Related Experiment Video
Updated: Jan 14, 2026
Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression
Published on: March 17, 2020
Systems Biology-Based Drug Repositioning Identifies Extracellular Matrix Module as a Therapeutic Target in Lung
Bin Yang1, Yuna Shao2, Ziyun Zhou1
1MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Abstract:
Systematic proteomic comparisons across cancer subtypes provide insights into tumor heterogeneity and accelerate discovery of therapeutic targets and drug repositioning. Here, we present a novel computational framework, signature-network-perturbation- based drug repositioning (SnpDR), integrating proteomic and pharmacogenomic data through differential modular analysis, drug response network construction, and multiscale perturbation response scanning. Applying SnpDR to compare the proteomic landscapes of lung adenocarcinoma and lung squamous cell carcinoma (LSCC), we identified the extracellular matrix (ECM) module as a central hub in LSCC, while LAMA1 emerged as a novel drug target. In vitro and in vivo experiments validated two repositioned drugs, Fingolimod and Piperlongumine, both targeting ECM components, significantly inhibited LSCC cell growth, proliferation and migration at concentrations below 10 μM. These results provide compelling evidence for the power of systems biology to identify subtype-specific therapeutic vulnerabilities. Our findings highlight a promising framework for precision oncology and underscore the potential of ECM-targeted interventions in LSCC.
Insights
We developed a new computational method (SnpDR) to find new cancer drug targets. This method identified extracellular matrix (ECM) targets and repositioned drugs for lung squamous cell carcinoma (LSCC).
Area of Science:
- Computational biology
- Proteomics
- Cancer research
Background:
- Cancer subtype proteomic comparisons reveal heterogeneity and therapeutic targets.
- Drug repositioning requires effective computational frameworks.
Purpose of the Study:
- To present a novel computational framework, signature-network-perturbation-based drug repositioning (SnpDR).
- To identify subtype-specific vulnerabilities and potential therapeutic targets in lung squamous cell carcinoma (LSCC).
Main Methods:
- Integrated proteomic and pharmacogenomic data using differential modular analysis.
- Constructed drug response networks and performed multiscale perturbation response scanning.
- Applied SnpDR to compare lung adenocarcinoma and LSCC proteomic landscapes.
Main Results:
- Identified the extracellular matrix (ECM) module as a central hub in LSCC.
- Discovered LAMA1 as a novel drug target.
- Validated Fingolimod and Piperlongumine as effective repositioned drugs inhibiting LSCC growth, proliferation, and migration.
Conclusions:
- SnpDR is a powerful framework for identifying subtype-specific therapeutic vulnerabilities.
- ECM-targeted interventions show promise for LSCC treatment.
- Systems biology approaches accelerate precision oncology discovery.
More Related Videos
08:40A Biomimetic Model for Liver Cancer to Study Tumor-Stroma Interactions in a 3D Environment with Tunable Bio-Physical Properties
Published on: August 7, 2020
07:39The Establishment of a Lung Colonization Assay for Circulating Tumor Cell Visualization in Lung Tissues
Published on: June 16, 2018
Related Concept Videos
01:27Transducer Mechanism: Enzyme-Linked Receptors
Major types that are helpful drug targets include:
02:57Targeted Cancer Therapies
There are several types of targeted therapies against...