Research Paper Volume 11, Issue 21 pp 9478—9491

Identifying hub genes of clear cell renal cell carcinoma associated with the proportion of regulatory T cells by weighted gene co-expression network analysis

Ye-Hui Chen1, *, , Shao-Hao Chen1, *, , Jian Hou1, *, , Zhi-Bin Ke1, *, , Yu-Peng Wu1, , Ting-Ting Lin1, , Yong Wei1, , Xue-Yi Xue1, , Qing-Shui Zheng1, , Jin-Bei Huang1, , Ning Xu1, ,

  • 1 Department of Urology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
* Equal contribution

Received: July 23, 2019       Accepted: October 21, 2019       Published: October 31, 2019
How to Cite

Copyright © 2019 Chen 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.


Background: Numerous patients with clear cell renal cell carcinoma (ccRCC) experience drug resistance after immunotherapy. Regulatory T (Treg) cells may work as a suppressor for anti-tumor immune response.

Purpose: We performed bioinformatics analysis to better understand the role of Treg cells in ccRCC.

Results: Module 10 revealed the most relevance with Treg cells. Functional annotation showed that biological processes and pathways were mainly related to activation of the immune system and the processes of immunoreaction. Four hub genes were selected: LCK, MAP4K1, SLAMF6, and RHOH. Further validation showed that the four hub genes well-distinguished tumor and normal tissues and were good prognostic biomarkers for ccRCC.

Conclusion: The identified hub genes facilitate our knowledge of the underlying molecular mechanism of how Treg cells affect ccRCC in anti-tumor immune therapy.

Methods: The CIBERSORT algorithm was performed to evaluate tumor-infiltrating immune cells based on the Cancer Genome Atlas cohort. Weighted gene co-expression network analysis was conducted to explore the modules related to Treg cells. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was built. Samples from the International Cancer Genomics Consortium database was used as a validation set.


RCC: renal cell carcinoma; ccRCC: clear cell renal cell carcinoma; Treg: regulatory T; WGCNA: weighted gene co-expression network analysis; TCGA: the cancer genome atlas; DEGs: differentially expressed genes; GO: gene ontology; PPI: protein-protein interaction; ICGC: international cancer genomics consortium; GS: gene significance; MS: module significance; ME: module eigengene; STRING: Search Tool for the Retrieval of Interacting Genes; HR: hazard ratio; ROC: receiver operating characteristic; AUC: area under the curve; LCK: LCK proto-oncogene, Src family tyrosine kinase; MAP4K1: Mitogen-Activated Protein Kinase Kinase Kinase Kinase 1; SLAMF6: SLAM Family Member 6; RHOH: Ras Homolog Family Member H; CRPC: castration-resistant prostate cancer; TCR: T cell receptor.