It is known that the cause of cancer could be due to the gain of function of an oncoprotein (OCP) or the lost of function of a tumor suppressor protein (TSP). These proteins are potential targets for drugs. Lung cancer is one of the leading causes of death in Taiwan. In this study, differential expressed genes (DEGs) are identified, using the Bioconductor package, via expression dataset generated from human lung adenocarcinoma tumor and adjacent non-tumor tissues. By integrating complementary resources, that is, microarray (ArrayExpress), protein-protein interaction (BioGrid), and protein complex (MIPS); it is found that certain cancer-related DEGs match with known protein complexes. After constructing the lung cancer protein-protein interaction network (PPIN), we performed graph theory analysis of PPIN. Highly dense modules (k-clique communities) are identified, which are potential cancer-related protein complexes. Up-clique and down-clique genes were used as queries to perform functional annotation clustering on DAVID. Over-represented or enriched biological processes and pathways are determined. Our findings suggest a potential relationship between those processes (as well as pathways) and cancer, which deserve further drug-gene interaction and potential drugs investigation.