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.


Systems biology

Drug repositioning

Co-expression network

Target chemotherapy


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


2352-3964 (eISSN)

Vol. 78 103963

Subject Categories

Pharmaceutical Sciences

Pharmacology and Toxicology

Cancer and Oncology





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4/5/2022 9