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| Bioinformatics-Based Prognostic Model for Ferroptosis in Hepatocellular Carcinoma |
| YU Yangyang, HAN Chao, LI Guang |
| The First Affiliated Hospital of China Medical University, Liaoning Shenyang 110000, China |
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Abstract Objective: To find the ferroptosis gene and independent prognostic factors, and to construct the prognostic model of ferroptosis in HCC patients. Methods: The transcriptomic data and clinical information of TCGA-LIHC were downloaded from the TCGA database, and the expression profiles of TCGA-LIHC genes were extracted using Perl language.The expression profiling data for ferroptosis-related genes were differentially analyzed using the limma package in R. Using the org.Hs.eg in the R language. Differential ferroptosis genes were id annotated using the org.Hs.eg.db package in R, and the GO and KEGG enrichment analysis of ferroptosis genes was performed using the colorspace, stringi and ggplot2 packages in R. The expression profile information of ferroptosis-related genes and the survival time and survival status of TCGA-LIHC patients were integrated using Perl language, and ferroptosis-related genes significantly associated with the survival prognosis of LIHC patients were screened by one-way cox analysis, and then the model was constructed using multi-factor cox analysis.Prognostic models of ferroptosis for hepatocellular carcinoma were grouped using the survival package in R and KM survival analysis was performed.Risk curves and survival status maps were plotted for the ferroptosis prognosis models using the pheatmap package in R. Patients’ survival time, survival status, age, sex, and TNM stage were integrated with an ferroptosis prognosis model using Perl to perform univariate and multifactor cox analyses to find factors that could be used as independent prognostic factors and ROC curves were used to assess the prognostic factors of the model.Results: The TCGA-LIHC contained 50 normal tissues and 374 hepatocellular carcinoma tissues.The differential analysis yielded 83 differential genes of TCGA-LIHC-related ferroptosis, including 70 upregulated genes and 13 downregulated genes.GO functional enrichment analysis showed that response to oxidative stress and cellular response to oxidative stress were the major ferroptosis-related biological processes; KEGG functional enrichment analysis revealed that Ferroptosis and Central carbon metabolism in cancer were the major biological pathways. Univariate cox analysis screened out 31 genes associated with ferroptosis prognosis, and multivariate cox analysis constructed a prognostic model of 10 ferroptosis-related genes (FANCD2, ZEB1, BLOC1S5-TXNDC5, HMOX1, GABARAPL1, FLT3, IDH1, G6PD, VDAC2, MYB). The coefficients in the multivariate cox regression were as follows: ferroptosis gene risk score = (-0.3774 FANCD2 expression) + (-0.3774 ZEB1 expression) + (-0.3774 BLOC1S5-TXNDC5 expression) + (-0.3774 HMOX1 expression) + (-0.3774 GABARAPL1 expression) + (-0.3774 FLT3 expression) + (-0.3774 IDH1 expression) + (-0.3774 G6PD expression) + (-0. 3774 VDAC2 expression) + (-0.3774 MYB expression).KM survival analysis showed showed a greater advantage in survival for patients with low expression of the prognostic model, P=1.992e 08. Risk curve and survival status further validated our results. Univariate and multivariate cox analyses showed P<0.001 and P=0.004 for clinical stage, and P<0.001 for riskScore, respectively. ROC curve evaluated the AUC of 0.736 for the ferroptosis model and the AUC of 0.701 for the clinical stage, which demonstrateed the reliability of the model.Conclusion: Through bioinformatics analysis, 10 ferroptosis gene models can be used to evaluate patient prognosis.
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2022, Vol. 28 
