Emerging Glyco-risk Prediction Model to Forecast Response to Immune Checkpoint Inhibitors in Colorectal Cancer

Background: Aberrant glycosylation is one of the most common post-translational modifications leading to heterogeneity in colorectal cancer (CRC). This study aims to construct a risk prediction model based on glycosyltransferase to forecast the response to immune checkpoint inhibitors in CRC patients.

Methods: Based on the TCGA dataset and glycosyltransferase genes, the NMF algorithm and WGCNA were used to identify molecular subtypes and co-expressed genes, respectively. Lasso and multivariate COX regression were used to identify prognostic glycosyltransferase genes and construct a glyco-risk prediction model in CRC patients. Univariate and multivariate Cox regression, Kaplan-Meier, and ROC curves were applied to further verify the prognostic performance of the model in CRC patients in the training and validation sets. We compared the responsiveness of immunotherapy and chemotherapy between the two groups. In vitro experiments and clinical specimens verified the specific function of the key glycosyltransferase genes in CRC.

Results: The CRC cohort was divided into two subtypes with prominent differences in survival based on the well-robust seven-gene glyco-risk prediction model (composed of ALG1L2, HAS1, PYGL, COLGALT2, B3GNT4, POFUT2, and GALNT7). The nomograms based on the risk model could predict the prognosis of CRC patients independently of other clinicopathologic characteristics. Our prediction model showed a better overall prediction performance than other models. Compared with the low-risk group, the high-risk CRC patients showed a lower immune infiltration state, but a higher TMB and a lower response to anti-PD-1, anti-PD-L1, and anti-CTLA-4 therapy. Clinical specimen validation showed an obvious difference in the expression of seven glycosyltransferase genes between the low- and high-risk groups. Significant reduction in POFUT2 expression in high-risk groups was associated with reduced N-glycans production.

Conclusion: Our study constructed a robust glyco-risk prediction model that could provide direction for immunotherapy and chemotherapy in CRC patients, which could help clinicians make personalized treatment decisions.