Insights into the Role of Sialylation in Cancer Metastasis, Immunity, and Therapeutic Opportunity

Sialyltransferase ST3GAL6 silencing reduces α2,3-sialylated glycans to regulate autophagy by decreasing HSPB8-BAG3 in the brain with hepatic encephalopathy

End-stage liver diseases, such as cirrhosis and liver cancer caused by hepatitis B, are often combined with hepatic encephalopathy (HE); ammonia poisoning is posited as one of its main pathogenesis mechanisms. Ammonia is closely related to autophagy, but the molecular mechanism of ammonia's regulatory effect on autophagy in HE remains unclear. Sialylation is an essential form of glycosylation. In the nervous system, abnormal sialylation affects various physiological processes, such as neural development and synapse formation. ST3 β‍-galactoside α2,‍3-sialyltransferase 6 (ST3GAL6) is one of the significant glycosyltransferases responsible for adding α2,3-linked sialic acid to substrates and generating glycan structures. We found that the expression of ST3GAL6 was upregulated in the brains of mice with HE and in astrocytes after ammonia induction, and the expression levels of α2,3-sialylated glycans and autophagy-related proteins microtubule-associated protein light chain 3 (LC3) and Beclin-1 were upregulated in ammonia-induced astrocytes. These findings suggest that ST3GAL6 is related to autophagy in HE. Therefore, we aimed to determine the regulatory relationship between ST3GAL6 and autophagy. We found that silencing ST3GAL6 and blocking or degrading α2,3-sialylated glycans by way of Maackia amurensis lectin-II (MAL-II) and neuraminidase can inhibit autophagy. In addition, silencing the expression of ST3GAL6 can downregulate the expression of heat shock protein β8 (HSPB8) and Bcl2-associated athanogene 3 (BAG3). Notably, the overexpression of HSPB8 partially restored the reduced autophagy levels caused by silencing ST3GAL6 expression. Our results indicate that ST3GAL6 regulates autophagy through the HSPB8-BAG3 complex.

Sialylated glycoproteins and sialyltransferases in digestive cancers: Mechanisms, diagnostic biomarkers, and therapeutic targets

Sialic acid (SA), as the ultimate epitope of polysaccharides, can act as a cap at the end of polysaccharide chains to prevent their overextension. Sialylation is the enzymatic process of transferring SA residues onto polysaccharides and is catalyzed by a group of enzymes known as sialyltransferases (SiaTs). It is noteworthy that the sialylation level of glycoproteins is significantly altered when digestive cancer occurs. And this alteration exhibits a close correlation with the progression of these cancers. In this review, from the perspective of altered SiaTs expression levels and changed glycoprotein sialylation patterns, we summarize the pathogenesis of gastric cancer (GC), colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), and hepatocellular carcinoma (HCC). Furthermore, we propose potential early diagnostic biomarkers and prognostic indicators for different digestive cancers. Finally, we summarize the therapeutic value of sialylation in digestive system cancers.

The prognostic value of sialylation-related long non-coding RNAs in lung adenocarcinoma

There has been increasing interest in the role of epigenetic modification in cancers recently. Among the various modifications, sialylation has emerged as a dominant subtype implicated in tumor progression, metastasis, immune evasion, and chemoresistance. The prognostic significance of sialylation-related molecules has been demonstrated in colorectal cancer. However, the potential roles and regulatory mechanisms of sialylation in lung adenocarcinoma (LUAD) have not been thoroughly investigated. Through Pearson correlation, univariate Cox hazards proportional regression, and random survival forest model analyses, we identified several prognostic long non-coding RNAs (lncRNAs) associated with aberrant sialylation and tumor progression, including LINC00857, LINC00968, LINC00663, and ITGA9-AS1. Based on the signatures of four lncRNAs, we classified patients into two clusters with different landscapes using a non-negative matrix factorization approach. Collectively, patients in Cluster 1 (C1) exhibited worse prognoses than those in Cluster 2 (C2), as well as heavier tumor mutation burden. Functional enrichment analysis showed the enrichment of several pro-tumor pathways in C1, differing from the upregulated Longevity and programmed cell death pathways in C2. Moreover, we profiled immune infiltration levels of important immune cell lineages in two subgroups using MCPcounter scores and single sample gene set enrichment analysis scores, revealing a relatively immunosuppressive microenvironment in C1. Risk analysis indicated that LINC00857 may serve as a pro-tumor regulator, while the other three lncRNAs may be protective contributors. Consistently, we observed upregulated LINC00857 in C1, whereas increased expressive levels of LINC00968, LINC00663, and ITGA9-AS1 were observed in C2. Finally, drug sensitivity analysis suggested that patients in the two groups may benefit from different therapeutic strategies, contributing to precise treatment in LUAD. By integrating multi-omics data, we identified four core sialylation-related lncRNAs and successfully established a prognostic model to distinguish patients with different characterizations. These findings may provide some insights into the underlying mechanism of sialylation, and offer a new stratification way as well as clinical guidance in LUAD.

Deep learning enhanced the diagnostic merit of serum glycome for multiple cancers

Protein glycosylation is associated with the pathogenesis of various cancers. The utilization of certain glycans in cancer diagnosis models holds promise, yet their accuracy is not always guaranteed. Here, we investigated the utility of deep learning techniques, specifically random forests combined with transfer learning, in enhancing serum glycome's discriminative power for cancer diagnosis (including ovarian cancer, non-small cell lung cancer, gastric cancer, and esophageal cancer). We started with ovarian cancer and demonstrated that transfer learning can achieve superior performance in data-disadvantaged cohorts (AUROC >0.9), outperforming the approach of PLS-DA. We identified a serum glycan-biomarker panel including 18 serum N-glycans and 4 glycan derived traits, most of which were featured with sialylation. Furthermore, we validated advantage of the transfer learning scheme across other cancer groups. These findings highlighted the superiority of transfer learning in improving the performance of glycans-based cancer diagnosis model and identifying cancer biomarkers, providing a new high-fidelity cancer diagnosis venue.

The pan-cancer multi-omics landscape of key genes of sialylation combined with RNA-sequencing validation

BACKGROUND

Sialylation, the process of salivary acid glycan synthesis, plays a pivotal function in tumor growth, immune escape, tumor metastasis, and resistance to drugs. However, the association between sialylation and prognosis, tumor microenvironment (TME), and treatment response in a variety of cancers remains unclear.

METHODS

A comprehensive survey of the expression profile, prognostic value, and genetic and epigenetic alterations of sialylation-related genes was performed in pan-cancer. Subsequently, the single-sample gene set enrichment analysis (ssGSEA) algorithm was used to compute sialylation pathway scores in pan-cancer. Correlations of sialylation pathway scores with clinical features, prognosis, and TME were evaluated using multiple algorithms. Finally, the efficacy of the sialylation pathway score in determining the effect of immunotherapy was evaluated. The expression of sialylation-related genes were verified by RNA-sequencing.

RESULTS

Significant differences were observed in sialylation-related genes expression between tumors and adjacent normal tissues for most cancer types. Sialylation pathway scores differed according to the type of tumor, where the poor prognosis was correlated with high sialylation pathway scores in uveal melanoma (UVM) and pancreatic adenocarcinoma (PAAD). In addition, sialylation pathway scores were positively associated with the ImmuneScore, StromalScore and immune-related pathways. Moreover, the level of immune cells infiltration was higher in tumors with higher sialylation pathway scores. Finally, patients with high sialylation pathway scores were more sensitive to immunotherapy.

CONCLUSION

Sialylation-related genes are essential in pan-cancer. The sialylation pathway score may be used as a biomarker in oncology patients.

Recent advances in the development of sialyltransferase inhibitors to control cancer metastasis: A comprehensive review

Metastasis accounts for the majority of cancer-associated mortalities, representing a huge health and economic burden. One of the mechanisms that enables metastasis is hypersialylation, characterized by an overabundance of sialylated glycans on the tumor surface, which leads to repulsion and detachment of cells from the original tumor. Once the tumor cells are mobilized, sialylated glycans hijack the natural killer T-cells through self-molecular mimicry and activatea downstream cascade of molecular events that result in inhibition of cytotoxicity and inflammatory responses against cancer cells, ultimately leading to immune evasion. Sialylation is mediated by a family of enzymes known as sialyltransferases (STs), which catalyse the transfer of sialic acid residue from the donor, CMP-sialic acid, onto the terminal end of an acceptor such as N-acetylgalactosamine on the cell-surface. Upregulation of STs increases tumor hypersialylation by up to 60% which is considered a distinctive hallmark of several types of cancers such as pancreatic, breast, and ovarian cancer. Therefore, inhibiting STs has emerged as a potential strategy to prevent metastasis. In this comprehensive review, we discuss the recent advances in designing novel sialyltransferase inhibitors using ligand-based drug design and high-throughput screening of natural and synthetic entities, emphasizing the most successful approaches. We analyse the limitations and challenges of designing selective, potent, and cell-permeable ST inhibitors that hindered further development of ST inhibitors into clinical trials. We conclude by analysing emerging opportunities, including advanced delivery methods which further increase the potential of these inhibitors to enrich the clinics with novel therapeutics to combat metastasis.

Integrating Bulk and Single-Cell RNA Sequencing Reveals Heterogeneity, Tumor Microenvironment, and Immunotherapeutic Efficacy Based on Sialylation-Related Genes in Bladder Cancer

Background

As known abnormal sialylation exerts crucial roles in the growth, metastasis, and immune evasion of cancers, but the molecular characteristics and roles in bladder cancer (BLCA) remain unclear. This study intends to establish BLCA risk stratification based on sialylation-related genes and elucidate its role in prognosis, tumor microenvironment, and immunotherapy of BLCA.

Methods

Bulk RNA-seq and scRNA-seq data were downloaded from open-access databases. The scRNA-seq data were processed using the R package "Seurat" to identify the core cell types. The tumor sub-typing of BLCA samples was performed by the R package "ConsensusClusterPlus" in the bulk RNA-seq data. Signature genes were identified by the R package "limma" and univariate regression analysis to calculate risk scores using the R package "GSVA" and establish risk stratification of BLCA patients. Finally, the differences in clinicopathological characteristics, tumor microenvironment, and immunotherapy efficacy between the different groups were investigated.

Results

5 core cell types were identified in the scRNA-seq dataset, with monocytes and macrophages presenting the greatest percentage, sialylation-related gene expression, and sialylation scores. The bulk RNA-seq samples were classified into 3 tumor subtypes based on 19 prognosis-related sialylation genes. The 10 differential expressed genes (DEGs) with the smallest p-values were collected as signature genes, and the risk score was calculated, with the samples divided into high and low-risk score groups. The results showed that patients in the high-risk score group exhibited worse survival outcomes, higher tumor grade, more advanced stage, more frequency of gene mutations, higher expression levels of immune checkpoints, and lower immunotherapy response.

Conclusion

We established a novel risk stratification of BLCA from a glycomics perspective, which demonstrated good accuracy in determining the prognostic outcome, clinicopathological characteristics, immune microenvironment, and immunotherapy efficacy of patients, and we are proposing to apply it to direct the choice of clinical treatment options for patients.

Aberrant protein glycosylation: Implications on diagnosis and Immunotherapy

Glycosylation-mediated post-translational modification is critical for regulating many fundamental processes like cell division, differentiation, immune response, and cell-to-cell interaction. Alterations in the N-linked or O-linked glycosylation pattern of regulatory proteins like transcription factors or cellular receptors lead to many diseases, including cancer. These alterations give rise to micro- and macro-heterogeneity in tumor cells. Here, we review the role of O- and N-linked glycosylation and its regulatory function in autoimmunity and aberrant glycosylation in cancer. The change in cellular glycome could result from a change in the expression of glycosidases or glycosyltransferases like N-acetyl-glucosaminyl transferase V, FUT8, ST6Gal-I, DPAGT1, etc., impact the glycosylation of target proteins leading to transformation. Moreover, the mutations in glycogenes affect glycosylation patterns on immune cells leading to other related manifestations like pro- or anti-inflammatory effects. In recent years, understanding the glycome to cancer indicates that it can be utilized for both diagnosis/prognosis as well as immunotherapy. Studies involving mass spectrometry of proteome, site- and structure-specific glycoproteomics, or transcriptomics/genomics of patient samples and cancer models revealed the importance of glycosylation homeostasis in cancer biology. The development of emerging technologies, such as the lectin microarray, has facilitated research on the structure and function of glycans and glycosylation. Newly developed devices allow for high-throughput, high-speed, and precise research on aberrant glycosylation. This paper also discusses emerging technologies and clinical applications of glycosylation.

Milk Polysialic Acid Levels Rapidly Decrease in Line with the N-Acetylneuraminic Acid Concentrations during Early Lactation in Dairy Cows

Sialylated milk oligosaccharides and glycoconjugates have several positive effects on the mucosal barrier, the gut microbiome, and an effective immune system. For this reason, they are important biomolecules for mammary gland health and optimal development of offspring. In milk, the major sialic acid, N-acetylneuraminic acid (Neu5Ac), can be attached as monosialyl-residues or as polymers. To investigate the sialylation processes during lactation of German Holstein cows, we analyzed udder tissue in addition to milk at different time points of lactation. The analysis of the milk samples revealed that both the levels of Neu5Ac and its polymer, polysialic acid (polySia), rapidly decreased during the first three days of lactation, and a high interindividual variance was observed. In mature milk, however, the sialylation status remains relatively constant. The results indicate that mammary gland epithelial cells are one source for milk polySia, since immunohistochemistry of udder tissue exhibited strong polySia staining in these cells. Furthermore, both polysialyltransferases, ST8SiaII and ST8SiaIV, are expressed. Based on known functions of monosialyl residues and polySia, we discuss the potential impact of these biomolecules and the consequences of the heterogeneous sialylation status of milk in relation to udder health and offspring health.