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Your search keyword '"Li, Zhiguang"' showing total 12 results
Start Over Author "Li, Zhiguang" Publisher biomed central
12 results on '"Li, Zhiguang"'

2. Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

Author

Gong, Binsheng, Li, Dan, Kusko, Rebecca, Novoradovskaya, Natalia, Zhang, Yifan, Wang, Shangzi, Pabón-Peña, Carlos, Zhang, Zhihong, Lai, Kevin, Cai, Wanshi, LoCoco, Jennifer S., Lader, Eric, Richmond, Todd A., Mittal, Vinay K., Liu, Liang-Chun, Johann, Jr, Donald J., Willey, James C., Bushel, Pierre R., Yu, Ying, Xu, Chang, Chen, Guangchun, Burgess, Daniel, Cawley, Simon, Giorda, Kristina, Haseley, Nathan, Qiu, Fujun, Wilkins, Katherine, Arib, Hanane, Attwooll, Claire, Babson, Kevin, Bao, Longlong, Bao, Wenjun, Lucas, Anne Bergstrom, Best, Hunter, Bhandari, Ambica, Bisgin, Halil, Blackburn, James, Blomquist, Thomas M., Boardman, Lisa, Burgher, Blake, Butler, Daniel J., Chang, Chia-Jung, Chaubey, Alka, Chen, Tao, Chierici, Marco, Chin, Christopher R., Close, Devin, Conroy, Jeffrey, Cooley Coleman, Jessica, Craig, Daniel J., Crawford, Erin, del Pozo, Angela, Deveson, Ira W., Duncan, Daniel, Eterovic, Agda Karina, Fan, Xiaohui, Foox, Jonathan, Furlanello, Cesare, Ghosal, Abhisek, Glenn, Sean, Guan, Meijian, Haag, Christine, Hang, Xinyi, Happe, Scott, Hennigan, Brittany, Hipp, Jennifer, Hong, Huixiao, Horvath, Kyle, Hu, Jianhong, Hung, Li-Yuan, Jarosz, Mirna, Kerkhof, Jennifer, Kipp, Benjamin, Kreil, David Philip, Łabaj, Paweł, Lapunzina, Pablo, Li, Peng, Li, Quan-Zhen, Li, Weihua, Li, Zhiguang, Liang, Yu, Liu, Shaoqing, Liu, Zhichao, Ma, Charles, Marella, Narasimha, Martín-Arenas, Rubén, Megherbi, Dalila B., Meng, Qingchang, Mieczkowski, Piotr A., Morrison, Tom, Muzny, Donna, Ning, Baitang, Parsons, Barbara L., Paweletz, Cloud P., Pirooznia, Mehdi, Qu, Wubin, Raymond, Amelia, Rindler, Paul, Ringler, Rebecca, Sadikovic, Bekim, Scherer, Andreas, Schulze, Egbert, Sebra, Robert, Shaknovich, Rita, Shi, Qiang, Shi, Tieliu, Silla-Castro, Juan Carlos, Smith, Melissa, López, Mario Solís, Song, Ping, Stetson, Daniel, Strahl, Maya, Stuart, Alan, Supplee, Julianna, Szankasi, Philippe, Tan, Haowen, Tang, Lin-ya, Tao, Yonghui, Thakkar, Shraddha, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkat J., Thomas, David, Tichý, Boris, Tom, Nikola, Garcia, Elena Vallespin, Verma, Suman, Walker, Kimbley, Wang, Charles, Wang, Junwen, Wang, Yexun, Wen, Zhining, Wirta, Valtteri, Wu, Leihong, Xiao, Chunlin, Xiao, Wenzhong, Xu, Shibei, Yang, Mary, Ying, Jianming, Yip, Shun H., Zhang, Guangliang, Zhang, Sa, Zhao, Meiru, Zheng, Yuanting, Zhou, Xiaoyan, Mason, Christopher E., Mercer, Timothy, Tong, Weida, Shi, Leming, Jones, Wendell, and Xu, Joshua

Published
2021
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5. A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

Author

Jones, Wendell, Gong, Binsheng, Novoradovskaya, Natalia, Li, Dan, Kusko, Rebecca, Richmond, Todd A., Johann, Jr, Donald J., Bisgin, Halil, Sahraeian, Sayed Mohammad Ebrahim, Bushel, Pierre R., Pirooznia, Mehdi, Wilkins, Katherine, Chierici, Marco, Bao, Wenjun, Basehore, Lee Scott, Lucas, Anne Bergstrom, Burgess, Daniel, Butler, Daniel J., Cawley, Simon, Chang, Chia-Jung, Chen, Guangchun, Chen, Tao, Chen, Yun-Ching, Craig, Daniel J., del Pozo, Angela, Foox, Jonathan, Francescatto, Margherita, Fu, Yutao, Furlanello, Cesare, Giorda, Kristina, Grist, Kira P., Guan, Meijian, Hao, Yingyi, Happe, Scott, Hariani, Gunjan, Haseley, Nathan, Jasper, Jeff, Jurman, Giuseppe, Kreil, David Philip, Łabaj, Paweł, Lai, Kevin, Li, Jianying, Li, Quan-Zhen, Li, Yulong, Li, Zhiguang, Liu, Zhichao, López, Mario Solís, Miclaus, Kelci, Miller, Raymond, Mittal, Vinay K., Mohiyuddin, Marghoob, Pabón-Peña, Carlos, Parsons, Barbara L., Qiu, Fujun, Scherer, Andreas, Shi, Tieliu, Stiegelmeyer, Suzy, Suo, Chen, Tom, Nikola, Wang, Dong, Wen, Zhining, Wu, Leihong, Xiao, Wenzhong, Xu, Chang, Yu, Ying, Zhang, Jiyang, Zhang, Yifan, Zhang, Zhihong, Zheng, Yuanting, Mason, Christopher E., Willey, James C., Tong, Weida, Shi, Leming, and Xu, Joshua

Published
2021
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7. A case-control study of a combination of single nucleotide polymorphisms and clinical parameters to predict clinically relevant toxicity associated with fluoropyrimidine and platinum-based chemotherapy in gastric cancer

Author

Cordova-Delgado, Miguel, Bravo, María Loreto, Cumsille, Elisa, Hill, Charlotte N., Muñoz-Medel, Matías, Pinto, Mauricio P., Retamal, Ignacio N., Lavanderos, María A., Miquel, Juan Francisco, Rodriguez-Fernandez, Maria, Liao, Yuwei, Li, Zhiguang, Corvalán, Alejandro H., Armisén, Ricardo, Garrido, Marcelo, Quiñones, Luis A., and Owen, Gareth I.

Published
2021
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11. Microarray platform consistency is revealed by biologically functional analysis of gene expression profiles.

Author

Li, Zhiguang, Su, Zhenqiang, Wen, Zhining, Shi, Leming, and Chen, Tao

Subjects
*DNA microarrays, *FUNCTIONAL analysis, *GENE expression, *CARCINOGENICITY, *LABORATORY rats
Abstract

Background: Several different microarray platforms are available for measuring gene expression. There are disagreements within the microarray scientific community for intra- and inter-platform consistency of these platforms. Both high and low consistencies were demonstrated across different platforms in terms of genes with significantly differential expression. Array studies for gene expression are used to explore biological causes and effects. Therefore, consistency should eventually be evaluated in a biological setting to reveal the functional differences between the examined samples, not just a list of differentially expressed genes (DEG). In this study, we investigated whether different platforms had a high consistency from the biologically functional perspective. Results: DEG data without filtering the different probes in microarrays from different platforms generated from kidney samples of rats treated with the kidney carcinogen, aristolochic acid, in five test sites using microarrays from Affymetrix, Applied Biosystems, Agilent, and GE health platforms (two sites using Affymetrix for intra-platform comparison) were input into the Ingenuity Pathway Analysis (IPA) system for functional analysis. The functions of the DEG lists determined by IPA were compared across the four different platforms and two test sites for Affymetrix platform. Analysis results showed that there is a very high level of consistency between the two test sites using the same platform or among different platforms. The top functions determined by the different platforms were very similar and reflected carcinogenicity and toxicity of aristolochic acid in the rat kidney. Conclusion: Our results demonstrate that highly consistent biological information can be generated from different microarray platforms. [ABSTRACT FROM AUTHOR]

Published
2009
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12. Integrated microRNA, mRNA, and protein expression profiling reveals microRNA regulatory networks in rat kidney treated with a carcinogenic dose of aristolochic acid.

Author

Li Z, Qin T, Wang K, Hackenberg M, Yan J, Gao Y, Yu LR, Shi L, Su Z, and Chen T

Subjects
Animals, Gene Expression Regulation, Neoplastic drug effects, Gene Regulatory Networks drug effects, High-Throughput Nucleotide Sequencing, Kidney metabolism, Kidney Neoplasms etiology, Male, MicroRNAs analysis, Molecular Sequence Data, Proteomics, RNA, Messenger analysis, Rats, Sequence Analysis, RNA, Aristolochic Acids toxicity, Carcinogens toxicity, Kidney drug effects, Kidney Neoplasms genetics, Kidney Neoplasms metabolism, RNA, Neoplasm analysis
Abstract

Background: Aristolochic Acid (AA), a natural component of Aristolochia plants that is found in a variety of herbal remedies and health supplements, is classified as a Group 1 carcinogen by the International Agency for Research on Cancer. Given that microRNAs (miRNAs) are involved in cancer initiation and progression and their role remains unknown in AA-induced carcinogenesis, we examined genome-wide AA-induced dysregulation of miRNAs as well as the regulation of miRNAs on their target gene expression in rat kidney., Results: We treated rats with 10 mg/kg AA and vehicle control for 12 weeks and eight kidney samples (4 for the treatment and 4 for the control) were used for examining miRNA and mRNA expression by deep sequencing, and protein expression by proteomics. AA treatment resulted in significant differential expression of miRNAs, mRNAs and proteins as measured by both principal component analysis (PCA) and hierarchical clustering analysis (HCA). Specially, 63 miRNAs (adjusted p value < 0.05 and fold change > 1.5), 6,794 mRNAs (adjusted p value < 0.05 and fold change > 2.0), and 800 proteins (fold change > 2.0) were significantly altered by AA treatment. The expression of 6 selected miRNAs was validated by quantitative real-time PCR analysis. Ingenuity Pathways Analysis (IPA) showed that cancer is the top network and disease associated with those dysregulated miRNAs. To further investigate the influence of miRNAs on kidney mRNA and protein expression, we combined proteomic and transcriptomic data in conjunction with miRNA target selection as confirmed and reported in miRTarBase. In addition to translational repression and transcriptional destabilization, we also found that miRNAs and their target genes were expressed in the same direction at levels of transcription (169) or translation (227). Furthermore, we identified that up-regulation of 13 oncogenic miRNAs was associated with translational activation of 45 out of 54 cancer-related targets., Conclusions: Our findings suggest that dysregulated miRNA expression plays an important role in AA-induced carcinogenesis in rat kidney, and that the integrated approach of multiple profiling provides a new insight into a post-transcriptional regulation of miRNAs on their target repression and activation in a genome-wide scale.

Published
2015
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