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Lung adenocarcinoma (LUAD) is a biologically and clinically heterogeneous disease that poses a major challenge for prognosis and treatment. In this study, we performed proteomic profiling in a cohort of 88 LUAD patients to identify molecular subgroups and investigate their clinical relevance. Unsupervised clustering of the proteomic data allowed us to identify two distinct patient groups with different demographic, clinical, and molecular characteristics. Cluster 1 consisted predominantly of older patients and showed increased expression of immune and inflammatory pathways, including significant enrichment of TNF and Toll-like receptor signaling. This suggests a stronger innate immune response that may be associated with better disease control. In contrast, cluster 2 was characterized by younger demographics, a higher proportion of female patients, and a greater frequency of smoking. This cluster showed less activation of immune-related pathways and a significantly shorter time to disease recurrence, suggesting a more aggressive clinical course and poorer prognosis. The differential expression of immune pathways between clusters underscores the role of the tumor microenvironment in disease progression and response to treatment. Our results demonstrate the value of integrating proteomic and clinical data to identify biologically distinct LUAD subtypes. This molecular stratification can improve the understanding of tumor behavior and inform personalized treatment strategies. Thus, proteomic profiling is a promising tool to guide biomarker-directed treatment of lung adenocarcinoma.