Emerging Role of AI in Single-Cell Sequencing Toward Biomarker Discovery, Therapeutic Target Identification and Precision Medicine in Lung Cancer.

Lung cancer, the most common malignancy worldwide, exists in numerous histological subtypes, rendering the diagnosis and prognosis of the disease a challenge to clinicians. The recent technological breakthroughs in single-cell sequencing (SCS) and Artificial Intelligence (AI) have expanded the existing knowledge of lung cancer biology and provided new opportunities in the area of precision medicine. SCS has the potential to expose the enormous complexity of tumors, showing heterogeneous cell populations and characterizing the tumor microenvironment, and thereby overcoming the inherent limitations of conventional bulk sequencing in resolving tumor heterogeneity. At the same time, AI's advanced machine learning (ML) and predictive modeling capabilities, when applied to SCS data, can facilitate the identification of subtle cellular states, dynamic cellular interactions, and complex molecular pathways often undetectable by traditional statistical methods. This review examines the emerging applications of SCS and AI in lung cancer, focusing on biomarker discovery, therapeutic target identification, and precision medicine. We outline how their integration may support the identification of new biomarkers and therapeutic targets, with implications for more personalized treatment approaches. Integration of single-cell biology knowledge with AI-based analytics may assist scientists and clinicians in addressing the shortcomings of traditional approaches, which may offer personalized therapies with the potential to improve patient care. The ultimate goal of this review is to address the challenges and future perspectives of this emerging trend by focusing on the potential of these technologies to fulfill the unmet needs in lung cancer diagnosis, prognosis, and treatment selection.