A Mood Semantic Awareness Model for Emotional Interactive Robots.

The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human-machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users' degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user's interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model's performance in terms of dialogue quality and user experience.

EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism.

A variety of technologies that could enhance driving safety are being actively explored, with the aim of reducing traffic accidents by accurately recognizing the driver's state. In this field, three mainstream detection methods have been widely applied, namely visual monitoring, physiological indicator monitoring and vehicle behavior analysis. In order to achieve more accurate driver state recognition, we adopted a multi-sensor fusion approach. We monitored driver physiological signals, electroencephalogram (EEG) signals and electrocardiogram (ECG) signals to determine fatigue state, while an in-vehicle camera observed driver behavior and provided more information for driver state assessment. In addition, an outside camera was used to monitor vehicle position to determine whether there were any driving deviations due to distraction or fatigue. After a series of experimental validations, our research results showed that our multi-sensor approach exhibited good performance for driver state recognition. This study could provide a solid foundation and development direction for future in-depth driver state recognition research, which is expected to further improve road safety.

CKD-516 potentiates the anti-cancer activity of docetaxel against epidermal growth factor receptor tyrosine kinase inhibitor-resistant lung cancer.

Lung cancer is the leading cause of cancer death. Although docetaxel has been used as a second- or third-line treatment for non-small cell lung cancer (NSCLC), the objective response rate is less than 10%. Hence, there is a need to improve the clinical efficacy of docetaxel monotherapy; combination therapy should be considered. Here, we show that CKD-516, a vascular disruption agent, can be combined with docetaxel to treat epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI)-resistant NSCLC. CKD-516 was orally bioavailable; neither CKD-516 nor docetaxel affected the mean plasma concentration-time profile or pharmacokinetic parameters of the other drug. CKD-516 and docetaxel synergistically inhibited the growth of H1975 (with an L858R/T790M double mutation of EGFR) and A549 (with a KRAS mutation) lung cancer cell lines. In addition, docetaxel plus CKD-516 delayed tumor growth in-and extended the lifespan of-tumor-bearing mice. Thus, combination CKD-516 and docetaxel therapy could be used to treat EGFR-TKI-resistant NSCLC.

A novel HDAC6 inhibitor, CKD-504, is effective in treating preclinical models of huntington's disease.

Huntington's disease (HD) is a neurodegenerative disorder, of which pathogenesis is caused by a polyglutamine expansion in the amino-terminus of huntingtin gene that resulted in the aggregation of mutant HTT proteins. HD is characterized by progressive motor dysfunction, cognitive impairment and neuropsychiatric disturbances. Histone deacetylase 6 (HDAC6), a microtubule-associated deacetylase, has been shown to induce transport- and release-defect phenotypes in HD models, whilst treatment with HDAC6 inhibitors ameliorates the phenotypic effects of HD by increasing the levels of α-tubulin acetylation, as well as decreasing the accumulation of mutant huntingtin (mHTT) aggregates, suggesting HDAC6 inhibitor as a HD therapeutics. In this study, we employed in vitro neural stem cell (NSC) model and in vivo YAC128 transgenic (TG) mouse model of HD to test the effect of a novel HDAC6 selective inhibitor, CKD-504, developed by Chong Kun Dang (CKD Pharmaceutical Corp., Korea). We found that treatment of CKD-504 increased tubulin acetylation, microtubule stabilization, axonal transport, and the decrease of mutant huntingtin protein in vitro. From in vivo study, we observed CKD-504 improved the pathology of Huntington's disease: alleviated behavioral deficits, increased axonal transport and number of neurons, restored synaptic function in corticostriatal (CS) circuit, reduced mHTT accumulation, inflammation and tau hyperphosphorylation in YAC128 TG mouse model. These novel results highlight CKD-504 as a potential therapeutic strategy in HD. [BMB Reports 2023; 56(3): 178-183].

Novel Histone Deacetylase 6 Inhibitor Confers Anti-inflammatory Effects and Enhances Gut Barrier Function.

Background/Aims: The purpose of the current study was to examine the anti-inflammatory effects of CKD-506, a novel histone deacetylase 6 inhibitor, on human peripheral blood mononuclear cells (PBMCs) and CD4+ T cells and to explore the relationship between CKD-506 and gut epithelial barrier function. Methods: Lipopolysaccharide-stimulated human PBMCs from inflammatory bowel disease (IBD) patients were treated with CKD-506, and tumor necrosis factor (TNF)-α expression was measured using an enzyme-linked immunosorbent assay. The proliferation of CD4+ T cells from IBD patients was evaluated using flow cytometric analysis. The effects of CKD-506 on gut barrier function in a cell line and colon organoids, based on examinations of mRNA production, goblet cell differentiation, and E-cadherin recovery, were investigated using quantitative reverse transcription polymerase chain reaction, immunofluorescence, and a fluorescein isothiocyanate-dextran permeability assay. Results: Secretion of TNF-α, a pivotal pro-inflammatory mediator in IBD, by lipopolysaccharide-triggered PBMCs was markedly decreased by CKD-506 treatment in a dose-dependent manner and to a greater extent than by tofacitinib or tubastatin A treatment. E-cadherin mRNA expression and goblet cell differentiation increased significantly and dose-dependently in HT-29 cells in response to CKD-506, and inhibition of E-cadherin loss after TNF-α stimulation was significantly reduced both in HT-29 cells and gut organoids. Caco-2 cells treated with CKD-506 showed a significant reduction in barrier permeability in a dose-dependent manner. Conclusions: The present study demonstrated that CKD-506 has anti-inflammatory effects on PBMCs and CD4 T cells and improves gut barrier function, suggesting its potential as a small-molecule therapeutic option for IBD.

An Ensemble Semantic Textual Similarity Measure Based on Multiple Evidences for Biomedical Documents.

With the increasing volume of the published biomedical literature, the fast and effective retrieval of the literature on the sequence, structure, and function of biological entities is an essential task for the rapid development of biology and medicine. To capture the semantic information in biomedical literature more effectively when biomedical documents are clustered, we propose a new multi-evidence-based semantic text similarity calculation method. Two semantic similarities and one content similarity are used, in which two semantic similarities include MeSH-based semantic similarity and word embedding-based semantic similarity. To fuse three different similarities more effectively, after, respectively, calculating two semantic and one content similarities between biomedical documents, feedforward neural network is applied to integrate the two semantic similarities. Finally, weighted linear combination method is used to integrate the semantic and content similarities. To evaluate the effectiveness, the proposed method is compared with the existing basic methods, and the proposed method outperforms the existing related methods. Based on the proven results of this study, this method can be used not only in actual biological or medical experiments such as protein sequence or function analysis but also in biological and medical research fields, which will help to provide, use, and understand thematically consistent documents.

Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering.

The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover informative representations and define relevant articles from the rapidly growing biomedical literature due to the unsupervised nature of document clustering. Moreover, empirical investigations demonstrated that traditional text clustering methods produce unsatisfactory results in terms of non-contextualized vector space representations because that neglect the semantic relationship between biomedical texts. Recently, pre-trained language models have emerged as successful in a wide range of natural language processing applications. In this paper, we propose the Gaussian Mixture Model-based efficient clustering framework that incorporates substantially pre-trained (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) BioBERT domain-specific language representations to enhance the clustering accuracy. Our proposed framework consists of main three phases. First, classic text pre-processing techniques are used biomedical document data, which crawled from the PubMed repository. Second, representative vectors are extracted from a pre-trained BioBERT language model for biomedical text mining. Third, we employ the Gaussian Mixture Model as a clustering algorithm, which allows us to assign labels for each biomedical document. In order to prove the efficiency of our proposed model, we conducted a comprehensive experimental analysis utilizing several clustering algorithms while combining diverse embedding techniques. Consequently, the experimental results show that the proposed model outperforms the benchmark models by reaching performance measures of Fowlkes mallows score, silhouette coefficient, adjusted rand index, Davies-Bouldin score of 0.7817, 0.3765, 0.4478, 1.6849, respectively. We expect the outcomes of this study will assist domain specialists in comprehending thematically cohesive documents in the healthcare field.

InfersentPPI: Prediction of Protein-Protein Interaction Using Protein Sentence Embedding With Gene Ontology Information.

Protein-protein interaction (PPI) prediction is meaningful work for deciphering cellular behaviors. Although many kinds of data and machine learning algorithms have been used in PPI prediction, the performance still needs to be improved. In this paper, we propose InferSentPPI, a sentence embedding based text mining method with gene ontology (GO) information for PPI prediction. First, we design a novel weighting GO term-based protein sentence representation method to generate protein sentences including multi-semantic information in the preprocessing. Gene ontology annotation (GOA) provides the reliability of relationships between proteins and GO terms for PPI prediction. Thus, GO term-based protein sentence can help to improve the prediction performance. Then we also propose an InferSent_PN algorithm based on the protein sentences and InferSent algorithm to extract relations between proteins. In the experiments, we evaluate the effectiveness of InferSentPPI with several benchmarking datasets. The result shows our proposed method has performed better than the state-of-the-art methods for a large PPI dataset.

EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare.

Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal, which can improve the reliability of emotional judgments, and, furthermore, deeply mine the potential influence correlations between music and emotions. Our proposed EER model (EEG-based Emotion Recognition Model) could identify 20 types of emotions based on 32 EEG channels, and the average recognition accuracy was above 90% and 80%, respectively. Our proposed music-based emotion classification model (MEC model) could classify eight typical emotion types of music based on nine music feature combinations, and the average classification accuracy was above 90%. In addition, the semantic mapping was analyzed according to the influence of different music types on emotional changes from different perspectives based on the two models, and the results showed that the joy type of music video could improve fear, disgust, mania, and trust emotions into surprise or intimacy emotions, while the sad type of music video could reduce intimacy to the fear emotion.

An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering.

Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similarity measurement method based on Hadoop MapReduce for big text data. At first, we preprocess and extract the semantic features from documents. Then, we calculate the document semantic similarity based on ontology network structure under MapReduce framework. Finally, based on the generated semantic document similarity, document clusters are generated via clustering algorithms. To validate the effectiveness, we use two kinds of open datasets. The experimental results show that the traditional methods can hardly work for more than ten thousand biomedical documents. The proposed method keeps efficient and accurate for big dataset and is of high parallelism and scalability.

Comprehensive Evaluation of Differential Methylation Analysis Methods for Bisulfite Sequencing Data.

Background: With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methylated regions in bisulfite sequencing data, and more are being developed continuously. Results: Here, we focused on a comprehensive evaluation of commonly used differential methylation analysis methods and describe the potential strengths and limitations of each method. We found that there are large differences among methods, and no single method consistently ranked first in all benchmarking. Moreover, smoothing seemed not to improve the performance greatly, and a small number of replicates created more difficulties in the computational analysis of BS-seq data than low sequencing depth. Conclusions: Data analysis and interpretation should be performed with great care, especially when the number of replicates or sequencing depth is limited.

Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification.

Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, we propose not only to build a classification model for five major subtypes using two kinds of losses, namely reconstruction loss and classification loss, but also to extract suitable features using a deep autoencoder. Furthermore, for considering the data imbalance problem, we apply an oversampling algorithm, the synthetic minority oversampling technique (SMOTE). For validation of our proposed autoencoder-based feature extraction approach for hematopoietic cancer subtype classification, we compared other traditional feature selection algorithms (principal component analysis, non-negative matrix factorization) and classification algorithms with the SMOTE oversampling approach. Additionally, we used the Shapley Additive exPlanations (SHAP) interpretation technique in our model to explain the important gene/protein for hematopoietic cancer subtype classification. Furthermore, we compared five widely used classification algorithms, including logistic regression, random forest, k-nearest neighbor, artificial neural network and support vector machine. The results of autoencoder-based feature extraction approaches showed good performance, and the best result was the SMOTE oversampling-applied support vector machine algorithm consider both focal loss and reconstruction loss as the loss function for autoencoder (AE) feature selection approach, which produced 97.01% accuracy, 92.60% recall, 99.52% specificity, 93.54% F1-measure, 97.87% G-mean and 95.46% index of balanced accuracy as subtype classification performance measures.

XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction.

Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, numerous studies have been proposed using artificial intelligence techniques to predict the risk of developing SiNCDs. However, determining the most significant features and developing interpretable models are rather challenging in such systems. In this study, we propose an efficient extreme gradient boosting (XGBoost) based framework incorporated with the hybrid feature selection (HFS) method for SiNCDs prediction among the general population in South Korea and the United States. Initially, HFS is performed in three stages: (I) significant features are selected by t-test and chi-square test; (II) multicollinearity analysis serves to obtain dissimilar features; (III) final selection of best representative features is done based on least absolute shrinkage and selection operator (LASSO). Then, selected features are fed into the XGBoost predictive model. The experimental results show that our proposed model outperforms several existing baseline models. In addition, the proposed model also provides important features in order to enhance the interpretability of the SiNCDs prediction model. Consequently, the XGBoost based framework is expected to contribute for early diagnosis and prevention of the SiNCDs in public health concerns.

GEN2VCF: a converter for human genome imputation output format to VCF format.

BACKGROUND: For a genome-wide association study in humans, genotype imputation is an essential analysis tool for improving association mapping power. When IMPUTE software is used for imputation analysis, an imputation output (GEN format) should be converted to variant call format (VCF) with imputed genotype dosage for association analysis. However, the conversion requires multiple software packages in a pipeline with a large amount of processing time. OBJECTIVE: We developed GEN2VCF, a fast and convenient GEN format to VCF conversion tool with dosage support. METHODS: The performance of GEN2VCF was compared to BCFtools, QCTOOL, and Oncofunco. The test data set was a 1 Mb GEN-formatted file of 5000 samples. To determine the performance of various sample sizes, tests were performed from 1000 to 5000 samples with a step size of 1000. Runtime and memory usage were used as performance measures. RESULTS: GEN2VCF showed drastically increased performances with respect to runtime and memory usage. Runtime and memory usage of GEN2VCF was at least 1.4- and 7.4-fold lower compared to other methods, respectively. CONCLUSIONS: GEN2VCF provides users with efficient conversion from GEN format to VCF with the best-guessed genotype, genotype posterior probabilities, and genotype dosage, as well as great flexibility in implementation with other software packages in a pipeline.

Acetylation changes tau interactome to degrade tau in Alzheimer's disease animal and organoid models.

Alzheimer's disease (AD) is an age-related neurodegenerative disease. The most common pathological hallmarks are amyloid plaques and neurofibrillary tangles in the brain. In the brains of patients with AD, pathological tau is abnormally accumulated causing neuronal loss, synaptic dysfunction, and cognitive decline. We found a histone deacetylase 6 (HDAC6) inhibitor, CKD-504, changed the tau interactome dramatically to degrade pathological tau not only in AD animal model (ADLPAPT ) brains containing both amyloid plaques and neurofibrillary tangles but also in AD patient-derived brain organoids. Acetylated tau recruited chaperone proteins such as Hsp40, Hsp70, and Hsp110, and this complex bound to novel tau E3 ligases including UBE2O and RNF14. This complex degraded pathological tau through proteasomal pathway. We also identified the responsible acetylation sites on tau. These dramatic tau-interactome changes may result in tau degradation, leading to the recovery of synaptic pathology and cognitive decline in the ADLPAPT mice.

Reconstruction error based deep neural networks for coronary heart disease risk prediction.

Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by predicting high-risk people who are to suffer from CHD, it is significant to reduce the risks of developing CHD. In recent years, some research works have been done using data mining to predict the risk of developing diseases based on medical tests. In this study, we have proposed a reconstruction error (RE) based deep neural networks (DNNs); this approach uses a deep autoencoder (AE) model for estimating RE. Initially, a training dataset is divided into two groups by their RE divergence on the deep AE model that learned from the whole training dataset. Next, two DNN classifiers are trained on each group of datasets separately by combining a RE based new feature with other risk factors to predict the risk of developing CHD. For creating the new feature, we use deep AE model that trained on the only high-risk dataset. We have performed an experiment to prove how the components of our proposed method work together more efficiently. As a result of our experiment, the performance measurements include accuracy, precision, recall, F-measure, and AUC score reached 86.3371%, 91.3716%, 82.9024%, 86.9148%, and 86.6568%, respectively. These results show that the proposed AE-DNNs outperformed regular machine learning-based classifiers for CHD risk prediction.

Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach.

Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named Entity Recognition (HNER) task using healthcare-domain ontology that can recognize health-related entities from large numbers of user messages from Twitter. For this task, we employ a deep learning architecture which is based on a recurrent neural network (RNN) with little feature engineering. To achieve our goal, we collected a large number of Twitter messages containing health-related information, and detected biomedical entities from the Unified Medical Language System (UMLS). A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features. The conditional random field (CRF) model predicted a sequence of labels that corresponded to a sequence of inputs, and the Viterbi algorithm was used to detect health-related entities from Twitter messages. We provide comprehensive results giving valuable insights for identifying medical entities in Twitter for various applications. The BiLSTM-CRF model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome HNER; a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of 73.47%, and F1-score of 84.51% for pharmacologic substance named entities. The ontology-based manual annotation results show that it is possible to perform high-quality annotation despite the complexity of medical terminology and the lack of context in tweets.

Risk Scoring System for Prognosis Estimation of Multivessel Disease Among Patients with ST-Segment Elevation Myocardial Infarction.

Multivessel disease (MVD) is an independent risk factor for poor prognosis in acute myocardial infarction patients. Although several global risk scoring systems (RSS) are in use in clinical practice, there is no dedicated RSS for MVD in ST-segment elevation myocardial infarction (STEMI). The primary objective of this study is to develop a novel RSS to estimate the prognosis of patients with MVD in STEMI.We used the Korean Acute Myocardial Infarction Registry (KAMIR) to identify 2,030 STEMI patients with MVD who underwent appropriate percutaneous coronary intervention (PCI). Their data were analyzed to develop a new RSS. The prognostic power of this RSS was validated with 2,556 STEMI patients with MVD in the Korean Working Group on Myocardial Infarction Registry (KORMI).Six prognostic factors related to all-cause death in STEMI patients with MVD were age, serum creatinine, Killip Class, lower body weight, decrease in left ventricular ejection fraction, and history of cerebrovascular disease. The RSS for all-cause death was constructed using these risk factors and their statistical weight. The RSS had appropriate performance (c-index: 0.72) in the KORMI validation cohort.We developed a novel RSS that estimates all-cause death in the year following discharge for patients with MVD in STEMI appropriately treated by PCI. This novel RSS was transformed into a simple linear risk score to yield a simplified estimate prognosis of MVD among STEMI patients.

Methylation Signature for Prediction of Progression Free Survival in Surgically Treated Clear Cell Renal Cell Carcinoma.

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.

ZNF492 and GPR149 methylation patterns as prognostic markers for clear cell renal cell carcinoma: Array­based DNA methylation profiling.

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 cancer­specific 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. Kaplan­Meier analysis of TCGA data indicated that only 4/22 loci were significantly associated with disease progression. In the internal validation set, Kaplan­Meier analysis revealed that hypermethylation of two loci, zinc finger protein 492 (ZNF492) and G protein­coupled receptor 149 (GPR149), was significantly associated with shorter time­to­progression. 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.