Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach
Journal article, 2022
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 treat patients who cannot get benefits from clinical drugs. Methods: We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the 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 of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed 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 repositioning researches 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.
ccRCC
Systems biology
Drug repositioning
Co-expression network
Target chemotherapy
Author
Xiangyu Li
Bash Biotech Inc.
Royal Institute of Technology (KTH)
Koeun Shong
Royal Institute of Technology (KTH)
Woonghee Kim
Royal Institute of Technology (KTH)
Meng Yuan
Royal Institute of Technology (KTH)
Hong Yang
Royal Institute of Technology (KTH)
Yusuke Sato
Institute for the Advanced Study of Human Biology
University of Tokyo
Haruki Kume
University of Tokyo
Seishi Ogawa
Institute for the Advanced Study of Human Biology
Karolinska Institutet
Hasan Turkez
Atatürk University
Saeed Shoaie
King's College London
Royal Institute of Technology (KTH)
Jan Borén
Sahlgrenska University Hospital
Jens B Nielsen
BioInnovation Institute
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Mathias Uhlen
Royal Institute of Technology (KTH)
C. Zhang
Royal Institute of Technology (KTH)
Zhengzhou University
Adil Mardinoglu
Royal Institute of Technology (KTH)
King's College London
EBioMedicine
2352-3964 (eISSN)
Vol. 78 103963Subject Categories
Pharmaceutical Sciences
Pharmacology and Toxicology
Cancer and Oncology
DOI
10.1016/j.ebiom.2022.103963
PubMed
35339898