Abstract
Introduction: This study on lung adenocarcinoma (LUAD), a common lung cancer subtype with high mortality. Aims: This study focuses on how tumor cell interactions affect immunotherapy responsiveness. Methods: Using public databases, we used non-negative matrix factorization clustering method, ssGSEA, CIBERSORT algorithm, immunophenotype score, survival analysis, protein-protein interaction network method to analyze gene expression data and coagulation-related genes. Results: We dividedLUAD patients into three coagulation-related subgroups with varying immune characteristics and survival rates. A cluster ofthreepatients, having the highest immune infiltration and survival rate, also showed the most potential for immunotherapy. We identified fivekey genes influencing patient survival using a protein-protein interaction network. Conclusion: This research offers valuable insights for forecasting prognosis and immunotherapy responsiveness in LUAD patients, helping to inform clinical treatment strategies.
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