Transcriptomic Profiling Identifies CSNK1E as a Key Mediator of Sorafenib Resistance in Hepatocellular Carcinoma

Transcriptomic Profiling Identifies CSNK1E as a Key Mediator of Sorafenib Resistance in 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 Transcriptomic Profiling Identifies CSNK1E as a Key Mediator of Sorafenib Resistance in Hepatocellular Carcinoma Xuanji Gao, Wanghu Liu, Weiwei Shao, Yulin Dong, Shajun Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7049920/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Sorafenib is the standard first-line therapy for advanced hepatocellular carcinoma (HCC), yet its clinical efficacy is often limited by primary or acquired resistance. The molecular mechanisms underlying this resistance remain poorly understood. Casein kinase 1 epsilon (CSNK1E), a known regulator of WNT/β-catenin signaling, may play a role in drug resistance, but its function in sorafenib response in HCC is unclear. Methods We analyzed transcriptomic data from 67 HCC patients treated with sorafenib, including 21 responders and 46 non-responders. Differential expression and LASSO regression analyses, integrated with TCGA data and protein-protein interaction networks, were used to identify resistance-related genes. Functional validation of CSNK1E was conducted using in vitro assays and xenograft models. Results A panel of eight genes ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, THBS3, and ZRANB2 was identified to be significantly upregulated in non-responders and associated with poor clinical outcomes. Among these, CSNK1E exhibited the highest prognostic relevance. Elevated CSNK1E expression was associated with unfavorable survival, increased TP53 mutation frequency, and copy number instability. Functional assays demonstrated that CSNK1E promoted HCC cell proliferation, migration, and resistance to sorafenib. Gene set enrichment analysis indicated that high CSNK1E expression was linked to activation of oncogenic pathways including WNT signaling and cell cycle progression. Conclusion CSNK1E is a key mediator of sorafenib resistance in HCC. Our eight-gene signature may serve as a predictive biomarker panel for sorafenib efficacy, offering a basis for personalized treatment strategies in HCC. Hepatocellular carcinoma Sorafenib Transcriptome analysis CSNK1E Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the third leading cause of cancer-related deaths worldwide. The global burden of HCC continues to rise due to the increasing prevalence of chronic liver diseases, including hepatitis B and C infections, as well as metabolic-associated fatty liver disease [ 1 – 3 ]. Although early-stage HCC can be managed with curative approaches such as surgical resection or liver transplantation, the majority of patients are diagnosed at intermediate or advanced stages when systemic therapy is the only available option [ 4 – 6 ]. Sorafenib, a multikinase inhibitor targeting RAF, VEGFR, and PDGFR, remains the standard first-line treatment for advanced HCC. However, its clinical efficacy is limited, with low objective response rates and the frequent emergence of drug resistance [ 7 , 8 ]. Understanding the molecular basis of sorafenib resistance is essential to improving treatment outcomes and identifying predictive biomarkers for individualized therapy [ 9 , 10 ]. HCC exhibits significant molecular heterogeneity, which contributes to the variability in treatment response. Although several signaling pathways including WNT/β-catenin, PI3K/AKT, and epithelial-mesenchymal transition, have been implicated in sorafenib resistance, few mechanistic insights have been translated into clinical practice [ 11 – 13 ]. In recent years, transcriptomic profiling has provided a powerful approach to unravel the molecular underpinnings of drug resistance across diverse cancer types [ 14 , 15 ]. Notably, genes such as Casein kinase 1 epsilon (CSNK1E) have been implicated in the regulation of oncogenic pathways, including WNT/β-catenin signaling, and are known to influence tumor progression. However, the role of CSNK1E in mediating sorafenib resistance in HCC remains largely unexplored [ 16 – 18 ]. Moreover, while numerous studies have sought to identify resistance-related biomarkers using genomic or transcriptomic data, most candidate markers fail to achieve clinical translation. One major limitation lies in the lack of integrative analysis across multiple dimensions of data such as somatic mutations, copy number variations, and gene regulatory networks to robustly validate the functional relevance of these genes [ 19 – 21 ]. A more comprehensive and multidimensional approach is therefore needed to identify reliable biomarkers and therapeutic targets that can be leveraged for precision oncology. In this study, we performed a systematic transcriptomic analysis of HCC patients treated with sorafenib to identify key genes associated with treatment response. By integrating differential expression profiling, data from The Cancer Genome Atlas (TCGA), protein-protein interaction (PPI) networks, and LASSO regression modeling, we identified a set of eight genes potentially critical to sorafenib response. We further characterized their expression patterns, genetic alterations, and prognostic significance. Among these, CSNK1E emerged as a top candidate due to its strong association with poor prognosis and therapeutic resistance. Functional assays and in vivo experiments confirmed its role in promoting HCC progression and sorafenib resistance. This study provides new insights into the molecular mechanisms underlying sorafenib response and proposes a gene signature that may inform personalized therapeutic strategies for HCC. Materials and Methods Patient Cohorts and Transcriptomic Data Acquisition To investigate gene signatures associated with sorafenib response in HCC, we collected transcriptomic data from a total of 67 HCC patients who received sorafenib treatment. The data were curated from the publicly available Gene Expression Omnibus (GEO) database (GSE109211), consisting of 21 patients who responded to sorafenib and 46 who were classified as non-responders based on the RECIST criteria [ 22 ]. In parallel, we downloaded RNA-sequencing data, somatic mutation profiles, and copy number variation (CNV) data of primary HCC tumors and adjacent normal tissues from The Cancer Genome Atlas (TCGA-LIHC) project via the UCSC Xena browser ( https://xena.ucsc.edu/ ), comprising 374 tumor samples and 50 matched normal tissues. For survival analyses, overall survival (OS) and clinical metadata including age, gender, tumor stage, tumor size, and histological grade were extracted and matched with gene expression profiles. Differential Gene Expression Analysis We utilized the R package limma to identify differentially expressed genes (DEGs) between sorafenib responders and non-responders. Data were first normalized using the quantile normalization method. Genes with an absolute log2 fold change (|log2FC|) > 1 and a Benjamini-Hochberg adjusted p-value (FDR) < 0.05 were considered statistically significant [ 23 , 24 ]. Similarly, we applied the same pipeline to compare gene expression between tumor tissues and normal liver tissues in the TCGA cohort. To identify sorafenib resistance-related genes specific to tumor biology, we intersected genes that were significantly upregulated in non-responders with those upregulated in tumor tissues compared to adjacent normals, yielding a subset of 250 candidate genes for further analysis. Protein–Protein Interaction (PPI) and Key Gene Identification To explore functional interactions among the 250 intersected genes, we conducted a PPI network analysis using the STRING database (Search Tool for the Retrieval of Interacting Genes/Proteins). An interaction score cutoff of 0.7 (high confidence) was used. To further identify key genes strongly associated with sorafenib response, we performed least absolute shrinkage and selection operator (LASSO) regression using the glmnet package in R. LASSO is a penalized regression approach that helps prevent overfitting while selecting the most predictive variables. Ten-fold cross-validation was applied to determine the optimal penalty parameter (lambda), and genes with non-zero coefficients were retained [ 25 ]. Principal Component Analysis and Consensus Clustering Principal component analysis (PCA) was performed using the FactoMineR package to reduce dimensionality and visualize sample distribution based on the expression profiles of the eight selected genes. To further assess whether these genes could stratify HCC patients into biologically distinct subtypes, we conducted consensus clustering using the ConsensusClusterPlus package (v1.60.0), with Euclidean distance and k-means clustering. The optimal number of clusters (k = 2) was determined based on the cumulative distribution function (CDF) and consensus matrices [ 26 ]. Receiver Operating Characteristic (ROC) Analysis To evaluate the diagnostic performance of each gene and the combined eight-gene signature in predicting sorafenib response, we performed ROC curve analysis using the pROC package in R. The area under the curve (AUC) was computed for each gene individually and for the LASSO-derived model. AUC values > 0.8 were considered indicative of strong predictive capacity. Copy Number Variation and Somatic Mutation Analysis We analyzed gene-level CNV data from TCGA using GISTIC2-processed thresholded CNV calls. The distribution of CNV events (amplification, gain, loss, deletion) for each candidate gene was visualized and statistically tested for association with expression levels. Somatic mutation data in mutation annotation format (MAF) were analyzed using the maftools package (v2.14). Co-occurrence and mutual exclusivity analysis were conducted to assess whether specific genes such as CSNK1E, TP53, and MUC16 tended to mutate together. Fisher’s exact test was used for significance testing, with FDR correction. Cell Culture and Genetic Manipulation Seven human HCC cell lines (Huh7, HepG2, Hep3B, PLC, MHCC97L, MHCC97H, and HCCLM3) were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin in a humidified incubator at 37°C with 5% CO₂. For gene knockdown, HCCLM3 cells were transfected with short hairpin RNA targeting CSNK1E (shCSNK1E) using Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer’s protocol. For overexpression, Huh7 cells were transduced with lentiviral particles carrying human CSNK1E cDNA under CMV promoter control, followed by puromycin selection. Efficiency was confirmed by qRT-PCR and Western blotting. Quantitative Real-Time PCR (qRT-PCR) Total RNA was extracted using TRIzol Reagent (Invitrogen), and 1 µg of RNA was reverse transcribed using PrimeScript RT Reagent Kit (Takara). qRT-PCR was performed using SYBR Green Master Mix (Roche) on a QuantStudio 5 real-time PCR system (Thermo Fisher). Primers for CSNK1E and GAPDH were designed using Primer-BLAST. Relative expression levels were calculated using the 2^−ΔΔCt method, with GAPDH serving as internal control. Western Blotting Proteins were extracted in RIPA buffer supplemented with protease and phosphatase inhibitors (Thermo Fisher). Samples were separated via SDS–PAGE and transferred to PVDF membranes. After blocking in 5% BSA, membranes were incubated overnight at 4°C with primary antibodies against CSNK1E (1:1000, Proteintech), GAPDH (1:5000, Proteintech), followed by HRP-conjugated secondary antibodies. Signals were detected using ECL chemiluminescence reagents (Thermo Fisher). Colony Formation Assay Transfected cells were seeded into 6-well plates (500 cells/well) and cultured for 10–14 days. Colonies were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, photographed, and manually counted. Colony numbers were normalized to control groups. Wound Healing and Migration Assays Cells were seeded into 6-well plates and grown to confluence. A sterile 200 µL pipette tip was used to scratch the monolayer, and images were captured at 0, 24, 48, and 72 hours. Migration was quantified by measuring wound closure using ImageJ. Experiments were performed in serum-free medium to avoid proliferation effects. Xenograft Tumorigenicity in Nude Mice Animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC). Mmale BALB/c nude mice (6 weeks old) were subcutaneously injected with 5×10⁶ HCCLM3 cells transfected with shCtrl or shCSNK1E (n = 5 per group). Tumor size was measured every 3 days using calipers and calculated as (length × width²)/2. After 3 weeks, mice were euthanized, and tumors were harvested, weighed, and photographed. Sorafenib Sensitivity Assays Cells were seeded into 96-well plates at 5,000 cells per well and treated with varying concentrations of sorafenib (0.5 to 16 µM, MCE) for 72 hours. Cell viability was measured using Cell Counting Kit-8, and absorbance at 450 nm was recorded. IC50 values were calculated using GraphPad Prism 9.0 based on non-linear regression models. Gene Set Enrichment Analysis (GSEA) To explore the downstream pathways associated with high CSNK1E expression, we performed GSEA using the clusterProfiler and enrichplot packages, referencing Hallmark, KEGG, and Reactome gene sets from the MSigDB database. Enrichment results were visualized with ridge and enrichment plots [ 27 ]. Statistical Analysis All analyses were performed in R (v4.2.1) and GraphPad Prism (v9.0). Comparisons between two groups were conducted using the Student’s t-test or Wilcoxon rank-sum test depending on data distribution. Correlation analyses were performed using Pearson or Spearman coefficients as appropriate. Survival curves were analyzed using the Kaplan–Meier method and log-rank test. A two-sided p value < 0.05 was considered statistically significant. Results Transcriptomic Analysis Identifies Eight Key Genes Associated with Sorafenib Response in Hepatocellular Carcinoma To identify critical genes associated with sorafenib response, we analyzed transcriptomic data from 67 hepatocellular carcinoma (HCC) patients treated with sorafenib, including 21 responders and 46 non-responders. Differential gene expression analysis using the LIMMA package identified a total of 2,279 differentially expressed genes (DEGs), among which 1,140 genes were upregulated and 1,139 genes downregulated in the non-responder group (Fig. 1 A). To further refine our analysis, we compared tumor tissues and adjacent normal tissues using TCGA HCC data, identifying 3,231 DEGs—2,692 genes were upregulated in tumor tissues and 539 downregulated (Fig. 1 B). Intersection analysis between the upregulated genes in non-responders and the tumor-upregulated genes in TCGA yielded 250 common genes (Fig. 1 C). Protein-protein interaction (PPI) network analysis revealed a complex interaction landscape among these 250 genes, suggesting their potential involvement in modulating sorafenib efficacy. To further pinpoint sorafenib response–associated genes, we performed least absolute shrinkage and selection operator (LASSO) regression, which identified eight key genes—ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, THBS3, and ZRANB2 (Fig. 1 E–F). Heatmap visualization showed that all eight genes were significantly upregulated in tumor tissues from non-responders. Functional Implication of the Eight Key Genes in HCC Progression To investigate the role of these eight genes in HCC pathogenesis, we conducted genomic characterization. Mutation analysis revealed that FLNC was mutated in approximately 2% of HCC samples, whereas ALDOA and ZRANB2 exhibited no detectable mutations (Fig. 2 A). Co-occurrence analysis indicated significant co-mutation events between CSNK1E and both FLNC and THBS3 (Fig. 2 B). Copy number variation (CNV) analysis showed a substantial amplification of THBS3, followed by BRD9; no notable CNVs were observed in the other genes (Fig. 2 C). Chromosomal mapping localized THBS3 to chromosome 1 and BRD9 to chromosome 5 (Fig. 2 D). Principal component analysis (PCA) based on gene expression clearly distinguished tumor from normal tissues (Fig. 2 E). Moreover, all eight genes were significantly overexpressed in paired tumor samples compared to adjacent non-tumorous tissues (Fig. 2 F). Correlation analysis at the mRNA level revealed strong positive correlations between DRAP1 and RPL27, and between CSNK1E and RPL27 (Fig. 2 G). Univariate Cox regression analysis using TCGA survival data demonstrated that high expression of ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, and ZRANB2 correlated with poor prognosis, whereas THBS3 was associated with better survival outcomes (Fig. 2 H). Consensus Clustering Identifies a Molecular Subtype Predictive of Sorafenib Response Differential expression analysis confirmed that all eight genes were significantly upregulated in the non-responder group (Fig. 3 A). To evaluate their predictive potential, we constructed receiver operating characteristic (ROC) curves. CSNK1E and BRD9 showed the highest predictive accuracy for sorafenib response, with area under the curve (AUC) values of 0.937 and 0.934, respectively; the remaining six genes also showed moderate predictive power (Fig. 3 B). PCA based on the expression of these eight genes clearly separated responders from non-responders into two distinct clusters (Fig. 3 C). We subsequently performed consensus clustering to classify patients into molecular subtypes. Two clusters were identified (Fig. 3 D), and PCA confirmed transcriptomic divergence between the two subtypes (Fig. 3 E). Notably, more than 90% of patients in Cluster A were responders, while only 4% of patients in Cluster B responded to sorafenib (Fig. 3 F). Kaplan–Meier analysis revealed significantly prolonged survival in Cluster A (Fig. 3 G). These findings suggest that the expression profile of these eight genes may serve as a robust biomarker panel for predicting therapeutic response to sorafenib. Prognostic Value of CSNK1E in Hepatocellular Carcinoma Among the eight identified genes, CSNK1E exhibited the highest LASSO coefficient weight, prompting further investigation. In both the GSE14520 and CHCC cohorts, CSNK1E was significantly overexpressed in HCC tissues compared to adjacent non-tumorous tissues (Fig. 4 A–B). Kaplan–Meier analysis revealed that lower CSNK1E expression correlated with significantly improved overall survival across the TCGA, GSE14520, and CHCC datasets. Multivariate Cox regression confirmed CSNK1E as an independent prognostic factor, even after adjusting for age, sex, tumor size, and clinical stage (Fig. 4 C–G). Stratifying patients based on median CSNK1E expression revealed that high-expression patients had a higher frequency of TP53 and MUC16 mutations, potentially contributing to its oncogenic role (Fig. 4 H). Moreover, high CSNK1E expression was associated with increased genomic instability in terms of CNV (Fig. 4 H). Functional Enrichment Analysis of CSNK1E We applied the ESTIMATE algorithm to evaluate immune and stromal infiltration in HCC. CSNK1E expression showed no significant correlation with immune scores (Fig. 5 A) and only a weak correlation with stromal scores, despite statistical significance (Fig. 5 B), suggesting a limited role in the tumor immune microenvironment. Gene set enrichment analysis (GSEA) indicated that high CSNK1E expression was significantly associated with the activation of extracellular matrix (ECM) remodeling, cell cycle progression, DNA replication, and WNT signaling pathways (Fig. 5 C). KEGG pathway analysis yielded consistent results, reinforcing the involvement of CSNK1E in these oncogenic processes (Fig. 5 D). Notably, CSNK1E expression negatively correlated with several metabolic pathways. Hallmark GSEA further supported the association between high CSNK1E expression and tumor-promoting pathways (Fig. 5 E). Given prior reports linking WNT activation to resistance against anti-angiogenic therapies, we explored the relationship between CSNK1E and WNT pathway activity using TCGA and CHCC data. Both datasets confirmed that high CSNK1E expression is significantly associated with WNT pathway activation (Fig. 5 F–G). CSNK1E Promotes HCC Cell Proliferation, Migration, and Sorafenib Resistance Quantitative PCR analysis showed that CSNK1E was most highly expressed in HCCLM3 cells and lowest in Huh7 cells (Fig. 6 A). Thus, we performed CSNK1E knockdown in HCCLM3 and overexpression in Huh7 cells (Fig. 6 B). Western blotting confirmed effective knockdown and overexpression, respectively (Fig. 6 C). Colony formation assays demonstrated that CSNK1E knockdown significantly inhibited, whereas overexpression promoted, HCC cell clonogenicity (Fig. 6 E–G). Scratch assays further revealed that CSNK1E enhances HCC cell migration (Fig. 6 H–J). In vivo, CSNK1E knockdown markedly suppressed tumor growth in a nude mouse xenograft model (Fig. 6 K–L). To assess its role in sorafenib resistance, we conducted drug sensitivity assays. CSNK1E knockdown reduced the IC50 of sorafenib in HCCLM3 cells from 6.391 µM to 4.644 µM (Fig. 6 M), whereas its overexpression in Huh7 cells increased the IC50 from 3.632 µM to 5.451 µM (Fig. 6 N). These results confirm a critical role for CSNK1E in mediating resistance to sorafenib in HCC. Discussion In this study, we identified and validated an eight-gene signature that is significantly associated with sorafenib resistance in HCC, with particular emphasis on the role of CSNK1E as a key modulator of therapeutic response. Through an integrative approach combining transcriptomic analysis, protein-protein interaction networks, clinical correlation, and functional validation in vitro and in vivo, we not only revealed the prognostic and predictive utility of this gene set but also provided mechanistic insights into how CSNK1E contributes to drug resistance and HCC progression. Our analysis first established that the eight genes including ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, THBS3, and ZRANB2 were upregulated in non-responders to sorafenib, and that their expression levels were higher in HCC tumors compared to adjacent non-tumor tissues. Among these, CSNK1E emerged with the strongest prognostic relevance and functional effect, highlighting it as a potential therapeutic target. These findings are consistent with and extend previous reports suggesting that WNT/β-catenin signaling, in which CSNK1E participates, plays a central role in the resistance of HCC to anti-angiogenic and multikinase therapies [ 28 , 29 ]. Our study further supports this mechanistic link by demonstrating that high CSNK1E expression is positively correlated with WNT signaling activity, both at the transcriptomic and pathway enrichment levels. CSNK1E, a member of the casein kinase 1 family, has been traditionally known for its role in circadian rhythm regulation and WNT signaling. However, emerging evidence has implicated this kinase in oncogenic processes such as cell cycle progression, DNA repair, and tumor immune evasion [ 30 , 31 ]. In the context of HCC, our data show that CSNK1E is overexpressed in tumors with high genomic instability and frequent TP53 mutations, supporting a model in which CSNK1E upregulation is part of a broader oncogenic phenotype associated with sorafenib resistance. Of particular importance, patients with high CSNK1E expression displayed significantly shorter overall survival in three independent HCC cohorts, and multivariate analysis confirmed that CSNK1E is an independent prognostic factor. These findings not only validate CSNK1E clinical relevance but also reinforce its potential utility as a biomarker to stratify patients who are less likely to benefit from sorafenib monotherapy. Functional assays further corroborated the pro-tumorigenic role of CSNK1E. Genetic perturbation experiments demonstrated that CSNK1E knockdown inhibited colony formation and migration in HCC cell lines, while its overexpression enhanced these malignant traits. These effects were recapitulated in vivo using a xenograft model, in which silencing CSNK1E markedly reduced tumor growth. Importantly, modulation of CSNK1E expression also influenced the sensitivity of HCC cells to sorafenib: knockdown sensitized cells to the drug, whereas overexpression conferred resistance. These observations suggest that CSNK1E actively contributes to drug tolerance mechanisms, possibly by maintaining proliferative and migratory signaling programs even in the presence of kinase inhibition. Mechanistically, GSEA linked high CSNK1E expression with several oncogenic processes, including extracellular matrix remodeling, DNA replication, cell cycle progression, and WNT pathway activation. These biological functions are known contributors to tumor aggressiveness and therapy resistance. Interestingly, CSNK1E expression showed a negative correlation with metabolic pathways, which may reflect a metabolic shift toward a proliferative state that supports tumor progression under treatment stress. This aligns with recent studies indicating that metabolic rewiring is a hallmark of drug-resistant HCC and could represent a secondary vulnerability in CSNK1E-high tumors [ 32 ]. In addition, in triple-negative breast cancer, CSNK1E promotes tumorigenesis via WNT and MYC signaling, and its inhibition sensitizes tumors to chemotherapy and targeted therapies [ 33 ]. Similarly, in glioblastoma, CSNK1E regulates cell survival and invasiveness through interaction with β-catenin and AKT [ 34 ]. These reports, combined with our current findings in HCC, suggest that CSNK1E may serve as a pan-cancer therapeutic target, particularly in tumors characterized by kinase inhibitor resistance and WNT pathway activation. In this study, the eight-gene signature derived from our analysis holds promise as a composite biomarker panel to predict sorafenib efficacy. Our consensus clustering analysis revealed that this gene set could stratify patients into distinct molecular subtypes with differential response rates and survival outcomes. More than 90% of patients in the CSNK1E-low cluster were responders, whereas only 4% of patients in the CSNK1E-high cluster responded to therapy. This striking contrast highlights the clinical utility of gene expression–based classifiers in guiding treatment decisions and supports the development of CSNK1E as part of a molecular diagnostic tool. It is worth noting that among the eight genes, several have been implicated in cancer biology beyond HCC. BRD9, a member of the SWI/SNF chromatin remodeling complex, has been shown to promote tumor growth and therapeutic resistance in colorectal and lung cancers, where its inhibition sensitizes tumors to checkpoint blockade. ALDOA, a glycolytic enzyme, is upregulated in various malignancies and associated with aerobic glycolysis and epithelial-mesenchymal transition (EMT)—both of which are hallmarks of drug resistance. ZRANB2, a zinc finger protein involved in RNA splicing, has been identified as a modulator of alternative splicing programs that affect apoptosis and drug sensitivity in breast cancer. Our findings thus underscore the broader oncogenic potential of this gene set and open avenues for cross-cancer comparative studies to explore conserved resistance mechanisms. Despite the promising findings, our study has several limitations. First, while the bioinformatic analyses were rigorously performed and validated in multiple datasets, prospective clinical validation of the eight-gene signature is necessary to assess its true predictive value in a real-world setting. Second, the mechanistic role of CSNK1E in sorafenib resistance warrants further exploration, particularly its downstream effectors and potential crosstalk with other signaling pathways such as PI3K/AKT and MAPK. Given the complexity of HCC biology, combinatorial therapeutic approaches targeting CSNK1E and other co-activated pathways may be required to overcome resistance. Third, our study focused primarily on tumor-intrinsic mechanisms. Although CSNK1E expression did not show a strong correlation with immune infiltration in the ESTIMATE algorithm, more comprehensive immune profiling such as spatial transcriptomics or single-cell RNA-seq may reveal microenvironmental interactions that influence sorafenib sensitivity. Conclusions In summary, our comprehensive analysis identifies CSNK1E as a central player in sorafenib resistance and HCC progression. By integrating transcriptomic profiling, functional validation, and clinical correlation, we demonstrate the prognostic and predictive relevance of CSNK1E and propose a robust eight-gene signature that stratifies patients according to therapeutic response. These findings provide a strong rationale for targeting CSNK1E as a strategy to overcome drug resistance and improve outcomes in HCC. Further translational and clinical studies are warranted to validate these findings and bring them closer to therapeutic application. Declarations Conflicts of interest: The authors declare no competing interests. Ethics approval: The experiment protocol was approved by the Experimental Animal Care and Use Committee of Nantong University. Funding This study was funded by the Natural Science Foundation of Nantong (JC2024076). Author Contribution X.G. and W.L. integrated and analyzed the data, and wrote the main manuscript text. W.S. participated in parts of molecular experiments and data analysis. Y.D. and S.Z. provided the initial idea of this study, designed this work, edited and revised the paper. All authors reviewed and approved the manuscript. Acknowledgments We thank the contributors of the TCGA and GEO databases for providing publicly available data. We also acknowledge the support from colleagues in our laboratory and the technical assistance from the bioinformatics core facility. References Alawyia B, Constantinou C. Hepatocellular Carcinoma: a Narrative Review on Current Knowledge and Future Prospects. Curr Treat Options Oncol. 2023;24(7):711–24. Hartke J, Johnson M, Ghabril M. The diagnosis and treatment of hepatocellular carcinoma. Semin Diagn Pathol. 2017;34(2):153–9. Galle PR, Dufour JF, Peck-Radosavljevic M, Trojan J, Vogel A. Systemic therapy of advanced hepatocellular carcinoma. Future Oncol. 2021;17(10):1237–51. Bejjani A, Finn RS. Evolution of Systemic Therapy in Advanced Hepatocellular Carcinoma. Surg Oncol Clin N Am. 2024;33(1):73–85. Llovet JM, De Baere T, Kulik L, Haber PK, Greten TF, Meyer T, Lencioni R. Locoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2021;18(5):293–313. Wang Y, Deng B. Hepatocellular carcinoma: molecular mechanism, targeted therapy, and biomarkers. Cancer Metastasis Rev. 2023;42(3):629–52. Tang W, Chen Z, Zhang W, Cheng Y, Zhang B, Wu F, Wang Q, Wang S, Rong D, Reiter FP, et al. The mechanisms of sorafenib resistance in hepatocellular carcinoma: theoretical basis and therapeutic aspects. Signal Transduct Target Ther. 2020;5(1):87. Xia S, Pan Y, Liang Y, Xu J, Cai X. The microenvironmental and metabolic aspects of sorafenib resistance in hepatocellular carcinoma. EBioMedicine. 2020;51:102610. Li Y, Yang W, Zheng Y, Dai W, Ji J, Wu L, Cheng Z, Zhang J, Li J, Xu X, et al. Targeting fatty acid synthase modulates sensitivity of hepatocellular carcinoma to sorafenib via ferroptosis. J Exp Clin Cancer Res. 2023;42(1):6. Pearson H, Marshall LV, Carceller F. Sorafenib in pediatric hepatocellular carcinoma from a clinician perspective. Pediatr Hematol Oncol. 2020;37(5):412–23. Chan YT, Zhang C, Wu J, Lu P, Xu L, Yuan H, Feng Y, Chen ZS, Wang N. Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma. Mol Cancer. 2024;23(1):189. Chidambaranathan-Reghupaty S, Fisher PB, Sarkar D. Hepatocellular carcinoma (HCC): Epidemiology, etiology and molecular classification. Adv Cancer Res. 2021;149:1–61. Yang X, Yang C, Zhang S, Geng H, Zhu AX, Bernards R, Qin W, Fan J, Wang C, Gao Q. Precision treatment in advanced hepatocellular carcinoma. Cancer Cell. 2024;42(2):180–97. Calderaro J, Ziol M, Paradis V, Zucman-Rossi J. Molecular and histological correlations in liver cancer. J Hepatol. 2019;71(3):616–30. Guo DZ, Zhang X, Zhang SQ, Zhang SY, Zhang XY, Yan JY, Dong SY, Zhu K, Yang XR, Fan J, et al. Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts. Mol Cancer. 2024;23(1):157. Chi SY, Hsu YC, Tsai CH, Huang SY, Chang SC, Cheng SP. Identifying CSNK1E as a therapeutic target in thyroid cancer among the core circadian clock genes. Histochem Cell Biol. 2025;163(1):30. Tiong KL, Chang KC, Yeh KT, Liu TY, Wu JH, Hsieh PH, Lin SH, Lai WY, Hsu YC, Chen JY, et al. CSNK1E/CTNNB1 are synthetic lethal to TP53 in colorectal cancer and are markers for prognosis. Neoplasia. 2014;16(5):441–50. Wang S, Wang X, Yang X, Liu F, Li J, Li W, Bai Z, Wang H, Mao J, Li T, et al. Comprehensive kinomic study via a chemical proteomic approach reveals kinome reprogramming in hepatocellular carcinoma tissues. Proteomics. 2022;22(4):e2100141. Cohen-Sharir Y, McFarland JM, Abdusamad M, Marquis C, Bernhard SV, Kazachkova M, Tang H, Ippolito MR, Laue K, Zerbib J, et al. Aneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition. Nature. 2021;590(7846):486–91. Martincorena I, Raine KM, Gerstung M, Dawson KJ, Haase K, Van Loo P, Davies H, Stratton MR, Campbell PJ. Universal Patterns of Selection in Cancer and Somatic Tissues. Cell. 2017;171(5):1029–e10411021. Olivier M, Taniere P. Somatic mutations in cancer prognosis and prediction: lessons from TP53 and EGFR genes. Curr Opin Oncol. 2011;23(1):88–92. Pinyol R, Montal R, Bassaganyas L, Sia D, Takayama T, Chau GY, Mazzaferro V, Roayaie S, Lee HC, Kokudo N, et al. Molecular predictors of prevention of recurrence in HCC with sorafenib as adjuvant treatment and prognostic factors in the phase 3 STORM trial. Gut. 2019;68(6):1065–75. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W. Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. Zhu S, Wu Q, Zhang B, Wei H, Li B, Shi W, Fang M, Zhu S, Wang L, Lang Zhou Y, et al. Autophagy-related gene expression classification defines three molecular subtypes with distinct clinical and microenvironment cell infiltration characteristics in colon cancer. Int Immunopharmacol. 2020;87:106757. Wang Q, Qiao W, Zhang H, Liu B, Li J, Zang C, Mei T, Zheng J, Zhang Y. Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma. Front Immunol. 2022;13:1019638. Zhang L, Zhang X, Guan M, Zeng J, Yu F, Lai F. Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response. Respir Res. 2024;25(1):206. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. Chen Y, Gao Z, Mohd-Ibrahim I, Yang H, Wu L, Fu Y, Deng Y. Pan-cancer analyses of bromodomain containing 9 as a novel therapeutic target reveals its diagnostic, prognostic potential and biological mechanism in human tumours. Clin Transl Med. 2024;14(2):e1543. Yang WS, Stockwell BR. Inhibition of casein kinase 1-epsilon induces cancer-cell-selective, PERIOD2-dependent growth arrest. Genome Biol. 2008;9(6):R92. Hong W, Wang X, Huang X, Chen P, Liu Y, Zheng Z, You X, Chen Y, Xie Z, Zhan G, et al. CSNK1E is involved in TGF-beta1 induced epithelial mesenchymal transformationas and related to melanoma immune heterogeneity. Front Pharmacol. 2024;15:1501849. Toyoshima M, Howie HL, Imakura M, Walsh RM, Annis JE, Chang AN, Frazier J, Chau BN, Loboda A, Linsley PS, et al. Functional genomics identifies therapeutic targets for MYC-driven cancer. Proc Natl Acad Sci U S A. 2012;109(24):9545–50. Lin YC, Sun DP, Hsieh TH, Chen CH. Targeting CK1delta and CK1epsilon as a New Therapeutic Approach for Clear Cell Renal Cell Carcinoma. Pharmacology. 2024;109(6):330–40. Kim SY, Dunn IF, Firestein R, Gupta P, Wardwell L, Repich K, Schinzel AC, Wittner B, Silver SJ, Root DE, et al. CK1epsilon is required for breast cancers dependent on beta-catenin activity. PLoS ONE. 2010;5(2):e8979. Varghese RT, Young S, Pham L, Liang Y, Pridham KJ, Guo S, Murphy S, Kelly DF, Sheng Z. Casein Kinase 1 Epsilon Regulates Glioblastoma Cell Survival. Sci Rep. 2018;8(1):13621. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 07 Jul, 2025 Submission checks completed at journal 07 Jul, 2025 First submitted to journal 04 Jul, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7049920","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481633425,"identity":"82583ad9-d471-4a64-823b-9ce52b684a48","order_by":0,"name":"Xuanji Gao","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University, Medical School of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Xuanji","middleName":"","lastName":"Gao","suffix":""},{"id":481633426,"identity":"b20f141e-7f9d-41cf-b219-d76d5f9e039e","order_by":1,"name":"Wanghu Liu","email":"","orcid":"","institution":"The Second 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09:22:09","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114440,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/a84c7f01664c51ac5a1f0943.html"},{"id":97667903,"identity":"9d0b36e1-f34b-4e0b-bb55-aa94026735ce","added_by":"auto","created_at":"2025-12-08 09:24:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":521412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of key genes associated with sorafenib resistance in HCC. (A)\u003c/strong\u003eVolcano plot showing DEGs between sorafenib responders and non-responders (n = 67). Red and blue dots represent significantly upregulated and downregulated genes, respectively (|log2FC| \u0026gt; 1, adjusted p \u0026lt; 0.05). \u003cstrong\u003e(B) \u003c/strong\u003eVolcano plot of DEGs between tumor and adjacent normal tissues in TCGA-LIHC cohort (n = 374 tumors, 50 normals). \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram showing 250 overlapping genes upregulated both in non-responders and tumor tissues.\u003cstrong\u003e (D)\u003c/strong\u003eProtein–protein interaction (PPI) network of the 250 intersected genes constructed using STRING database. \u003cstrong\u003e(E) \u003c/strong\u003eLASSO regression model identifying 8 genes most predictive of sorafenib response. \u003cstrong\u003e(F)\u003c/strong\u003eCoefficient profiles of the selected genes from the LASSO model. \u003cstrong\u003e(G)\u003c/strong\u003e Heat map reveals the expression differences of 8 key genes between sorafenib responders and non-responders.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/f314c656537d0f84424383a8.png"},{"id":97667786,"identity":"2d6392da-ae6a-4040-81be-300ba9ea114c","added_by":"auto","created_at":"2025-12-08 09:24:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic characterization and expression profiles of the 8 key genes. (A) \u003c/strong\u003eSomatic mutation frequencies of the 8 candidate genes in TCGA-HCC cohort. \u003cstrong\u003e(B)\u003c/strong\u003eCo-occurrence and mutual exclusivity analysis of mutations among CSNK1E, FLNC, and THBS3. \u003cstrong\u003e(C)\u003c/strong\u003e CNV distribution for each of the 8 genes; \u003cstrong\u003e(D)\u003c/strong\u003eChromosomal locations of 8 genes. \u003cstrong\u003e(E)\u003c/strong\u003e PCA analysis based on the expression of the 8 genes showing distinct separation between tumor and normal samples. \u003cstrong\u003e(F)\u003c/strong\u003e Boxplots showing significantly higher expression of all 8 genes in tumor tissues compared to matched adjacent tissues. \u003cstrong\u003e(G) \u003c/strong\u003eCorrelation matrix showing significant positive correlations among 8 genes. \u003cstrong\u003e(H) \u003c/strong\u003eForest plot of univariate Cox regression analysis indicating prognostic values of the 8 genes in overall survival.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/7992f28680d15319dcccdee9.png"},{"id":97422864,"identity":"cd9fd686-6f8b-4659-9d30-fb276879478c","added_by":"auto","created_at":"2025-12-04 08:46:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular subtyping based on the 8-gene signature and its association with sorafenib response.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eBoxplots showing significantly higher expression of all 8 genes in sorafenib non-responders. \u003cstrong\u003e(B) \u003c/strong\u003eROC curve analysis demonstrating the predictive ability of individual genes for sorafenib response. \u003cstrong\u003e(C)\u003c/strong\u003e PCA based on the 8-gene signature clearly distinguishes responders from non-responders.\u003cstrong\u003e (D)\u003c/strong\u003eConsensus clustering of sorafenib-treated HCC patients based on the 8 genes, revealing two distinct molecular subtypes. \u003cstrong\u003e(E)\u003c/strong\u003e PCA confirms transcriptional differences between cluster A and B.\u003cstrong\u003e (F)\u003c/strong\u003e Bar plot showing responder distribution across clusters. 90% of responders were in cluster A. \u003cstrong\u003e(G)\u003c/strong\u003e Kaplan–Meier survival curves showing significantly better overall survival for cluster A patients compared to cluster B.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/2b078b4695b13efa5398f482.png"},{"id":97666420,"identity":"cdcca6de-1519-4cce-9100-ac1066536e11","added_by":"auto","created_at":"2025-12-08 09:21:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical significance and prognostic relevance of CSNK1E in HCC.\u003c/strong\u003e \u003cstrong\u003e(A–B)\u003c/strong\u003eCSNK1E is significantly upregulated in tumor compared with normal tissues in GSE14520 (A) and CHCC (B) cohorts. \u003cstrong\u003e(C–E)\u003c/strong\u003eKaplan–Meier survival curves in TCGA, GSE14520, and CHCC datasets show that high CSNK1E expression is associated with shorter overall survival. \u003cstrong\u003e(F–G)\u003c/strong\u003eMultivariate Cox regression analysis incorporating clinical variables (age, sex, tumor size, stage) identifies CSNK1E as an independent risk factor in CHCC (F) and TCGA (G) cohort. \u003cstrong\u003e(H)\u003c/strong\u003e Gene mutation frequencies and copy number instability analysis in CSNK1E high and low-expression groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/bfa18f990c37a309e5fe6ffb.png"},{"id":97667756,"identity":"21b16699-dcc8-44e3-9560-9a4d1fd225cf","added_by":"auto","created_at":"2025-12-08 09:24:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway enrichment analysis of CSNK1E in HCC\u003c/strong\u003e. \u003cstrong\u003e(A) \u003c/strong\u003eCorrelation between CSNK1E expression and immune score (ESTIMATE algorithm). \u003cstrong\u003e(B)\u003c/strong\u003e Correlation between CSNK1E expression and stromal score (ESTIMATE algorithm). \u003cstrong\u003e(C)\u003c/strong\u003e GSEA showing positive enrichment of ECM, cell cycle, DNA replication, and WNT signaling pathways in CSNK1E-high tumors. \u003cstrong\u003e(D)\u003c/strong\u003e KEGG pathway enrichment confirms involvement in WNT, cell cycle, and DNA replication pathways; metabolic pathways are negatively enriched. \u003cstrong\u003e(E)\u003c/strong\u003e Hallmark gene set enrichment reveals activation of oncogenic signatures in CSNK1E-high group. \u003cstrong\u003e(F–G) \u003c/strong\u003eCSNK1E expression is positively correlated with WNT pathway activation in both TCGA (F) and CHCC cohort (G).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/0f8fb9da61f085d0ae42b22a.png"},{"id":97422868,"identity":"df7335fb-f20f-466a-8d49-2b7cf5dfbc07","added_by":"auto","created_at":"2025-12-04 08:46:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2155172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional validation of CSNK1E in vitro and in vivo. (A)\u003c/strong\u003e CSNK1E mRNA levels in seven HCC cell lines. \u003cstrong\u003e(B-C)\u003c/strong\u003e qRT-PCR validation of CSNK1E knockdown in HCCLM3 and overexpression in Huh7. \u003cstrong\u003e(D)\u003c/strong\u003e Western blot confirming effective knockdown and overexpression of CSNK1E protein. \u003cstrong\u003e(E–G)\u003c/strong\u003eColony formation assays. CSNK1E knockdown significantly reduces, while overexpression enhances clonogenic ability. \u003cstrong\u003e(H–J)\u003c/strong\u003eWound healing assays. CSNK1E promotes HCC cell migration. \u003cstrong\u003e(K–L) \u003c/strong\u003eTumor volume and weight of xenografts from HCCLM3 cells. \u003cstrong\u003e(M–N)\u003c/strong\u003eSorafenib IC50 values. knockdown of CSNK1E sensitizes HCCLM3 cells, while overexpression induces resistance in Huh7 cells.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/205645fa36e5c7393c3dd2af.png"},{"id":97677553,"identity":"b605297d-5f32-4cb0-9ff0-00296deb4cfd","added_by":"auto","created_at":"2025-12-08 09:53:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4169283,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7049920/v1/63b16d6b-29c9-4cf3-a390-2820628ca7b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic Profiling Identifies CSNK1E as a Key Mediator of Sorafenib Resistance in Hepatocellular Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the most common primary liver malignancy and the third leading cause of cancer-related deaths worldwide. The global burden of HCC continues to rise due to the increasing prevalence of chronic liver diseases, including hepatitis B and C infections, as well as metabolic-associated fatty liver disease [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although early-stage HCC can be managed with curative approaches such as surgical resection or liver transplantation, the majority of patients are diagnosed at intermediate or advanced stages when systemic therapy is the only available option [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Sorafenib, a multikinase inhibitor targeting RAF, VEGFR, and PDGFR, remains the standard first-line treatment for advanced HCC. However, its clinical efficacy is limited, with low objective response rates and the frequent emergence of drug resistance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Understanding the molecular basis of sorafenib resistance is essential to improving treatment outcomes and identifying predictive biomarkers for individualized therapy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHCC exhibits significant molecular heterogeneity, which contributes to the variability in treatment response. Although several signaling pathways including WNT/β-catenin, PI3K/AKT, and epithelial-mesenchymal transition, have been implicated in sorafenib resistance, few mechanistic insights have been translated into clinical practice [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In recent years, transcriptomic profiling has provided a powerful approach to unravel the molecular underpinnings of drug resistance across diverse cancer types [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, genes such as Casein kinase 1 epsilon (CSNK1E) have been implicated in the regulation of oncogenic pathways, including WNT/β-catenin signaling, and are known to influence tumor progression. However, the role of CSNK1E in mediating sorafenib resistance in HCC remains largely unexplored [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, while numerous studies have sought to identify resistance-related biomarkers using genomic or transcriptomic data, most candidate markers fail to achieve clinical translation. One major limitation lies in the lack of integrative analysis across multiple dimensions of data such as somatic mutations, copy number variations, and gene regulatory networks to robustly validate the functional relevance of these genes [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A more comprehensive and multidimensional approach is therefore needed to identify reliable biomarkers and therapeutic targets that can be leveraged for precision oncology.\u003c/p\u003e\u003cp\u003eIn this study, we performed a systematic transcriptomic analysis of HCC patients treated with sorafenib to identify key genes associated with treatment response. By integrating differential expression profiling, data from The Cancer Genome Atlas (TCGA), protein-protein interaction (PPI) networks, and LASSO regression modeling, we identified a set of eight genes potentially critical to sorafenib response. We further characterized their expression patterns, genetic alterations, and prognostic significance. Among these, CSNK1E emerged as a top candidate due to its strong association with poor prognosis and therapeutic resistance. Functional assays and in vivo experiments confirmed its role in promoting HCC progression and sorafenib resistance. This study provides new insights into the molecular mechanisms underlying sorafenib response and proposes a gene signature that may inform personalized therapeutic strategies for HCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003ePatient Cohorts and Transcriptomic Data Acquisition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate gene signatures associated with sorafenib response in HCC, we collected transcriptomic data from a total of 67 HCC patients who received sorafenib treatment. The data were curated from the publicly available Gene Expression Omnibus (GEO) database (GSE109211), consisting of 21 patients who responded to sorafenib and 46 who were classified as non-responders based on the RECIST criteria [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In parallel, we downloaded RNA-sequencing data, somatic mutation profiles, and copy number variation (CNV) data of primary HCC tumors and adjacent normal tissues from The Cancer Genome Atlas (TCGA-LIHC) project via the UCSC Xena browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), comprising 374 tumor samples and 50 matched normal tissues. For survival analyses, overall survival (OS) and clinical metadata including age, gender, tumor stage, tumor size, and histological grade were extracted and matched with gene expression profiles.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferential Gene Expression Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe utilized the R package limma to identify differentially expressed genes (DEGs) between sorafenib responders and non-responders. Data were first normalized using the quantile normalization method. Genes with an absolute log2 fold change (|log2FC|)\u0026thinsp;\u0026gt;\u0026thinsp;1 and a Benjamini-Hochberg adjusted p-value (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, we applied the same pipeline to compare gene expression between tumor tissues and normal liver tissues in the TCGA cohort. To identify sorafenib resistance-related genes specific to tumor biology, we intersected genes that were significantly upregulated in non-responders with those upregulated in tumor tissues compared to adjacent normals, yielding a subset of 250 candidate genes for further analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProtein\u0026ndash;Protein Interaction (PPI) and Key Gene Identification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore functional interactions among the 250 intersected genes, we conducted a PPI network analysis using the STRING database (Search Tool for the Retrieval of Interacting Genes/Proteins). An interaction score cutoff of 0.7 (high confidence) was used. To further identify key genes strongly associated with sorafenib response, we performed least absolute shrinkage and selection operator (LASSO) regression using the glmnet package in R. LASSO is a penalized regression approach that helps prevent overfitting while selecting the most predictive variables. Ten-fold cross-validation was applied to determine the optimal penalty parameter (lambda), and genes with non-zero coefficients were retained [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrincipal Component Analysis and Consensus Clustering\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) was performed using the FactoMineR package to reduce dimensionality and visualize sample distribution based on the expression profiles of the eight selected genes. To further assess whether these genes could stratify HCC patients into biologically distinct subtypes, we conducted consensus clustering using the ConsensusClusterPlus package (v1.60.0), with Euclidean distance and k-means clustering. The optimal number of clusters (k\u0026thinsp;=\u0026thinsp;2) was determined based on the cumulative distribution function (CDF) and consensus matrices [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eReceiver Operating Characteristic (ROC) Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the diagnostic performance of each gene and the combined eight-gene signature in predicting sorafenib response, we performed ROC curve analysis using the pROC package in R. The area under the curve (AUC) was computed for each gene individually and for the LASSO-derived model. AUC values\u0026thinsp;\u0026gt;\u0026thinsp;0.8 were considered indicative of strong predictive capacity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCopy Number Variation and Somatic Mutation Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed gene-level CNV data from TCGA using GISTIC2-processed thresholded CNV calls. The distribution of CNV events (amplification, gain, loss, deletion) for each candidate gene was visualized and statistically tested for association with expression levels. Somatic mutation data in mutation annotation format (MAF) were analyzed using the maftools package (v2.14). Co-occurrence and mutual exclusivity analysis were conducted to assess whether specific genes such as CSNK1E, TP53, and MUC16 tended to mutate together. Fisher\u0026rsquo;s exact test was used for significance testing, with FDR correction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Culture and Genetic Manipulation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeven human HCC cell lines (Huh7, HepG2, Hep3B, PLC, MHCC97L, MHCC97H, and HCCLM3) were cultured in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM, Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin in a humidified incubator at 37\u0026deg;C with 5% CO₂. For gene knockdown, HCCLM3 cells were transfected with short hairpin RNA targeting CSNK1E (shCSNK1E) using Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer\u0026rsquo;s protocol. For overexpression, Huh7 cells were transduced with lentiviral particles carrying human CSNK1E cDNA under CMV promoter control, followed by puromycin selection. Efficiency was confirmed by qRT-PCR and Western blotting.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantitative Real-Time PCR (qRT-PCR)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal RNA was extracted using TRIzol Reagent (Invitrogen), and 1 \u0026micro;g of RNA was reverse transcribed using PrimeScript RT Reagent Kit (Takara). qRT-PCR was performed using SYBR Green Master Mix (Roche) on a QuantStudio 5 real-time PCR system (Thermo Fisher). Primers for CSNK1E and GAPDH were designed using Primer-BLAST. Relative expression levels were calculated using the 2^\u0026minus;ΔΔCt method, with GAPDH serving as internal control.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern Blotting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eProteins were extracted in RIPA buffer supplemented with protease and phosphatase inhibitors (Thermo Fisher). Samples were separated via SDS\u0026ndash;PAGE and transferred to PVDF membranes. After blocking in 5% BSA, membranes were incubated overnight at 4\u0026deg;C with primary antibodies against CSNK1E (1:1000, Proteintech), GAPDH (1:5000, Proteintech), followed by HRP-conjugated secondary antibodies. Signals were detected using ECL chemiluminescence reagents (Thermo Fisher).\u003c/p\u003e\u003cp\u003e\u003cb\u003eColony Formation Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTransfected cells were seeded into 6-well plates (500 cells/well) and cultured for 10\u0026ndash;14 days. Colonies were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, photographed, and manually counted. Colony numbers were normalized to control groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWound Healing and Migration Assays\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells were seeded into 6-well plates and grown to confluence. A sterile 200 \u0026micro;L pipette tip was used to scratch the monolayer, and images were captured at 0, 24, 48, and 72 hours. Migration was quantified by measuring wound closure using ImageJ. Experiments were performed in serum-free medium to avoid proliferation effects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eXenograft Tumorigenicity in Nude Mice\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC). Mmale BALB/c nude mice (6 weeks old) were subcutaneously injected with 5\u0026times;10⁶ HCCLM3 cells transfected with shCtrl or shCSNK1E (n\u0026thinsp;=\u0026thinsp;5 per group). Tumor size was measured every 3 days using calipers and calculated as (length \u0026times; width\u0026sup2;)/2. After 3 weeks, mice were euthanized, and tumors were harvested, weighed, and photographed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSorafenib Sensitivity Assays\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells were seeded into 96-well plates at 5,000 cells per well and treated with varying concentrations of sorafenib (0.5 to 16 \u0026micro;M, MCE) for 72 hours. Cell viability was measured using Cell Counting Kit-8, and absorbance at 450 nm was recorded. IC50 values were calculated using GraphPad Prism 9.0 based on non-linear regression models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene Set Enrichment Analysis (GSEA)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the downstream pathways associated with high CSNK1E expression, we performed GSEA using the clusterProfiler and enrichplot packages, referencing Hallmark, KEGG, and Reactome gene sets from the MSigDB database. Enrichment results were visualized with ridge and enrichment plots [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were performed in R (v4.2.1) and GraphPad Prism (v9.0). Comparisons between two groups were conducted using the Student\u0026rsquo;s t-test or Wilcoxon rank-sum test depending on data distribution. Correlation analyses were performed using Pearson or Spearman coefficients as appropriate. Survival curves were analyzed using the Kaplan\u0026ndash;Meier method and log-rank test. A two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eTranscriptomic Analysis Identifies Eight Key Genes Associated with Sorafenib Response in Hepatocellular Carcinoma\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify critical genes associated with sorafenib response, we analyzed transcriptomic data from 67 hepatocellular carcinoma (HCC) patients treated with sorafenib, including 21 responders and 46 non-responders. Differential gene expression analysis using the LIMMA package identified a total of 2,279 differentially expressed genes (DEGs), among which 1,140 genes were upregulated and 1,139 genes downregulated in the non-responder group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). To further refine our analysis, we compared tumor tissues and adjacent normal tissues using TCGA HCC data, identifying 3,231 DEGs\u0026mdash;2,692 genes were upregulated in tumor tissues and 539 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Intersection analysis between the upregulated genes in non-responders and the tumor-upregulated genes in TCGA yielded 250 common genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Protein-protein interaction (PPI) network analysis revealed a complex interaction landscape among these 250 genes, suggesting their potential involvement in modulating sorafenib efficacy. To further pinpoint sorafenib response\u0026ndash;associated genes, we performed least absolute shrinkage and selection operator (LASSO) regression, which identified eight key genes\u0026mdash;ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, THBS3, and ZRANB2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u0026ndash;F). Heatmap visualization showed that all eight genes were significantly upregulated in tumor tissues from non-responders.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Implication of the Eight Key Genes in HCC Progression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the role of these eight genes in HCC pathogenesis, we conducted genomic characterization. Mutation analysis revealed that FLNC was mutated in approximately 2% of HCC samples, whereas ALDOA and ZRANB2 exhibited no detectable mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Co-occurrence analysis indicated significant co-mutation events between CSNK1E and both FLNC and THBS3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Copy number variation (CNV) analysis showed a substantial amplification of THBS3, followed by BRD9; no notable CNVs were observed in the other genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Chromosomal mapping localized THBS3 to chromosome 1 and BRD9 to chromosome 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Principal component analysis (PCA) based on gene expression clearly distinguished tumor from normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Moreover, all eight genes were significantly overexpressed in paired tumor samples compared to adjacent non-tumorous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Correlation analysis at the mRNA level revealed strong positive correlations between DRAP1 and RPL27, and between CSNK1E and RPL27 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Univariate Cox regression analysis using TCGA survival data demonstrated that high expression of ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, and ZRANB2 correlated with poor prognosis, whereas THBS3 was associated with better survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConsensus Clustering Identifies a Molecular Subtype Predictive of Sorafenib Response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDifferential expression analysis confirmed that all eight genes were significantly upregulated in the non-responder group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To evaluate their predictive potential, we constructed receiver operating characteristic (ROC) curves. CSNK1E and BRD9 showed the highest predictive accuracy for sorafenib response, with area under the curve (AUC) values of 0.937 and 0.934, respectively; the remaining six genes also showed moderate predictive power (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). PCA based on the expression of these eight genes clearly separated responders from non-responders into two distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We subsequently performed consensus clustering to classify patients into molecular subtypes. Two clusters were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), and PCA confirmed transcriptomic divergence between the two subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Notably, more than 90% of patients in Cluster A were responders, while only 4% of patients in Cluster B responded to sorafenib (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Kaplan\u0026ndash;Meier analysis revealed significantly prolonged survival in Cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These findings suggest that the expression profile of these eight genes may serve as a robust biomarker panel for predicting therapeutic response to sorafenib.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrognostic Value of CSNK1E in Hepatocellular Carcinoma\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the eight identified genes, CSNK1E exhibited the highest LASSO coefficient weight, prompting further investigation. In both the GSE14520 and CHCC cohorts, CSNK1E was significantly overexpressed in HCC tissues compared to adjacent non-tumorous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B). Kaplan\u0026ndash;Meier analysis revealed that lower CSNK1E expression correlated with significantly improved overall survival across the TCGA, GSE14520, and CHCC datasets. Multivariate Cox regression confirmed CSNK1E as an independent prognostic factor, even after adjusting for age, sex, tumor size, and clinical stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;G). Stratifying patients based on median CSNK1E expression revealed that high-expression patients had a higher frequency of TP53 and MUC16 mutations, potentially contributing to its oncogenic role (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). Moreover, high CSNK1E expression was associated with increased genomic instability in terms of CNV (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Enrichment Analysis of CSNK1E\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe applied the ESTIMATE algorithm to evaluate immune and stromal infiltration in HCC. CSNK1E expression showed no significant correlation with immune scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and only a weak correlation with stromal scores, despite statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), suggesting a limited role in the tumor immune microenvironment. Gene set enrichment analysis (GSEA) indicated that high CSNK1E expression was significantly associated with the activation of extracellular matrix (ECM) remodeling, cell cycle progression, DNA replication, and WNT signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). KEGG pathway analysis yielded consistent results, reinforcing the involvement of CSNK1E in these oncogenic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Notably, CSNK1E expression negatively correlated with several metabolic pathways. Hallmark GSEA further supported the association between high CSNK1E expression and tumor-promoting pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Given prior reports linking WNT activation to resistance against anti-angiogenic therapies, we explored the relationship between CSNK1E and WNT pathway activity using TCGA and CHCC data. Both datasets confirmed that high CSNK1E expression is significantly associated with WNT pathway activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF\u0026ndash;G).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCSNK1E Promotes HCC Cell Proliferation, Migration, and Sorafenib Resistance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQuantitative PCR analysis showed that CSNK1E was most highly expressed in HCCLM3 cells and lowest in Huh7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Thus, we performed CSNK1E knockdown in HCCLM3 and overexpression in Huh7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Western blotting confirmed effective knockdown and overexpression, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Colony formation assays demonstrated that CSNK1E knockdown significantly inhibited, whereas overexpression promoted, HCC cell clonogenicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u0026ndash;G). Scratch assays further revealed that CSNK1E enhances HCC cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH\u0026ndash;J). In vivo, CSNK1E knockdown markedly suppressed tumor growth in a nude mouse xenograft model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK\u0026ndash;L). To assess its role in sorafenib resistance, we conducted drug sensitivity assays. CSNK1E knockdown reduced the IC50 of sorafenib in HCCLM3 cells from 6.391 \u0026micro;M to 4.644 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eM), whereas its overexpression in Huh7 cells increased the IC50 from 3.632 \u0026micro;M to 5.451 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eN). These results confirm a critical role for CSNK1E in mediating resistance to sorafenib in HCC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified and validated an eight-gene signature that is significantly associated with sorafenib resistance in HCC, with particular emphasis on the role of CSNK1E as a key modulator of therapeutic response. Through an integrative approach combining transcriptomic analysis, protein-protein interaction networks, clinical correlation, and functional validation in vitro and in vivo, we not only revealed the prognostic and predictive utility of this gene set but also provided mechanistic insights into how CSNK1E contributes to drug resistance and HCC progression.\u003c/p\u003e\u003cp\u003eOur analysis first established that the eight genes including ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, THBS3, and ZRANB2 were upregulated in non-responders to sorafenib, and that their expression levels were higher in HCC tumors compared to adjacent non-tumor tissues. Among these, CSNK1E emerged with the strongest prognostic relevance and functional effect, highlighting it as a potential therapeutic target. These findings are consistent with and extend previous reports suggesting that WNT/β-catenin signaling, in which CSNK1E participates, plays a central role in the resistance of HCC to anti-angiogenic and multikinase therapies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our study further supports this mechanistic link by demonstrating that high CSNK1E expression is positively correlated with WNT signaling activity, both at the transcriptomic and pathway enrichment levels.\u003c/p\u003e\u003cp\u003eCSNK1E, a member of the casein kinase 1 family, has been traditionally known for its role in circadian rhythm regulation and WNT signaling. However, emerging evidence has implicated this kinase in oncogenic processes such as cell cycle progression, DNA repair, and tumor immune evasion [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In the context of HCC, our data show that CSNK1E is overexpressed in tumors with high genomic instability and frequent TP53 mutations, supporting a model in which CSNK1E upregulation is part of a broader oncogenic phenotype associated with sorafenib resistance. Of particular importance, patients with high CSNK1E expression displayed significantly shorter overall survival in three independent HCC cohorts, and multivariate analysis confirmed that CSNK1E is an independent prognostic factor. These findings not only validate CSNK1E clinical relevance but also reinforce its potential utility as a biomarker to stratify patients who are less likely to benefit from sorafenib monotherapy. Functional assays further corroborated the pro-tumorigenic role of CSNK1E. Genetic perturbation experiments demonstrated that CSNK1E knockdown inhibited colony formation and migration in HCC cell lines, while its overexpression enhanced these malignant traits. These effects were recapitulated in vivo using a xenograft model, in which silencing CSNK1E markedly reduced tumor growth. Importantly, modulation of CSNK1E expression also influenced the sensitivity of HCC cells to sorafenib: knockdown sensitized cells to the drug, whereas overexpression conferred resistance. These observations suggest that CSNK1E actively contributes to drug tolerance mechanisms, possibly by maintaining proliferative and migratory signaling programs even in the presence of kinase inhibition. Mechanistically, GSEA linked high CSNK1E expression with several oncogenic processes, including extracellular matrix remodeling, DNA replication, cell cycle progression, and WNT pathway activation. These biological functions are known contributors to tumor aggressiveness and therapy resistance. Interestingly, CSNK1E expression showed a negative correlation with metabolic pathways, which may reflect a metabolic shift toward a proliferative state that supports tumor progression under treatment stress. This aligns with recent studies indicating that metabolic rewiring is a hallmark of drug-resistant HCC and could represent a secondary vulnerability in CSNK1E-high tumors [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, in triple-negative breast cancer, CSNK1E promotes tumorigenesis via WNT and MYC signaling, and its inhibition sensitizes tumors to chemotherapy and targeted therapies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, in glioblastoma, CSNK1E regulates cell survival and invasiveness through interaction with β-catenin and AKT [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These reports, combined with our current findings in HCC, suggest that CSNK1E may serve as a pan-cancer therapeutic target, particularly in tumors characterized by kinase inhibitor resistance and WNT pathway activation.\u003c/p\u003e\u003cp\u003eIn this study, the eight-gene signature derived from our analysis holds promise as a composite biomarker panel to predict sorafenib efficacy. Our consensus clustering analysis revealed that this gene set could stratify patients into distinct molecular subtypes with differential response rates and survival outcomes. More than 90% of patients in the CSNK1E-low cluster were responders, whereas only 4% of patients in the CSNK1E-high cluster responded to therapy. This striking contrast highlights the clinical utility of gene expression\u0026ndash;based classifiers in guiding treatment decisions and supports the development of CSNK1E as part of a molecular diagnostic tool. It is worth noting that among the eight genes, several have been implicated in cancer biology beyond HCC. BRD9, a member of the SWI/SNF chromatin remodeling complex, has been shown to promote tumor growth and therapeutic resistance in colorectal and lung cancers, where its inhibition sensitizes tumors to checkpoint blockade. ALDOA, a glycolytic enzyme, is upregulated in various malignancies and associated with aerobic glycolysis and epithelial-mesenchymal transition (EMT)\u0026mdash;both of which are hallmarks of drug resistance. ZRANB2, a zinc finger protein involved in RNA splicing, has been identified as a modulator of alternative splicing programs that affect apoptosis and drug sensitivity in breast cancer. Our findings thus underscore the broader oncogenic potential of this gene set and open avenues for cross-cancer comparative studies to explore conserved resistance mechanisms.\u003c/p\u003e\u003cp\u003eDespite the promising findings, our study has several limitations. First, while the bioinformatic analyses were rigorously performed and validated in multiple datasets, prospective clinical validation of the eight-gene signature is necessary to assess its true predictive value in a real-world setting. Second, the mechanistic role of CSNK1E in sorafenib resistance warrants further exploration, particularly its downstream effectors and potential crosstalk with other signaling pathways such as PI3K/AKT and MAPK. Given the complexity of HCC biology, combinatorial therapeutic approaches targeting CSNK1E and other co-activated pathways may be required to overcome resistance. Third, our study focused primarily on tumor-intrinsic mechanisms. Although CSNK1E expression did not show a strong correlation with immune infiltration in the ESTIMATE algorithm, more comprehensive immune profiling such as spatial transcriptomics or single-cell RNA-seq may reveal microenvironmental interactions that influence sorafenib sensitivity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our comprehensive analysis identifies CSNK1E as a central player in sorafenib resistance and HCC progression. By integrating transcriptomic profiling, functional validation, and clinical correlation, we demonstrate the prognostic and predictive relevance of CSNK1E and propose a robust eight-gene signature that stratifies patients according to therapeutic response. These findings provide a strong rationale for targeting CSNK1E as a strategy to overcome drug resistance and improve outcomes in HCC. Further translational and clinical studies are warranted to validate these findings and bring them closer to therapeutic application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e\u003cp\u003e The experiment protocol was approved by the Experimental Animal Care and Use Committee of Nantong University.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by the Natural Science Foundation of Nantong (JC2024076).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX.G. and W.L. integrated and analyzed the data, and wrote the main manuscript text. W.S. participated in parts of molecular experiments and data analysis. Y.D. and S.Z. provided the initial idea of this study, designed this work, edited and revised the paper. All authors reviewed and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eWe thank the contributors of the TCGA and GEO databases for providing publicly available data. We also acknowledge the support from colleagues in our laboratory and the technical assistance from the bioinformatics core facility.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlawyia B, Constantinou C. Hepatocellular Carcinoma: a Narrative Review on Current Knowledge and Future Prospects. Curr Treat Options Oncol. 2023;24(7):711\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartke J, Johnson M, Ghabril M. The diagnosis and treatment of hepatocellular carcinoma. Semin Diagn Pathol. 2017;34(2):153\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalle PR, Dufour JF, Peck-Radosavljevic M, Trojan J, Vogel A. Systemic therapy of advanced hepatocellular carcinoma. Future Oncol. 2021;17(10):1237\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBejjani A, Finn RS. Evolution of Systemic Therapy in Advanced Hepatocellular Carcinoma. Surg Oncol Clin N Am. 2024;33(1):73\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLlovet JM, De Baere T, Kulik L, Haber PK, Greten TF, Meyer T, Lencioni R. Locoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2021;18(5):293\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Deng B. Hepatocellular carcinoma: molecular mechanism, targeted therapy, and biomarkers. Cancer Metastasis Rev. 2023;42(3):629\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang W, Chen Z, Zhang W, Cheng Y, Zhang B, Wu F, Wang Q, Wang S, Rong D, Reiter FP, et al. The mechanisms of sorafenib resistance in hepatocellular carcinoma: theoretical basis and therapeutic aspects. Signal Transduct Target Ther. 2020;5(1):87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia S, Pan Y, Liang Y, Xu J, Cai X. The microenvironmental and metabolic aspects of sorafenib resistance in hepatocellular carcinoma. EBioMedicine. 2020;51:102610.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Yang W, Zheng Y, Dai W, Ji J, Wu L, Cheng Z, Zhang J, Li J, Xu X, et al. Targeting fatty acid synthase modulates sensitivity of hepatocellular carcinoma to sorafenib via ferroptosis. J Exp Clin Cancer Res. 2023;42(1):6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePearson H, Marshall LV, Carceller F. Sorafenib in pediatric hepatocellular carcinoma from a clinician perspective. Pediatr Hematol Oncol. 2020;37(5):412\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan YT, Zhang C, Wu J, Lu P, Xu L, Yuan H, Feng Y, Chen ZS, Wang N. Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma. Mol Cancer. 2024;23(1):189.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChidambaranathan-Reghupaty S, Fisher PB, Sarkar D. Hepatocellular carcinoma (HCC): Epidemiology, etiology and molecular classification. Adv Cancer Res. 2021;149:1\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang X, Yang C, Zhang S, Geng H, Zhu AX, Bernards R, Qin W, Fan J, Wang C, Gao Q. Precision treatment in advanced hepatocellular carcinoma. Cancer Cell. 2024;42(2):180\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalderaro J, Ziol M, Paradis V, Zucman-Rossi J. Molecular and histological correlations in liver cancer. J Hepatol. 2019;71(3):616\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo DZ, Zhang X, Zhang SQ, Zhang SY, Zhang XY, Yan JY, Dong SY, Zhu K, Yang XR, Fan J, et al. Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts. Mol Cancer. 2024;23(1):157.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChi SY, Hsu YC, Tsai CH, Huang SY, Chang SC, Cheng SP. Identifying CSNK1E as a therapeutic target in thyroid cancer among the core circadian clock genes. Histochem Cell Biol. 2025;163(1):30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTiong KL, Chang KC, Yeh KT, Liu TY, Wu JH, Hsieh PH, Lin SH, Lai WY, Hsu YC, Chen JY, et al. CSNK1E/CTNNB1 are synthetic lethal to TP53 in colorectal cancer and are markers for prognosis. Neoplasia. 2014;16(5):441\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Wang X, Yang X, Liu F, Li J, Li W, Bai Z, Wang H, Mao J, Li T, et al. Comprehensive kinomic study via a chemical proteomic approach reveals kinome reprogramming in hepatocellular carcinoma tissues. Proteomics. 2022;22(4):e2100141.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen-Sharir Y, McFarland JM, Abdusamad M, Marquis C, Bernhard SV, Kazachkova M, Tang H, Ippolito MR, Laue K, Zerbib J, et al. Aneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition. Nature. 2021;590(7846):486\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartincorena I, Raine KM, Gerstung M, Dawson KJ, Haase K, Van Loo P, Davies H, Stratton MR, Campbell PJ. Universal Patterns of Selection in Cancer and Somatic Tissues. Cell. 2017;171(5):1029\u0026ndash;e10411021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlivier M, Taniere P. Somatic mutations in cancer prognosis and prediction: lessons from TP53 and EGFR genes. Curr Opin Oncol. 2011;23(1):88\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePinyol R, Montal R, Bassaganyas L, Sia D, Takayama T, Chau GY, Mazzaferro V, Roayaie S, Lee HC, Kokudo N, et al. Molecular predictors of prevention of recurrence in HCC with sorafenib as adjuvant treatment and prognostic factors in the phase 3 STORM trial. Gut. 2019;68(6):1065\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W. Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu S, Wu Q, Zhang B, Wei H, Li B, Shi W, Fang M, Zhu S, Wang L, Lang Zhou Y, et al. Autophagy-related gene expression classification defines three molecular subtypes with distinct clinical and microenvironment cell infiltration characteristics in colon cancer. Int Immunopharmacol. 2020;87:106757.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Q, Qiao W, Zhang H, Liu B, Li J, Zang C, Mei T, Zheng J, Zhang Y. Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma. Front Immunol. 2022;13:1019638.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang L, Zhang X, Guan M, Zeng J, Yu F, Lai F. Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response. Respir Res. 2024;25(1):206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Gao Z, Mohd-Ibrahim I, Yang H, Wu L, Fu Y, Deng Y. Pan-cancer analyses of bromodomain containing 9 as a novel therapeutic target reveals its diagnostic, prognostic potential and biological mechanism in human tumours. Clin Transl Med. 2024;14(2):e1543.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang WS, Stockwell BR. Inhibition of casein kinase 1-epsilon induces cancer-cell-selective, PERIOD2-dependent growth arrest. Genome Biol. 2008;9(6):R92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHong W, Wang X, Huang X, Chen P, Liu Y, Zheng Z, You X, Chen Y, Xie Z, Zhan G, et al. CSNK1E is involved in TGF-beta1 induced epithelial mesenchymal transformationas and related to melanoma immune heterogeneity. Front Pharmacol. 2024;15:1501849.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToyoshima M, Howie HL, Imakura M, Walsh RM, Annis JE, Chang AN, Frazier J, Chau BN, Loboda A, Linsley PS, et al. Functional genomics identifies therapeutic targets for MYC-driven cancer. Proc Natl Acad Sci U S A. 2012;109(24):9545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin YC, Sun DP, Hsieh TH, Chen CH. Targeting CK1delta and CK1epsilon as a New Therapeutic Approach for Clear Cell Renal Cell Carcinoma. Pharmacology. 2024;109(6):330\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim SY, Dunn IF, Firestein R, Gupta P, Wardwell L, Repich K, Schinzel AC, Wittner B, Silver SJ, Root DE, et al. CK1epsilon is required for breast cancers dependent on beta-catenin activity. PLoS ONE. 2010;5(2):e8979.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarghese RT, Young S, Pham L, Liang Y, Pridham KJ, Guo S, Murphy S, Kelly DF, Sheng Z. Casein Kinase 1 Epsilon Regulates Glioblastoma Cell Survival. Sci Rep. 2018;8(1):13621.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Sorafenib, Transcriptome analysis, CSNK1E, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7049920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7049920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSorafenib is the standard first-line therapy for advanced hepatocellular carcinoma (HCC), yet its clinical efficacy is often limited by primary or acquired resistance. The molecular mechanisms underlying this resistance remain poorly understood. Casein kinase 1 epsilon (CSNK1E), a known regulator of WNT/β-catenin signaling, may play a role in drug resistance, but its function in sorafenib response in HCC is unclear.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed transcriptomic data from 67 HCC patients treated with sorafenib, including 21 responders and 46 non-responders. Differential expression and LASSO regression analyses, integrated with TCGA data and protein-protein interaction networks, were used to identify resistance-related genes. Functional validation of CSNK1E was conducted using in vitro assays and xenograft models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA panel of eight genes ALDOA, BRD9, CSNK1E, DRAP1, FLNC, RPL27, THBS3, and ZRANB2 was identified to be significantly upregulated in non-responders and associated with poor clinical outcomes. Among these, CSNK1E exhibited the highest prognostic relevance. Elevated CSNK1E expression was associated with unfavorable survival, increased TP53 mutation frequency, and copy number instability. Functional assays demonstrated that CSNK1E promoted HCC cell proliferation, migration, and resistance to sorafenib. Gene set enrichment analysis indicated that high CSNK1E expression was linked to activation of oncogenic pathways including WNT signaling and cell cycle progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCSNK1E is a key mediator of sorafenib resistance in HCC. Our eight-gene signature may serve as a predictive biomarker panel for sorafenib efficacy, offering a basis for personalized treatment strategies in HCC.\u003c/p\u003e","manuscriptTitle":"Transcriptomic Profiling Identifies CSNK1E as a Key Mediator of Sorafenib Resistance in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 08:46:41","doi":"10.21203/rs.3.rs-7049920/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-25T23:00:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65127071077795335689994014970170445638","date":"2026-01-12T17:04:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T11:23:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-07T08:59:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T08:58:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2025-07-05T01:35:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7183372d-417c-40aa-938b-eb5f900889ab","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T11:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 08:46:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7049920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7049920","identity":"rs-7049920","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}