Journal of Jilin University(Medicine Edition) ›› 2025, Vol. 51 ›› Issue (1): 172-181.doi: 10.13481/j.1671-587X.20250121

• Research in clinical medicine • Previous Articles    

Construction of prediction model for gastric cancer mismatch repair based on preoperative inflammatory indicators and clinicopathological features in gastric cancer patients

Xiuzhen WEI1,2,3,Yaling DONG1,2,Zhibo ZHU1,2,Zhengjie ZHANG1,3,Yuanjun TAN2,Jie BAI1,2,Xiayi SU2,Baihong ZHANG2()   

  1. 1.First Clinical Medical School,Gansu University of Chinese Medicine,Lanzhou 730030,China
    2.Department of Oncology,940th Hospital of Joint Logistics Support Force of People’s Liberation Army,Lanzhou 730050,China
    3.Department of Gastroenterology,Liangzhou Hospital,Wuwei City,Gansu Province,Wuwei 733000,China
  • Received:2024-02-01 Accepted:2024-04-14 Online:2025-01-28 Published:2025-03-06
  • Contact: Baihong ZHANG E-mail:bhzhang@126.com

Abstract:

Objective To discuss the associations of mismatch repair (MMR) in gastric cancer with preoperative inflammatory indicators and clinicopathological features in the gastric cancer patients, and to construct a gastric cancer MMR predictive model based on preoperative inflammatory indicators and clinicopathological features of the gastric cancer patients, and to provide new ideas for evaluation of MMR status in gastric cancer. Methods The data of 254 gastric cancer patients who underwent surgical treatment from September 2020 to October 2023 were included. According to the expression of MMR protein, the patients were divided into MMR normal (proficiout MMR, pMMR) group and MMR deficient (dMMR) group. The preoperative inflammatory indicators and clinicopathological features data of the gastric cancer patients in two groups were collected. The associations between inflammatory indicators, clinicopathological features, and MMR in dMMR group and pMMR group were analyzed usingChi-square test. The independent predictive factors for dMMR were selected to construct the nomogram. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive efficacy, and decision curve was used to evaluate the practicality of the predication model. Results A total of 254 gastric cancer patients were included in the study, with 221 patients (87%) in pMMR group and 33 patients (13%) in dMMR group. There were statistically significant differences (P<0.05) in age, tumor location, tumor differentiation degree, maximum tumor diameter, platelet-to-lymphocyte ratio (PLR), alkaline phosphatase (AKP), alkaline phosphatase-to-albumin ratio (AAR), fibrinogen(FB)-to-lymphocyte (FLR), FB-to-albumin(AL) (FAR), D-dimer (D-D), and FB of the gastric cancer patients between dMMR group and pMMR group. Univariate and multivariate Logistic regression analysis revealed maximum tumor diameter [odd ratio(OR)=2.958, 95% confidence interval (CI):1.196-7.314, P=0.019], tumor location (OR=4.013,95%CI:1.596-10.089, P=0.003), tumor differentiation (OR=3.006, 95%CI: 1.250-7.230, P=0.014), FAR (OR=2.793, 95%CI:1.179-6.616, P=0.020), and carbohydrate antigen 199(CA199) (OR=0.279, 95%CI:0.084-0.929, P=0.038) were the independent predictors of dMMR. The area under the ROC curve (AUC) value of the gastric cancer MMR prediction model constructed based on inflammatory indicators and clinical pathological characteristics was 0.800 with the sensitivity of 0.851 and the specificity of 0.606. The calibration curve of the nomogram was found to fit the ideal curve well,and in Hosmer-Lemeshow test P=0.412, the clinical decision curve showed a better net benefit. Conclusion The preoperative inflammatory indicators and clinicopathological features are associated with MMR in gastric cancer; maximum tumor diameter, tumor location, tumor differentiation, CA199, and FAR are the independent predictors of dMMR. The prediction model based on the above predictors could predict the MMR status of the dMMR gastric cancer patients.

Key words: Stomach neoplasm, Deficient mismatch repair, Microsatellite instability, Inflammatory indicator, Prediction model

CLC Number: 

  • R735.2