Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach.

ABSTRACT:

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 in

SUBMITTER: Li X 

PROVIDER: S-EPMC8960981 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature