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

Published Date: Mar 4, 2022

Abstract

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.

Authors

Chen Zhao

Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China.
Kewei Xiong

School of Mathematics and Statistics, Central China Normal University, Wuhan, 430079 Hubei Province, China.
Fangrui Zhao

Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei Province, China.
Abdalla Adam

School of Medicine, Wuhan University, Wuhan, 430072 Hubei Province, China.
Xiangpan Li

Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei Province, China.

Keywords

Female Gene Expression Regulation, Neoplastic Gene Regulatory Networks Glycosylation Humans Machine Learning Middle Aged Ovarian Neoplasms Prognosis Protein Processing, Post-Translational

External Resources

View on PubMed Access via DOI PubMed (35281472)

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

Abstract

Authors

Keywords

External Resources

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