Search

Your search keyword '"Rodriguez-Rodriguez L"' showing total 10 results
Start Over Author "Rodriguez-Rodriguez L" Publisher nature publishing group
10 results on '"Rodriguez-Rodriguez L"'

1. GWAS for systemic sclerosis identifies multiple risk loci and highlights fibrotic and vasculopathy pathways

Author

Lopez-Isac, Elena, Acosta-Herrera, Marialbert, Kerick, Martin, Assassi, Shervin, Satpathy, Ansuman T., Granja, Jeffrey, Mumbach, Maxwell R., Beretta, Lorenzo, Simeon, Carmen P., Carreira, Patricia, Ortego-Centeno, Norberto, Castellvi, Ivan, Bossini-Castillo, Lara, David Carmona, F., Orozco, Gisela, Hunzelmann, Nicolas, Distler, Joerg H. W., Franke, Andre, Lunardi, Claudio, Moroncini, Gianluca, Gabrielli, Armando, de Vries-Bouwstra, Jeska, Wijmenga, Cisca, Koeleman, Bobby P. C., Nordin, Annika, Padyukov, Leonid, Hoffmann-Vold, Anna-Maria, Lie, Benedicte, Rios, R., Callejas, J. L., Vargas-Hitos, J. A., Garcia-Portales, R., Camps, M. T., Fernandez-Nebro, A., Gonzalez-Escribano, M. F., Garcia-Hernandez, F. J., Castillo, M. J., Aguirre, M. A., Gomez-Gracia, I., Fernandez-Gutierrez, B., Rodriguez-Rodriguez, L., Garcia de la Pena, P., Vicente, E., Andreu, J. L., Fernandez de Castro, M., Lopez-Longo, F. J., Martinez, L., Fonollosa, V, Guillen, A., Espinosa, G., Tolosa, C., Pros, A., Rodriguez-Carballeira, M., Narvaez, F. J., Rubio-Rivas, M., Ortiz-Santamaria, V, Madronero, A. B., Gonzalez-Gay, M. A., Diaz, B., Trapiella, L., Sousa, A., Egurbide, M. V., Fanlo-Mateo, P., Saez-Comet, L., Diaz, F., Hernandez, V, Beltran, E., Roman-Ivorra, J. A., Grau, E., Alegre-Sancho, J. J., Freire, M., Blanco-Garcia, F. J., Oreiro, N., Witte, T., Kreuter, A., Riemekasten, G., Airo, P., Magro, C., Voskuyl, A. E., Vonk, M. C., Hesselstrand, R., Proudman, Susanna, Stevens, Wendy, Nikpour, Mandana, Zochling, J., Sahhar, J., Roddy, J., Nash, P., Tymms, K., Rischmueller, M., Lester, S., Vyse, Timothy, Herrick, Ariane L., Worthington, Jane, Denton, Christopher P., Allanore, Yannick, Brown, Matthew A., Radstake, Timothy R. D. J., Fonseca, Carmen, Chang, Howard Y., Mayes, Maureen D., Martin, Javier, Lopez-Isac, Elena, Acosta-Herrera, Marialbert, Kerick, Martin, Assassi, Shervin, Satpathy, Ansuman T., Granja, Jeffrey, Mumbach, Maxwell R., Beretta, Lorenzo, Simeon, Carmen P., Carreira, Patricia, Ortego-Centeno, Norberto, Castellvi, Ivan, Bossini-Castillo, Lara, David Carmona, F., Orozco, Gisela, Hunzelmann, Nicolas, Distler, Joerg H. W., Franke, Andre, Lunardi, Claudio, Moroncini, Gianluca, Gabrielli, Armando, de Vries-Bouwstra, Jeska, Wijmenga, Cisca, Koeleman, Bobby P. C., Nordin, Annika, Padyukov, Leonid, Hoffmann-Vold, Anna-Maria, Lie, Benedicte, Rios, R., Callejas, J. L., Vargas-Hitos, J. A., Garcia-Portales, R., Camps, M. T., Fernandez-Nebro, A., Gonzalez-Escribano, M. F., Garcia-Hernandez, F. J., Castillo, M. J., Aguirre, M. A., Gomez-Gracia, I., Fernandez-Gutierrez, B., Rodriguez-Rodriguez, L., Garcia de la Pena, P., Vicente, E., Andreu, J. L., Fernandez de Castro, M., Lopez-Longo, F. J., Martinez, L., Fonollosa, V, Guillen, A., Espinosa, G., Tolosa, C., Pros, A., Rodriguez-Carballeira, M., Narvaez, F. J., Rubio-Rivas, M., Ortiz-Santamaria, V, Madronero, A. B., Gonzalez-Gay, M. A., Diaz, B., Trapiella, L., Sousa, A., Egurbide, M. V., Fanlo-Mateo, P., Saez-Comet, L., Diaz, F., Hernandez, V, Beltran, E., Roman-Ivorra, J. A., Grau, E., Alegre-Sancho, J. J., Freire, M., Blanco-Garcia, F. J., Oreiro, N., Witte, T., Kreuter, A., Riemekasten, G., Airo, P., Magro, C., Voskuyl, A. E., Vonk, M. C., Hesselstrand, R., Proudman, Susanna, Stevens, Wendy, Nikpour, Mandana, Zochling, J., Sahhar, J., Roddy, J., Nash, P., Tymms, K., Rischmueller, M., Lester, S., Vyse, Timothy, Herrick, Ariane L., Worthington, Jane, Denton, Christopher P., Allanore, Yannick, Brown, Matthew A., Radstake, Timothy R. D. J., Fonseca, Carmen, Chang, Howard Y., Mayes, Maureen D., and Martin, Javier

Abstract

Systemic sclerosis (SSc) is an autoimmune disease that shows one of the highest mortality rates among rheumatic diseases. We perform a large genome-wide association study (GWAS), and meta-analysis with previous GWASs, in 26,679 individuals and identify 27 independent genome-wide associated signals, including 13 new risk loci. The novel associations nearly double the number of genome-wide hits reported for SSc thus far. We define 95% credible sets of less than 5 likely causal variants in 12 loci. Additionally, we identify specific SSc subtype-associated signals. Functional analysis of high-priority variants shows the potential function of SSc signals, with the identification of 43 robust target genes through HiChIP. Our results point towards molecular pathways potentially involved in vasculopathy and fibrosis, two main hallmarks in SSc, and highlight the spectrum of critical cell types for the disease. This work supports a better understanding of the genetic basis of SSc and provides directions for future functional experiments.

Published
2019

2. GWAS for systemic sclerosis identifies multiple risk loci and highlights fibrotic and vasculopathy pathways

Author

Lopez-Isac, E, Acosta-Herrera, M, Kerick, M, Assassi, S, Satpathy, AT, Granja, J, Mumbach, MR, Beretta, L, Simeon, CP, Carreira, P, Ortego-Centeno, N, Castellvi, I, Bossini-Castillo, L, David Carmona, F, Orozco, G, Hunzelmann, N, Distler, JHW, Franke, A, Lunardi, C, Moroncini, G, Gabrielli, A, de Vries-Bouwstra, J, Wijmenga, C, Koeleman, BPC, Nordin, A, Padyukov, L, Hoffmann-Vold, A-M, Lie, B, Rios, R, Callejas, JL, Vargas-Hitos, JA, Garcia-Portales, R, Camps, MT, Fernandez-Nebro, A, Gonzalez-Escribano, MF, Garcia-Hernandez, FJ, Castillo, MJ, Aguirre, MA, Gomez-Gracia, I, Fernandez-Gutierrez, B, Rodriguez-Rodriguez, L, Garcia de la Pena, P, Vicente, E, Andreu, JL, Fernandez de Castro, M, Lopez-Longo, FJ, Martinez, L, Fonollosa, V, Guillen, A, Espinosa, G, Tolosa, C, Pros, A, Rodriguez-Carballeira, M, Narvaez, FJ, Rubio-Rivas, M, Ortiz-Santamaria, V, Madronero, AB, Gonzalez-Gay, MA, Diaz, B, Trapiella, L, Sousa, A, Egurbide, MV, Fanlo-Mateo, P, Saez-Comet, L, Diaz, F, Hernandez, V, Beltran, E, Roman-Ivorra, JA, Grau, E, Alegre-Sancho, JJ, Freire, M, Blanco-Garcia, FJ, Oreiro, N, Witte, T, Kreuter, A, Riemekasten, G, Airo, P, Magro, C, Voskuyl, AE, Vonk, MC, Hesselstrand, R, Proudman, S, Stevens, W, Nikpour, M, Zochling, J, Sahhar, J, Roddy, J, Nash, P, Tymms, K, Rischmueller, M, Lester, S, Vyse, T, Herrick, AL, Worthington, J, Denton, CP, Allanore, Y, Brown, MA, Radstake, TRDJ, Fonseca, C, Chang, HY, Mayes, MD, Martin, J, Lopez-Isac, E, Acosta-Herrera, M, Kerick, M, Assassi, S, Satpathy, AT, Granja, J, Mumbach, MR, Beretta, L, Simeon, CP, Carreira, P, Ortego-Centeno, N, Castellvi, I, Bossini-Castillo, L, David Carmona, F, Orozco, G, Hunzelmann, N, Distler, JHW, Franke, A, Lunardi, C, Moroncini, G, Gabrielli, A, de Vries-Bouwstra, J, Wijmenga, C, Koeleman, BPC, Nordin, A, Padyukov, L, Hoffmann-Vold, A-M, Lie, B, Rios, R, Callejas, JL, Vargas-Hitos, JA, Garcia-Portales, R, Camps, MT, Fernandez-Nebro, A, Gonzalez-Escribano, MF, Garcia-Hernandez, FJ, Castillo, MJ, Aguirre, MA, Gomez-Gracia, I, Fernandez-Gutierrez, B, Rodriguez-Rodriguez, L, Garcia de la Pena, P, Vicente, E, Andreu, JL, Fernandez de Castro, M, Lopez-Longo, FJ, Martinez, L, Fonollosa, V, Guillen, A, Espinosa, G, Tolosa, C, Pros, A, Rodriguez-Carballeira, M, Narvaez, FJ, Rubio-Rivas, M, Ortiz-Santamaria, V, Madronero, AB, Gonzalez-Gay, MA, Diaz, B, Trapiella, L, Sousa, A, Egurbide, MV, Fanlo-Mateo, P, Saez-Comet, L, Diaz, F, Hernandez, V, Beltran, E, Roman-Ivorra, JA, Grau, E, Alegre-Sancho, JJ, Freire, M, Blanco-Garcia, FJ, Oreiro, N, Witte, T, Kreuter, A, Riemekasten, G, Airo, P, Magro, C, Voskuyl, AE, Vonk, MC, Hesselstrand, R, Proudman, S, Stevens, W, Nikpour, M, Zochling, J, Sahhar, J, Roddy, J, Nash, P, Tymms, K, Rischmueller, M, Lester, S, Vyse, T, Herrick, AL, Worthington, J, Denton, CP, Allanore, Y, Brown, MA, Radstake, TRDJ, Fonseca, C, Chang, HY, Mayes, MD, and Martin, J

Abstract

Systemic sclerosis (SSc) is an autoimmune disease that shows one of the highest mortality rates among rheumatic diseases. We perform a large genome-wide association study (GWAS), and meta-analysis with previous GWASs, in 26,679 individuals and identify 27 independent genome-wide associated signals, including 13 new risk loci. The novel associations nearly double the number of genome-wide hits reported for SSc thus far. We define 95% credible sets of less than 5 likely causal variants in 12 loci. Additionally, we identify specific SSc subtype-associated signals. Functional analysis of high-priority variants shows the potential function of SSc signals, with the identification of 43 robust target genes through HiChIP. Our results point towards molecular pathways potentially involved in vasculopathy and fibrosis, two main hallmarks in SSc, and highlight the spectrum of critical cell types for the disease. This work supports a better understanding of the genetic basis of SSc and provides directions for future functional experiments.

Published
2019

3. GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer

Author

Pharoah, PDP, Tsai, Y-Y, Ramus, SJ, Phelan, CM, Goode, EL, Lawrenson, K, Buckley, M, Fridley, BL, Tyrer, JP, Shen, H, Weber, R, Karevan, R, Larson, MC, Song, H, Tessier, DC, Bacot, F, Vincent, D, Cunningham, JM, Dennis, J, Dicks, E, Aben, KK, Anton-Culver, H, Antonenkova, N, Armasu, SM, Baglietto, L, Bandera, EV, Beckmann, MW, Birrer, MJ, Bloom, G, Bogdanova, N, Brenton, JD, Brinton, LA, Brooks-Wilson, A, Brown, R, Butzow, R, Campbell, I, Carney, ME, Carvalho, RS, Chang-Claude, J, Chen, YA, Chen, Z, Chow, W-H, Cicek, MS, Coetzee, G, Cook, LS, Cramer, DW, Cybulski, C, Dansonka-Mieszkowska, A, Despierre, E, Doherty, JA, Doerk, T, du Bois, A, Duerst, M, Eccles, D, Edwards, R, Ekici, AB, Fasching, PA, Fenstermacher, D, Flanagan, J, Gao, Y-T, Garcia-Closas, M, Gentry-Maharaj, A, Giles, G, Gjyshi, A, Gore, M, Gronwald, J, Guo, Q, Halle, MK, Harter, P, Hein, A, Heitz, F, Hillemanns, P, Hoatlin, M, Hogdall, E, Hogdall, CK, Hosono, S, Jakubowska, A, Jensen, A, Kalli, KR, Karlan, BY, Kelemen, LE, Kiemeney, LA, Kjaer, SK, Konecny, GE, Krakstad, C, Kupryjanczyk, J, Lambrechts, D, Lambrechts, S, Le, ND, Lee, N, Lee, J, Leminen, A, Lim, BK, Lissowska, J, Lubinski, J, Lundvall, L, Lurie, G, Massuger, LFAG, Matsuo, K, McGuire, V, McLaughlin, JR, Menon, U, Modugno, F, Moysich, KB, Nakanishi, T, Narod, SA, Ness, RB, Nevanlinna, H, Nickels, S, Noushmehr, H, Odunsi, K, Olson, S, Orlow, I, Paul, J, Pejovic, T, Pelttari, LM, Permuth-Wey, J, Pike, MC, Poole, EM, Qu, X, Risch, HA, Rodriguez-Rodriguez, L, Rossing, MA, Rudolph, A, Runnebaum, I, Rzepecka, IK, Salvesen, HB, Schwaab, I, Severi, G, Shridhar, V, Shu, X-O, Sieh, W, Southey, MC, Spellman, P, Tajima, K, Teo, S-H, Terry, KL, Thompson, PJ, Timorek, A, Tworoger, SS, van Altena, AM, van den Berg, D, Vergote, I, Vierkant, RA, Vitonis, AF, Wang-Gohrke, S, Wentzensen, N, Whittemore, AS, Wik, E, Winterhoff, B, Woo, YL, Wu, AH, Yang, HP, Zheng, W, Ziogas, A, Zulkifli, F, Goodman, MT, Hall, P, Easton, DF, Pearce, CL, Berchuck, A, Chenevix-Trench, G, Iversen, E, Monteiro, ANA, Gayther, SA, Schildkraut, JM, Sellers, TA, Pharoah, PDP, Tsai, Y-Y, Ramus, SJ, Phelan, CM, Goode, EL, Lawrenson, K, Buckley, M, Fridley, BL, Tyrer, JP, Shen, H, Weber, R, Karevan, R, Larson, MC, Song, H, Tessier, DC, Bacot, F, Vincent, D, Cunningham, JM, Dennis, J, Dicks, E, Aben, KK, Anton-Culver, H, Antonenkova, N, Armasu, SM, Baglietto, L, Bandera, EV, Beckmann, MW, Birrer, MJ, Bloom, G, Bogdanova, N, Brenton, JD, Brinton, LA, Brooks-Wilson, A, Brown, R, Butzow, R, Campbell, I, Carney, ME, Carvalho, RS, Chang-Claude, J, Chen, YA, Chen, Z, Chow, W-H, Cicek, MS, Coetzee, G, Cook, LS, Cramer, DW, Cybulski, C, Dansonka-Mieszkowska, A, Despierre, E, Doherty, JA, Doerk, T, du Bois, A, Duerst, M, Eccles, D, Edwards, R, Ekici, AB, Fasching, PA, Fenstermacher, D, Flanagan, J, Gao, Y-T, Garcia-Closas, M, Gentry-Maharaj, A, Giles, G, Gjyshi, A, Gore, M, Gronwald, J, Guo, Q, Halle, MK, Harter, P, Hein, A, Heitz, F, Hillemanns, P, Hoatlin, M, Hogdall, E, Hogdall, CK, Hosono, S, Jakubowska, A, Jensen, A, Kalli, KR, Karlan, BY, Kelemen, LE, Kiemeney, LA, Kjaer, SK, Konecny, GE, Krakstad, C, Kupryjanczyk, J, Lambrechts, D, Lambrechts, S, Le, ND, Lee, N, Lee, J, Leminen, A, Lim, BK, Lissowska, J, Lubinski, J, Lundvall, L, Lurie, G, Massuger, LFAG, Matsuo, K, McGuire, V, McLaughlin, JR, Menon, U, Modugno, F, Moysich, KB, Nakanishi, T, Narod, SA, Ness, RB, Nevanlinna, H, Nickels, S, Noushmehr, H, Odunsi, K, Olson, S, Orlow, I, Paul, J, Pejovic, T, Pelttari, LM, Permuth-Wey, J, Pike, MC, Poole, EM, Qu, X, Risch, HA, Rodriguez-Rodriguez, L, Rossing, MA, Rudolph, A, Runnebaum, I, Rzepecka, IK, Salvesen, HB, Schwaab, I, Severi, G, Shridhar, V, Shu, X-O, Sieh, W, Southey, MC, Spellman, P, Tajima, K, Teo, S-H, Terry, KL, Thompson, PJ, Timorek, A, Tworoger, SS, van Altena, AM, van den Berg, D, Vergote, I, Vierkant, RA, Vitonis, AF, Wang-Gohrke, S, Wentzensen, N, Whittemore, AS, Wik, E, Winterhoff, B, Woo, YL, Wu, AH, Yang, HP, Zheng, W, Ziogas, A, Zulkifli, F, Goodman, MT, Hall, P, Easton, DF, Pearce, CL, Berchuck, A, Chenevix-Trench, G, Iversen, E, Monteiro, ANA, Gayther, SA, Schildkraut, JM, and Sellers, TA

Abstract

Genome-wide association studies (GWAS) have identified four susceptibility loci for epithelial ovarian cancer (EOC), with another two suggestive loci reaching near genome-wide significance. We pooled data from a GWAS conducted in North America with another GWAS from the UK. We selected the top 24,551 SNPs for inclusion on the iCOGS custom genotyping array. We performed follow-up genotyping in 18,174 individuals with EOC (cases) and 26,134 controls from 43 studies from the Ovarian Cancer Association Consortium. We validated the two loci at 3q25 and 17q21 that were previously found to have associations close to genome-wide significance and identified three loci newly associated with risk: two loci associated with all EOC subtypes at 8q21 (rs11782652, P = 5.5 × 10(-9)) and 10p12 (rs1243180, P = 1.8 × 10(-8)) and another locus specific to the serous subtype at 17q12 (rs757210, P = 8.1 × 10(-10)). An integrated molecular analysis of genes and regulatory regions at these loci provided evidence for functional mechanisms underlying susceptibility and implicated CHMP4C in the pathogenesis of ovarian cancer.

Published
2013

4. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis

Author

Eyre, S, Bowes, J, Diogo, D, Lee, A, Barton, A, Martin, P, Zhernakova, A, Stahl, E, Viatte, S, McAllister, K, Amos, CI, Padyukov, L, Toes, REM, Huizinga, TWJ, Wijmenga, C, Trynka, G, Franke, L, Westra, H-J, Alfredsson, L, Hu, X, Sandor, C, de Bakker, PIW, Davila, S, Khor, CC, Heng, KK, Andrews, R, Edkins, S, Hunt, SE, Langford, C, Symmons, D, Concannon, P, Onengut-Gumuscu, S, Rich, SS, Deloukas, P, Gonzalez-Gay, MA, Rodriguez-Rodriguez, L, Arlsetig, L, Martin, J, Rantapaa-Dahlqvist, S, Plenge, RM, Raychaudhuri, S, Klareskog, L, Gregersen, PK, Worthington, J, Eyre, S, Bowes, J, Diogo, D, Lee, A, Barton, A, Martin, P, Zhernakova, A, Stahl, E, Viatte, S, McAllister, K, Amos, CI, Padyukov, L, Toes, REM, Huizinga, TWJ, Wijmenga, C, Trynka, G, Franke, L, Westra, H-J, Alfredsson, L, Hu, X, Sandor, C, de Bakker, PIW, Davila, S, Khor, CC, Heng, KK, Andrews, R, Edkins, S, Hunt, SE, Langford, C, Symmons, D, Concannon, P, Onengut-Gumuscu, S, Rich, SS, Deloukas, P, Gonzalez-Gay, MA, Rodriguez-Rodriguez, L, Arlsetig, L, Martin, J, Rantapaa-Dahlqvist, S, Plenge, RM, Raychaudhuri, S, Klareskog, L, Gregersen, PK, and Worthington, J

Abstract

Using the Immunochip custom SNP array, which was designed for dense genotyping of 186 loci identified through genome-wide association studies (GWAS), we analyzed 11,475 individuals with rheumatoid arthritis (cases) of European ancestry and 15,870 controls for 129,464 markers. We combined these data in a meta-analysis with GWAS data from additional independent cases (n = 2,363) and controls (n = 17,872). We identified 14 new susceptibility loci, 9 of which were associated with rheumatoid arthritis overall and five of which were specifically associated with disease that was positive for anticitrullinated peptide antibodies, bringing the number of confirmed rheumatoid arthritis risk loci in individuals of European ancestry to 46. We refined the peak of association to a single gene for 19 loci, identified secondary independent effects at 6 loci and identified association to low-frequency variants at 4 loci. Bioinformatic analyses generated strong hypotheses for the causal SNP at seven loci. This study illustrates the advantages of dense SNP mapping analysis to inform subsequent functional investigations.

Published
2012

5. Gene expression associated with endocrine therapy resistance in estrogen receptor-positive breast cancer.

Author

Jones V, Yin HH, Yuan YC, Wang Y, Li SM, Aljaber D, Sanchez A, Quinones C, Schmolze D, Yuan Y, Mortimer J, Yee L, Kruper L, Jovanovic-Talisman T, Tomsic J, Sanchez N, Chavez T, O'Regan RM, Khan QJ, Davis M, Kalinsky K, Meisel J, Kittles R, Rodriguez-Rodriguez L, and Seewaldt V

Subjects
Humans, Female, Middle Aged, Aged, Adult, Neoplasm Recurrence, Local genetics, Antineoplastic Agents, Hormonal therapeutic use, Antineoplastic Agents, Hormonal pharmacology, Biomarkers, Tumor genetics, Biomarkers, Tumor metabolism, Gene Expression Profiling, Aged, 80 and over, Breast Neoplasms genetics, Breast Neoplasms drug therapy, Breast Neoplasms pathology, Breast Neoplasms metabolism, Drug Resistance, Neoplasm genetics, Receptors, Estrogen metabolism, Receptors, Estrogen genetics, Gene Expression Regulation, Neoplastic
Abstract

Despite endocrine therapy (ET), approximately 20-40% of Stage I-III estrogen receptor-positive breast cancer (ER + BC) patients experience recurrence. Recurrence while on ET is indicative of ET resistance. This study aimed to identify differentially expressed genes (DEGs) associated with recurrence during ET (ET resistance) and to explore gene expression differences across PAM50 molecular subtypes. Eighty tumor specimens from 79 patients treated at the City of Hope Comprehensive Cancer Center (2012-2016) were analyzed using NanoString technology. Fourteen patients (17.7%) experienced recurrence over a median follow-up of 68 months (range 35-104 months). Key upregulated DEGs in the recurrence group included EZH2 (log2 fold change[log2FC]: 0.67, p = 0.0017), WNT11 (log2FC: 1.08, p = 0.0088), ITGB6 (log2FC: 0.80, p = 0.0312), and TOP2A (log2FC: 0.79, p = 0.0381). Downregulated DEGs included SNAI2 (log2FC: - 0.63, p = 0.0055), ITPR1 (log2FC: - 0.75, p = 0.0083), CD10 (log2FC: - 0.70, p = 0.0092), PTEN (log2FC: - 0.29, p = 0.0163), VRD (log2FC: - 0.46, p = 0.0184), and WNT5A (log2FC: - 0.76, p = 0.0272). EZH2 and TOP2A were positively correlated with proliferation scores, while WNT11 and ITGB6 emerged as potential biomarkers independently associated with recurrence. These findings suggest novel biomarker candidates that could help overcome ET resistance, reduce recurrence, and improve outcomes in ER + BC., Competing Interests: Declarations. Competing interests: Yuan Yuan declares Grant/Research Support from Imugene, Genentech, Pfizer, Merck, Edenbridge, Agenus; Consultant for Gilead Sciences and Novartis; On the Speakers Bureau for AstraZeneca, Daiichi-Sankyo, Merck, Pfizer and Gilead Sciences. Kevin Kalinsky declares Employment/Stock: Spouse—EQRX, Grail (Prior Employee); Advisory/Consulting: Eli-Lilly, Pfizer, Novartis, AstraZeneca, Daiichi Sankyo, Puma, 4D Pharma, Oncosec, Immunomedics, Puma, Merck, Seattle Genetics, Mersana, and Cyclocel; Institutional Research Funding: Genentech/Roche, Novartis, Eli-Lilly, AstraZeneca, Daiichi Sankyo, Ascentage. The remaining authors have no conflicts of interest., (© 2025. The Author(s).)

Published
2025
Full Text
View/download PDF

6. Publisher Correction: A predominant involvement of the triple seropositive patients and others with rheumatoid factor in the association of smoking with rheumatoid arthritis.

Author

Regueiro C, Rodriguez-Rodriguez L, Lopez-Mejias R, Nuño L, Triguero-Martinez A, Perez-Pampin E, Corrales A, Villalba A, Lopez-Golan Y, Abasolo L, Remuzgo-Martínez S, Ortiz AM, Herranz E, Martínez-Feito A, Conde C, Mera-Varela A, Balsa A, Gonzalez-Alvaro I, González-Gay MÁ, Fernandez-Gutierrez B, and Gonzalez A

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020
Full Text
View/download PDF

7. A predominant involvement of the triple seropositive patients and others with rheumatoid factor in the association of smoking with rheumatoid arthritis.

Author

Regueiro C, Rodriguez-Rodriguez L, Lopez-Mejias R, Nuño L, Triguero-Martinez A, Perez-Pampin E, Corrales A, Villalba A, Lopez-Golan Y, Abasolo L, Remuzgo-Martínez S, Ortiz AM, Herranz E, Martínez-Feito A, Conde C, Mera-Varela A, Balsa A, Gonzalez-Alvaro I, González-Gay MÁ, Fernandez-Gutierrez B, and Gonzalez A

Subjects
Anti-Citrullinated Protein Antibodies immunology, Arthritis, Rheumatoid complications, Arthritis, Rheumatoid immunology, Autoantibodies blood, Autoantibodies immunology, Female, HLA-DRB1 Chains immunology, Humans, Immunologic Tests, Male, Patients, Peptides, Cyclic immunology, Protein Carbamylation immunology, Rheumatoid Factor blood, Rheumatoid Factor immunology, Risk Factors, Smoking adverse effects, Anti-Citrullinated Protein Antibodies blood, Arthritis, Rheumatoid blood, Epitopes immunology, Seroepidemiologic Studies
Abstract

The major environmental risk factor for rheumatoid arthritis (RA) is smoking, which according to a widely accepted model induces protein citrullination in the lungs, triggering the production of anti-citrullinated protein antibodies (ACPA) and RA development. Nevertheless, some research findings do not fit this model. Therefore, we obtained six independent cohorts with 2253 RA patients for a detailed analysis of the association between smoking and RA autoantibodies. Our results showed a predominant association of smoking with the concurrent presence of the three antibodies: rheumatoid factor (RF), ACPA and anti-carbamylated protein antibodies (ACarPA) (3 Ab vs. 0 Ab: OR = 1.99, p = 2.5 × 10 -8 ). Meta-analysis with previous data (4491 patients) confirmed the predominant association with the concurrent presence of the three antibodies (3 Ab vs. 0 Ab: OR = 2.00, p = 4.4 ×10 -16 ) and revealed that smoking was exclusively associated with the presence of RF in patients with one or two antibodies (RF + 1+2 vs. RF - 0+1+2 : OR = 1.32, p = 0.0002). In contrast, no specific association with ACPA or ACarPA was found. Therefore, these results showed the need to understand how smoking favors the concordance of RA specific antibodies and RF triggering, perhaps involving smoking-induced epitope spreading and other hypothesized mechanisms.

Published
2020
Full Text
View/download PDF

8. Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach.

Author

Lezcano-Valverde JM, Salazar F, León L, Toledano E, Jover JA, Fernandez-Gutierrez B, Soudah E, González-Álvaro I, Abasolo L, and Rodriguez-Rodriguez L

Subjects
Aged, Blood Sedimentation, Cohort Studies, Female, Humans, Longitudinal Studies, Male, Middle Aged, Models, Theoretical, Spain, Arthritis, Rheumatoid blood, Arthritis, Rheumatoid mortality, Machine Learning
Abstract

We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clínico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79-0.80 and 0.43-0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.

Published
2017
Full Text
View/download PDF

9. Rab1 in cell signaling, cancer and other diseases.

Author

Yang XZ, Li XX, Zhang YJ, Rodriguez-Rodriguez L, Xiang MQ, Wang HY, and Zheng XF

Subjects
Animals, Cardiomyopathies genetics, Cardiomyopathies metabolism, Carrier Proteins, Gene Expression Regulation, Neoplastic, Humans, Neoplasms genetics, Protein Binding, Protein Interaction Domains and Motifs, rab1 GTP-Binding Proteins chemistry, rab1 GTP-Binding Proteins genetics, Disease Susceptibility, Neoplasms metabolism, Signal Transduction, rab1 GTP-Binding Proteins metabolism
Abstract

The endoplasmic reticulum (ER) and Golgi membrane system have major roles in cell signaling and regulation of the biosynthesis/transport of proteins and lipids in response to environmental cues such as amino acid and cholesterol levels. Rab1 is the founding member of the Rab small GTPase family, which is known to mediate dynamic membrane trafficking between ER and Golgi. Growing evidence indicate that Rab1 proteins have important functions beyond their classical vesicular transport functions, including nutrient sensing and signaling, cell migration and presentation of cell-surface receptors. Moreover, deregulation of RAB1 expression has been linked to a myriad of human diseases such as cancer, cardiomyopathy and Parkinson's disease. Further investigating these new physiological and pathological functions of Rab1 should provide new opportunities for better understanding of the disease processes and may lead to more effective therapeutic interventions.

Published
2016
Full Text
View/download PDF

10. Genetics of rheumatoid arthritis contributes to biology and drug discovery.

Author

Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, Kochi Y, Ohmura K, Suzuki A, Yoshida S, Graham RR, Manoharan A, Ortmann W, Bhangale T, Denny JC, Carroll RJ, Eyler AE, Greenberg JD, Kremer JM, Pappas DA, Jiang L, Yin J, Ye L, Su DF, Yang J, Xie G, Keystone E, Westra HJ, Esko T, Metspalu A, Zhou X, Gupta N, Mirel D, Stahl EA, Diogo D, Cui J, Liao K, Guo MH, Myouzen K, Kawaguchi T, Coenen MJ, van Riel PL, van de Laar MA, Guchelaar HJ, Huizinga TW, Dieudé P, Mariette X, Bridges SL Jr, Zhernakova A, Toes RE, Tak PP, Miceli-Richard C, Bang SY, Lee HS, Martin J, Gonzalez-Gay MA, Rodriguez-Rodriguez L, Rantapää-Dahlqvist S, Arlestig L, Choi HK, Kamatani Y, Galan P, Lathrop M, Eyre S, Bowes J, Barton A, de Vries N, Moreland LW, Criswell LA, Karlson EW, Taniguchi A, Yamada R, Kubo M, Liu JS, Bae SC, Worthington J, Padyukov L, Klareskog L, Gregersen PK, Raychaudhuri S, Stranger BE, De Jager PL, Franke L, Visscher PM, Brown MA, Yamanaka H, Mimori T, Takahashi A, Xu H, Behrens TW, Siminovitch KA, Momohara S, Matsuda F, Yamamoto K, and Plenge RM

Subjects
Alleles, Animals, Arthritis, Rheumatoid metabolism, Arthritis, Rheumatoid pathology, Asian People genetics, Case-Control Studies, Computational Biology, Drug Repositioning, Female, Genome-Wide Association Study, Hematologic Neoplasms genetics, Hematologic Neoplasms metabolism, Humans, Male, Mice, Mice, Knockout, Polymorphism, Single Nucleotide genetics, White People genetics, Arthritis, Rheumatoid drug therapy, Arthritis, Rheumatoid genetics, Drug Discovery, Genetic Predisposition to Disease genetics, Molecular Targeted Therapy
Abstract

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results