screening and diagnostic tests for cancer. [ABSTRACT FROM AUTHOR]

ABSTRACT BIRB-796, a selective inhibitor of p38 mitogen-activated protein kinase, has entered clinical trials for the treatment of autoimmune diseases. Levels of alanine transaminase, a biomarker of hepatic toxicity in clinical pathology, were found to be increased in Crohn's disease patients treated with BIRB-796. The purpose of the present study was to clarify the molecular mechanism(s) of this hepatotoxicity. A toxicogenomic analysis using a highly sensitive DNA chip, 3D-Gene™ Mouse Oligo chip 24k, indicated that BIRB-796 treatment activated the nuclear factor (erythroid-derived 2)-like 2 signaling pathway, which plays a key role in the response to oxidative stress. A reactive intermediate of BIRB-796 was detected by the glutathione-trapping method using mouse and human liver microsomes. The production of this reactive metabolite in the liver may be one of the causes of BIRB-796's hepatotoxicity. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective: Hepatocellular carcinoma (HCC) is difficult to manage due to the high frequency of post-surgical recurrence. Early detection of the HCC recurrence after liver resection is important in making further therapeutic options, such as salvage liver transplantation. In this study, we utilized microRNA expression profiling to assess the risk of HCC recurrence after liver resection. Methods: We examined microRNA expression profiling in paired tumor and non-tumor liver tissues from 73 HCC patients who satisfied the Milan Criteria. We constructed prediction models of recurrence-free survival using the Cox proportional hazard model and principal component analysis. The prediction efficiency was assessed by the leave-one-out crossvalidation method, and the time-averaged area under the ROC curve (ta-AUROC). Results: The univariate Cox analysis identified 13 and 56 recurrence-related microRNAs in the tumor and non-tumor tissues, such as miR-96. The number of recurrence-related microRNAs was significantly larger in the non-tumor-derived microRNAs (N-miRs) than in the tumor-derived microRNAs (T-miRs, P<0.0001). The best ta-AUROC using the whole dataset, T-miRs, NmiRs, and clinicopathological dataset were 0.8281, 0.7530, 0.7152, and 0.6835, respectively. The recurrence-free survival curve of the low-risk group stratified by the best model was significantly better than that of the high-risk group (Log-rank: P = 0.00029). The T-miRs tend to predict early recurrence better than late recurrence, whereas N-miRs tend to predict late recurrence better (P<0.0001). This finding supports the concept of early recurrence by the dissemination of primary tumor cells and multicentric late recurrence by the 'field effect'. Conclusion: microRNA profiling can predict HCC recurrence in Milan criteria cases. [ABSTRACT FROM AUTHOR]

Rapid and multiplex detection system using an ultrasensitive DNA microarray was developed and utilized for the analysis of six pharmacokinetically relevant single nucleotide polymorphisms (SNPs) (MDR1-C1236T, MDR1-G2677TA, MDR1-C3435T, CYP3A5-A6986G, CYP2C19-G681A, CYP2C19-G636A) from blood samples derived from liver transplant patients. The SNP detection system is comprised of three processes: multiplex PCR, single base extension with fluorescently labelled di-deoxy-nucleotides and detection by DNA microarray. The entire workflow of this system completes within 5 h. The final genotype call was obtained statistically by Mahalanobis distance which was calculated from the bi-coloured fluorescent signals detected by the microarray. In order to detect the six SNPs, this system required only 50 copies of genomic DNA, and the obtained detection calls completely matched with the results by the sequencing-based genotyping method. With the high sensitivity and rapid processing, our SNP detection system utilizing ultrasensitive microarray is a promising device applicable for diagnostic utility. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. Sarcomas are rare malignant tumours of bone and soft tissue whose diagnosis remain difficult. Here, the authors analyse serum samples from over 1000 patients and using separate discovery, training and validation cohorts, identify and validate a 7-microRNA index that distinguishes malignant sarcomas from benign disease. [ABSTRACT FROM AUTHOR]

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. Daichi Shigemizu et al. developed a risk prediction model using potential miRNA biomarkers of different dementias identified by a supervised principal component analysis logistic regression method. Their models achieved high accuracy when tested on a validation cohort and demonstrate the potential application of miRNA-based risk prediction models. [ABSTRACT FROM AUTHOR]

A major obstacle to improving prognoses in ovarian cancer is the lack of effective screening methods for early detection. Circulating microRNAs (miRNAs) have been recognized as promising biomarkers that could lead to clinical applications. Here, to develop an optimal detection method, we use microarrays to obtain comprehensive miRNA profiles from 4046 serum samples, including 428 patients with ovarian tumors. A diagnostic model based on expression levels of ten miRNAs is constructed in the discovery set. Validation in an independent cohort reveals that the model is very accurate (sensitivity, 0.99; specificity, 1.00), and the diagnostic accuracy is maintained even in early-stage ovarian cancers. Furthermore, we construct two additional models, each using 9-10 serum miRNAs, aimed at discriminating ovarian cancers from the other types of solid tumors or benign ovarian tumors. Our findings provide robust evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer. Screening methods for early detection of ovarian cancer is technically difficult. Here, the authors investigated circulating microRNA in human blood serum and developed a model using 10 microRNAs to discern between ovarian cancer and being ovarian tumors, solid tumors, and non-cancer patients. [ABSTRACT FROM AUTHOR]