Clustering-based identification of immune-related gene signatures in hepatocellular carcinoma

Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive. The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments. Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells. This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

BackgroundCurrently, some studies have demonstrated that miR-34a could serve as a suppressor of several cancers including hepatocellular carcinoma (HCC). Previously, we discovered that miR-34a was downregulated in HCC and involved in the tumorigenesis and progression of HCC; however, the mechanism remains unclear. The purpose of this study was to estimate the expression of miR-34a in HCC by applying the microarray profiles and analyzing the predicted targets of miR-34a and their related biological pathways of HCC.MethodsGene expression omnibus (GEO) datasets were conducted to identify the difference of miR-34a expression between HCC and corresponding normal tissues and to explore its relationship with HCC clinicopathologic features. The natural language processing (NLP), gene ontology (GO), pathway and network analyses were performed to analyze the genes associated with the carcinogenesis and progression of HCC and the targets of miR-34a predicted in silico. In addition, the integrative analysis was performed to explore the targets of miR-34a which were also relevant to HCC.ResultsThe analysis of GEO datasets demonstrated that miR-34a was downregulated in HCC tissues, and no heterogeneity was observed (Std. Mean Difference(SMD) = 0.63, 95% confidence intervals(95%CI):[0.38, 0.88], P < 0.00001; Pheterogeneity = 0.08 I2 = 41%). However, no association was found between the expression value of miR-34a and any clinicopathologic characteristics. In the NLP analysis of HCC, we obtained 25 significant HCC-associated signaling pathways. Besides, we explored 1000 miR-34a-related genes and 5 significant signaling pathways in which CCND1 and Bcl-2 served as necessary hub genes. In the integrative analysis, we found 61 hub genes and 5 significant pathways, including cell cycle, cytokine-cytokine receptor interaction, notching pathway, p53 pathway and focal adhesion, which proposed the relevant functions of miR-34a in HCC.ConclusionOur results may lead researchers to understand the molecular mechanism of miR-34a in the diagnosis, prognosis and therapy of HCC. Therefore, the interaction between miR-34a and its targets may promise better prediction and treatment for HCC. And the experiments in vivo and vitro will be conducted by our group to identify the specific mechanism of miR-34a in the progress and deterioration of HCC.

BackgroundCuproptosis 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.MethodsThe 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.ResultsA 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.ConclusionOur 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.

Due to their great variety of targets, microRNAs (miRNAs) play a key role in number of physiological processes and in oncogenesis. The identification of specific miRNA signatures in various types of tumors, including hepatocellular carcinoma (HCC), highlighted the dual role of miRNAs, both oncogenic and tumor suppressive. HCC is a cancer of poor prognosis that mainly develops on an injured liver. Here, we will review the current knowledge concerning the deregulation of miRNA expression at all stages of hepatocarcinogenesis, including the underlying liver disease, in particular steatohepatitis and fibrosis. We will develop the data concerning the identification of specific miRNA signatures in hepatocellular carcinoma, either in tumor or in sera. Since HCC appears in a context of chronic inflammation after recurrent liver injury, it also constitutes one of the best example in which miRNAs are key mediators for the communication between tumor cells and tumor microenvironment. The role of miRNAs in liver inflammation will be detailed in this chapter. To conclude, all studies focusing on miRNAs in HCC argue for their possible use as diagnostic and prognostic biomarkers. Promisingly, miRNAs appear as potent therapeutic targets to improve HCC treatment, for which surgery remains the most frequent therapeutic option.KeywordsLiver diseaseHepatocellular carcinomaInflammationMicroRNA signatureMicroRNA-based therapies

Background: Hepatocellular carcinoma (HCC) originates from the hepatocytes and accounts for 90% of liver cancer. The study intends to identify novel prognostic biomarkers for predicting the prognosis of HCC patients based on TCGA and GSE14520 cohorts. Methods: Differential analysis was employed to obtain the DEGs (Differentially Expressed Genes) of the TCGA-LIHC-TPM cohort. The lasso regression analysis was applied to build the prognosis model through using the TCGA cohort as the training group and the GSE14520 cohort as the testing group. Next, based on the prognosis model, we performed the following analyses: the survival analysis, the independent prognosis analysis, the clinical feature analysis, the mutation analysis, the immune cell infiltration analysis, the tumor microenvironment analysis, and the drug sensitivity analysis. Finally, the survival time of HCC patients was predicted by constructing nomograms. Results: Through the lasso regression analysis, we obtained a prognosis model of ten genes including BIRC5 (baculoviral IAP repeat containing 5), CDK4 (cyclin-dependent kinase 4), DCK (deoxycytidine kinase), HSPA4 (heat shock protein family A member 4), HSP90AA1 (heat shock protein 90 α family class A member 1), PSMD2 (Proteasome 26S Subunit Ubiquitin Receptor, Non-ATPase 2), IL1RN (interleukin 1 receptor antagonist), PGF (placental growth factor), SPP1 (secreted phosphoprotein 1), and STC2 (stanniocalcin 2). First, we found that the risk score is an independent prognosis factor and is related to the clinical features of HCC patients, covering AFP (α-fetoprotein) and stage. Second, we observed that the p53 mutation was the most obvious mutation between the high-risk and low-risk groups. Third, we also discovered that the risk score is related to some immune cells, covering B cells, T cells, dendritic, macrophages, neutrophils, etc. Fourth, the high-risk group possesses a lower TIDE score, a higher expression of immune checkpoints, and higher ESTIMATE score. Finally, nomograms include the clinical features and risk signatures, displaying the clinical utility of the signature in the survival prediction of HCC patients. Conclusions: Through the comprehensive analysis, we constructed an immune-related prognosis model to predict the survival of HCC patients. In addition to predicting the survival time of HCC patients, this model significantly correlates with the tumor microenvironment. Furthermore, we concluded that these ten immune-related genes (BIRC5, CDK4, DCK, HSPA4, HSP90AA1, PSMD2, IL1RN, PGF, SPP1, and STC2) serve as novel targets for antitumor immunity. Therefore, this study plays a significant role in exploring the clinical application of immune-related genes.

In this preliminary study, we examined whether imaging-based phenotypes are associated with reported predictive gene signatures in hepatocellular carcinoma (HCC). Thirty-eight patients (M/F 30/8, mean age 61years) who underwent pre-operative CT or MR imaging before surgery as well as transcriptome profiling were included in this IRB-approved single-centre retrospective study. Eleven qualitative and four quantitative imaging traits (size, enhancement ratios, wash-out ratio, tumour-to-liver contrast ratios) were assessed by three observers and were correlated with 13 previously reported HCC gene signatures using logistic regression analysis. Thirty-nine HCC tumours (mean size 5.7 ± 3.2cm) were assessed. Significant positive associations were observed between certain imaging traits and gene signatures of aggressive HCC phenotype (G3-Boyault, Proliferation-Chiang profiles, CK19-Villanueva, S1/S2-Hoshida) with odds ratios ranging from 4.44-12.73 (P <0.045). Infiltrative pattern at imaging was significantly associated with signatures of microvascular invasion and aggressive phenotype. Significant but weak associations were also observed between each enhancement ratio and tumour-to-liver contrast ratios and certain gene expression profiles. This preliminary study demonstrates a correlation between phenotypic imaging traits with gene signatures of aggressive HCC, which warrants further prospective validation to establish imaging-based surrogate markers of molecular phenotypes in HCC. • There are associations between imaging and gene signatures of aggressive hepatocellular carcinoma. • Infiltrative type is associated with gene signatures of microvascular invasion and aggressiveness. • Infiltrative type may be a surrogate marker of microvascular invasion gene signature.

Background: Hepatocellular carcinoma (HCC) is among malignancies with the highest fatality toll globally and minimal therapeutic options. Necroptosis is a programmed form of necrosis or inflammatory cell death, which can affect prognosis and microenvironmental status of HCC. Therefore, we aimed to explore the prognostic value of necroptosis-related lncRNAs (NRLs) in HCC and the role of the tumor microenvironment (TME) in immunotherapy. Methods: The RNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). NRLs were identified by Pearson correlation analysis. The signature was constructed using the LASSO–Cox regression analysis and evaluated using the receiver operating characteristic curve (ROC) and the area under the Kaplan–Meier curve. The nomogram was built based on clinical information and risk score. Gene set enrichment analysis (GSEA), immunoassay, half-maximum inhibitory concentration (IC50) analysis of the risk group, and the HCC subtype identification based on NRLs were also carried out. Finally, we detected the expression of lncRNAs in HCC tissues and cell lines in vitro. Results: A total of 508 NRLs were screened out, and seven NRLs were constructed as a risk stratification system to classify patients into distinct low- and high-risk groups. Patients in the high-risk group had a significantly lower overall survival (OS) than those in the low-risk group. Using multivariate Cox regression analysis, we found that the risk score was an independent predictor of OS. Functional analysis showed that the immune status of different patients was different. The IC50 analysis of chemotherapy demonstrated that patients in the high-risk group were more sensitive to commonly prescribed drugs. qRT-PCR showed that three high-risk lncRNAs were upregulated in drug-resistant cells, and the expression in HCC tissues was higher than that in adjacent tissues. Conclusion: The prediction signature developed in this study can be used to assess the prognosis and microenvironment of HCC patients, and serve as a new benchmark for HCC treatment selection.

Background: Hepatocellular carcinoma (HCC) is the world’s second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment.Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software.Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation.Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.

Background: Epithelial–mesenchymal transition (EMT) plays a critical role in the recurrence and metastasis of hepatocellular carcinoma (HCC). Some long noncoding (lnc)RNAs are involved in this process through the regulation of EMT-related transcription factors.Methods: In this study, we established a novel EMT-related lncRNA signature in HCC and identified hub lncRNAs that can serve as potential therapeutic targets. Differentially expressed lncRNAs were identified by screening HCC patient data from The Cancer Genome Atlas, and a correlation analysis was performed to identify those associated with EMT. The EMT-related lncRNA signature was established by univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. After verifying the prognostic accuracy of the signature, its relationships to immune cell infiltration and immune checkpoint targets were explored. LINC01116 was identified as a hub lncRNA and its role in HCC was investigated in vitro and in vivo.Results: A 5-lncRNA signature was developed for HCC and its prognostic accuracy was assessed by survival, time-dependent receiver operating characteristic curve, clinical correlation, and Cox regression analyses. The correlation analysis showed that the lncRNA signature was closely related to immune cell infiltration and 10 immune checkpoint targets and also predicted the prognosis of HCC patients with high accuracy. In vitro and in vivo experiments revealed that LINC01116 stimulated cell proliferation, cell cycle progression, and tumor metastasis. We also found that LINC01116 was closely related to immune regulation.Conclusions: These results demonstrate that LINC01116 is an immune-related oncogene that is associated with both EMT and immune regulation in HCC. Moreover, the EMT-related lncRNA signature that includes LINC01116 can guide risk stratification and clinical decision-making in HCC management.

BackgroundDrug resistance reflects the evolution of tumors and represents the leading factor behind recurrence and death. Lenvatinib is the first-line therapy for hepatocellular carcinoma (HCC), but its effectiveness is limited by rapid development of resistance. Therefore, we aimed to identify lenvatinib resistance-related genes and assess their influence on prognosis and treatment response in HCC.MethodsThe GSE186191 dataset served as the discovery cohort to identify lenvatinib resistance-related genes. A Venn diagram analysis delineated the intersection between lenvatinib resistance-related genes and prognostic-associated genes derived from The Cancer Genome Atlas (TCGA) database. The LASSO Cox regression model was implemented to construct a multigene signature in the TCGA cohort. A nomogram was built by integrating the TNM stage and our prognostic model. The gene signature and nomogram were further validated using HCC patients from the International Cancer Genome Consortium (ICGC) cohort. Mutation signatures, therapeutic response, functional enrichment, and immune profile analyses were performed in the two groups. Two lenvatinib-resistant (LR) HCC cells were established using a concentration gradient increment method. PFKFB4 expression was detected via qRT-PCR and western blot assay. The CCK-8 assay and flow cytometry were utilized to evaluate the proliferation and apoptosis of LR cells under different interventions.ResultsWe developed a lenvatinib resistance-related gene signature (ALPK3, SLC2A2, CTSV, and PFKFB4), and demonstrated that’s a precise, independent, and specific prognostic model for HCC patients. High-risk patients were characterized by a predisposition to TP53 mutations, aggressive tumor features, and treatment resistance. The risk score was significantly associated with immune cell infiltration, immune checkpoint expression, angiogenesis, and tumor stemness. PFKFB4 was overexpressed in LR cells, and its knockdown significantly enhances the antiproliferative and pro-apoptotic effects of lenvatinib on resistant cells.ConclusionsThe lenvatinib resistance-related prognostic signature exhibits strong predictive power for prognosis in HCC patients and may serve as an effective tool for guiding treatment decisions. PFKFB4 promotes tumor progression and lenvatinib resistance, highlighting its potential as a novel therapeutic target for HCC.Clinical trial numberNot applicable.

The aim of this study was to reveal the function of the long non-coding RNA (lncRNA) RP11-556E13.1 (RP11) and its clinical significance in hepatocellular carcinoma (HCC). LncRNA and mRNA expression profiling was performed using lncRNA and mRNA microarrays in HCC and adjacent tissues. Human tissue samples were analyzed by semiquantitative real-time polymerase chain reaction (sqRT-PCR) to evaluate the expression of RP11. Smart silencer RNA (siRNA) was used to knockdown the expression of RP11 in HCC cells. The function of RP11 was determined by some cell function experiments in HCC cells. Western blotting (WB) was performed to detect proteins that were presumably associated with these function changes. An Affymetrix Human HTA2.0 microarray was used to detect the underlying mechanism of RP11 in HCC. lncRNA RP11 was the most significantly upregulated lncRNA in HCC tissues compared with the adjacent tissues (p < 0.05, fold change = 20.24). The expression of RP11 was significantly higher in HCC tissues compared to adjacent tissues in 112 tissue pairs (p < 0.05). The higher the expression of RP11 in HCC tissues, the bigger the tumor size, the poorer the histological differentiation, and the lower the overall survival rate of the patients (all p < 0.05). After the knockdown of RP11, HCC cells displayed inhibited proliferation, increased apoptosis rate, and G1/S arrest. Moreover, the expression of cleaved PARP1 and cleaved caspase-3 was increased. GO enrichment and KEGG pathway enrichment analysis showed some important pathways that might be related to the knockdown of RP11 in HCC cells. lncRNA RP11 is an HCC-promoting gene and a potential prognostic predictor of HCC.

Most genes are alternatively spliced and increasing number of evidences show that alternative splicing (AS) is modified and related to tumor progression. Systematic profiles of AS signature in hepatocellular carcinoma (HCC) is absent and urgently needed. Here, differentially spliced AS transcripts between HCC and non-HCC tissues were compared, prognosis-associated AS events by using univariate Cox regression analysis were selected. Our gene functional enrichment analysis demonstrated the potential pathways enriched by survival-associated AS. Prognostic AS signatures were then constructed for HCC prognosis prediction by Lasso regression model. We also analyzed splicing factors (SFs) regulating underlying mechanisms by Pearson correlation and then built corresponding regulatory networks. In addition, we explored the performance of AS signature in the mutated HCC samples. Genome-wide AS events in 377 HCC patients from TCGA were profiled. Among 34 163 AS events in 8985 genes, 3950 AS events in 2403 genes associated with overall survival (OS) significantly for HCC were detected. In addition, computational algorithm results showed that metabolic and ribosome pathways may be the potential molecular mechanisms regulating the poor prognosis. More importantly, survival-associated AS signatures revealed high performance in predicting HCC prognosis. The area under curve for AS signature was 0.806 in all HCC and 0.944 in TP53 mutated HCC samples at 2000 days of OS. We submitted prognostic SFs to build the AS regulatory network, from which we found prognostic AS events were significantly enriched in metabolism-related pathways. A robust AS signature for HCC patients and revealed the regulatory splicing networks contributing to the potential significantly enriched metabolism-related pathways.

BackgroundDNA damage repair (DDR) may affect tumorigenesis and therapeutic response in hepatocellular carcinoma (HCC). Long noncoding RNAs (LncRNAs) can regulate DDR and play a vital role in maintaining genomic stability in cancers. Here, we identified a DDR-related prognostic signature in HCC and explored its potential clinical value.MethodsData of HCC samples were obtained from the Cancer Genome Atlas (TCGA), and a list of DDR-related genes was extracted from the Molecular Signatures database (MSigDB). A DDR-related lncRNAs signature associated to overall survival (OS) was constructed using the least absolute shrinkage and selection operator-cox regression, and was further validated by the Kaplan-Meier curve and receiver operating characteristic curve. A nomogram integrating other clinical risk factors was established. Moreover, the relationships between the signature with somatic mutation, immune landscape and drug sensitivity were explored.ResultsThe prognostic model of 5 DDR-related lncRNAs was constructed and classified patients into two risk groups at median cut-off. The low-risk group had a better OS, and the signature was an independent prognostic indicator in HCC. A nomogram of the signature combined with TNM stage was constructed. TP53 gene was more frequently mutated in the high-risk group. Marked differences in immune cells were observed, such as CD4 + T cells, NK cells and macrophages, between the two groups. Moreover, an increase in the expression of immune checkpoint molecules was found in the high-risk group. The low-risk group presented with a significantly higher response to sorafenib or cisplatin. Finally, potential value of this signature was validated in real-world HCC patients.ConclusionOur findings provided a promising insight into DDR-related lncRNAs in HCC and a personalized prediction tool for prognosis and therapeutic response.

Ferroptosis has been reported to regulate multiple biological behaviors. However, the prognostic and oncologic values of ferroptosis-related genes (FRGs) have not been comprehensively elucidated in hepatocellular carcinoma (HCC). Here, we aimed to construct FRGs-associated signature for stratification of the prognosis of HCC patients. A list of FRGs was generated from FerrDb. Public databases were used to extract expression matrices and clinical information. TCGA cohort was randomly divided into a training set and a validation set. Prognostic signature for Overall Survival (OS) was established in training set and validated in internal cohorts (TCGA validation set and entire set) and external cohort (ICGC cohort). Additionally, the role of signature in HCC was well investigated by analysis of mutations, gene set enrichment analysis (GSEA), analysis of immune infiltrates, and analysis of response to immune checkpoint blockade (ICB) treatment. The oncogenic effects of ZFP69B on HCC were also investigated in vitro. We identified 12 FRGs-based signature for OS with LASSO regression. Patients were partitioned into different risk groups based on the signature. Overall, patients in different groups had different prognosis. The signature independently predicted OS in multivariate Cox regression analyses. Anti-tumor immune cells including activated CD8 T cells, cytolytic activity, and Th1 cells were negatively correlated with risk score in both TCGC and ICGC cohorts. Furthermore, low-risk patients responded better to ICB than high-risk patients. In addition, knockdown of ZFP69B reduced proliferation, migration, and invasion, and promoted erastin-induced ferroptosis of HCC cells. We developed a prognostic signature based on FRGs to predict OS of HCC patients. And the signature may facilitate clinicians in identifying those who are likely to benefit from ICIs. The results also indicated that ZFP69B might regulate the process of ferroptosis and could be used as a novel potential target for HCC.

Abnormal accumulation of copper could induce cell death and tumor growth, and affect tumor immune escape by regulating programmed cell death ligand 1 (PD-L1) expression. This study aims to establish and verify a risk signature based on cuproptosis- and immune-related genes (CIRGs) for hepatocellular carcinoma (HCC) management. HCC RNA-seq and clinical data were obtained from open databases. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were utilized to screen CIRGs and develop a risk signature. The signature's value for clinical applications, functional enrichment, tumor mutation burden (TMB), and immune profile analyses were investigated systematically. A risk signature was developed utilizing seven CIRGs, and it performed well in predicting the prognosis of HCC patients in both the training and external validation cohorts. The model's risk score was discovered to be related to important clinical features. Top 15 mutated genes in HCC were significantly different among different risk groups. High-risk patients showed higher TMB, and high TMB was closely identified with a poorer prognosis. Immune profile analyses showed that immune infiltration level was higher in low-risk patients than high-risk patients, and the level of immune checkpoint genes expression varied significantly between patients in two different risk groups. Low-risk patients responded well to immunotherapy treatment, whereas high-risk patients were more sensitive to sorafenib, doxorubicin, gemcitabine and AKT (also known as protein kinase B) inhibitors. The established risk signature based on CIRGs can not only well predict the prognosis of HCC patients but is also promising in evaluating TMB and treatment response to immunotherapy, targeted therapy and chemotherapy, which has the potential to assist in the clinical management of HCC.