Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning

Zhao, Chen; Xiong, Kewei; Zhao, Fangrui; Adam, Abdalla; Li, Xiangpan

doi:10.1155/2022/3665617

Cited by 9 publications

(9 citation statements)

References 64 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis Identified Glycosyltransferase Signature to Predict Glioma Prognosis and TAM Phenotype

Liu

et al. 2023

BioMed Research International

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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.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis Identified Glycosyltransferase Signature to Predict Glioma Prognosis and TAM Phenotype

Liu

et al. 2023

BioMed Research International

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer

Jia

et al. 2023

J Ovarian Res

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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.

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“… 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.…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

Liu

Antwi

et al. 2022

Front. Genet.

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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.

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Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning

Cited by 9 publications

References 64 publications

Comprehensive Analysis Identified Glycosyltransferase Signature to Predict Glioma Prognosis and TAM Phenotype

Comprehensive Analysis Identified Glycosyltransferase Signature to Predict Glioma Prognosis and TAM Phenotype

A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

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