Cancer is a large class of diseases and colorectal cancer is one of the leading types of cancer. Systems level analysis of complex diseases like cancer requires the analysis of relationships between different types of clinical data as well as molecular data. Common or specific network features of colorectal cancer together with the other cancer types could be identified by using different network approaches, such as the analysis of clinical data associations, molecular signaling pathways of cancers, and specific interaction networks of cancers. Firstly, a clinical network analysis has been performed on relationships between different types of cancer and the drugs. We generated two cancer networks, one of cancer types that share Food and Drug Administration (FDA) approved drugs, and another of cancer types that share clinical trials of FDA approved drugs. Breast cancer is the only cancer type with significant weighted degree values in both cancer networks. Lung cancer is significantly connected in the FDA approval based cancer network, whereas ovarian cancer and lymphoma are significantly connected in the clinical trial based cancer network. We defined global and local lethality values representing death rates relative to other cancers vs. within a cancer. Correlation and linear regression analyses suggests that global lethality impacts the drug approval and trial numbers, whereas, local lethality impacts the amount of drug sharing in trials and approvals. However, this effect may not apply to pancreatic, liver, and esophagus cancers as the sharing of drugs for these cancers is very low. We also showed a weak overlap between the mutation and drug target based cancer networks. Secondly, we analyzed the cancer pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, which provides a collective of signaling pathway members involved in cancer progression. However, the KEGG cancer pathways, unlike signaling pathways, were analyzed extensively with gene expression and mutation data. We transformed the colorectal cancer pathway into subgroups based on their position and analyzed the relative expression levels of adenoma and carcinoma samples as well as the distribution of mutation targets. The gene expression values of the early stage pathway members are significantly higher than the rest of the pathway members in colorectal adenoma tissues. The colorectal cancer pathway shows some degree of coherence in only the carcinoma samples. The correlated gene pairs responsible for the coherence of the colorectal cancer pathway in the carcinoma samples are supported, in part, by the literature and may suggest novel regulatory associations. Thirdly, we compared colorectal cancer samples not only to a control sample set but against a wide variety of samples and conditions, in contrast to current integrative network approaches that identify specific genes by comparing pair-wise control (i.e. normal) to treated (i.e. disease) samples. We were able to identify a distinctly expressed set of genes which were significantly associated with colorectal cancer in the literature unlike the pair-wise approach. We integrated these specific genes with the PPI data to construct a colorectal cancer-specific network. We identified a potential regulatory relationship between glucocorticoid receptor (GR) and ring finger protein 43 (RNF43) which may play a role in colorectal cancer. In HCT116 colorectal cancer cell line, knocking-down GR levels with siRNA resulted in increased RNF43 levels and inducing the colorectal cancer cells with dexamethasone, which is an activating ligand for GR, resulted in decreased RNF43 levels. On the other hand, knocking-down RNF43 levels with siRNA resulted in decreased GR levels. Our study suggests GR might regulate RNF43 negatively, whereas there might not be such a negative regulation from RNF43 to GR.
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