Integration of bulk and single-cell RNA-seq data identifies a cellular senescence-related prognostic signature in liver hepatocellular carcinoma

Integration of bulk and single-cell RNA-seq data identifies a cellular senescence-related prognostic signature in liver hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integration of bulk and single-cell RNA-seq data identifies a cellular senescence-related prognostic signature in liver hepatocellular carcinoma Rentong Liu, Bing Li, Yiqian Luo, Xiuwei Yan, Yuanyuan Wang, Song Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9198475/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Liver Hepatocellular carcinoma (LIHC) is a highly heterogeneous malignancy with poor prognosis, underscoring the urgent need for reliable biomarkers and therapeutic targets. Cellular senescence plays a dual role in tumor progression, yet the landscape of cellular senescence-related genes (CSRGs) in LIHC remains incompletely characterized. In this study, we curated 167 CSRGs from the Gene Ontology database and performed integrative analysis using transcriptomic data from The Cancer Genome Atlas and International Cancer Genome Consortium cohorts, combined with single-cell RNA-sequencing data from the Tumor Immune Single-Cell Hub database. Fifteen prognostic CSRGs were identified and used to stratify LIHC patients into two distinct molecular subtypes with divergent clinical outcomes. Cluster 2 exhibited an immune-excluded phenotype characterized by high immune infiltration but impaired effector function, correlating with poor prognosis, whereas Cluster 1 represented an immune-desert phenotype. A CSRGs-based risk score was constructed and validated as an independent prognostic factor across cohorts. Single-cell analysis identified EEF1E1 as a key CSRG specifically enriched in malignant hepatocytes, with high EEF1E1 expression correlating with advanced clinicopathological features and immune cell infiltration. Functional experiments demonstrated that EEF1E1 knockdown significantly suppressed LIHC cell migration and invasion. Collectively, this study provides a comprehensive characterization of CSRGs in LIHC and establishes a robust prognostic signature with potential clinical utility, identifying EEF1E1 as a functionally relevant oncogenic driver and a promising therapeutic target in LIHC. Hepatocellular carcinoma Cellular senescence Prognostic signature Tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Liver Hepatocellular Carcinoma (LIHC) is the sixth most common malignancy and the third leading cause of cancer-related mortality worldwide, accounting for approximately 780,000 deaths annually [ 1 , 2 ]. Despite advances in surgical resection, locoregional therapies, and systemic treatments such as tyrosine kinase inhibitors and immune checkpoint inhibitors, the prognosis of LIHC patients remains dismal, with a five-year survival rate of less than 20% [ 3 , 4 ]. The high heterogeneity of LIHC at both the genomic and phenotypic levels poses a major challenge for precise risk stratification and individualized treatment [ 5 , 6 ]. Therefore, identifying reliable biomarkers and novel therapeutic targets that reflect the underlying biological heterogeneity is urgently needed to improve patient outcomes. Cellular senescence is a fundamental biological process characterized by irreversible cell cycle arrest, which serves as a critical barrier against tumor initiation and progression [ 7 , 8 ]. However, emerging evidence has revealed a paradoxical role of senescence in cancer. While oncogene-induced senescence can suppress tumorigenesis, the accumulation of senescent cells within the tumor microenvironment (TME) can paradoxically promote tumor growth, metastasis, and therapy resistance through the secretion of a complex mixture of pro-inflammatory cytokines, growth factors, and proteases, collectively termed the senescence-associated secretory phenotype (SASP) [ 9 , 10 ]. The SASP can induce chronic inflammation, remodel the extracellular matrix, recruit immunosuppressive cells, and facilitate immune evasion, thereby creating a pro-tumorigenic niche [ 11 , 12 ]. Given the dual nature of cellular senescence, the expression patterns and functional roles of senescence-related genes (CSRGs) in LIHC warrant comprehensive investigation.zhu Recent advances in high-throughput sequencing technologies have enabled the systematic characterization of the molecular landscape of LIHC. However, most studies have focused on the role of individual senescence-associated genes or pathways, and a comprehensive analysis integrating bulk transcriptomic and single-cell RNA-sequencing (scRNA-seq) data to identify CSRGs with prognostic and immunologic significance in LIHC is lacking [ 13 ]. scRNA-seq offers a powerful approach to dissect cellular heterogeneity within the TME, allowing the identification of cell-type-specific expression patterns that are obscured in bulk tissue analyses [ 14 ]. By integrating bulk and single-cell data, it is possible to uncover previously unrecognized associations between senescence-related genes, immune cell infiltration, and clinical outcomes. In this study, we performed a comprehensive integrative analysis to characterize the landscape of CSRGs in LIHC. Using transcriptomic data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we identified 15 prognostic CSRGs that were upregulated in LIHC tumor tissues. Unsupervised consensus clustering based on these genes stratified patients into two distinct molecular subtypes with divergent clinical outcomes and immune phenotypes. We further constructed and validated a CSRGs-based risk score that independently predicted overall survival and demonstrated strong associations with immune infiltration and immune checkpoint gene expression. Through single-cell resolution analysis, we identified EEF1E1 as a key CSRG specifically enriched in malignant hepatocytes and experimentally validated its oncogenic role in promoting cell migration and invasion. Collectively, our findings provide novel insights into the role of cellular senescence in LIHC and offer potential biomarkers for risk stratification and therapeutic targeting. 2. Materials and Methods 2.1 Data Collection and Preprocessing Transcriptomic profiles and corresponding clinical information of patients with LIHC were obtained from TCGA database ( https://portal.gdc.cancer.gov ). The LIHC dataset included 374 tumor samples and 50 adjacent normal tissue samples. FPKM values were converted to TPM for normalization. For external validation, the ICGC LIHC dataset was downloaded from the ICGC Data Portal ( https://dcc.icgc.org ). Samples with incomplete survival information or ambiguous histopathological diagnosis were excluded from further analysis. 2.2 Identification of Cellular Senescence-Related Genes A total of 167 CSRGs were curated from the Gene Ontology (GO) database using the following accession terms: GO:0090398 (cellular senescence), GO:2000772 (regulation of cellular senescence), GO:2000773 (negative regulation of cellular senescence), and GO:2000774 (positive regulation of cellular senescence). These genes were retrieved from the Molecular Signatures Database (MSigDB) and relevant published literature. 2.3 Differential Expression Analysis and Prognostic Gene Screening Differential expression analysis between LIHC tumor tissues and adjacent normal tissues was performed using the limma package in R [ 15 ]. Genes with |log₂ FC| > 1 and a false discovery rate (FDR) < 0.05 were considered significantly upregulated. Univariate Cox regression analysis was applied to identify CSRGs significantly associated with overall survival (OS) in the TCGA cohort. Genes with a P value < 0.05 were considered prognostic. 2.4 Consensus Clustering for Molecular Subtype Identification Based on the expression profiles of the overlapping prognostic CSRGs, unsupervised consensus clustering was performed using the ConsensusClusterPlus R package[ 16 ]. A total of 1,000 iterations with an 80% sampling rate were conducted to ensure stability. The optimal number of clusters (k) was determined by evaluating the empirical cumulative distribution function (CDF) curves, the relative change in the area under the CDF curve, and the consensus matrix. Patients were stratified into two distinct molecular subtypes (Cluster 1 and Cluster 2). 2.5 Immune Microenvironment Analysis The immune landscape of LIHC patients was evaluated using multiple algorithms. The relative proportions of immune cell infiltration were estimated using the TIMER [ 17 ], MCP-counter [ 18 ], and xCell [ 19 ] algorithms implemented via the IOBR package [ 20 ]. The activity of the cancer-immunity cycle was assessed using the Tumor Immune Estimation Resource (TIP) database ( http://biocc.hrbmu.edu.cn/TIP/index.jsp ), which visualizes anti-tumor immune responses across seven sequential steps: release of cancer cell antigens (Step 1), cancer antigen presentation (Step 2), priming and activation (Step 3), trafficking of immune cells to tumors (Step 4), infiltration of immune cells into tumors (Step 5), recognition of cancer cells by T cells (Step 6), and killing of cancer cells (Step 7) [ 21 ]. 2.6 Construction and Validation of a Prognostic Risk Signature Least absolute shrinkage and selection operator (LASSO) penalized regression was performed using the glmnet R package to select the most informative CSRGs and construct a risk score model [ 22 ]. Ten-fold cross-validation was applied to determine the optimal tuning parameter (λ). The risk score for each patient was calculated as follows: $$\:Risk\:score\:={\sum\:}_{i=1}^{n}Coef\:\left({X}_{i}\right)*\:Exp\:\left({X}_{i}\right)$$ where Coef (Xi) represents the regression coefficient of each selected gene, and Exp (Xi) represents its expression level. represents its expression level. Patients were divided into high-risk and low-risk groups based on the median risk score. The predictive performance of the risk signature was evaluated using Kaplan-Meier survival curves with log-rank tests and time-dependent receiver operating characteristic (ROC) curves with the area under the curve (AUC) calculated using the survivalROC R package. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were performed to visualize the clustering of high- and low-risk groups. 2.7 Independent Prognostic Analysis and Nomogram Construction Univariate and multivariate Cox regression analyses were performed to assess whether the risk score served as an independent prognostic factor, adjusting for clinicopathological variables including age, gender, histological grade, and pathological stage. A nomogram was constructed integrating the risk score and significant clinicopathological features to predict 1-, 3-, and 5-year overall survival[ 23 ]. Calibration curves were generated to evaluate the agreement between predicted and observed survival probabilities. 2.8 Single-Cell RNA-Seq Data Analysis Single-cell RNA-sequencing data of LIHC samples were obtained from the Tumor Immune Single-Cell Hub (TISCH) database (GSE166635) [ 24 ]. Dimensionality reduction was performed using uniform manifold approximation and projection (UMAP) implemented via the Seurat R package. Cells were clustered based on canonical marker genes, and the expression patterns of the seven prognostic CSRGs were visualized across different cell types using violin plots. 2.9 Cell Culture and siRNA Transfection Human LIHC cell lines HepG-2 and HuH-7 were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin at 37°C in a 5% CO₂ atmosphere. Small interfering RNA (siRNA) targeting EEF1E1 and negative control siRNA were synthesized and transfected using Lipofectamine 3000 reagent according to the manufacturer’s instructions. Knockdown efficiency was confirmed by RT‑qPCR and western blot analysis 48 hours post-transfection. 2.10 RNA Extraction and Quantitative Real-Time PCR (RT‑qPCR) Total RNA was extracted from cell lines and tissue samples using TRIzol reagent. Reverse transcription was performed using the PrimeScript RT Reagent Kit. RT‑qPCR was conducted using SYBR Green Master Mix on a real-time PCR system. Relative gene expression levels were calculated using the 2⁻ΔΔCt method, with GAPDH as the internal control. All reactions were performed in triplicate. 2.11 Western Blot Analysis Cells and tissues were lysed in RIPA buffer containing protease and phosphatase inhibitors. Protein concentrations were quantified using the BCA assay. Equal amounts of protein were separated by SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% non-fat milk and incubated overnight at 4°C with primary antibodies against EEF1E1 (1:1000, 10805-1-AP, Proteintech) and β-tubulin (1:4000, AF1216, Beyotime). After incubation with HRP-conjugated secondary antibodies, protein bands were visualized using enhanced chemiluminescence substrate. 2.12 Wound Healing and Transwell Invasion Assays For wound healing assays, cells were seeded in six-well plates and grown to confluence. A sterile pipette tip was used to create a linear scratch. Wound closure was monitored and photographed at 0 and 24 hours. The percentage of wound closure was quantified using ImageJ software. For Transwell invasion assays, cells were suspended in serum-free medium and seeded into the upper chamber of Matrigel-coated inserts. The lower chamber was filled with medium containing 10% FBS as a chemoattractant. After 24 hours, non‑invading cells were removed, and invading cells were fixed, stained with crystal violet, and counted in five random fields per well. 2.13 Statistical Analysis All statistical analyses were performed using R software (version 4.1.2). Comparisons between two groups were performed using the Wilcoxon rank-sum test. Comparisons among multiple groups were performed using the Kruskal-Wallis test. Categorical variables were compared using the chi‑square test or Fisher’s exact test. Survival curves were estimated using the Kaplan‑Meier method and compared using the log‑rank test. A P value < 0.05 was considered statistically significant.: Significance levels: *p < 0.05, **p < 0.01,***p < 0.001. 3. Results 3.1 Identification of cellular senescence-related molecular subtypes in hepatocellular carcinoma A total of 167 CSRGs were initially retrieved from the GO database based on the following accession terms: GO:0090398, GO:2000772, GO:2000773, and GO:2000774. Univariate Cox regression analysis identified 43 CSRGs significantly associated with overall survival in patients with LIHC. Concurrently, differential expression analysis between LIHC and adjacent normal tissues using the limma package (logFC > 1) yielded 2,016 upregulated genes. Intersection of these two gene sets revealed 15 prognostic CSRGs that were upregulated in tumor tissues (Fig. 1 A). To explore the underlying heterogeneity among LIHC patients, we performed unsupervised consensus clustering based on the expression profiles of these 15 genes. Analysis of the CDF and consensus matrices indicated that k = 2 represented the optimal clustering solution, allowing the stratification of patients into two distinct molecular subtypes, designated Cluster 1 and Cluster 2 (Fig. 1 B– 1 D). Significant differences in clinical outcomes were observed between the two subtypes. Kaplan–Meier survival analysis demonstrated that patients in Cluster 1 had markedly longer overall survival compared to those in Cluster 2 (Fig. 1 E). We further characterized the association between the identified subtypes, CSRG expression, and clinicopathological features. Heatmaps revealed distinct expression patterns of the 15 prognostic CSRGs between Cluster 1 and Cluster 2, accompanied by distinct distributions of clinical annotations (Fig. 1 F). Notably, Cluster 2 was associated with a more aggressive clinical phenotype. In the TCGA cohort, patients in Cluster 2 exhibited significantly higher proportions of advanced histological grade and pathological stage compared to those in Cluster 1 (Fig. 1 G). 3.2 Immunogenomic Landscape of CSRGs-Based Molecular Subtypes in LIHC To further characterize the biological distinction between the two subtypes, we investigated the landscape of the TME. Using the IOBR package, we employed three independent algorithms—TIMER, MCP-counter, and xCell—to estimate the infiltration abundance of diverse immune cell populations. Notably, Cluster 2 exhibited significantly higher enrichment scores for the majority of immune cell types compared to Cluster 1, suggesting a more inflamed, albeit potentially immunosuppressive, TIME (Fig. 2 A–C). Given the pivotal role of immune checkpoint pathways in tumor immune evasion, we next examined the expression profiles of canonical immune checkpoint genes between the two clusters. Consistent with the immune infiltration patterns, most immune checkpoint genes, including PD-L1, PDCD1, and CTLA-4, were significantly upregulated in Cluster 2 relative to Cluster 1 (Fig. 2 D). To gain deeper insight into the dynamic processes underlying these immune phenotypes, we leveraged the TIP database to assess the activity of distinct immune cell populations across the cancer-immunity cycle. Intriguingly, Cluster 1 exhibited significantly higher activity in steps 5, 6, and 7, which correspond to immune cell infiltration, killing of tumor cells, and release of tumor antigens, respectively. In contrast, Cluster 2 showed enhanced enrichment in steps 1 and 4, representing antigen release and trafficking of immune cells to the tumor site (Fig. 2 E). These divergent patterns suggest that while Cluster 2 displays a globally higher abundance of immune cells, the effective execution of anti-tumor immunity may be arrested at intermediate steps. Based on these findings, we propose that Cluster 2 corresponds to an immune-excluded phenotype, characterized by immune cell accumulation at the periphery without effective tumor infiltration, whereas Cluster 1 represents an immune-desert phenotype, marked by minimal immune engagement across the majority of the cancer-immunity cycle [ 25 ]. 3.3 Construction and Validation of a CSRGs-Based Prognostic Risk Signature. To develop a quantitative prognostic model based on the identified CSRGs, we performed LASSO penalized regression analysis using LIHC datasets from TCGA and the ICGC. A risk score was subsequently constructed for each patient, enabling stratification into high- and low-risk groups (Fig. 3 A, 3 B). Kaplan–Meier survival analysis revealed that patients in the low-risk group had significantly prolonged overall survival compared to those in the high-risk group across both cohorts (both P < 0.001; Fig. 3 C). The distribution of risk scores and survival status further illustrated that patients with higher risk scores experienced shorter survival times and increased mortality (Fig. 3 D). Time-dependent ROC analysis demonstrated favorable predictive performance of the risk score. In the TCGA cohort, the AUC values for 1-, 3-, and 5-year overall survival were 0.766, 0.696, and 0.667, respectively. Comparable performance was observed in the ICGC cohort, with AUC values of 0.716, 0.735, and 0.747 for the respective time points (Fig. 3 E). Dimensionality reduction analyses, including PCA and t-SNE, showed clear separation between high- and low-risk groups, indicating robust clustering based on the CSRGs-derived risk score (Fig. 3 F, 3 G). Subgroup survival analyses stratified by various clinicopathological features consistently showed that patients in the low-risk group experienced superior survival outcomes compared to those in the high-risk group across different clinical strata, including histological grade, pathological stage, and other key variables (Fig. 3 H– 3 K, Figure S1 A-C). Collectively, these findings validate the prognostic utility of the CSRGs-based risk signature across independent cohorts and clinical subgroups. To evaluate the clinical utility of the LASSO-derived risk score, we performed univariate and multivariate Cox regression analyses. In both models, the risk score emerged as an independent prognostic factor for overall survival in LIHC patients (Fig. 4 A, B). To facilitate clinical translation, a nomogram integrating the risk score with key clinicopathological features was constructed, which demonstrated robust predictive performance as evidenced by well-calibrated calibration curves (Fig. 4 C, D). 3.4 Immune Landscape in High- and Low-Risk Groups. To further characterize the immune microenvironment associated with the CSRGs‑based risk signature, we evaluated immune cell infiltration and immune checkpoint gene expression between the high‑ and low‑risk groups. Using three independent algorithms—TIMER, MCP‑counter, and xCell—implemented, we observed that patients in the high‑risk group exhibited significantly higher enrichment scores for the majority of immune cell types compared with those in the low‑risk group (Fig. 5 A–C; Figure S2A–C). Consistent with this pattern, the expression levels of canonical immune checkpoint genes, including PD‑L1, PD‑1, and CTLA‑4, were markedly upregulated in the high‑risk group relative to the low‑risk group (Fig. 5 D; Figure S3D). These findings indicate that the risk score not only captures prognostic heterogeneity but also reflects distinct immune phenotypes, with the high‑risk group displaying a more inflamed yet potentially immunosuppressive tumor microenvironment. 3.5 Identification of EEF1E1 as a Key CSRG in LIHC To further pinpoint the most functionally relevant CSRGs within the prognostic signature, we analyzed single‑cell RNA‑sequencing data from the GSE166635 dataset obtained from the TISCH database. Dimensionality reduction via UMAP revealed distinct cellular clusters within the LIHC tumor microenvironment (Fig. 6 A). Violin plots demonstrated that among the seven prognostic CSRGs, EEF1E1 was specifically and significantly enriched in malignant hepatocytes compared with other cell types, including immune and stromal populations (Fig. 6 B, C; Figure S3A–C). This selective expression pattern prompted us to focus on EEF1E1 as a candidate key regulator. We next validated the expression of EEF1E1 at the transcriptional level using the GEPIA database. Consistent with the single‑cell findings, EEF1E1 expression was significantly upregulated in LIHC tumor tissues compared with adjacent normal tissues (Fig. 6 D). Immunohistochemical analysis from the Human Protein Atlas (HPA) database further confirmed elevated EEF1E1 protein expression in LIHC tumor samples (Fig. 6 E). To explore the clinical relevance of EEF1E1, we analyzed its association with clinicopathological features. Notably, high EEF1E1 expression was significantly correlated with advanced histological grade and pathological stage, indicating its potential role in tumor aggressiveness (Fig. 6 F; Figure S3D). Given the established link between EEF1E1 and immune infiltration patterns observed in the risk model, we further investigated its correlation with immune cell infiltration using the TIMER database. EEF1E1 expression showed significant positive correlations with the infiltration levels of multiple immune cell types, including B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells (Fig. 6 G). These findings suggest that EEF1E1 may contribute to the immune‑modulatory landscape of LIHC. 3.6 Functional Validation of EEF1E1 in LIHC Cell Lines Given the consistent association of EEF1E1 with poor prognosis, immune infiltration, and specific enrichment in malignant hepatocytes, we further investigated its functional role in LIHC through loss‑of‑function experiments. siRNA‑mediated knockdown of EEF1E1 was performed in both HepG‑2 and HuH‑7 cell lines. The efficiency of knockdown was confirmed by RT‑qPCR and western blot analysis, demonstrating significant reduction of EEF1E1 at both the mRNA and protein levels in siEEF1E1‑treated cells compared with controls (Fig. 7 A–C). Functional assays revealed that EEF1E1 silencing markedly suppressed the migratory capacity of LIHC cells, as evidenced by wound healing assays (Fig. 7 D, E). Similarly, Transwell invasion assays demonstrated that knockdown of EEF1E1 significantly attenuated the invasive ability of both HepG‑2 and HuH‑7 cells (Fig. 7 F, G). Collectively, these findings suggest that EEF1E1 promotes cell migration and invasion in LIHC, supporting its role as a functionally relevant oncogenic driver within the CSRGs‑based prognostic signature. 4 Discussion In the present study, we comprehensively characterized the landscape of CSRGs in LIHC and constructed a robust prognostic signature with potential clinical utility. By integrating bulk transcriptomic and single-cell RNA-sequencing data from multiple independent cohorts, we identified two distinct molecular subtypes (Cluster 1 and Cluster 2) with divergent prognostic outcomes and immune phenotypes. Furthermore, we developed and validated a CSRGs-based risk score that independently predicted overall survival and demonstrated significant associations with tumor immune infiltration and immune checkpoint gene expression. Notably, through single-cell resolution analysis, we pinpointed EEF1E1 as a key CSRG specifically enriched in malignant hepatocytes, and functional experiments confirmed its oncogenic role in promoting LIHC cell migration and invasion. Collectively, our findings provide novel insights into the role of cellular senescence in LIHC and offer a potential biomarker and therapeutic target for this devastating disease. Cellular senescence is a complex biological process characterized by irreversible cell cycle arrest, which has been implicated in both tumor suppression and tumor promotion depending on the context [ 26 , 27 ]. While the tumor-suppressive functions of senescence are well established, accumulating evidence suggests that senescent cells can acquire a SASP that promotes chronic inflammation, immune evasion, and tumor progression [ 28 , 29 ]. In hepatocellular carcinoma, the role of senescence-related genes remains incompletely understood, and systematic characterization of CSRGs in this malignancy is lacking. Our study addresses this gap by providing a comprehensive analysis of CSRGs in LIHC and revealing their prognostic and immunologic significance. Through unsupervised consensus clustering based on 15 prognostic CSRGs, we stratified LIHC patients into two distinct molecular subtypes with markedly different clinical outcomes. Cluster 1 was characterized by favorable prognosis, whereas Cluster 2 was associated with aggressive clinicopathological features and poor survival. These findings align with previous studies demonstrating that aberrant expression of senescence-associated genes correlates with tumor progression and therapy resistance in various cancers [ 30 , 31 ]. Notably, the two subtypes exhibited distinct immune landscapes: Cluster 2 displayed higher overall immune cell infiltration but paradoxically showed impaired effector functions in the later stages of the cancer-immunity cycle. This pattern is reminiscent of the immune-excluded phenotype, in which immune cells accumulate at the tumor periphery but fail to infiltrate the tumor parenchyma and exert effective anti-tumor responses [ 25 ]. In contrast, Cluster 1 exhibited an immune-desert phenotype with minimal immune cell presence. These observations underscore the complex interplay between cellular senescence and immune modulation, suggesting that CSRGs may orchestrate immune evasion mechanisms that contribute to poor outcomes in LIHC. The prognostic risk signature we constructed based on CSRGs demonstrated excellent predictive performance across both TCGA and ICGC validation cohorts, with AUC values exceeding 0.70 for 1-, 3-, and 5-year survival. Importantly, the risk score retained independent prognostic significance after adjusting for conventional clinicopathological variables, indicating its potential value as a complementary tool for risk stratification. In addition, the risk score showed strong correlations with immune cell infiltration and immune checkpoint gene expression, with high-risk patients exhibiting a more inflamed but potentially immunosuppressive tumor microenvironment. These findings suggest that high-risk patients may derive greater benefit from ICB therapy, a hypothesis that warrants further investigation in clinical trials. Indeed, recent studies have highlighted the potential of combining senescence-targeting agents with immunotherapy to overcome resistance and enhance therapeutic efficacy [ 32 , 33 ]. Among the 15 prognostic CSRGs, we identified EEF1E1 as a particularly intriguing candidate due to its specific enrichment in malignant hepatocytes at single-cell resolution. EEF1E1, also known as eukaryotic translation elongation factor 1 epsilon 1, has been implicated in aminoacyl-tRNA biosynthesis and cellular stress responses [ 34 – 36 ]. While its role in cancer remains incompletely characterized, emerging evidence suggests that EEF1E1 may contribute to tumor progression through mechanisms involving metabolic reprogramming and immune modulation [ 37 ]. Our validation studies confirmed that EEF1E1 is significantly upregulated in LIHC tumor tissues at both the mRNA and protein levels, and its high expression correlates with advanced histological grade and pathological stage. Moreover, EEF1E1 expression showed significant positive correlations with infiltration of multiple immune cell types, including T cells and macrophages, further supporting its potential involvement in shaping the immune landscape. Functional experiments demonstrated that EEF1E1 knockdown markedly suppressed the migratory and invasive capacities of LIHC cells, establishing EEF1E1 as a functionally relevant driver of tumor aggressiveness. These findings position EEF1E1 as a promising therapeutic target in LIHC, and future studies should explore the molecular mechanisms by which EEF1E1 promotes tumor progression and immune modulation. Despite the strengths of our study, several limitations should be acknowledged. First, while we validated our findings in independent cohorts, all analyses were retrospective and derived from publicly available datasets, which may be subject to inherent biases. Prospective studies are needed to confirm the clinical utility of our risk signature. Second, the functional experiments were limited to in vitro assays, and in vivo studies using orthotopic mouse models are required to further establish the oncogenic role of EEF1E1 and its therapeutic potential. Third, the exact molecular mechanisms by which CSRGs, particularly EEF1E1, regulate immune cell infiltration and function remain to be elucidated. Future mechanistic studies should investigate whether EEF1E1 modulates the senescence-associated secretory phenotype or directly interacts with immune signaling pathways. Additionally, although our risk signature showed promise for predicting ICB response, this hypothesis requires validation in patient cohorts receiving immunotherapy. Finally, the present study focused on CSRGs identified from the Gene Ontology database; as our understanding of cellular senescence evolves, additional senescence-related genes may emerge and should be incorporated into future models. In conclusion, this study provides a comprehensive characterization of CSRGs in LIHC and establishes a robust prognostic signature with potential clinical applications. We identify two distinct molecular subtypes with divergent immune phenotypes and demonstrate that the CSRGs-based risk score independently predicts patient outcomes. Furthermore, we pinpoint EEF1E1 as a key functional driver of LIHC aggressiveness and a promising therapeutic target. Our findings contribute to a deeper understanding of the interplay between cellular senescence and tumor immunity in LIHC and may inform the development of personalized therapeutic strategies for this challenging malignancy. Declarations Acknowledgements The authors gratefully acknowledge databases like TCGA, ICGC, and MSigDB for offering convenient access to datasets. Authors’ contributions Rentong Liu and Yiwei Cheng conceived and designed the study, performed data acquisition and analysis, and drafted the manuscript. Xiuwei Yan conducted the in vitro experiments. Bing Li and Yiqian Luo contributed to data interpretation and statistical analysis. Yuanyuan Wang and Song Jiang participated in bioinformatics analysis and figure preparation. Chengjie Gan assisted in manuscript revision and proofreading. All authors read and approved the final manuscript. Yiwei Cheng, as the corresponding author, had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Funding This work was supported by The Hospital-level Fund of the 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army (No. 2025-M-02). Data availability All data used in this study are publicly available. The bulk RNA‑seq and clinical data of the LIHC cohort were obtained from the TCGA portal (https://portal.gdc.cancer.gov/). The external validation dataset was downloaded from the ICGC Data Portal (https://dcc.icgc.org/). The single‑cell RNA‑seq dataset GSE166635 was accessed through the TISCH database (http://tisch.comp-genomics.org/). The cancer‑immunity cycle activity analysis was performed using the TIP database (http://biocc.hrbmu.edu.cn/TIP/index.jsp). Cellular senescence‑related gene sets were retrieved from the GO database using the following accession terms: GO:0090398 (cellular senescence), GO:2000772 (regulation of cellular senescence), GO:2000773 (negative regulation of cellular senescence), and GO:2000774 (positive regulation of cellular senescence). Additional gene sets were obtained from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/). The GEPIA database (http://gepia.cancer-pku.cn/) and the HPA (https://www.proteinatlas.org/) were used for expression validation. All other data generated or analyzed during this study are included in this published article. Declaration of competing interest The authors declare no competing interests. Consent to publish Not applicable. This study contains no individual person’s data in any form (including individual details, images, or videos). Ethics approval and consent to participate This study was conducted using publicly available datasets (TCGA, ICGC, TISCH, GEPIA, HPA) and in vitro cell line experiments. All datasets were obtained in accordance with the data access policies of the respective repositories, and ethical approval was not required. The cell lines used in this study were commercially obtained and no human or animal subjects were involved. References Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. https://doi.org/10.3322/caac.21660 . Vogel A, Meyer T, Sapisochin G, et al. Hepatocellular carcinoma. Lancet. 2022;400:1345–62. https://doi.org/10.1016/s0140-6736(22)01200-4 . Villanueva A. Hepatocellular carcinoma. 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Jia Q, Wu W, Wang Y, et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun. 2018;9:5361. https://doi.org/10.1038/s41467-018-07767-w . Liu Y, Pagacz J, Wolfgeher DJ, et al. Senescent cancer cell vaccines induce cytotoxic t cell responses targeting primary tumors and disseminated tumor cells. J Immunother Cancer. 2023;11. https://doi.org/10.1136/jitc-2022-005862 . Gabai Y, Assouline B, Ben-Porath I. Senescent stromal cells: Roles in the tumor microenvironment. Trends Cancer. 2023;9:28–41. https://doi.org/10.1016/j.trecan.2022.09.002 . Zhang W, Zhang K, Shi J, et al. The impact of the senescent microenvironment on tumorigenesis: Insights for cancer therapy. Aging Cell. 2024;23:e14182. https://doi.org/10.1111/acel.14182 . Dun Y, Zhang W, Du Y, et al. High-intensity interval training mitigates sarcopenia and suppresses the myoblast senescence regulator eef1e1. J Cachexia Sarcopenia Muscle. 2024;15:2574–85. https://doi.org/10.1002/jcsm.13600 . Pu X, Zhang C, Jin J, et al. Phase separation of eef1e1 promotes tumor stemness via pten/akt-mediated DNA repair in hepatocellular carcinoma. Cancer Lett. 2025;613:217508. https://doi.org/10.1016/j.canlet.2025.217508 . Das AS, Rabolli CP, Martens CR, et al. Aimp3 maintains cardiac homeostasis by regulating the editing activity of methionyl-trna synthetase. Nat Cardiovasc Res. 2025;4:876–90. https://doi.org/10.1038/s44161-025-00670-w . Ren L, Chen D, Xu T, et al. Ct radiomics combined with metabolic-biomarkers enables early recurrence prediction in hepatocellular carcinoma. J Hepatocell Carcinoma. 2025;12:2183–96. https://doi.org/10.2147/jhc.S547186 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9198475","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627915067,"identity":"0651c04a-81c6-4ebf-bf71-a013fd65a045","order_by":0,"name":"Rentong Liu","email":"","orcid":"","institution":"The 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Rentong","middleName":"","lastName":"Liu","suffix":""},{"id":627915068,"identity":"d54fe3e3-a03f-425e-946b-91364c1719f9","order_by":1,"name":"Bing Li","email":"","orcid":"","institution":"The 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Li","suffix":""},{"id":627915069,"identity":"585c21a7-7b71-4b9b-8b66-e40d15fab84d","order_by":2,"name":"Yiqian Luo","email":"","orcid":"","institution":"The 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Yiqian","middleName":"","lastName":"Luo","suffix":""},{"id":627915070,"identity":"a3bae703-b5f5-49bb-87fd-f8ee95aa8a67","order_by":3,"name":"Xiuwei Yan","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiuwei","middleName":"","lastName":"Yan","suffix":""},{"id":627915071,"identity":"4c1952c7-0c45-4908-8689-662c3440c253","order_by":4,"name":"Yuanyuan Wang","email":"","orcid":"","institution":"The 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Wang","suffix":""},{"id":627915072,"identity":"be07205b-9ac0-4271-8055-8cc97edde2df","order_by":5,"name":"Song Jiang","email":"","orcid":"","institution":"The 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Jiang","suffix":""},{"id":627915073,"identity":"4fc2b491-18a7-44e2-b330-7165e66a0f3d","order_by":6,"name":"Chengjie Gan","email":"","orcid":"","institution":"The 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Chengjie","middleName":"","lastName":"Gan","suffix":""},{"id":627915075,"identity":"44973e39-ecd9-4728-acb0-b3c46d93de43","order_by":7,"name":"Yiwei Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACfvnHBx98MPgvJ8/eQKQWyYa0ZMMZFczGhj0HiNRicCDHTJrnDHNiw40EYl124Iyx4cw2tsTGmY833mCosYkmqIOxsa3wwcc2HuN26bRiC4ZjabkNhLQwMzNvBtoiIds4O8dMgrHhMGEtbGwMZtK8bQaMDTfPEKmFh4cF5P0ExYYbPERqkZBgAwXyAWAgA/2SQIxf7G8wg6LyADAqD2+88aHGhrAWZGAgkUCKcogWUnWMglEwCkbByAAA08JB8RvCuvgAAAAASUVORK5CYII=","orcid":"","institution":"The 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army","correspondingAuthor":true,"prefix":"","firstName":"Yiwei","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2026-03-23 09:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9198475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9198475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107639972,"identity":"d9bb5f4d-0afa-4192-ad69-3f6a9dec511c","added_by":"auto","created_at":"2026-04-23 13:11:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":890784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of CSRGs‑based molecular subtypes in LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram showing the overlap between upregulated genes in tumor tissues (logFC \u0026gt; 1) and prognosis‑associated cellular senescence‑related genes (CSRGs) identified by univariate Cox regression analysis. A total of 15 overlapping genes were selected for further analysis.\u003c/p\u003e\n\u003cp\u003e(B–D) Consensus clustering of LIHC patients based on the expression profiles of the 15 prognostic CSRGs. (B) Empirical CDF curves for k = 2 to 10. (C) Relative change in the area under the CDF curve, indicating that k = 2 represents the optimal clustering solution. (D) Consensus matrix for k = 2, clearly delineating two distinct molecular subtypes.\u003c/p\u003e\n\u003cp\u003e(E) Kaplan‑Meier survival curves for patients in Cluster 1 and Cluster 2 in the TCGA cohort.\u003c/p\u003e\n\u003cp\u003e(F) Heatmap displaying the expression patterns of the 15 prognostic CSRGs across the two subtypes. Clinical annotations, including histological grade and pathological stage, are shown at the top.\u003c/p\u003e\n\u003cp\u003e(G) Bar plots illustrating the distribution of Clinical pathological features between Cluster 1 and Cluster 2.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/0dfe8e91fb6c090a5cf5bec4.png"},{"id":107639969,"identity":"c438cad8-2093-498b-be08-6de93dc605c8","added_by":"auto","created_at":"2026-04-23 13:11:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1033268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunogenomic landscape of CSRGs‑based molecular subtypes in LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C) Immune cell infiltration abundance in Cluster 1 and Cluster 2 estimated by three independent algorithms: (A) TIMER, (B) MCP‑counter, and (C) xCell.\u003c/p\u003e\n\u003cp\u003e(D) Expression levels of canonical immune checkpoint genes, including PD‑L1, PD‑1, and CTLA‑4, in the two subtypes. Most checkpoint genes were significantly upregulated in Cluster 2 relative to Cluster 1.\u003c/p\u003e\n\u003cp\u003e(E) Activity profiling of the cancer‑immunity cycle steps using the TIP database.\u003c/p\u003e\n\u003cp\u003e(*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/f50a5dbd31e8ead0032eea29.png"},{"id":107640058,"identity":"3fffdea9-d8aa-45db-8abb-fc8bd47494df","added_by":"auto","created_at":"2026-04-23 13:11:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":907624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of a CSRGs‑based prognostic risk signature in LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) LASSO penalized regression analysis for feature selection. (A) Partial likelihood deviance curves showing the optimal tuning parameter (λ) selection via ten‑fold cross‑validation. (B) Coefficient profiles of the candidate CSRGs as a function of the log(λ) sequence.\u003c/p\u003e\n\u003cp\u003e(C) Kaplan‑Meier survival curves comparing overall survival between high‑risk and low‑risk groups in the TCGA (left) and ICGC (right) cohorts.\u003c/p\u003e\n\u003cp\u003e(D) Distribution of risk scores (upper panel) and survival status (lower panel) in the TCGA (left) and ICGC (right) cohorts.\u003c/p\u003e\n\u003cp\u003e(E) Time‑dependent receiver operating characteristic (ROC) curves for the risk score predicting 1‑, 3‑, and 5‑year overall survival in the TCGA (left) and ICGC (right) cohorts.\u003c/p\u003e\n\u003cp\u003e(F, G) Dimensionality reduction analyses showing clear separation between high‑risk and low‑risk groups. (F) PCA. (G) t‑SNE.\u003c/p\u003e\n\u003cp\u003e(H–K) Subgroup survival analyses stratified by clinicopathological features n the TCGA dataset. In each subgroup, patients in the low‑risk group consistently demonstrated superior survival outcomes compared with those in the high‑risk group, including across different (H) Age, (I) Gender, (J) histological grade, and (K) pathological stages.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/5cba05e094b53fb38b3edae2.png"},{"id":107639967,"identity":"74597a12-e29c-4f6c-88c2-a62ff4484d6e","added_by":"auto","created_at":"2026-04-23 13:11:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndependent prognostic value and clinical utility of the CSRGs‑based risk signature.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) Forest plots illustrating the results of univariate and multivariate Cox regression analyses for overall survival in LIHC patients. In the TCGA cohort, the risk score was identified as an independent prognostic factor in both models (univariate: HR = 1.680, 95% CI = 1.369–2.062, P \u0026lt; 0.001; multivariate: HR = 2.203, 95% CI = 1.519–3.195, P \u0026lt; 0.001). Consistent results were observed in the ICGC cohort, where the risk score also demonstrated independent prognostic significance (univariate: HR = 1.533, 95% CI = 1.236–1.901, P \u0026lt; 0.001; multivariate: HR = 1.631, 95% CI = 1.298–2.050, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e(C) Nomogram constructed integrating the risk score with key clinicopathological variables (age, gender, histological grade, and pathological stage) for predicting 1‑, 3‑, and 5‑year overall survival probability in the TCGA (left) and ICGC (right) cohort.\u003c/p\u003e\n\u003cp\u003e(D) Calibration curves demonstrating optimal agreement between predicted and observed survival probabilities at 1, 3, and 5 years. Curves for both the TCGA (left) and ICGC (right) cohorts are shown, confirming robust predictive accuracy of the nomogram across independent datasets.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/2faade6c39649146fc6c9de7.png"},{"id":107639981,"identity":"0d76f7fe-a30b-48f6-abee-49709bff7281","added_by":"auto","created_at":"2026-04-23 13:11:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":668459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune landscape between high‑ and low‑risk groups in the TCGA cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C) Bar plots showing the infiltration abundance of diverse immune cell types estimated by (A) TIMER, (B) MCP‑counter, and (C) xCell algorithms in the TCGA cohort.\u003c/p\u003e\n\u003cp\u003e(D) Expression levels of canonical immune checkpoint genes, in high‑risk versus low‑risk groups.\u003c/p\u003e\n\u003cp\u003e(*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/eaea8fd93720044563be073f.png"},{"id":107640051,"identity":"739bc8cf-6448-4f7d-8a21-5c17b30f9887","added_by":"auto","created_at":"2026-04-23 13:11:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1061443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and validation of EEF1E1 as a key CSRG in LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) UMAP plot derived from single‑cell RNA‑sequencing data of the GSE166635 dataset, illustrating distinct cellular clusters within the LIHC tumor microenvironment.\u003c/p\u003e\n\u003cp\u003e(B) UMAP plot showing the expression distribution of EEF1E1 projected onto the same dimensional reduction space (right).\u003c/p\u003e\n\u003cp\u003e(C) Violin plots showing the expression distribution of EEF1E1 across distinct cell types in the GSE166635 single‑cell dataset.\u003c/p\u003e\n\u003cp\u003e(D) Box plots showing the expression levels of EEF1E1 in LIHC tumor tissues compared with adjacent normal tissues.\u003c/p\u003e\n\u003cp\u003e(E) The protein expression of EEF1E1 in HPA dataset.\u003c/p\u003e\n\u003cp\u003e(F)The association between EEF1E1 expression and clinical pathological features in the TCGA dataset.\u003c/p\u003e\n\u003cp\u003e(G) Correlation analysis between EEF1E1 expression and immune cell infiltration levels using the TIMER database.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/0ebc06411936346543cf2455.png"},{"id":107639980,"identity":"93c5b61a-7122-437a-a466-0ca1713c34e7","added_by":"auto","created_at":"2026-04-23 13:11:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":593173,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEEF1E1 knockdown suppresses migration and invasion of LIHC cells in vitro.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C) Validation of EEF1E1 knockdown efficiency in HepG‑2 and HuH‑7 cells. (A, B) RT‑qPCR analysis showing significantly reduced EEF1E1 mRNA expression following transfection with siEEF1E1 compared with control siRNA. (C) Western blot analysis confirming decreased EEF1E1 protein expression in both cell lines after siRNA‑mediated knockdown.\u003c/p\u003e\n\u003cp\u003e(D, E) Wound healing assays assessing cell migration capacity. Representative images (D) and quantitative analysis (E) demonstrate that EEF1E1 knockdown significantly impaired the migratory ability of HepG‑2 and HuH‑7 cells compared with control groups. Scale bar: 200 μm.\u003c/p\u003e\n\u003cp\u003e(F, G) Transwell assays evaluating cell invasion capacity. Representative images (F) and quantitative analysis (G) show that downregulation of EEF1E1 markedly reduced the invasive ability of both cell lines. Scale bar: 200 μm.\u003c/p\u003e\n\u003cp\u003e(P \u0026lt; 0.05, *P \u0026lt; 0.01, **P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/890aa32157f0e0c73a985bed.png"},{"id":107706069,"identity":"08926bab-17e0-4588-8237-ff4fc6068bd6","added_by":"auto","created_at":"2026-04-24 09:17:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6002004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/f235360c-dd41-43a4-b78e-e6d96e134c1c.pdf"},{"id":107639965,"identity":"d3d9d448-c42d-44bf-bf58-080a6cc789c6","added_by":"auto","created_at":"2026-04-23 13:11:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1336932,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9198475/v1/ec82cd0606a50f7c7b26b60a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integration of bulk and single-cell RNA-seq data identifies a cellular senescence-related prognostic signature in liver hepatocellular carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLiver Hepatocellular Carcinoma (LIHC) is the sixth most common malignancy and the third leading cause of cancer-related mortality worldwide, accounting for approximately 780,000 deaths annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advances in surgical resection, locoregional therapies, and systemic treatments such as tyrosine kinase inhibitors and immune checkpoint inhibitors, the prognosis of LIHC patients remains dismal, with a five-year survival rate of less than 20% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The high heterogeneity of LIHC at both the genomic and phenotypic levels poses a major challenge for precise risk stratification and individualized treatment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, identifying reliable biomarkers and novel therapeutic targets that reflect the underlying biological heterogeneity is urgently needed to improve patient outcomes.\u003c/p\u003e \u003cp\u003eCellular senescence is a fundamental biological process characterized by irreversible cell cycle arrest, which serves as a critical barrier against tumor initiation and progression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, emerging evidence has revealed a paradoxical role of senescence in cancer. While oncogene-induced senescence can suppress tumorigenesis, the accumulation of senescent cells within the tumor microenvironment (TME) can paradoxically promote tumor growth, metastasis, and therapy resistance through the secretion of a complex mixture of pro-inflammatory cytokines, growth factors, and proteases, collectively termed the senescence-associated secretory phenotype (SASP) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The SASP can induce chronic inflammation, remodel the extracellular matrix, recruit immunosuppressive cells, and facilitate immune evasion, thereby creating a pro-tumorigenic niche [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given the dual nature of cellular senescence, the expression patterns and functional roles of senescence-related genes (CSRGs) in LIHC warrant comprehensive investigation.zhu\u003c/p\u003e \u003cp\u003eRecent advances in high-throughput sequencing technologies have enabled the systematic characterization of the molecular landscape of LIHC. However, most studies have focused on the role of individual senescence-associated genes or pathways, and a comprehensive analysis integrating bulk transcriptomic and single-cell RNA-sequencing (scRNA-seq) data to identify CSRGs with prognostic and immunologic significance in LIHC is lacking [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. scRNA-seq offers a powerful approach to dissect cellular heterogeneity within the TME, allowing the identification of cell-type-specific expression patterns that are obscured in bulk tissue analyses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. By integrating bulk and single-cell data, it is possible to uncover previously unrecognized associations between senescence-related genes, immune cell infiltration, and clinical outcomes.\u003c/p\u003e \u003cp\u003eIn this study, we performed a comprehensive integrative analysis to characterize the landscape of CSRGs in LIHC. Using transcriptomic data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we identified 15 prognostic CSRGs that were upregulated in LIHC tumor tissues. Unsupervised consensus clustering based on these genes stratified patients into two distinct molecular subtypes with divergent clinical outcomes and immune phenotypes. We further constructed and validated a CSRGs-based risk score that independently predicted overall survival and demonstrated strong associations with immune infiltration and immune checkpoint gene expression. Through single-cell resolution analysis, we identified EEF1E1 as a key CSRG specifically enriched in malignant hepatocytes and experimentally validated its oncogenic role in promoting cell migration and invasion. Collectively, our findings provide novel insights into the role of cellular senescence in LIHC and offer potential biomarkers for risk stratification and therapeutic targeting.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection and Preprocessing\u003c/h2\u003e \u003cp\u003eTranscriptomic profiles and corresponding clinical information of patients with LIHC were obtained from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The LIHC dataset included 374 tumor samples and 50 adjacent normal tissue samples. FPKM values were converted to TPM for normalization. For external validation, the ICGC LIHC dataset was downloaded from the ICGC Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dcc.icgc.org\u003c/span\u003e\u003cspan address=\"https://dcc.icgc.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Samples with incomplete survival information or ambiguous histopathological diagnosis were excluded from further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of Cellular Senescence-Related Genes\u003c/h2\u003e \u003cp\u003eA total of 167 CSRGs were curated from the Gene Ontology (GO) database using the following accession terms: GO:0090398 (cellular senescence), GO:2000772 (regulation of cellular senescence), GO:2000773 (negative regulation of cellular senescence), and GO:2000774 (positive regulation of cellular senescence). These genes were retrieved from the Molecular Signatures Database (MSigDB) and relevant published literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Differential Expression Analysis and Prognostic Gene Screening\u003c/h2\u003e \u003cp\u003eDifferential expression analysis between LIHC tumor tissues and adjacent normal tissues was performed using the limma package in R [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Genes with |log₂ FC| \u0026gt; 1 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly upregulated. Univariate Cox regression analysis was applied to identify CSRGs significantly associated with overall survival (OS) in the TCGA cohort. Genes with a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered prognostic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Consensus Clustering for Molecular Subtype Identification\u003c/h2\u003e \u003cp\u003eBased on the expression profiles of the overlapping prognostic CSRGs, unsupervised consensus clustering was performed using the ConsensusClusterPlus R package[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A total of 1,000 iterations with an 80% sampling rate were conducted to ensure stability. The optimal number of clusters (k) was determined by evaluating the empirical cumulative distribution function (CDF) curves, the relative change in the area under the CDF curve, and the consensus matrix. Patients were stratified into two distinct molecular subtypes (Cluster 1 and Cluster 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Immune Microenvironment Analysis\u003c/h2\u003e \u003cp\u003eThe immune landscape of LIHC patients was evaluated using multiple algorithms. The relative proportions of immune cell infiltration were estimated using the TIMER [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], MCP-counter [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and xCell [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] algorithms implemented via the IOBR package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The activity of the cancer-immunity cycle was assessed using the Tumor Immune Estimation Resource (TIP) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocc.hrbmu.edu.cn/TIP/index.jsp\u003c/span\u003e\u003cspan address=\"http://biocc.hrbmu.edu.cn/TIP/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which visualizes anti-tumor immune responses across seven sequential steps: release of cancer cell antigens (Step 1), cancer antigen presentation (Step 2), priming and activation (Step 3), trafficking of immune cells to tumors (Step 4), infiltration of immune cells into tumors (Step 5), recognition of cancer cells by T cells (Step 6), and killing of cancer cells (Step 7) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Construction and Validation of a Prognostic Risk Signature\u003c/h2\u003e \u003cp\u003eLeast absolute shrinkage and selection operator (LASSO) penalized regression was performed using the glmnet R package to select the most informative CSRGs and construct a risk score model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Ten-fold cross-validation was applied to determine the optimal tuning parameter (λ). The risk score for each patient was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Risk\\:score\\:={\\sum\\:}_{i=1}^{n}Coef\\:\\left({X}_{i}\\right)*\\:Exp\\:\\left({X}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Coef (Xi) represents the regression coefficient of each selected gene, and Exp (Xi) represents its expression level. represents its expression level. Patients were divided into high-risk and low-risk groups based on the median risk score. The predictive performance of the risk signature was evaluated using Kaplan-Meier survival curves with log-rank tests and time-dependent receiver operating characteristic (ROC) curves with the area under the curve (AUC) calculated using the survivalROC R package. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were performed to visualize the clustering of high- and low-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Independent Prognostic Analysis and Nomogram Construction\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were performed to assess whether the risk score served as an independent prognostic factor, adjusting for clinicopathological variables including age, gender, histological grade, and pathological stage. A nomogram was constructed integrating the risk score and significant clinicopathological features to predict 1-, 3-, and 5-year overall survival[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Calibration curves were generated to evaluate the agreement between predicted and observed survival probabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Single-Cell RNA-Seq Data Analysis\u003c/h2\u003e \u003cp\u003eSingle-cell RNA-sequencing data of LIHC samples were obtained from the Tumor Immune Single-Cell Hub (TISCH) database (GSE166635) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Dimensionality reduction was performed using uniform manifold approximation and projection (UMAP) implemented via the Seurat R package. Cells were clustered based on canonical marker genes, and the expression patterns of the seven prognostic CSRGs were visualized across different cell types using violin plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Cell Culture and siRNA Transfection\u003c/h2\u003e \u003cp\u003eHuman LIHC cell lines HepG-2 and HuH-7 were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin at 37\u0026deg;C in a 5% CO₂ atmosphere. Small interfering RNA (siRNA) targeting EEF1E1 and negative control siRNA were synthesized and transfected using Lipofectamine 3000 reagent according to the manufacturer\u0026rsquo;s instructions. Knockdown efficiency was confirmed by RT‑qPCR and western blot analysis 48 hours post-transfection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 RNA Extraction and Quantitative Real-Time PCR (RT‑qPCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from cell lines and tissue samples using TRIzol reagent. Reverse transcription was performed using the PrimeScript RT Reagent Kit. RT‑qPCR was conducted using SYBR Green Master Mix on a real-time PCR system. Relative gene expression levels were calculated using the 2⁻ΔΔCt method, with GAPDH as the internal control. All reactions were performed in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Western Blot Analysis\u003c/h2\u003e \u003cp\u003eCells and tissues were lysed in RIPA buffer containing protease and phosphatase inhibitors. Protein concentrations were quantified using the BCA assay. Equal amounts of protein were separated by SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% non-fat milk and incubated overnight at 4\u0026deg;C with primary antibodies against EEF1E1 (1:1000, 10805-1-AP, Proteintech) and β-tubulin (1:4000, AF1216, Beyotime). After incubation with HRP-conjugated secondary antibodies, protein bands were visualized using enhanced chemiluminescence substrate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Wound Healing and Transwell Invasion Assays\u003c/h2\u003e \u003cp\u003eFor wound healing assays, cells were seeded in six-well plates and grown to confluence. A sterile pipette tip was used to create a linear scratch. Wound closure was monitored and photographed at 0 and 24 hours. The percentage of wound closure was quantified using ImageJ software. For Transwell invasion assays, cells were suspended in serum-free medium and seeded into the upper chamber of Matrigel-coated inserts. The lower chamber was filled with medium containing 10% FBS as a chemoattractant. After 24 hours, non‑invading cells were removed, and invading cells were fixed, stained with crystal violet, and counted in five random fields per well.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.1.2). Comparisons between two groups were performed using the Wilcoxon rank-sum test. Comparisons among multiple groups were performed using the Kruskal-Wallis test. Categorical variables were compared using the chi‑square test or Fisher\u0026rsquo;s exact test. Survival curves were estimated using the Kaplan‑Meier method and compared using the log‑rank test. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.: Significance levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of cellular senescence-related molecular subtypes in hepatocellular carcinoma\u003c/h2\u003e \u003cp\u003eA total of 167 CSRGs were initially retrieved from the GO database based on the following accession terms: GO:0090398, GO:2000772, GO:2000773, and GO:2000774. Univariate Cox regression analysis identified 43 CSRGs significantly associated with overall survival in patients with LIHC. Concurrently, differential expression analysis between LIHC and adjacent normal tissues using the limma package (logFC\u0026thinsp;\u0026gt;\u0026thinsp;1) yielded 2,016 upregulated genes. Intersection of these two gene sets revealed 15 prognostic CSRGs that were upregulated in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eTo explore the underlying heterogeneity among LIHC patients, we performed unsupervised consensus clustering based on the expression profiles of these 15 genes. Analysis of the CDF and consensus matrices indicated that k\u0026thinsp;=\u0026thinsp;2 represented the optimal clustering solution, allowing the stratification of patients into two distinct molecular subtypes, designated Cluster 1 and Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026ndash;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eSignificant differences in clinical outcomes were observed between the two subtypes. Kaplan\u0026ndash;Meier survival analysis demonstrated that patients in Cluster 1 had markedly longer overall survival compared to those in Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). We further characterized the association between the identified subtypes, CSRG expression, and clinicopathological features. Heatmaps revealed distinct expression patterns of the 15 prognostic CSRGs between Cluster 1 and Cluster 2, accompanied by distinct distributions of clinical annotations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Notably, Cluster 2 was associated with a more aggressive clinical phenotype. In the TCGA cohort, patients in Cluster 2 exhibited significantly higher proportions of advanced histological grade and pathological stage compared to those in Cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 Immunogenomic Landscape of CSRGs-Based Molecular Subtypes in LIHC\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo further characterize the biological distinction between the two subtypes, we investigated the landscape of the TME. Using the IOBR package, we employed three independent algorithms\u0026mdash;TIMER, MCP-counter, and xCell\u0026mdash;to estimate the infiltration abundance of diverse immune cell populations. Notably, Cluster 2 exhibited significantly higher enrichment scores for the majority of immune cell types compared to Cluster 1, suggesting a more inflamed, albeit potentially immunosuppressive, TIME (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C). Given the pivotal role of immune checkpoint pathways in tumor immune evasion, we next examined the expression profiles of canonical immune checkpoint genes between the two clusters. Consistent with the immune infiltration patterns, most immune checkpoint genes, including PD-L1, PDCD1, and CTLA-4, were significantly upregulated in Cluster 2 relative to Cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo gain deeper insight into the dynamic processes underlying these immune phenotypes, we leveraged the TIP database to assess the activity of distinct immune cell populations across the cancer-immunity cycle. Intriguingly, Cluster 1 exhibited significantly higher activity in steps 5, 6, and 7, which correspond to immune cell infiltration, killing of tumor cells, and release of tumor antigens, respectively. In contrast, Cluster 2 showed enhanced enrichment in steps 1 and 4, representing antigen release and trafficking of immune cells to the tumor site (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These divergent patterns suggest that while Cluster 2 displays a globally higher abundance of immune cells, the effective execution of anti-tumor immunity may be arrested at intermediate steps. Based on these findings, we propose that Cluster 2 corresponds to an immune-excluded phenotype, characterized by immune cell accumulation at the periphery without effective tumor infiltration, whereas Cluster 1 represents an immune-desert phenotype, marked by minimal immune engagement across the majority of the cancer-immunity cycle [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction and Validation of a CSRGs-Based Prognostic Risk Signature.\u003c/h2\u003e \u003cp\u003eTo develop a quantitative prognostic model based on the identified CSRGs, we performed LASSO penalized regression analysis using LIHC datasets from TCGA and the ICGC. A risk score was subsequently constructed for each patient, enabling stratification into high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Kaplan\u0026ndash;Meier survival analysis revealed that patients in the low-risk group had significantly prolonged overall survival compared to those in the high-risk group across both cohorts (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The distribution of risk scores and survival status further illustrated that patients with higher risk scores experienced shorter survival times and increased mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Time-dependent ROC analysis demonstrated favorable predictive performance of the risk score. In the TCGA cohort, the AUC values for 1-, 3-, and 5-year overall survival were 0.766, 0.696, and 0.667, respectively. Comparable performance was observed in the ICGC cohort, with AUC values of 0.716, 0.735, and 0.747 for the respective time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Dimensionality reduction analyses, including PCA and t-SNE, showed clear separation between high- and low-risk groups, indicating robust clustering based on the CSRGs-derived risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Subgroup survival analyses stratified by various clinicopathological features consistently showed that patients in the low-risk group experienced superior survival outcomes compared to those in the high-risk group across different clinical strata, including histological grade, pathological stage, and other key variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-C). Collectively, these findings validate the prognostic utility of the CSRGs-based risk signature across independent cohorts and clinical subgroups.\u003c/p\u003e \u003cp\u003eTo evaluate the clinical utility of the LASSO-derived risk score, we performed univariate and multivariate Cox regression analyses. In both models, the risk score emerged as an independent prognostic factor for overall survival in LIHC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). To facilitate clinical translation, a nomogram integrating the risk score with key clinicopathological features was constructed, which demonstrated robust predictive performance as evidenced by well-calibrated calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Immune Landscape in High- and Low-Risk Groups.\u003c/h2\u003e \u003cp\u003eTo further characterize the immune microenvironment associated with the CSRGs‑based risk signature, we evaluated immune cell infiltration and immune checkpoint gene expression between the high‑ and low‑risk groups. Using three independent algorithms\u0026mdash;TIMER, MCP‑counter, and xCell\u0026mdash;implemented, we observed that patients in the high‑risk group exhibited significantly higher enrichment scores for the majority of immune cell types compared with those in the low‑risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;C; Figure S2A\u0026ndash;C). Consistent with this pattern, the expression levels of canonical immune checkpoint genes, including PD‑L1, PD‑1, and CTLA‑4, were markedly upregulated in the high‑risk group relative to the low‑risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD; Figure S3D). These findings indicate that the risk score not only captures prognostic heterogeneity but also reflects distinct immune phenotypes, with the high‑risk group displaying a more inflamed yet potentially immunosuppressive tumor microenvironment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Identification of EEF1E1 as a Key CSRG in LIHC\u003c/h2\u003e \u003cp\u003eTo further pinpoint the most functionally relevant CSRGs within the prognostic signature, we analyzed single‑cell RNA‑sequencing data from the GSE166635 dataset obtained from the TISCH database. Dimensionality reduction via UMAP revealed distinct cellular clusters within the LIHC tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Violin plots demonstrated that among the seven prognostic CSRGs, EEF1E1 was specifically and significantly enriched in malignant hepatocytes compared with other cell types, including immune and stromal populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C; Figure S3A\u0026ndash;C). This selective expression pattern prompted us to focus on EEF1E1 as a candidate key regulator.\u003c/p\u003e \u003cp\u003eWe next validated the expression of EEF1E1 at the transcriptional level using the GEPIA database. Consistent with the single‑cell findings, EEF1E1 expression was significantly upregulated in LIHC tumor tissues compared with adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Immunohistochemical analysis from the Human Protein Atlas (HPA) database further confirmed elevated EEF1E1 protein expression in LIHC tumor samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). To explore the clinical relevance of EEF1E1, we analyzed its association with clinicopathological features. Notably, high EEF1E1 expression was significantly correlated with advanced histological grade and pathological stage, indicating its potential role in tumor aggressiveness (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF; Figure S3D).\u003c/p\u003e \u003cp\u003eGiven the established link between EEF1E1 and immune infiltration patterns observed in the risk model, we further investigated its correlation with immune cell infiltration using the TIMER database. EEF1E1 expression showed significant positive correlations with the infiltration levels of multiple immune cell types, including B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). These findings suggest that EEF1E1 may contribute to the immune‑modulatory landscape of LIHC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Functional Validation of EEF1E1 in LIHC Cell Lines\u003c/h2\u003e \u003cp\u003eGiven the consistent association of EEF1E1 with poor prognosis, immune infiltration, and specific enrichment in malignant hepatocytes, we further investigated its functional role in LIHC through loss‑of‑function experiments. siRNA‑mediated knockdown of EEF1E1 was performed in both HepG‑2 and HuH‑7 cell lines. The efficiency of knockdown was confirmed by RT‑qPCR and western blot analysis, demonstrating significant reduction of EEF1E1 at both the mRNA and protein levels in siEEF1E1‑treated cells compared with controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;C). Functional assays revealed that EEF1E1 silencing markedly suppressed the migratory capacity of LIHC cells, as evidenced by wound healing assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, E). Similarly, Transwell invasion assays demonstrated that knockdown of EEF1E1 significantly attenuated the invasive ability of both HepG‑2 and HuH‑7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, G). Collectively, these findings suggest that EEF1E1 promotes cell migration and invasion in LIHC, supporting its role as a functionally relevant oncogenic driver within the CSRGs‑based prognostic signature.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn the present study, we comprehensively characterized the landscape of CSRGs in LIHC and constructed a robust prognostic signature with potential clinical utility. By integrating bulk transcriptomic and single-cell RNA-sequencing data from multiple independent cohorts, we identified two distinct molecular subtypes (Cluster 1 and Cluster 2) with divergent prognostic outcomes and immune phenotypes. Furthermore, we developed and validated a CSRGs-based risk score that independently predicted overall survival and demonstrated significant associations with tumor immune infiltration and immune checkpoint gene expression. Notably, through single-cell resolution analysis, we pinpointed EEF1E1 as a key CSRG specifically enriched in malignant hepatocytes, and functional experiments confirmed its oncogenic role in promoting LIHC cell migration and invasion. Collectively, our findings provide novel insights into the role of cellular senescence in LIHC and offer a potential biomarker and therapeutic target for this devastating disease.\u003c/p\u003e \u003cp\u003eCellular senescence is a complex biological process characterized by irreversible cell cycle arrest, which has been implicated in both tumor suppression and tumor promotion depending on the context [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While the tumor-suppressive functions of senescence are well established, accumulating evidence suggests that senescent cells can acquire a SASP that promotes chronic inflammation, immune evasion, and tumor progression [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In hepatocellular carcinoma, the role of senescence-related genes remains incompletely understood, and systematic characterization of CSRGs in this malignancy is lacking. Our study addresses this gap by providing a comprehensive analysis of CSRGs in LIHC and revealing their prognostic and immunologic significance.\u003c/p\u003e \u003cp\u003eThrough unsupervised consensus clustering based on 15 prognostic CSRGs, we stratified LIHC patients into two distinct molecular subtypes with markedly different clinical outcomes. Cluster 1 was characterized by favorable prognosis, whereas Cluster 2 was associated with aggressive clinicopathological features and poor survival. These findings align with previous studies demonstrating that aberrant expression of senescence-associated genes correlates with tumor progression and therapy resistance in various cancers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, the two subtypes exhibited distinct immune landscapes: Cluster 2 displayed higher overall immune cell infiltration but paradoxically showed impaired effector functions in the later stages of the cancer-immunity cycle. This pattern is reminiscent of the immune-excluded phenotype, in which immune cells accumulate at the tumor periphery but fail to infiltrate the tumor parenchyma and exert effective anti-tumor responses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, Cluster 1 exhibited an immune-desert phenotype with minimal immune cell presence. These observations underscore the complex interplay between cellular senescence and immune modulation, suggesting that CSRGs may orchestrate immune evasion mechanisms that contribute to poor outcomes in LIHC.\u003c/p\u003e \u003cp\u003eThe prognostic risk signature we constructed based on CSRGs demonstrated excellent predictive performance across both TCGA and ICGC validation cohorts, with AUC values exceeding 0.70 for 1-, 3-, and 5-year survival. Importantly, the risk score retained independent prognostic significance after adjusting for conventional clinicopathological variables, indicating its potential value as a complementary tool for risk stratification. In addition, the risk score showed strong correlations with immune cell infiltration and immune checkpoint gene expression, with high-risk patients exhibiting a more inflamed but potentially immunosuppressive tumor microenvironment. These findings suggest that high-risk patients may derive greater benefit from ICB therapy, a hypothesis that warrants further investigation in clinical trials. Indeed, recent studies have highlighted the potential of combining senescence-targeting agents with immunotherapy to overcome resistance and enhance therapeutic efficacy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the 15 prognostic CSRGs, we identified EEF1E1 as a particularly intriguing candidate due to its specific enrichment in malignant hepatocytes at single-cell resolution. EEF1E1, also known as eukaryotic translation elongation factor 1 epsilon 1, has been implicated in aminoacyl-tRNA biosynthesis and cellular stress responses [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. While its role in cancer remains incompletely characterized, emerging evidence suggests that EEF1E1 may contribute to tumor progression through mechanisms involving metabolic reprogramming and immune modulation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our validation studies confirmed that EEF1E1 is significantly upregulated in LIHC tumor tissues at both the mRNA and protein levels, and its high expression correlates with advanced histological grade and pathological stage. Moreover, EEF1E1 expression showed significant positive correlations with infiltration of multiple immune cell types, including T cells and macrophages, further supporting its potential involvement in shaping the immune landscape. Functional experiments demonstrated that EEF1E1 knockdown markedly suppressed the migratory and invasive capacities of LIHC cells, establishing EEF1E1 as a functionally relevant driver of tumor aggressiveness. These findings position EEF1E1 as a promising therapeutic target in LIHC, and future studies should explore the molecular mechanisms by which EEF1E1 promotes tumor progression and immune modulation.\u003c/p\u003e \u003cp\u003eDespite the strengths of our study, several limitations should be acknowledged. First, while we validated our findings in independent cohorts, all analyses were retrospective and derived from publicly available datasets, which may be subject to inherent biases. Prospective studies are needed to confirm the clinical utility of our risk signature. Second, the functional experiments were limited to in vitro assays, and in vivo studies using orthotopic mouse models are required to further establish the oncogenic role of EEF1E1 and its therapeutic potential. Third, the exact molecular mechanisms by which CSRGs, particularly EEF1E1, regulate immune cell infiltration and function remain to be elucidated. Future mechanistic studies should investigate whether EEF1E1 modulates the senescence-associated secretory phenotype or directly interacts with immune signaling pathways. Additionally, although our risk signature showed promise for predicting ICB response, this hypothesis requires validation in patient cohorts receiving immunotherapy. Finally, the present study focused on CSRGs identified from the Gene Ontology database; as our understanding of cellular senescence evolves, additional senescence-related genes may emerge and should be incorporated into future models.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides a comprehensive characterization of CSRGs in LIHC and establishes a robust prognostic signature with potential clinical applications. We identify two distinct molecular subtypes with divergent immune phenotypes and demonstrate that the CSRGs-based risk score independently predicts patient outcomes. Furthermore, we pinpoint EEF1E1 as a key functional driver of LIHC aggressiveness and a promising therapeutic target. Our findings contribute to a deeper understanding of the interplay between cellular senescence and tumor immunity in LIHC and may inform the development of personalized therapeutic strategies for this challenging malignancy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge databases like TCGA, ICGC, and MSigDB for offering convenient access to datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRentong Liu and Yiwei Cheng conceived and designed the study, performed data acquisition and analysis, and drafted the manuscript. Xiuwei Yan conducted the in vitro experiments. Bing Li and Yiqian Luo contributed to data interpretation and statistical analysis. Yuanyuan Wang and Song Jiang participated in bioinformatics analysis and figure preparation. Chengjie Gan assisted in manuscript revision and proofreading. All authors read and approved the final manuscript. Yiwei Cheng, as the corresponding author, had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The Hospital-level Fund of the 967th Hospital of the Joint Logistic Support Force of the Chinese People\u0026apos;s Liberation Army (No. 2025-M-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available. The bulk RNA‑seq and clinical data of the LIHC cohort were obtained from the TCGA portal (https://portal.gdc.cancer.gov/). The external validation dataset was downloaded from the ICGC Data Portal (https://dcc.icgc.org/). The single‑cell RNA‑seq dataset GSE166635 was accessed through the TISCH database (http://tisch.comp-genomics.org/). The cancer‑immunity cycle activity analysis was performed using the TIP database (http://biocc.hrbmu.edu.cn/TIP/index.jsp). Cellular senescence‑related gene sets were retrieved from the GO database using the following accession terms: GO:0090398 (cellular senescence), GO:2000772 (regulation of cellular senescence), GO:2000773 (negative regulation of cellular senescence), and GO:2000774 (positive regulation of cellular senescence). Additional gene sets were obtained from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/). The GEPIA database (http://gepia.cancer-pku.cn/) and the HPA (https://www.proteinatlas.org/) were used for expression validation. All other data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study contains no individual person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using publicly available datasets (TCGA, ICGC, TISCH, GEPIA, HPA) and in vitro cell line experiments. All datasets were obtained in accordance with the data access policies of the respective repositories, and ethical approval was not required. The cell lines used in this study were commercially obtained and no human or animal subjects were involved.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogel A, Meyer T, Sapisochin G, et al. Hepatocellular carcinoma. 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J Hepatocell Carcinoma. 2025;12:2183\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/jhc.S547186\u003c/span\u003e\u003cspan address=\"10.2147/jhc.S547186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Cellular senescence, Prognostic signature, Tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-9198475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9198475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLiver Hepatocellular carcinoma (LIHC) is a highly heterogeneous malignancy with poor prognosis, underscoring the urgent need for reliable biomarkers and therapeutic targets. Cellular senescence plays a dual role in tumor progression, yet the landscape of cellular senescence-related genes (CSRGs) in LIHC remains incompletely characterized. In this study, we curated 167 CSRGs from the Gene Ontology database and performed integrative analysis using transcriptomic data from The Cancer Genome Atlas and International Cancer Genome Consortium cohorts, combined with single-cell RNA-sequencing data from the Tumor Immune Single-Cell Hub database. Fifteen prognostic CSRGs were identified and used to stratify LIHC patients into two distinct molecular subtypes with divergent clinical outcomes. Cluster 2 exhibited an immune-excluded phenotype characterized by high immune infiltration but impaired effector function, correlating with poor prognosis, whereas Cluster 1 represented an immune-desert phenotype. A CSRGs-based risk score was constructed and validated as an independent prognostic factor across cohorts. Single-cell analysis identified EEF1E1 as a key CSRG specifically enriched in malignant hepatocytes, with high EEF1E1 expression correlating with advanced clinicopathological features and immune cell infiltration. Functional experiments demonstrated that EEF1E1 knockdown significantly suppressed LIHC cell migration and invasion. Collectively, this study provides a comprehensive characterization of CSRGs in LIHC and establishes a robust prognostic signature with potential clinical utility, identifying EEF1E1 as a functionally relevant oncogenic driver and a promising therapeutic target in LIHC.\u003c/p\u003e","manuscriptTitle":"Integration of bulk and single-cell RNA-seq data identifies a cellular senescence-related prognostic signature in liver hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 13:11:09","doi":"10.21203/rs.3.rs-9198475/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T12:09:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T21:26:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T15:41:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65127071077795335689994014970170445638","date":"2026-04-23T18:19:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295483907611515955953093678367694475627","date":"2026-04-20T12:21:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T07:04:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264668410602411409374993812584476731386","date":"2026-04-17T02:43:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T07:02:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T08:28:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T06:46:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-03-31T02:56:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7de1f4c4-a197-44a9-8cc7-4ee15a8f636c","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T12:09:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T21:26:06+00:00","index":70,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T15:41:22+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T12:46:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 13:11:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9198475","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9198475","identity":"rs-9198475","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}