GLT8D1 overexpression as a novel prognostic biomarker in human cutaneous melanoma

Genome-wide association study reveals 2 copy number variations associated with the variation of plumage color in the white duck hybrid population

Plumage color is an intuitive external poultry characteristic with rich manifestations and complex genetic mechanisms. In our previous study, we observed that there were more dark variations in plumage color in the F2 population derived from the hybridization of 2 white duck varieties. Therefore, based on the statistics of plumage color of 308 F2 populations, we further used the resequencing data of these individuals to detect copy number variations (CNVs) in the whole genome and conducted genome-wide association studies (GWAS) to determine the genetic basis related to plumage color traits. The CNV detection revealed 9,337 CNVs, with an average length of 15,950 bp and a total length of 142.02 MB, accounting for approximately 12.91% of the reference genome. The CNV distribution on the chromosomes was relatively uniform, and the number of CNVs on each chromosome positively correlated with the length of the chromosome. In the pure black plumage group, 2,101 CNVs were only identified, and 1,714 were specifically identified in the pure white plumage group. Ten CNVs were randomly selected for validation using quantitative real-time PCR, and 9 CNVs had the same CNV types as predicted, with an accuracy of 90%. Based on GWAS, we identified 2 CNVs potentially associated with plumage color variations, with the associated CNV regions covering 9 genes. Enrichment analysis of these 9 candidate genes showed significant enrichment of 3 pathways (ribosome biogenesis in eukaryotes, RNA transport, and protein export) and 17 gene ontology terms. Among these, VWA5A can downregulate MITF by binding to the regulatory factors SOX10. The occurrence of CNV may indirectly contribute to duck plumage color variation by affecting the regulatory factors of the switch gene MITF in the melanogenesis pathway. These findings have improved the understanding of the genetic basis of duck plumage color variation and have been beneficial for developing and using plumage color traits in subsequent poultry breeding.

Glycosyltransferase 8 domain-containing protein 1 (GLT8D1) is a UDP-dependent galactosyltransferase

Glycosyltransferases (GTs) are enzymes that catalyze the formation of glycosidic bonds and hundreds of GTs have been identified so far in humans. Glycosyltransferase 8 domain-containing protein 1 (GLT8D1) has been associated with central nervous system diseases and cancer. However, evidence on its enzymatic properties, including its substrates, has been scarcely described. In this paper, we have produced and purified recombinant secretory GLT8D1. The enzyme was found to be N-glycosylated. Differential scanning fluorimetry was employed to analyze the stabilization of GLT8D1 by Mn2+ and nucleotides, revealing UDP as the most stabilizing nucleotide scaffold. GLT8D1 displayed glycosyltransferase activity from UDP-galactose onto N-acetylgalactosamine but with a low efficiency. Modeling of the structure revealed similarities with other GT-A fold enzymes in CAZy family GT8 and glycosyltransferases in other families with galactosyl-, glucosyl-, and xylosyltransferase activities, each with retaining catalytic mechanisms. Our study provides novel structural and functional insights into the properties of GLT8D1 with implications in pathological processes.

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

BACKGROUND

Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics.

PURPOSE

The purpose of this study was to explore the potential application value of pathomics signatures.

METHODS

Pathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature-gene links.

RESULTS

The clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e-04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high-risk group based on gene expression and pathomics signatures was significantly lower than that of the low-risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p-values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc. CONCLUSION: This study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature-based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.

Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction

Clinical outcome prediction is important for stratified therapeutics. Machine learning (ML) and deep learning (DL) methods facilitate therapeutic response prediction from transcriptomic profiles of cells and clinical samples. Clinical transcriptomic DL is challenged by the low-sample sizes (34-286 subjects), high-dimensionality (up to 21,653 genes) and unordered nature of clinical transcriptomic data. The established methods rely on ML algorithms at accuracy levels of 0.6-0.8 AUC/ACC values. Low-sample DL algorithms are needed for enhanced prediction capability. Here, an unsupervised manifold-guided algorithm was employed for restructuring transcriptomic data into ordered image-like 2D-representations, followed by efficient DL of these 2D-representations with deep ConvNets. Our DL models significantly outperformed the state-of-the-art (SOTA) ML models on 82% of 17 low-sample benchmark datasets (53% with >0.05 AUC/ACC improvement). They are more robust than the SOTA models in cross-cohort prediction tasks, and in identifying robust biomarkers and response-dependent variational patterns consistent with experimental indications.

Glycosyltransferase GLT8D1 and GLT8D2 serve as potential prognostic biomarkers correlated with Tumor Immunity in Gastric Cancer

BACKGROUND

Glycosylation involved in various biological function, aberrant glycosylation plays an important role in cancer development and progression. Glycosyltransferase 8 domain containing 1 (GLT8D1) and GLT8D2, as members of the glycosyltransferase family proteins, are associated with transferase activity. However, the association between GLT8D1/2 and gastric cancer (GC) remains unclear. We aimed to investigate the potential prognostic value and oncogenic role of GLT8D1/2 in GC.

METHODS

The relationship between GLT8D1/2 and GC was evaluated through comprehensive bioinformatics approaches. A series of factors like gene expression patterns, Kaplan-Meier survival analyses, Cox regression analyses, prognostic nomogram, calibration curves, ROC curves, function enrichment analyses, tumor immunity association, genetic alterations, and DNA methylation were included. Data and statistical analyses were performed using R software (v3.6.3).

RESULTS

Both GLT8D1 and GLT8D2 expression were significantly upregulated in GC tissues(n = 414) compared with normal tissues(n = 210), and high expression of GLT8D1/2 was remarkably correlated with poor prognosis for GC patients. Cox regression analyses implied that GLT8D1/2 could act as independent prognostic factors in GC. Furthermore, gene function analyses indicated that multiple signaling pathways involving tumor oncogenesis and development enriched, such as mTOR, cell cycle, MAPK, Notch, Hedgehog, FGF, and PI3K-Akt signaling pathways. Moreover, GLT8D1/2 was significantly associated with immune cell infiltration, immune checkpoint genes, and immune regulators TMB/MSI.

CONCLUSION

GLT8D1/2 may serve as potential prognostic markers of poor prognosis in GC correlated with tumor immunity. The study provided an insight into identifying potential biomarkers and targets for prognosis, immunotherapy response, and therapy in GC.

Integrated single-cell and bulk RNA sequencing analysis identifies a cancer-associated fibroblast-related gene signature for predicting survival and therapy in gastric cancer

As the dominant component of the tumor microenvironment, cancer-associated fibroblasts (CAFs), play a vital role in tumor progression. An increasing number of studies have confirmed that CAFs are involved in almost every aspect of tumors including tumorigenesis, metabolism, invasion, metastasis and drug resistance, and CAFs provide an attractive therapeutic target. This study aimed to explore the feature genes of CAFs for potential therapeutic targets and reliable prediction of prognosis in patients with gastric cancer (GC). Bioinformatic analysis was utilized to identify the feature genes of CAFs in GC by performing an integrated analysis of single-cell and transcriptome RNA sequencing using R software. Based on these feature genes, a CAF-related gene signature was constructed for prognostic prediction by LASSO. Simultaneously, survival analysis and nomogram were performed to validate the prognostic predictive value of this gene signature, and qRT-PCR and immunohistochemical staining verified the expression of the feature genes of CAFs. In addition, small molecular drugs for gene therapy of CAF-related gene signatures in GC patients were identified using the connectivity map (CMAP) database. A combination of nine CAF-related genes was constructed to characterize the prognosis of GC, and the prognostic potential and differential expression of the gene signature were initially validated. Additionally, three small molecular drugs were deduced to have anticancer properties on GC progression. By integrating single-cell and bulk RNA sequencing analyses, a novel gene signature of CAFs was constructed. The results provide a positive impact on future research and clinical studies involving CAFs for GC.

Apprising Diagnostic and Prognostic Biomarkers in Cutaneous Melanoma—Persistent Updating

The incidence of melanoma, a very aggressive skin cancer, has increased over the past few decades. Although there are well-established clinical, dermoscopic and histopathological criteria, the diagnosis is often performed late, which has important implications on the patient’s clinical outcome. Unfortunately, melanoma is one of the most challenging tumors to diagnose because it is a heterogeneous neoplasm at the clinical, histopathological, and molecular level. The use of reliable biomarkers for the diagnosis and monitoring of disease progression is becoming a standard of care in modern medicine. In this review, we discuss the latest studies, which highlight findings from the genomics, epitranscriptomics, proteomics and metabolomics areas, pointing out different genes, molecules and cells as potential diagnostic and prognostic biomarkers in cutaneous melanoma.

Emerging Roles of Post-Translational Modifications in Skin Diseases: Current Knowledge, Challenges and Future Perspectives

Post-translational modifications (PTMs) of proteins represent as a key step in regulating their biological functions and dynamic interaction with other players. This process is fine-tuned by a myriad of enzymes named “writers, readers and erasers” whose actions are precisely controlled. Either the mutation, aberration in the expression of the aforementioned enzymes or their substrates have shown to participate in the pathogenesis of various skin diseases such as melanoma, vitiligo, psoriasis, eczema, atopic dermatitis and inherited dermatological diseases. It is becoming increasingly clear that key transcriptional factors, inflammation-related molecules are prone to PTMs. Despite their importance in regulating key processes including inflammation, keratinocyte apoptosis, proliferation and differentiation, PTMs have received less attention due to the challenges involved. Here in this review we summarize the role of the most common types and the newly discovered PTMs, including acetylation, glycosylation, citrullination, PARylation and sumoylation in dermatoses and surveys the recent progress in PTM-based therapeutic approaches in skin diseases.

Enzymatic activity of glycosyltransferase GLT8D1 promotes human glioblastoma cell migration

Glioblastoma (GBM) is the most aggressive primary brain tumor characterized by infiltrative growth of malignant glioma cells into the surrounding brain parenchyma. In this study, our analysis of GBM patient cohorts revealed a significantly higher expression of Glycosyltransferase 8 domain containing 1 (GLT8D1) compared to normal brain tissue and could be associated with impaired patient survival. Increased in vitro expression of GLT8D1 significantly enhanced migration of two different sphere-forming GBM cell lines. By in silico analysis we predicted the 3D-structure as well as the active site residues of GLT8D1. The introduction of point mutations in the predicted active site reduced its glycosyltransferase activity in vitro and consequently impaired GBM tumor cell migration. Examination of GLT8D1 interaction partners by LC-MS/MS implied proteins associated with cytoskeleton and intracellular transport as potential substrates. In conclusion, we demonstrated that the enzymatic activity of glycosyltransferase GLT8D1 promotes GBM cell migration.

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The glycosyltransferase GLT8D1 is enriched in GBM tissue and cells

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In silico analysis predicts the 3D structure and the active site of GLT8D1

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Enzymatically active GLT8D1 promotes GBM migration

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Manipulation of GLT8D1 enzymatic activity decreases GBM migration

Identification of a Glycosyltransferase Signature for Predicting Prognosis and Immune Microenvironment in Neuroblastoma

Neuroblastoma (NB) is one of the most common solid tumors in children. Glycosyltransferases (GTs) play a crucial role in tumor development and immune escape and have been used as prognostic biomarkers in various tumors. However, the biological functions and prognostic significance of GTs in NB remain poorly understood. The expression data from Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were collected as training and testing data. Based on a progression status, differentially expressed GTs were identified. We constructed a GTscore through support vector machine, least absolute shrinkage and selection operator, and Cox regression in NB, which included four prognostic GTs and was an independent prognostic risk factor for NB. Patients in the high GTscore group had an older age, MYCN amplification, advanced International Neuroblastoma Staging System stage, and high risk. Samples with high GTscores revealed high disialoganglioside (GD2) and neuron-specific enolase expression levels. In addition, a lack of immune cell infiltration was observed in the high GTscore group. This GTscore was also associated with the expression of chemokines (CCL2, CXCL9, and CXCL10) and immune checkpoint genes (cytotoxic T-lymphocyte–associated protein 4, granzyme H, and granzyme K). A low GTscore was also linked to an enhanced response to anti–PD-1 immunotherapy in melanoma patients, and one type of tumor was also derived from neuroectodermal cells such as NB. In conclusion, the constructed GTscore revealed the relationship between GT expression and the NB outcome, GD2 phenotype, and immune infiltration and provided novel clues for the prediction of prognosis and immunotherapy response in NB.

High glycosyltransferase 8 domain containing two protein levels contribute to poor prognosis in urothelial carcinoma

OBJECTIVE

To examine the expression levels of the glycosyltransferase 8 domain containing protein 2 and its clinical implications in urothelial carcinoma patients.

METHODS

Data mining, immunohistochemistry together with H-score calculation was carried out to evaluate the glycosyltransferase 8 domain containing protein 2 levels on tissue specimens from urothelial carcinoma patients, retrospectively. Correlations between glycosyltransferase 8 domain containing protein 2 H-score and imperative clinicopathological factors were measured. The indication of glycosyltransferase 8 domain containing protein 2 level on disease-specific and metastasis-free survivals were next analyzed.

RESULTS

In upper tract urothelial carcinomas (n = 340) and bladder urothelial carcinomas (n = 295), 170 (50%) and 148 (50%) patients, respectively, were identified to have high glycosyltransferase 8 domain containing protein 2 expression. The glycosyltransferase 8 domain containing protein 2 levels were correlated to several clinicopathological characteristics and patient survival. Upregulation of the glycosyltransferase 8 domain containing protein 2 was correlated to primary tumor (P < 0.001), nodal metastasis (P < 0.001), histological grade (P < 0.001), vascular invasion (P < 0.001), perineural invasion (P < 0.05) and mitotic rate (P < 0.001). High glycosyltransferase 8 domain containing protein 2 levels independently predicted poor disease-specific survival (P = 0.049) and metastasis-free survival (P = 0.008) in upper tract urothelial carcinoma and urinary bladder urothelial carcinoma, respectively. Gene Ontology enrichment analysis additionally showed that multiple biological processes were enriched including "ECM organization" (Gene Ontology:0030198), "extracellular structure organization" (Gene Ontology:0043062), "biological adhesion" (Gene Ontology:0022610), "cell adhesion" (Gene Ontology:0007155), "collagen fibril organization" (Gene Ontology:0030199) and "vasculature development" (Gene Ontology:0001944).

CONCLUSIONS

The present findings suggest that upregulation of the glycosyltransferase 8 domain containing protein 2 is an independent and disadvantageous prognosticator in urothelial carcinoma. High glycosyltransferase 8 domain containing protein 2 level might play a crucial role in progression of urothelial carcinoma.

Germinal GLT8D1, GATAD2A and SLC25A39 mutations in a patient with a glomangiopericytal tumor and five different sarcomas over a 10-year period

Soft tissue sarcoma represents about 1% of all adult cancers. Occurrence of multiple sarcomas in a same individual cannot be fortuitous. A 72-year-old patient had between 2007 and 2016 a glomangiopericytal tumor of the right forearm and a succession of sarcomas of the extremities: a leiomyosarcoma of the left buttock, a myxofibrosarcoma (MFS) of the right forearm, a MFS of the left scapula, a left latero-thoracic MFS and two undifferentiated sarcomas on the left forearm. Pathological examination of the six locations was not in favor of disease with local/distant recurrences but could not confirm different diseases. An extensive molecular analysis including DNA-array, RNA-sequencing and DNA-Sanger-sequencing, was thus performed to determine the link between them. The genomic profile of the glomangiopericytal tumor and the six sarcomas revealed that five sarcomas were different diseases and one was the local recurrence of the glomangiopericytal tumor. While the chromosomal alterations in the six tumors were different, a common somatic CDKN2A/CDKN2B deletion was identified. RNA-sequencing of five tumors identified mutations in GLT8D1, GATAD2A and SLC25A39 in all samples. The germline origin of these mutations was confirmed by Sanger-sequencing. Innovative molecular analysis methods have made possible a better understanding of the complex tumorigenesis of multiple sarcomas.

AFF4 facilitates melanoma cell progression by regulating c-Jun activity

Melanoma is characterized by high mortality and poor prognosis due to metastasis. AFF4 (AF4/FMR2 family member 4), as a scaffold protein, is a component of the super elongation complex (SEC), and is involved in the progression of tumors, e.g., leukemia, head and neck squamous cell carcinoma (HNSCC). However, few studies on AFF4 have focused on melanoma. Here, AFF4 expression levels and clinicopathological features were evaluated in melanoma tissue samples. Then, we performed cell proliferation, migration and invasion assays in A375 and A2058 cells lines in vitro to evaluate the role of AFF4 in melanoma. The effects of AFF4 knockdown in vivo were characterized via a xenograft mouse model. Finally, the correlation between c-Jun and AFF4 protein levels in melanoma was analyzed by rescue assay and immunohistochemistry (IHC). We found that AFF4 expression was upregulated in melanoma tumor tissues and that AFF4 protein expression was also closely related to the prognosis of patients with cutaneous melanoma. Moreover, AFF4 could promote the invasion and migration of melanoma cells by mediating epithelial to mesenchymal transition (EMT). AFF4 might regulate c-Jun activity to promote the invasion and migration of melanoma cells. Importantly, c-Jun was regulated by the AFF4 promoted melanoma tumorigenesis in vivo. Taken together, AFF4 may be a novel oncogene that promotes melanoma progression through regulation of c-Jun activity.

Role of FUT8 expression in clinicopathology and patient survival for various malignant tumor types: a systematic review and meta-analysis

Dysregulation of α(1,6)-fucosyltransferase (FUT8) plays significant roles in development of a variety of malignant tumor types. We collected as many relevant articles and microarray datasets as possible to assess the prognostic value of FUT8 expression in malignant tumors. For this purpose, we systematically searched PubMed, Embase, Web of Science, Springer, Chinese National Knowledge Infrastructure (CNKI), and Wan Fang, and eventually identified 7 articles and 35 microarray datasets (involving 6124 patients and 10 tumor types) for inclusion in meta-analysis. In each tumor type, FUT8 expression showed significant (p< 0.05) correlation with one or more clinicopathological parameters; these included patient gender, molecular subgroup, histological grade, TNM stage, estrogen receptor, progesterone receptor, and recurrence status. In regard to survival prognosis, FUT8 expression level was associated with overall survival in non-small cell lung cancer (NSCLC), breast cancer, diffuse large B cell lymphoma, gastric cancer, and glioma. FUT8 expression was also correlated with disease-free survival in NSCLC, breast cancer, and colorectal cancer, and with relapse-free survival in pancreatic ductal adenocarcinoma. For most tumor types, survival prognosis of patients with high FUT8 expression was related primarily to clinical features such as gender, tumor stage, age, and pathological category. Our systematic review and meta-analysis confirmed the association of FUT8 with clinicopathological features and patient survival rates for numerous malignant tumor types. Verification of prognostic value of FUT8 in these tumor types will require a large-scale study using standardized methods of detection and analysis.

Identification of a five-miRNA signature predicting survival in cutaneous melanoma cancer patients

Background

Cutaneous melanoma (CM) is the deadliest form of skin cancer. Numerous studies have revealed that microRNAs (miRNAs) are expressed abnormally in melanoma tissues. Our work aimed to assess multiple miRNAs using bioinformatic analysis in order to predict the prognoses of cutaneous melanoma patients.

Methods

The microarray dataset GSE35579 was downloaded from the Gene Expression Omnibus (GEO) database to detect the differential expression of miRNAs (DEMs), including 41 melanoma (primary and metastatic) tissues and 11 benign nevi. Clinical information and miRNA sequencing data of cutaneous melanoma tissues were downloaded from the Cancer Genome Atlas database (TCGA) to assess the prognostic values of DEMs. Additionally, the target genes of DEMs were anticipated using miRanda, miRmap, TargetScan, and PicTar. Finally, functional analysis was performed using selected target genes on the Annotation, Visualization and Integrated Discovery (DAVID) website.

Results

After performing bioinformatic analysis, a total of 185 DEMs were identified: 80 upregulated miRNAs and 105 downregulated miRNAs. A five-miRNA (miR-25, miR-204, miR-211, miR-510, miR-513c) signature was discovered to be a potential significant prognostic biomarker of cutaneous melanoma when using the Kaplan–Meier survival method (P = 0.001). Univariate and multivariate Cox regression analyses showed that the five-miRNA signature could be an independent prognostic marker (HR = 0.605, P = 0.006) in cutaneous melanoma patients. Biological pathway analysis indicated that the target genes may be involved in PI3K-Akt pathways, ubiquitin-mediated proteolysis, and focal adhesion.

Conclusion

The identified five-miRNA signature may serve as a prognostic biomarker, or as a potential therapeutic target, in cutaneous melanoma patients.