Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data. AIM: To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients. METHODS: Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model. RESULTS: More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility. CONCLUSION: This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.

CircRNA_100290 promotes colorectal cancer progression through miR-516b-induced downregulation of FZD4 expression and Wnt/ß-catenin signaling.

Accumulating evidence indicates that circular RNAs (circRNA) exert crucial functions in the development and advance of cancers. CircRNA_100290 has been reported to promote proliferation in oral cancer. However, whether it participates in colorectal cancer (CRC) remains unclear. Here, our report showed that circRNA_100290 level was significantly increased in CRC tissues and cell lines. Besides, circRNA_100290 expression was positively correlated with tumor metastasis while inversely correlated with prognosis. Silencing circRNA_100290 markedly reduced cell proliferation rate, inhibited migration and invasion abilities, but promoted apoptosis in vitro. Mechanistically, our data revealed circRNA_100290 was a competing endogenous RNA (ceRNA) of FZD4 by sponging miR-516b, leading to activation of Wnt/ß-catenin pathway. Rescue assay indicated that FZD4-induced activation of ß-catenin pathway is indispensable for the function of circRNA_100290 in CRC. In summary, our study for the first time revealed a novel regulatory loop of circRNA_100290/miR-516b/FZD4/Wnt/ß-catenin implicated in CRC progression.