Networks are widely recognized as key determinants of structure and function in systems that span the biological, physical, and social sciences. They are static pictures of the interactions among the components of complex systems. Often, much effort is required to identify networks as part of particular patterns as well as to visualize and interpret them.

Altered expression of microRNAs (miRNAs) hallmarks many cancer types. The study of the associations of miRNA expression profile and cancer phenotype could help identify the links between deregulation of miRNA expression and oncogenic pathways. Expression profiling of 866 human miRNAs in 19 colorectal and 17 pancreatic cancers and in matched adjacent normal tissues was investigated. Classical paired t-test and random forest analyses were applied to identify miRNAs associated with tissue-specific tumors. Network analysis based on a computational approach to mine associations between cancer types and miRNAs was performed. The merge between the two statistical methods used to intersect the miRNAs differentially expressed in colon and pancreatic cancers allowed the identification of cancer-specific miRNA alterations. By miRNA-network analysis, tissue-specific patterns of miRNA deregulation were traced: the driving miRNAs were miR-195, miR-1280, miR-140-3p and miR-1246 in colorectal tumors, and miR-103, miR-23a and miR-15b in pancreatic cancers. MiRNA expression profiles may identify cancer-specific signatures and potentially useful biomarkers for the diagnosis of tissue specific cancers. miRNA-network analysis help identify altered miRNA regulatory networks that could play a role in tumor pathogenesis.

Type 2 diabetes mellitus (DM) is a common metabolic disorder that affects nearly 250 million people worldwide,^1,2 and is associated with major diabetes-related complications, including retinopathy, nephropathy, neuropathy, and cardiovascular disease.³ Type 2 DM is a complex disease in which both genetic and environmental factors interact to produce hyperglycemia.^4,5 Insulin resistance in muscle, liver, and adipose tissue is a major feature of most patients with type 2 DM,⁶ making these tissues resistant to both endogenous and exogenous insulin.⁶

Brain invasion by glioblastoma determines prognosis, recurrence, and lethality in patients, but no master factor coordinating the invasive properties of glioblastoma has been identified. Here we report evidence favoring such a role for the noncanonical WNT family member Wnt5a. We found the most invasive gliomas to be characterized by Wnt5a overexpression, which correlated with poor prognosis and also discriminated infiltrating mesenchymal glioblastoma from poorly motile proneural and classical glioblastoma. Indeed, Wnt5a overexpression associated with tumor-promoting stem-like characteristics (TPC) in defining the character of highly infiltrating mesenchymal glioblastoma cells (Wnt5aHigh). Inhibiting Wnt5a in mesenchymal glioblastoma TPC suppressed their infiltrating capability. Conversely, enforcing high levels of Wnt5a activated an infiltrative, mesenchymal-like program in classical glioblastoma TPC and Wnt5aLow mesenchymal TPC. In intracranial mouse xenograft models of glioblastoma, inhibiting Wnt5a activity blocked brain invasion and increased host survival. Overall, our results highlight Wnt5a as a master regulator of brain invasion, specifically TPC, and they provide a therapeutic rationale to target it in patients with glioblastoma. Cancer Res; 77(4); 996–1007. ©2016 AACR.