Decoding Lung Cancer: PLAU as a Critical Driver and Diagnostic Marker

Abstract

Lung cancer remains a leading cause of cancer-related mortality, and understanding its underlying molecular mechanisms is critical for developing targeted therapies. We developed a novel tool called the Transcriptome-TCGA Integration (TTI), which combines in vitro transcriptome data with clinical datasets from the Cancer Genome Atlas (TCGA). TTI allows for the identification of key genes and pathways involved in lung cancer, providing valuable insights into tumor biology. The TTI tool successfully identified PLAU as a key driver of lung cancer progression, promoting NF-κB pathway activation and EMT. Inhibition of PLAU led to decreased NF-κB activity, reduced cell viability, and diminished migration and invasion capabilities in lung cancer cells. Furthermore, cfDNA analysis revealed that PLAU inhibition lowered cfDNA-derived PLAU levels, offering a potential diagnostic marker for tumor dynamics. TTI is a versatile tool that not only elucidates critical molecular pathways in lung cancer but also holds significant potential for application across other cancer types. By integrating AI and machine learning, the TTI tool can be further developed into an AI-driven platform for precision oncology, enabling predictive treatment models and cross-cancer analyses. Our study demonstrates the utility of TTI in advancing cancer research and personalized medicine, with implications for future therapeutic developments in multiple cancer contexts.