Xiaosi Yu1, Juan Guo1, Qian Zhou1, Wenjie Huang1, Chen Xu1, Xinghua Long2. 1. Department of Labortory Medicine, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, China. 2. Department of Labortory Medicine, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, China. zhoulongxinghua@qq.com.
Abstract
PURPOSE: To find immune-related genes with prognostic value in breast cancer, and construct a prognostic risk assessment model to make a more accurate assessment. Moreover, looking for potential immune markers for breast cancer immunotherapy. METHODS: The breast cancer (BC) data were retrieved from The Cancer Genome Atlas (TCGA) database as a training set. Through the Weighted gene co-expression network analysis (WGCNA), Kaplan-Meier (KM) analysis, lasso regression analysis and stepwise backward Cox regression analysis, screening for prognosis-related immune genes, a prognostic index was built, and external validation with two data sets of Gene Expression Omnibus (GEO) database was performed. Transcription factor (TF) regulatory network was constructed to identify key transcription factors that regulate prognostic immune genes. Gene set enrichment analysis (GSEA) was used to explore the signal pathways differences between high and low-risk groups, estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment. RESULTS: We obtained 10 prognosis-related immune genes, and the index showed accurate prognostic value. We also identified 7 prognostic transcription factors. Multiple signaling pathways that inhibit tumor progression were enriched in the low-risk group, and risk score was significantly negatively related to the degree of immune infiltration and the expression level of immune checkpoint genes. CONCLUSION: We successfully constructed an independent prognostic index, which not only has a stronger predictive ability than the tumor pathological stage, but also can reflect the immune infiltration of breast cancer patients.
PURPOSE: To find immune-related genes with prognostic value in breast cancer, and construct a prognostic risk assessment model to make a more accurate assessment. Moreover, looking for potential immune markers for breast cancer immunotherapy. METHODS: The breast cancer (BC) data were retrieved from The Cancer Genome Atlas (TCGA) database as a training set. Through the Weighted gene co-expression network analysis (WGCNA), Kaplan-Meier (KM) analysis, lasso regression analysis and stepwise backward Cox regression analysis, screening for prognosis-related immune genes, a prognostic index was built, and external validation with two data sets of Gene Expression Omnibus (GEO) database was performed. Transcription factor (TF) regulatory network was constructed to identify key transcription factors that regulate prognostic immune genes. Gene set enrichment analysis (GSEA) was used to explore the signal pathways differences between high and low-risk groups, estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment. RESULTS: We obtained 10 prognosis-related immune genes, and the index showed accurate prognostic value. We also identified 7 prognostic transcription factors. Multiple signaling pathways that inhibit tumor progression were enriched in the low-risk group, and risk score was significantly negatively related to the degree of immune infiltration and the expression level of immune checkpoint genes. CONCLUSION: We successfully constructed an independent prognostic index, which not only has a stronger predictive ability than the tumor pathological stage, but also can reflect the immune infiltration of breast cancerpatients.
Entities:
Keywords:
Biomarkers; Breast neoplasms; Immunotherapy; Prognosis; Tumor microenvironment
Authors: Carsten Denkert; Gunter von Minckwitz; Silvia Darb-Esfahani; Bianca Lederer; Barbara I Heppner; Karsten E Weber; Jan Budczies; Jens Huober; Frederick Klauschen; Jenny Furlanetto; Wolfgang D Schmitt; Jens-Uwe Blohmer; Thomas Karn; Berit M Pfitzner; Sherko Kümmel; Knut Engels; Andreas Schneeweiss; Arndt Hartmann; Aurelia Noske; Peter A Fasching; Christian Jackisch; Marion van Mackelenbergh; Peter Sinn; Christian Schem; Claus Hanusch; Michael Untch; Sibylle Loibl Journal: Lancet Oncol Date: 2017-12-07 Impact factor: 41.316
Authors: Y Asano; S Kashiwagi; W Goto; K Kurata; S Noda; T Takashima; N Onoda; S Tanaka; M Ohsawa; K Hirakawa Journal: Br J Surg Date: 2016-03-08 Impact factor: 6.939
Authors: Kaisheng Yuan; Ruiqi Zeng; Pengteng Deng; Aiping Zhang; Huiqian Liu; Ning Wang; Yongxi Tang; Zhikang Yin; Hang Liu Journal: Int J Gen Med Date: 2022-02-15