In this study, we have analyzed human primary lung adenocarcinoma tumors using global mass spectrometry to elucidate the biological mechanisms behind relapse post surgery. In total, we identified over 3000 proteins with high confidence. Supervised multivariate analysis was used to select 132 proteins separating the prognostic groups. Based on in-depth bioinformatics analysis, we hypothesized that the tumors with poor prognosis had a higher glycolytic activity and HIF activation. By measuring the bioenergetic cellular index of the tumors, we could detect a higher dependency of glycolysis among the tumors with poor prognosis. Further, we could also detect an up-regulation of HIF1α mRNA expression in tumors with early relapse. Finally, we selected three proteins that were upregulated in the poor prognosis group (cathepsin D, ENO1, and VDAC1) to confirm that the proteins indeed originated from the tumor and not from a stromal or inflammatory component. Overall, these findings show how in-depth analysis of clinical material can lead to an increased understanding of the molecular mechanisms behind tumor progression.