Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

Guangzhen Wu; Yingkun Xu; Chenglin Han; Zilong Wang; Jiayi Li; Qifei Wang; Xiangyu Che

doi:10.1155/2020/6657013

Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

J Oncol. 2020 Dec 30:2020:6657013. doi: 10.1155/2020/6657013. eCollection 2020.

Authors

Guangzhen Wu¹, Yingkun Xu², Chenglin Han², Zilong Wang², Jiayi Li³, Qifei Wang¹, Xiangyu Che¹

Affiliations

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.