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3. Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Author

Walsh, Naomi, Zhang, Han, Hyland, Paula L, Yang, Qi, Mocci, Evelina, Zhang, Mingfeng, Childs, Erica J, Collins, Irene, Wang, Zhaoming, Arslan, Alan A, Beane-Freeman, Laura, Bracci, Paige M, Brennan, Paul, Canzian, Federico, Duell, Eric J, Gallinger, Steven, Giles, Graham G, Goggins, Michael, Goodman, Gary E, Goodman, Phyllis J, Hung, Rayjean J, Kooperberg, Charles, Kurtz, Robert C, Malats, Núria, LeMarchand, Loic, Neale, Rachel E, Olson, Sara H, Scelo, Ghislaine, Shu, Xiao O, Van Den Eeden, Stephen K, Visvanathan, Kala, White, Emily, Zheng, Wei, PanScan and PanC4 consortia, Albanes, Demetrius, Andreotti, Gabriella, Babic, Ana, Bamlet, William R, Berndt, Sonja I, Borgida, Ayelet, Boutron-Ruault, Marie-Christine, Brais, Lauren, Bueno-de-Mesquita, Bas, Buring, Julie, Chaffee, Kari G, Chanock, Stephen, Cleary, Sean, Cotterchio, Michelle, Foretova, Lenka, Fuchs, Charles, M Gaziano, J Michael, Giovannucci, Edward, Hackert, Thilo, Haiman, Christopher, Hartge, Patricia, Hasan, Manal, Helzlsouer, Kathy J, Herman, Joseph, Holcatova, Ivana, Holly, Elizabeth A, Hoover, Robert, Janout, Vladimir, Klein, Eric A, Laheru, Daniel, Lee, I-Min, Lu, Lingeng, Mannisto, Satu, Milne, Roger L, Oberg, Ann L, Orlow, Irene, Patel, Alpa V, Peters, Ulrike, Porta, Miquel, Real, Francisco X, Rothman, Nathaniel, Sesso, Howard D, Severi, Gianluca, Silverman, Debra, Strobel, Oliver, Sund, Malin, Thornquist, Mark D, Tobias, Geoffrey S, Wactawski-Wende, Jean, Wareham, Nick, Weiderpass, Elisabete, Wentzensen, Nicolas, Wheeler, William, Yu, Herbert, Zeleniuch-Jacquotte, Anne, Kraft, Peter, Li, Donghui, Jacobs, Eric J, Petersen, Gloria M, Wolpin, Brian M, Risch, Harvey A, Amundadottir, Laufey T, Yu, Kai, Klein, Alison P, Stolzenberg-Solomon, Rachael Z, Wareham, Nicholas [0000-0003-1422-2993], and Apollo - University of Cambridge Repository

Subjects
Pancreatic Neoplasms, Models, Statistical, Case-Control Studies, Humans, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Carcinoma, Pancreatic Ductal, Genome-Wide Association Study
Abstract

Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.

Published
2018

4. Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Author

Walsh, Naomi, Zhang, Han, Hyland, Paula L., Yang, Qi, Mocci, Evelina, Zhang, Mingfeng, Childs, Erica J., Collins, Irene, Wang, Zhaoming, Arslan, Alan A., Beane-Freeman, Laura, Bracci, Paige M., Brennan, Paul, Canzian, Federico, Duell, Eric J., Gallinger, Steven, Giles, Graham G., Goggins, Michael, Goodman, Gary E., Goodman, Phyllis J., Hung, Rayjean J., Kooperberg, Charles, Kurtz, Robert C., Malats, Núria, LeMarchand, Loic, Neale, Rachel E., Olson, Sara H., Scelo, Ghislaine, Shu, Xiao O., Van Den Eeden, Stephen K., Visvanathan, Kala, White, Emily, Zheng, Wei, Albanes, Demetrius, Andreotti, Gabriella, Babic, Ana, Bamlet, William R., Berndt, Sonja I., Borgida, Ayelet, Boutron-Ruault, Marie-Christine, Brais, Lauren, Bueno-de-Mesquita, Bas, Buring, Julie, Chaffee, Kari G., Chanock, Stephen, Cleary, Sean, Cotterchio, Michelle, Foretova, Lenka, Fuchs, Charles, M. Gaziano, J. Michael, Giovannucci, Edward, Hackert, Thilo, Haiman, Christopher, Hartge, Patricia, Hasan, Manal, Helzlsouer, Kathy J., Herman, Joseph, Holcatova, Ivana, Holly, Elizabeth A., Hoover, Robert, Janout, Vladimir, Klein, Eric A., Laheru, Daniel, Lee, I-Min, Lu, Lingeng, Mannisto, Satu, Milne, Roger L., Oberg, Ann L., Orlow, Irene, Patel, Alpa V., Peters, Ulrike, Porta, Miquel, Real, Francisco X., Rothman, Nathaniel, Sesso, Howard D., Severi, Gianluca, Silverman, Debra, Strobel, Oliver, Sund, Malin, Thornquist, Mark D., Tobias, Geoffrey S., Wactawski-Wende, Jean, Wareham, Nick, Weiderpass, Elisabete, Wentzensen, Nicolas, Wheeler, William, Yu, Herbert, Zeleniuch-Jacquotte, Anne, Kraft, Peter, Li, Donghui, Jacobs, Eric J., Petersen, Gloria M., Wolpin, Brian M., Risch, Harvey A., Amundadottir, Laufey T., Yu, Kai, Klein, Alison P., Stolzenberg-Solomon, Rachael Z., Walsh, Naomi, Zhang, Han, Hyland, Paula L., Yang, Qi, Mocci, Evelina, Zhang, Mingfeng, Childs, Erica J., Collins, Irene, Wang, Zhaoming, Arslan, Alan A., Beane-Freeman, Laura, Bracci, Paige M., Brennan, Paul, Canzian, Federico, Duell, Eric J., Gallinger, Steven, Giles, Graham G., Goggins, Michael, Goodman, Gary E., Goodman, Phyllis J., Hung, Rayjean J., Kooperberg, Charles, Kurtz, Robert C., Malats, Núria, LeMarchand, Loic, Neale, Rachel E., Olson, Sara H., Scelo, Ghislaine, Shu, Xiao O., Van Den Eeden, Stephen K., Visvanathan, Kala, White, Emily, Zheng, Wei, Albanes, Demetrius, Andreotti, Gabriella, Babic, Ana, Bamlet, William R., Berndt, Sonja I., Borgida, Ayelet, Boutron-Ruault, Marie-Christine, Brais, Lauren, Bueno-de-Mesquita, Bas, Buring, Julie, Chaffee, Kari G., Chanock, Stephen, Cleary, Sean, Cotterchio, Michelle, Foretova, Lenka, Fuchs, Charles, M. Gaziano, J. Michael, Giovannucci, Edward, Hackert, Thilo, Haiman, Christopher, Hartge, Patricia, Hasan, Manal, Helzlsouer, Kathy J., Herman, Joseph, Holcatova, Ivana, Holly, Elizabeth A., Hoover, Robert, Janout, Vladimir, Klein, Eric A., Laheru, Daniel, Lee, I-Min, Lu, Lingeng, Mannisto, Satu, Milne, Roger L., Oberg, Ann L., Orlow, Irene, Patel, Alpa V., Peters, Ulrike, Porta, Miquel, Real, Francisco X., Rothman, Nathaniel, Sesso, Howard D., Severi, Gianluca, Silverman, Debra, Strobel, Oliver, Sund, Malin, Thornquist, Mark D., Tobias, Geoffrey S., Wactawski-Wende, Jean, Wareham, Nick, Weiderpass, Elisabete, Wentzensen, Nicolas, Wheeler, William, Yu, Herbert, Zeleniuch-Jacquotte, Anne, Kraft, Peter, Li, Donghui, Jacobs, Eric J., Petersen, Gloria M., Wolpin, Brian M., Risch, Harvey A., Amundadottir, Laufey T., Yu, Kai, Klein, Alison P., and Stolzenberg-Solomon, Rachael Z.

Abstract

Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.

Published
2019
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6. Abstract 4140: Secular trends in incidence and mortality of female cancers in Shanghai, China (1973-2009)

Author

Huang, Zhezhou, primary, Beeghly-Fadiel, Alicia C., additional, Zheng, Ying, additional, Wen, Wanqing, additional, Gao, Yutang, additional, Wu, Chunxiao, additional, Bao, Pingping, additional, Zhong, Weijian, additional, Jin, Fan, additional, Xiang, Yongbing, additional, Zheng, Wei, additional, Shu, Xiao O., additional, and Lu, Wei, additional

Published
2014
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8. Abstract 2167: Infertility and risk of incident endometrial carcinoma: a pooled analysis from the Epidemiology of Endometrial Cancer Consortium

Author

Yang, Hannah P., primary, Cook, Linda S., additional, Weiderpass, Elisabete, additional, Adami, Hans-Olov, additional, Anderson, Kristin E., additional, Cai, Hui, additional, Cerhan, James R., additional, Clendenen, Tess, additional, Felix, Ashley S., additional, Friedenreich, Christine, additional, Garcia-Closas, Montserrat, additional, Goodman, Marc T., additional, Liang, Xiaolin, additional, Lissowska, Jolanta, additional, Lu, Lingeng, additional, Magliocco, Anthony M., additional, McCann, Susan E., additional, Moysich, Kristen B., additional, Olson, Sara H., additional, Pike, Malcolm C., additional, Polidoro, Silvia, additional, Ricceri, Fulvio, additional, Risch, Harvey, additional, Sacerdote, Carlotta, additional, Setiawan, V. Wendy, additional, Shu, Xiao O., additional, Spurdle, Amanda B., additional, Trabert, Britton, additional, Webb, Penelope M., additional, Wentzensen, Nicolas, additional, Xiang, Yong-Bing, additional, Xu, Youming, additional, Yu, Herbert, additional, Zeleniuch-Jacquotte, Anne, additional, and Brinton, Louise A., additional

Published
2014
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9. A Cohort Study of Ginseng, Soy and Other Complementary Medicine Use and Breast Cancer Survival

Author

VANDERBILT UNIV MEDICAL CENTER ATLANTA GA, Shu, Xiao O., VANDERBILT UNIV MEDICAL CENTER ATLANTA GA, and Shu, Xiao O.

Abstract

Although experimental data suggest that ginseng has immunostimulating and cancer inhibitory properties, its effect and safety have not been adequately evaluated among breast cancer survivors. Soy has also been shown in laboratory studies to have anti-estrogenic and anti-cancer effects but epidemiologic data on soy and breast cancer recurrence is sparse. Limited data are available on the effectiveness and safety of other botanic and "natural" remedies that are marketed as anti-cancer agents. Potential beneficial or harmful pharmacological interactions of ginseng, soy, and other complementary medicines with conventional cancer therapies have not been well studied. Funded by the DOD, we are conducting a large cohort study in Shanghai, China, to test the following hypotheses: 1) Ginseng use and high soy intake reduce breast cancer recurrence and improve overall survival; 2) Ginseng use increases quality of life among breast cancer survivors. We will also evaluate the potential effect of Chinese traditional medicine and other herbal or "natural" remedies on survival, recurrence, and quality of life. Interactions with conventional cancer therapies will be examined. The study is progressing as planed. To date, l98l breast cancer survivors have been recruited with an overall participation rate of 80%. The 18-month follow-up survey has been completed for 634 cases. No adverse events have been reported during the study period.

Published
2004

13. Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Author

Walsh, Naomi, Zhang, Han, Hyland, Paula L, Yang, Qi, Mocci, Evelina, Zhang, Mingfeng, Childs, Erica J, Collins, Irene, Wang, Zhaoming, Arslan, Alan A, Beane-Freeman, Laura, Bracci, Paige M, Brennan, Paul, Canzian, Federico, Duell, Eric J, Gallinger, Steven, Giles, Graham G, Goggins, Michael, Goodman, Gary E, Goodman, Phyllis J, Hung, Rayjean J, Kooperberg, Charles, Kurtz, Robert C, Malats, Núria, LeMarchand, Loic, Neale, Rachel E, Olson, Sara H, Scelo, Ghislaine, Shu, Xiao O, Van Den Eeden, Stephen K, Visvanathan, Kala, White, Emily, Zheng, Wei, PanScan And PanC4 Consortia, Albanes, Demetrius, Andreotti, Gabriella, Babic, Ana, Bamlet, William R, Berndt, Sonja I, Borgida, Ayelet, Boutron-Ruault, Marie-Christine, Brais, Lauren, Bueno-De-Mesquita, Bas, Buring, Julie, Chaffee, Kari G, Chanock, Stephen, Cleary, Sean, Cotterchio, Michelle, Foretova, Lenka, Fuchs, Charles, M Gaziano, J Michael, Giovannucci, Edward, Hackert, Thilo, Haiman, Christopher, Hartge, Patricia, Hasan, Manal, Helzlsouer, Kathy J, Herman, Joseph, Holcatova, Ivana, Holly, Elizabeth A, Hoover, Robert, Janout, Vladimir, Klein, Eric A, Laheru, Daniel, Lee, I-Min, Lu, Lingeng, Mannisto, Satu, Milne, Roger L, Oberg, Ann L, Orlow, Irene, Patel, Alpa V, Peters, Ulrike, Porta, Miquel, Real, Francisco X, Rothman, Nathaniel, Sesso, Howard D, Severi, Gianluca, Silverman, Debra, Strobel, Oliver, Sund, Malin, Thornquist, Mark D, Tobias, Geoffrey S, Wactawski-Wende, Jean, Wareham, Nick, Weiderpass, Elisabete, Wentzensen, Nicolas, Wheeler, William, Yu, Herbert, Zeleniuch-Jacquotte, Anne, Kraft, Peter, Li, Donghui, Jacobs, Eric J, Petersen, Gloria M, Wolpin, Brian M, Risch, Harvey A, Amundadottir, Laufey T, Yu, Kai, Klein, Alison P, and Stolzenberg-Solomon, Rachael Z

Subjects
Pancreatic Neoplasms, Models, Statistical, Case-Control Studies, Humans, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, 3. Good health, Carcinoma, Pancreatic Ductal, Genome-Wide Association Study
Abstract

Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified., This work was supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. This publication has emanated from research supported in part by a Grant from Science Foundation Ireland under Grant number [15/SIRG/3482](NW) and Health Research Board/Irish Cancer Society (CPFPR-2012–2)(NW). This work was also supported by RO1 CA154823 and federal funds from the National Cancer Institute (NCI), US National Institutes of Health, under contract number HHSN261200800001E. Please see the Supplementary Materials (available online) for a complete list of funding acknowledgments

14. Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer.

Author

Walsh N, Zhang H, Hyland PL, Yang Q, Mocci E, Zhang M, Childs EJ, Collins I, Wang Z, Arslan AA, Beane-Freeman L, Bracci PM, Brennan P, Canzian F, Duell EJ, Gallinger S, Giles GG, Goggins M, Goodman GE, Goodman PJ, Hung RJ, Kooperberg C, Kurtz RC, Malats N, LeMarchand L, Neale RE, Olson SH, Scelo G, Shu XO, Van Den Eeden SK, Visvanathan K, White E, Zheng W, Albanes D, Andreotti G, Babic A, Bamlet WR, Berndt SI, Borgida A, Boutron-Ruault MC, Brais L, Brennan P, Bueno-de-Mesquita B, Buring J, Chaffee KG, Chanock S, Cleary S, Cotterchio M, Foretova L, Fuchs C, M Gaziano JM, Giovannucci E, Goggins M, Hackert T, Haiman C, Hartge P, Hasan M, Helzlsouer KJ, Herman J, Holcatova I, Holly EA, Hoover R, Hung RJ, Janout V, Klein EA, Kurtz RC, Laheru D, Lee IM, Lu L, Malats N, Mannisto S, Milne RL, Oberg AL, Orlow I, Patel AV, Peters U, Porta M, Real FX, Rothman N, Sesso HD, Severi G, Silverman D, Strobel O, Sund M, Thornquist MD, Tobias GS, Wactawski-Wende J, Wareham N, Weiderpass E, Wentzensen N, Wheeler W, Yu H, Zeleniuch-Jacquotte A, Kraft P, Li D, Jacobs EJ, Petersen GM, Wolpin BM, Risch HA, Amundadottir LT, Yu K, Klein AP, and Stolzenberg-Solomon RZ

Subjects
Case-Control Studies, Genetic Predisposition to Disease, Humans, Models, Statistical, Polymorphism, Single Nucleotide, Carcinoma, Pancreatic Ductal genetics, Genome-Wide Association Study methods, Pancreatic Neoplasms genetics
Abstract

Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes., Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided., Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets., Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified., (Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.)

Published
2019
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