Yushan Liu1,2, Wenfen Fu1,2, Wei Chen1,2, Yuxiang Lin1,2, Jie Zhang1,2, Chuangui Song1,2. 1. Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China. 2. Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China.
Abstract
PURPOSE: Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients. METHODS: The RNA-seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value. RESULTS: Based on the immune score, we found that the high-score group showed better clinical outcomes than the low-score group. Twenty-five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low-risk group had longer overall survival than the high-risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25-gene prognostic signatures and several clinicopathological prognostic factors was constructed. CONCLUSIONS: This study revealed the significance of immune infiltration and constructed a 25-gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.
PURPOSE:Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancerpatients. METHODS: The RNA-seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value. RESULTS: Based on the immune score, we found that the high-score group showed better clinical outcomes than the low-score group. Twenty-five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low-risk group had longer overall survival than the high-risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25-gene prognostic signatures and several clinicopathological prognostic factors was constructed. CONCLUSIONS: This study revealed the significance of immune infiltration and constructed a 25-gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.
Authors: Nam K Yoon; Erin L Maresh; Dejun Shen; Yahya Elshimali; Sophia Apple; Steve Horvath; Vei Mah; Shikha Bose; David Chia; Helena R Chang; Lee Goodglick Journal: Hum Pathol Date: 2010-12 Impact factor: 3.466
Authors: Sahar M A Mahmoud; Emma Claire Paish; Desmond G Powe; R Douglas Macmillan; Matthew J Grainge; Andrew H S Lee; Ian O Ellis; Andrew R Green Journal: J Clin Oncol Date: 2011-04-11 Impact factor: 44.544
Authors: Fatima Cardoso; Laura J van't Veer; Jan Bogaerts; Leen Slaets; Giuseppe Viale; Suzette Delaloge; Jean-Yves Pierga; Etienne Brain; Sylvain Causeret; Mauro DeLorenzi; Annuska M Glas; Vassilis Golfinopoulos; Theodora Goulioti; Susan Knox; Erika Matos; Bart Meulemans; Peter A Neijenhuis; Ulrike Nitz; Rodolfo Passalacqua; Peter Ravdin; Isabel T Rubio; Mahasti Saghatchian; Tineke J Smilde; Christos Sotiriou; Lisette Stork; Carolyn Straehle; Geraldine Thomas; Alastair M Thompson; Jacobus M van der Hoeven; Peter Vuylsteke; René Bernards; Konstantinos Tryfonidis; Emiel Rutgers; Martine Piccart Journal: N Engl J Med Date: 2016-08-25 Impact factor: 91.245
Authors: Peter Schmid; Sylvia Adams; Hope S Rugo; Andreas Schneeweiss; Carlos H Barrios; Hiroji Iwata; Véronique Diéras; Roberto Hegg; Seock-Ah Im; Gail Shaw Wright; Volkmar Henschel; Luciana Molinero; Stephen Y Chui; Roel Funke; Amreen Husain; Eric P Winer; Sherene Loi; Leisha A Emens Journal: N Engl J Med Date: 2018-10-20 Impact factor: 91.245
Authors: Gaynor J Bates; Stephen B Fox; Cheng Han; Russell D Leek; José F Garcia; Adrian L Harris; Alison H Banham Journal: J Clin Oncol Date: 2006-12-01 Impact factor: 44.544
Authors: Marcus Schmidt; Veronika Weyer-Elberich; Jan G Hengstler; Anne-Sophie Heimes; Katrin Almstedt; Aslihan Gerhold-Ay; Antje Lebrecht; Marco J Battista; Annette Hasenburg; Ugur Sahin; Konstantine T Kalogeras; Pirkko-Liisa Kellokumpu-Lehtinen; George Fountzilas; Ralph M Wirtz; Heikki Joensuu Journal: Breast Cancer Res Date: 2018-02-26 Impact factor: 6.466
Authors: Javier Valdés-Ferrada; Natalia Muñoz-Durango; Alejandra Pérez-Sepulveda; Sabrina Muñiz; Irenice Coronado-Arrázola; Francisco Acevedo; Jorge A Soto; Susan M Bueno; Cesar Sánchez; Alexis M Kalergis Journal: Front Immunol Date: 2020-07-09 Impact factor: 7.561
Authors: Richard Buus; Ivana Sestak; Ralf Kronenwett; Sean Ferree; Catherine A Schnabel; Frederick L Baehner; Elizabeth A Mallon; Jack Cuzick; Mitch Dowsett Journal: J Clin Oncol Date: 2020-10-27 Impact factor: 44.544