Although colorectal cancer (CRC) is one of the most common malignancies worldwide, the current therapeutic approaches for advanced CRC are ineffective. In this study, we investigated the involvement of the VAV3 oncogene in tumor progression and in the prognosis of human CRC. The two patient cohorts in this study comprised 354 CRC cases from 1998 to 2005 with documented pathologic and clinical factors and clinical outcomes. VAV3 protein levels were significantly correlated with the depth of invasion (P = 0.0259), the nodal status (P < 0.0001), distant metastasis (P = 0.0354), the stage (P < 0.0001), and poor disease-free survival (P = 0.003). Multivariate Cox regression analysis showed that VAV3 overexpression is an independent prognostic marker for CRC (P = 0.041). In vitro experiments indicated that VAV3 knockdown inhibited CRC cell growth, spread, and xenograft proliferation. Mechanistic studies further revealed that VAV3 overexpression could dysregulate the expression of cell cycle control- and metastasis-related molecules by activating the PI3K-AKT signaling pathway in both CRC cells and xenografts. This study suggests that VAV3 overexpression could be a useful marker for predicting the outcomes of CRC patients and that VAV3 targeting represents a potential modality for treating CRC.
cAMP signaling controls a variety of cellular functions. In addition to the well-known signal transducer cAMP-dependent protein kinase, a more recently discovered transducer is the exchange protein directly activated by cAMP (EPAC). EPAC responses are mediated by small G proteins, which regulate biologic functions such as cell adhesion, migration and proliferation. Recently, the clinical importance of EPAC1 has received increased attention. This study investigated the correlations between the expression of EPAC1 and various clinicopathologic parameters as well as the survival of the patients with gastric cancer (GC). The patient cohort in this study consisted of 141 cases of GC that presented from 1999 through 2011; documented clinicopathologic parameters and clinical outcomes were available for all cases. Immunoblotting, immunohistochemistry and quantitative real-time PCR were used to examine EPAC1 expression in gastric cells and tissues. siRNA technology was used to study the effect of EPAC1 knockdown on cell proliferation and invasion. An increase in EPAC1 expression was found in GC cells and tissues. The overexpression of EPAC1 was associated with the depth of invasion (P=0.0021), stage (P=0.0429), and vascular invasion (P=0.0049) and was correlated with poor disease-free survival (P=0.0029) and overall survival (P=0.0024). A univariate Cox regression analysis showed that the overexpression of EPAC1 was a prognostic marker for GC (P=0.038). Furthermore, cell studies indicated that the knockdown of EPAC1 in GC cells suppressed cell proliferation and invasion. The overexpression of EPAC1 can be used as a marker to predict the outcome of patients with GC, and EPAC1 represents a potential therapeutic modality for treating GC.
The study was to enhance adherence to quality-of-care guidelines for colorectal cancer (CRC) patients through plotting graphical representations. Rasch analysis was performed to examine the unidimensional measurement of the 13 core indicators. An author-made Excel module was applied to plot the so-called Wright map and KIDMAP in education field to report physicians' adherence to the quality-of-life guidelines. We found that the scale of the quality-of-care guidelines for patients with colon cancer is unidimensional. A total of 15 (3.8%) and 14 (3.5%) persons' response patterns (i.e., Outfit MNSQs >2.0 and 4.0, respectively) are aberrantly dispersed from the majority of sample according to their estimated parameters of persons and indicators. It can be used for investigating the root cause of the 1ow measures and/or the most unexpected aberrant pattern of responses using Rasch analysis once any one indicator of unexpectedly aberrant treatment (p < .05) presents. The Rasch model can deal with these binary and/or missing data frequently seen in clinical settings. We confirm this computer module can contribute to ensuring that hospitals adhere to the treatment guidelines for patients with colon cancer.
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