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. Purpose 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 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 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 The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.
Highlights
Ovarian cancer (OC) is one of the most common gynecological neoplasms with the fourth highest incidence and third highest mortality in the world [1]
The number in the parentheses represented the rankings of weight. With these inadequacies and challenges of OC research, this research is aimed at investigating the clinicopathologic features of glycosylation-related messenger RNAs (GRmRNAs) for the prognostic and tumor microenvironment (TME) prediction of patients with OC
Our study found that overexpressed ALG8 was located in OC tissues and was associated with favorable survival outcomes
Summary
Ovarian cancer (OC) is one of the most common gynecological neoplasms with the fourth highest incidence and third highest mortality in the world [1]. Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. 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. 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. 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
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