Related Experiment Videos
Integrative Network-Based Transcriptomic Analysis Identifies Niclosamide as a Candidate Repositioned Drug for Breast
Busra Aydin1, Beyza Nur Okutan2, Fatmanur Elif Sara2
1Department of Bioengineering, Faculty of Engineering and Architecture, Konya Food and Agriculture University, Konya, Turkey.
Purpose:
Breast cancer (BC) is a highly heterogeneous malignancy, and current treatments often suffer from toxicity, limited selectivity, and high cost. This study aimed to integrate transcriptome-level data, multi-layered network analysis, and drug repositioning strategies to identify candidate diagnostic and prognostic biomarkers for BC and propose potential repositioned drug candidates.
Methods:
Differentially expressed genes (DEGs) were identified from the GSE42568 dataset (|log2FC| > 1 and p < 0.05). Functional enrichment analyses were conducted using GO and KEGG. Three biological interaction layers - protein-protein interactions, transcription factors, and miRNA-mRNA interactions were constructed, and hub nodes were identified using topological metrics. Kaplan-Meier analyses assessed survival associations. PCA evaluated sample separation across datasets in GSE42568, GSE113865, and GSE22820. Drug repositioning was performed using L1000CDS2, and in vitro validation of a top drug candidate (niclosamide) and an exploratory comparative compound (amitriptyline) was performed using MCF-7 cells, including viability and combination assays.
Results:
A total of 4266 DEGs were identified. Network analyses revealed 37 hub signatures, 11 of which-ESR1, RECQL4, FOS, BCL2, CXCL8, TRIM25, EGR1, CDH1, KRAS, PTGS2, and IL6 were associated with survival outcomes. PCA demonstrated clear separation between healthy and BC samples. Drug repositioning identified eight candidates, with niclosamide as the top hit. In vitro assays showed marked reduction in cell viability at 5 µM niclosamide and 25 µM amitriptyline after 24 h treatment. No significant additive effect was observed in the combination treatment.
Conclusion:
This integrative approach revealed candidate BC-specific biomarkers and identified niclosamide as a potential repositioned therapeutic. These findings remain exploratory and do not provide definitive clinical evidence. Further validation across additional models and clinical settings is required.
Insights
This study identified 11 key breast cancer (BC) biomarkers linked to survival and proposed niclosamide as a potential repositioned drug candidate for BC treatment. Further validation is needed.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Breast cancer (BC) is a complex disease with treatment challenges including toxicity and cost.
- Identifying novel biomarkers and therapeutic strategies is crucial for improving BC patient outcomes.
Purpose of the Study:
- To integrate multi-omics data and network analysis for BC biomarker discovery.
- To identify potential drug candidates for BC through drug repositioning.
Main Methods:
- Differential gene expression analysis and functional enrichment (GO, KEGG).
- Multi-layered network construction (PPI, TF, miRNA-mRNA) to identify hub genes.
- Survival analysis (Kaplan-Meier) and principal component analysis (PCA).
- Drug repositioning using L1000CDS2 and in vitro validation of niclosamide and amitriptyline.
Main Results:
- Identified 4266 differentially expressed genes (DEGs) and 37 hub signatures.
- Eleven hub genes (e.g., ESR1, BCL2, IL6) were significantly associated with BC survival.
- PCA confirmed clear separation between healthy and BC samples.
- Niclosamide emerged as a top drug repositioning candidate, showing efficacy in vitro.
Conclusions:
- An integrative approach successfully identified BC biomarkers and a potential therapeutic agent, niclosamide.
- Findings highlight novel targets and drug candidates for BC.
- Further clinical validation is necessary to confirm the therapeutic potential.
Related Concept Videos
02:57Targeted Cancer Therapies
There are several types of targeted therapies against specific...
01:29Pharmacogenomics: Identification of New Drug Targets