Can we use graph mining algorithms to find patterns in tumor molecular mechanisms? Can we model disease progression with multiple time-specific graph comparison algorithms? In this paper, we will focus on this area. Our main contributions are 1) we proposed the Temporal-Omics (Temp-O) workflow to model tumor progression in non-small cell lung cancer (NSCLC) using graph comparisons between multiple stage-specific graphs, and 2) we showed that temporal structures are meaningful in the tumor progression of NSCLC. Other identified temporal structures that were not highlighted in this paper may also be used to gain insights to possible novel mechanisms. Importantly, the Temp-O workflow is generic; while we applied it on NSCLC, it can be applied in other cancers and diseases. We used gene expression data from tumor samples across disease stages to model lung cancer progression, creating stage-specific tumor graphs. Validating our findings in independent datasets showed that differences in temporal network structures capture diverse mechanisms in NSCLC. Furthermore, results showed that structures are consistent and potentially biologically important as we observed that genes with similar protein names were captured in the same cliques for all cliques in all datasets. Importantly, the identified temporal structures are meaningful in the tumor progression of NSCLC as they agree with the molecular mechanism in the tumor progression or carcinogenesis of NSCLC. In particular, the identified major histocompatibility complex of class II temporal structures capture mechanisms concerning carcinogenesis; the proteasome temporal structures capture mechanisms that are in early or late stages of lung cancer; the ribosomal cliques capture the role of ribosome biosynthesis in cancer development and sustainment. Further, on a large independent dataset we validated that temporal network structures identified proteins that are prognostic for overall survival in NSCLC adenocarcinoma.