Identification of Potential Drug Therapy for Dermatofibrosarcoma Protuberans with Bioinformatics and Deep Learning
Muge Liu1, Fan Yang1, Yingbin Xu1
1Department of Burn Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510030, China.
Background:
Dermatofibrosarcoma protuberans (DFSP) is a rare mesenchymal tumor that is primarily treated with surgery. Targeted therapy is a promising approach to help reduce the high rate of recurrence. This study aims to identify the potential target genes and explore the candidate drugs acting on them effectively with computational methods.
Methods:
Identification of genes associated with DFSP was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-Protein Interaction (PPI) network was constructed by using the Search Tools for the Retrieval of Interacting (STRING) database and visualized in Cytoscape. The gene candidates were identified after a literature review. Drugs targeting these genes were selected from Pharmaprojects. The binding affinity scores of Drug-Target Interaction (DTI) were predicted by a deep learning algorithm Deep Purpose.
Results:
A total of 121 genes were found to be associated with DFSP by text mining. The top 3 statistically functionally enriched pathways of GO and KEGG analysis included 36 genes, and 18 hub genes were further screened out by constructing a PPI networking and literature retrieval. A total of 42 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 10 drugs with top affinity scores were predicted by DeepPurpose, including 3 platelet-derived growth factor receptor beta kinase (PDGFRB) inhibitors, 2 platelet-derived growth factor receptor alpha kinase (PDGFRA) inhibitors, 2 Erb-B2 receptor tyrosine kinase 2 (ErbB-2) inhibitors, 1 tumor protein p53 (TP53) stimulant, 1 vascular endothelial growth factor receptor (VEGFR) antagonist, and 1 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitor.
Conclusion:
Text mining and bioinformatics are useful methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the drug candidates.
Insights
This study identifies potential drug targets for dermatofibrosarcoma protuberans (DFSP) using bioinformatics. Computational analysis revealed 10 promising candidate drugs, including PDGFRB and PDGFRA inhibitors, for treating this rare skin cancer.
Area of Science:
- Oncology
- Bioinformatics
- Computational Biology
Background:
- Dermatofibrosarcoma protuberans (DFSP) is a rare skin cancer with high recurrence rates after surgery.
- Targeted therapy offers a promising strategy to improve treatment outcomes for DFSP.
- Identifying specific molecular targets is crucial for developing effective targeted therapies.
Approach:
- Utilized text mining (pubmed2ensembl) to identify DFSP-associated genes.
- Performed Gene Ontology (GO) and KEGG pathway analysis for functional enrichment.
- Constructed a Protein-Protein Interaction (PPI) network using STRING and visualized in Cytoscape.
- Screened candidate drugs targeting identified hub genes using Pharmaprojects.
- Employed the deep learning tool DeepPurpose for predicting drug-target interactions (DTI) and binding affinity.
Key Points:
- Identified 121 DFSP-associated genes through text mining.
- Filtered down to 18 key hub genes via pathway and network analysis.
- Discovered 42 potential drugs targeting these hub genes.
- Predicted 10 top-ranked drugs with high affinity scores, including inhibitors for PDGFRB, PDGFRA, and ErbB-2, a TP53 stimulant, a VEGFR antagonist, and a PTGS2 inhibitor.
Conclusions:
- Bioinformatics and text mining are effective for identifying therapeutic targets in DFSP.
- DeepPurpose demonstrates efficiency in predicting drug-target interactions and selecting candidate drugs for DFSP.
- This computational approach facilitates drug discovery for rare cancers like DFSP.
