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Updated: Sep 20, 2025
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks
Habib MotieGhader1,2, Parinaz Tabrizi-Nezhadi3,4, Mahshid Deldar Abad Paskeh5
1Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran. habib_moti@ut.ac.ir.
Abstract:
Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF-TG (transcription factors-target genes) network was drawn. Afterward, a list of drugs targeting TF-TG genes was obtained from the DGIdb V4.0 database, and two drug-gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF-TG, and drug-gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF-TG, and drug-gene interaction networks.
Insights
This study identified 16 potential drugs for non-small cell lung cancer (NSCLC) treatment by computationally analyzing gene networks and drug interactions. These findings offer new avenues for repurposing existing medications to combat NSCLC effectively.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Non-small cell lung cancer (NSCLC) is the predominant type of lung cancer.
- Drug repositioning (DR) offers a strategy to identify new therapeutic uses for existing drugs.
- Identifying novel treatments for NSCLC remains a critical area of cancer research.
Purpose of the Study:
- To computationally identify candidate drugs for non-small cell lung cancer (NSCLC) treatment.
- To apply a drug repositioning strategy using transcriptomics and network analysis.
- To discover potential therapeutic agents for NSCLC patients.
Main Methods:
- Obtained NSCLC transcriptomics data from the GEO database (accession: GSE21933).
- Reconstructed gene co-expression networks using WGCNA and identified significant gene modules.
- Constructed transcription factor-target gene (TF-TG) networks and drug-gene interaction networks.
Main Results:
- Identified two significant gene modules (purple and magenta) in NSCLC.
- Generated TF-TG networks and drug-gene interaction networks (drug-TG and drug-TF).
- Selected 16 candidate drugs for NSCLC treatment, including Methotrexate and Dexamethasone, identified through network analysis.
Conclusions:
- The study successfully proposed 16 drugs as potential candidates for NSCLC treatment.
- Computational analysis of gene co-expression, TF-TG, and drug-gene interaction networks is effective for drug repositioning.
- The identified drugs warrant further investigation for their efficacy in NSCLC therapy.
