Prediction of Drug Candidates for Clear Cell Renal Cell Carcinoma Using A Systems Biology-Based Drug Repositioning Approach

Summary

Background
The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma(ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treatpatients who cannot get benefits from clinical drugs.

Methods
We proposed a systematic approach which included the target prediction based on the co-expression networkanalysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based onthe analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line.

Findings
First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC,which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes ofpatients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets basedon the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposeddrug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model andobserved that these drugs inhibited the protein levels of their corresponding target genes and cell viability.

Interpretation
These findings proved the usefulness and efficiency of our approach to improve the drug repositioningresearches for cancer treatment and precision medicine.

Funding
This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.

Copyright

2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Keywords: Systems biology; Co-expression network; Target chemotherapy; Drug repositioning; ccRCC