Sage Journals: Discover world-class research

Abstract

BACKGROUND:

Hepatocellular carcinoma (HCC) is one of the most serious malignant tumors with a poor prognosis worldwide. Cuproptosis is a novel copper-dependent cell death form, involving mitochondrial respiration and lipoylated components of the tricarboxylic acid (TCA) cycle. Long non-coding RNAs (lncRNAs) have been demonstrated to affect the tumorigenesis, growth, and metastasis of HCC.

OBJECTIVE:

We explored the potential roles of cuproptosis-related lncRNAs in predicting the prognosis for HCC.

METHODS:

The RNA-seq transcriptome data, mutation data, and clinical information data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analyses were performed to identify a prognostic cuproptosis-related lncRNA signature. The receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of the lncRNA signature for HCC. The enrichment pathways, immune functions, immune cell infiltration, tumor mutation burden, and drug sensitivity were also analyzed.

RESULTS:

We constructed a prognostic model consisting of 8 cuproptosis-related lncRNAs for HCC. The patients were divided into high-risk group and low-risk group according to the riskscore calculated using the model. Kaplan-Meier analysis revealed that the high-risk lncRNA signature was correlated with poor overall survival [hazard ratio (HR) $=$ 1.009, 95% confidence interval (CI) $=$ 1.002–1.015; $p=$ 0.010)] of HCC. A prognostic nomogram incorporated the lncRNA signature and clinicopathological features were constructed and showed favorable performance for predicting prognosis of HCC patients. In addition, the most immune-related functions were significantly different between the high-risk and low-risk groups. Tumor mutation burden (TMB) and immune checkpoints were also expressed differently between the two risk groups. Finally, HCC patients with low-risk score were more sensitive to several chemotherapy drugs.

CONCLUSIONS:

The novel cuproptosis-related lncRNA signature could be used to predict prognosis and evaluate the effect of chemotherapy for HCC.

Keywords

Hepatocellular carcinoma cuproptosis lncRNAs prognosis immune checkpoint

1. Introduction

Hepatocellular carcinoma (HCC), accounting for $>$ 90% of the incidence of primary liver cancers (including cholangiocarcinomas, hepatoblastomas, and HCC), is the fifth common malignancy and the second cancer-related death worldwide [1]. It is estimated that there are approximately 850,000 new cases of HCC being diagnosed annually, leading to 800,000 deaths per year [1]. The main risk factors associated with HCC occurrence were chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, alcohol consumption, metabolic syndromes, and aflatoxin B1 (AFB1) intake [2]. Besides, gene mutation (TP53, TERT, MYC, and CTNNB1), DNA damage, and epigenetic alterations (DNA methylation) were also critical to the development and progression of HCC [3, 4, 5]. In addition, several signaling pathways, such as Wnt/ $\beta$ -catenin, TP53, and PI3K/Akt/mTOR might also contribute to the occurrence, progression, and metastasis of HCC [6, 7]. Although several advanced therapies have been applied in clinical practice, including sorafenib and regorafenib, however, the overall survival (OS) rate of HCC still remains poor [8]. Considering the asymptomatic nature and poor survival rate of HCC, researchers are now focusing on the molecular landscape and cell biology of HCC to seek new diagnostic biomarkers and treatment strategies for HCC.

Long noncoding RNAs (lncRNAs), a member of non-coding RNAs (ncRNAs) with longer than 200 nucleotides in length, were transcribed by RNA polymerase II, capped, polyadenylated, and edited [9]. lncRNAs might exert their regulatory roles in gene expression through their binding with RNA, DNA, and proteins. Currently, emerging evidence has demonstrated the key roles of many lncRNAs in the tumorigenicity, growth, metastasis, and drug resistance of HCC, such as lncRNA H19, lncRNA HULC, and lncRNA MALAT1 [9, 10]. Therefore, lncRNAs might be potential diagnostic and therapeutic targets in HCC.

Copper is an essential endogenous metal associated with various biological functions, such as mitochondrial respiration, iron metabolism, and oxygen radical detoxification [11]. The regulation of copper concentrations in the cytoplasm is based on the redox signal generated and transduced by mitochondria [12]. Recently, Tsvetkov et al. uncovered a new form of nonapoptotic cell death pathway, copper-dependent cell death (namely cuproptosis) [13]. The new proposed cuproptosis was different from apoptosis, ferroptosis, or necroptosis, which was triggered by targeting copper to mitochondria [13]. The mechanism of cuproptosis depended on the accumulation of copper, active mitochondrial respiration, and the participation of pivotal components of the tricarboxylic acid (TCA) cycle [13]. The discovery of cuproptosis raised new attention on copper level homeostasis in mitochondria and its potential roles in the development and progression of cancers. Previous studies have shown that the altered metabolizing activities of the TCA cycle were an important feature in HCC [14, 15]. In addition, copper could improve the anti-metastatic activity of disulfiram (DSF) in HCC. DSF/copper suppressed the metastasis and epithelial–mesenchymal transition (EMT) of HCC through NF- $\kappa$ B, and TGF- $\beta$ signaling pathways, indicating the potential role of copper for HCC treatment [16, 17].

In this study, we constructed a prognostic model for HCC based on 8 differentially expressed cuproptosis-related lncRNAs. The model showed better prognostic value for HCC patients compared with other clinicopathological features. Then a nomogram incorporated the cuproptosis-related lncRNA signature and clinicopathological features to predict the survival rate of HCC patients. We also explored the potential immune functions of the lncRNA signature to elaborate on its potential therapeutic value.

2. Materials and methods

2.1 Data collection

Table 1
The clinical characteristics of HCC patients in the TCGA dataset

Characteristics	Number of samples
Age (years)
$<$ 55/ $\geqslant$ 55	120/254
Gender
Male/female	253/121
Grade
G1/G2/G3/G4/unknown	55/180/123/13/3
Stage
I/II/III/IV/unknown	175/86/86/5/22
T stage
T1/T2/T3/T4/Nx	185/93/83/13/2
N stage
N0/N1/Nx	257/4/113
M stage
M0/M1/Nx	272/4/98

HCC, Hepatocellular carcinoma; TCGA, The Cancer Genome Atlas.

RNA-sequence transcriptome data of 424 samples (374 tumor tissues and 50 normal tissues) was downloaded from the TCGA database (https://portal.gdc. cancer.gov/repository). Besides, clinicopathological data and survival information were also collected. The clinicopathological characteristics including age, gender, TNM stage, and grade stage were shown in Table 1. The mutation data of HCC samples were downloaded from the TCGA database (https://portal.gdc.cancer.gov/ repository). The raw data of gene expression were normalized using the Perl language (http://www.perl.org/).

2.2 Identification of cuproptosis-related lncRNAs

To identify cuproptosis-related lncRNAs, 19 cuproptosis-related genes were retrieved from the previous literature conducted by Tsvetkov et al. Pearson’s correlation analysis was used to assess the relationships between the cuproptosis-related lncRNAs and HCC calculated by R software with the “limma” package. The threshold values $|\text{cor}|\geqslant$ 0.4 and $p<$ 0.001 was considered to be significant. Finally, 994 cuproptosis-related lncRNAs were identified. Significant differential expression of cuproptosis-related lncRNAs was set as false discovery rate (FDR) $<$ 0.05 and $|\text{log}_{2}\text{FC}(\text{FoldChange})|\geqslant$ 1.

2.3 Construction of the cuproptosis-related lncRNA-based prognostic model

The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to screen a multigene signature of the cuproptosis-related lncRNAs by the R software with the “glmnet” package. For the training group, the lncRNA-based prognostic risk score for each patient sample was calculated by the following formula with the expression level multiplied regression model ( $\beta$ ): risk score $=$ $\beta$ lncRNA1 $\times$ lncRNA1 expression $+$ $\beta$ lncRNA2 $\times$ lncRNA2 expression $+\ldots+$ $\beta$ lncRNAn $\times$ lncRNAn expression. Then the patients were divided into high-risk group and low-risk group based on the median value of the risk score. The OS rates between the two risk groups were performed by the R software with the “survival” and “survminer” packages training group and test group. Kaplan–Meier curve was implemented to visualize the survival. The receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of the lncRNA-based prognostic model and the independent risk factors using the R software with the “timeROC” package. Next, univariate and multivariate Cox regression analyses were carried out to assess the prognostic value of the risk score model and other clinicopathological features, including age, gender, pathologic staging, and so on.

2.4 Predictive nomogram

We construct a prognostic nomogram to predict the 1-, 3-, and 5-year OS of HCC patients. All independent prognostic factors and the lncRNA-based prognostic signature were included in the construction of the prognostic nomogram using the R software with the “regplot” package. The calibration curve, concordance index (C-index), and ROC curve were used to assess the accuracy of the model.

2.5 Functional enrichment analysis

The R software with the “clusterProfler” package was used to perform Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of cuproptosis-related differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) was performed to identify the potential functional pathways between the high-risk group and low-risk group according to the cuproptosis-related lncRNA signature. Enriched biological processes and pathways with $p<$ 0.05 and FDR $<$ 0.05 were considered to be significant. c5.go. v7.4 symbols.gmt was used as a reference get set selected from the MSigDB Database (http://software.broadinstitute.org/gsea/msigdb/index.jsp). In addition, we performed principal component analysis (PCA) to assess the distribution of HCC patients with different risk scores (namely whole genes, cuproptosis genes, cuproptosis-related lncRNAs, and the lncRNA signature). The R software with the “scatterplot3d” package was used to draw the PCA plots.

2.6 Immune-related function analysis

We further compared the 13 immune-related functions in the high-risk group and low-risk group by calculating each HCC sample gene set enrichment analysis (ssGSEA) scores using the R software with the “gsva” package. Besides, CIBERSORT was used to analyze the differences of 22 types of infiltrating immune cells between high-risk group and low-risk group (http://cibersort.stanford.edu/). The annotated gene set file was present in Supplement files.

2.7 Analysis of tumor mutation burden, checkpoint, and drug sensitivity

Tumor mutation burden (TMB) analysis was performed to explore the gene mutation profile between the high-risk and low-risk groups using the R software with the “maftools” package. The potential immune checkpoints, including CTLA-4, PD-1, and PD-L1 were conducted using the R software with the “limma” package between the two risk groups. According to Genomics of Drug Sensitivity in Cancer (GDSC) 2016 (www.cancerrxgene.org/), the IC50 values for various chemotherapeutic and targeted drugs recommended for HCC treatment were calculated using the R software with the “pRRophetic” package. The Wilcoxon test was used to compare the IC50 between the two risk groups.

2.8 Cell culture and quantitative real-time PCR (qRT-PCR) assay

Table 2
The used primer sequences for qRT-PCR

Primer	Sequence (5’-3’)	Products
TNFRSF4-F	CCTGGTGTCTGTGGTAGA
TNFRSF4-R	ATTGCGTCCGAGCTATTG	175bp
TNFSF4-F	GGAGGCTGGTAAGAGTGT
TNFSF4-R	GGTGTGATGAGGAGAGGAT	145bp
PDCD1LG2-F	TCTTCCTCCTGCTAATGTTG
PDCD1LG2-R	GTTATTGCTCCAAGGTTCAC	163bp
TNFRSF18-F	TTGGCTTCCAGTGTATCG
TNFRSF18-R	CAGTGAGAAACCCGAACT	102bp
GAPDH-F	CCTCTGACTTCAACAGCGAC
GAPDH-R	TCCTCTTGTGCTCTTGCTGG	200bp

The human normal liver cell (LC) was a gift from the Medical School of Ningbo University, Ningbo, China, which was derived from stem cells isolated from a healthy human liver. Two human malignant HCC cell lines, HepG2 and Huh-7, were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM, Mediatech lnc., VA, USA) comprising 10% fetal bovine serum (FBS, PAN, Seratech, Germany) at the humidified chamber of 37 ${}^{\circ}$ C and 5% CO ${}_{2}$ . According to the corresponding manufacturer’s instructions, Total RNA Kit II (Code No. R6934-01, OMEGA) was used to extract the total RNA from cell lines, and the RevertAid First Strand cDNA Synthesis Kit (Code No. K1622, Thermo Scientific) were used for cDNA synthesis. qRT-PCR was carried out using the TB Green R Premix Ex TaqTM II kit (Takara). GAPDH was used as an internal control. The relative gene expression level was quantified through a 2 ${}^{-\Delta\Delta\textit{Ct}}$ method. The qRT-PCR assay was repeated three times for each amplified gene. All primers were synthesized by Shanghai Sangon Biotech. The primer sequences were shown in Table 2.

2.9 Statistical analysis

Data were analyzed using the R software (version 4.0.5; http://www.r-project.org) with the Bioconductor packages. Pearson correlation coefficients were used to assess the correlations between cuproptosis-related lncRNA expression and genes. Univariate and multivariate Cox regression analyses were used to evaluate the prognostic value of the related factors for OS of HCC patients. Survival analysis of HCC patients based on the cuproptosis-related lncRNA signature was performed using the Kaplan–Meier curve. ROC curve was used to compare the sensitivity and specificity between the cuproptosis-related lncRNA signature and other clinicopathological features for HCC patients. The relationships between the risk score of the model and infiltrating immune cells were evaluated using Spearman’s correlation coefficients test. $P$ value $<$ 0.05 was determined to be statistically significant. ${}^{*}p<$ 0.05; ${}^{**}p<$ 0.01; ${}^{***}p<$ 0.001; and ${}^{****}p<$ 0.0001.

3. Results

3.1 Identification of cuproptosis-related differentially expressed lncRNAs

We firstly collected the transcriptome data of 420 tissues (370 HCC tissues and 50 normal tissues) from TCGA database. Then, the patients were divided randomly into a training group (185 patient samples) and a test group (185 patient samples). Based on the study conducted by Tsvetkov et al., 19 cuproptosis-related genes were collected simultaneously(Supplementary Table 1) [13]. Subsequently, 994 cuproptosis-related lncRNAs were identified by Pearson’s correlation analysis ( $|\text{cor}|\geqslant$ 0.4, $p<$ 0.001; Supplementary Table 2).

3.2 Identification of a prognostic cuproptosis-related lncRNA signature

Figure 1.

Identification of cuproptosis-related lncRNA signature with prognostic value for hepatocellular carcinoma (HCC) patients. (A, B) LASSO Cox regression with a 10-fold cross-validation for the prognostic value of the cuproptosis-related 8 lncRNAs. (C) Forest plot of the prognostic values of the cuproptosis-related lncRNA signature using multivariate Cox regression analysis. (D) A heatmap showed the Pearson’s correlations between the 8 lncRNAs and the 19 cuproptosis genes.

Figure 2.

Prognostic risk scores calculated using the cuproptosis-related lncRNA prognostic signature. (A) Risk scores distribution of the high-risk and low-risk patients. (B) Survival status distribution of the high-risk and low-risk patients. (C) Heatmaps of the 8 lncRNAs expressions in the high-risk and low-risk groups. (D) Kaplan–Meier curves for overall survival (OS) of HCC patients in the high-risk and low-risk groups.

Univariate Cox regression analysis identified 204 significant cuproptosis-related lncRNAs (Supplementary Table 3), which were included in the LASSO algorithm and multivariate Cox regression analysis. Finally, 8 cuproptosis-related lncRNAs (AC004112.1, AC007064.2, AC012186.2, AC026412.3, AL031985.3, AL133477.1, AL365361.1, and TMCC1-AS1) were identified as independent prognosis predictors of HCC, which were used to establish a prognostic cuproptosis-related lncRNA signature (Fig. 1A–C and Supplementary Table 4). Among these 8 lncRNAs, five lncRNAs (AC012186.2, AC026412.3, AL031985.3, AL133477.1, and TMCC1-AS1) were associated with unfavorable prognosis (Supplementary Fig. 1). A heatmap showed the correlations between the 8 lncRNAs and the 19 cuproptosis-related genes (Fig. 1D and Supplementary Table 5). The risk score formula was as follows: ( $-$ 1.3404) $\times$ AC004112.1 $+$ 0.65757 $\times$ AC007064.2 $+$ 1.0647 $\times$ AC012186.2 $+$ 1.31381 $\times$ AC026412.3 $+$ 0.78154 $\times$ AL031985.3 $+$ 0.36935 $\times$ AL133477.1 $+$ ( $-$ 1.71627) $\times$ AL365361.1 $+$ 0.84393 $\times$ TMCC1-AS1. We calculated the risk score for each patient based on the cuproptosis-related lncRNA signature levels. Therefore, patients were divided into the low-risk group ( $n=$ 200) and the high-risk group ( $n=$ 170) based on the median risk score as the threshold value. A prognostic curve and a scatter diagram indicated the risk score and the survival status of each HCC patient (Fig. 2A–B). The results showed that more dead cases were mainly distributed in the high-risk group (Fig. 2B). Besides, a heatmap exhibited the expressions of the 8 cuproptosis-related lncRNA signatures (Fig. 2C). Kaplan-Meier survival curve showed that the OS in high-risk group was associated with worse survival compared with that in the low-risk group (Fig. 2D), suggesting that the newly constructed signature effectively predicts survival of HCC patients.

3.3 Evaluation of cuproptosis-related lncRNA signature as an independent prognostic factor for HCC

Figure 3.

Identification of the cuproptosis-related lncRNA signature as an independent prognostic factor. Univariate (A) and multivariate Cox regression analyses (B) of the associations between clinicopathological factors (including the lncRNA signature) and OS. (C) Time-dependent ROC curves for OS of the lncRNA signature at 1, 3, and 5 years. Principal component analysis (PCA) of the high-risk and low-risk groups based on the (D) whole-genes, (E) cuproptosis genes, cuproptosis-related lncRNAs, and (F) the risk model (the lncRNA signature). Patients with the high-risk and low-risk scores are indicated in blue and red, respectively. (H) ROC curves for validation of the prognostic value of the cuproptosis-related lncRNA signature. (I) A heatmap for cuproptosis-related lncRNA prognostic signature and clinicopathological features.

Next, we performed the univariate and multivariate Cox regression analyses to evaluate the independent prognostic factor of the cuproptosis-related lncRNA signature for HCC patients. The results of univariate Cox regression showed that the model [hazard ratio (HR): 1.007, 95% confidence interval (CI): 1.001–1.013; $p=$ 0.014; Fig. 3A], the stage [HR (95%CI): 1.879 (1.466–2.408); $p<$ 0.001; Fig. 3A], T stage [HR (95%CI): 1.816 (1.443–2.287); $p<$ 0.001; Fig. 3A], and the M stage [HR (95%CI): 3.924 (1.230–22.519); $p=$ 0.021; Fig. 3A] were significantly prognostic factors for HCC patients. Meanwhile, multivariate Cox regression analysis suggested that the model [HR (95%CI): 1.009 (1.002–1.015); $p=$ 0.010; Fig. 3B] was the only independent prognostic indicator for HCC patients. Besides, a time-dependent ROC curve showed that the AUC of the risk model was 0.756 at 1 year, 0.784 at 3 years, and 0.774 at 5 years (Fig. 3C). Then, we performed a principal component analysis (PCA) to compare the distributions of whole genes, cuproptosis genes, cuproptosis-related lncRNAs, and risk model between the high-risk group and low-risk group. As shown in Fig. 3D-G, the high-risk group and low-risk group could not be clearly distinguished using the whole genes, cuproptosis genes, or cuproptosis-related lncRNAs, while could be effectively distinguished using the model (the lncRNA signature), further supporting the predictive value of the model. In addition, the area under the curve (AUC) of the lncRNA signature was 0.756, which showed better performance than conventional clinicopathological characteristics in predicting the HCC prognosis (Fig. 3H). A heatmap exhibited the associations between cuproptosis-related lncRNA prognostic signature and clinicopathological characteristics (Fig. 3I).

3.4 Correlations between the risk model and clinicopathological features

Figure 4.

Kaplan-Meier survival curves analysis in 7 subgroups of HCC patients. (A) Age $<$ 55 years and $\geqslant$ 55 years; (B) Males and females; (C) Stage I $+$ II and III $+$ IV; (D) T1 $+$ T2 and T3 $+$ T4; (E) N0 and N1; (F) M0 and M1; (G) G1 $+$ G2 and G3 $+$ G4.

Kaplan-Meier survival curve was performed to evaluate the OS rates stratified by clinicopathological factors between the high-risk group and low-risk group. Results were presented as subgroups stratified by age, gender, clinical stage, grade, T stage, N stage, and M stage (Fig. 4A–G). Although the differences were not observed in the OS of two subgroups (patients with N1 and M1) between the two risk groups, the other 12 clinical groups showed that the patients in the high-risk group had a worse prognosis compared to patients in the low-risk group (Fig. 4A–G).

3.5 Construction of the predictive nomogram

Figure 5.

A nomogram of (OS prediction for HCC patients. (A) A nomogram incorporated the cuproptosis-related lncRNA signature and clinicopathological factors for predicting 1-, 3-, and 5-year survival of HCC patients. (B) Calibration curve of the 1-, 3-, and 5-year survival prediction accuracy for the nomogram. (C) Concordance index (C-index) for the clinical practicality evaluation of the nomogram.

To evaluate the 1-year, 3-year, and 5-year survival probabilities for HCC patients, we constructed a clinically diagnostic nomogram incorporating the cuproptosis-related lncRNA signature and several clinicopathological features (Fig. 5A). The calibration curves of the nomogram for 1-year, 3-year, and 5-year indicated that the nomogram-predicted OS was close to the actual OS rates (Fig. 5B). Besides, the C-index of the risk model was $>$ 0.7, superior to other indicators, indicating a good predicted ability of the nomogram for HCC patients (Fig. 5C).

3.6 Functional enrichment analysis

Figure 6.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for differentially expressed genes (DEGs) between the high-risk group and the low-risk group. (A) Bubble plots of GO. (B) Bubble plots of KEGG.

GO enrichment and KEGG pathway analyses were used to explore the functional roles of the DEGs in patients with HCC. GO analysis showed that the DEGs were mostly enriched in humoral immune response, immunoglobulin complex, adaptive immune response, lymphocyte mediated immunity, and antigen binding (Fig. 6A and Supplementary Table 6). KEGG analysis indicated that DEGs were mostly enriched in the cell cycle signaling pathway, oocyte meiosis, human T-cell leukemia virus 1 infection, and cytokine-cytokine receptor interaction (Fig. 6B and Supplementary Table 6).

3.7 Immune function analysis

Figure 7.

Immune-related functions analysis of the cuproptosis-related lncRNA signature. (A) A boxplot of the ssGSEA scores of 13 immune-related functions between the high-risk and low-risk groups. (B) A boxplot of CIBERSORT analysis of 22 subtypes of immune infiltration cells between the high-risk and low-risk groups.

Figure 8.

Analysis of tumor mutation burden (TMB), checkpoint, and drug sensitivity in the high-risk and low-risk groups. (A–B) Waterfall plot of 20 mutated genes in the high-risk and low-risk groups. (C) The TMB level in the high-risk and low-risk groups. (D) The expression level of potential immune checkpoints in the high-risk and low-risk groups. (E) The expression levels of TNFRSF4, TNFSF4, PDCD1LG2, and TNFRSF14 in HCC cell lines. (F–K) Drug sensitivity of 5-fluorouracil, doxorubicin, gemcitabine, paclitaxel, sorafenib, and sunitinib for HCC treatment in the high-risk and low-risk groups.

To further evaluate the associations between the risk score and immune function status, we conducted the enrichment scores of immune cell subpopulations and related functions based on ssGSEA. We found that the immune-related functions, including Type interferon (IFN) response, antigen presenting cells (APCs) co-stimulation and co-inhibition, CC chemokine receptor (CCR), T cell co-inhibition and co-inhibition, human leukocyte antigen (HLA), checkpoint, cytolytic activity, and inflammation promoting were all significantly lower in the high-risk group compared with that in the low-risk group (All $p<$ 0.001, Fig. 7A and Supplementary Table 7). Besides, the CIBERSORT algorithm was used to compare the proportions of 22 subtypes of immune infiltration cells in HCC between the high-risk group and low-risk group(Supplementary Table 7 and Supplementary Fig. 2). As shown in Fig. 7B, the proportions of naïve B cells, plasma cells, CD8 ${}^{+}$ T cells, monocytes, and M0 macrophages were significantly different between the high-risk and low-risk groups. We also observed higher proportions of resting memory CD4 ${}^{+}$ T cells, regulatory T cells (Tregs), M1 macrophages, and M2 macrophages in the high-risk group, (Fig. 7B). These results revealed that those infiltrating-immune cells might play important roles on the prognosis of HCC patients.

3.8 Analysis of TMB, checkpoint, and drug sensitivity in the high-risk and low-risk groups

Then, we analyzed mutational status and TMB in the high-risk and low-risk groups. The top 20 genes with the highest mutation frequency were presented in a waterfall plot. As shown in Fig. 8A and B, missense mutations were the most common genetic alteration among somatic mutations. TP53, CTNNB1, and TTN were the three genes with the highest mutation frequency in both the high-risk and low-risk groups (Fig. 8A and B). Besides, the TMB between the two risk groups was significantly different ( $p$ = 0.022, Fig. 8C). Next, the expressions of immune checkpoints between the high-risk and low-risk groups were also explored. The expressions of CD274 (PD-L1) and PDCD1LG2 (PD-L2) were significantly higher in the low-risk group than that in the high-risk group, while PDCD1 (PD-1) and CTLA4 were no significantly different (Fig. 8D). The results suggested that patients with low-risk might respond better to several chemotherapy drugs. Subsequently, we evaluated the expression levels of TNFRSF4, TNFSF4, PDCD1LG2, and TNFRSF18 in two HCC cell lines, HepG2 and Huh-7. TNFSF4 expression was significant decreased both in HepG2 and Huh-7 cell lines, while the expressions of TNFRSF4, PDCD1LG2, and TNFRSF18 were dramatically increased (Fig. 8E). Finally, we compared the clinical drug sensitivity in the two risk groups. The IC50s of 5-fluorouracil, doxorubicin, gemcitabine, paclitaxel, sorafenib, and sunitinib were significantly different (Fig. 8F–K). HCC patients with low-risk score were more sensitive to those chemotherapy drugs (Fig. 8F–K).

4. Discussion

In China, HCC ranks as the fourth highest incidence of all malignant tumors, with approximately 470,000 new cases and 420,000 deaths annually, which accounts for approximately 50% of the total deaths caused by HCC globally [18]. Therefore, HCC raises a great economic and societal burden in China as well as affects patients’ quality of life.

Previous studies have described the lncRNA signatures for HCC prognosis and immune response. Xu et al. constructed a 9 ferroptosis-related lncRNAs (CTD-2033A16.3, CTD-2116N20.1, CTD-2510F5.4, DDX11-AS1, LINC00942, LINC01224, LINC01231, LINC01508, and ZFPM2-AS1) prognostic model for HCC [19]. Besides, an autophagy-related four-lncRNA signature (LUCAT1, AC099850.3, ZFPM2-AS1, and AC009005.1) had been reported to be associated with regulatory mechanisms in HCC prognosis [20]. In the current study, we firstly identified a novel 8 cuproptosis-related lncRNA signature based on the patients with HCC of TCGA dataset. We also demonstrated that the lncRNA signature had a valuable predictive performance for HCC OS with higher AUC (0.756). Overall, 5 lncRNAs (AC004112.1, AC026412.3, AL031985.3, AL365361.1, and TMCC1-AS1) out of the 8 candidate lncRNAs had been reported to be correlated with development and prognosis in various tumors. AC004112.1 was a member of seven-lncRNA signature, which was used to assess prognostic risk for papillary thyroid carcinoma [21]. AC026412.3 and AL031985.3 were two components of a pyroptosis-related lncRNA risk signature related to prognosis and immune infiltration of HCC, suggesting a potential link between pyroptosis and immune regulation in HCC [22]. Besides, AL031985.3 and TMCC1-AS1 had also been integrated into several models correlated with ferroptosis and autophagy to predict the outcomes, immune cell infiltration, and immunotherapy response for HCC patients [23, 24]. AL365361.1 was also reported to be associated with OS in neck squamous cell carcinoma and ovarian cancer [25, 26]. Next, we found that synapse associated protein 1 (SYAP1) might be a target gene of lncRNA AC012186.2 using starbase (http://starbase.sysu.edu.cn/). However, no molecules interacting with the AC007064.2 and AL133477.1 were observed, which were needed to be validated in the future.

More importantly, we also explored the potential pathways involved in DEGs between the high-risk and low-risk groups. In our study, GO and KEGG enrichment analysis showed that many immune-related biological processes and pathways were enriched, such as humoral immune response, adaptive immune response, lymphocyte mediated immunity, and cell cycle. Therefore, we then explored the feature of immune-related functions and subtypes of infiltrating immune cells between the high-risk group and low-risk group. Results showed that all 13 immune-related functions were significant lower in the high-risk group than that in the low-risk group except for MHC class I. The impaired immune functions in the high-risk group might contribute to HCC carcinogenesis [27]. For example, T cell activation and function were regulated by co-stimulatory and co-inhibitory pathway. The proliferation, differentiation, and survival of T cells required co-stimulatory signals delivered by the APCs, making co-stimulation necessary to induce immune responses [27]. Antitumor immune responses of T cells also depended on efficient presentation of tumor antigens and co-stimulatory signals by host APCs [27]. The weaken co-stimulatory and co-inhibitory of APCs and T cells in the high-risk patients might lead to reduced tumor antigen presentation and killing, thus facilitating the immune escape of HCC. Therefore, co-stimulatory and co-inhibitory markers have been used as therapeutic targets of tumor immunotherapy [27, 28]. Moreover, type II IFN response played important functions on immune surveillance, promoting tumor antigen recognition and elimination in cancers [29]. Evidence indicated that IFN could directly activate tumor suppressor gene p53, which might lead to the apoptosis of several tumor cells [30]. Narkwa et al. reported that AFB1 down-regulated the type I IFN signaling pathway through inhibiting the JAK1, STAT1, and OAS3 to induce occurrence of HCC in HepG2 cells [31]. Therefore, the impaired immune functions in the high-risk group might compromise the roles of innate immune system in supervising and defensing HCC.

In addition, CIBERSORT analysis indicated that the proportion of M0 macrophages was significantly higher in the high-risk group compared to that in the low-risk group. Tumor-associated macrophages (TAMs) were essential members of TIICs in promoting tumor inflammation, tumor initiation, progression, and extracellular matrix remodelings, such as M1 macrophages and M2 macrophages [32]. Different from M1 or M2, M0 macrophages might be another type of TAMs or an incompletely differentiated M2 by transcriptomic profiling analysis in ovarian cancer and glioblastoma [33, 34]. Evidence has indicated that M0 macrophages were positive with poor prognosis in lung cancer [35]. Moreover, it was reported that M0 macrophages were associated with worse OS in HCC, indicating the potential predictive role of M0 macrophages for patients with HCC [36].

In fact, TMB has now been used as an immunotherapy biomarker of cancer therapy. The tumors with the higher TMB were more likely to respond better to immune checkpoint blockade (ICB) agents [37]. Our study showed that TP53 and CTNNB1 were the highest mutated genes in the high-risk group and the low-risk group, respectively. TP53 mutation (12%–48%) was the most common mutation in HCC patients [38]. TP53 inactivation, as well as FGF19 and/or CCND1 amplifications were reported to be associated with chromosomal instability of HCC [39]. Besides, TP53 inactivation might lead to dysregulation of cell-cycle activity, which was correlated with poorly differentiated, vascular invasion, and poor prognosis for HCC [40]. CTTNB, encoding $\beta$ -catenin, was considered as activating mutation associated with higher rates of TERT promoter mutations and initiation of the Wnt/ $\beta$ -catenin pathway [38, 41]. Besides, the higher TMB in high-risk group might indicate a good response to ICB therapy than low-risk group. However, the effective response to ICB therapy was also influenced by the expressions of several immune checkpoints, such as CTLA-4, PD-1, and PD-L1. Though the expressions of CTLA-4 and PD-1 were no significant in the two risk groups, the higher PD-L1 and PD-L2 expressions in low-risk group suggested that patients with low-risk may benefit more from anti-PD-L1 and anti-PDL2 immunotherapy. In addition, six antitumor drugs, including 5-fluorouracil, doxorubicin, gemcitabine, paclitaxel, sorafenib, and sunitinib, were more sensitivity to patients with low-risk scores, indicating these risk model might affect the therapeutic effect in HCC through cuproptosis.

The strength of this study was that we explored the correlations between cuproptosis-related lncRNAs and the prognosis of HCC based on the TCGA database. The constructed cuproptosis-related 8-lncRNA signature showed a good performance in predicting the prognosis for HCC patients compared with other clinical features. In addition, we also assessed the immune-related functions and pathways related to the cuproptosis-related 8-lncRNA signature, which might provide potential insights for the patient selection for more effective anti-tumor immunotherapies. However, there were several limitations in our study. First, our results were based on a retrospectively single database source. Second, our findings were mainly based on integrative bioinformatics, thus prospective and multicenter clinical studies were needed to verify the prognostic value of our model. Third, further functional experiments were needed to elucidate the biological mechanisms of how cuproptosis-related lncRNAs regulated the process of HCC.

Taken together, we firstly constructed a novel cuproptosis-related lncRNA signature for predicting the prognosis of HCC patients. A predictive nomogram based on the cuproptosis-related lncRNA signature was constructed for HCC. Besides, the cuproptosis-related lncRNA signature might be correlated with immune infiltration levels, especially M0 macrophages, and predict the immunotherapy response of HCC.

Author contributions

For every author, his or her contribution to the manuscript needs to be provided using the following categories:

Conception: Jianshuai Jiang, Yanqing Liu.

Interpretation or analysis of data: Yanqing Liu.

Preparation of the manuscript: Yanqing Liu.

Revision for important intellectual content: Jianshuai Jiang, Yanqing Liu.

Supervision: Jianshuai Jiang.

Ethic statements

This study design followed the local legislation and institutional requirements in accordance with the Declaration of Helsinki. Our research was approved by the Ethical Committee of the Ningbo First Hospital, and written informed consent was waived because the data were obtained from public databases.

Supplementary data

The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-220259.

sj-pdf-1-cbm-10.3233_CBM-220259.pdf - Supplemental material

Supplemental material, sj-pdf-1-cbm-10.3233_CBM-220259.pdf

sj-pdf-2-cbm-10.3233_CBM-220259.pdf - Supplemental material

Supplemental material, sj-pdf-2-cbm-10.3233_CBM-220259.pdf

sj-xls-1-cbm-10.3233_CBM-220259.xls - Supplemental material

Supplemental material, sj-xls-1-cbm-10.3233_CBM-220259.xls

sj-xls-2-cbm-10.3233_CBM-220259.xls - Supplemental material

Supplemental material, sj-xls-2-cbm-10.3233_CBM-220259.xls

sj-xlsx-1-cbm-10.3233_CBM-220259.xlsx - Supplemental material

Supplemental material, sj-xlsx-1-cbm-10.3233_CBM-220259.xlsx

sj-xlsx-2-cbm-10.3233_CBM-220259.xlsx - Supplemental material

Supplemental material, sj-xlsx-2-cbm-10.3233_CBM-220259.xlsx

sj-xlsx-3-cbm-10.3233_CBM-220259.xlsx - Supplemental material

Supplemental material, sj-xlsx-3-cbm-10.3233_CBM-220259.xlsx

sj-xlsx-4-cbm-10.3233_CBM-220259.xlsx - Supplemental material

Supplemental material, sj-xlsx-4-cbm-10.3233_CBM-220259.xlsx

Footnotes

Acknowledgments

This study was supported by the supported by the Medical and Health Science and Technology Projects of Zhejiang Province (No.2022KY308).

Conflict of interest

The authors declare no conflict of interests.

References

Sung

Ferlay

Siegel

R.L.

Laversanne

Soerjomataram

Jemal

and Bray

, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA: A Cancer Journal for Clinicians 71 (2021), 209–249.

Forner

Llovet

J.M.

and Bruix

, Hepatocellular carcinoma, Lancet (London, England) 379 (2012), 1245–1255.

Llovet

J.M.

Montal

Sia

and Finn

R.S.

, Molecular therapies and precision medicine for hepatocellular carcinoma, Nature Reviews Clinical Oncology 15 (2018), 599–616.

Villanueva

Hoshida

Battiston

Tovar

Sia

Alsinet

Cornella

Liberzon

Kobayashi

Kumada

Thung

S.N.

Bruix

Newell

April

Fan

J.B.

Roayaie

Mazzaferro

Schwartz

M.E.

and Llovet

J.M.

, Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma, Gastroenterology 140 (2011), 1501–1512.

Bayo

Fiore

E.J.

Dominguez

L.M.

Real

Malvicini

Rizzo

Atorrasagasti

García

M.G.

Argemi

Martinez

E.D.

and Mazzolini

G.D.

, A comprehensive study of epigenetic alterations in hepatocellular carcinoma identifies potential therapeutic targets, Journal of Hepatology 71 (2019), 78–90.

Dang

Takai

Forgues

Pomyen

Mou

Xue

Ray

K.C.H.

Morris

Q.D.

Hughes

T.R.

and Wang

X.W.

, Oncogenic Activation of the RNA Binding Protein NELFE and MYC Signaling in Hepatocellular Carcinoma, Cancer Cell 32 (2017), 101–114.

Severi

van Malenstein

Verslype

and van Pelt

J.F.

, Tumor initiation and progression in hepatocellular carcinoma: risk factors, classification, and therapeutic targets, Acta Pharmacologica Sinica 31 (2010), 1409–1420.

Bruix

Qin

Merle

Granito

Huang

Y.H.

Bodoky

Pracht

Yokosuka

Rosmorduc

Breder

Gerolami

Masi

Ross

P.J.

Song

Bronowicki

J.P.

Ollivier-Hourmand

Kudo

Cheng

A.L.

Llovet

J.M.

Finn

R.S.

LeBerre

M.A.

Baumhauer

Meinhardt

and Han

, Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial, Lancet (London, England) 389 (2017), 56–66.

Wong

L.S.

and Wong

C.M.

, Decoding the Roles of Long Noncoding RNAs in Hepatocellular Carcinoma, International Journal of Molecular Sciences 22 (2021).

10.

Huang

Zhou

J.K.

Peng

and Huang

, The role of long noncoding RNAs in hepatocellular carcinoma, Molecular Cancer 19 (2020), 77.

11.

Robinson

N.J.

and Winge

D.R.

, Copper metallochaperones, Annual Review of Biochemistry 79 (2010), 537–562.

12.

Cobine

P.A.

Moore

S.A.

and Leary

S.C.

, Getting out what you put in: Copper in mitochondria and its impacts on human disease, Biochimica et Biophysica Acta Molecular Cell Research 1868 (2021), 118867.

13.

Tsvetkov

Coy

Petrova

Dreishpoon

Verma

Abdusamad

Rossen

Joesch-Cohen

Humeidi

Spangler

R.D.

Eaton

J.K.

Frenkel

Kocak

Corsello

S.M.

Lutsenko

Kanarek

Santagata

and Golub

T.R.

, Copper induces cell death by targeting lipoylated TCA cycle proteins, Science (New York, NY) 375 (2022), 1254–1261.

14.

Huang

Tan

Yin

Gao

Wang

and Xu

, Metabolic characterization of hepatocellular carcinoma using nontargeted tissue metabolomics, Cancer Research 73 (2013), 4992–5002.

15.

Fukuda

Ebara

Okuyama

Sugiura

Yoshikawa

Saisho

Shimizu

Motoji

Shigematsu

and Watayo

, Increased metabolizing activities of the tricarboxylic acid cycle and decreased drug metabolism in hepatocellular carcinoma, Carcinogenesis 23 (2002), 2019–2023.

16.

Wang

L.H.

Zhang

H.T.

Wang

Y.T.

Liu

Zhou

W.L.

Yuan

X.Z.

T.Y.

C.F.

and Yang

J.Y.

, Disulfiram combined with copper inhibits metastasis and epithelial-mesenchymal transition in hepatocellular carcinoma through the NF-κB and TGF-β pathways, Journal of Cellular and Molecular Medicine 22 (2018), 439–451.

17.

Chiba

Suzuki

Yuki

Zen

Oshima

Miyagi

Saraya

Koide

Motoyama

Ogasawara

Ooka

Tawada

Nakatsura

Hayashi

Yamashita

Kaneko

Miyazaki

Iwama

and Yokosuka

, Disulfiram eradicates tumor-initiating hepatocellular carcinoma cells in ROS-p38 MAPK pathway-dependent and -independent manners, PloS One 9 (2014), e84807.

18.

Zou

Lei

Luo

Xue

Yao

Lai

Ung

C.O.L.

and Hu

, Economic Burden and Quality of Life of Hepatocellular Carcinoma in Greater China: A Systematic Review, Frontiers in Public Health 10 (2022), 801981.

19.

Peng

Liang

Chen

Cai

Zeng

Gao

Wang

Gong

and Yan

, Construction of a Ferroptosis-Related Nine-lncRNA Signature for Predicting Prognosis and Immune Response in Hepatocellular Carcinoma, Frontiers in Immunology 12 (2021), 719175.

20.

Zhang

Zhou

and Mo

, Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma, Journal of Cellular Physiology 235 (2020), 9304–9316.

21.

Guo

Pan

Zeng

Zhou

and Wang

, Identification of prognostic signature with seven LncRNAs for papillary thyroid carcinoma, Advances in Medical Sciences 67 (2022), 103–113.

22.

Wang

and Guo

, Comprehensive Analysis of the Correlation Between Pyroptosis-Related LncRNAs and Tumor Microenvironment, Prognosis, and Immune Infiltration in Hepatocellular Carcinoma, Frontiers in Genetics 13 (2022), 867627.

23.

Chen

Z.A.

Tian

Yao

D.M.

Zhang

Feng

Z.J.

and Yang

C.J.

, Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma, Frontiers in Oncology 11 (2021), 738477.

24.

Jia

Chen

and Liu

, Prognosis-Predictive Signature and Nomogram Based on Autophagy-Related Long Non-coding RNAs for Hepatocellular Carcinoma, Frontiers in Genetics 11 (2020), 608668.

25.

Zhong

Hong

Chen

Kong

Deng

Sun

and Liang

, Transcriptome analysis reveals the link between lncRNA-mRNA co-expression network and tumor immune microenvironment and overall survival in head and neck squamous cell carcinoma, BMC Medical Genomics 13 (2020), 57.

26.

Cao

Zhang

Zhu

Huo

Bao

and Su

, Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer, Frontiers in Oncology 12 (2022), 780950.

27.

O’Neill

R.E.

and Cao

, Co-stimulatory and co-inhibitory pathways in cancer immunotherapy, Advances in Cancer Research 143 (2019), 145–194.

28.

Ford

M.L.

Adams

A.B.

and Pearson

T.C.

, Targeting co-stimulatory pathways: transplantation and autoimmunity, Nature Reviews Nephrology 10 (2014), 14–24.

29.

von Locquenghien

Rozalén

and Celià-Terrassa

, Interferons in cancer immunoediting: sculpting metastasis and immunotherapy response, The Journal of Clinical Investigation 131 (2021).

30.

Bekisz

Baron

Balinsky

Morrow

and Zoon

K.C.

, Antiproliferative Properties of Type I and Type II Interferon, Pharmaceuticals (Basel, Switzerland) 3 (2010), 994–1015.

31.

Narkwa

P.W.

Blackbourn

D.J.

and Mutocheluh

, Aflatoxin B(1) inhibits the type 1 interferon response pathway via STAT1 suggesting another mechanism of hepatocellular carcinoma, Infectious Agents and Cancer 12 (2017), 17.

32.

Qian

B.Z.

and Pollard

J.W.

, Macrophage diversity enhances tumor progression and metastasis, Cell 141 (2010), 39–51.

33.

Zhang

Mao

Wang

Zhang

Tian

Lei

Han

Suo

Gao

Guo

Irwin

D.M.

Niu

and Tan

, Apoptotic SKOV3 cells stimulate M0 macrophages to differentiate into M2 macrophages and promote the proliferation and migration of ovarian cancer cells by activating the ERK signaling pathway, International Journal of Molecular Medicine 45 (2020), 10–22.

34.

Gabrusiewicz

Rodriguez

Wei

Hashimoto

Healy

L.M.

Maiti

S.N.

Thomas

Zhou

Wang

Elakkad

Liebelt

B.D.

Yaghi

N.K.

Ezhilarasan

Huang

Weinberg

J.S.

Prabhu

S.S.

Rao

Sawaya

Langford

L.A.

Bruner

J.M.

Fuller

G.N.

Bar-Or

Colen

R.R.

Curran

M.A.

Bhat

K.P.

Antel

J.P.

Cooper

L.J.

Sulman

E.P.

and Heimberger

A.B.

, Glioblastoma-infiltrated innate immune cells resemble M0 macrophage phenotype, JCI Insight 1 (2016).

35.

Liu

Yang

Zhao

Zhu

and Hou

, The prognostic landscape of tumor-infiltrating immune cell and immunomodulators in lung cancer, Biomedicine & Pharmacotherapy 95 (2017), 55–61.

36.

You

J.A.

Gong

Jin

Chi

and Sun

, WGCNA, LASSO and SVM Algorithm Revealed RAC1 Correlated M0 Macrophage and the Risk Score to Predict the Survival of Hepatocellular Carcinoma Patients, Frontiers in Genetics 12 (2021), 730920.

37.

Chan

T.A.

Yarchoan

Jaffee

Swanton

Quezada

S.A.

Stenzinger

and Peters

, Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic, Annals of Oncology: Official Journal of the European Society for Medical Oncology 30 (2019), 44–56.

38.

Zucman-Rossi

Villanueva

Nault

J.C.

and Llovet

J.M.

, Genetic Landscape and Biomarkers of Hepatocellular Carcinoma, Gastroenterology 149 (2015), 1226–1239.

39.

Wang

Lim

H.Y.

Shi

Lee

Deng

Xie

Zhu

Wang

Pocalyko

Yang

W.J.

Rejto

P.A.

Mao

Park

C.K.

and Xu

, Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma, Hepatology (Baltimore, Md) 58 (2013), 706–717.

40.

Calderaro

Couchy

Imbeaud

Amaddeo

Letouzé

Blanc

J.F.

Laurent

Hajji

Azoulay

Bioulac-Sage

Nault

J.C.

and Zucman-Rossi

, Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification, Journal of Hepatology 67 (2017), 727–738.

41.

Lachenmayer

Alsinet

Savic

Cabellos

Toffanin

Hoshida

Villanueva

Minguez

Newell

Tsai

H.W.

Barretina

Thung

Ward

S.C.

Bruix

Mazzaferro

Schwartz

Friedman

S.L.

and Llovet

J.M.

, Wnt-pathway activation in two molecular classes of hepatocellular carcinoma and experimental modulation by sorafenib, Clinical Cancer Research 18 (2012), 4997–5007.

A novel cuproptosis-related lncRNA signature predicts the prognosis and immunotherapy for hepatocellular carcinoma

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Data collection

Table 1 The clinical characteristics of HCC patients in the TCGA dataset

2.3 Construction of the cuproptosis-related lncRNA-based prognostic model

2.4 Predictive nomogram

2.5 Functional enrichment analysis

2.6 Immune-related function analysis

2.7 Analysis of tumor mutation burden, checkpoint, and drug sensitivity

2.8 Cell culture and quantitative real-time PCR (qRT-PCR) assay

Table 2 The used primer sequences for qRT-PCR

3. Results

3.1 Identification of cuproptosis-related differentially expressed lncRNAs

3.2 Identification of a prognostic cuproptosis-related lncRNA signature

4. Discussion

Author contributions

Ethic statements

Supplementary data

sj-pdf-1-cbm-10.3233_CBM-220259.pdf - Supplemental material

sj-pdf-2-cbm-10.3233_CBM-220259.pdf - Supplemental material

sj-xls-1-cbm-10.3233_CBM-220259.xls - Supplemental material

sj-xls-2-cbm-10.3233_CBM-220259.xls - Supplemental material

sj-xlsx-1-cbm-10.3233_CBM-220259.xlsx - Supplemental material

sj-xlsx-2-cbm-10.3233_CBM-220259.xlsx - Supplemental material

sj-xlsx-3-cbm-10.3233_CBM-220259.xlsx - Supplemental material

sj-xlsx-4-cbm-10.3233_CBM-220259.xlsx - Supplemental material

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
The clinical characteristics of HCC patients in the TCGA dataset

Table 2
The used primer sequences for qRT-PCR