Background: Distinguishing indeterminate pulmonary nodules remains a significant challenge in the early diagnosis of lung cancer, while proteomic biomarkers derived from the lesioned pulmonary lobe may provide crucial insights into their malignant progression. Methods: Our study cohort included three groups: the lesioned lobe of lung cancer patients (LC-L), the paired non-lesioned lobe of lung cancer patients (LC-N), and the lesioned lobe of patients with benign nodules (BN-L). Data-independent acquisition (DIA) proteomic analysis was performed to identify potential biomarkers for early diagnosis of lung cancer, followed by evaluation in a larger, independent cohort. Results: We identified 4,305 proteins, among which 715 and 738 were differentially expressed between LC-L and LC-N, and between LC-L and BN-L, respectively. Integration of these results revealed 134 up-regulated and 44 down-regulated proteins (p < 0.05; |FC| > 1.2). Notably, 14 proteins exhibited a |FC| > 3, and 5 of them were either involved in biological metabolism or had been previously reported as cancer-associated. KEGG pathway enrichment analysis of the 134 up-regulated proteins demonstrated significant enrichment in metabolic pathways and the TCA cycle. We further validated 9 candidate proteins by ELISA and confirmed that 3 proteins (SUCLA2, FKBP9, TAP1) exhibited expression patterns consistent with those observed in the discovery cohort. Among them, SUCLA2 and FKBP9 reached statistical significance, while TAP1 showed a strong trend (p = 0.061). To our knowledge, this is the first report showing that SUCLA2 and FKBP9 are up-regulated in the lesioned lobes of lung cancer patients, independent of benign nodules. Conclusions: These candidate biomarkers and their associated metabolic reprogramming pathways provide a new direction for improving the identification ability of uncertain nodules, and expand our understanding of the pathophysiology of early lung cancer.