Background Little is known about epigenetic silencing of genes by promoter hypermethylation in renal cell carcinoma (RCC). The aim of this study was to identify prognostic methylation markers in surgically treated clear cell RCC (ccRCC). Methods Methylation patterns were assayed using the Infinium HumanMethylation450 BeadChip array on pairs of ccRCC and normal tissue from 12 patients. Using quantitative PSQ analysis, tumor-specific hypermethylated genes were validated in 25 independent cohorts and their clinical relevance was also verified in 152 independent cohorts. Results Using genome-wide methylation array, Zinc finger protein 278 ( ZNF278 ), Family with sequence similarity 155 member A ( FAM155A ) and Dipeptidyl peptidase 6 ( DPP6 ) were selected for tumor-specific hypermethylated genes in primary ccRCC. The promoter methylation of these genes occurred more frequently in ccRCC than normal kidney in independent validation cohort. The hypermethylation of three genes were associated with advanced tumor stage and high grade tumor in ccRCC. During median follow-up of 39.2 (interquartile range, 15.4–79.1) months, 22 (14.5%) patients experienced distant metastasis. Multivariate analysis identified the methylation status of these three genes, either alone, or in a combined risk score as an independent predictor of distant metastasis. Conclusion The promoter methylation of ZNF278 , FAM155A and DPP6 genes are associated with aggressive tumor phenotype and early development of distant metastasis in patients with surgically treated ccRCC. These potential methylation markers, either alone, or in combination, could provide novel targets for development of individualized therapeutic and prevention regimens.

Very young breast cancer patients are more common in Asian countries than Western countries and are thought to have worse prognosis than older patients. The aim of the current study was to identify molecular characteristics of young patients with estrogen receptor ( ER )‐positive breast cancer by analyzing mutations and copy number variants ( CNV ), and by applying expression profiling. The whole exome and transcriptome of 47 Korean young breast cancer ( KYBR ) patients (age <35) were analyzed. Genomic profiles were constructed using mutations, CNV and differential gene expression from sequencing data. Pathway analyses were also performed using gene sets to identify biological processes. Our data were compared with young ER + breast cancer patients in The Cancer Genome Atlas ( TCGA ) dataset. TP 53 , PIK 3 CA and GATA 3 were highly recurrent somatic mutation genes. APOBEC ‐associated mutation signature was more frequent in KYBR compared with young TCGA patients. Integrative profiling was used to classify our patients into 3 subgroups based on molecular characteristics. Group A showed luminal A‐like subtype and IGF 1R signal dysregulation. Luminal B patients were classified into groups B and C, which showed chromosomal instability and enrichment for APOBEC 3A/B deletions, respectively. Group B was characterized by 11q13 ( CCND 1) amplification and activation of the ubiquitin‐mediated proteolysis pathway. Group C showed 17q12 ( ERBB 2) amplification and lower ER and progesterone receptor expression. Group C was also distinguished by immune activation and lower epithelial‐mesenchyme transition ( EMT ) degree compared with group B. This study showed that integrative genomic profiling could classify very young patients with breast cancer into molecular subgroups that are potentially linked to different clinical characteristics.

The present study aimed to identify novel methylation markers of clear cell renal cell carcinoma (ccRCC) using microarray methylation analysis and evaluate their prognostic relevance in patient samples. To identify cancer-specific methylated biomarkers, microarray profiling of ccRCC samples from our institute (n=12) and The Cancer Genome Atlas (TCGA) database (n=160) were utilized, and the prognostic relevance of candidate genes were investigated in another TCGA dataset (n=153). For validation, pyrosequencing analyses with ccRCC samples from our institute (n=164) and another (n=117) were performed and the potential clinical application of selected biomarkers was examined. We identified 22 CpG island loci that were commonly hypermethylated in ccRCC. Kaplan-Meier analysis of TCGA data indicated that only 4/22 loci were significantly associated with disease progression. In the internal validation set, Kaplan-Meier analysis revealed that hypermethylation of two loci, zinc finger protein 492 (ZNF492) and G protein-coupled receptor 149 (GPR149), was significantly associated with shorter time-to-progression. Multivariate Cox regression models revealed that hypermethylation of ZNF492 [hazard ratio (HR), 5.44; P=0.001] and GPR149 (HR, 7.07; P<0.001) may be independent predictors of tumor progression. Similarly, the methylation status of these two genes was significantly associated with poor outcomes in the independent external validation cohort. Collectively, the present study proposed that the novel methylation markers ZNF492 and GPR149 could be independent prognostic indicators in patients with ccRCC.

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In this study, an integrated deep learning framework was developed for classifying the periodontitis stages of each individual tooth using dental panoramic radiographs. Based on actual patient panoramic radiographs data, the bone loss by periodontitis and cementoenamel junction boundaries were detected, while the tooth number and tooth length were identified using data from AIHub, an open database platform. The two factors were integrated to classify and to evaluate the periodontitis staging on dental panoramic radiography. Periodontitis is classified into four stages based on the criteria of the radiographic bone level, as suggested at the relevant international conference in 2017. For the integrated deep learning framework developed in this study, the classification performance was evaluated by comparing the results of dental specialists, which indicated that the integrated framework had an accuracy of 0.929, with a recall and precision of 0.807 and 0.724, respectively, in average across all four stages. The novel framework was thus shown to exhibit a relatively high level of performance, and the findings in this study are expected to assist dental specialists with detecting the periodontitis stage and subsequent effective treatment. A systematic application will be developed in the future, to provide ancillary data for diagnosis and basic data for the treatment and prevention of periodontal disease.