Research Paper Volume 16, Issue 14 pp 11248—11274
A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning
Figure 4. Consensus clustering based on the DEGs and assessment of the Stemness Subtypes. (A) Consensus clustering matrix for EC patients for DEGs in EC. (B) Consensus clustering distribution function (CDF). (C) Relative changes in the area under the CDF curve. (D) The heatmap shows the of 145 DEGs (including 3 up-regulated and 142 down-regulated genes) between different Stemness Subtypes and the clinical characteristics (TMB, TCGA subtypes, continuous variable of mRNAsi and categorical variable) in the TCGA database. (E, F) Survival curve of patients in different subtypes. Patients in subtype I had a promising prognosis in both OS and DFS. (G) Thermogram shows the activation state of KEGG pathways in different Stemness Subtype I and II after processing by GSVA. The yellow node represents high enrichment scores, and the blue node represents low enrichment scores, p < 0.05.