Literature DB >> 25883093

Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications.

Angelo Gámez-Pozo1, Julia Berges-Soria1, Jorge M Arevalillo2, Paolo Nanni3, Rocío López-Vacas1, Hilario Navarro2, Jonas Grossmann3, Carlos A Castaneda4, Paloma Main5, Mariana Díaz-Almirón6, Enrique Espinosa7, Eva Ciruelos8, Juan Ángel Fresno Vara9.   

Abstract

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER(+)) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER(+) and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention. ©2015 American Association for Cancer Research.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 25883093     DOI: 10.1158/0008-5472.CAN-14-1937

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  16 in total

1.  Downregulation of antigen presentation-associated pathway proteins is linked to poor outcome in triple-negative breast cancer patient tumors.

Authors:  Martin H Pedersen; Brian L Hood; Hans Christian Beck; Thomas P Conrads; Henrik J Ditzel; Rikke Leth-Larsen
Journal:  Oncoimmunology       Date:  2017-03-16       Impact factor: 8.110

2.  Genetic Profile and Functional Proteomics of Anal Squamous Cell Carcinoma: Proposal for a Molecular Classification.

Authors:  Lucía Trilla-Fuertes; Ismael Ghanem; Angelo Gámez-Pozo; Joan Maurel; Laura G-Pastrián; Marta Mendiola; Cristina Peña; Rocío López-Vacas; Guillermo Prado-Vázquez; Elena López-Camacho; Andrea Zapater-Moros; Victoria Heredia; Miriam Cuatrecasas; Pilar García-Alfonso; Jaume Capdevila; Carles Conill; Rocío García-Carbonero; Ricardo Ramos-Ruiz; Claudia Fortes; Carlos Llorens; Paolo Nanni; Juan Ángel Fresno Vara; Jaime Feliu
Journal:  Mol Cell Proteomics       Date:  2020-02-27       Impact factor: 5.911

Review 3.  Tiny giants of gene regulation: experimental strategies for microRNA functional studies.

Authors:  Bruno R Steinkraus; Markus Toegel; Tudor A Fulga
Journal:  Wiley Interdiscip Rev Dev Biol       Date:  2016-03-07       Impact factor: 5.814

4.  Molecular characterization of breast cancer cell response to metabolic drugs.

Authors:  Lucía Trilla-Fuertes; Angelo Gámez-Pozo; Jorge M Arevalillo; Mariana Díaz-Almirón; Guillermo Prado-Vázquez; Andrea Zapater-Moros; Hilario Navarro; Rosa Aras-López; Irene Dapía; Rocío López-Vacas; Paolo Nanni; Sara Llorente-Armijo; Pedro Arias; Alberto M Borobia; Paloma Maín; Jaime Feliú; Enrique Espinosa; Juan Ángel Fresno Vara
Journal:  Oncotarget       Date:  2018-01-08

5.  Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics.

Authors:  Angelo Gámez-Pozo; Lucía Trilla-Fuertes; Guillermo Prado-Vázquez; Cristina Chiva; Rocío López-Vacas; Paolo Nanni; Julia Berges-Soria; Jonas Grossmann; Mariana Díaz-Almirón; Eva Ciruelos; Eduard Sabidó; Enrique Espinosa; Juan Ángel Fresno Vara
Journal:  PLoS One       Date:  2017-06-08       Impact factor: 3.240

6.  MiRNAs Predict the Prognosis of Patients with Triple Negative Breast Cancer: A Meta-Analysis.

Authors:  Yanli Liu; Yuchao Zhang; Qingfu Li; Junfang Li; Xiaotian Ma; Jinfang Xing; Shouhua Rong; Zhong Wu; Yuan Tian; Jing Li; Liting Jia
Journal:  PLoS One       Date:  2017-01-13       Impact factor: 3.240

7.  Bayesian networks established functional differences between breast cancer subtypes.

Authors:  Lucía Trilla-Fuertes; Angelo Gámez-Pozo; Jorge M Arevalillo; Rocío López-Vacas; Elena López-Camacho; Guillermo Prado-Vázquez; Andrea Zapater-Moros; Mariana Díaz-Almirón; María Ferrer-Gómez; Hilario Navarro; Paolo Nanni; Pilar Zamora; Enrique Espinosa; Paloma Maín; Juan Ángel Fresno Vara
Journal:  PLoS One       Date:  2020-06-11       Impact factor: 3.240

8.  Proteome Profiling of Primary Pancreatic Ductal Adenocarcinomas Undergoing Additive Chemoradiation Link ALDH1A1 to Early Local Recurrence and Chemoradiation Resistance.

Authors:  V O Oria; P Bronsert; A R Thomsen; M C Föll; C Zamboglou; Luciana Hannibal; S Behringer; M L Biniossek; C Schreiber; A L Grosu; L Bolm; D Rades; T Keck; M Werner; U F Wellner; O Schilling
Journal:  Transl Oncol       Date:  2018-08-30       Impact factor: 4.243

9.  Urothelial cancer proteomics provides both prognostic and functional information.

Authors:  Guillermo de Velasco; Lucia Trilla-Fuertes; Angelo Gamez-Pozo; Maria Urbanowicz; Gustavo Ruiz-Ares; Juan M Sepúlveda; Guillermo Prado-Vazquez; Jorge M Arevalillo; Andrea Zapater-Moros; Hilario Navarro; Rocio Lopez-Vacas; Ray Manneh; Irene Otero; Felipe Villacampa; Jesus M Paramio; Juan Angel Fresno Vara; Daniel Castellano
Journal:  Sci Rep       Date:  2017-11-17       Impact factor: 4.379

10.  Functional proteomics outlines the complexity of breast cancer molecular subtypes.

Authors:  Angelo Gámez-Pozo; Lucía Trilla-Fuertes; Julia Berges-Soria; Nathalie Selevsek; Rocío López-Vacas; Mariana Díaz-Almirón; Paolo Nanni; Jorge M Arevalillo; Hilario Navarro; Jonas Grossmann; Francisco Gayá Moreno; Rubén Gómez Rioja; Guillermo Prado-Vázquez; Andrea Zapater-Moros; Paloma Main; Jaime Feliú; Purificación Martínez Del Prado; Pilar Zamora; Eva Ciruelos; Enrique Espinosa; Juan Ángel Fresno Vara
Journal:  Sci Rep       Date:  2017-08-30       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.