Ruiqi Liu1†
Yingyi Liu
Lichuan Wu- 1School of Medicine, Guangxi University, Nanning, China
- 2Guangxi Scientific Research Center of Traditional Chinese Medicine, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
Background: Hepatocellular carcinoma is the third most deadly malignant tumor in the world with a poor prognosis. Although immunotherapy represents a promising therapeutic approach for HCC, the overall response rate of HCC patients to immunotherapy is less than 30%. Therefore, it is of great significance to explore prognostic factors and investigate the associated tumor immune microenvironment features.
Methods: By analyzing RNA-seq data of the TCGA-LIHC cohort, the set of cuproptosis related genes was extracted via correlation analysis as a generalization feature. Then, a random forest cox prognostic model was constructed and the cuproptosis random forest cox score was built by random forest feature filtering and univariate multivariate cox regression analysis. Subsequently, the prognosis prediction of CRFCS was evaluated via analyzing data of independent cohorts from GEO and ICGC by using KM and ROC methods. Moreover, mutation characterization, immune cell infiltration, immune evasion, and drug sensitivity of CRFCS in HCC were assessed.
Results: A cuproptosis random forest cox score was built based on a generalization feature of four cuproptosis related genes. Patients in the high CRFCS group exhibited a lower overall survival. Univariate multivariate Cox regression analysis validated CRFCS as an independent prognostic indicator. ROC analysis revealed that CRFCS was a good predictor of HCC (AUC =0.82). Mutation analysis manifested that microsatellite instability (MSI) was significantly increased in the high CRFCS group. Meanwhile, tumor microenvironment analysis showed that the high CRFCS group displayed much more immune cell infiltration compared with the low CRFCS group. The immune escape assessment analysis demonstrated that the high CRFCS group displayed a decreased TIDE score indicating a lower immune escape probability in the high CRFCS group compared with the low CRFCS group. Interestingly, immune checkpoints were highly expressed in the high CRFCS group. Drug sensitivity analysis revealed that HCC patients from the high CRFCS group had a lower IC50 of sorafenib than that from the low CRFCS group.
Conclusions: In this study, we constructed a cuproptosis random forest cox score (CRFCS) model. CRFCS was revealed to be a potential independent prognostic indicator of HCC and high CRFCS samples showed a poor prognosis. Interestingly, CRFCS were correlated with TME characteristics as well as clinical treatment efficacy. Importantly, compared with the low CRFCS group, the high CRFCS group may benefit from immunotherapy and sorafenib treatment.
1 Introduction
Liver cancer remains one of the most lethal cancers, with 830,000 deaths worldwide in 2020, accounting for 8.3% of cancer related deaths (1). Hepatocellular carcinoma (HCC) is the most frequent of all primary liver cancers, comprising 75-85% of cases (2). Due to the lack of diagnostic marker, most of the HCC patients are diagnosed at advanced stages with a poor prognosis (3). Therapies such as traditional cytotoxic drugs are rarely effective. Over the last decade, sorafenib and lenvatinib are the only systemic drugs that have been proven to be clinically effective in the therapy of part of the advanced HCC patients (4). Therefore, it is crucial to find valid prognostic models as well as treatment strategies.
Immune checkpoint inhibitor (ICIs) therapy is one of the fastest-developing immunotherapy strategies, which effectively breaks the dilemma of cancer treatment, especially in advanced cancer. However, the efficacy of immunotherapy varies widely among patients (5). HCC is intimately correlated with inflammation and has a complicated tumor microenvironment (TME) (6). Immune checkpoint therapy is being used for HCC treatment recently. The sensitivity of immunotherapy in HCC varies significantly due to the heterogeneity and complexity of the TME (7). Revealing the potential TME characteristics of HCC patients is hence crucial for predicting the efficacy of immunotherapy.
Copper (Cu) is a required element for human health. Disturbance of intracellular coppers is associated with diverse pathologies (8). Previous studies have demonstrated that Cu levels are significantly increased in tumor tissues and cancer patients derived serum (9–12). The elevated levels of Cu are reported to be involved in tumor cell proliferation, angiogenesis, and metastasis (13, 14). Cu may also increase the incidence of HCC in Wilson’s disease patients (15). Both copper chelators and copper ionophores have been exploited as antitumor drugs and tested in clinical trials (16–18). Besides, Cu homeostasis is essential for maintaining normal immune function (19–21) and elevated Cu levels in tumor cells contribute to immune escape by enhancing PD-L1 expression (22). These findings suggest that Cu plays an important role in tumorigenesis and TME shaping. The Cu metabolism is recognized as a unique vulnerability in cancer (23) and targeting Cu metabolism might be an alternative strategy for cancer treatment (24). Recently, a novel Cu induced programmed cell death termed cuproptosis was revealed which occurs by targeting lipoylated TCA cycle proteins (25). Previous studies have shown that cuproptosis-related signature and genes are closely related to TME in colorectal cancer (26), breast cancer (27), lung cancer (28), bladder cancer (29), kidney renal clear cancer (30), and so forth. However, the relationships between cuproptosis-related genes and prognosis, immune microenvironment, and drug sensitivity of liver cancer has not been fully elucidated.
In this study, cuproptosis-related gene sets were derived by correlation analysis as generalization features. Then a random forest Cox prognostic model was constructed, and the cuproptosis random forest Cox score (CRFCS) was built by random forest feature filtering and univariate multivariate Cox regression. The HCC patients were clustered according to CRFCS and investigated in terms of prognosis analysis, mutational characteristics, tumor microenvironment, prediction of immune evasion, immune checkpoint, and drug sensitivity.
2 Materials and methods
2.1 Data acquisition and processing
The mRNA expression data, somatic mutation data, and corresponding clinical information of HCC were downloaded from the TCGA database via the R package “TCGAbiolinks”. The clinical and mRNA expression data of GSE116174 and ICGC-LIHC-US cohorts were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the ICGC database (https://dcc.icgc.org/projects/), respectively. Then, the mRNA data were converted to TPM format and normalized by log2 transformation.
2.2 Development of cuproptosis random forest cox score (CRFCS)
The cuproptosis-associated gene set was derived as a generalization feature by correlation analysis based on the TCGA-LIHC cohort. We used the method “rfsrc” in the R package “randomForestSRC” to construct a random forest model and selected features. The Cox regression was constructed based on the mentioned characteristics, and Regression coefficients were obtained by the “coxph” method in the “survival” package. The Cuproptosis Random Forest Cox Score (CRFCS) was established by the following formula:
Where is the expression of feature gene i, and is the characteristic co-efficient of feature gene i.
2.3 Survival analysis
Kaplan-Meier (K-M) survival analysis and visualization were conducted with the “survival” and “survminer” packages. The time-related receiver operating characteristic curve (time ROC) was performed by the R package “pROC” to evaluate the prediction performance of CRFCS in the training and test sets.
2.4 Processing and analysis of mutation profile
The analysis and visualization of mutation profile were performed by the “maftools” package. We plotted the mutation waterfall by the method “oncoplot”. After removing the loci falling into the CNV region, the Mutant-Allele Tumor Heterogeneity (MATH) score of the samples was calculated by the “inferHeterogeneity” method (31). MSI scores were calculated by the “MSIsensor” method (32).
2.5 TME cell infiltration assessment
The immune cell infiltration was estimated by both ssGSEA and CIBERSORT algorithms. For the ssGSEA method, we used the TME-infiltrating gene set from Charoentong et al., which includes 28 immune cell types (33). We evaluated the enrichment fraction of each sample in the cohort via the ssGSEA method to characterize the immune cell invasion in each sample. The CIBERSORT algorithm worked in conjunction with the immune infiltration signature matrix LM22 to evaluate the invasion of various immune cells in the samples. In the case of stromal cells, we estimated the stromal cell infiltration by evaluating the expression of markers for each stromal cell.
2.6 Immune evasion prediction
The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm is used to assess the immune evasion mechanism of tumors (34). The effect of both T-cell dysfunction and T-cell exclusion mechanisms on immune evasion was evaluated separately by the TIDE algorithm and the TIDE score was used to predict the degree of immune evasion of the samples.
2.7 Drugs sensitivity prediction
The IC50 values of the drugs in the training set samples were evaluated by the “pRRopheticPredict” method of the R package “pRRophetic”, with the dataset “cgp2016”. We calculated the correlation between IC50 values and CRFCS subgroups to investigate the association between CRFCS and drug sensitivity.
2.8 Statistical analysis
The analysis and visualization of the data were performed in R (version 4.1.1). The Wilcoxon test was used to compare the data between the two groups. Charts were mainly visualized by the “ ggplot2 “ package. The p-value<0.05 was regarded as statistically significant (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001).
3 Results
3.1 The expressions and prognosis analysis of cuproptosis-related genes in HCC
We initially evaluated the expressions of ten genes in HCC which were reported to be crucial regulators of cuproptosis (25). It was noticed that among these ten genes, all of them except FDX1 were significantly highly expressed in HCC (Figure 1A), indicating that the cuproptosis process might be associated with HCC. To further explore the prognosis of cuproptosis genes in HCC, we performed a correlation analysis between cuproptosis gene expression and HCC patients’ survival (OS) (Figure 1B). The results displayed that genes DLAT (HR =1.71, p =0.003), PDHA1 (HR =1.42, p =0.046), GLS (HR =1.49, p =0.023), and CDKN2A (HR =1.78, p =0.001) had prominent prognostic significance in HCC, and patients with high expression of these four genes exhibited shorter survival (Figures 1C–F).
Figure 1 The Expressions and Prognosis Analysis of Cuproptosis-Related Genes in HCC. (A) Differential expression of cuproptosis-related genes in the TCGA-LIHC cohort. (***p<0.001; ns stands for not significant) (B) Correlation between cuproptosis-related gene expression and survival data (OS) of HCC patients. The horizontal dotted line stands for p=0.05. The vertical dotted line represents HR=1. (C–F) Kaplan-Meier curves of DLAT (C), PDHA1 (D), GLS (E), and CDKN2A (F).
3.2 Construction of cuproptosis random forest cox score (CRFCS) model
Given that cuproptosis may be involved in the progression of HCC, a more robust prognostic model was constructed using the above-mentioned cuproptosis genes with prominent prognostic significance (DLAT, PDHA1, GLS, and CDKN2A). First and foremost, correlation analysis of the above genes was initially conducted via analyzing data from TCGA-LIHC cohort to enhance the generalization ability of the model. For each cuproptosis gene listed above, the top 25 expression-related genes were identified as generalized features based on correlation coefficients. For the gene sets after the generalization of features, GO/KEGG analysis was performed to ensure that the characteristics were not distorted by generalization. The results indicated that the gene set after features generalization remained associated with key pathways of cuproptosis, such as the TCA cycle (Figures 2A, B). Training the gene set as input of the random forest model, the out-of-bag error of the model stabilized when the number of trees was approximately around 1000 (Figure 2C). The random forest model derived the variable importance (VIMP) ranking of the input features (Figure 2D). We selected the top 20% of the ranked features to be involved in the construction of the Cox model. Excluding the features not significant in the univariate Cox test, 17 features were obtained and model scores were established according to the steps in Materials and Methods (Figure 2E).
Figure 2 Construction of CRFCS. (A, B) GO (A) and KEGG (B) enrichment analysis of gene sets after generalizing features. (C) Trend of out-of-bag error (oob) of random forest model with the number of trees (nTree). (D) Ranking of variable importance (VIMP) of features. (E) Multivariate cox test of feature genes.
3.3 Prognosis prediction of CRFCS
To evaluate the accuracy of the model’s predictions, we validated CRFCS in the training set TCGA-LIHC and the external validation set ICGC-LIHC-US and GSE116174. We divided the samples of each set into high and low score groups by the median of CRFCS. In the TCGA-LIHC set, the contemporaneous surviving rate of the high CRFCS subgroup samples was much lower than that of the low CRFCS subgroup. The HR for the CRFCS subgroups was 2.86 (1.96-4.16), with a p-value less than 0.001 (Figure 3A). Likewise, the survival of the high CRFCS subgroup samples was shorter in both validation cohorts. In the ICGC-LIHC-US cohort, the HR of the CRFCS subgroup was 2.69 (1.65-4.38) with a p-value less than 0.001 (Figure 3B) while the HR value was 2.78 (1.24-6.23) with a p-value of 0.013 in the GSE116174 cohort (Figure 3C). Subsequently, ROC analysis was performed to evaluate the diagnostic potency of CRFCS in HCC. The results demonstrated that CRFCS was a strong predictor in both training and validation cohorts (Figures 3D–F). The AUC values for predicting OS were 0.820 at 1 year, 0.727 at 3 years, and 0.670 at 5 years in the TCGA-LIHC training cohort (Figure 3D). While AUC values for predicting OS were 0.720 at 1 year, 0.671 at 3 years, and 0.664 at 5 years in the ICGC-LIHC-US cohort (Figure 3E) and 0.727 at 1 year, 0.665 at 3 years, and 0.713 at 5 years in the GSE116174 cohort (Figure 3F). Also, we performed univariate and multivariate Cox analyses of CRFCS in order to examine the potential of CRFCS as an OS-independent prognostic factor for HCC. The results showed a hazard ratio of 2.708 (2.087-3.514) for CRFCS in the univariate analysis with a p-value less than 0.001 (Figure 3G). In the multifactorial analysis, the hazard ratio was 2.437 (1.825-3.254) with a p-value less than 0.001 (Figure 3H). These results implied that CRFCS was a potential independent predictor of HCC.
Figure 3 Prognosis prediction of CRFCS. (A, C) Kaplan-Meier curves of CRFCS subgroups for the training cohort TCGA-LIHC (A), the external validation cohorts ICGC-LIHC-US (B), and GSE116174 (C). (D–F) AUC curves for the prediction of overall survival (OS) by CRFCS in samples of TCGA-LIHC (D), ICGC-LIHC-US (E), and GSE116174 (F). (G, H) Univariate (G) and multivariate analysis (H) of CRFCS.
3.4 CRFCS and mutation features
Mutational features are an integral part of the cancer process landscape. We investigated the mutational characteristics of the CRFCS subgroup of HCC. The top 3 high-frequency mutated genes in the high-CRFCS subgroup were TP53 (29%), TTN (24%), and CTNNB1 (20%) (Figure 4A) while CTNNB1 (31%), TNN (23%), and ALB (15%) were identified as the top 3 mutated genes in the low CRFCS subgroup (Figure 4B). We also found that Microsatellite Instability (MSI) score was significantly higher in the high CRFCS subgroup than in the low CRFCS group (p<0.001) (Figure 4C). Then, we evaluated the MATH scores which were positively correlated with tumor heterogeneity. The results revealed that the MATH scores between the two groups were not significant (Figure 4D).
Figure 4 CRFCS and mutation characteristics. (A, B) Mutation oncoplots of high CRFCS group (A) and low CRFCS group (B), including genes with top 30 mutation frequency. (C) Differences in MSI scores between high and low CRFCS subgroups. *** stands for p<0.001. (D) Differences in MATH scores between CRFCS subgroups.
3.5 CRFCS and TME
Immunotherapy is vital for the treatment of patients with advanced cancer and TME features are essential indicators of the efficacy of immune checkpoint inhibitors (ICIs). The level of various immune-related cellular infiltrates in TCGA-LIHC cohort samples was assessed by the ssGSEA method (Figures 5A, B). The results displayed a positive correlation between the CRFCS and the level of some anti-tumor immune cell infiltration, such as activated CD4 T cells (p<0.0001), activated dendritic cells (p =0.0142), central memory CD4 T cells (p<0.0001), central memory CD8 T cells (p =0.0025), and effector memory CD4 T cell (p<0.0001). Similarly, infiltrations of pro-tumor immune cells including regulatory T cells (p<0.0001), type 2 T helper cells (p<0.0001), immature dendritic cells (p =0.0239), and MDSC (p =0.0173) were also positively correlated with CRFCS. In addition, some neutral immune infiltrates such as eosinophil (p<0.0001) and mast cell (p =0.0189) were negatively related to CRFCS. We also evaluated the immune infiltration of the samples with the CIBERSORT algorithm (Figure 5C). Higher infiltration levels of T cells CD4 memory activated (p<0.001), T cells follicular helper (p<0.01), T cells regulatory (Tregs) (p<0.01), Macrophages M0 (p<0.001) and dendritic cells resting (p<0.01) were observed in the high CRFCS subgroup. In contrast, B cells naïve (p<0.05), T cells CD4 memory resting (p<0.05), NK cells activated (p<0.05), monocytes (p<0.05) and mast cells resting (p<0.001) had higher levels in the low CRFCS subgroup. Considering both methods together, the infiltration levels of activated CD4 T cells and regulatory T cells were significantly higher in the high-CRFCS subgroup, while the infiltration level of Mast cells resting was lower. Infiltration of stromal cells is also an integral part of TME. We also assessed the levels of stromal cell-related markers in the TCGA-LIHC cohort samples. The analysis showed that the levels of most markers of diverse stromal cells including CAF, EC, MSC, TAM, M1, and M2 in the samples were positively correlated with CRFCS (Figure 5D). Regulatory T cells was reported to suppress the immune response and promote tumorigenic immune escape (35). We then assessed the extent of immune escape between high and low CRFCS subgroups by the TIDE algorithm and the results showed that the high CRFCS group displayed a decreased TIDE score compared with the low CRFCS group (Figure 5E), indicating that samples with high CRFCS had lower levels of immune escape.
Figure 5 CRFCS and immune infiltration. (A) Heat map of ssGSEA score of various immune cells in high and low CRFCS groups via analyzing TCGA-LIHC cohort data. (B) The correlations between immune score and CRFCS. (C) Immune infiltration landscape of TCGA-LIHC cohort samples assessed by the CIBERSORT algorithm. (D) Correlation of stromal cell-associated markers with CRFCS. (E) The level of immune escape between high and low CRFCS subgroups was assessed by the TIDE algorithm. (*p<0.05; **p<0.01; ***p<0.001; ns stands for not significant).
3.6 CRFCS and drug-sensitivity
Next, we assessed the drug-sensitivity of CRFCS in HCC by applying the R package of “pRRophetic”. By analyzing data from TCGA, we found that the high CRFCS group had a lower IC50 of sorafenib compared with the low CRFCS group (Figure 6A). To verify these results, an external data from ICGC-LIHC-US was analyzed which confirmed that the high CRFCS group are more sensitive to sorafenib (Figure 6B). Immunotherapy delivers more opportunities to patients with advanced HCC (36). It is well recognized that TME characteristic can significantly influence the outcome of immunotherapy (37). TME is classified into three subtypes: immune-desert, immune-inflamed, and immune-excluded. The immune-inflamed type which is highly expressed with immune checkpoint such as PD1 and PD-L1 is considered to be very sensitive to immunotherapy (38). Therefore, we evaluated the expression profile of immune checkpoint in CRFCS. Our results displayed that the immune checkpoints including PD-L1, PD1, TIGIT, TIM3, and CTLA4 were significantly highly expressed in the high CRFCS group compared with the low CRFCS group in both TCGA and ICGC HCC cohorts (Figures 6C, D). These results suggested that high CRFCS group might be more responsive to immunotherapy.
Figure 6 CRFCS and drug sensitivity. (A, B) Drug sensitivity of sorafenib in high and low CRFCS subgroups via analyzing data from TCGA cohort (A) and ICGC-LIHC (B). (C, D) Differential expression of immune checkpoints between high and low CRFCS subgroups via analyzing data from TCGA cohort (C) and ICGC-LIHC (D). (**p<0.01; ***p<0.001).
4 Discussion
Cuproptosis, a recently discovered new programmed cell death induced by excessive accumulation of intracellular Cu, is distinct from known cell death forms including apoptosis, pyroptosis, ferroptosis, necrosis. To dissect the specific regulators of cuproprosis, Tsvetkov et al. used genome-wide CRISPR/Cas9 screens and identify ten crucial cuproptosis-specific genes including FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A (25). These ten genes are closely associated with HCC progression and TME. Zhang et al. identified FDX1 as an immunotherapy predictor of HCC (39). Yan et al. discovered that inhibition of LIPT1 restrained HCC cell proliferation and invasion (40). Zhou et al. found that overexpression of DLAT increased HCC cell growth and invasion and may facilitate cancer cell evade immune system (41). Sun et al. reported that activation of PDHA1 suppressed the Warburg effect and promoted HCC apoptosis (42). Yang et al. demonstrated that knockdown PDHB induced metabolic reprogramming of the tricarboxylic acid (TCA) cycle leading to glutamine depletion and inhibition of HCC cell proliferation (43). Yang et al. reported that over-expression of MTF1 contributed to the proliferation of HCC cells (44). Dong et al. found that GLS1 promoted HCC cell proliferation via activating AKT/GSK3β/Cyclin D1 pathway (45). Xu et al. revealed that upregulation of CDKN2A significantly inhibited ACTR5 induced HCC cell proliferation (46). Considering the role of these ten crucial cuproptosis-specific genes in HCC, constructing a model based on these ten genes might provide potential insights for evaluation the TME and immunotherapy efficacy of HCC.
Since the discovery of cuproptosis, the role of cuptoptosis in liver cancer prognosis and TME has been gradually evaluated. Previous studies mainly explored this issue by constructing Lasso cox model, which directly entered the target genes as model inputs (47–53). The Lasso model is applied to analyze multicollinearity data (54). Usually, nonlinear data might be generated when performing log normalization of the expression matrix. From this perspective, the lasso cox model might not be the ideal strategy. The random forest model is a set of binary trees constructed with recursive partitioning (RPART), which enables the random forest to handle nonlinear data due to the combination of trees (55). Therefore, the random forest model with nonlinear data as the application object is more suitable. Meanwhile, the random forest model is better at learning potential crossover features consisting of multidimensional features (56) and shows strong robustness when applied to large feature sets (57). These reasons led us to use the random forest model to construct the prognostic model. In addition, considering that there might be noise differences between individual data of each sample, some features may be lost due to the presence of data noise if the target genes are considered only, we trained the model using gene clusters related to cuproptosis genes as model inputs to generalize the features. We generalize the features by acquiring highly correlated genes of crucial cuproptosis genes when constructing the model so that the model could learn as much information as possible about the implicit features in the data. This makes the output of the model smoother and less susceptible to fluctuations caused by noise in the data, thus improving the robustness of the model. The generalized input data combined with the random forest model can better learn the potential cross features in the data.
Microsatellite instability (MSI) is closely correlated with tumor immunotherapy efficacy. High MSI (MSI-H) in tumor samples usually cause additional mutant antigens and sensitize patient to immunotherapy (58). However, MSI-H also tends to increase tumor heterogeneity, which in turn results in poorer immunotherapy efficacy (59). In the present study, the mutation landscape of CRFCS subgroups was investigated which showed that the MSI scores were significantly higher in the high-CRFCS subgroup sample than in the low-CRFCS group while no significant difference between high and low CRFCS subgroups was observed in the tumor heterogeneity score MATH (Figure 4). These results suggested that high CRFCS subgroups may have better immunotherapeutic efficacy. Besides, studies exist demonstrated that tumor patients with high expression of immune checkpoints are more sensitive to immunotherapy (38). We evaluated the expression of immune checkpoints in high and low CRFCS group. Our results showed that the immune checkpoints including PD-L1, PD1, TIGIT, TIM3, and CTLA4 were remarkably highly expressed in the high CRFCS group compared with the low CRFCS group (Figures 6C, D). In addition, evidence displayed that Treg cells cause immune escape through several mechanisms, which in turn impede the anti-tumor immune response (60). To estimate the tumor immune escape effect between the CRFCS subgroups, we calculated the TIDE scores of the samples. The results showed that the high-CRFCS subgroup had significantly lower TIDE scores (Figure 5E), indicating that samples of the high-CRFCS subgroup had a lower probability of immune escape and were less prone to be resistant to immunotherapy. Combining the results above, it might be inferred that the high CRFCS group might be more suitable to receive immunotherapy than the low CRFCS group.
Although a cuproptosis related model termed CRFCS was successfully constructed to evaluate prognosis and TME characteristic in HCC, some limitations should not be neglected. First, cuproptosis was discovered in 2022, only several genes were confirmed as crucial cuproptosis-specific genes, more genes need to be identified to provide systematic and comprehensive understanding of cuproptosis. Second, our study was performed based on integrative bioinformatic analysis, it would be more valid to carry out functional experiments in vitro and in vivo. Finally, the data involved in this study were retrieved from public dataset, it would be better to use large-scale of local datasets to verify our findings.
5 Conclusions
In aggregate, we constructed a cuproptosis random forest cox score (CRFCS) model. CRFCS was identified to be an independent prognostic indicator of HCC and high CRFCS samples showed a poor prognosis. Interestingly, CRFCS were correlated with TME characteristics as well as clinical treatment efficacy. Patients with high CRFCS had a better clinical prognosis for immunotherapy and sorafenib.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number (s) can be found below: https://portal.gdc.cancer.gov/, TCGA-LIHC; https://www.ncbi.nlm.nih.gov/geo/, GSE116174; https://dcc.icgc.org/projects/, ICGC-LIHC-US.
Author contributions
LW and JW designed and planned the study concept. RL, YL, and FZ performed experiments and analyzed data. RL and LW drafted the manuscript. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the National Natural Science Foundation of China (82260715), the Guangxi Natural Science Foundation (2017GXNSFBA198240, 2018GXNSFAA050055, and 2021GXNSFAA075038), Youth Fund Project of Natural Science Research Project of Guangxi University of Traditional Chinese Medicine (No. 2016QN007), and Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues (No. CICAR2016-P6).
Acknowledgments
This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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Keywords: cuproptosis, hepatocellular carcinoma, prognostic signature, immunotherapy, tumor microenvironment
Citation: Liu R, Liu Y, Zhang F, Wei J and Wu L (2023) A cuproptosis random forest cox score model-based evaluation of prognosis, mutation characterization, immune infiltration, and drug sensitivity in hepatocellular carcinoma. Front. Immunol. 14:1146411. doi: 10.3389/fimmu.2023.1146411
Received: 17 January 2023; Accepted: 20 March 2023;
Published: 30 March 2023.
Edited by:
Lin-Lin Bu, Wuhan University, ChinaReviewed by:
Kosuke Kawaguchi, Kyoto University, JapanShucai Xie, Department of Critical Care Medicine, Central South University, China
Copyright © 2023 Liu, Liu, Zhang, Wei and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Lichuan Wu, richard_wu@gxu.edu.cn; Jinrui Wei, weijr@gxtcmu.edu.cn
†These authors have contributed equally to this work