Background Cuproptosis is a new type of copper-induced cell death that is characterized by the aggregation of lipoylated tricarboxylic acid (TCA) cycle proteins. However, its role in hepatocellular carcinoma (HCC) remains unclear. The goal of this research is to develop a cuproptosis-related signature predicting the prognosis of HCC. Methods The cuproptosis-related genes were defined using Pearson correlation coefficients. LASSO-Cox regression analysis was used to evaluate the prognostic values of cuproptosis-related genes to construct a cuproptosis-related prognostic model. The immune microenvironment analysis was performed by “ssGSEA” to reveal the associated immune cell infiltration patterns with the cuproptosis-related genes signature. The expression levels of one of the prognostic genes PDXK were then verified in HCC samples by Western Blot and immunohistochemistry. The potential roles of target genes in cuproptosis were further explored during in-vitro experiments. Results A total of 136 cuproptosis-related genes were discovered using Pearson correlation analysis in HCC. A cuproptosis-related signature that included 5 cuproptosis-related genes (PDXK, HPN, SLC25A28, RNFT1, CLEC3B) was established in the TCGA-LIHC training cohort. TCGA validation cohort and another two external validation cohorts confirmed the robustness of the signature’s predictive value. Moreover, a nomogram using the risk score was created to best predict the survival of HCC patients. The immune microenvironment analysis revealed distinct immune infiltrations patterns between different risk groups based on the signature model. Furthermore, the upregulation of PDXK was confirmed in HCC tumor tissues in 30 clinical HCC specimens. The knockdown of PDXK reduced the proliferation, migration and invasion of HCC cells. Besides, the expression of PDXK was upregulated after the induction of cuproptosis by elesclomol–CuCL2, which could be suppressed when pretreated with a copper ion chelator. And PDXK deficiency increased the sensitivity of HCC cells to cuproptosis inducer. Conclusion Our study identified a new cuproptosis-related gene signature that could predict the prognosis of HCC patient. Besides, the upregulated PDXK could promote the proliferation and metastasis of HCC. And PDXK deficiency facilities cuproptosis in HCC. Therefore, these fundings highlighted that PDXK might serve as a potential diagnostic and therapeutic target for HCC.

BackgroundCuproptosis is a novel form of copper-induced cell death that targets lipoylated tricarboxylic acid (TCA) cycle proteins. However, its prognostic role in lung adenocarcinoma (LUAD) remains unclear. This study aimed to establish a cuproptosis-related prognostic signature for patients with LUAD.MethodsTranscriptome data of LUAD samples were extracted from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The prognostic value of cuproptosis-related genes (CRGs) was investigated using Cox regression analysis to develop a cuproptosis-related prognostic model. Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO) and gene set variation analysis (GSVA) were conducted to characterize different biological activities or pathways between high- or low-CRG groups. The expression pattern and prognostic values of CRGs were validated in 37 paired tumor–normal samples using quantitative PCR. Furthermore, in vitro experiments were performed to investigate the relationship between cuproptosis and CRG expression and to explore the function of target genes in cuproptosis.ResultsAmong the 36 CRGs, 17 genes were upregulated, and 3 genes were downregulated in LUAD. A total of 385 CRGs were identified using Pearson correlation analysis. A cuproptosis-related signature was constructed using least absolute shrinkage and selection operator (LASSO) analysis. The prognostic value of the cuproptosis-related signature was validated in six external validation cohorts and in LUAD specimens from our facility. Patients in the high-risk group based on the CRG signature score had shorter overall survival than those in the low-risk group in both the datasets and clinical specimens. In vitro experiments revealed that the expression of BARX1, GFRA3, and KHDRBS2 was upregulated after cuproptosis was induced by elesclomol–CuCL2, whereas the upregulation was suppressed on pretreatment with tetrathiomolybdate (TTM), a chelator of copper. Further, the cell proliferation assay revealed that the BARX1 and GFRA3 deficiency facilities the cuproptosis induced by elesclomol–CuCL2.ConclusionThis study established a new CRG signature that can be used to predict the OS of LUAD patients. Moreover, the knockdown of BARX1 and GFRA3 could increase the sensitivity of LUAD cells to the cuproptosis.

ObjectiveEnergy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.MethodsTranscriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.ResultsA total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.ConclusionThis study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.