Guangzhen Wu1, Yingkun Xu2, Chenglin Han2, Zilong Wang2, Jiayi Li3, Qifei Wang1, Xiangyu Che1. 1. Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China. 2. Department of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China. 3. School of Business, Hanyang University, Seoul, Republic of Korea.
Abstract
PURPOSE: To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation. MATERIALS AND METHODS: KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis (P < 0.05). The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve. RESULTS: Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues (P < 0.05). Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712). CONCLUSION: We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.
PURPOSE: To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation. MATERIALS AND METHODS: KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis (P < 0.05). The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve. RESULTS: Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues (P < 0.05). Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712). CONCLUSION: We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.
Authors: Shirin Khambata-Ford; Christopher R Garrett; Neal J Meropol; Mark Basik; Christopher T Harbison; Shujian Wu; Tai W Wong; Xin Huang; Chris H Takimoto; Andrew K Godwin; Benjamin R Tan; Smitha S Krishnamurthi; Howard A Burris; Elizabeth A Poplin; Manuel Hidalgo; Jose Baselga; Edwin A Clark; David J Mauro Journal: J Clin Oncol Date: 2007-08-01 Impact factor: 44.544