Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.
Journal
Oxidative medicine and cellular longevity
ISSN: 1942-0994
Titre abrégé: Oxid Med Cell Longev
Pays: United States
ID NLM: 101479826
Informations de publication
Date de publication:
2022
2022
Historique:
received:
31
10
2021
revised:
10
02
2022
accepted:
21
02
2022
entrez:
14
3
2022
pubmed:
15
3
2022
medline:
25
3
2022
Statut:
epublish
Résumé
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. This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients. The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels. A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts. The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.
Sections du résumé
Background
UNASSIGNED
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
UNASSIGNED
This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients.
Methods
UNASSIGNED
The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels.
Results
UNASSIGNED
A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts.
Conclusion
UNASSIGNED
The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.
Identifiants
pubmed: 35281472
doi: 10.1155/2022/3665617
pmc: PMC8916863
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3665617Informations de copyright
Copyright © 2022 Chen Zhao et al.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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