Characteristics and clinical predictive value of gut microbiota and metabolites related to neoadjuvant chemotherapy effect in patients with colorectal cancer liver metastases
10.3760/cma.j.cn114452-20240105-00007
- VernacularTitle:结直肠癌肝转移患者新辅助化疗效果相关的肠道菌群和代谢物特征及其临床预测价值
- Author:
Xinya ZHANG
1
;
Yifan WANG
;
Jinming LI
;
Shujun ZHANG
;
Peilong LI
;
Chuanxin WANG
;
Lutao DU
Author Information
1. 山东大学第二医院检验医学中心，济南 250033
- Keywords:
Colorectal cancer;
Liver metastasis;
Metagenome;
Metabonomics;
Neoadjuvant chemotherapy;
Efficacy prediction
- From:
Chinese Journal of Laboratory Medicine
2024;47(7):779-788
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To screen the characteristic gut microbiota and fecal metabolites related to the efficacy of oxaliplatin-based neoadjuvant chemotherapy in patients with colorectal cancer liver metastasis, to analyze the relationship between gut microbiota and fecal metabolites, and to evaluate the predictive value of relevant markers for the efficacy of neoadjuvant chemotherapy in patients with colorectal cancer liver metastasis.Methods:This is a case-control study, 34 patients with colorectal cancer liver metastasis who were treated in Qilu Hospital of Shandong University from October 2021 to July 2022 were selected as the research objects, and were divided into chemotherapy effective group (20 cases) and chemotherapy ineffective group (14 cases) according to the efficacy evaluation criteria. Logistic regression was used to construct a prediction model to screen the microbiota and metabolic markers capable of predicting the effect of chemotherapy, and the receiver operating characteristic (ROC) curve and survival analysis curve were plotted to evaluate the predictive effect of related microbiota and metabolites on the efficacy of neoadjuvant chemotherapy.Results:There was no significant difference in the α and β diversity of gut microbiota between the patients in the chemotherapy effective group and in the ineffective group (all P>0.05). In terms of species, the relative abundance of 5 species was up-regulated and 10 species were down-regulated in the chemotherapy-effective group compared with the chemotherapy-ineffective group, and the difference was statistically significant (all P<0.05), among which Prevotella salivae could effectively predict the chemotherapy effect (AUC=0.750, P=0.007), with a sensitivity of 80.0% and a specificity of 71.4%. The overall survival of patients with high abundance (17 cases) was lower than that of patients with low abundance (17 cases) ( χ 2=5.239, P=0.022). In terms of metabolites, 20 metabolites were up-regulated and 4 metabolites were down-regulated in the chemotherapy-effective group compared with the chemotherapy-ineffective group, and the difference was statistically significant (all P<0.05), among which threonine and prostaglandin F2α-1-ethanolamide could distinguish between patients who responded to chemotherapy and those who did not respond to chemotherapy (AUC=0.743, 0.707, all P<0.05), and the overall survival of patients with high levels of relative abundance (17 cases) was higher than that of patients with low levels (17 cases) ( χ 2=4.748, 5.407, all P<0.05). The Logistic regression model of Prevotella salivae and prostaglandin F2α-1-ethanolamide was obtained through screening analysis, and the ROC curve results showed that the model had a good predictive value (AUC=0.836, sensitivity: 90.0%, specificity: 78.6%), and the overall survival of patients with high predict probability (17 cases) predicted by the model was higher than that of patients with low predict probability (17 cases) ( χ 2=9.260, P=0.002). Conclusion:Prevotella salivae and prostaglandin F2α-1-ethanolamide can be used as predictive biomarkers of neoadjuvant chemotherapy for colorectal cancer liver metastasis, and the model has good clinical reference value for prognosis assessment of patients in this cohort.
