CCT4 as a prognostic biomarker correlated with immune infiltration and hepatocyte dedifferentiation in hepatocellular carcinoma

Introduction

Primary liver cancer ranks as the sixth most common cancer globally, with hepatocellular carcinoma (HCC) accounting for 75% to 85% of cases (1). The identification of HCC metastasis and high recurrence risk can significantly influence prognostic management. In past clinical management of HCC, numerous biomarkers such as alpha-fetoprotein (AFP) and glypican 3 (GPC3), des-gamma carboxyprothrombin (DCP), Golgi glycoprotein 73 (GP73) (2), gamma-glutamyl transferase (GGT), and alpha-L-fucosidase (AFU) (3) have been developed, but their sensitivity or specificity remains limited. Therefore, identifying HCC prognostic biomarkers is crucial for improving clinical treatment outcomes in HCC patients.

The chaperonin containing TCP1 complex (CCT) is a large macromolecular complex composed of 16 subunits forming a back-to-back double-ring structure, with each ring containing eight distinct subunits: α, β, γ, δ, ε, ζ, η, and θ (CCT1–8). CCT may contribute to malignant cellular transformation by affecting the folding and assembly of actin and tubulin, leading to aberrant conformations of microtubules and microfilaments (4). The CCT gene family has been implicated in various cancers. Pan-cancer analysis revealed that CCT2 is expressed to varying degrees in most cancers (5). CCT5 has been shown to interact with cyclin D1 to promote the migration and invasion of non-small cell lung cancer cells (6).

In HCC, several CCT family members such as CCT1, CCT3, CCT4, CCT6A, CCT7, and CCT8, demonstrate diagnostic and prognostic values through roles in proliferation, cell cycle control, epithelial-mesenchymal transition (EMT), and oncogenic signaling, with high expression of all except CCT6B consistently associated with liver cancer (7-13). Although most CCT family members, including CCT4, are upregulated in HCC (Figure S1A-S1D), and their expression levels exhibit strong positive correlations across multiple datasets (Figure S1E,S1F), CCT4 remains less studied compared to well-characterized members like CCT3. Notably, recent studies in glioblastoma (14), osteosarcoma (15), and breast cancer (16) have implicated CCT4 in promoting tumor growth and survival via mTOR and STAT3 signaling pathways, highlighting its oncogenic potential beyond general chaperonin function. Furthermore, no study has yet explored CCT4 at single-cell resolution in HCC, presenting a novel opportunity to uncover its cell type-specific roles and tumor microenvironment interactions.

In this study, we investigated CCT4 expression in HCC using bioinformatics methods, analyzed its correlations with HCC prognosis, immune cell infiltration, and drug sensitivity, as well as its expression characteristics at the single-cell level, to further elucidate its mechanistic roles in HCC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1277/rc).

Methods

Data sources

Transcriptome sequencing data for 50 normal tissues and 369 HCC tissues were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) in transcripts per million (TPM) format. Microarray data (GSE14520) and single-cell sequencing data (GSE149614) were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE14520 includes 225 tumor tissues and 220 normal tissues (17), while GSE149614 comprises 10 patients (18). Only patients with a survival time greater than 0 were included in the analysis. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Functional enrichment analysis

Co-expression genes of CCT4 were identified in the cBioPortal database (https://www.cbioportal.org/) (19), with a co-expression threshold of |correlation coefficient| >0.4 and q-value <0.05. To investigate the biological processes (BP) and metabolic pathways involving these co-expression genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R package clusterProfiler (v4.8.2) (20). Based on the median CCT4 gene expression, HCC patients in TCGA were divided into high-expression and low-expression groups, and Gene Set Enrichment Analysis (GSEA) (v4.3.2) software was used to analyze gene sets influenced by high CCT4 expression (21).

Construction of prediction nomogram

The proportional hazards assumption was tested using the R package survival (v3.5-7), followed by univariate and multivariate Cox regression analyses to identify independent prognostic factors. Based on the multivariate results, a nomogram was constructed using the rms (v6.7-1) package in R, with TCGA data as the training set. The model predicted 1-, 2-, 3-, and 5-year overall survival (OS) of HCC patients based on total risk scores. The GSE14520 dataset served as an external validation cohort to assess predictive performance. Model reliability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots.

Relationship between CCT4 expression and immune infiltration

The CIBERSORT algorithm was used to estimate the infiltration levels of 22 immune cell types in tumor tissues (22). Differences in immune infiltration were compared between high and low CCT4 expression groups. Additionally, Spearman correlation analysis was performed to assess the relationships between CCT4 expression, immune cell infiltration, and immune checkpoint molecules.

Drug sensitivity analysis

Gene expression and drug sensitivity data for the same samples were collected from the CellMiner database (https://discover.nci.nih.gov/cellminer/home.do) (23). Drugs validated by clinical trials and the Food and Drug Administration (FDA) were selected, and drug sensitivity data were filtered accordingly. Subsequently, Spearman correlation analysis was performed to assess the relationship between CCT4 expression data and drug sensitivity data.

Preprocessing of single-cell RNA sequencing (scRNA-seq) data

The R package Seurat (v4.3.0.1) (24) was used to preprocess the GSE149614 data. Data were normalized and converted into a Seurat Object. Cells with gene counts below 200 or above 8,000 or with mitochondrial RNA proportions exceeding 10% were filtered out (18). The FindVariableFeatures function was applied to identify the top 2,000 highly variable genes, followed by principal component analysis (PCA) for dimensionality reduction. Batch effects were corrected using the Harmony (v0.1.1) package (25), and t-distributed stochastic neighbor embedding (t-SNE) was employed for dimensionality reduction to achieve visualization of clustering in two-dimensional space.

Cell annotation of HCC single-cell data was manually performed based on canonical marker genes curated from published studies (18,26,27). Cell types were identified based on the following markers: T/natural killer (NK) cells (CD3E, CD3D, CD3G, NKG7, CD8A); hepatocytes (ALB, SERPINA1, HNF4A, TF, TTR); myeloid cells (CD86, CD14, CD163, CD1C, CLEC4C); endothelial cells (VWF, PECAM1, FCGR2B, ICAM2, KDR); fibroblasts (ACTA2, COL1A1, COL1A2, COL3A1, PDGFRB); B cells (IGHG1, JCHAIN, CD79A, CD19); and cholangiocytes (EPCAM, KRT19, CD24).

Prediction of tumor cell stemness

Cellular Trajectory Reconstruction Analysis using gene Counts and Expression (CytoTRACE) (v0.3.3) predicts cellular stemness at the single-cell level using gene expression and intrinsic stemness gene sets (28). For hepatocyte populations, CytoTRACE scoring was applied to assess cell differentiation states and illustrate differentiation trends across different cell populations.

Cell-cell communication

Cell communication analysis was performed using the CellChat (v1.6.1) package (29). The normalized expression matrix processed by Seurat was imported into CellChat, followed by analysis of cell-cell communication between the hepatocyte cluster and other cells.

Statistical analysis

Statistical analysis, data processing, and graph generation were performed using R (v 4.3.1; R Foundation for Statistical Computing, Vienna, Austria). The Kaplan-Meier method was used to assess OS. To evaluate variance in continuous data between groups, the Wilcoxon rank-sum test or Student’s t-test was applied. For multiple group comparisons, the Kruskal-Wallis test was used. Spearman correlation coefficients were employed to explore relationships between variables. A P value <0.05 was considered statistically significant.

Results

CCT4 overexpression in HCC is associated with clinical features

Analysis of HCC transcriptome data from TCGA database revealed CCT4 mRNA expression levels were significantly higher in HCC than in normal liver tissues (Figure 1A), and in HCC tissues compared with adjacent non-tumor tissues (Figure 1B). ROC curve analysis of the TCGA dataset indicated excellent diagnostic performance of CCT4 with an area under the curve (AUC) of 0.92 (Figure 1C). Similar patterns were observed in independent validation datasets: the GSE14520 cohort demonstrated significantly elevated CCT4 expression in HCC versus normal liver tissues (Figure 1D), which was replicated in matched pairs of tumor and normal adjacent tissue samples (Figure 1E). Consistently, ROC analysis of the GEO dataset yielded an AUC of 0.92 for CCT4’s diagnostic capability (Figure 1F).

Figure 1 Expression of CCT4 and its association with clinical features in HCC. (A) The expression of CCT4 is upregulated in HCC tissues compared with normal tissues in TCGA. (B) CCT4 expression in 50 paired HCC and adjacent normal tissue samples from TCGA. (C) ROC analysis for TCGA. (D) The expression level of CCT4 is upregulated in HCC tissues compared with normal tissues in GSE14520. (E) CCT4 expression in 214 paired HCC and adjacent normal tissue samples from GSE14520. (F) ROC analysis for GSE14520. (G-J) The association between CCT4 expression and age (G), grade (H) and TNM stage (I) and gender (J) in HCC. AUC, area under the curve; HCC, hepatocellular carcinoma; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; TNM, tumor node metastasis.

Next, the expression levels of CCT4 across different clinical characteristic groups were analyzed. The results showed that CCT4 expression was associated with age (Figure 1G), histological grade (Figure 1H), and tumor node metastasis (TNM) stage (Figure 1I), but not significantly correlated with gender (Figure 1J). These findings demonstrated that CCT4 was significantly overexpressed in HCC tissues and possessed strong diagnostic potential. Moreover, its expression correlated with patient age, tumor grade, and TNM stage, suggesting a role for CCT4 in HCC progression and its potential as a clinical biomarker.

CCT4 co-expressed genes are enriched in ribosome biogenesis and related pathways

Using the established screening criteria, 408 co-expression genes of CCT4 were identified. GO enrichment analysis showed that these co-expression genes were primarily associated with ribonucleoprotein complex biogenesis, cytoplasmic translation, and ribosome biogenesis in BP (Figure 2A). In cellular components (CC), they were associated with ribosomal subunits, ribosomes, and cytosolic ribosomes (Figure 2B). They were also linked to structural constituents of ribosomes, ATP-dependent protein folding chaperones, and protein folding chaperones in molecular functions (MF) (Figure 2C). KEGG analysis indicated enrichment predominantly in ribosome and spliceosome-related pathways (Figure 2D).

Figure 2 GO, KEGG enrichment analysis of CCT4 co-expression genes and GSEA. (A-C) GO enriched terms for CCT4 co-expression genes. (D) KEGG enriched terms for CCT4 co-expression genes. (E-H) GSEA results for CCT4. BP, biological process; CC, cellular component; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

GSEA revealed that the CCT4 high-expression group was mainly enriched in DNA replication initiation, structural constituent of nuclear pore, and chromosomal breakage induced by cross linking agents (Figure 2E-2H). The co-expression and enrichment analyses indicated that CCT4 was closely linked to ribosome biogenesis, protein folding, and RNA processing pathways, suggesting its involvement in fundamental cellular functions and genomic stability in HCC.

CCT4 serves as an independent prognostic biomarker and improves survival prediction in HCC

To investigate the correlation between CCT4 expression and clinical prognosis, univariate and multivariate Cox regression models were constructed. The univariate Cox regression model indicated that TNM stage and CCT4 expression levels were associated with patient prognosis (Figure 3A). The multivariate Cox regression model further revealed that TNM stage and CCT4 expression levels served as independent prognostic markers for HCC (Figure 3B). Subsequently, patients were divided into high and low expression groups based on the median CCT4 expression value. Kaplan-Meier survival curve analysis showed that higher CCT4 expression correlated with lower survival rates in HCC patients (Figure 3C). The nomogram, depicted in Figure 3D, was used to estimate 1-, 2-, 3-, and 5-year survival rates for HCC patients, with the predictive model achieving a C-index of 0.707 (Figure 3E). Calibration curves demonstrated overlap between predicted probabilities and actual observations in both training and validation groups, indicating optimal predictive accuracy (Figure 3F,3G). In the TCGA and GSE14520 datasets, this model also exhibited significant predictive value for HCC patients (Figure 3H,3I, AUC >0.7 at 1, 2, and 3 years). CCT4 expression served as an independent prognostic factor for HCC, with higher levels predicting poorer survival. The developed nomogram integrating CCT4 and clinical features provided accurate survival predictions, supporting its clinical utility for patient risk stratification.

Figure 3 CCT4 is an independent prognostic factor for overall survival in HCC patients. (A,B) Univariate and multivariate Cox regression analyses of prognostic parameters in the TCGA dataset. (C) Kaplan-Meier analysis of CCT4 expression in HCC. (D) A nomogram for predicting the 1-, 2-, 3- and 5-year OS of patients with HCC. (E) The predictive effect of the nomogram; CCT4 expression and TNM stage of HCC on OS were evaluated by C-index. (F,G) Nomogram calibration curves for OS prediction at 1, 2, 3, and 5 years, using TCGA (F) and GSE14520 (G) dataset. (H,I) ROC analysis using TCGA (H) and GSE14520 (I) dataset. AUC, area under curve; CI, confidence interval; HCC, hepatocellular carcinoma; HR, hazard ratio; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; TNM, tumor node metastasis.

CCT4 expression alters immune cell infiltration landscape in HCC

Among the 22 immune cell types, T cells CD4 naive, M0 macrophages, dendritic cells resting, eosinophils, and neutrophils exhibited higher infiltration in the CCT4 high-expression group, whereas naive B cells, γδ T cells, monocytes, and M1 macrophages showed higher infiltration in the CCT4 low-expression group (Figure 4A). Correlation analysis revealed that dendritic cells, M0 macrophages, eosinophils, and memory B cells were positively correlated with CCT4 expression, while NK cells resting, mast cells resting, T cells CD4 memory resting, M1 macrophages, and γδ T cells were negatively correlated with CCT4 expression (Figure 4B).

Figure 4 Correlation between CCT4 expression and immune cell infiltration in HCC. (A) Box plot of the distribution of immune cells in TCGA. (B) Correlation coefficient of CCT4 expression and immune cells. (C) The correlation between CCT4 expression and immune checkpoint. HCC, hepatocellular carcinoma; NK, natural killer; TCGA, The Cancer Genome Atlas; Treg, regulatory T cells.

Further analysis explored the correlation between CCT4 expression and 48 immune checkpoints. Using a threshold of |correlation coefficient| >0.3 and P value <0.05, nine immune checkpoints—CD44, CD200, NRP1, CD276, CD86, TNFSF4, HAVCR2, CD200R1, and LAIR1—were found to be positively correlated with CCT4 expression (Figure 4C). CCT4 expression correlated with variations in immune cell populations and immune checkpoint expression, revealing its potential involvement in shaping immune responses within the HCC microenvironment.

CCT4 expression associates with drug response in HCC

Among targeted therapies for HCC (Figure 5A), the first-line drug sorafenib showed a significant positive correlation between its half maximal inhibitory concentration (IC₅₀) and CCT4 expression (r=0.31, P=0.02), indicating that elevated CCT4 levels may be associated with resistance to this standard frontline treatment. No significant correlations were found between CCT4 expression and other targeted drugs. Among chemotherapeutic agents used for HCC (Figure 5B), only docetaxel exhibited a significant negative correlation with CCT4 expression (r=−0.26, P=0.044), suggesting that high CCT4 expression enhances sensitivity to this drug.

Figure 5 Drug sensitivity analysis of CCT4. (A) CCT4-associated targeted therapies. (B) CCT4-associated chemotherapeutic agents. (C) Drugs significantly correlated with CCT4 expression. Positive & negative: P<0.05; non-significant, P>0.05.

Additionally, several other drugs displayed notable correlations with CCT4 expression (Figure 5C): IC₅₀ values of ribociclib (r=0.30, P=0.02) and foretinib (r=0.30, P=0.02) were positively correlated with CCT4 expression, whereas vemurafenib (r=−0.30, P=0.02), entosplenitib (r=−0.33, P=0.01), vorasidenib (r=−0.35, P=0.007), and odanacatib (r=−0.36, P=0.006) showed negative correlations. These findings indicated that CCT4 expression influenced drug sensitivity and resistance in HCC, highlighting its potential as a predictive biomarker to guide personalized treatment strategies.

CCT4 is highly expressed in hepatocytes, particularly in pro-metastatic clusters

After dimensionality reduction and clustering of normal and tumor group samples from GSE149614, seven clusters were annotated: T/NK cells, hepatocytes, myeloid cells, endothelial cells, fibroblasts, B cells, and cholangiocytes (Figure 6A). The proportion of T/NK cells was reduced, whereas the proportion of hepatocytes was increased in primary tumor (PT), portal vein tumor thrombus (PVTT), and metastatic lymph node (MLN) tissues, compared with the non-tumor liver (NTL) tissues (Figure 6B). Cells with CCT4 expression greater than 0 were classified as CCT4 positive, and those with expression equal to 0 as CCT4 negative. In PT, PVTT, and MLN tissues, hepatocytes were markedly more abundant among CCT4-positive cells compared with CCT4-negative cells (Figure 6C).

Figure 6 Expression analysis of CCT4 gene in HCC cells. (A) t-SNE plots representing the dimensionality reduction of all cells. (B) Relative proportions of cell types, ordered by tissue type. (C) Relative proportions of CCT4-positive and CCT4-negative cells across four tissue types. (D) t-SNE plots representing the dimensionality reduction of hepatocytes. (E) Tissue distribution patterns of non-malignant, pro-tumorigenic, and pro-metastatic hepatocyte clusters across NTL, PT, PVTT, and MLN samples. Dot size represents R_O/E, and dot color indicates the log-transformed P values from a two-sided Chi-squared test. (F) Density plot of CCT4 expression. (G) The boxplot illustrates CCT4 expression levels across non-malignant, pro-tumorigenic, and pro-metastatic groups. HCC, hepatocellular carcinoma; MLN, metastatic lymph node; NK, natural killer cell; NTL, non-tumor liver; PT, primary tumor; PVTT, portal vein tumor thrombus; R_O/E, the ratio of observed to expected cell numbers; t-SNE, t-distributed stochastic neighbor embedding.

A total of 22,699 hepatocytes were identified and classified into 13 clusters (Figure 6D). Hepatocytes enriched in NTL tissues were defined as non-malignant cells, those enriched in PT tissues as pro-tumorigenic cells, and those enriched in PVTT and MLN tissues as pro-metastatic cells. Among these, four clusters were designated as non-malignant hepatocytes, three clusters as pro-tumorigenic hepatocytes, and six clusters as pro-metastatic hepatocytes (Figure 6E). CCT4 expression was higher in pro-metastatic hepatocytes compared with the other groups (Figure 6F,6G).

In summary, CCT4 was predominantly expressed in hepatocytes, with the highest levels observed in pro-metastatic clusters, implicating its potential role in metastatic progression and the need for further study.

Pro-metastatic hepatocytes exhibit low differentiation and altered immune cell interactions associated with high CCT4 expression

CytoTRACE scoring of all hepatocyte clusters showed that the pro-metastatic hepatocytes had the highest score, indicating the lowest differentiation level (Figure 7A-7C). CCT4 expression was highest in the pro-metastatic hepatocytes (Figure 7D), suggesting a link between low hepatocyte differentiation and high CCT4 expression.

Figure 7 Expression analysis of CCT4 in hepatocytes. (A) The differentiation of hepatocytes evaluated by boxplot. (B,C) Differentiation of hepatocyte subtypes. (D) The expression of CCT4 in different clusters of hepatocytes. (E,F) Interaction strength between pro-metastatic hepatocytes and cells originating from NTL (E) and PT (F) tissues. CytoTRACE, Cellular Trajectory Reconstruction Analysis using gene Counts and Expression; NTL, non-tumor liver; PT, primary tumor.

Cell communication analysis was conducted for the pro-metastatic hepatocyte clusters. In NTL tissues, pro-metastatic hepatocytes exhibited strong interaction intensities with T/NK cells and myeloid cells (Figure 7E). However, these interactions with T/NK cells and myeloid cells were markedly attenuated in PT tissues (Figure 7F). In contrast, interactions between pro-metastatic hepatocytes and fibroblasts as well as endothelia were enhanced in PT tissues compared to NTL tissues.

Altogether, high CCT4 expression in poorly differentiated pro-metastatic hepatocytes may drive HCC progression, accompanied by weakened interactions with immune cells in tumor tissues, reflecting dynamic changes in the tumor microenvironment.

Discussion

This study analyzed HCC patient data from the TCGA-LIHC and GSE14520 datasets. The analysis revealed that CCT4 is highly expressed in HCC, consistent with a previous report (9). Furthermore, high CCT4 expression was associated with younger age, higher pathological grade, and advanced TNM stage. CCT4 expression also showed a positive correlation with the IC₅₀ values of sorafenib, ribociclib and foretinib. This suggests that CCT4 expression is linked to resistance to these drugs, positioning CCT4 as a potential predictor of chemotherapy drug sensitivity in HCC.

To investigate the potential functional roles of CCT4 in HCC, we performed enrichment analysis of CCT4 co-expressed genes. CCT4 is primarily recognized as a molecular chaperone that facilitates proper protein folding and preserves protein stability. Our analysis, however, also linked CCT4 to ribosome synthesis and RNA splicing—processes typically mediated by RNA-binding proteins (RBPs) in cancer (30). Søndergaard et al. (11) revealed that CCT3, a paralog of CCT4, functions as a non-canonical RBP and exerts a critical role in lipid metabolism in HCC. Given the structural and functional similarities between CCT3 and CCT4 (31), CCT4 may likewise act as a non-canonical RBP, extending its role beyond chaperoning.

Additionally, CCT4 expression was evaluated using univariate and multivariate Cox regression analysis and survival analysis. The results identified CCT4 as an independent prognostic factor in HCC, with high CCT4 expression linked to unfavorable survival outcomes. A nomogram was constructed using CCT4 expression and TNM staging. Assessments via C-index, calibration curves, and ROC analysis demonstrated that this model effectively predicts OS in HCC. Recent research utilizing multivariate Cox regression and linear regression analyses has developed a predictive model incorporating three molecular chaperones (CCT4, CCT6A, and CCT6B), with CCT4 demonstrating particularly strong prognostic significance in HCC outcomes (32).

Single-cell transcriptomic analysis of HCC (GSE149614) revealed that CCT4 is enriched in hepatocytes, particularly within poorly differentiated pro-metastatic clusters. These clusters were more frequent among cells with high CCT4 expression, reflecting a strong link between CCT4 expression and hepatocyte dedifferentiation. During HCC progression, hepatocytes represent the epithelial origin of the tumor, and their dedifferentiation is a hallmark of EMT, a process that confers invasive and metastatic potential to tumor cells (33,34). Previous studies have identified epithelial cell-associated molecules such as keratin 1 as biomarkers and therapeutic targets in aggressive cancers (35), and selective splicing events, such as those driven by LINC01089, as inducers of EMT in HCC (36). Given that CCT4 is highly expressed in poorly differentiated, pro-metastatic hepatocytes, it may contribute to EMT-driven phenotypic changes and malignant progression in HCC. This raises the possibility that CCT4 could serve as both a biomarker and a potential therapeutic target for limiting hepatocyte dedifferentiation and metastasis in HCC.

Cell-cell communication analysis revealed that pro-metastatic hepatocytes in NTL tissues exhibited robust interactions with T/NK cells and myeloid cells, whereas these interactions were markedly diminished in PT tissues. The attenuated communication between pro-metastatic hepatocytes and T cells in tumor tissues likely reflects T cell dysfunction or exhaustion (37), resulting in weakened anti-tumor immunity and immune evasion. Similarly, reduced pro-metastatic hepatocyte-myeloid interactions may reflect myeloid polarization toward immunosuppressive M2-like macrophages, impairing their tumoricidal function and promoting tumor progression (38). Immune infiltration analysis demonstrated a negative correlation between CCT4 expression and infiltration levels of M1 macrophages, resting NK cells, resting CD4 memory T cells, and γδ T cells, further supporting the notion of diminished immune cell activity and weakened cellular crosstalk in the tumor microenvironment.

This study has limitations. Our reliance on TCGA, GEO, and single-cell datasets, without in vitro or in vivo validation, restricts deeper mechanistic understanding. Nevertheless, our findings highlight CCT4 as a promising biomarker for stratifying HCC patients and guiding personalized targeted therapy. Its roles in immune evasion and pro-metastatic hepatocyte dedifferentiation also point to immunotherapy prospects. Future studies should experimentally confirm CCT4’s mechanisms and pursue targeted therapies to improve HCC patient outcomes.

Conclusions

This study conducted a comprehensive analysis using the bulk RNA-seq and scRNA-seq. The analysis revealed a high expression state of CCT4 in HCC and its significant association with HCC patient prognosis. It further examined CCT4’s correlations with immune cell infiltration and targeted drug sensitivity. Additionally, the study explored the relationship between CCT4 and pro-metastatic hepatocytes at the single-cell level. These findings establish a foundation for further investigating CCT4’s roles in HCC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1277/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1277/prf

Funding: This study was supported by Yunnan Fundamental Research Kunming Medical University Projects (No. 202201AY070001-123), Kunming Medical University First-Class Discipline Team Construction Project (No. 2024XKTDPY17) and Yunnan Province University Student Innovative Experiment Program Project (No. S202310678005).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1277/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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