Expression Profiling Identifies Bezafibrate as Potential Therapeutic Drug for Lung Adenocarcinoma

Area of Science:

• Oncology
• Pharmacology
• Computational Biology

Background:

• Drug repositioning using gene expression patterns offers a novel therapeutic strategy.
• Lung adenocarcinoma (AC) urgently requires new therapeutic compounds.
• In silico methods can accelerate the discovery of potential drug candidates.

Purpose of the Study:

• To identify novel drugs for lung adenocarcinoma using a gene expression-based approach.
• To validate the efficacy of a computationally predicted drug, bezafibrate, against lung AC.
• To investigate the molecular mechanisms and in vivo effects of bezafibrate in lung AC.

Main Methods:

• Connectivity Map (CMap) analysis for in silico drug prediction.
• In vitro anti-proliferation and cell cycle arrest assays.
• In silico molecular docking to identify drug targets.
• In vivo xenograft studies in nude mice.

Main Results:

• Bezafibrate was computationally predicted as a potential treatment for lung AC.
• In vitro studies confirmed bezafibrate's anti-proliferative effects and ability to induce cell cycle arrest.
• Bezafibrate targets cyclin-dependent kinase 2 (CDK2), down-regulating its mRNA and protein expression.
• Bezafibrate inhibited lung AC tumor growth in vivo.

Conclusions:

• Bezafibrate is a potential therapeutic option for lung adenocarcinoma.
• In silico drug screening is a viable strategy for identifying novel anti-cancer agents.
• Targeting CDK2 with bezafibrate offers a promising avenue for lung AC treatment.