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.

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.

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.

Serum miRNA-based Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

PURPOSE: Sentinel lymph node biopsy (SLNB) is the gold-standard procedure for evaluating axillary lymph node (ALN) status in patients with breast cancer. However, the morbidity of SLNB is not negligible, and the procedure is invasive for patients without ALN metastasis. Here, we developed a diagnostic model for evaluating ALN status using a combination of serum miRNAs and clinicopathologic factors as a novel less-invasive biomarker.Experimental Design: Preoperative serum samples were collected from patients who underwent surgery for primary breast cancer or breast benign diseases between 2008 and 2014. A total of 958 serum samples (921 cases of primary breast cancer, including 630 cases in the no ALN metastasis group and 291 cases in the ALN metastasis group, and 37 patients with benign breast diseases) were analyzed by miRNA microarray. Samples were randomly divided into training and test sets. Logistic LASSO regression analysis was used to construct diagnostic models in the training set, which were validated in the test set. RESULTS: An optimal diagnostic model was identified using a combination of two miRNAs (miR-629-3p and miR-4710) and three clinicopathologic factors (T stage, lymphovascular invasion, and ultrasound findings), which showed a sensitivity of 0.88 (0.84-0.92), a specificity of 0.69 (0.61-0.76), an accuracy of 0.818, and an area under the receiver operating characteristic curve of 0.86 in the test set. CONCLUSIONS: Serum miRNA profiles may be useful for the diagnosis of ALN metastasis before surgery in a less-invasive manner than SLNB.

Circulating miRNA panels for specific and early detection in bladder cancer.

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.

Integrated extracellular microRNA profiling for ovarian cancer screening.

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.

Exciplex ensemble modulated by excitation mode in intramolecular charge-transfer dyad: effects of temperature, solvent polarity, and wavelength on photochemistry and photophysics of tethered naphthalene-dicyanoethene system.

Solvent, temperature, and excitation wavelength significantly affected the photochemical outcomes of a naphthalene-dicyanoethene system tethered by different number (n) of methylene groups (1-3). The effect of irradiation wavelength was almost negligible for 2a but pronounced for 3a. The temperature dependence and theoretical calculations indicated the diversity of exciplex conformations, an ensemble of which can be effectively altered by changing excitation wavelength to eventually switch the regioselectivity of photoreactions.

Targeted gene disruption identifies three PPR-DYW proteins involved in RNA editing for five editing sites of the moss mitochondrial transcripts.

In plant organelles, RNA editing frequently occurs in many transcripts, but little is known about its molecular mechanism. Eleven RNA editing sites are present in the moss Physcomitrella patens mitochondria. Recently PpPPR_71, one member of 10 DYW-subclass pentatricopeptide repeat (PPR-DYW) proteins, has been identified as a site-specific recognition factor for RNA editing in the mitochondrial transcript. In this study, we disrupted three genes encoding a PPR-DYW protein-PpPPR_56, PpPPR_77, and PpPPR_91-to investigate whether they are involved in RNA editing. Transient expression of an N-terminal amino acid sequence fused to the green fluorescent protein (GFP) suggests that the three PPR-DYW proteins are targeted to mitochondria. Disruption of each gene by homologous recombination revealed that PpPPR_56 was involved in RNA editing at the nad3 and nad4 sites, PpPPR_77 at the cox2 and cox3 sites, and PpPPR_91 at the nad5-2 site in the mitochondrial transcripts. The nucleotide sequences surrounding the two editing sites targeted by a single PPR-DYW protein share 42 to 56% of their identities. Thus, moss PPR-DYW proteins seem to be site-specific factors for RNA editing in mitochondrial transcripts.