Nomogram model for prognosis of early colorectal cancer after endoscopic therapy: integration of intestinal microbiota and clinicopathological parameters - Zhu, Yanli; Yang, Fang; Meng, Lijun; Zhang, Xiaoling; Chang, Yongsheng; Zhang, Lanfang (Frontiers in Oncology, 2025)
ObjectiveTo integrate the characteristics of intestinal flora and clinical pathological parameters in patients with early colorectal cancer, and to construct and validate a nomogram model for predicting the prognosis of endoscopic therapy.MethodsThe data of 80 patients with early colorectal cancer receiving endoscopic treatment from January 2019 to June 2024 were retrospectively collected. They were randomly divided into a training set (n = 56) and a validation set (n = 24) at a ratio of 7:3. The factors related to prognosis were screened by univariate analysis and multivariate Logistic regression analysis regression, so as to construct Nomogram model, calculate C-index, and draw the calibration curve. The clinical application value of the model was evaluated using decision curve analysis (DCA).ResultsThere was no significant difference in the incidence of poor prognosis, intestinal flora, and clinical pathological parameters between the training set and the validation set (all P>0.05). Multivariate Logistic regression analysis showed that tumor diameter, Shannon-Wiener index, relative abundance of Fusobacterium nucleatum, relative abundance of Bacteroides fragilis, relative abundance of Bifidobacterium, and relative abundance of Lactobacilli were the independent influencing factors for poor prognosis of endoscopic therapy (all P < 0.05). The nomogram prediction model was further constructed, and the nomogram model had good calibration and fitting between prediction and reality in the training set and the validation set. ROC curves were shown in the training set and the validation set; AUC of the nomogram model for predicting the prognosis of endoscopic therapy was 0.979(95% CI: 0. 946-1.000) and 0. 821(95% CI: 0.516-1.000).ConclusionNomogram model based on intestinal flora and clinical pathological parameters can effectively predict the prognosis of early colorectal cancer patients after endoscopic treatment with good accuracy and reliability, which is expected to provide an important reference for the development of clinical individualized treatment plan and guide the accurate treatment and management of patients.