Kannan R.*, Natarajan P., Ganesh H. , Dinesh Siva N., Indhuja R.
Sankaralingam Bhuvaneswari College of Pharmacy, Anaikuttam, Sivakasi, Tamil Nadu, India
Affiliated with Tamil Nadu Dr. M.G.R. Medical University, Chennai, Tamil Nadu, India
*Address for Corresponding Author
Kannan R.
Department of Pharmacology
Sankaralingam Bhuvaneswari College of Pharmacy, Sivakasi, Tamil Nadu, India
Abstract
To date, non-small-cell lung cancer (NSCLC) still accounts for most cancer-related mortality, making an efficient model a pressing need for a comprehensive approach. The human lung adenocarcinoma-derived A549 cell line has been widely used for research in NSCLC due to cell reproducibility, KRAS mutation, and involvement of key oncogenic signaling. The A549 cell line emphasizes important aspects of cancer biology, namely apoptosis resistance, metabolism, and sustained proliferation driven by PI3K/AKT/mTOR, NF-κB, and KRAS/RAF/MEK/ERK. Oncogenic KRAS expression and NF-κB-mediated transcription induce chemoresistance through increased expression of efflux transporters (MDR1) and anti-apoptotic proteins (BCL-2, XIAP). In addition, A549 cell monolayers maintain cancer stem-like populations (e.g., CD90+ and high-ALDH1 cells), sustaining cancer growth and relapse. Innovative strategies support A549 model applications. CRISPR-Cas9 functional genomics facilitates genetic vulnerability mapping, while 3D cell cultures and organoids model a cancer microenvironment. Integration with artificial intelligence (AI) technology also helps to facilitate predictive models and screening of medications. A549, despite limitations like the lack of patient heterogeneity and the simplification of the microenvironment, is extremely useful in combination with other models or systems. In conclusion, A549 remains at the forefront, integrating knowledge and innovation to promote precise oncology to ensure NSCLC treatment efficacy.
Keywords: A549 cell line, non-small-cell lung cancer, KRAS, PI3K/AKT/mTOR, NF-κB, drug resistance, cancer stem cells, CRISPR, organoids, artificial intelligence
