Identification and validation of a novel glycolysis-related gene signature for predicting the prognosis in ovarian cancer

Fig. 1. Flow chart for the research

Fig. 2. Identification of glycolysis-related DEGs in the TCGA training set and functional analysis of DEGs. a GSEA of two gene sets that were significantly differentially enriched between normal and tumor tissues. b Heat map of representative DEGs. The colours in the heat map represent the expression levels (blue to red represents the spectrum from low to high. c The results of GO enrichment analysis are shown in a bubble chart: molecular function (MF), biological process (BP), and cellular component (CC). d KEGG enrichment analysis of DEGs

Fig. 3. Construction and validation of the risk signature in the TCGA training set. a, b Five glycolysis-related genes (ANGPTL4, PYGB, IRS2, ISG20 and SEH1L) were selected to construct a risk signature according to univariate Cox regression analysis (a) and multivariate Cox regression analysis (b). c Differential mRNA level between ovarian tumor tissues and normal tissues in TCGA training set. *P < 0.05; **P < 0.01; ***P < 0.001. d The correlations between the mRNA levels of the five genes. e PCA based on mRNA data between the low-risk (n = 187) and high-risk groups (n = 188). f The Kaplan–Meier survival curves of the low-risk group and the high-risk group were distinct, indicating the prognostic value of the risk signature. g ROC curves were used to assess the efficiency of the risk signature for predicting 5-, 7- and 9-year survival. h The risk score distribution, the expression profiles of the five genes and the survival status of patients

Fig. 4. External validation of the risk signature in the ICGC external test set. a Kaplan–Meier survival curves showed the prognostic value of the risk signature in the ICGC external test set (n3 = 93). b The risk score distribution (When the risk score of the high-risk group was above 10 points, it was recorded as 10, which was convenient for visual display and did not affect the results), the expression profiles of the five genes and the survival status of patients

Fig. 5. Construction of a nomogram for predicting the 1-, 3- and 5-year survival of OC patients. a, b Univariate Cox regression analysis (a) and multivariate Cox regression analysis (b) were applied to evaluate the contribution of each variable to OC survival. c A nomogram for predicting the 1-, 3- and 5-year survival rates of OC patients was established. d–f Calibration curves showed the actual rate versus predicted probability of 1- (d), 3- (e) and 5-year (f) survival

Fig. 6. GSVA and immune infiltration analysis of the risk signature between low- and high-risk group. a GSVA revealed KEGG pathways associated with the risk signature. b The top 10 risk signature-related KEGG pathways. c Infiltration abundance of 22 immune cells in each sample; samples were sorted by risk score from left (lowest) to right (highest). d The differences in the infiltration level of 22 immune cells between the low-risk and high-risk groups. The green line is the low-risk group, and the red line represents the high-risk group

Fig. 7. Expression and function analysis of unreported signature genes ISG20 and SEH1L in OC. a, b Immunohistochemistry (a) and western blotting (b) analysis of ISG20 and SEH1L protein levels in paired tumor tissues and adjacent normal tissues. Scale bar = 50 μm. c qRT-PCR analysis of ISG20 and SEH1L mRNA levels in paired tumor tissues and adjacent normal tissues. *P < 0.05. d Western blotting analysis of ISG20 or SEH1L expression in Caov3 cells transfected with pLVX-ISG20-IRES-Neo, pLVX-SEH1L-IRES-Neo or empty vector. e Cell proliferation was detected by the CCK-8 assay. **P < 0.01; ***P < 0.001. f Cell migration and invasion were evaluated by Transwell assays. g Tumor growth curve. h. Xenograft tumors. Scale bar = 1 cm. i The weights of xenograft tumors. **P < 0.01; ***P < 0.001