An integrative pan-cancer analysis of the molecular and biological features of glycosyltransferases

Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer

BACKGROUND

This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs.

METHODS

Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group.

RESULTS

In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy.

CONCLUSION

A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Elucidating the multifaceted role of MGAT1 in hepatocellular carcinoma: integrative single-cell and spatial transcriptomics reveal novel therapeutic insights

Background

Glycosyltransferase-associated genes play a crucial role in hepatocellular carcinoma (HCC) pathogenesis. This study investigates their impact on the tumor microenvironment and molecular mechanisms, offering insights into innovative immunotherapeutic strategies for HCC.

Methods

We utilized cutting-edge single-cell and spatial transcriptomics to examine HCC heterogeneity. Four single-cell scoring techniques were employed to evaluate glycosyltransferase genes. Spatial transcriptomic findings were validated, and bulk RNA-seq analysis was conducted to identify prognostic glycosyltransferase-related genes and potential immunotherapeutic targets. MGAT1's role was further explored through various functional assays.

Results

Our analysis revealed diverse cell subpopulations in HCC with distinct glycosyltransferase gene activities, particularly in macrophages. Key glycosyltransferase genes specific to macrophages were identified. Temporal analysis illustrated macrophage evolution during tumor progression, while spatial transcriptomics highlighted reduced expression of these genes in core tumor macrophages. Integrating scRNA-seq, bulk RNA-seq, and spatial transcriptomics, MGAT1 emerged as a promising therapeutic target, showing significant potential in HCC immunotherapy.

Conclusion

This comprehensive study delves into glycosyltransferase-associated genes in HCC, elucidating their critical roles in cellular dynamics and immune cell interactions. Our findings open new avenues for immunotherapeutic interventions and personalized HCC management, pushing the boundaries of HCC immunotherapy.

Sex-specific survival gene mutations are discovered as clinical predictors of clear cell renal cell carcinoma

Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.

C1GALT1 induces the carcinogenesis of thyroid cancer through regulation by miR-141-3p and GLUT1

Core 1 β 1,3-galactosyltransferase 1 (C1GALT1) acts as an important glycosyltransferase in the occurrence and development of tumor glycosylation. However, the regulatory mechanisms of C1GALT1 in thyroid cancer (TC) is still unclear. In this study, we discovered that the expression level of C1GALT1 was significantly increased in thyroid adenocarcinoma tissues and cell lines (p < 0.01). Meanwhile, gene silencing of C1GALT1 inhibited the proliferation (CCK-8 assay), migration (wound healing), and invasion (Transwell) of TC cells (p < 0.05). Further investigation indicated that miR-141-3p had a negative correlation with C1GALT1 and suppressed cancer carcinogenesis in TC cells. Moreover, we first found that glucose transporter 1 (GLUT1) was a downstream element of C1GALT1 and was positively correlated with C1GALT1 levels in TC. The GLUT1 could reverse the inhibitory effects of siRNA C1GALT1 on cell development (p < 0.05). These data suggest that the miR-141-3p/C1GALT1/GLUT1 axis plays an essential role during TC progression and may be a probable biomarker or therapeutic target for thyroid cancer patients.

Identification of glycosyltransferase-related genes signature and integrative analyses in patients with ovarian cancer

BACKGROUND

Glycosyltransferases (GT) play a crucial role in glycosylation reactions, and aberrant expression of glycosyltransferase-related genes (GTs) leads to abnormal glycosylation, which is associated with tumor progression. However, the prognostic value of aberrant expression of GTs in ovarian cancer (OC) and the correlation between GTs and tumor microenvironment (TME) remain unknown.

METHODS

TCGA and GSE53963 databases were used to obtain data on OC patient samples. The association of GTs with OC was analyzed. Molecular subtypes were identified by consensus unsupervised clustering, followed by immune infiltration and functional enrichment analyses. Survival analysis was performed using Kaplan-Meier curves and log-rank tests. Least Absolute Shrinkage and Selection Operator (LASSO) and multifactorial cox regression were used to screen for signature genes associated with OC and used to establish prognostic models.

RESULT

OC patients were categorized into 5 GTs clusters using consensus unsupervised cluster analysis. Clusters D and E showed significant differences between survival, signaling pathways and immune infiltration. Then, a risk model was developed based on the 12 signature genes, which provides a more accurate evaluation of the prognosis of OC patients. We categorized patients into high-risk and low-risk groups based on the risk score and found that the survival of patients in the high-risk group was significantly lower than that in the low-risk group. Moreover, the risk score was significantly correlated with tumor microenvironment, immune infiltration, and chemotherapy sensitivity.

CONCLUSION

Overall, we performed a comprehensive analysis of GTs in OC patients and developed a risk model for OC. Our findings will provide a new insight to OC prognosis and treatment.

Glycosylation-related genes mediated prognostic signature contribute to prognostic prediction and treatment options in ovarian cancer: based on bulk and single‑cell RNA sequencing data

BACKGROUND

Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy.

METHODS

AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs signature. We evaluated the differences in immune cell infiltration, enrichment of immune checkpoints, immunotherapy response, and gene mutation status among different risk groups. Finally, RT-qPCR, Wound-healing assay, Transwell assay were performed to verify the effect of the CYBRD1 on OC.

RESULTS

A total of 1187 GRGs were obtained and a GRGs signature including 16 genes was established. The OC patients were divided into high- and low- risk group based on the median riskscore and the patients in high-risk group have poor outcome. We also found that the patients in low-risk group have higher immune cell infiltration, enrichment of immune checkpoints and immunotherapy response. The results of laboratory test showed that CYBRD1 can promote the invasion, and migration of OC and is closely related to the poor prognosis of OC patients.

CONCLUSIONS

Our study established a GRGs signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prognosis prediction and treatment strategy for OC.

Integrative proteomics and n-glycoproteomics reveal the synergistic anti-tumor effects of aspirin- and gemcitabine-based chemotherapy on pancreatic cancer cells

OBJECTIVE AND DESIGN

Pancreatic cancer is a highly malignant tumor that is well known for its poor prognosis. Based on glycosylation, we performed integrated quantitative N-glycoproteomics to investigate the synergistic anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells and explore the potential molecular mechanisms of chemotherapy in pancreatic cancer.

METHODS AND RESULTS

Two pancreatic cancer cell lines (PANC-1 and BxPC-3) were treated with gemcitabine, aspirin, and a combination (gemcitabine + aspirin). We found that the addition of aspirin enhanced the inhibitory effect of gemcitabine on the activity of PANC-1 and BxPC-3 cells. Quantitative N-glycoproteome, proteome, phosphorylation, and transcriptome data were obtained from integrated multi-omics analysis to evaluate the anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells. Mfuzz analysis of intact N-glycopeptide profiles revealed two consistent trends associated with the addition of aspirin, which showed a strong relationship between N-glycosylation and the synergistic effect of aspirin. Further analysis demonstrated that the dynamic regulation of sialylation and high-mannose glycoforms on ECM-related proteins (LAMP1, LAMP2, ITGA3, etc.) was a significant factor for the ability of aspirin to promote the anti-tumor activity of gemcitabine and the drug resistance of pancreatic cancer cells.

CONCLUSIONS

In-depth analysis of N-glycosylation-related processes and pathways in pancreatic cancer cells can provide new insight for future studies regarding pancreatic cancer therapeutic targets and drug resistance mechanisms.

Integrative Analyses of Pyrimidine Salvage Pathway-Related Genes Revealing the Associations Between UPP1 and Tumor Microenvironment

Background

The pyrimidine salvage pathway plays a critical role in tumor progression and patient outcomes. The roles of pyrimidine salvage pathway-related genes (PSPGs) in cancer, however, are not fully understood. This study aims to depict the characteristics of PSPGs across various cancers.

Methods

An integrative pan-cancer analysis of six PSPGs (CDA, UCK1, UCK2, UCKL1, UPP1, and UPP2) was conducted using TCGA data, single-cell RNA sequencing datasets, and patient samples. Single-cell transcriptome analysis and RT-qPCR were used to validate the relation between UPP1 and cytokines. Flow cytometry was performed to validate the role of UPP1 in immune checkpoint regulation. The correlation between UPP1 and tumor associated neutrophils (TAN) were investigated and validated by single-cell transcriptome analysis and tissue microarrays (TMAs).

Results

PSPGs showed low mutation rates but significant copy number variations, particularly amplifications in UCKL1, UPP1, and UCK2 across various cancers. DNA methylation patterns varied, with notable negative correlations between methylation and gene expression in UPP1. PSPGs were broadly up-regulated in multiple cancers, with correlations to clinical staging and prognosis. Proteomic data further confirmed these findings. Functional analysis revealed PSPGs' associations with tumor proliferation, metastasis, and various signaling pathways. UPP1 showed strong correlations with the tumor microenvironment (TME), particularly with cytokines, immune checkpoints, and various immune cells. Single-cell transcriptome analysis confirmed these associations, highlighting UPP1's influence on cytokine expression and immune checkpoint regulation. In esophageal squamous cell carcinoma (ESCC), UPP1-high tumor cells were significantly associated with immunosuppressive cells in the TME. Spatial analysis using TMAs revealed that UPP1+ tumor cells were predominantly located at the invasive margin and closely associated with neutrophils, correlating with poorer patient prognosis.

Conclusion

Our study depicted the multi-dimensional view of PSPGs in cancer, with a particular focus on UPP1's role in the TME. Targeting UPP1 holds promise as a potential strategy for cancer therapy.

A combined analysis of multi-omics data reveals the prognostic values and immunotherapy response of LAG3 in human cancers

Lymphocyte-activation gene 3 (LAG3) is a highly anticipated immune checkpoint in the context of cancer, exerting regulatory control over immune cell proliferation and function to reinforce the advancement of cancers. However, the comprehensive functional analysis of LAG3 across various cancer types remains undisclosed; thus, this study aims to investigate the pan-cancer expression profile of LAG3. We have investigated the expression profile, prognostic significance, and genetic alterations of LAG3 in various cancers while elucidating its characteristic in immune response regulation. Our findings demonstrated that elevated LAG3 expression is significantly associated with favorable prognosis in patients with cutaneous melanoma (SKCM), and it may be a potential biomarker for SKCM. Furthermore, multiple immune algorithms have highlighted the important regulatory role of LAG3 for the tumor-infiltrating immune cells including CD8 + T cells, B cells, dendritic cells (DCs), macrophages, and natural killer (NK) cells. We also examined the distribution of LAG3 at the single-cell level and explored its functional significance. A comprehensive and systematic analysis of LAG3 would facilitate a comprehensive evaluation of LAG3 in cancer biology and provide valuable insights for cancer management.

Targeting protein glycosylation to regulate inflammation in the respiratory tract: novel diagnostic and therapeutic candidates for chronic respiratory diseases

Protein glycosylation is a widespread posttranslational modification that can impact the function of proteins. Dysregulated protein glycosylation has been linked to several diseases, including chronic respiratory diseases (CRDs). CRDs pose a significant public health threat globally, affecting the airways and other lung structures. Emerging researches suggest that glycosylation plays a significant role in regulating inflammation associated with CRDs. This review offers an overview of the abnormal glycoenzyme activity and corresponding glycosylation changes involved in various CRDs, including chronic obstructive pulmonary disease, asthma, cystic fibrosis, idiopathic pulmonary fibrosis, pulmonary arterial hypertension, non-cystic fibrosis bronchiectasis, and lung cancer. Additionally, this review summarizes recent advances in glycomics and glycoproteomics-based protein glycosylation analysis of CRDs. The potential of glycoenzymes and glycoproteins for clinical use in the diagnosis and treatment of CRDs is also discussed.