[Retracted] CDC20 May Serve as a Potential Biomarker-Based Risk Score System in Predicting the Prognosis of Patients with Hepatocellular Carcinoma

Figure 1

Workflow of the study. In step 1, differentially expressed genes (DEGs) of TCGA, GSE138178, GSE84006, and GSE77509 between HCC and adjacent nontumor liver tissues were identified by differential expression analysis. Functional enrichment analyses were performed using the intersection of DEGs in the four datasets. In step 2, a coexpression network was done to screen the module genes, in which 998 OS-related genes were identified by Cox and Kaplan-Meier analyses using the module genes. An of OS-related genes was obtained by AUC analysis to construct the protein-protein interaction (PPI) network. In step 3, 8 feature genes were identified by least absolute shrinkage and the selection operator (LASSO) model. Then, univariate Cox model and feature gene-based risk score demonstrated a high expression of CDC20 as a poor overall survival- (OS-) related gene in HCC patients. In step 4, expression profiles of the four datasets were performed on the single sample GSEA to explore immune cell infiltration and the correlation of upregulated type-2 T helper cells and CDC20. In step 5, the correlation analysis of CDC20 and its biological pathways showed that the cell cycle was significantly correlated with CDC20 in HCC. AUC: area under the curve; DEG: differentially expressed genes; GSEA: gene set enrichment analysis; K-M: Kaplan-Meier; HCC: hepatocellular cancer; LASSO: least absolute shrinkage and selection operator; OS: overall survival; PPI: protein-protein interaction; ROC: receiver-operating characteristic curve; TCGA: The Cancer Genome Atlas. WGCNA: weighted gene coexpression network analysis.