A novel model based on liquid‐liquid phase separation–Related genes correlates immune microenvironment profiles and predicts prognosis of lung squamous cell carcinoma

OBJECTIVE: The aim of the study was to construct and validate a robust prognostic model based on liquid‐liquid phase separation (LLPS)–related genes in lung squamous cell carcinoma (LUSC). METHODS: The Cancer Genome Atlas dataset was used as the discovery set to identify the LLPS‐related differentially expressed genes (DEGs) between LUSC and normal tissue. These DEGs were screened by the LASSO Cox regression analysis to identify the genes with nonzero coefficient, which were next included in the multivariate Cox regression analysis to construct the prediction model. The dataset GSE41271 was adopted as the validation set to verify the efficacy of the model. Enrichment analysis and the CIBERSORT were performed to illustrate potential immune mechanisms underlying the prediction model. RESULTS: A total of 48 LLPS‐related genes were aberrantly expressed in LUSC. Among them, 7 genes were selected by the LASSO Cox regression analysis to construct the prediction model. Risk index (RI) was calculated according to the model for each patient. The prognosis was significantly different between the patients with high and low RI in the discovery set and the validation set (p < 0.001 and p = 0.028, respectively). The multivariate survival analysis confirmed RI as an independent prognostic factor in LUSC (in the discovery set: p < 0.001, HR = 2.643, 95% CI = 1.986–3.518; in the validation set: p = 0.042, HR = 2.144, 95% CI = 1.026–4.480). A series of pathways involving immune cells were found to be related to RI. The distribution pattern of immune cells and chemokines varied according to the value of RI. CONCLUSION: The prediction model based on LLPS‐related genes was constructed and validated as a robust prognostic tool for LUSC using multiple datasets. LLPS might have an impact on LUSC through immune pathways.