FUT11 is a target gene of HIF1α that promotes the progression of hepatocellular carcinoma

Integration of RNAseq transcriptomics and N-glycomics reveal biosynthetic pathways and predict structure-specific N-glycan expression

The processes involved in protein N-glycosylation represent new therapeutic targets for diseases but their stepwise and overlapping biosynthetic processes make it challenging to identify the specific glycogenes involved. In this work, we aimed to elucidate the interactions between glycogene expression and N-glycan abundance by constructing supervised machine-learning models for each N-glycan composition. Regression models were trained to predict N-glycan abundance (response variable) from glycogene expression (predictors) using paired LC-MS/MS N-glycomic and 3'-TagSeq transcriptomic datasets from cells derived from multiple tissue origins and treatment conditions. The datasets include cells from several tissue origins - B cell, brain, colon, lung, muscle, prostate - encompassing nearly 400 N-glycan compounds and over 160 glycogenes filtered from an 18 000-gene transcriptome. Accurate models (validation R 2 > 0.8) predicted N-glycan abundance across cell types, including GLC01 (lung cancer), CCD19-Lu (lung fibroblast), and Tib-190 (B cell). Model importance scores ranked glycogene contributions to N-glycan predictions, revealing significant glycogene associations with specific N-glycan types. The predictions were consistent across input cell quantities, unlike LC-MS/MS glycomics which showed inconsistent results. This suggests that the models can reliably predict N-glycosylation even in samples with low cell amounts and by extension, single-cell samples. These findings can provide insights into cellular N-glycosylation machinery, offering potential therapeutic strategies for diseases linked to aberrant glycosylation, such as cancer, and neurodegenerative and autoimmune disorders.

Protein O-Fucosyltransferases: Biological Functions and Molecular Mechanisms in Mammals

Domain-specific O-fucosylation is an unusual type of glycosylation, where the fucose is directly attached to the serine or threonine residues in specific protein domains via an O-linkage. O-fucosylated proteins play critical roles in a wide variety of biological events and hold important therapeutic values, with the most studied being the Notch receptors and ADAMTS proteins. O-fucose glycans modulate the function of the proteins they modify and are closely associated with various diseases including cancer. In mammals, alongside the well-documented protein O-fucosyltransferase (POFUT) 1-mediated O-fucosylation of epidermal growth factor-like (EGF) repeats and POFUT2-mediated O-fucosylation of thrombospondin type 1 repeats (TSRs), a new type of O-fucosylation was recently identified on elastin microfibril interface (EMI) domains, mediated by POFUT3 and POFUT4 (formerly FUT10 and FUT11). In this review, we present an overview of our current knowledge of O-fucosylation, integrating the latest findings and with a particular focus on its biological functions and molecular mechanisms.

Construction and Evaluation of an M2 Macrophage-Related Prognostic Model for Colon Cancer

Colon cancer (CC) is a primary human malignancy. Recently, the mechanism of the tumor microenvironment (TME) in CC has been a hot topic of research. However, there is uncertainty regarding the contribution of M2 macrophages and related genes to the prognosis for CC. M2 macrophage-related genes (M2RGs) were obtained from The Cancer Genome Atlas (TCGA) database. Immune cell infiltration in CC tissue was assessed by Cibersort. Based on the TCGA-COAD training set, a Least Absolute Shrinkage and Selection Operator (LASSO) Cox risk model was constructed and its efficiency was evaluated by analyzing risk profiles and survival profiles. Using gene set enrichment analysis (GSEA), the functional distinctions between high-risk and low-risk categories were further investigated. Finally, potential immune checkpoints, immunotherapy efficiency, and clinical treatment of high-risk patients were evaluated. A total of 1063 M2RGs were identified in TCGA-COAD, 32 of these were confirmed to be strongly related to overall survival (OS), and 14 of these were picked to construct an OS-oriented prognostic model in CC patients. The M2RG signature had a positive correlation with unfavorable prognosis according to the survival analysis. Correlation analysis revealed that the risk model was positively associated with clinicopathological characteristics, immune cell infiltration, immune checkpoint inhibitor targets, the risk of immune escape, and the efficiency of anti-cancer medications. The risk model created using M2RGs may be useful in predicting the prognosis of CC.

FUT11 expression in gastric cancer: its prognostic significance and role in immune regulation

BACKGROUND

Gastric cancer (GC) is a malignant digestive tract tumor with a high recurrence rate and poor prognosis. Fucosylation is important in tumor glycosylation, in which the key enzyme is fucosyltransferase (FUT). FUT11 is a member of the fucosyltransferase family and has been closely associated with the development of multiple cancers. However, the specific relationship between FUT11 and GC prognosis and its molecular mechanism has not been fully studied. This study explored FUT11 expression, clinical correlation, and its role in GC occurrence and development to deepen understanding of its function.

METHODS

FUT11 expression in 33 cancers was preliminarily analyzed using the Tumor Immunoassay Resource (TIMER2.0) database. FUT11 expression in GC was evaluated using The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD) and Gene Expression Profiling Interactive Analysis (GEPIA2) data and verified using the Gene Expression Omnibus (GEO) GSE65801 dataset. Furthermore, we studied the survival prognosis of FUT11 in GC and analyzed its effect on the survival rate of patients with GC using the KM-plotter. We also performed COX regression analysis on TCGA GC clinical data and analyzed FUT11 expression in the pathway using the STRING and LinkedOmics databases. Moreover, the relationship between FUT11 and GC immune infiltration level was examined, and the Kaplan-Meier survival analysis diagram was constructed. The FUT11 genetic variation information was retrieved using cBioPortal, and its drug sensitivity was analyzed using CellMiner. Finally, differential FUT11 expression in GC tissues was verified using immunohistochemistry.

RESULTS

The data mining and analysis demonstrated that FUT11 expression was abnormally elevated in GC tissues and correlated with poor patient prognosis. The FUT11 expression level was an independent prognostic factor for GC. The difference in FUT11 expression level resulted in different degrees of immune cell infiltration in the patients with GC, which might regulate the tumor microenvironment. FUT11 affected GC development by participating in cancer pathways such as PI3K-AKT, neuroactive ligand-receptor, and MAPK. Immunohistochemical staining revealed that FUT11 was highly expressed in GC.

CONCLUSIONS

This study revealed that FUT11 expression is significantly increased in GC tissues. This increase is associated with poor prognosis and might affect immune regulation. FUT11 might have immunological and targeted therapeutic value, providing a new approach to GC treatment.

Prognosis and immunotherapy response prediction based on M2 macrophage-related genes in colon cancer

BACKGROUND

M2 macrophage were revealed to play a crucial role in immune evasion and immunotherapies. This study aims to explore the potential significance of M2 macrophage-related genes in colon adenocarcinoma (COAD) by analysizing the transcriptome data in a comprehensive way.

METHODS

We collected RNA-sequencing (RNA-seq) data of COAD from The Cancer Genome Atlas (TCGA) and Gene Expression Ominibus (GEO) databases. We calculated the immune infiltration scores of every sample using CIBERSORT algorithm. Through weighted gene co-expression network analysis (WGCNA), we picked out M2 macrophage-related genes. With these genes we screened out prognosis related genes which were utilized to construct a signature to assess the prognosis of patients. To extend the potential application of the signature, we also calculated the correlations with immune infiltration. Finally, we applied techniques such as quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunoblotting (Western Blotting) to validate the RNF32 gene in cellular in vitro assays.

RESULTS

Seven M2 macrophage-related genes signature was constructed, which was an excellent prognostic predictor in two independent groups. The high-risk group showed lower immune infiltration and poorer response to immunotherapies than those of the low-risk group. The cell vitro experiments showed that the expression level of RNF32 was upregulated in colon cancer cell lines compared with normal cell lines. Moreover, we found that RNF32 may promote the proliferation, migration and invasion of cancer cells in vitro by inhibiting apoptosis.

CONCLUSION

A novel M2 macrophage-related gene signature affects the prognosis and immune characteristics of colon cancer.

A Strategy based on Bioinformatics and Machine Learning Algorithms Reveals Potential Mechanisms of Shelian Capsule against Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying its efficacy remain elusive. This study sought to uncover the potential mechanisms of SLC in HCC treatment using bioinformatics methods.

METHODS

Bioactive components of SLC were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and HCC-related microarray chip data were sourced from the Gene Expression Omnibus (GEO) database. The selection criteria for components included OB ≧ 30% and DL ≧ 0.18. By integrating the results of differential expression analysis and weighted gene co-expression network analysis (WGCNA), disease-related genes were identified. Therapeutic targets were determined as shared items between candidate targets and disease genes. Protein-protein interaction (PPI) network analysis was conducted for concatenated genes, with core protein clusters identified using the MCODE plugin. Machine learning algorithms were applied to identify signature genes within therapeutic targets. Subsequently, immune cell infiltration analysis, single-cell RNA sequencing (sc-RNA seq) analysis, molecular docking, and ADME analysis were performed for the screened genes.

RESULTS

A total of 153 SLC ingredients and 170 candidate targets were identified, along with 494 HCCrelated disease genes. Overlapping items between disease genes and drug candidates represented therapeutic genes, and PPI network analysis was conducted using concatenated genes. MCODE1 and MCODE2 cluster genes underwent Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Four signature genes (TOP2A, CYP1A2, CYP2B6, and IGFBP3) were identified from 28 therapeutic genes using 3 machine learning algorithms, with ROC curves plotted. Molecular docking validated the interaction modes and binding abilities between signature genes and corresponding compounds, with free binding energy all <-7 kcal/mol. Finally, ADME analysis revealed similarities between certain SLC components and the clinical drugs Sorafenib and Lenvatinib.

CONCLUSION

In summary, our study revealed that the mechanism underlying the anti-HCC effects of SLC involves interactions at three levels: components (quercetin, beta-sitosterol, kaempferol, baicalein, stigmasterol, and luteolin), pathways (PI3K-Akt signaling pathway, TNF signaling pathway, and IL-17 signaling pathway), and targets (TOP2A, CYP1A2, CYP2B6, and IGFBP3). This study provides preliminary insights into the potential pharmacological mechanisms of SLC in HCC treatment, aiming to support its clinical application and serve as a reference for future laboratory investigations.

Brain metastasis-associated fibroblasts secrete fucosylated PVR/CD155 that induces breast cancer invasion

Brain metastasis cancer-associated fibroblasts (bmCAFs) are emerging as crucial players in the development of breast cancer brain metastasis (BCBM), but our understanding of the underlying molecular mechanisms is limited. In this study, we aim to elucidate the pathological contributions of fucosylation (the post-translational modification of proteins by the dietary sugar L-fucose) to tumor-stromal interactions that drive the development of BCBM. Here, we report that patient-derived bmCAFs secrete high levels of polio virus receptor (PVR), which enhance the invasive capacity of BC cells. Mechanistically, we find that HIF1α transcriptionally upregulates fucosyltransferase 11, which fucosylates PVR, triggering its secretion from bmCAFs. Global phosphoproteomic analysis of BC cells followed by functional verification identifies cell-cell junction and actin cytoskeletal signaling as modulated by bmCAF-secreted, -fucosylated PVR. Our findings delineate a hypoxia- and fucosylation-regulated mechanism by which bmCAFs contribute to the invasiveness of BCBM in the brain.

Knockdown of FUT11 inhibits the progression of gastric cancer via the PI3K/AKT pathway

Gastric cancer (GC) is a common and highly malignant tumor of the digestive tract. Members of the focused fucosyltransferase (FUT) family participate in the advancement of various types of cancer. However, research of FUT family members in the progression of GC known to be limited. The purpose of the research was to determine the function of important affiliates of the FUT family in GC and to explore its impacts on the proliferation and migration of GC cells and molecular mechanisms. For the study, fucosyltransferase11 (FUT11) was confirmed to be the only affiliate of the FUT family that was upmodulated in GC tissues and linked to poor survival according to GEPIA data. Furthermore, compared with adjacent noncancerous tissues, the expression of FUT11 was increased in GC tissues. The elevated FUT11 expression suggested that the overall survival (OS) rate of GC is low. Inhibition of FUT11 significantly reduced the proliferation and migration and suppressed the PI3K/AKT pathway by down-regulated collagen type VI alpha 3 chain (COL6A3) in GC cells. The present study has demonstrated that reinstating the expression of COL6A3 in gastric cancer (GC) cells can counteract the inhibitory impact of FUT11 knockdown on the proliferation and migration of GC cells. In conclusion, FUT11 may serve as a novel biomarker for GC, as it modulates GC cell proliferation and migration through the PI3K/AKT signaling pathway.

Chronic hypoxia is associated with transcriptomic reprogramming and increased genomic instability in cancer cells

Hypoxia afflicts the microenvironment of solid tumors fueling malignancy. We investigated the impact of long hypoxia exposure on transcriptional remodeling, tumor mutational burden (TMB), and genomic instability of cancer cells that were grouped based on their inherent sensitivity or resistance to hypoxia. A hypoxia score was used as a metric to distinguish between the most hypoxia-sensitive (hypoxia high (HH)), and most resistant (hypoxia low (HL)) cancer cells. By applying whole exome sequencing and microarray analysis, we showed that the HH group was indeed more sensitive to hypoxia, having significantly higher TMB (p = 0.03) and copy number losses (p = 0.03), as well as a trend of higher transcriptional response. Globally cells adapted by decreasing expression of genes involved in metabolism, proliferation, and protein maturation, and increasing alternative splicing. They accumulated mutations, especially frameshift insertions, and harbored increased copy number alterations, indicating increased genomic instability. Cells showing highest TMB simultaneously experienced a significant downregulation of DNA replication and repair and chromosomal maintenance pathways. A sixteen-gene common response to chronic hypoxia was put forth, including genes regulating angiogenesis and proliferation. Our findings show that chronic hypoxia enables survival of tumor cells by metabolic reprogramming, modulating proliferation, and increasing genomic instability. They additionally highlight key adaptive pathways that can potentially be targeted to prevent cancer cells residing in chronically hypoxic tumor areas from thriving.

Dry and wet experiments reveal the significant role of FUT11 in clear cell renal cell carcinoma

BACKGROUND

Renal cancer is one of the most common urogenital tumors worldwide. Although numerous traditional and relatively new therapeutic strategies have been adopted for clear cell renal cell carcinoma (ccRCC) patients, their effects are not satisfactory enough for the improvement of patients. The pathogenesis and progression of ccRCC requires further investigations.

METHODS

Using a series of bioinformatic analyses, the expression levels, clinical relevance, and prognostic potential of FUT11 as well as its correlations with immune cells in ccRCC were investigated. The mRNA and protein expression levels of FUT11 in renal cancer cell lines and human tissues were determined using quantitative real-time-polymerase chain reaction (RT-PCR) and Western blot analyses. MTT, colony formation, Edu, and wound healing assays were performed to explore the function of FUT11 in renal cancer cell lines. The immunohistochemical staining of human and mouse tissues was performed to reveal the correlations between the expression levels of FUT11 and the infiltration level of immune cell subtypes. Using mouse xenograft models, the role of FUT11 was further investigated in-vivo.

RESULTS

The data mining and corresponding analyses indicated that the expression levels of FUT11 were elevated in renal cancer and independently correlated with the prognosis of ccRCC patients. The cibersort and ssGSEA algorithms revealed differential infiltration levels of immune cells between the patients with distinct expression levels of FUT11; these results were verified by the consequent human renal cancer tissues and animal models. The MTT, colony formation, EdU, and wound healing assays showed that the decreased expression level of FUT11 could promote the proliferation and migration of renal cancer cell lines. The animal models-based analysis showed similar results.

CONCLUSIONS

In conclusion, this study identified a novel important molecule correlated with the prognosis of ccRCC patients and revealed its immune-related role and its function in the proliferation and migration of renal cancer cells. This study might provide a novel basis for the treatment of renal cancer.

Comparative transcriptomics of high-altitude Vulpes and their low-altitude relatives

The harsh environment of Qinghai-Tibet Plateau (QTP) imposes strong selective stresses (e.g., hypoxia, high UV-radiation, and extreme temperature) to the native species, which have driven striking phenotypic and genetic adaptations. Although the mechanisms of high-altitude adaptation have been explored for many plateau species, how the phylogenetic background contributes to genetic adaption to high-altitude of Vulpes is largely unknown. In this study, we sequenced transcriptomic data across multiple tissues of two high-altitude Vulpes (Vulpes vulpes montana and Vulpes ferrilata) and their low-altitude relatives (Vulpes corsac and Vulpes lagopus) to search the genetic and gene expression changes caused by high-altitude environment. The results indicated that the positive selection genes (PSGs) identified by both high-altitude Vulpes are related to angiogenesis, suggesting that angiogenesis may be the result of convergent evolution of Vulpes in the face of hypoxic selection pressure. In addition, more PSGs were detected in V. ferrilata than in V. v. montana, which may be related to the longer adaptation time of V. ferrilata to plateau environment and thus more genetic changes. Besides, more PSGs associated with high-altitude adaptation were identified in V. ferrilata compared with V. v. montana, indicating that the longer the adaptation time to the high-altitude environment, the more genetic alterations of the species. Furthermore, the result of expression profiles revealed a tissue-specific pattern between Vulpes. We also observed that differential expressed genes in the high-altitude group exhibited species-specific expression patterns, revealed a convergent expression pattern of Vulpes in high-altitude environment. In general, our research provides a valuable transcriptomic resource for further studies, and expands our understanding of high-altitude adaptation within a phylogenetic context.