Figure - available from: Oxidative Medicine and Cellular Longevity
This content is subject to copyright. Terms and conditions apply.
Estimation of model effectiveness and a nomogram prediction. (a) Different stratifications of clinical phenotypes in the high- and low-risk groups. (b) Connections among risk subtypes, vital status, and age stratifications. (c, d) Principal component analysis and t-distribution stochastic neighbor embedding for sample discrimination in the first two dimensions. (e) Nomogram for 1-, 3-, and 5-year overall survival prediction. The red line shows an example of how to predict the prognosis. (f, g) DCA of 1-year and 3-year survival probability. The upper lines indicate more net benefit (∗∗∗P<0.001, ∗∗P<0.01, and ∗P<0.05).
Source publication
Background:
Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signatures and tumor environment of OC.
Purpose:
This study is aimed at developing a novel model with glyc...
Similar publications
Pyroptosis is a form of inflammatory programmed cell death. However, because of no specific molecular biomarker, pyroptosis has not been considered as a novel therapeutic method to treat cutaneous melanoma (CM). Here, we identified pyroptosis genes that associate with the prognosis of CM patients and constructed an effective model for the prognosti...
Objective:
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal types of cancer. Various microRNAs have been identified to play an important role in PDAC. The study aimed to explore the role of miR-429 in PDAC.
Patients and methods:
The expression and prognostic value of miR-429 were first analyzed using The Cancer Genome Atlas (TCG...
Background:
Thyroid cancer (TC) is a rapidly increasing incidence of endocrine malignancies, occupying 3% of new cancer incidence, of which 10% has a heterogeneous prognosis. Ferroptosis is a form of cell death distinct from apoptosis, which involves antitumor drug-related research. Long noncoding RNAs (lncRNAs) could affect cancer prognosis by re...
Background:
A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).
Methods:
From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs)...
Background:
Lung adenocarcinoma (LUAD) is a major cause for global cancer-related deaths. Research reports demonstrate that lymph node metastasis (LNM) is pertinent to the survival rate of LUAD patients, and crux lies in the lack of biomarkers that could distinguish patients with LNM. We aimed to verify the LNM-related prognostic biomarkers in LUA...
Citations
... At the same time, the emerging WGCNA is increasingly being used for associations between diseases and associated phenotypes and highly corrected gene module. [34][35][36] Several studies have elucidated the role of hub genes and their underlying molecular mechanisms in HCC patients through WGCNA analysis. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. ...
The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the “limma” software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein–protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the “limma” software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.
... This may offer an opportunity to predict and treat cancer outcomes. Glycosylation-related gene-based signatures predicted the prognosis of ovarian cancer and head and neck squamous cell carcinoma [30,31]. However, the role of GRGs in pancreatic cancer is poorly studied; therefore, this study determined their correlation. ...
Background:
Tumor initiation and progression are closely associated with glycosylation. However, glycosylated molecules have not been the subject of extensive studies as prognostic markers for pancreatic cancer. The objectives of this study were to identify glycosylation-related genes in pancreatic cancer and use them to construct reliable prognostic models.
Materials and methods:
The Cancer Genome Atlas and Gene Expression Omnibus databases were used to assess the differential expression of glycosylation-related genes; four clusters were identified based on consistent clustering analysis. Kaplan-Meier analyses identified three glycosylation-related genes associated with overall survival. LASSO analysis was then performed on The Cancer Genome Atlas and International Cancer Genome Consortium databases to identify glycosylation-related signatures. We identified 12 GRGs differently expressed in pancreatic cancer and selected three genes (SEL1L, TUBA1C, and SDC1) to build a prognostic model. Thereafter, patients were divided into high and low-risk groups. Eventually, we performed Quantitative real-time PCR (qRT-PCR) to validate the signature.
Results:
Clinical outcomes were significantly poorer in the high-risk group than in the low-risk group. There were also significant correlations between the high-risk group and several risk factors, including no-smoking history, drinking history, radiotherapy history, and lower tumor grade. Furthermore, the high-risk group had a higher proportion of immune cells. Eventually, three glycosylation-related genes were validated in human PC cell lines.
Conclusion:
This study identified the glycosylation-related signature for pancreatic cancer. It is an effective predictor of survival and can guide treatment decisions.
... It can construct gene coexpression networks, calculate the adjacency of genes, screen out hub gene modules, and evaluate the associations with specific clinical features. WGCNA has been widely used in various diseases, such as in cancers (9,10), coronary artery disease, and heart failure (11,12). Currently, there is a paucity of data utilizing WGCNA to identify key modules or genes related to POAF. ...
Background
Atrial fibrillation (AF) is the most common complication in patients undergoing cardiac surgery. However, the pathogenesis of postoperative AF (POAF) is elusive, and research related to this topic is sparse. Our study aimed to identify key gene modules and genes and to conduct a circular RNA (circRNA)-microRNA (miRNA)-messenger RNA (mRNA) regulatory network analysis of POAF on the basis of bioinformatic analysis.
Methods
The GSE143924 and GSE97455 data sets from the Gene Expression Omnibus (GEO) database were analyzed. Weighted gene co-expression network analysis (WGCNA) was used to identify the key gene modules and genes related to POAF. A circRNA-miRNA-mRNA regulatory network was also built according to differential expression analysis. Functional enrichment analysis was further performed according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
Results
WGCNA identified 2 key gene modules and 44 key genes that were significantly related to POAF. Functional enrichment analysis of these key genes implicated the following important biological processes (BPs): endosomal transport, protein kinase B signaling, and transcription regulation. The circRNA-miRNA-mRNA regulatory network suggested that KLF10 may take critical part in POAF. Moreover, 2 novel circRNAs, hsa_circRNA_001654 and hsa_circRNA_005899, and 2 miRNAs, hsa-miR-19b-3p and hsa-miR-30a-5p, which related with KLF10, were involved in the network.
Conclusions
Our study provides foundational expression profiles following POAF based on WGCNA. The circRNA-miRNA-mRNA network offers insights into the BPs and underlying mechanisms of POAF.
... The glycosylation of N-linked proteins plays a crucial role in multiple of cancer processes, including cell survival, proliferation, migration, metastasis, and anti-tumor immunity [17][18][19]. Therefore, abnormalities expression of glycosyltransferase is considered to be predictive markers and therapeutic targets in tumors [20]. We used the Kaplan-Meier plot to determine the prognostic value of the ALG3. ...
Background:
The abnormal expression of Alpha-1,3-mannosyltransferase (ALG3) has been implicated in tumor promotion. However, the clinical significance of ALG3 in Lung Adenocarcinoma (LUAD) remains poorly understood. Therefore, we aimed to assess the prognostic value of ALG3 and its association with immune infiltrates in LUAD.
Methods:
The transcriptional expression profiles of ALG3 were obtained from the Cancer Genome Atlas (TCGA), comparing lung adenocarcinoma tissue with normal tissues. To determine the prognostic significance of AGL3, Kaplan-Meier plotter, and Cox regression analysis were employed. Logistic regression was utilized to analyze the association between ALG3 expression and clinical characteristics. Additionally, a receiver operating characteristic (ROC) curve and a nomogram were constructed. To explore the underlying mechanisms, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene set enrichment analysis (GSEA) was conducted. The relationship between AGL3A mRNA expression and immune infiltrates was investigated using the tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB). Furthermore, an in vitro experiment was performed to assess the impact of ALG3 mRNA on lung cancer stemness abilities and examine key signaling pathway proteins.
Results:
Our results revealed the ALG3 mRNA and protein expression in patients with LUAD was much higher than that in adjacent normal tissues. High expression of ALG3 was significantly associated with N stage (N0, HR = 1.98, P = 0.002), pathological stage (stage I, HR = 2.09, P = 0.003), and the number of pack years (<40, HR = 2.58, P = 0.001). Kaplan-Meier survival analysis showed that high expression of ALG3 was associated with poor overall survival (P < 0.001), disease-free survival (P < 0.001), and progression-free interval (P = 0.007). Through multivariate analysis, it was determined that elevated ALG3 expression independently impacted overall survival (HR = 1.325, P = 0.04). The Tumor Immune Estimation Resource discovered a link between ALG3 expression and tumor-infiltrating immune cells in LUAD. Additionally, ROC analysis proved that ALG3 is a reliable diagnostic marker for LUAD (AUC:0.923). Functional pathways analysis identified that ALG3 is negatively correlated with FAT4. We performed qRT-PCR to assess that knockdown ALG3 expression significantly upregulated FAT4 expression. Spheroid assay and flow cytometry analysis results showed that downregulated of ALG3 inhibited H1975 cell line stemness. Western blot analysis revealed that decreased ALG3 inhibited the YAP/TAZ signal pathway.
Conclusion:
High expression of ALG3 is strongly associated with poor prognosis and immune infiltrates in LUAD.
... A translational study showed that the expression levels of 210 GTs genes could differentiate between six types of cancer, including breast cancer and ovarian cancer. Moreover, glycosylation has the potential to act as a prognostic indicator, as a signature of glycosylation-related genes (GRGs) was shown to predict clinical outcomes in ovarian cancer patients (15). Other post-translational regulatory mechanisms, such as ubiquitination (16), phosphorylation (17) and epigenetic modifications (18) have also been reported as potential biomarkers in UM prognostic models. ...
Background
Increasing evidence suggests a correlation between glycosylation and the onset of cancer. However, the clinical relevance of glycosylation-related genes (GRGs) in uveal melanoma (UM) is yet to be fully understood. This study aimed to shed light on the impact of GRGs on UM prognosis.
Methods
To identify the most influential genes in UM, we employed the AUCell and WGCNA algorithms. The GRGs signature was established by integrating bulk RNA-seq and scRNA-seq data. UM patients were separated into two groups based on their risk scores, the GCNS_low and GCNS_high groups, and the differences in clinicopathological correlation, functional enrichment, immune response, mutational burden, and immunotherapy between the two groups were examined. The role of the critical gene AUP1 in UM was validated through in vitro and in vivo experiments.
Results
The GRGs signature was comprised of AUP1, HNMT, PARP8, ARC, ALG5, AKAP13, and ISG20. The GCNS was a significant prognostic factor for UM, and high GCNS correlated with poorer outcomes. Patients with high GCNS displayed heightened immune-related characteristics, such as immune cell infiltration and immune scores. In vitro experiments showed that the knockdown of AUP1 led to a drastic reduction in the viability, proliferation, and invasion capability of UM cells.
Conclusion
Our gene signature provides an independent predictor of UM patient survival and represents a starting point for further investigation of GRGs in UM. It offers a novel perspective on the clinical diagnosis and treatment of UM.
... As a result of tumor mutations, new antigens are produced, and the exposure of these antigens may provide new targeting opportunities for immunotherapy. For instance, Zhao et al. developed a model incorporating 4 GT genes, ALG8, DCTN4, DCTN6, and UBB, to predict ovarian cancer (OC) patients and immune function [23]. The results showed that high-risk patients were at higher risk compared to low-risk patients, and tumor purity together with tumor mutational load was negatively correlated with risk scores. ...
Glycosylation is the most common posttranslational modification of proteins. Glycosyltransferase gene differential expression dictates the glycosylation model and is epigenetically regulating glioma progression and immunity. This study is aimed at identifying the glycosyltransferase gene signature to predict the prognosis and immune characteristics of glioma. The glycosyltransferase gene signature of glioma was identified in the TCGA database and validated in the CGGA database. Glioma patients were then divided into high- and low-risk groups based on risk scores to compare survival differences and predictive capacity. Subsequently, validation of glycosyltransferase gene signature merits by comparing with other signatures and utility in clinical judgment. The immune cell infiltration, immune pathways, and immune checkpoint expression level were also analyzed and compared in the high- and low-risk groups. Finally, the signature and its gene function were tested in our cohort and in vitro experiments. Eight glycosyltransferase genes were identified to establish the glycosyltransferase signature to predict the prognosis of glioma patients. The survival time was shorter in the high-risk group compared to the low-risk group based on glycosyltransferase signature and was confirmed in an independent external cohort. The glycosyltransferase signature displayed outstanding predictive capacity than other signatures in the TCGA and CGGA database cohorts. Furthermore, patients in the high-risk group were positively correlated with TAM infiltration, immune checkpoint expression level, and protumor immune pathways in TCGA cohorts. Validation of clinical tissue specimens revealed that the high-risk group was closely associated with infiltration of M2 TAMs. High-risk genes in the signature promote glioma proliferation, invasion, and macrophage recruitment in an in vitro validation of U87 and U251 cell lines. This carefully constructed that glycosyltransferase signature can predict the prognosis and immune profile of gliomas and help us evaluate subsequent macrophage-targeted therapies as well as other immune microenvironment modulation therapeutic strategies.
... ALG8 is an alpha-1,3-glucosyltransferase and ALG8-CGD (congenital disorders of glycosylation) is a widely studied monogenic disorder of glycosylation that involves multisystem disorders [68]. In malignancies, ALG8 was a variate of a risk predictive model established for gastric cancer [69] and ovarian cancer [70]. However, consistent with Zhao et al. 's study [70], ALG8 in our study was overexpressed in OC tissues and led to favourable outcomes. ...
... In malignancies, ALG8 was a variate of a risk predictive model established for gastric cancer [69] and ovarian cancer [70]. However, consistent with Zhao et al. 's study [70], ALG8 in our study was overexpressed in OC tissues and led to favourable outcomes. In summary, except for ALG8, other five GTs are all key enzymes that involves in the synthesis of cancer associated glycans and may exist a close tie between each other. ...
Background
Ovarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we aimed to determine whether OC patients could be stratified by glycosyltransferase gene profiles to better predict the prognosis and efficiency of immune checkpoint blockade therapies (ICBs).
Methods
We retrieved transcriptome data across 420 OC and 88 normal tissue samples using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively. An external validation dataset containing 185 OC samples was downloaded from the Gene Expression Omnibus (GEO) database. Knockdown and pathway prediction of B4GALT5 were conducted to investigate the function and mechanism of B4GALT5 in OC proliferation, migration and invasion.
Results
A total of 50 differentially expressed GT genes were identified between OC and normal ovarian tissues. Two clusters were stratified by operating consensus clustering, but no significant prognostic value was observed. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene signature was built that classified OC patients in the TCGA cohort into a low- or high-risk group. Patients with high scores had a worse prognosis than those with low scores. This risk signature was further validated in an external GEO dataset. Furthermore, the risk score was an independent risk predictor, and a nomogram was created to improve the accuracy of prognostic classification. Notably, the low-risk OC patients exhibited a higher degree of antitumor immune cell infiltration and a superior response to ICBs. B4GALT5, one of six hub genes, was identified as a regulator of proliferation, migration and invasion in OC.
Conclusion
Taken together, we established a reliable GT-gene-based signature to predict prognosis, immune status and identify OC patients who would benefit from ICBs. GT genes might be a promising biomarker for OC progression and a potential therapeutic target for OC.
... Several prognostic models based on glycosylation-related genes have been emerging for predicting cancer prognosis. For example, Zhao et al. harnessed 4 glycosylation-related mRNAs to calculate the risk score in ovarian cancer, and the risk score was negatively correlated with tumor purity and tumor mutation burden, which suggested that low-risk scores might predict a better benefit from ICI treatment (14). Our previous study screened 9 glycosyltransferase genes to develop a prognostic signature in BC, and the high-risk-group patients showed shorter survival probability and more immunosuppressive profile (15). ...
Aberrant glycosylation, a post-translational modification of proteins, is regarded to engage in tumorigenesis and malignant progression of breast cancer (BC). The altered expression of glycosyltransferases causes abnormal glycan biosynthesis changes, which can serve as diagnostic hallmarks in BC. This study attempts to establish a predictive signature based on glycosyltransferase-related lncRNAs (GT-lncRNAs) in BC prognosis and response to immune checkpoint inhibitors (ICIs) treatment. We firstly screened out characterized glycosyltransferase-related genes (GTGs) through NMF and WGCNA analysis and identified GT-lncRNAs through co-expression analysis. By using the coefficients of 8 GT-lncRNAs, a risk score was calculated and its median value divided BC patients into high- and low-risk groups. The analyses unraveled that patients in the high-risk group had shorter survival and the risk score was an independent predictor of BC prognosis. Besides, the predictive efficacy of our risk score was higher than other published models. Moreover, ESTIMATE analysis, immunophenoscore (IPS), and SubMAP analysis showed that the risk score could stratify patients with distinct immune infiltration, and patients in the high-risk group might benefit more from ICIs treatment. Finally, the vitro assay showed that MIR4435-2HG might promote the proliferation and migration of BC cells, facilitate the polarization of M1 into M2 macrophages, enhance the migration of macrophages and increase the PD-1/PD-L1/CTLA4 expression. Collectively, our well-constructed prognostic signature with GT-lncRNAs had the ability to identify two subtypes with different survival state and responses to immune therapy, which will provide reliable tools for predicting BC outcomes and making rational follow-up strategies.
... However, there are a few glycosylation regulatory prognostic models for glioma. A recent study developed a prognostic model to predict the prognosis of ovarian cancer patients based on glycosylationrelated genes (Zhao et al., 2022). Zhang et al. constructed an endoplasmic reticulum (ER) stress risk model and proved that it was an independent prognostic factor . ...
Glycosylation modification plays a vital role in tumor progression and is highly associated with glioma prognosis. However, the influence of glycosylation modification on the tumor microenvironment (TME) and omic features of glioma remains unclear. Differentially expressed glycosylation-related genes between adjacent and tumor tissues of The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets were identified. We performed unsupervised clustering to classify patients into different molecular phenotypes, and analyzed their TME het-erogeneity, including immunocyte infiltration, immune pathways and tumor purity. Subsequently, we developed a prognostic predicting system named GlycoScore by stepwise least absolute shrinkage and selection operator-Cox regression to evaluate the modification pattern and its association with somatic mutation, clinical significance , immune fractions and drug resistance. Two clustering clusters were identified and presented distinct clinical outcomes and biological functions characterized by hot and cold tumors respectively. Patients with higher GlycoScores exhibited poor prognosis, less mutation counts, and were more sensitive to chemothera-peutics. We also confirmed that the GlycoScore severed as an independent risk factor. Cancer hallmarks such as cell cycle, hippo, and TGFβ were active in the high-GlycoScore group. The combination of tumor mutation burden and the GlycoScore presented an excellent performance in prognostic stratification. Our study suggests that glycosylation is essential for modeling TME of glioma and the GlycoScore is a promising prognostic signature and indicator of immunotherapeutic efficacy.
... Yan et al. (2020) constructed a prognostic feature model of ovarian cancer based on immune cell infiltration (Yan et al., 2020). Zhao et al. (2022) constructed a prognostic model of ovarian cancer through WGCNA and machine learning methods (Zhao et al., 2022). However, there is no research to analyze the diagnostic signature genes of ovarian cancer through machine learning algorithms and medical big data. ...
... Yan et al. (2020) constructed a prognostic feature model of ovarian cancer based on immune cell infiltration (Yan et al., 2020). Zhao et al. (2022) constructed a prognostic model of ovarian cancer through WGCNA and machine learning methods (Zhao et al., 2022). However, there is no research to analyze the diagnostic signature genes of ovarian cancer through machine learning algorithms and medical big data. ...
Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.
Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.
Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.
Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.
