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Text mining‑based drug discovery in cutaneous squamous cell carcinoma
Yuyan Pan1, Yong Zhang1, Jiaqi Liu1
1Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, P.R. China.
Oncology Reports
|October 2, 2018
Summary
This study used computational tools to identify 121 genes and 10 molecular pathways linked to cutaneous squamous cell carcinoma (cSCC). Researchers found 55 potential drugs, including chemotherapy agents and tyrosine kinase inhibitors (TKIs), for cSCC treatment.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Cutaneous squamous cell carcinoma (cSCC) is a prevalent skin cancer with limited therapeutic options.
- Existing drug therapies for cSCC show restricted efficacy and utility.
Purpose of the Study:
- To identify genes and molecular pathways associated with cSCC using computational approaches.
- To explore potential drug treatments targeting identified molecular pathways for cSCC.
Main Methods:
- Text mining and GeneCodis were employed to identify cSCC-related genes.
- Protein-protein interaction (PPI) analysis was conducted using STRING and Cytoscape.
- cBioPortal was utilized for gene characteristic analysis and drug selection, followed by drug-gene interaction analysis.
Main Results:
- A total of 121 genes associated with cSCC were identified through text mining.
- Gene enrichment analysis revealed 11 key genes involved in 10 distinct molecular pathways.
- Fifty-five drugs, including chemotherapy agents, tyrosine kinase inhibitors (TKIs), and others, were identified as potential treatments targeting these pathways.
Conclusions:
- In silico text mining and pathway analysis offer a viable method for discovering candidate drugs for cSCC.
- This approach can identify drugs targeting specific genes and pathways relevant to cSCC, paving the way for novel treatment strategies.
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