ABSTRACT
Objective To construct a prognostic model for cuprotosis-related genes (CRGs) in patients with hepatocellular carcinoma (HCC). Methods Differential expression of CRGs in HCC was analyzed on the basis of datasets from the TCGA database. The potential mechanisms of CRGs and their related genes in HCC were explored through GO and KEGG enrichment analyses. The prognostic value of the CRGs was evaluated through Kaplan-Meier survival analysis, and the relationship between CRG expression and immune cell infiltration was investigated. CRGs significantly correlated with prognosis in patients with HCC were identified. A prognostic model was established through univariate, Lasso regression, and multivariate Cox regression analyses. The patients were divided into two groups by risk score. ROC curve was used in evaluating the prognostic model. The relationship of risk score or clinical factors with prognosis was analyzed through univariate and multivariate Cox regression analyses. Results A total of 11 differentially expressed CRGs in HCC were obtained. The main enriched GO item of CRGs and their related genes was oxidoreductase activity, acting on the aldehyde or oxo group of donors, and the main enriched KEGG pathway was carbon metabolism. The expression of CRGs was significantly correlated with pDC, T helper cells and other immune cells (P < 0.05). Three CRGs (CDKN2A, DLAT, and LIPT1) were screened and a prognostic model was constructed. There was significant difference in overall survival between the high- and low-risk groups (P < 0.001). The risk score is an independent risk factor for poor prognosis (P < 0.001). Conclusion The prognostic model for CRGs in patients with HCC is constructed using TCGA database data. This model may be used in evaluating patient prognosis.
ABSTRACT
Objective To explore the relationship of cuprotosis-related genes with survival rate and prognosis in patients with liver cancer. Methods By collecting clinical information and corresponding RNA-seq data of patients with liver cancer in the TCGA database, the differential expression levels of 10 cuprotosis-related genes in liver cancer and normal tissues was analyzed. Novel liver cancer subtypes were identified through consistent clustering, and differences in overall survival and clinicopathological factors were compared between the two subtypes. Univariate Cox regression analysis was used in screening cuprotosis genes associated with prognosis, and LASSO regression analysis was used in constructing a risk model. Results FDX1 was down-regulated, and the other nine genes were up-regulated in HCC tissues compared with normal tissues. Cluster analysis showed that the prognosis of Cluster1 was poor. Five prognostic genes (LIPT1, DLAT, MTF1, GLS, and CDKN2A) were screened out through univariate Cox regression analysis and LASSO regression analysis for risk model construction. The risk score of this prognostic model was identified as an independent prognostic factor compared with other clinical features. Conclusion Through bioinformatics analysis, a liver cancer prognosis model of five cuprotosis-related genes is constructed, which may be used as molecular markers for tumor diagnosis and are potential therapeutic targets.