Comprehensive Analysis of Sialylation-Related Signature and Development of a Novel Prognostic Model in Diffuse Large B-Cell Lymphoma

Sialylation, a form of glycosylation modification, represents a novel epigenetic alteration mediated by sialyltransferases on terminal sialic acid residues. Aberrant sialylation, including α2,3-sialylation, α2,6-sialylation and α2,8-sialylation, significantly facilitates tumor invasion, migration, and immune evasion. However, the role of sialylation in diffuse large B-cell lymphoma (DLBCL) remains undefined. Herein, we aimed to investigate the sialylation-related signature in DLBCL as well as the prognostic value, and identify the involved immune infiltration features associated with sialylation.

We first assessed the expression profile of sialylation-related genes (SRGs) by analyzing RNA sequencing data of DLBCL patients. The results indicated that mRNA expression levels of SRGs were significantly dysregulated in DLBCL. Subsequently, we evaluated the clinical significance of these SRGs in DLBCL. 15 genes exhibited significant associations with overall survival (OS) in DLBCL patients based on univariate Cox regression analysis. Among them, NANP, ST6GALNAC5, and ST8SIA1 were selected for the risk model based on the minimum criteria of the LASSO Cox regression. This model allowed us to categorize DLBCL patients into high-risk and low-risk groups. Notably, the low-risk group exhibited lower mortality rates and longer survival times compared to the high-risk group. Furthermore, DLBCL patients with high risks experienced significantly shorter OS (P < 0.001). The excellent predictive performance of this model was confirmed by receiver operating characteristic (ROC) curves.

To validate the expression levels of SRGs in DLBCL, we conducted quantitative reverse transcription polymerase chain reaction and western blot analysis on the above three SRGs in DLBCL cell lines. Our results indicated down-regulation of ST6GALNAC5 and ST8SIA1, while NANP was significantly up-regulated in DLBCL cell lines compared to normal samples. Overall, these findings underscore the outstanding predictive efficacy and superiority of this model.

It was reported that in the tumor microenvironment, sialylation contributed to immune suppression by engaging immunoregulatory Siglecs on infiltrating immune cells. We further analyzed the immune status of different risk groups using the ESTIMATE, ssGSEA, and CIBERSORT. The low-risk group showed increased activity immune infiltration and immune checkpoint pathways compared to the high-risk group. The risk scores corresponded with the immune profile, and the elevated immune activity might contribute to the antitumor effect of DLBCL. These findings further support the critical role of SRGs in immune regulation within DLBCL.

In summary, our study identified for the first time that sialylation is closely associated with the development and progression of DLBCL. A comprehensive prognostic model was developed based on the characteristics of SRGs in DLBCL, achieving accurate prognosis prediction for DLBCL patients. Importantly, targeting sialylation in conjunction with immunotherapies holds promise as an alternative treatment strategy for DLBCL. These findings offer potential avenues for identifying high-risk population and establishing individualized treatment in DLBCL.

No relevant conflicts of interest to declare.