Non-small Cell Lung Cancer
Non-small-cell lung cancer (NSCLC) accounts for >85% of lung cancers, and its incidence is increasing. The NSCLC is further divided into Adenocarcinoma (40%), Squamous Cell Carcinoma (27%) and large cell carcinoma (8%). The two most commonly exploited genetic aberrations of NSCLC adenocarcinoma are the epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) gene rearrangements.
We explored expression differences between NSCLC and normal cells and predicted potential target sites for the detection and diagnosis of NSCLC. Systems biology analyses of NSCLC have recently been applied to reveal prognostic and treatment predictive factors as well as to explore and decipher novel signaling pathways that might be used to develop innovative therapeutic approaches.
Databases and Computational Approaches
- Gene Expression Omnibus Database and GEO2R Tools
- String Database
- Kyoto Encyclopedia of Genes
- Genotype Clustering of the Data Set
- Computational Biological Modeling (CBM)
- Kaplan-Meier Curves Analysis
- Gene Ontology Analysis
- Genomes/Gene Set Enrichment Analysis
- Cytoscape and MCODE(Molecular Complex Detection) Plug-in
- Protein-protein Interaction (PPI) Network Analysis
- Co-expression Network Analysis
Identify Good Molecular Targets
Because of the heterogeneity of many tumors, it is very challenging work to identify good molecular targets. We utilize a computational biology approach, based on appropriate re-analyses of datasets followed by reliable data filtering, to analyze integrative and combinatorial deregulated interaction networks in NSCLC. However, resistant subclones of overexpressed and mutated genes may prevent them from being good molecular targets. Therefore, the best target is a core gene whose mutation occurs early in oncogenesis and dysregulates a key pathway that drives tumor growth in all of the subclones. Examples include mutations in the genes ABL, HER-2, KIT, EGFR, and probably BRAF, in non-small cell lung cancer. Applications of unsupervised learning algorithms have been developed that identify biological processes and protein complexes to which NSCLC cell lines are sensitive upon gene differential expression, thus finding candidate therapeutic targets.
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.
