Multi-omics integration and machine learning identify NPC2 as a prognostic and treatment-responsive regulator in lung adenocarcinoma.

Background: This study aims to define a novel molecular subtype of LUAD by integrating multiple omics data. Additionally, we develop and validate an Artificial Intelligence Derived Prognostic Index (AIDPI) that predicts the prognosis of LUAD patients, identifies potential therapeutic targets. Methods: This study employed ten clustering algorithms from the R package "MOVICS" to integrate multi-omics data of LUAD sourced from TCGA database for molecular typing. Subsequently, an Artificial Intelligence Derived Prognostic Index (AIDPI) was constructed as the most effective indicator for predicting the overall survival rate of LUAD patients. The biological functions and mechanisms of NPC2 in lung adenocarcinoma were elucidated through both Results: Through multi-omics clustering, we identified two subtypes of lung adenocarcinoma associated with prognosis, with the CS1 subtype exhibiting the most favorable prognostic outcomes. The low AIDPI group exhibited a more positive prognosis, accompanied by increased immune cell infiltration and activation of immune pathways. Meanwhile, NPC2 was recognized as a standalone risk factor for LUAD, with its high expression significantly improving the overall survival of LUAD patients. Functionally, the overexpression of NPC2 promotes tumorigenesis in LUAD both Conclusion: The comprehensive analysis of multiple omics data significantly enhances the molecular classification of lung adenocarcinoma. Furthermore, AIDPI is a potential biomarker that predicts the prognosis of LUAD patients. NPC2 inhibits the progression of LUAD by suppressing the PI3K/AKT signaling pathway and enhancing the chemotherapy sensitivity to Ribociclib.