Integrative multi-omics and machine learning reveal critical functions of proliferating cells in prognosis and personalized treatment of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a major cause of cancer-related mortality globally. Proliferating cells, crucial components of the tumor immune microenvironment (TIME), play a significant role in cancer progression and immunotherapy response. Herein, we utilized multi-omics data and employed a multifaceted approach to delineate the proliferating cell landscape in LUAD. The Scissor algorithm was applied to identify Scissor+ proliferating cell genes associated with prognosis. An integrative machine learning program, comprising 111 algorithms, was developed to construct a Scissor+ proliferating cell risk score (SPRS). The SPRS model demonstrated superior performance in predicting prognosis and clinical outcomes compared to 30 previously published models. The role of SPRS and five pivotal genes in immunotherapy response was evaluated, and their expression was experimentally verified. Multifactorial analysis confirmed SPRS as an independent prognostic factor affecting LUAD patient survival. High- and low-SPRS groups exhibited different biological functions and immune cell infiltration in the TIME. High SPRS patients showed resistance to immunotherapy but increased sensitivity to chemotherapeutic and targeted therapeutic agents. Our study elucidates the dynamics of proliferating cells in LUAD, enhancing prognostic accuracy and highlighting the potential of SPRS and its constituent genes for personalized therapeutic interventions.