Expressions of TP53, P16 and K-ras in gallbladder high-grade intraepithelial neoplasia and early carcinoma and establishment of a random forest prediction model / 西安交通大学学报(医学版)

ABSTRACT

【Objective】 To explore the different expressions of TP53, P16 and K-ras in gallbladder high-grade intraepithelial neoplasia and early carcinoma, and establish their mutation random forest prediction model. 【Methods】 We retrospectively analyzed the clinicopathological data of 71 patients who underwent cholecystectomy at The First Affiliated Hospital of Xi’an Jiaotong University from January 2013 to December 2018, including 20 cases of chronic cholecystitis, 28 cases of gallbladder high-grade intraepithelial neoplasia, and 23 cases of early gallbladder carcinoma. The immunohistochemical SP method was conducted to detect the expressions of TP53, P16 and K-ras in the gallbladder pathological tissues; the correlation between the above genes and clinicopathological data was analyzed. A random forest prediction model of each gene mutation was established based on the clinicopathological data and gene expression. 【Results】 The positive expressions of TP53, P16 and K-ras were related to the gallbladder with cholecystolithiasis or polyps and gallbladder pathological tissue type. The positive rates of the three genes in the gallbladder polyps were significantly higher than those in the cholecystolithiasis group (P<0.05). The positive rates of the three genes in the latter two groups of gallbladder high-grade intraepithelial neoplasia and early gallbladder carcinoma were significantly higher than those in the chronic cholecystitis (P<0.05), while there was no statistical difference between the latter two groups (P>0.05). The mutations of TP53, P16 and K-ras had a certain correlation (χ2=6.285, 19.595, 4.070, r=0.298, 0.525, 0.239, P<0.05). TP53, P16 and K-ras mutation prediction models based on random forest had good accuracy (AUC=77.42%, 80.06%, 71.75%, accuracy=76.06%, 76.06%, 67.61%). 【Conclusion】 TP53, P16 and K-ras gene mutations promote the transformation of chronic cholecystitis to gallbladder carcinoma. The mutation prediction model based on random forest has a good accuracy, which can provide an important reference for carcinogenesis and early diagnosis of gallbladder carcinoma.