DOI: 10.57760/sciencedb.j00217.02019
title: Construction and validation of a prognostic model for hepatocellular carcinoma based on disulfidptosis/ferroptosis
title: 基于双硫死亡/铁死亡的肝癌预后模型的构建和验证

description: Objective TCGA and ICGC data were used to construct a prognostic model for Hepatocellular carcinoma (HCC) based on Disulfidptosis-associated genes (DAGs) and Ferroptosis-associated genes (FAGs). We also explored the immune characteristics and antitumour drug sensitivity of different risk groups of HCC.Methods The HCC transcriptome and clinical data were downloaded from the official websites of TCGA and ICGC databases, and the expressions of DAGs and FAGs were extracted, followed by differential expression analysis and prognostic analysis to screen out the differentially expressed and prognostically relevant DAGs and FAGs (DFAGs). Gene-gene interrelationships were explored with protein-interaction network (PPI) analysis and correlation network analysis. HCC prognostic models were constructed by lasso algorithm (LASSO) regression analysis. The prognostic value of risk factors was observed with univariate and multivariate Cox regression analyses, Kaplan-Meier analyses, receiver operating characteristic (ROC) curves, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). GO and KEGG enrichment analyses were used to further explore the associated mechanisms. Single sample gene set enrichment analysis (ssGSEA) was used to observe the effects of risk factors on immune cells and immune function. Finally, the accuracy of the model was validated using the ICGC database.Results In this study, a novel prognostic model for HCC was constructed based on four DFAGs (SLC7A11, SLC1A5, G6PD, and LRPPRC), with a risk score (risk sore) = (0.035 × SLC7A11 expression) + (0.0442 × SLC1A5 expression) + (0.1597 × G6PD expression) + ( 0.0132 × LRPPRC expression), and univariate and multivariate COX analyses showed significant independent prognostic value of the model (P &lt; 0.01). High risk score patients were shown to be significantly associated with poorer overall survival (OS) in both TCGA and ICGC (P &lt; 0.05). Enrichment analysis showed that risk-differential genes have multiple effects on HCC, which are closely related to immune response, cell cycle, glycolysis, and gluconeogenesis. The scoring of immune cell infiltration (ADC, CD8+ T cells, DC, IDC, macrophages, pDCs, Tfh, Th1 cells, Th2 cells, TIL, Treg) was significantly higher in the high-risk group than in the low-risk group, whereas the immune scoring of NK cells and mast cells was significantly lower than that of the low-risk group. type II interferon (Type_II_IFN_Reponse), CC chemokine receptor (CCR), and immune check-point were significantly different between high and low risk groups.Conclusion In this study, we constructed the first HCC prognostic model associated with DFAGs, which has a good predictive value.There are significant differences in the level of immune cell infiltration and immune function among different risk groups of HCC. There were significant differences in sensitivity to targeted and chemotherapeutic agents. It provides new ideas for prognostic assessment, immunotherapy and individualised treatment of HCC.
description: Objective TCGA and ICGC data were used to construct a prognostic model for Hepatocellular carcinoma (HCC) based on Disulfidptosis-associated genes (DAGs) and Ferroptosis-associated genes (FAGs). We also explored the immune characteristics and antitumour drug sensitivity of different risk groups of HCC.Methods The HCC transcriptome and clinical data were downloaded from the official websites of TCGA and ICGC databases, and the expressions of DAGs and FAGs were extracted, followed by differential expression analysis and prognostic analysis to screen out the differentially expressed and prognostically relevant DAGs and FAGs (DFAGs). Gene-gene interrelationships were explored with protein-interaction network (PPI) analysis and correlation network analysis. HCC prognostic models were constructed by lasso algorithm (LASSO) regression analysis. The prognostic value of risk factors was observed with univariate and multivariate Cox regression analyses, Kaplan-Meier analyses, receiver operating characteristic (ROC) curves, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). GO and KEGG enrichment analyses were used to further explore the associated mechanisms. Single sample gene set enrichment analysis (ssGSEA) was used to observe the effects of risk factors on immune cells and immune function. Finally, the accuracy of the model was validated using the ICGC database.Results In this study, a novel prognostic model for HCC was constructed based on four DFAGs (SLC7A11, SLC1A5, G6PD, and LRPPRC), with a risk score (risk sore) = (0.035 × SLC7A11 expression) + (0.0442 × SLC1A5 expression) + (0.1597 × G6PD expression) + ( 0.0132 × LRPPRC expression), and univariate and multivariate COX analyses showed significant independent prognostic value of the model (P &lt; 0.01). High risk score patients were shown to be significantly associated with poorer overall survival (OS) in both TCGA and ICGC (P &lt; 0.05). Enrichment analysis showed that risk-differential genes have multiple effects on HCC, which are closely related to immune response, cell cycle, glycolysis, and gluconeogenesis. The scoring of immune cell infiltration (ADC, CD8+ T cells, DC, IDC, macrophages, pDCs, Tfh, Th1 cells, Th2 cells, TIL, Treg) was significantly higher in the high-risk group than in the low-risk group, whereas the immune scoring of NK cells and mast cells was significantly lower than that of the low-risk group. type II interferon (Type_II_IFN_Reponse), CC chemokine receptor (CCR), and immune check-point were significantly different between high and low risk groups.Conclusion In this study, we constructed the first HCC prognostic model associated with DFAGs, which has a good predictive value.There are significant differences in the level of immune cell infiltration and immune function among different risk groups of HCC. There were significant differences in sensitivity to targeted and chemotherapeutic agents. It provides new ideas for prognostic assessment, immunotherapy and individualised treatment of HCC.