Background: A gel-free proteomic approach was utilized to perform in-depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long-term goal of this study is to generate a large-scale, label-free proteomic data set from histologically well-classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers. Methods and Results: Cases of early-stage (I-II) and advanced-stage (III-IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung adenocarcinomas were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). From the analysis of ADC and normal specimens, 5,933 protein groups were identified. To examine the protein expression profile of ADC, a peak area-based quantitation method was used. In early- and advanced-stage ADC, 33 and 39 proteins were differentially-expressed respectively between normal and tumor tissue (adjusted p-value < 0.01, fold change ≥ 4). For early- and advanced stage ADC tumors compared to normal patient-matched tissue, 11 and 22 proteins and 23 and 16 proteins were identified as down- and up-regulated, respectively. In silico functional analysis of the up-regulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most over-represented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes. Conclusions: Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage-specific drug targets for rational intervention.