Genes Associated with Recurrence of Hepatocellular Carcinoma: Integrated Analysis by Gene Expression and Methylation Profiling

Gene expression is suppressed by DNA methylation. The goal of this study was to identify genes whose CpG site methylation and mRNA expression are associated with recurrence after surgical resection for hepatocellular carcinoma (HCC). Sixty-two HCCs were examined by both whole genome DNA methylation and transcriptome analysis. The Cox model was used to select genes associated with recurrence. A validation was performed in an independent cohort of 66 HCC patients. Among fifty-nine common genes, increased CpG site methylation and decreased mRNA expression were associated with recurrence for 12 genes (Group A), whereas decreased CpG site methylation and increased mRNA expression were associated with recurrence for 25 genes (Group B). The remaining 22 genes were defined as Group C. Complement factor H (CFH) and myosin VIIA and Rab interacting protein (MYRIP) in Group A; proline/serine-rich coiled-coil 1 (PSRC1), meiotic recombination 11 homolog A (MRE11A), and myosin IE (MYO1E) in Group B; and autophagy-related protein LC3 A (MAP1LC3A), and NADH dehydrogenase 1 alpha subcomplex assembly factor 1 (NDUFAF1) in Group C were validated. In conclusion, potential tumor suppressor (CFH, MYRIP) and oncogenes (PSRC1, MRE11A, MYO1E) in HCC are reported. The regulation of individual genes by methylation in hepatocarcinogenesis needs to be validated

Clinical Characteristics of a Nationwide Hospital-based Registry of Mild-to-Moderate Alzheimer's Disease Patients in Korea: A CREDOS (Clinical Research Center for Dementia of South Korea) Study

With rapid population aging, the socioeconomic burden caused by dementia care is snowballing. Although a few community-based studies of Alzheimer's disease (AD) have been performed in Korea, there has never been a nationwide hospital-based study thereof. We aimed to identify the demographics and clinical characteristics of mild-to-moderate AD patients from the Clinical Research Center for Dementia of Korea (CREDOS) registry. A total of 1,786 patients were consecutively included from September 2005 to June 2010. Each patient underwent comprehensive neurological examination, interview for caregivers, laboratory investigations, neuropsychological tests, and brain MRI. The mean age was 74.0 yr and the female percentage 67.0%. The mean period of education was 7.1 yr and the frequency of early-onset AD (< 65 yr old) was 18.8%. Among the vascular risk factors, hypertension (48.9%) and diabetes mellitus (22.3%) were the most frequent. The mean score of the Korean version of Mini-Mental State Examination (K-MMSE) was 19.2 and the mean sum of box scores of Clinical Dementia Rating (CDR-SB) 5.1. Based on the well-structured, nationwide, and hospital-based registry, this study provides the unique clinical characteristics of AD and emphasizes the importance of vascular factors in AD in Korea

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Background Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimers disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The time orientation and 3-word recall score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.The publication costs, design of the study, data management and writing the manuscript for this article were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538), Korea Ministry of Health & Welfare, and from the Original Technology Research Program for Brain Science through the National Research Foundation of Korea funded by the Korean Government (MSIP; No. 2014M3C7A1064752)

Rare Exonic Minisatellite Alleles in MUC2 Influence Susceptibility to Gastric Carcinoma

BACKGROUND: Mucins are the major components of mucus and their genes share a common, centrally-located region of sequence that encodes tandem repeats. Mucins are well known genes with respect to their specific expression levels; however, their genomic levels are unclear because of complex genomic properties. In this study, we identified eight novel minisatellites from the entire MUC2 region and investigated how allelic variation in these minisatellites may affect susceptibility to gastrointestinal cancer. METHODOLOGY/PRINCIPLE FINDINGS: We analyzed genomic DNA from the blood of normal healthy individuals and multi-generational family groups. Six of the eight minisatellites exhibited polymorphism and were transmitted meiotically in seven families, following Mendelian inheritance. Furthermore, a case-control study was performed that compared genomic DNA from 457 cancer-free controls with DNA from individuals with gastric (455), colon (192) and rectal (271) cancers. A statistically significant association was identified between rare exonic MUC2-MS6 alleles and the occurrence of gastric cancer: odds ratio (OR), 2.56; 95% confidence interval (CI), 1.31-5.04; and p = 0.0047. We focused on an association between rare alleles and gastric cancer. Rare alleles were divided into short (40, 43 and 44) and long (47, 50 and 54), according to their TR (tandem repeats) lengths. Interestingly, short rare alleles were associated with gastric cancer (OR = 5.6, 95% CI: 1.93-16.42; p = 0.00036). Moreover, hypervariable MUC2 minisatellites were analyzed in matched blood and cancer tissue from 28 patients with gastric cancer and in 4 cases of MUC2-MS2, minisatellites were found to have undergone rearrangement. CONCLUSIONS/SIGNIFICANCE: Our observations suggest that the short rare MUC2-MS6 alleles could function as identifiers for risk of gastric cancer. Additionally, we suggest that minisatellite instability might be associated with MUC2 function in cancer cells

Phase II randomized trial of neoadjuvant metformin plus letrozole versus placebo plus letrozole for estrogen receptor positive postmenopausal breast cancer (METEOR)

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background Neoadjuvant endocrine therapy with an aromatase inhibitor has shown efficacy comparable to that of neoadjuvant chemotherapy in patients with postmenopausal breast cancer. Preclinical and clinical studies have shown that the antidiabetic drug metformin has anti-tumor activity. This prospective, multicenter, phase II randomized, placebo controlled trial was designed to evaluate the direct anti-tumor effect of metformin in non-diabetic postmenopausal women with estrogen-receptor (ER) positive breast cancer. Methods/Design Patients meeting the inclusion criteria and providing written informed consent will be randomized to 24 weeks of neoadjuvant treatment with letrozole (2.5 mg/day) and either metformin (2000 mg/day) or placebo. Target accrual number is 104 patients per arm. The primary endpoint will be clinical response rate, as measured by calipers. Secondary endpoints include pathologic complete response rate, breast conserving rate, change in Ki67 expression, breast density change, and toxicity profile. Molecular assays will be performed using samples obtained before treatment, at week 4, and postoperatively. Discussion This study will provide direct evidence of the anti-tumor effect of metformin in non-diabetic, postmenopausal patients with ER-positive breast cancer. Trial registration ClinicalTrials.gov Identifier NCT0158936

Phase II randomized trial of neoadjuvant metformin plus letrozole versus placebo plus letrozole for estrogen receptor positive postmenopausal breast cancer (METEOR)

This study is being supported by grant no 04-2012-0290 from the SNUH Research fund and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP)(No. 2013005540). Letrozole and metformin are being supplied by the pharmaceutical company, Shin Poong Pharm. Co., Ltd.Background : Neoadjuvant endocrine therapy with an aromatase inhibitor has shown efficacy comparable to that of neoadjuvant chemotherapy in patients with postmenopausal breast cancer. Preclinical and clinical studies have shown that the antidiabetic drug metformin has anti-tumor activity. This prospective, multicenter, phase II randomized, placebo controlled trial was designed to evaluate the direct anti-tumor effect of metformin in non-diabetic postmenopausal women with estrogen-receptor (ER) positive breast cancer. Methods/Design : Patients meeting the inclusion criteria and providing written informed consent will be randomized to 24 weeks of neoadjuvant treatment with letrozole (2.5 mg/day) and either metformin (2000 mg/day) or placebo. Target accrual number is 104 patients per arm. The primary endpoint will be clinical response rate, as measured by calipers. Secondary endpoints include pathologic complete response rate, breast conserving rate, change in Ki67 expression, breast density change, and toxicity profile. Molecular assays will be performed using samples obtained before treatment, at week 4, and postoperatively. Discussion : This study will provide direct evidence of the anti-tumor effect of metformin in non-diabetic, postmenopausal patients with ER-positive breast cancer. Trial registration : ClinicalTrials.gov Identifier NCT01589367Peer Reviewe

The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

This paper proposes a development of automatic rib sequence labeling systems on chest computed tomography (CT) images with two suggested methods and three-dimensional (3D) region growing. In clinical practice, radiologists usually define anatomical terms of location depending on the rib&rsquo;s number. Thus, with the manual process of labeling 12 pairs of ribs and counting their sequence, it is necessary to refer to the annotations every time the radiologists read chest CT. However, the process is tedious, repetitive, and time-consuming as the demand for chest CT-based medical readings has increased. To handle the task efficiently, we proposed an automatic rib sequence labeling system and implemented comparison analysis on two methods. With 50 collected chest CT images, we implemented intensity-based image processing (IIP) and a convolutional neural network (CNN) for rib segmentation on this system. Additionally, three-dimensional (3D) region growing was used to classify each rib&rsquo;s label and put in a sequence label. The IIP-based method reported a 92.0% and the CNN-based method reported a 98.0% success rate, which is the rate of labeling appropriate rib sequences over whole pairs (1st to 12th) for all slices. We hope for the applicability thereof in clinical diagnostic environments by this method-efficient automatic rib sequence labeling system