Objective To examine the expression of cell division cycle associated 2(CDCA 2) in pancreatic ductal adenocarcinoma(PDAC) and investigate its role in prognosis of PDAC patients.Methods This retrospective study include...Objective To examine the expression of cell division cycle associated 2(CDCA 2) in pancreatic ductal adenocarcinoma(PDAC) and investigate its role in prognosis of PDAC patients.Methods This retrospective study included 155 PDAC patients who underwent surgical treatment and complete post-operative follow-up.Clinicopathologic data were collected through clinical database.Tissue microarray was constructed and immunohistochemistry was performed to detect CDCA2 expression in the PDAC tumor tissues and adjacent non-tumor tissues.Clinicopathological characteristics between high and low CDCA2 expression were compared.Correlation of CDCA2 expressions with patients' survival was analyzed using Kaplan-Meier method and Cox regression analysis.Results Expression of CDCA2 in PDAC cells was significantly higher than that in adjacent non-tumor tissues(U=4056.5,P<0.001).Univariate analysis showed that CDCA2 expression [hazard ratio(HR)=1.574,95% confidence interval(CI)=1.014-2.443,P=0.043] and node metastasis(HR=1.704,95%CI=1.183-2.454,P=0.004) were significantly associated with prognosis.Cox regression analysis showed CDCA2 expression was not an independent prognostic risk factor(HR=1.418,95%CI=0.897-2.242,P=0.135) for PDCA patients.Stratification survival analysis demonstrated CDCA2 expression as an independent prognostic risk factor in male patients(HR=2.554,95%CI=1.446-4.511,P=0.003) or in non-perineural invasion patients(HR=2.290,95%CI=1.146-4.577,P=0.012).Conclusions CDCA2 is highly expressed in PDAC tumor tissue.Although CDCA2 is not an independent prognostic risk factor for PDAC patients,it might be used to help predict prognosis of male or non-perineural invasion patients of PDAC.展开更多
Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class unifo...Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.展开更多
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2012AA02A212)
文摘Objective To examine the expression of cell division cycle associated 2(CDCA 2) in pancreatic ductal adenocarcinoma(PDAC) and investigate its role in prognosis of PDAC patients.Methods This retrospective study included 155 PDAC patients who underwent surgical treatment and complete post-operative follow-up.Clinicopathologic data were collected through clinical database.Tissue microarray was constructed and immunohistochemistry was performed to detect CDCA2 expression in the PDAC tumor tissues and adjacent non-tumor tissues.Clinicopathological characteristics between high and low CDCA2 expression were compared.Correlation of CDCA2 expressions with patients' survival was analyzed using Kaplan-Meier method and Cox regression analysis.Results Expression of CDCA2 in PDAC cells was significantly higher than that in adjacent non-tumor tissues(U=4056.5,P<0.001).Univariate analysis showed that CDCA2 expression [hazard ratio(HR)=1.574,95% confidence interval(CI)=1.014-2.443,P=0.043] and node metastasis(HR=1.704,95%CI=1.183-2.454,P=0.004) were significantly associated with prognosis.Cox regression analysis showed CDCA2 expression was not an independent prognostic risk factor(HR=1.418,95%CI=0.897-2.242,P=0.135) for PDCA patients.Stratification survival analysis demonstrated CDCA2 expression as an independent prognostic risk factor in male patients(HR=2.554,95%CI=1.446-4.511,P=0.003) or in non-perineural invasion patients(HR=2.290,95%CI=1.146-4.577,P=0.012).Conclusions CDCA2 is highly expressed in PDAC tumor tissue.Although CDCA2 is not an independent prognostic risk factor for PDAC patients,it might be used to help predict prognosis of male or non-perineural invasion patients of PDAC.
基金Supported by the CRSRI Open Research Program(CKWV2013225/KY)the Priority Academic Program Development of Jiangsu Higher Education Institution+2 种基金the Open Project Foundation of Key Laboratory of the Yellow River Sediment of Ministry of Water Resource(2014006)the State Key Lab of Urban Water Resource and Environment(HIT)(ES201409)the Open Project Program of State Key Laboratory of Food Science and Technology,Jiangnan University(SKLF-KF-201310)
文摘Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.
