B4GALT3 promotes cell proliferation and invasion in glioblastoma

Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives

Glioblastoma (GBM), the predominant and primary malignant intracranial tumor, poses a formidable challenge due to its immunosuppressive microenvironment, thereby confounding conventional therapeutic interventions. Despite the established treatment regimen comprising surgical intervention, radiotherapy, temozolomide administration, and the exploration of emerging modalities such as immunotherapy and integration of medicine and engineering technology therapy, the efficacy of these approaches remains constrained, resulting in suboptimal prognostic outcomes. In recent years, intensive scrutiny of the inhibitory and immunosuppressive milieu within GBM has underscored the significance of cellular constituents of the GBM microenvironment and their interactions with malignant cells and neurons. Novel immune and targeted therapy strategies have emerged, offering promising avenues for advancing GBM treatment. One pivotal mechanism orchestrating immunosuppression in GBM involves the aggregation of myeloid-derived suppressor cells (MDSCs), glioma-associated macrophage/microglia (GAM), and regulatory T cells (Tregs). Among these, MDSCs, though constituting a minority (4-8%) of CD45+ cells in GBM, play a central component in fostering immune evasion and propelling tumor progression, angiogenesis, invasion, and metastasis. MDSCs deploy intricate immunosuppressive mechanisms that adapt to the dynamic tumor microenvironment (TME). Understanding the interplay between GBM and MDSCs provides a compelling basis for therapeutic interventions. This review seeks to elucidate the immune regulatory mechanisms inherent in the GBM microenvironment, explore existing therapeutic targets, and consolidate recent insights into MDSC induction and their contribution to GBM immunosuppression. Additionally, the review comprehensively surveys ongoing clinical trials and potential treatment strategies, envisioning a future where targeting MDSCs could reshape the immune landscape of GBM. Through the synergistic integration of immunotherapy with other therapeutic modalities, this approach can establish a multidisciplinary, multi-target paradigm, ultimately improving the prognosis and quality of life in patients with GBM.

Development and validation of glycosyltransferase related-gene for the diagnosis and prognosis of head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous cancer characterized by difficulties in early diagnosis and outcome prediction. Aberrant glycosylated structures produced by the aberrant expression of glycosyltransferases are prevalent in HNSCC. In this study, we aim to construct glycosyltransferase-related gene signatures with diagnostic and prognostic value to better stratify patients with HNSCC and improve their diagnosis and prognosis.

METHODS

Bioinformatic tools were used to process data of patients with HNSCC from The Cancer Genome Atlas (TCGA) database. The prognostic model was formatted using univariate and multivariate Cox regression methods, while the diagnostic signature was constructed using support vector machine (SVM) and LASSO analysis. The results were verified using the Gene Expression Omnibus (GEO) cohort. The tumor microenvironment and benefits of immune checkpoint inhibitor (ICI) therapy in subgroups defined by glycosyltransferase-related genes were analyzed. Molecular biology experiments, including western blotting, cell counting kit (CCK)-8, colony formation, wound healing, and Transwell assays, were conducted to confirm the oncogenic function of beta-1,4-galactosyltransferase 3 (B4GALT3) in HNSCC.

RESULTS

We established a five-gene prognostic signature and a 15-gene diagnostic model. Based on the median risk score, patients with low risk had longer overall survival than those in the high-risk group, which was consistent with the results of the GEO cohort. The concrete results suggested that high-risk samples were related to a high tumor protein (TP)53 mutation rate, high infiltration of resting memory cluster of differentiation (CD)4 T cells, resting natural killer (NK) cells, and M0 macrophages, and benefited from ICI therapy. In contrast, the low-risk subgroup was associated with a low TP53 mutation rate; and high infiltration of naive B cells, plasma cells, CD8 T cells, and resting mast cells; and benefited less from ICI therapy. In addition, the diagnostic model had an area under curve (AUC) value of 0.997 and 0.978 in the training dataset and validation cohort, respectively, indicating the high diagnostic potential of the model. Ultimately, the depletion of B4GALT3 significantly hindered the proliferation, migration, and invasion of HNSCC cells.

CONCLUSIONS

We established two new biomarkers that could provide clinicians with diagnostic, prognostic, and treatment guidance for patients with HNSCC.

Beta-1,4-galactosyltransferase-3 deficiency suppresses the growth of immunogenic tumors in mice

Background

Beta-1,4-galactosyltransferase-3 (B4GALT3) belongs to the family of beta-1,4-galactosyltransferases (B4GALTs) and is responsible for the transfer of UDP-galactose to terminal N-acetylglucosamine. B4GALT3 is differentially expressed in tumors and adjacent normal tissues, and is correlated with clinical prognosis in several cancers, including neuroblastoma, cervical cancer, and bladder cancer. However, the exact role of B4GALT3 in the tumor immune microenvironment (TIME) remains unclear. Here, we aimed to elucidate the function of B4GALT3 in the TIME.

Methods

To study the functions of B4GALT3 in cancer immunity, either weakly or strongly immunogenic tumor cells were subcutaneously transplanted into wild-type (WT) and B4galt3 knockout (KO) mice. Bone marrow transplantation and CD8+ T cell depletion experiments were conducted to elucidate the role of immune cells in suppressing tumor growth in B4galt3 KO mice. The cell types and gene expression in the tumor region and infiltrating CD8+ T cells were analyzed using flow cytometry and RNA sequencing. N-glycosylated proteins from WT and B4galt3 KO mice were compared using the liquid chromatography tandem mass spectrometry (LC-MS/MS)-based glycoproteomic approach.

Results

B4galt3 KO mice exhibited suppressed growth of strongly immunogenic tumors with a notable increase in CD8+ T cell infiltration within tumors. Notably, B4galt3 deficiency led to changes in N-glycan modification of several proteins, including integrin alpha L (ITGAL), involved in T cell activity and proliferation. In vitro experiments suggested that B4galt3 KO CD8+ T cells were more susceptible to activation and displayed increased downstream phosphorylation of FAK linked to ITGAL.

Conclusion

Our study demonstrates that B4galt3 deficiency can potentially boost anti-tumor immune responses, largely through enhancing the influx of CD8+ T cells. B4GALT3 might be suppressing cancer immunity by synthesizing the glycan structure of molecules on the CD8+ T cell surface, as evidenced by the changes in the glycan structure of ITGAL in immune cells. Importantly, B4galt3 KO mice showed no adverse effects on growth, development, or reproduction, underscoring the potential of B4GALT3 as a promising and safe therapeutic target for cancer treatment.

Construction and validation of a prognostic marker and risk model for HCC ultrasound therapy combined with WGCNA identification

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with a highly aggressive and metastatic nature. Ultrasound remains a routine monitoring tool for screening, treatment and post-treatment recheck of HCC. Therefore, it is of great significance to explore the role of ultrasound therapy and related genes in prognosis prediction and clinical diagnosis and treatment of HCC.

Methods: Gene co-expression networks were developed utilizing the R package WGCNA as per the expression profiles and clinical features of TCGA HCC samples, key modules were identified by the correlation coefficients between clinical features and modules, and hub genes of modules were determined as per the GS and MM values. Ultrasound treatment differential expression genes were identified using R package limma, and univariate Cox analysis was conducted on the intersection genes of ultrasound differential expression genes and hub genes of key HCC modules to screen the signatures linked with HCC prognosis and construct a risk model. The median risk score was used as the threshold point to classify tumor samples into high- and low-risk groups, and the R package IOBR was used to assess the proportion of immune cells in high- and low-risk groups, R package maftools to assess the genomic mutation differences in high- and low-risk groups, R package GSVA’s ssgsea algorithm to assess the HALLMARK pathway enrichment analysis, and R package pRRophetic to analyze drug sensitivity in patients with HCC.

Results: WGCNA analysis based on the expression profiles and clinical data of the TCGA LIHC cohort identified three key modules with two major clinical features associated with HCC. The intersection of ultrasound-related differential genes and module hub genes was selected for univariate Cox analysis to identify prognostic factors significantly associated with HCC, and a risk score model consisting of six signatures was finally developed to analyze the prognosis of individuals with HCC. The risk model showed strength in the training set, overall set, and external validation set. The percentage of immune cell infiltration, genomic mutations, pathway enrichment scores, and chemotherapy drug resistance were significantly different between high- and low-risk groups according to the risk scores. Expression of model genes correlated with tumor immune microenvironment and clinical tumor characteristics while generally differentially expressed in pan-cancer tumor and healthy samples. In the immunotherapy dataset, patients in the high-risk group had a worse prognosis with immunotherapy, indicating that subjects in the low-risk group are more responsive to immunotherapy.

Conclusion: The 6-gene signature constructed by ultrasound treatment of HCC combined with WGCNA analysis can be used for prognosis prediction of HCC patients and may become a marker for immune response.

An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment

Glioblastoma multiforme (GBM) is the most aggressive and common malignant primary brain tumor. Patients with GBM often have poor prognoses, with a median survival of ∼15 months. Enhanced understanding of the molecular biology of central nervous system tumors has led to modifications in their classifications, the most recent of which classified these tumors into new categories and made some changes in their nomenclature and grading system. This review aims to give a panoramic view of the last 3 years' findings in glioblastoma characterization, its heterogeneity, and current advances in its treatment. Several molecular parameters have been used to achieve an accurate and personalized characterization of glioblastoma in patients, including epigenetic, genetic, transcriptomic and metabolic features, as well as age- and sex-related patterns and the involvement of several noncoding RNAs in glioblastoma progression. Astrocyte-like neural stem cells and outer radial glial-like cells from the subventricular zone have been proposed as agents involved in GBM of IDH-wildtype origin, but this remains controversial. Glioblastoma metabolism is characterized by upregulation of the PI3K/Akt/mTOR signaling pathway, promotion of the glycolytic flux, maintenance of lipid storage, and other features. This metabolism also contributes to glioblastoma's resistance to conventional therapies. Tumor heterogeneity, a hallmark of GBM, has been shown to affect the genetic expression, modulation of metabolic pathways, and immune system evasion. GBM's aggressive invasion potential is modulated by cell-to-cell crosstalk within the tumor microenvironment and altered expressions of specific genes, such as ANXA2, GBP2, FN1, PHIP, and GLUT3. Nevertheless, the rising number of active clinical trials illustrates the efforts to identify new targets and drugs to treat this malignancy. Immunotherapy is still relevant for research purposes, given the amount of ongoing clinical trials based on this strategy to treat GBM, and neoantigen and nucleic acid-based vaccines are gaining importance due to their antitumoral activity by inducing the immune response. Furthermore, there are clinical trials focused on the PI3K/Akt/mTOR axis, angiogenesis, and tumor heterogeneity for developing molecular-targeted therapies against GBM. Other strategies, such as nanodelivery and computational models, may improve the drug pharmacokinetics and the prognosis of patients with GBM.

Glycosyltransferases in Cancer: Prognostic Biomarkers of Survival in Patient Cohorts and Impact on Malignancy in Experimental Models

Background: Glycosylation changes are a main feature of cancer. Some carbohydrate epitopes and expression levels of glycosyltransferases have been used or proposed as prognostic markers, while many experimental works have investigated the role of glycosyltransferases in malignancy. Using the transcriptomic data of the 21 TCGA cohorts, we correlated the expression level of 114 glycosyltransferases with the overall survival of patients. Methods: Using the Oncolnc website, we determined the Kaplan−Meier survival curves for the patients falling in the 15% upper or lower percentile of mRNA expression of each glycosyltransferase. Results: Seventeen glycosyltransferases involved in initial steps of N- or O-glycosylation and of glycolipid biosynthesis, in chain extension and sialylation were unequivocally associated with bad prognosis in a majority of cohorts. Four glycosyltransferases were associated with good prognosis. Other glycosyltransferases displayed an extremely high predictive value in only one or a few cohorts. The top were GALNT3, ALG6 and B3GNT7, which displayed a p < 1 × 10−9 in the low-grade glioma (LGG) cohort. Comparison with published experimental data points to ALG3, GALNT2, B4GALNT1, POFUT1, B4GALT5, B3GNT5 and ST3GAL2 as the most consistently malignancy-associated enzymes. Conclusions: We identified several cancer-associated glycosyltransferases as potential prognostic markers and therapeutic targets.

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

Although many prognostic models have been developed to help determine personalized prognoses and treatments, the predictive efficiency of these prognostic models in hepatocellular carcinoma (HCC), which is a highly heterogeneous malignancy, is less than ideal. Recently, aberrant glycosylation has been demonstrated to universally participate in tumour initiation and progression, suggesting that dysregulation of glycosyltransferases can serve as novel cancer biomarkers. In this study, a total of 568 RNA-sequencing datasets of HCC from the TCGA database and ICGC database were analysed and integrated via bioinformatic methods. LASSO regression analysis was applied to construct a prognostic signature. Kaplan-Meier survival, ROC curve, nomogram, and univariate and multivariate Cox regression analyses were performed to assess the predictive efficiency of the prognostic signature. GSEA and the "CIBERSORT" R package were utilized to further discover the potential biological mechanism of the prognostic signature. Meanwhile, the differential expression of the prognostic signature was verified by western blot, qRT-PCR and immunohistochemical staining derived from the HPA. Ultimately, we constructed a prognostic signature in HCC based on a combination of six glycosyltransferases, whose prognostic value was evaluated and validated successfully in the testing cohort and the validation cohort. The prognostic signature was identified as an independent unfavourable prognostic factor for OS, and a nomogram including the risk score was established and showed the good performance in predicting OS. Further analysis of the underlying mechanism revealed that the prognostic signature may be potentially associated with metabolic disorders and tumour-infiltrating immune cells.

Finding New Ways How to Control BACE1

Recently, all applications of BACE1 inhibitors failed as therapeutical targets for Alzheimer´s disease (AD) due to severe side effects. Therefore, alternative ways for treatment development are a hot research topic. The present analysis investigates BACE1 protein-protein interaction networks and attempts to solve the absence of complete knowledge about pathways involving BACE1. A bioinformatics analysis matched the functions of the non-substrate interaction network with Voltage-gated potassium channels, which also appear as top priority protein nodes. Targeting BACE1 interactions with PS1 and GGA-s, blocking of BACE1 access to APP by BRI3 and RTN-s, activation of Wnt signaling and upregulation of β-catenin, and brain delivery of the extracellular domain of p75^NTR, are the main alternatives to the use of BACE 1 inhibitors highlighted by the analysis. The pathway enrichment analysis also emphasized substrates and substrate candidates with essential biological functions, which cleavage must remain controlled. They include ephrin receptors, ROBO1, ROBO2, CNTN-s, CASPR-s, CD147, CypB, TTR, APLP1/APLP2, NRXN-s, and PTPR-s. The analysis of the interaction subnetwork of BACE1 functionally related to inflammation identified a connection to three cardiomyopathies, which supports the hypothesis of the common molecular mechanisms with AD. A lot of potential shows the regulation of BACE1 activity through post-translational modifications. The interaction network of BACE1 and its phosphorylation enzyme CSNK1D functionally match the Circadian clock, p53, and Hedgehog signaling pathways. The regulation of BACE1 glycosylation could be achieved through N-acetylglucosamine transferases, α-(1→6)-fucosyltransferase, β-galactoside α-(2→6)-sialyltransferases, galactosyltransferases, and mannosidases suggested by the interaction network analysis of BACE1-MGAT3. The present analysis proposes possibilities for the alternative control of AD pathology.

Integrated bioinformatic analysis identifies UBE2Q1 as a potential prognostic marker for high grade serous ovarian cancer

Background

High grade serous ovarian cancer (HGSOC) accounts for nearly 60% of total cases of epithelial ovarian cancer. It is the most aggressive subtype, which shows poor prognosis and low patient survival. For better management of HGSOC patients, new prognostic biomarkers are required to facilitate improved treatment strategies and ensure suitable healthcare decisions.

Methods

We performed genome wide expression analysis of HGSOC patient samples to identify differentially expressed genes (DEGs) using R based Limma package, Clust and other statistical tools. The identified DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to identify co-expression patterns of relevant genes. Module trait and gene ontology analyses were performed to establish important gene co-expression networks and their biological functions. Overlapping the most relevant DEG cluster 4 with prominent WGCNA cyan module identified strongest correlation of UBE2Q1 with ovarian cancer and its prognostic significance on survival probability of ovarian cancer patients was investigated. The predictive value of UBE2Q1 as a potential biomarker was analysed by correlating its expression with 12-months relapse free survival of patients in response to platin/taxane, the standard first-line chemotherapy for ovarian cancer, and analysing area under the ROC curve.

Results

An integrated gene expression analysis and WGCNA, identified UBE2Q1 as a potential prognostic marker associated with poor relapse-free survival and response outcome to platin/taxane treatment of patients with high grade serous ovarian cancer.

Conclusions

Our study identifies a potential UBE2Q1 – B4GALT3 functional axis in ovarian cancer, where only the E2 conjugating enzyme showed a poor prognostic impact on the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07928-z.