Lung Cancer
Lung cancer (LC) is the second most common cancer in the world. It is broadly classified into two categories - Non-Small Cell Lung Carcinoma (NSCLC) consisting of 80% of all lung cancer cases and Small Cell Lung Carcinoma (SCLC) recorded in 20% of all lung cancers. Major advances have been made in the diagnosis and staging for LC using computational technologies. Systems biology-based approaches contribute to generating biomarkers and novel therapeutic targets in LC.
The advancement in research and technology has been slowly shifting the focus of lung cancer diagnosis, prognosis, and treatment towards understanding the underlying cause of disease progression using protein-protein interaction (PPI) networks, gene co-expression networks, and molecular pathways. Individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities.
Workflow of Genes Prediction Involved in Lung Cancer
- Import the genes related to lung cancer to Search Tool for the Retrieval of Interacting genes/proteins (STRING).
- Construct the protein-protein interaction (PPI) Network and pathway enrichment.
- Use GO ontology and Reactome databases for describing the genes, the average length of survival, and constructing networks.
- Use the ClusterONE plugin of Cytoscape software to analyze and cluster networks.
- Define hubs and bottleneck nodes based on their degree and betweenness.
Our computational biology platform has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.
Data Analysis Methodology in Lung Cancer Research
- Construct functional protein network
- Discover biomarker genes
- Functional and pathway analysis of biomarker genes
- Survival analysis using biomarker genes
CD ComputaBio provides available services and tools potentially valuable for lung cancer basic and translational research. We have years of experience performing computational analyses of related data sets and aiding lung cancer translational research. Contact us for more service details.
