F Wu1, W Chen2, X Kang3, L Jin4, J Bai4, H Zhang4, X Zhang5. 1. Ambuiatory Surgery Treatment Department, Cangzhou Central Hospital, Cangzhou, 061001, Hebei Province, China. 2. Department of Diagnostic Imaging, Affiliated Hospital of North China University of Science and Technology, Tangshan, 063000, Hebei, China. 3. Ultrasound Department II, Cangzhou Central Hospital, Cangzhou, 061001, Hebei Province, China. 4. Department of Thyroid and Mammary Gland III, Cangzhou Central Hospital, No. 16 Xinhua West Road, Yunhe District, Cangzhou, 061001, Hebei Province, China. 5. Department of Thyroid and Mammary Gland III, Cangzhou Central Hospital, No. 16 Xinhua West Road, Yunhe District, Cangzhou, 061001, Hebei Province, China. zhangxiaoyu93956466@21cn.com.
Abstract
BACKGROUND: Breast cancer (BRCA) is a malignant cancer that threatened the life of female with unsatisfactory prognosis. The aim of this study was to identify prognostic nuclear receptors (NRs) signature of BRCA. METHODS: BRCA patient samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Consensus clustering analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to evaluate, select NRs as prognostic factors and build Risk Score model. GSEA analysis was explored to check signaling differences between High- and Low-Risk group. Nomogram model basing on age and Risk Score was established to predict the 1-, 3- and 5-year survival. Model performance was assessed by a time-dependent receiver operating characteristic (ROC) curve and calibration plot. CIBERSORT, ESTIMATE and TIMER algorithm were introduced to evaluate the immune landscape. RESULTS: NR3C1, NR4A3, THRA, RXRG, NR2F6, NR1D2 and RORB were optimized as a prognostic signature for BRCA. This seven-NR-based Risk Score could effectively predict overall survival status. The area under the curve (AUC) of 1-, 3- and 5-year overall survival are 0.702, 0.734 and 0.722 in TCGA training cohort, and 0.630, 0.721 and 0.823 in GEO validation cohort, respectively. Calibration plot demonstrated satisfactory agreement between predictive and observed outcomes. Nomogram model worked well on predicting survival probabilities. Multiple cancer-related pathways were highly enriched in High-Risk group. High- and Low-Risk groups showed significant differed immune cell infiltration. There exists an obvious connection between Risk Score and immune checkpoints LAG3, PD1 and TIM3. CONCLUSION: The seven-NR-based Risk Score represents a promising signature for estimating overall survival in patients with BRCA, and is correlated with the immune microenvironment.
BACKGROUND: Breast cancer (BRCA) is a malignant cancer that threatened the life of female with unsatisfactory prognosis. The aim of this study was to identify prognostic nuclear receptors (NRs) signature of BRCA. METHODS: BRCA patient samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Consensus clustering analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to evaluate, select NRs as prognostic factors and build Risk Score model. GSEA analysis was explored to check signaling differences between High- and Low-Risk group. Nomogram model basing on age and Risk Score was established to predict the 1-, 3- and 5-year survival. Model performance was assessed by a time-dependent receiver operating characteristic (ROC) curve and calibration plot. CIBERSORT, ESTIMATE and TIMER algorithm were introduced to evaluate the immune landscape. RESULTS: NR3C1, NR4A3, THRA, RXRG, NR2F6, NR1D2 and RORB were optimized as a prognostic signature for BRCA. This seven-NR-based Risk Score could effectively predict overall survival status. The area under the curve (AUC) of 1-, 3- and 5-year overall survival are 0.702, 0.734 and 0.722 in TCGA training cohort, and 0.630, 0.721 and 0.823 in GEO validation cohort, respectively. Calibration plot demonstrated satisfactory agreement between predictive and observed outcomes. Nomogram model worked well on predicting survival probabilities. Multiple cancer-related pathways were highly enriched in High-Risk group. High- and Low-Risk groups showed significant differed immune cell infiltration. There exists an obvious connection between Risk Score and immune checkpoints LAG3, PD1 and TIM3. CONCLUSION: The seven-NR-based Risk Score represents a promising signature for estimating overall survival in patients with BRCA, and is correlated with the immune microenvironment.
Authors: D J Mangelsdorf; C Thummel; M Beato; P Herrlich; G Schütz; K Umesono; B Blumberg; P Kastner; M Mark; P Chambon; R M Evans Journal: Cell Date: 1995-12-15 Impact factor: 41.582
Authors: Xavier Farré; Roderic Espín; Alexandra Baiges; Eline Blommaert; Wonji Kim; Krinio Giannikou; Carmen Herranz; Antonio Román; Berta Sáez; Álvaro Casanova; Julio Ancochea; Claudia Valenzuela; Piedad Ussetti; Rosalía Laporta; José A Rodríguez-Portal; Coline H M van Moorsel; Joanne J van der Vis; Marian J R Quanjel; Mireia Tena-Garitaonaindia; Fermín Sánchez de Medina; Francesca Mateo; María Molina-Molina; Sungho Won; David J Kwiatkowski; Rafael de Cid; Miquel Angel Pujana Journal: ERJ Open Res Date: 2022-01-24