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17 results on '"CHEN Kang"'

6. Publisher Correction: Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Author

Li, Yuan-Kuei, Hsu, Huan-Ming, Lin, Meng-Chiung, Chang, Chi-Wen, Chu, Chi-Ming, Chang, Yu-Jia, Yu, Jyh-Cherng, Chen, Chien-Ting, Jian, Chen-En, Sun, Chien-An, Chen, Kang-Hua, Kuo, Ming-Hao, Cheng, Chia-Shiang, Chang, Ya-Ting, Wu, Yi-Syuan, Wu, Hao-Yi, Yang, Ya-Ting, Lin, Chen, Lin, Hung-Che, Hu, Je-Ming, and Chang, Yu-Tien

Published
2021
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7. Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Author

Li, Yuan-Kuei, Hsu, Huan-Ming, Lin, Meng-Chiung, Chang, Chi-Wen, Chu, Chi-Ming, Chang, Yu-Jia, Yu, Jyh-Cherng, Chen, Chien-Ting, Jian, Chen-En, Sun, Chien-An, Chen, Kang-Hua, Kuo, Ming-Hao, Cheng, Chia-Shiang, Chang, Ya-Ting, Wu, Yi-Syuan, Wu, Hao-Yi, Yang, Ya-Ting, Lin, Chen, Lin, Hung-Che, Hu, Je-Ming, and Chang, Yu-Tien

Published
2021
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11. Author Correction: Diversity in gut bacterial community of school-age children in Asia

Author

Nakayama, Jiro, Watanabe, Koichi, Jiang, Jiahui, Matsuda, Kazunori, Chao, Shiou-Huei, Haryono, Pri, La-ongkham, Orawan, Sarwoko, Martinus-Agus, Sujaya, I. Nengah, Zhao, Liang, Chen, Kang-Ting, Chen, Yen-Po, Chiu, Hsueh-Hui, Hidaka, Tomoko, Huang, Ning-Xin, Kiyohara, Chikako, Kurakawa, Takashi, Sakamoto, Naoshige, Sonomoto, Kenji, Tashiro, Kousuke, Tsuji, Hirokazu, Chen, Ming-Ju, Leelavatcharamas, Vichai, Liao, Chii-Cherng, Nitisinprasert, Sunee, Rahayu, Endang S., Ren, Fa-Zheng, Tsai, Ying-Chieh, and Lee, Yuan-Kun

Published
2019
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12. Diversity in gut bacterial community of school-age children in Asia

Author

Nakayama, Jiro, Watanabe, Koichi, Jiang, Jiahui, Matsuda, Kazunori, Chao, Shiou-Huei, Haryono, Pri, La-ongkham, Orawan, Sarwoko, Martinus-Agus, Sujaya, I. Nengah, Zhao, Liang, Chen, Kang-Ting, Chen, Yen-Po, Chiu, Hsueh-Hui, Hidaka, Tomoko, Huang, Ning- Xin, Kiyohara, Chikako, Kurakawa, Takashi, Sakamoto, Naoshige, Sonomoto, Kenji, Tashiro, Kousuke, Tsuji, Hirokazu, Chen, Ming-Ju, Leelavatcharamas, Vichai, Liao, Chii-Cherng, Nitisinprasert, Sunee, Rahayu, Endang S., Ren, Fa-Zheng, Tsai, Ying-Chieh, and Lee, Yuan-Kun

Published
2015
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13. The ELISA Detectability and Potency of Pegfilgrastim Decrease in Physiological Conditions: Key Roles for Aggregation and Individual Variability

Author

Xie, Tao, primary, Fang, Hui, additional, Ouyang, Weiming, additional, Angart, Phillip, additional, Chiang, Meng-Jung, additional, Bhirde, Ashwinkumar A., additional, Sheikh, Faruk, additional, Lynch, Patrick, additional, Shah, Ankit B., additional, Patil, Sharadrao M., additional, Chen, Kang, additional, Shen, Meiyu, additional, Agarabi, Cyrus, additional, Donnelly, Raymond P., additional, Brorson, Kurt, additional, Schrieber, Sarah J., additional, Howard, Kristina E., additional, Rogstad, Sarah M., additional, and Frucht, David M., additional

Published
2020
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14. Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer.

Author

Chu, Chi-Ming, Hsu, Huan-Ming, Chang, Chi-Wen, Li, Yuan-Kuei, Chang, Yu-Jia, Yu, Jyh-Cherng, Chen, Chien-Ting, Jian, Chen-En, Lin, Meng-Chiung, Chen, Kang-Hua, Kuo, Ming-Hao, Cheng, Chia-Shiang, Chang, Ya-Ting, Wu, Yi-Syuan, Wu, Hao-Yi, Yang, Ya-Ting, Hu, Je-Ming, and Chang, Yu-Tien

Subjects
GENE expression, PREDICTION models, BIOMARKERS, BREAST cancer prognosis, DNA microarrays
Abstract

Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis. [ABSTRACT FROM AUTHOR]

Published
2021
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16. Glycosylation of Dentin Matrix Protein 1 is critical for osteogenesis

Author

Bo Jing, Qi Zhang, Yuteng Weng, Zhongshuang Liu, Yao Sun, Chen Kang, Yi Liu, Chenyang Zhang, and Zuolin Wang

Subjects
Extracellular Matrix Proteins, Glycosylation, Osteoblasts, Multidisciplinary, biology, Bone morphogenetic protein 8A, Bone Matrix, Gene Expression Regulation, Developmental, Bone morphogenetic protein 10, Article, DMP1, Bone remodeling, carbohydrates (lipids), Bone morphogenetic protein 7, Mice, chemistry.chemical_compound, Bone morphogenetic protein 6, stomatognathic system, chemistry, Proteoglycan, Biochemistry, Osteogenesis, biology.protein, Animals
Abstract

Proteoglycans play important roles in regulating osteogenesis. Dentin matrix protein 1 (DMP1) is a highly expressed bone extracellular matrix protein that regulates both bone development and phosphate metabolism. After glycosylation, an N-terminal fragment of DMP1 protein was identified as a new proteoglycan (DMP1-PG) in bone matrix. In vitro investigations showed that Ser89 is the key glycosylation site in mouse DMP1. However, the specific role of DMP1 glycosylation is still not understood. In this study, a mutant DMP1 mouse model was developed in which the glycosylation site S89 was substituted with G89 (S89G-DMP1). The glycosylation level of DMP1 was down-regulated in the bone matrix of S89G-DMP1 mice. Compared with wild type mice, the long bones of S89G-DMP1 mice showed developmental changes, including the speed of bone remodeling and mineralization, the morphology and activities of osteocytes and activities of both osteoblasts and osteoclasts. These findings indicate that glycosylation of DMP1 is a key posttranslational modification process during development and that DMP1-PG functions as an indispensable proteoglycan in osteogenesis.

Published
2015
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