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

Xiuzhen WEI, Yaling DONG, Zhibo ZHU, Zhengjie ZHANG, Yuanjun TAN, Jie BAI, Xiayi SU, Baihong ZHANG

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J Jilin Univ Med Ed ›› 2025, Vol. 51 ›› Issue (1) : 172-181. DOI: 10.13481/j.1671-587X.20250121
Research in clinical medicine

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

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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

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Xiuzhen WEI , Yaling DONG , Zhibo ZHU , et al . Construction of prediction model for gastric cancer mismatch repair based on preoperative inflammatory indicators and clinicopathological features in gastric cancer patients. Journal of Jilin University(Medicine Edition). 2025, 51(1): 172-181 https://doi.org/10.13481/j.1671-587X.20250121

References

1
ZHENG R S CHEN R HAN B F, et al. Cancer incidence and mortality in China, 2022[J]. Zhonghua Zhong Liu Za Zhi202446(3): 221-231.
2
CHIA N Y TAN P. Molecular classification of gastric cancer[J]. Ann Oncol201627(5): 763-769.
3
DAGHER O K SCHWAB R D BROOKENS S K, et al. Advances in cancer immunotherapies[J]. Cell2023186(8): 1814-1814.e1.
4
CHAO J FUCHS C S SHITARA K, et al. Assessment of pembrolizumab therapy for the treatment of microsatellite instability-high gastric or gastroesophageal junction cancer among patients in the KEYNOTE-059, KEYNOTE-061, and KEYNOTE-062 clinical trials[J]. JAMA Oncol20217(6): 895-902.
5
ANDRÉ T BERTON D CURIGLIANO G, et al. Antitumor activity and safety of dostarlimab monotherapy in patients with mismatch repair deficient solid tumors: a nonrandomized controlled trial[J]. JAMA Netw Open20236(11): e2341165.
6
ZHAN P C YANG S LIU X, et al. A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study[J]. BMC Cancer202424(1):404.
7
SILVA J R MASCARENHAS-LEMOS L NETO D N C, et al. Role of endoscopic biopsies and morphologic features in predicting microsatellite instability status in gastric cancer: A multicenter comparative study of endoscopic biopsies and surgical specimens[J]. Am J Surg Pathol202347(9):990-1000.
8
OZER M VEGIVINTI C T R SYED M, et al. Neoadjuvant immunotherapy for patients with dMMR/MSI-high gastrointestinal cancers: a changing paradigm[J]. Cancers202315(15): 3833.
9
GUAN W L HE Y XU R H. Gastric cancer treatment: recent progress and future perspectives[J]. J Hematol Oncol202316(1): 57.
10
WANG J Y XIU J FARRELL A, et al. Mutational analysis of microsatellite-stable gastrointestinal cancer with high tumour mutational burden: a retrospective cohort study[J]. Lancet Oncol202324(2): 151-161.
11
SHITARA K VAN CUTSEM E BANG Y J, et al. Efficacy and safety of pembrolizumab or pembrolizumab plus chemotherapy vs chemotherapy alone for patients with first-line, advanced gastric cancer: the KEYNOTE-062 phase 3 randomized clinical trial[J]. JAMA Oncol20206(10): 1571-1580.
12
JANJIGIAN Y Y SHITARA K MOEHLER M, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial[J]. Lancet2021398(10294): 27-40.
13
AJANI J A D’AMICO T A BENTREM D J, et al. Gastric cancer, version 2.2022, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw202220(2): 167-192.
14
HANAHAN D WEINBERG R A. Hallmarks of cancer: the next generation[J]. Cell2011144(5): 646-674.
15
HANAHAN D. Hallmarks of cancer: new dimensions[J]. Cancer Discov202212(1): 31-46.
16
WANG F H ZHANG X T LI Y F, et al. The Chinese society of clinical oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer, 2021[J]. Cancer Commun (Lond)202141(8): 747-795.
17
ZHAO F X LI E X SHEN G S, et al. Correlation between mismatch repair and survival of patients with gastric cancer after 5-FU-based adjuvant chemotherapy[J]. J Gastroenterol202358(7): 622-632.
18
MESTRALLET G BROWN M BOZKUS C C, et al. Immune escape and resistance to immunotherapy in mismatch repair deficient tumors[J]. Front Immunol202314: 1210164.
19
杨军, 徐志杰, 朱卫东, 等. 微卫星不稳定性(MSI)检测技术专家共识[J]. 临床与实验病理学杂志202440(3): 228-235.
20
CUI M Y LI P MAO Y, et al. Implication of microsatellite instability in Chinese cohort of human cancers[J]. Cancer Manag Res202012: 10287-10295.
21
ZHU Y J WANG P WANG B Z, et al. Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer[J]. Insights Imaging202314(1): 151.
22
CHEN S DU W Z CAO Y H, et al. Preoperative contrast-enhanced CT imaging and clinicopathological characteristics analysis of mismatch repair-deficient colorectal cancer[J]. Cancer Imaging202323(1): 97.
23
BELKOUCHI Y NEBOT-BRAL L LAWRANCE L, et al. Predicting immunotherapy outcomes in patients with MSI tumors using NLR and CT global tumor volume[J]. Front Oncol202212: 982790.

魏秀珍和张百红参与实验的整体设计及论文撰写,董亚玲、朱志博、张政杰和谈元郡参与文献检索、数据收集及数据整理,白洁和苏夏艺参与论文的统计学分析及论文修改。

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