Published papers

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.

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.

© 2018, Macmillan Publishers Ltd., part of Springer Nature. The efficacy of Helicobacter pylori eradication therapy for the prevention of preneoplastic lesions in gastric cancer remains controversial. A new placebo-controlled trial and a large-scale observational study tackle this problem and show the positive effects of eradication therapy.

© 2019 American Association for Cancer Research. 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 ¼ 123). A diagnostic model was constructed using combinations of candidate miRNAs in the training set (n ¼ 484). The performance of the diagnostic model was evaluated in the validation set (n ¼ 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.

The clinical manifestations of COVID-19 vary broadly, ranging from asymptomatic infection to acute respiratory failure and death. But the predictive biomarkers for characterizing the variability are still lacking. Since emerging evidence indicates that extracellular vesicles (EVs) and extracellular RNAs (exRNAs) are functionally involved in a number of pathological processes, we hypothesize that these extracellular components may be key determinants and/or predictors of COVID-19 severity. To test our hypothesis, we collected serum samples from 31 patients with mild COVID-19 symptoms at the time of their admission for discovery cohort. After symptomatic treatment without corticosteroids, 9 of the 31 patients developed severe/critical COVID-19 symptoms. We analyzed EV protein and exRNA profiles to look for correlations between these profiles and COVID-19 severity. Strikingly, we identified three distinct groups of markers (antiviral response-related EV proteins, coagulation-related markers, and liver damage-related exRNAs) with the potential to serve as early predictive biomarkers for COVID-19 severity. As the best predictive marker, EV COPB2 protein, a subunit of the Golgi coatomer complex, exhibited significantly higher abundance in patients remained mild than developed severe/critical COVID-19 and healthy controls in discovery cohort (AUC 1.00 (95% CI: 1.00-1.00)). The validation set included 40 COVID-19 patients and 39 healthy controls, and showed exactly the same trend between the three groups with excellent predictive value (AUC 0.85 (95% CI: 0.73-0.97)). These findings highlight the potential of EV COPB2 expression for patient stratification and for making early clinical decisions about strategies for COVID-19 therapy.