Systems Biology-Based Drug Repositioning Identifies Extracellular Matrix Module as a Therapeutic Target in Lung Squamous Cell Carcinoma
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".