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Individualized immune-related gene signature predicts immune status and oncologic outcomes in clear cell renal call carcinoma patients

  • Xiong Y.,
  • Liu L.,
  • Bai Q.,
  • Xia Y.,
  • Wang J.,
  • Guo J.
1 Zhognshan Hospital, Fudan University, Dept. of Urology, Shanghai, China

Publication: March 2019

Introduction & Objectives

To develop an individualized immune-related gene signature that predicts oncologic outcomes and immune status of ccRCC.

Materials & Methods

Our study retrospectively analyzed expression profile of ccRCC including 1 microarray data set and 1 RNA-Seq data set. The immune related gene pair (IGP) index was constructed and validated based on pairwise comparison in 634ccRCC patients. Association with overall survival (OS), progression-free interval (PFI) and disease specific survival (DSS) was evaluated by Kaplan-Meier analysis, univariate and multivariate cox regression survival analysis. Prognostic values of different risk models were compared using Harrel’s C-index.


The IGP index of 17 gene pairs was an adverse independent risk factor in multivariate analyses for OS (HR, 1.718; P=0.001), PFI (HR, 1.550; P=0.006) and DSS (HR, 2.201; P=0.001) in ccRCC patients. It showed comparable prognostic accuracy with ccA/ccB signature (C-index for OS, 0.657 vs 0.640; P=0.686) and better intra tumor homogeneity. Immunosuppressive immune cell, markers and pathways referring to immune suppression were all enriched in high immune risk tumors. The integrated immune-clinical prognostic score outperformed ccA/ccB signature and UISS risk model in terms of C-index for estimation of OS (P<0.001), PFI (P<0.001) and DSS (P<0.001).


The proposed IGP index is a robust and promising biomarker for estimating oncologic outcomes in ccRCC. High immune risk tumors are highly immunosuppressive.

Commented by Dr. Giuseppe Procopio

Predictive biomarkers remain an unmet medical need in RCC.

Several investigations were presented in this interesting session to assess the meaning of genomic profil in RCC patients.

We are moving on how to translate molecular features of the disease into a personalized approach for kidney cancer patients.