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

Figure 6

Biological functions and TME landscape. (a) Significant enriched pathways in the high- and low-risk groups. The extremum located in the left part indicates a positive association between risk scores and pathway activity, and vice versa. (b) The relationships of risk and tumor immune-infiltrations according to the evidence from the TIMER database. (c, d) The differences of tumor infiltrating of 16 cell types and score of immune pathways between the risk groups by ssGSEA. The lines in the boxes represent the median values. The black dots represent outliers. Asterisks indicate significance. (e) The associations between risk and immune-infiltrations by CIBERSORT algorithm. (f) Proportions of multiple tumor-infiltrating cells (TME: tumor microenvironment; ssGSEA: single-sample gene set enrichment analysis).

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