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Background: Insulin receptor substrate 4 (IRS-4) is an adaptor protein for which new evidence suggests plays a role in tumour promotion.Methods: We described nuclear IRS-4 in RKO colon cancer cell lines in biopsies of patients with colorectal cancer (CRC) (n = 20) and in matched adjacent normal colorectal (MANC) tissue (n = 20).Results: Treatment with physiological doses of IGF-1 promoted nuclear influx of IRS-4 from cellular cytosol in RKO cells. When exogenous IRS-4 was overexpressed in RKO cells, there was an increase in cyclin D1, cyclin E, E2F1, pRB Ser 809/811 and pRB Ser 705 levels compared with the empty vector-transfected cells. Some of these changes returned to control values after wortmannin treatment. Subcellular fractionation showed an overexpression of IRS-4 in the cytoplasm, membrane, and nuclei of tumour samples, whereas the levels of the protein were barely detectable in the three compartments of normal samples. Immunohistochemical studies showed positive nuclear IRS-4 staining in over 74% of the tumour cells. IRS-4 was strongly overexpressed in tumoural tissues from CRC patients compared to MANC tissues. The up-regulation of IRS-4 in CRC samples correlated significantly with the increase of several G1 checkpoint proteins including cyclin D1 (r = 0.6662), Rb (r = 0.7779), pRb Serine 809/811 (r = 0.6864), pRb serine 705 (r = 0.6261) and E2F1 (r = 0.8702).Conclusions: Taken together, our findings suggest that IRS-4 promotes retinoblastoma-cyclin-dependent kinase activation and it may serve as a pharmacological target since its expression is very low in normal tissue, including colonic epithelium. [ABSTRACT FROM AUTHOR]

Abstract: Accurate and early estimations are essential for effective decision making in software project management. Nowadays, classical estimation models are being replaced by data mining models due to their application simplicity and the rapid production of profitable results. In this work, a method for mining association rules that relate project attributes is proposed. It deals with the problem of discretizing continuous data in order to generate a manageable number of high confident association rules. The method was validated by applying it to data from a Software Project Simulator. The association model obtained allows us to estimate the influence of certain management policy factors on various software project attributes simultaneously. [Copyright &y& Elsevier]