Differential analysis of urinary stable downregulated proteins in male patients after colorectal cancer surgery.

Colorectal cancer is a common malignant tumor that threatens men's health, and there are currently ineffective biomarkers for noninvasive monitoring of postoperative tumor changes in clinical practice. Urine is an easily accessible sample that can reveal physiological and pathological changes in the body. To identify specific proteins stably downregulated postoperatively and thereby provide novel potential biomarkers for colorectal cancer following surgery monitoring, this study analyzed urinary proteomic changes in male colorectal cancer patients before and after surgery. This study included 56 male colorectal cancer patients, separated into three groups according on urine sample collection timing: preoperative, postoperative 4 ± 1 day, and postoperative≥8 days. Label-free quantitative proteomics was used to examine urine protein expression profiles. Bioinformatics research revealed differently expressed proteins with a consistent decrease trend postoperatively. Their clinical diagnostic efficacy was assessed using correlation analysis and receiver operating characteristic (ROC) curves. This study identified 10 proteins exhibiting a stable downward trend post-surgery through urinary proteomics analysis. Correlation analysis revealed a significant positive correlation between PSMA7 and IDH1 (P < 0.05), as well as between RPS3 and RACK1 (P < 0.05). The diagnostic performance of the four proteins (PSMA7, IDH1, RACK1, RPS3) was 0.888, 0.831, 0.823, and 0.805, respectively (all P < 0.05). The combined diagnostic model achieved an AUC of 0.912. The urinary levels of IDH1, PSMA7, RPS3, and RACK1 demonstrated stable downregulation across postoperative stages with certain diagnostic efficacy, and these proteins hold promise as candidate novel non-invasive biomarkers for auxiliary monitoring of urinary proteomic changes in male colorectal cancer patients following surgery. However, their clinical utility warrants further validation in large-scale independent cohorts.