A propensity score-adjusted analysis of the effects of ubiquitin E3 ligase copy number variation in peripheral blood leukocytes on colorectal cancer risk

Abstract

Background: The ubiquitin ligases E3 (E3s) plays a key role in the specific protein degradation in many carcinogenic biological processes. Colorectal cancer (CRC) development may be affected by the copy number variation (CNV) of E3s. Prior studies may have underestimated the impact of potential confounding factors' effects on the association between gene CNV and CRC risk, and CRC risk predictive model integrating gene CNV patterns is lacking. Our research sought to assess the genes CNVs of MDM2, SKP2, FBXW7, β-TRCP, and NEDD4-1 and CRC risk by using propensity score (PS) adjustment and developing models that integrate CNV patterns for CRC risk predictions. Methods: This study comprising 1036 participants used traditional regression and different PS techniques to adjust the confounding factors to evaluate the relationships between five gene CNVs and CRC risk, and to establish a CRC risk predictive model. The AUC was applied to evaluate the effect of the model. The categorical net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were analyzed to evaluate the discriminatory accuracy improvement among the models. Results: Compared to variable adjustment, the odds ratios (ORs) tended to be conservative and accurate with narrow confidence intervals (CIs) after PS adjustment. After PS adjustment, MDM2 amplification was related to increased CRC risk (Amp-pattern: OR = 8.684, 95% CI: 1.213-62.155, P = 0.031), whereas SKP2 deletion and the (del+amp) genotype were associated with reduced CRC risk (Del-pattern: OR = 0.323, 95% CI: 0.106-0.979, P = 0.046; Var-pattern: OR = 0.339, 95% CI: 0.135-0.854, P = 0.024). The predictive model integrating the gene CNV pattern could correctly reclassify 1.7% of the subjects. Conclusions: MDM2 amplification and SKP2 CNVs are associated with increased and decreased CRC risk, respectively; abnormal CNV-integrated model is more precise for predicting CRC risk. Further studies are needed to verify these encouraging outcomes.