GAL3ST4

Last update 08 May 2025

Basic Info

Synonyms

Beta-galactose-3-O-sulfotransferase 4, FLJ12116, Gal-beta-1,3-GalNAc 3'-sulfotransferase

+ [3]

Introduction

Catalyzes the transfer of sulfate to beta-1,3-linked galactose residues in O-linked glycoproteins. Good substrates include asialofetuin, Gal-beta-1,3-GalNAc and Gal-beta-1,3 (GlcNAc-beta-1,6)GalNAc.

100 Clinical Results associated with GAL3ST4

100 Translational Medicine associated with GAL3ST4

0 Patents (Medical) associated with GAL3ST4

Literatures (Medical) associated with GAL3ST4

01 Jan 2025·Gene

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Article

Author: Wang, Wen ; Jiao, Yunjia ; Du, Xiaojing ; Ye, Zhaoming

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

01 Feb 2022·Journal of Biological ChemistryQ2 · BIOLOGY

Installation of O-glycan sulfation capacities in human HEK293 cells for display of sulfated mucins

Q2 · BIOLOGY

Article

Author: Yang, Zhang ; Sun, Lingbo ; Ye, Zilu ; Konstantinidi, Andriana ; Vakhrushev, Sergey Y ; Clausen, Henrik ; Zhang, Yuecheng ; Leffler, Hakon ; Büll, Christian ; Kahl-Knutson, Barbro ; Narimatsu, Yoshiki ; Hansen, Lars ; Nason, Rebecca

The human genome contains at least 35 genes that encode Golgi sulfotransferases that function in the secretory pathway, where they are involved in decorating glycosaminoglycans, glycolipids, and glycoproteins with sulfate groups. Although a number of important interactions by proteins such as selectins, galectins, and sialic acid-binding immunoglobulin-like lectins are thought to mainly rely on sulfated O-glycans, our insight into the sulfotransferases that modify these glycoproteins, and in particular GalNAc-type O-glycoproteins, is limited. Moreover, sulfated mucins appear to accumulate in respiratory diseases, arthritis, and cancer. To explore further the genetic and biosynthetic regulation of sulfated O-glycans, here we expanded a cell-based glycan array in the human embryonic kidney 293 (HEK293) cell line with sulfation capacities. We stably engineered O-glycan sulfation capacities in HEK293 cells by site-directed knockin of sulfotransferase genes in combination with knockout of genes to eliminate endogenous O-glycan branching (core2 synthase gene GCNT1) and/or sialylation capacities in order to provide simplified substrates (core1 Galβ1-3GalNAcα1-O-Ser/Thr) for the introduced sulfotransferases. Expression of the galactose 3-O-sulfotransferase 2 in HEK293 cells resulted in sulfation of core1 and core2 O-glycans, whereas expression of galactose 3-O-sulfotransferase 4 resulted in sulfation of core1 only. We used the engineered cell library to dissect the binding specificity of galectin-4 and confirmed binding to the 3-O-sulfo-core1 O-glycan. This is a first step toward expanding the emerging cell-based glycan arrays with the important sulfation modification for display and production of glycoconjugates with sulfated O-glycans.

01 Jan 2022·Frontiers in genetics

A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

Article

Author: Li, Shihao ; Lou, Zheng ; Xu, Jun ; Zhang, Wei ; Lai, Yi ; Hong, Yonggang ; Meng, Ronggui ; Xin, Cheng ; Hao, Liqiang ; Ji, Liqiang ; Wang, Ye

Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC.Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature.Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis.Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.

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GAL3ST4

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