Investigating Liquid–Liquid Phase Separation in Lung Adenocarcinoma to Improve Prognostic Accuracy and Treatment Efficacy

Liquid–Liquid Phase Separation (LLPS) refers to the separation of biomacromolecules into separate liquid phases within the cells, plays a critical role in lung cancer pathogenesis. Using machine learning, we developed an LLPS-associated signature (LLPSAS) based on 79 key genes. The LLPSAS demonstrated superior prognostic performance compared to 140 existing lung adenocarcinoma (LUAD) prognostic models. Patients stratified by LLPSAS risk scores revealed significantly lower overall survival in the high-risk group. Comparative analysis between the high-risk and low-risk groups showed distinct pathway enrichment, genomic alterations, tumour immune microenvironment (TIME) profiles, immunotherapy responses and drug sensitivities. The low-risk group exhibited an inflamed TIME, suggesting potentially better immunotherapy response. Furthermore, potential effective small molecule drugs were identified for high-risk patients. Finally, immunohistochemistry confirmed upregulation of LLPS-associated proteins (PLK1, HMMR, PRC1) in LUAD tissues, and immunofluorescence validated their LLPS occurrence. Conclusively, the LLPSAS provides a valuable tool for LUAD prognosis and treatment optimisation.