Renal Cancer

Renal cancer is difficult to treat with a 5-year survival rate of 10% in metastatic patients. The target therapy options for patients include tyrosine kinase inhibitors, mTOR inhibitors, and monoclonal antibodies against VEGF, PD-1 or PD-L1. However, lack of validated biomarkers and scarce knowledge of the biological processes occurring during renal cancer progression are the main reasons for therapy failure.

Research Tasks

• Investigation of mechanisms regulating renal cancer progression
• Dynamics research of primary tumour cells in the kidney
• Construction of models reflecting spatial tumour growth

Analysis Services

• Gene co-expression network analysis
• Transcriptomic and methylome analysis
• High-throughput multi-omics data analysis
• Principal component analysis (PCA)
• Gene Ontology (GO) enrichment analysis
• Machine learning and mathematical modeling analysis

Statistics Methods

• Univariate Cox regression model
• Kaplan-Meier method

Drug Repositioning

Computational drug repositioning based on systems biology methods has become a powerful tool to identify potential drug-target interactions and drug-disease interactions. 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.

• Drug-based repositioning
• Disease-based repositioning
• Profile-based repositioning

Profile-based repositioning methods have been employed to identify potentially valuable drugs for the treatment of clear cell renal cell carcinoma.

Why Choose Us?

Our mission is to uncover the mechanisms of renal cancer behavior, study multi-omic data, and determine their relationship with biological outcomes. We utilize microarray, deep sequencing platforms, advanced biostatistical approaches, and computational analysis methods to detect biological signals in high-dimensional data. We are also interested in how the machine learning-based integration of different datasets can aid in the discovery of new cancer subgroups and biomarkers.

In addition, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

Reference

1. Xiangyu Li, et al. Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach. eBioMedicine. 2022.