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.

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