Research Paper Volume 14, Issue 17 pp 6917—6935

Identification of immune subtypes to guide immunotherapy and targeted therapy in clear cell renal cell carcinoma

Chen Xu1, *, , Yang Li2, *, , Wei Su3, *, , Zhenfan Wang1, , Zheng Ma1, , Lei Zhou1, , Yongqiang Zhou1, , Jianchun Chen1, &, , Minjun Jiang1, &, , Ming Liu4, &, ,

  • 1 Department of Urology, Suzhou Ninth People’s Hospital, Soochow University, Suzhou 215000, China
  • 2 Department of Urology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Huinan Town, Pudong, Shanghai 201399, China
  • 3 Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
  • 4 The State Key Laboratory of Pharmaceutical Biotechnology, Department of Hematology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, China-Australia Institute of Translational Medicine, School of Life Sciences, Nanjing University, Nanjing 210023, China
* Equal contribution

Received: April 5, 2022       Accepted: August 17, 2022       Published: September 1, 2022
How to Cite

Copyright: © 2022 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Accumulating pieces of evidence suggested that immunotypes may indicate the overall immune landscape in the tumor microenvironment, which were closely related to therapeutic response. The purpose of this study was to classify and define the immune subtypes of clear cell renal cell carcinoma (ccRCC), so as to authenticate the potential immune subtypes that respond to immunotherapy. Transcriptome expression profile and mutation profile data of ccRCC, as well as clinical characteristics used in this study were obtained from TCGA database. There were significant differences in the infiltration of immune cells, immune checkpoints, and antigens between ccRCC and para-cancerous tissues. According to immune components, patients with ccRCC were divided into three immune subtypes, with different clinical and molecular characteristics. Compared with other subtypes, IS2 showed cold immune phenotype, and was associated with better survival. IS1 represented complex immune populations and was associated with poor overall survival (OS) and progression free survival (PFS). Further analysis indicated that expression of immune checkpoints also differed among the three subtypes, and was abnormally up-regulated in IS3. Pathway enrichment analysis indicated that the mTOR signaling pathway was abnormally enriched in IS3, while the TGF_BETA, ANGIOGENESIS and receptor tyrosine kinase signaling pathways were abnormally enriched in IS2. Furthermore, there was an abnormal enrichment of the epithelial-to-mesenchymal transition (EMT) signaling pathway in IS1, which may be associated with a higher rate of metastasis. Finally, SCG2 was screened as a specific antigen of ccRCC, which was not only related to poor prognosis, but also significantly associated with immune cells and immune checkpoints. In conclusion, the immune subtypes of ccRCC may provide new insights into the tumor biology and the precise clinical management of this disease.


ARG: expressed adipose-related gene; CAFs: cancer-associated fibroblasts; ccRCC: clear cell renal cell carcinoma; CSC: cancer stem cell; CTLA-4: cytotoxic T lymphocyte associated protein 4; DFS: disease-free survival; EC: endothelial cell; EMT: epithelial-to-mesenchymal transition; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumours using Expression data; IAG: immune-associated gene; ICD: immunogenic cell death; ICGC: International Cancer Genome Consortium; ICI: immune checkpoint inhibitor; ICP: immune checkpoint; KM: Kaplan-Meier; MCS: mesenchymal stem cell; mRNAsi: mRNA expression-based stemness index; mTOR: mammalian target of rapamycin; NMF: Non-Negative Matrix Factorization; OS: overall survival; PCA: Principal Component Analysis; PD-1: programmed death-1; PD-L1: programmed death ligand-1; PFS: progression free survival; RCC: renal cell carcinoma; ssGSEA: single sample gene set enrichment analysis; TCGA: The Cancer Genome Atlas; TKI: Tyrosine Kinase Inhibitor; Treg: regulatory T cell; t-SNE: t-Distributed Stochastic Neighbor Embedding.