Research Paper Volume 12, Issue 10 pp 9205—9223

Identification of potential novel differentially-expressed genes and their role in invasion and migration in renal cell carcinoma

Shen-Nan Shi2, *, , Xia Qin3, *, , Shuo Wang1, *, , Wen-Fu Wang1, , Yao-Feng Zhu1, , Yu Lin4, , Zun-Lin Zhou1, , Ben-Kang Shi1, , Xi-Gao Liu1, ,

  • 1 Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, China
  • 2 Department of General Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
  • 3 The Graduate School of Second Military Medical University, Shanghai, China
  • 4 The Graduate School of Fujian Medical University, Fuzhou, Fujian, China
* Equal contribution

Received: December 1, 2019       Accepted: April 17, 2020       Published: May 18, 2020
How to Cite

Copyright © 2020 Shi 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.


Clear cell renal cell carcinoma (ccRCC) remains one of the most common cancer types globally, and while it has been extensively studied, the molecular basis for its pathology remains incompletely understood. Herein, we profiled three previously published datasets (GSE66272, GSE100666, and GSE105261) in a single integrated analysis aimed at identifying disease-associated patterns of gene expression that may offer mechanistic insight into the drivers of this disease. We pooled expression data from 39 normal kidney samples and 39 kidney tumors, leading us to identify 310 differentially expressed genes (DEGs) that were linked to kidney cancer in all three analyzed datasets. Of these genes, 133 and 177 were up- and down-regulated, respectively, in cancer samples. We then incorporated these DEGs into a protein-protein interaction network with the STRING and Cytoscape tools, and we were able to identify signaling pathways significantly enriched for these DEGs. The relationship between DEG expression and ccRCC patient survival was further evaluated using a Kaplan-Meier approach, leading us to identify TIMP1 as an independent prognostic factor in ccRCC patients. When TIMP1 expression was disrupted in ccRCC cell lines, this impaired their migratory and invasive capabilities. In summary, we employed an integrative bioinformatics approach to identify ccRCC-related DEGs and associated signaling pathways. Together these findings offer novel insight into the mechanistic basis for ccRCC, potentially helping to identify novel therapeutic targets for the treatment of this deadly disease.


RCC: Renal cell carcinoma; ccRCC: Clear cell renal cell carcinoma; RNA-Seq: RNA sequencing; GEO: Gene Expression Omnibus; DEGs: Differentially Expressed Genes; DAVID: Database for Annotation, Visualization and Integrated Discovery; GO: Gene Ontology; MF: Molecular function; CC: Cellular components; BP: Biological processes; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: Protein-protein interaction; TIMP1: Tissue Inhibitor of Metalloproteinases 1; RMA: Robust Multi-Array Average; KNN: K-Nearest Neighbor; TCGA: The Cancer Genome Atlas; DFS: Disease-free survival; OS: Overall survival; CI: Confidence interval; GSEA: Gene set enrichment analysis; GEPIA: Gene Expression Profiling Interactive Analysis.