Identification of prognostic genes associated with phase separation in lung adenocarcinoma and construction of prognostic models

Lung adenocarcinoma (LUAD) is a common histological subtype of lung cancer, but its prognosis remains poor. Recent studies have suggested that liquid-liquid phase separation-related genes (LRGs) can significantly predict the prognosis of low-grade tumors. Identifying potential LRGs associated with prognosis in LUAD could have significant clinical value for predicting patient outcomes. Data were sourced from public databases. Differentially expressed LRGs (DE-LRGs) were identified through differential expression analysis and by taking intersections between datasets. Regression analysis and the Least Absolute Shrinkage and Selection Operator (Lasso) method were used to shortlist prognostic genes, and a multivariate Cox regression model was developed to create a prognostic risk model. Tumor samples were stratified into high- and low-risk groups based on the median risk score. Independent prognostic analyses and the construction of a nomogram were performed in conjunction with clinical characteristics. Immune characteristics of the two risk groups were also analyzed. Additionally, single-cell RNA sequencing (scRNA-seq) data were used to identify cell clusters and annotate known cell types. A total of 389 DE-LRGs were identified, and 7 prognostic genes were selected to construct the risk model. Patients in the high-risk group exhibited lower survival rates, and the nomogram demonstrated high predictive accuracy. Significant differences were observed in clinical characteristics, immune status, and drug sensitivity between the high- and low-risk groups. Based on scRNA-seq data, 8 distinct cell types were annotated, with the prognostic genes GRIA1 and BCAN showing higher expression levels in fibroblasts and mast cells, respectively. Seven prognostic genes were identified, and the resulting prognostic model accurately predicted the survival outcomes of LUAD patients. This model provides valuable insights for the prognosis and personalized treatment of LUAD patients.