In this study, we performed a retrospective analysis on 312 PAAD patients to illustrate the potential association of B3GNT3 and PAAD. As a result, we observed that B3GNT3 expression differed in normal and tumor tissues of PAAD. The expression of B3GNT3 was obviously up-regulated in tumor tissues compared with normal tissues both in TCGA and GEO datasets. Meanwhile, increased B3GNT3 expression significantly correlated with histologic grade and unfavorable prognosis. And the multivariate cox regression analyses suggested that B3GNT3 expression was an independent prognostic factor for PAAD patients. According to our knowledge, there was limited reports concerning the impact of B3GNT3 on PAAD.
O-glycosylation is one of the major post-translational modifications of proteins. O-glycans was not only presented on the surface of normal cells, but also the tumors. Prior reports have well studied that O-glycans participated in various biological and cellular signaling, including immune modulation, signal transduction, protein folding, tumorigenesis, etc.[20–23]. As an O-glycosyltransferases, B3GNT3 should transfer donor substrate sugars via a β1,3-linkage and get involved in the development and progression of tumors. In 2018, a study indicated that elevated B3GNT3 expression played an important role in the maintenance of stemness in pancreatic cancer stem cells (PCSCs). Based on molecular and functional assays, the results revealed that B3GNT3 could modulate CSC markers including CD44v6, ESA, SOX2 and OCT3/4 and influence the self-renewal potential of PCSCs. But, a later study from their group, regarding glycosyltransferases to PAAD pathogenesis, implicated that B3GNT3 negatively regulated the proliferation, migration and stem cell markers in vitro. Therefore, B3GNT3 likely acted as a tumor-promoting role but the clear mechanisms were still unknown. Interestingly, as an O-glycosyltransferases, the single nucleotide polymer-phisms (SNPs) of B3GNT3 also might affect levels of circulating tumor biomarkers including CA19-9, CEA and AFP. By SNP-defined ranges for each tumor marker, diagnostic specificity and sensitivity of CA19-9 were improved in the identification of patients with PAAD[26, 27]. As is known, CA19-9 shows many shortcomings of unsatisfactory diagnostic accuracy in the routine management of PAAD. And about 10–15 % of PAAD patients do not secrete CA19-9 due to their Lewis antigen status. Hence, B3GNT3 may be a potential biomarker and when in combination with CA19-9 might provide additional information for the screening and diagnosis of PAAD.
The tumor microenvironment (TME) consists of various cell types and extracellular component, which densely surrounds tumor cells. Tumor infiltrating immune cells (TIICs) are important components of TME and mainly include T cells, B cells, macrophages, dendritic cells (DCs), and natural killer (NK) cells, etc. TIICs play an important role in tumor initiation, progression and metastasis by regulating antitumor immune responses and remodeling an immunosuppressive microenvironment. Surrounded by densely immunosuppressive micro-environment, tumor cells finally escaped from immune surveillance and proliferated rapidly. Although the underlying mechanisms remain indefinite, evidence has shown that TILs proportion was a positive prognostic factor in various tumors[28, 29]. Based on RNA expression analyzed by CIBERSORT, one study indicated that higher levels of activated memory CD4 + T cells, activated mast cells and activated NK cells were associated with favorable overall survival in cervical cancer. PAAD is characterized by the dense fibrotic tumor stroma and the immunosuppressive microenvironment, which inhibit the infiltration of immune cells and make common immune therapy ineffectiveness. Hence, PAAD is thought to be immune-quiescent. Increasing evidence has suggested that tumor-infiltrating immune cells were positively correlated with long-term survival. As well as TNM classification system, quantification of TIICs may be a possible tool for additional immunological subtype classification in PAAD patients[32, 19]. In our study, we also analyzed the correlation between B3GNT3 expression and TIICs profiles in PAAD using CIBERSORT method and the “correlation” module of GEPIA. Our results revealed that there existed a negatively correlation between B3GNT3 expression level and infiltrating immune cells level. In addition, the “correlation” module analysis furtherly implicated the negative correlation between B3GNT3 expression and the multiple immune marker genes. Our results implicated that high B3GNT3 might lead to poor prognosis by hampering immune cells infiltrating from circulation into tumors. However, the possible molecular mechanism is unclear and further study are needed to address this issue.
According to functional enrichment analysis, B3GNT3 was mainly involved in various molecular signaling pathways including pathways in cancer, p53 signaling pathway, TGF beta signaling pathway, etc. Catabolic and transport processes of proteins were also enriched. Besides, ubiquitination and adheres were related to the B3GNT3 signature. All these biological functions were reported to be vital in the tumorigenesis and progression of PAAD[33–35]. Hence, biological analysis revealed B3GNT3 promoting the development of PAAD through multiple pathways.
Although our study found successfully the close relationship between B3GNT3 expression and PAAD, some limitations of this study should be acknowledged. Firstly, both TCGA and GEO are powerful databases with a comprehensive landscape of genomic alterations. But some of the shortcomings should not be ignored: (1) The sample sizes of PAAD are relatively small in TCGA and GEO dataset. For example, there were only seven cases of stage Ⅲ and Ⅳ patients in TCGA, which might lead to statistical errors in analyzing the correlation between B3GNT3 expression and stage. (2) Corresponding clinical information of PAAD patients is inadequate in GEO, which makes it impossible to analyze effect of B3GNT3 on clinicopathological factors. Secondly, our results were based on high-throughput RNA‐sequencing profiles and computational algorithm. Although the practicability and accuracy of this method has been testified by many studies, further experiments in vivo and vitro are necessary to verify the results in the future.