TY - JOUR TI - Computational Cellular Mathematical Model Aids Understanding the cGAS-STING in NSCLC Pathogenicity AU - Khandibharad, Shweta AU - Gulhane, Pooja AU - Singh, Shailza VL - 15 IS - 5 PY - 2025 DA - 2025/03/05 SP - e5223 C1 - Bio-protocol 2025;15:e5223 DO - 10.21769/BioProtoc.5223 UR - https://doi.org/10.21769/BioProtoc.5223 AB - Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. According to 2020 reports, globally, 2.2 million cases are reported every year, with the mortality number being as high as 1.8 million patients. To study NSCLC, systems biology offers mathematical modeling as a tool to understand complex pathways and provide insights into the identification of biomarkers and potential therapeutic targets, which aids precision therapy. Mathematical modeling, specifically ordinary differential equations (ODEs), is used to better understand the dynamics of cancer growth and immunological interactions in the tumor microenvironment. This study highlighted the dual role of the cyclic GMP-AMP synthase–stimulator of interferon genes (cGAS/STING) pathway's classical involvement in regulating type 1 interferon (IFN I) and pro-inflammatory responses to promote tumor regression through senescence and apoptosis. Alternative signaling was induced by nuclear factor kappa B (NF-κB), mutated tumor protein p53 (p53), and programmed death-ligand1 (PD-L1), which lead to tumor growth. We identified key regulators in cancer progression by simulating the model and validating it with the following model estimation parameters: local sensitivity analysis, principal component analysis, rate of flow of metabolites, and model reduction. Integration of multiple signaling axes revealed that cGAS-STING, phosphoinositide 3-kinases (PI3K), and Ak strain transforming (AKT) may be potential targets that can be validated for cancer therapy. KW - MATLAB KW - Sensitivity KW - Flux KW - Model reduction KW - Crosstalk KW - Systems biology JF - Bio-protocol SN - 2331-8325 PB - Bio-protocol LLC. BIO101 - False