Y.S., H.K. 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, and 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. model, which provided new chance for the tumor treatment. Implications of all the available evidence We demonstrated the feasibility of the integrated approach combining the disease-target and drug-target prediction in the treatment of kidney cancer. Besides that, this approach could be also broadly applied to other cancers, which provides new insight into cancer treatment and precision medicine. Alt-text: Unlabelled box Introduction Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC), accounts for 70% of all RCC cases.1 Surgery (radical or partial nephrectomy) is the standard primary treatment for patients with localized tumors. The first-line and second-line target therapy options for patients with relapsed after nephrectomy or advanced stage tumor include tyrosine kinase inhibitors (axitinib, sorafenib, pazopanib, and sunitinib, etc.), mTOR inhibitors (everolimus and tesirolimus), and monoclonal antibodies against VEGF, PD-1 or PD-L1 (bevacizumab, pembrolizumab and avelumab, etc.). However, the National Comprehensive Cancer Network (NCCN, version: 1.2022) has reported that the response rates of the single-agent or combinatory regimens based on these drugs range from 6% to 50% in different clinical trials.2 Moreover, the average duration of disease control with these drugs is only 8-9 months for the first-line setting and 5-6 months for the second-line setting.3 Therefore, there is a need to discover more tolerated and effective drugs to widen the options for single-agent or combinatory regimens for ccRCC patients. Computational drug repositioning based on systems biology methods has become a powerful tool to identify potential drug-target interactions and drug-disease interactions.4 The advantage of drug repositioning is that the pharmacology and safety of the repositioned drugs have been well-characterized, dramatically decreasing the cost and duration taken by traditional drug development and reducing the risk of attrition in clinical phases.5,6 In general, current drug repositioning strategies can be classified into drug-based, disease-based and profile-based.7 Usually, drug-based and disease-based approaches are conducted by comparing drug-drug or disease-disease similarity or applying existing drug treatment knowledge to predict new disease-drug associations.8,9 In Rabbit polyclonal to CNTF contrast, profile-based approaches are conducted by analyzing the high-throughput multi-omics data associated with diseases and drugs, which do not rely on prior knowledge about a particular drug or disease and have increased ability to discover new drug-disease pairs.7 Recently, several studies have employed profile-based repositioning methods to identify potentially valuable drugs for the treatment of ccRCC. A widely used method is selecting the drug that has a reversed effect on the disease signature genes.10,11 The idea of this method is that if the perturbation of gene expression induced by a drug (drug-perturbed signatures) is negatively correlated with the dysregulation in the tumor tissues compared to normal tissues (disease-specific signatures), this drug Oxyclozanide turns out to have therapeutic value for this tumor type. During the application of the above approach, ConnectivityMap (CMap)12 is the most commonly used drug-perturbed gene expression data source, and it has been recently updated and integrated into the LINCS Data Portal.13 To Oxyclozanide date, the LINCS data portal includes more than three million gene expression profiles associated with more than 20,000 drugs, gene overexpression and Oxyclozanide knockdown in up to more than 200 cell lines.13 However,.