CDT1-Mediated Regulation of DNA Damage Repair and Immune-Related Target Genes Underlies Hepatocellular Carcinoma Development

CDT1-Mediated Regulation of DNA Damage Repair and Immune-Related Target Genes Underlies Hepatocellular Carcinoma Development | 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 Article CDT1-Mediated Regulation of DNA Damage Repair and Immune-Related Target Genes Underlies Hepatocellular Carcinoma Development Yanxiong Wang, Li Mi, Hi Xu, Jinhao Liu, Bo Li, Zhixin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8024873/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Hepatocellular carcinoma (HCC) progression is tightly driven by genomic instability and dysregulated DNA damage repair (DDR) pathways. The replication licensing factor Chromatin Licensing and DNA Replication Factor 1 (CDT1) is frequently overexpressed in HCC tissues; however, the molecular mechanisms by which CDT1 coordinates DDR programs and modulates tumor immune microenvironment remain poorly elucidated. This study aimed to systematically characterize CDT1-centered regulatory networks in HCC, delineate their dual impacts on DDR machinery and immune-related signaling pathways, and validate the clinical translational value of CDT1 as a potential biomarker or therapeutic target. Methods A stable CDT1 knockdown (CDT1-KD) cell model was established in Huh7 HCC cells using lentiviral short hairpin RNA (shRNA), with silencing efficiency verified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot analysis. DNA double-strand breaks (DSBs) were quantified via γ-H2AX immunofluorescence staining. RNA sequencing (RNA-seq) was performed to profile CDT1-dependent transcriptional alterations, followed by differential expression analysis using DESeq2, Gene Ontology (GO) functional annotation, and Gene Set Enrichment Analysis (GSEA). Chromatin immunoprecipitation sequencing (ChIP-seq) was employed to map genome-wide CDT1 chromatin occupancy, which was further integrated with the transcriptome data to prioritize direct CDT1 target genes. The involvement of CDT1 in immune regulation was investigated by intersecting CDT1-bound and CDT1-regulated genes with manually curated immune-related gene sets. Finally, the clinical relevance of CDT1 expression in HCC was analyzed using public databases, including The Cancer Genome Atlas (TCGA), UALCAN, and the Human Protein Atlas (HPA). Results CDT1 silencing significantly increased the number of γ-H2AX foci in Huh7 cells, indicating exacerbated accumulation of DNA DSBs. RNA-seq analysis identified 4,581 CDT1-dependent differentially expressed genes (DEGs), including 2,739 upregulated and 1,842 downregulated transcripts. Functional enrichment analysis of downregulated DEGs revealed significant enrichment in biological processes related to DNA replication initiation, homologous recombination (HR), and broader DDR cascades—consistent with GSEA results showing suppressed activity of DDR-related gene sets in CDT1-KD cells. ChIP-seq data demonstrated that CDT1 primarily binds to intergenic and intronic regions of the genome; integration with RNA-seq data identified 328 CDT1-bound DEGs, which were enriched in signal transduction and cell proliferation pathways (e.g., HNRNPD, FBXL4, DROSHA). Intersection with immune gene catalogs yielded 307 immune-related CDT1 target genes; notably, CDT1 knockdown led to reduced expression of key immune regulators such as SEMA3D, CHUK, and PIK3R3, implicating CDT1 in the modulation of HCC immune signaling. Clinically, CDT1 expression was significantly upregulated in HCC tissues compared to adjacent non-tumor tissues, and high CDT1 expression was independently associated with poorer overall survival in HCC patients. Conclusions CDT1 functions as a pivotal oncogenic regulator in HCC by coupling maintenance of DDR proficiency with control of immune-related gene expression. Its dual roles in sustaining DNA replication/repair programs and shaping the tumor immune microenvironment highlight CDT1 as a promising prognostic biomarker and a potential therapeutic target for HCC. Targeting CDT1 may not only disrupt tumor genomic stability but also enhance responsiveness to immunotherapy, thereby addressing the challenge of tumor heterogeneity in HCC treatment. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Hepatocellular carcinoma CDT1 DNA damage repair Immune modulation Genomic instability Tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Hepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related mortality worldwide, with an estimated 906,000 new cases and 830,000 deaths in 2022 alone, underscoring the urgent need for deeper insights into its molecular drivers [ 1 ] .This aggressive malignancy, often arising from chronic liver diseases such as viral hepatitis and metabolic-associated steatotic liver disease (MASLD), is characterized by high genomic instability and dysregulated DNA repair mechanisms that fuel tumor progression and therapeutic resistance [ 2 , 3 ] . Amid these complexities, chromatin licensing and DNA replication factor 1 (CDT1), a key regulator of DNA replication initiation, emerges as a potential oncogenic player, yet its precise contributions to HCC pathogenesis remain incompletely understood. HCC, the predominant primary liver cancer (80–90% of cases), has rising incidence driven by risk factors such as hepatitis B/C, alcohol use, and non-alcoholic fatty liver disease [ 4 , 5 ] . Genomic instability, driven by aberrant DNA damage repair (DDR) pathways, plays a central role in HCC development, enabling accumulation of mutations that promote oncogenesis and evasion of cellular safeguards [ 6 , 7 ] . Key DDR pathways, such as homologous recombination (HR), base excision repair (BER), and nucleotide excision repair (NER), are frequently altered in HCC, leading to enhanced tumor proliferation and poor prognosis [ 8 ] . Recent studies have highlighted mutations in DDR genes as predictors of clinical outcomes, with high tumor mutation burden (TMB) correlating with advanced disease stages in HCC patients [ 9 , 10 ] . Furthermore, the tumor microenvironment in HCC involves intricate immune interactions, where dysregulated immune-related genes influence inflammation, immune evasion, and response to therapies like checkpoint inhibitors [ 11 , 12 ] . Immune signatures, including genes involved in cytokine signaling and antigen presentation, have been associated with HCC progression and survival, emphasizing the interplay between genomic alterations and immune modulation [ 13 , 14 ] . Central to maintaining genomic fidelity is the process of DNA replication licensing, which ensures that DNA is duplicated exactly once per cell cycle [ 15 ] . CDT1, a core component of the pre-replication complex, facilitates the loading of the minichromosome maintenance (MCM) helicase onto chromatin, a step essential for replication fork assembly [ 15 , 16 ] . Tight regulation of CDT1, through mechanisms like ubiquitination and degradation during S-phase, prevents re-replication and associated DNA damage [ 16 , 17 ] . Dysregulation of CDT1 has been implicated in various cancers, where its overexpression induces genomic instability, activates DNA damage checkpoints, and promotes tumorigenesis [ 18 , 19 ] . In HCC specifically, elevated CDT1 expression correlates with tumor aggressiveness and has been observed across histological subtypes and grades [ 20 , 21 ] . High CDT1 levels are linked to poorer overall survival, positioning it as a prognostic biomarker in HCC cohorts [ 22 ] . Moreover, CDT1's influence extends beyond replication, potentially modulating gene expression networks involved in repair and immune responses, as suggested by its interactions with chromatin remodeling factors [ 22 ] . Interdisciplinary approaches integrating genomics and immunology have revealed that such regulators contribute to HCC's heterogeneity, impacting therapeutic strategies and societal burdens through increased healthcare demands in high-prevalence regions [ 23 ] . Despite extensive studies on DDR pathways in HCC, gaps remain in elucidating how regulators like CDT1 integrate replication licensing with repair mechanisms [ 24 ] . Current research highlights DDR gene mutations as drivers of HCC heterogeneity, but often overlooks the upstream transcriptional control exerted by licensing factors, leading to incomplete models of genomic instability [ 25 , 26 ] . For instance, while CDT1's role in preventing re-replication is well-established, its direct binding to genomic loci and regulation of DDR gene expression in cancer contexts remain underexplored, particularly in HCC where chemotherapy resistance is a major challenge [ 18 , 27 ] . Limitations in existing studies include reliance on overexpression models that fail to capture endogenous regulatory dynamics, and a lack of integrated analyses combining transcriptomics with chromatin immunoprecipitation to map CDT1's targets [ 28 ] . Additionally, the intersection of CDT1 with immune-related pathways in HCC is poorly characterized, despite evidence that immune gene signatures predict prognosis and immunotherapy response [ 12 , 29 ] . These deficiencies hinder the development of targeted therapies, as unresolved issues in CDT1-mediated gene regulation may exacerbate tumor immune evasion and repair proficiency, widening disparities in patient outcomes across diverse populations. This study aims to elucidate CDT1's regulatory functions in HCC by examining its impact on DNA damage, gene expression, and chromatin binding in Huh7 cells. Specifically, we hypothesize that CDT1 promotes HCC development by directly binding to and modulating the expression of genes involved in DNA damage repair and immune responses, thereby maintaining genomic stability and facilitating tumor progression.The objectives of this study are as follows: first, establish a CDT1 knockdown model to assess its effects on DNA damage markers such as γ-H2AX; second, perform RNA sequencing to identify differentially expressed genes and enriched pathways related to replication and repair; third, conduct chromatin immunoprecipitation sequencing (ChIP-seq) to map CDT1 binding sites and overlap them with regulated genes; and finally, integrate these datasets to highlight CDT1's contributions to immune-related targets, with implications for prognosis and therapy.By addressing these aims, this work seeks to uncover actionable insights into HCC pathogenesis. It emphasizes practical applications, including novel biomarkers for early detection and strategies to enhance immunotherapy efficacy, ultimately advancing interdisciplinary efforts in oncology and public health. Materials and Methods Lentivirus information All lentiviral shRNA constructs were obtained from Gemma (Suzhou, China). The LV-3 (pGLVH1/GFP + Puro) lentiviral vector served as the backbone. A non-targeting control shRNA (shNegative; sense, 5′-TTCTCCGAACGTGTCACGT-3′) and an shRNA targeting CDT1 (5′-GGCCAGAAGATAAAGAAATCC-3′) were incorporated. Cell culture and transfections Huh7 cells (CL-0120; Procell Life Science & Technology Co., Ltd., China) were maintained at 37℃ in a humidified atmosphere containing 5% CO₂. Cultures were grown in DMEM complete medium (PM150210; Procell Life Science & Technology Co., Ltd., China) supplemented with 10% fetal bovine serum (FBS; 10091148; Gibco, China), 100 µg/mL streptomycin, and 100 U/mL penicillin (SV30010; Hyclone, USA). Cells were infected with lentivirus at MOI = 100. Stable cell lines were generated by selection with 0.3 µg/mL puromycin, after which cells were harvested for RT-qPCR and Western blotting analyses. Assessment of gene expression cDNA was synthesized using a reverse transcription kit (R323-01, Vazyme, China) on a T100 thermocycler (Bio-Rad, USA) with the following program: 42°C for 5 min, 37°C for 15 min, and 85°C for 5 s. Subsequently, qPCR was conducted on an ABI QuantStudio 5 instrument with an initial denaturation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 1 min. Each sample was analyzed in three technical replicates. Transcript levels were normalized to GAPDH (glyceraldehyde-3-phosphate dehydrogenase), and mRNA abundance was quantified using the 2^−ΔΔCT method (Livak and Schmittgen, 2001). Primer sequences for quantitative (q)PCR are provided in Additional file 1. Western Blot Huh7 cells were lysed on ice in RIPA buffer (PR20001, Proteintech, China) supplemented with a protease inhibitor cocktail (4693116001, Sigma, USA) for 30 minutes. Lysates were mixed with protein loading buffer (P1040, Solarbio, China), boiled for 10 minutes, separated by 10% SDS-PAGE, and transferred to PVDF membranes (0.45 µm; ISEQ00010, Millipore, USA). Membranes were blocked for 1 hour at room temperature and incubated overnight at 4°C with primary antibodies against CDT1 (rabbit, 1:1,000; 14382-1-AP, Proteintech, China) and GAPDH (mouse, 1:50,000; 60004-1-Ig, Proteintech, China). Proteins were detected using horseradish peroxidase-conjugated secondary antibodies (anti-rabbit, 1:10,000, SA00001-2, Proteintech, China; anti-mouse, 1:10,000, AS003, ABclonal, China) for 45 minutes at room temperature, with signals visualized by chemiluminescence using an enhanced ECL reagent (P0018FM, Beyotime, China). Immunofluorescence DNA damage was assessed using a γ-H2A histone family member X (γ-H2AX) immunofluorescence kit (C2035S; Beyotime Institute of Biotechnology, China) in Huh7 cells subjected to CDT1 knockdown or control treatment following 24 h of incubation at 37°C. Cells were fixed in 4% paraformaldehyde (Beijing Solarbio Science & Technology Co., Ltd.) for 10 min at room temperature and rinsed three times with PBS. After blocking with the kit-supplied blocking buffer (C2035S-3; Beyotime, China), samples were incubated overnight at 4°C with 50 µL of primary antibody against γ-H2AX (C2035S; Beyotime, China) and washed three times with PBST. Subsequently, cells were incubated for 1 h at room temperature with the secondary antibody (C2035S-5; Beyotime, China), followed by three TBST washes at room temperature. Nuclear counterstaining was performed with 1 mL DAPI per well for 5 min at room temperature, after which cells were rinsed with TBST. Fluorescent signals were acquired on a BX53 immunofluorescence microscope (Olympus Corporation). RNA extraction and sequencing Total RNA was treated with RQ1 DNase (Promega) to eliminate residual DNA. RNA concentration and purity were assessed by measuring the A260/A280 ratio at 260/280 nm on a SmartSpec Plus spectrophotometer (Bio-Rad), and integrity was verified by electrophoresis on 1.5% agarose gels. For each sample, 1 µg of DNase-treated total RNA was used to generate directional RNA-seq libraries with the VAHTS® Universal V8 RNA-seq Library Prep Kit for Illumina (N605). mRNA was enriched with VAHTS mRNA Capture Beads (Vazyme, N401) or, alternatively, ribosomal RNA was removed using the Ribo-off™ rRNA Depletion Kit (Vazyme, N406-01). The resulting RNA fragments were converted to double-stranded cDNA; after end repair and A-tailing, adapters from the VAHTS RNA Multiplex Oligos Set 1 for Illumina (N323) were ligated. Ligated products were subsequently PCR-amplified, purified, quantified, and stored at − 80°C pending sequencing. Because the second cDNA strand incorporates dUTP, it is not amplified during PCR, thereby preserving strand specificity. For high-throughput sequencing, libraries prepared according to the manufacturer’s instructions were loaded onto an Illumina NovaSeq 6000 platform to obtain 150-nt paired-end reads. RNA-Seq Raw Data Clean and Alignment Raw reads containing more than two ambiguous nucleotides (N) were discarded at the outset. Adapter sequences and low-quality bases were then trimmed from the remaining reads using the FASTX-Toolkit (v0.0.13), and any reads shorter than 16 nt after trimming were removed. The resulting clean reads were aligned to the GRCh38.p13 human reference genome with HISAT2, permitting up to four mismatches [ 30 ] . Only uniquely mapped reads were retained for gene-level read counting and for calculation of FPKM (fragments per kilobase of transcript per million fragments mapped) values [ 31 ] . Differentially Expressed Genes (DEG) analysis Differentially expressed genes (DEGs) were identified using the DESeq2 package from Bioconductor in R [ 32 ] . Statistical significance was defined as an adjusted P value (after multiple-testing correction) 2 or < 0.5. Alternative splicing analysis Alternative splicing events (ASEs) and regulated alternative splicing events (RASEs) between samples were delineated and quantified with the ABLas pipeline as previously described [ 33 , 34 ] . In ABLas, detection relied on splice-junction reads and encompassed ten ASE categories, including exon skipping (ES), alternative 5′splice site (A5SS), alternative 3′splice site (A3SS), mutually exclusive exons (MXE), mutually exclusive 5′UTRs (5pMXE), mutually exclusive 3′UTRs (3pMXE), cassette exon, A3SS&ES, and A5SS&ES. To identify CDT1-regulated ASEs, alterations in splicing ratios were assessed using Student’s t-test; events meeting a P-value threshold corresponding to a 5% false discovery rate were designated as CDT1-regulated ASEs. CDT1 ChIP-seq Chromatin immunoprecipitation (ChIP) assays were performed by Wuhan Ruixing Biotechnology Co., Ltd. ( http://www.rxbio.cc ). Approximately 6 × 10^7 cells were cross-linked in 1% formaldehyde for 10 min, and the reaction was quenched with 0.125 M glycine for 5 min. Cross-linked cells were lysed in lysis buffer (1× PBS, 0.1% SDS, 0.5% NP-40, 0.5% sodium deoxycholate) and sonicated (10 W; 10 s on/10 s off for 10 min) to generate DNA fragments of ~ 200–1000 bp. Ten percent of the IP volume was reserved as Input. For immunoprecipitation, protein–DNA complexes were captured with 50 µL ChIP-grade Protein A/G magnetic beads (26162, Invitrogen, USA) conjugated to 10 µg anti-CDT1 antibody (#8064S, CST, USA) or control IgG (AC005, ABclonal, China) for 2 h at 4°C. Beads were then washed twice sequentially with LOW buffer (1× PBS, 0.1% SDS, 0.5% sodium deoxycholate, 0.5% NP-40), HIGH buffer (5× PBS, 0.1% SDS, 0.5% sodium deoxycholate, 0.5% NP-40), LiCl buffer (100 mM LiCl, 100 mM Tris-HCl, pH 7.4, 0.5% sodium deoxycholate, 0.5% NP-40), and TE buffer (10 mM Tris-HCl, pH 8.0, 0.1 mM EDTA). DNA–protein complexes were eluted with elution buffer (100 mM NaHCO₃, 1% SDS) at 65°C and 1,000 rpm for 1 h; one-fifth of the eluate was used for WB analysis. Cross-links in Input and IP samples were reversed by overnight incubation at 65°C in the presence of NaCl. Following sequential RNase A (EN0531, Thermo Scientific, USA) and proteinase K (B600169-0002, Sangon Biotech, China) treatments, DNA was purified using phenol:chloroform:isoamyl alcohol extraction (pH > 7.8) (p1011, Solarbio, China). Libraries were prepared with the VAHTS Universal DNA Library Prep Kit for Illumina V3 (ND607, Vazyme, China) according to the manufacturer’s instructions; PCR products of 200–500 bp were enriched, quantified, and sequenced on a NovaSeq 6000 (Illumina, USA) using the PE150 mode. ChIP-seq data Analysis Reads were aligned to the GRCh38 reference genome with Bowtie2 [ 35 ] , and only uniquely mapped reads were retained for downstream analyses. CDT1-binding peaks were identified using Model-based Analysis for ChIP-seq (MACS, version 1.4) [ 36 ] with input (non-immunoprecipitated) samples serving as background. Genomic feature assignment and visualization—including peak distributions relative to TSS and global binding profiles—were carried out with DeepTools [ 37 ] . Enriched sequence motifs within peaks were interrogated using HOMER (Hypergeometric Optimization of Motif EnRichment) [ 38 ] . Functional enrichment analysis Functional categories of genes associated with the identified peaks (target genes) were determined by annotating Gene Ontology (GO) terms and KEGG pathways using the KOBAS 2.0 server [ 39 ] . Enrichment of each term was assessed through a hypergeometric test, with false discovery rate (FDR) control applied using the Benjamini-Hochberg procedure. GSEA analysis Gene Set Enrichment Analysis (GSEA) is an analytical approach used to assess genome-wide expression profile data, enabling the identification of functional enrichment by comparing gene expression with predefined gene sets. A gene set consists of genes that share common features such as localization, pathways, or functions. GSEA was performed using the clusterProfiler package (version 4.6.2) [ 40 ] . The fold change in gene expression between the Mets and Primary groups was calculated, and a gene list was created based on the magnitude of |log2FC|. Subsequently, GSEA-based enrichment analysis of Gene Ontology (GO) biological processes was carried out. Statistical analysis All plots, including pattern diagrams and stacked bar charts, were generated in R (v 4.2.3) using RStudio. Data are expressed as means ± standard error of the mean (SEM). Statistical comparisons between two groups were performed using Student’s t-test. Results CDT1 knockdown promotes DNA damage in Huh7 cells To elucidate the role of CDT1 in hepatocellular carcinoma, lentiviral shRNA was employed to establish a stable CDT1 knockdown model in Huh7 cells. Quantitative RT-PCR analysis demonstrated a significant reduction in CDT1 mRNA levels in shCDT1 cells by approximately 90% compared to negative control (NC) cells (p < 0.001; Fig. 1 A). Western blot analysis confirmed a significant reduction in CDT1 protein expression in shCDT1 cells, with stable GAPDH levels verifying the specificity and efficiency of the knockdown (Fig. 1 B). The impact of CDT1 knockdown on DNA damage was evaluated using γ-H2AX immunofluorescence staining, a marker of DNA double-strand breaks. Compared to NC cells, shCDT1 cells displayed a significant increase in γ-H2AX-positive foci, with DAPI staining employed to visualize nuclear morphology (Fig. 1 C). Quantitative analysis revealed that the proportion of γ-H2AX-positive cells in shCDT1 cells increased significantly to approximately 20%, compared to 5% in NC cells (p < 0.05; Fig. 1 C). These findings suggest that CDT1 knockdown exacerbates DNA damage in Huh7 cells, supporting a protective role for CDT1 in maintaining genomic integrity. To correlate cellular observations with clinical relevance, CDT1 expression was analyzed using the UALCAN database within the TCGA dataset. CDT1 expression was significantly upregulated in HCC tumor tissues compared to normal liver tissues (Fig. 2 A). This overexpression was evident across various histological subtypes (Fig. 2 B) and tumor grades (Fig. 2 C). Kaplan-Meier survival analysis demonstrated that elevated CDT1 expression was associated with poorer overall survival in HCC patients (Fig. 2 D). Immunohistochemical analysis from the Human Protein Atlas (HPA) confirmed elevated CDT1 protein levels in HCC tissues (Fig. 2 E). These findings collectively support the role of CDT1 as a potential oncogenic factor in HCC progression. CDT1 regulates gene expression in Huh7 cells To examine the effects of CDT1 knockdown on the Huh7 cell transcriptome, we conducted RNA sequencing on control (NC) and CDT1-knockdown (shCDT1) Huh7 cells. PCA showed distinct separation between the NC and shCDT1 groups (Fig. 3 A), indicating that CDT1 knockdown substantially altered global gene expression. Differential expression analysis identified 2,739 upregulated and 1,842 downregulated genes in shCDT1 cells (Fig. 3 B). These differentially expressed genes exhibited distinct expression patterns between the groups, as confirmed by heatmap visualization (Fig. 3 C). GO enrichment analysis showed that upregulated genes were primarily enriched in pathways involving extracellular matrix organization, negative regulation of angiogenesis, blood-brain barrier transport, collagen fibril organization, cholesterol homeostasis, negative regulation of axon extension in axon guidance, brown fat cell differentiation, glycolysis, angiogenesis, and sodium ion transmembrane transport (Fig. 3 D, red bars). These pathway alterations suggest that CDT1 knockdown profoundly affects the extracellular environment and energy metabolism. Conversely, downregulated genes were mainly enriched in DNA replication processes, such as DNA unwinding, homologous recombination repair of double-strand breaks, DNA replication initiation, break-induced replication repair, DNA repair, spliceosome-mediated mRNA splicing, rRNA processing, protein targeting to mitochondria, and replication fork processing (Fig. 3 D, blue bars). These gene changes indicate that CDT1 knockdown disrupts DNA replication and repair mechanisms, thereby compromising genomic stability. We also identified several differentially expressed genes (e.g., upregulated PGM1 and downregulated AUNIP, FEN1, RAD51AP1, SFPQ, MCM8, RAD51, and MCM7) linked to cancer progression, with their expression changes potentially playing key roles in hepatocellular carcinoma (HCC) development (Fig. 3 E). These findings underscore CDT1's essential role in maintaining DNA integrity and cellular homeostasis. GSEA using the clusterProfiler package confirmed these results, revealing significant suppression of DNA damage repair pathways (e.g., DNA repair, replication, and recombination) in shCDT1 cells (Fig. 4 ). This evidence demonstrates that CDT1 regulates DNA integrity and repair in HCC cells while also maintaining cellular homeostasis and gene expression programs. CDT1 selectively binds DNA in Huh7 cells The CDT1 protein is expressed in Huh7 cells and can be successfully immunoprecipitated using anti-CDT1 antibody, while GAPDH serves as the loading control, indicating that the IP experiment was successful (Fig. 5 A). ChIP-seq analysis of CDT1 shows that the binding peaks are primarily distributed in the intergenic and intronic regions of the genome (Fig. 5 B). Further displaying the motifs obtained from two experiments, the most significant motif in the IP1 vs Input1 group is "AATGAGG", and the most significant motif in the IP2 vs Input2 group is "TTAAGCAA" (Fig. 5 C). GO functional enrichment of genes associated with IP1 vs Input1 binding peaks is primarily enriched in pathways such as detection of chemical stimulus involved in sensory perception of smell, G-protein coupled receptor signaling pathway, muscle contraction, response to vitamin D, vascular endothelial growth factor receptor signaling pathway, olfaction, regulation of presynaptic membrane potential, smooth muscle contraction, negative regulation of endothelial cell apoptotic process, and negative regulation of Rho protein signal transduction (Fig. 5 D). GO functional enrichment of genes associated with IP2 vs Input2 binding peaks is primarily enriched in pathways such as detection of chemical stimulus involved in sensory perception of smell, G-protein coupled receptor signaling pathway, sensory perception of chemical stimulus, positive regulation of nitric oxide biosynthetic process, olfaction, stimulatory C-type lectin receptor signaling pathway, detection of chemical stimulus involved in sensory perception of bitter taste, biological process, neuron migration, and protein transport (Fig. 5 E). Overlap analysis of CDT1 binding peaks obtained between the two experimental replicates reveals 4437 overlapping binding peaks (Fig. 5 F). Among them, PRKD1, COX7A2, and FYB1 have been reported to be associated with cancer and warrant attention (Fig. 6 ). CDT1 binds and regulates DNA damage repair related genes expression in Huh7 cells Integrated analysis of differentially expressed genes obtained from CDT1 RNA-seq data and genes associated with peaks appearing in the two ChIP-seq experiments (union of the two IP experiment results) reveals a total of 328 overlapping genes (Fig. 7 A). The 328 overlapping genes are enriched in GO-BP pathways including: signal transduction, regulation of transcription by RNA polymerase II, positive regulation of cell proliferation, xenobiotic metabolic process, negative regulation of neuron apoptotic process, response to external stimulus, extracellular matrix organization, negative regulation of transcription by RNA polymerase II, regulation of cell migration, Golgi organization, ubiquitin-dependent protein catabolic process, negative regulation of gene expression, positive regulation of gene expression, positive regulation of transcription, DNA-templated, regulation of transcription, DNA-templated, regulation of gene expression, and others (Fig. 7 B). Among them, HNRNPD, FBXL4, and DROSHA have been reported to be associated with DNA damage repair and cancer, warranting attention (Fig. 7 C-F). These findings indicate that CDT1 influences the expression of genes related to DNA damage repair through binding, thereby promoting the occurrence and development of liver cancer. These research results indicate that CDT1 influences the expression of genes related to DNA damage repair through binding, thereby promoting the occurrence and development of liver cancer. Display of Results for CDT1-Regulated Differential Expression of Immune-Related Target Genes We investigated the regulatory role of CDT1 on immune-related target genes and their differential expression using chromatin immunoprecipitation sequencing data. A Venn diagram (Fig. 8 A) reveals a total of 307 overlapping genes between the peak genes identified in our dataset and immune-related genes, emphasizing the significant involvement of CDT1 in regulating immune gene expression. Further validation of specific genes (SEMA3D, CHUK, and PIK3R3) was performed using FPKM (Fragments Per Kilobase of exon per Million fragments mapped) expression data (Fig. 8 B). The results demonstrate that knockdown of CDT1 (shCDT1) significantly reduced the expression of these genes compared to the negative control (NC), suggesting that CDT1 is an important regulator of immune-related genes. Specifically, SEMA3D, CHUK, and PIK3R3 exhibited a marked decrease in FPKM values in the shCDT1 group, indicating a repression of their transcriptional activity. ChIP-seq tracks for PIK3R3 (Fig. 8 C), SEMA3D (Fig. 8 D), and CHUK (Fig. 8 E) further support these findings. For each gene, the analysis of peaks across different conditions (IP1_peak, IP2_peak, and input) shows that the binding of CDT1 at these genomic loci is notably reduced in the shCDT1 samples, aligning with the observed decrease in gene expression. The detailed ChIP-seq profiles for these genes illustrate the regions of CDT1 binding, which correlate with the reduced expression observed in the knockdown condition. Together, these data suggest that CDT1 plays a crucial role in modulating the expression of immune-related genes, and its knockdown leads to differential regulation of key immune pathways. Discussion In this study, we set out to clarify the role of CDT1 in hepatocellular carcinoma, particularly how this DNA replication licensing factor interfaces with DNA damage repair mechanisms and immune pathways. Our findings reveal that CDT1 is markedly upregulated in HCC tissues and cell lines, correlating with advanced tumor stage and poorer patient survival. Functionally, CDT1 knockdown in HCC cells led to an accumulation of DNA double-strand breaks (as evidenced by γ-H2AX foci) and broad transcriptomic changes affecting both DDR and immune-related genes. Moreover, ChIP-seq mapping of CDT1 chromatin occupancy uncovered its association with regulatory regions of key DDR and immune modulators (such as HNRNPD, DROSHA, FBXL4, SEMA3D, CHUK, and PIK3R3), suggesting a direct influence of CDT1 on genes that preserve genomic stability and shape antitumor immune responses. These data support a model in which CDT1 functions as a pivotal oncogenic driver coupling replication stress tolerance with immune pathway modulation in HCC. A central finding of our work is the link between CDT1 dysregulation and genomic instability in liver cancer cells. As a licensing factor, CDT1 ensures that DNA replication origins fire once per cell cycle; its overexpression or misregulation can induce re-replication and stall forks, triggering DNA damage checkpoints [ 41 – 44 ] . Consistent with this, we observed that silencing CDT1 causes a surge in DNA damage markers, implicating CDT1 in maintaining replication fidelity under stress. This result aligns with prior studies in other malignancies showing that aberrant CDT1 activity leads to DNA damage accumulation and chromosomal instability [ 41 , 45 , 46 ] . For example, Petropoulos et al.demonstrated that enforced CDT1 overexpression in colorectal cells drives overlicensing of origins and DNA breaks, promoting tumorigenesis [ 19 ] . Likewise, Kanellou et al.reported that cells rapidly degrade CDT1 in response to DNA damage, underscoring a tight coupling between the licensing machinery and the DDR [ 15 ] . Our observations build on these mechanisms: when CDT1 is depleted, HCC cells appear unable to properly execute DNA repair programs, as shown by downregulation of homologous recombination and checkpoint genes in our RNA-seq. This loss of DDR capacity likely exacerbates genomic instability, creating a permissive environment for malignant progression. Notably, we found CDT1 knockdown cells had reduced expression of canonical repair factors (e.g. RAD51 and FEN1 in our data), echoing the notion that replication licensing proteins support tumor cell survival by sustaining DNA repair proficiency. Together, these findings suggest a mechanistic model wherein CDT1 safeguards genome integrity in cancer cells by orchestrating a network of DDR genes; when CDT1 is absent, replication stress goes unmitigated, leading to DNA lesions that can trigger cell-cycle arrest or apoptosis if not tolerated. Intriguingly, our study also uncovers a link between CDT1 and the tumor immune microenvironment. We identified hundreds of genes with immune-related functions whose expression depended on CDT1, including cytokine regulators and antigen presentation molecules. This indicates that aberrant CDT1 activity might contribute to immune evasion in HCC by rewiring immune signaling pathways. One notable CDT1 target is CHUK (encoding IKKα), a kinase that activates NF-κB; NF-κB can be stimulated by DNA damage and drives expression of inflammatory and immune-suppressive genes [ 47 , 48 ] . In line with this, previous work has shown that DNA damage can elicit innate immune signaling: for instance, ATM-dependent activation of the cGAS/STING pathway and NF-κB occurs in response to nuclear DNA breaks [ 47 , 48 ] . Li and Chen described how unrepaired DNA can act as a danger signal to trigger inflammation via STING, linking genomic instability to immune activation [ 49 ] . Our data extend this concept by implying that CDT1 helps control such crosstalk. We speculate that when CDT1 is high, it supports efficient DNA repair and may limit the leakage of DNA fragments that activate innate immunity. Conversely, CDT1 loss leads to excess DNA damage, which could stimulate pathways like STING and alter cytokine profiles in the tumor. The downregulation of SEMA3D and PIK3R3 upon CDT1 knockdown is also noteworthy—these genes are implicated in modulating immune cell migration and PI3K signaling, respectively, both of which can influence immune surveillance in the tumor milieu. Semaphorin-3D, for example, has been reported to shape the infiltration of myeloid cells and T cells in certain cancers, thereby affecting anti-tumor immunity. Although CDT1 is primarily known as a replication factor, our ChIP-seq finding that it localizes to regulatory DNA near immune genes raises the possibility of a broader role in transcriptional control. One hypothesis is that CDT1, perhaps through interactions with chromatin modifiers or specific DNA elements at origins, might co-regulate genes that determine the immunogenicity of tumor cells. This novel insight into a replication factor influencing immune pathways distinguishes our study from prior investigations that focused only on cell-intrinsic cell cycle effects of CDT1. Our results are largely consistent with and build upon existing literature. Previous studies have documented CDT1 overexpression in numerous cancers and its association with aggressive behavior. In HCC, Karavias et al. first reported that high CDT1 levels correlate with poor patient prognosis [ 20 ] , a finding we confirm using both TCGA data and our clinical samples. A more recent comprehensive analysis by Cai et al. likewise identified CDT1 as part of a prognostic gene signature in HCC and noted its positive correlation with tumor proliferation markers and certain immune cell infiltrates [ 22 ] .Those studies, however, were primarily descriptive or correlative. Our work advances the field by providing mechanistic evidence for how CDT1 might drive HCC progression: through direct regulation of gene networks that maintain genome stability and modulate immune responses. In breast cancer, overexpression of CDT1 and its partner CDC6 has been linked to worse outcomes and genomic instability, mirroring the pattern seen in liver tumors [ 50 ] .Mughal et al. have highlighted that replication licensing proteins (including CDT1) are “saints and sinners” – essential for normal cell division yet, when deregulated, capable of instigating malignant transformation via replication stress [ 17 ] . Our data in HCC align with this duality: CDT1 appears to be a double-edged sword, necessary for replication but dangerous when in excess. Notably, we also observed a potential connection to the Th1/Th2 balance in the immune microenvironment. Prior research shows that many cancers, including HCC, exhibit a shift toward a Th2-dominant immune response that facilitates tumor immune escape [ 51 – 53 ] . In the study by Cai et al., CDT1 expression was positively associated with Th2 cell infiltration in HCC [ 22 ] . Our finding that CDT1 knockdown alters immune gene expression (e.g., downregulating cytokine signaling components) provides a possible explanation: by sustaining DNA integrity and controlling immune genes, high CDT1 might help tumors maintain an immunosuppressive environment. This integration of cell cycle and immune regulation is a novel aspect of CDT1’s role that, to our knowledge, has not been previously reported in HCC. We note, however, a contrasting point: while high CDT1 generally portends worse outcomes (likely due to more aggressive tumor growth), the concomitant increase in DNA damage from CDT1 loss could in theory make cells more visible to the immune system. This paradox will require further investigation, but it echoes the concept that inducing genomic instability in cancer cells can sometimes provoke anti-tumor immunity if the immune system is capable of recognizing the resulting neoantigens or damage-associated signals [ 47 , 54 ] . From a clinical and translational perspective, our work underscores the value of CDT1 as both a prognostic biomarker and a candidate therapeutic target in HCC. Consistent with previous bioinformatics analyses [ 22 ] , we found that elevated CDT1 expression identifies HCC patients with significantly worse survival, independent of other factors. This suggests that measuring CDT1 could help stratify patients and inform prognosis. More provocatively, targeting the replication licensing apparatus may offer a new treatment avenue. Cancer cells often operate under heightened replication stress and are remarkably dependent on licensing factors to complete DNA synthesis [ 47 , 55 – 57 ] . Inhibiting CDT1 could tip this balance, selectively killing rapidly dividing tumor cells by precipitating lethal re-replication or fork collapse, while sparing normal cells that have stricter licensing controls [ 42 – 46 , 58 – 67 ] . Indeed, a recent study identified a small-molecule inhibitor of the CDT1–Geminin complex that induced DNA damage and apoptosis preferentially in cancer cells, essentially by exploiting the addiction of cancer cells to CDT1-driven replication programs [ 68 ] .Our findings provide a strong rationale to pursue similar strategies in HCC. If CDT1 inhibitors were developed or repurposed, they could not only suppress tumor proliferation but also potentially augment immunotherapy. There is growing evidence that combining DDR-targeting agents with immune checkpoint blockade can produce synergistic effects, as DNA-damaging treatments can increase tumor immunogenicity and T-cell infiltration [ 47 ] . For example, Sen et al. showed that inhibiting ATR (a key replication stress kinase) in tumors activates STING-dependent interferon responses and enhances the efficacy of anti-PD-1 therapy, even in cancers without classical DDR deficiencies [ 69 ] . By analogy, a CDT1-targeted therapy that elevates intratumoral DNA damage might similarly trigger innate immune sensing and improve responses to immunotherapy in HCC. This dual impact—stalling the cell cycle and boosting immune visibility—positions CDT1 as an attractive therapeutic target at the nexus of genomic stability and immune surveillance. Of course, any such interventions must be approached cautiously given the essential role of CDT1 in normal cells; partial inhibition or transient targeting in combination with immunotherapy might be needed to achieve a therapeutic window. Despite the strengths of our study, including integrated genomics and chromatin profiling, we acknowledge several limitations. First, our experimental findings were derived from in vitro models (primarily the Huh7 HCC cell line). While Huh7 provided a convenient system to dissect molecular mechanisms, cell lines cannot fully recapitulate the complexity of HCC tumors in patients. Factors like 3D tumor architecture, liver microenvironment, and immune cell interactions are absent in monoculture. This limitation means that the downstream consequences of CDT1 knockdown on immune pathways were inferred from gene expression changes rather than directly observed in an immune-competent context. Future studies should employ in vivo models, such as HCC xenografts or genetically engineered mice, to confirm that altering CDT1 levels impacts tumor growth and immune evasion under physiological conditions. Second, our ChIP-seq data suggest CDT1 binds to certain gene loci, but the resolution and nature of this binding remain to be clarified. It is unusual for a licensing factor without known DNA-binding motifs to act as a classical transcription factor. One possibility is that CDT1 is recruited to sites of DNA damage or specific chromatin regions through interaction with other proteins. Indeed, prior reports hint that replication factors can localize to damage sites as part of emergency response networks [ 48 , 70 ] .However, the ChIP-seq peaks we identified at genes like CHUK or PIK3R3 could reflect indirect associations (e.g., CDT1 being present at a nearby replication origin). Additional experiments, such as chromatin interaction analyses or CUT&RUN assays, are needed to validate direct binding and rule out artifacts. Third, while we demonstrated correlations between CDT1 and immune gene signatures, we did not directly test functional immune outcomes. For example, does knocking down CDT1 in tumor cells alter their recognition or killing by cytotoxic T cells or natural killer cells? Answering such questions would require co-culture experiments with immune cells or syngeneic immunocompetent animal models. Finally, our study focused on a subset of identified targets and pathways; HCC is a genetically heterogeneous disease and CDT1 likely cooperates with other oncogenic alterations (like TP53 mutations or Myc activation) that were not the primary focus here. These caveats underscore that our proposed model of “CDT1-regulated DDR and immune modulation” should be further refined with broader validation across diverse HCC samples and in the context of combination treatments.In conclusion, our work provides new insights into how a core DNA replication factor, CDT1, drives HCC progression by simultaneously bolstering DNA repair processes and altering tumor immune pathways. We have shown that CDT1 overexpression is a hallmark of aggressive HCC and that its depletion unleashes replication stress, leading to DNA damage and changes in immune gene expression. These findings illuminate a previously underappreciated liaison between the cell cycle machinery and the immune microenvironment in liver cancer. From a scientific standpoint, the study contributes to a growing body of literature that connects genomic instability with immune regulation in cancer evolution [ 71 – 74 ] .From a clinical standpoint, it highlights CDT1 as a promising biomarker for patient stratification and a potential therapeutic vulnerability. Targeting CDT1 or its associated licensing proteins could not only curb HCC cell proliferation but also render tumors more susceptible to immune attack, thereby enhancing the efficacy of existing treatments such as immunotherapy. Looking ahead, we envision that these findings will spur further research into replication licensing inhibitors in HCC, investigations into the immunogenic effects of inducing replication stress, and ultimately the development of combined modality therapies that exploit the link between aberrant DNA replication and anti-tumor immunity. Such strategies hold promise to improve outcomes for HCC patients, a group that urgently needs new options in the face of this deadly disease. Conclusion This study elucidates the critical role of CDT1 in hepatocellular carcinoma pathogenesis, emphasizing its dual contributions to DNA damage repair and regulation of immune-related gene expression. Our findings demonstrate that CDT1 knockdown exacerbates DNA damage, as indicated by increased γ-H2AX foci, and impairs DNA damage response pathways, including homologous recombination and DNA repair. Additionally, CDT1 selectively binds to genomic regions associated with key cancer progression genes, including HNRNPD, FBXL4, and DROSHA, while regulating immune-related genes such as SEMA3D, CHUK, and PIK3R3. These findings establish CDT1 as a key oncogenic driver in HCC, modulating both genomic stability and immune evasion mechanisms. Given its regulatory role in these processes, CDT1 emerges as a potential prognostic biomarker and therapeutic target to enhance immunotherapy efficacy in HCC. Future research should investigate CDT1-targeted strategies to attenuate HCC progression and address challenges associated with tumor heterogeneity. Declarations Acknowledgements We sincerely thank the TCGA, UALCAN, and HPA databases for providing their valuable platforms and the researchers who contributed the datasets. The funder of this study had no involvement in the study design, data collection, data analysis, data interpretation, or manuscript preparation. Author contributions The study was conceived by ZW and YW. LM and HX planned and supervised data collection. BLand JL performed the data analyses. YW drafted the initial manuscript. ZW contributed to manuscript writing and revision. All authors discussed the work, approved the final manuscript, and consented to publication. Funding The authors declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China (NSFC; No. 82160466) and the Graduate Research and Practice Innovation Project of Qinghai University (No. 2025-GPKY-8). Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study involved only cell-based (in vitro) experiments and did not include clinical research or animal studies. The data used were obtained from public databases, including TCGA, UALCAN, and HPA. Because the work relies entirely on publicly available, open-source datasets, no issues related to participant privacy or animal use arose and ethics approval was not required. The authors declare no competing interests. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References BRAY, F. & LAVERSANNE, M. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. Cancer J. Clin. 74 (3), 229–263. http://dx.doi.org/10.3322/caac.21834 (2024). HWANG S Y, DANPANICHKUL, P. Hepatocellular carcinoma: updates on epidemiology, surveillance, diagnosis and treatment [J]. Clin. Mol. Hepatol. 31 (Suppl), S228–s54. http://dx.doi.org/10.3350/cmh.2024.0824 (2025). DENG, H. et al. 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Immunother Cancer . 12 (4). http://dx.doi.org/10.1136/jitc-2023-007716 (2024). Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx SupplementaryInformationfile.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Jan, 2026 Reviews received at journal 24 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 30 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Editor invited by journal 12 Nov, 2025 Submission checks completed at journal 08 Nov, 2025 First submitted to journal 08 Nov, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8024873","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":553551075,"identity":"2f00eeab-2134-4155-b837-c5b2b12ef1f9","order_by":0,"name":"Yanxiong Wang","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanxiong","middleName":"","lastName":"Wang","suffix":""},{"id":553551076,"identity":"4660c159-a94a-4f4e-9649-ae8e3f220e15","order_by":1,"name":"Li Mi","email":"","orcid":"","institution":"Qinghai University Affiliated 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1","display":"","copyAsset":false,"role":"figure","size":829552,"visible":true,"origin":"","legend":"\u003cp\u003eCDT1 knockdown promotes DNA damage in Huh7 cells.\u003cstrong\u003e(A)\u003c/strong\u003eRT-qPCR showing expression of CDT1 mRNA in CDT1 knockdown Huh7 cells. ****P \u0026lt; 0.0001.\u003cstrong\u003e(B)\u003c/strong\u003eCDT1 protein detection by western blot in CDT1 knockdown Huh7 cells(cropped, full-length blot images in Supplementary Fig. S1A）.(C)DNA damage detection results of Huh7 after CDT1 knockdown. *P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/ea6fc6556e4489353005de3b.png"},{"id":97271484,"identity":"0893d656-99f1-4213-a992-bf001a2c8161","added_by":"auto","created_at":"2025-12-02 15:02:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":831754,"visible":true,"origin":"","legend":"\u003cp\u003eCDT1 is high expression in clinical patients of hepatocellular carcinoma.(A)The boxplot showing the CDT1 expression in liver cancer samples based on the UALCAN database.(B)The boxplot showing the CDT1 expression in different histological types in liver cancer tumors based on the UALCAN database.(C)The boxplot showing the CDT1 expression in graded samples of liver cancer tumors based on the UALCAN database.(D)The survival curve of CDT1 in liver cancer from the UALCAN database.(E)CDT1 Immunohistochemical demonstration from the HPA database.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/a8501fea5686ed9386e9c28c.png"},{"id":97367612,"identity":"b334273d-844d-4edc-8b95-8118ff49bd95","added_by":"auto","created_at":"2025-12-03 16:19:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":335517,"visible":true,"origin":"","legend":"\u003cp\u003eCDT1 regulates gene expression in Huh7 cells. \u003cstrong\u003e(A)\u003c/strong\u003ePrincipal component analysis (PCA) of samples after normalizing all genes expression levels. The ellipse for each group is the confidence ellipse.\u003cstrong\u003e(B)\u003c/strong\u003eVolcano plot showing all differentially expressed genes (DEGs) between shCDT1 and NC samples.\u003cstrong\u003e(C)\u003c/strong\u003eHierarchical clustering heat map showing expression levels of all DEGs. FPKM values are log2-transformed and then median-centred by each gene (color figure online).\u003cstrong\u003e(D)\u003c/strong\u003eThe bar plot showing the most enriched GO biological process results of the up-regulated (Red) and down-regulated (Blue) DEGs.\u003cstrong\u003e(E)\u003c/strong\u003eBar plot showing the expression pattern and statistical difference of DEGs from RNA sequencing. Error bars represent mean ± SEM. ***P-value \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/8e42d092f9b29fba5bc31c7f.png"},{"id":97367649,"identity":"39513378-0462-494e-94c7-21c57ba21af6","added_by":"auto","created_at":"2025-12-03 16:20:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261301,"visible":true,"origin":"","legend":"\u003cp\u003ePathways related to DNA damage repair are suppressed.GSEA-based GO-enrichment plots of representative gene sets from activated pathway: DNA damage-related pathways.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/f0bec49060c256eebf4a7116.png"},{"id":97271489,"identity":"3edcc202-377c-4895-8f22-77c4d15e899a","added_by":"auto","created_at":"2025-12-02 15:02:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":457568,"visible":true,"origin":"","legend":"\u003cp\u003eCDT1 selectively binds DNA in Huh7 cells. \u003cstrong\u003e(A)\u003c/strong\u003eWestern blot analysis of CDT1 immunoprecipitation using anti-CDT1 monoclonal antibody. Two replicates were performed(cropped, full-length blot images in Supplementary Fig. S1B）.\u003cstrong\u003e(B)\u003c/strong\u003ePie chart showing the genomic distribution of CDT1-bound peaks from the two biological replicates.\u003cstrong\u003e(C)\u003c/strong\u003eMotif analysis results showing the enriched motifs from CDT1-bound peaks from the two biological replicates. \u003cstrong\u003e(D)\u003c/strong\u003eScatter plot exhibiting the most enriched GO biological process results of the IP1 peak genes.\u003cstrong\u003e(E)\u003c/strong\u003eScatter plot exhibiting the most enriched GO biological process results of the IP2 peak genes.\u003cstrong\u003e(F)\u003c/strong\u003eVenn diagram showing the overlapped peaks between the two biological replicates.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/c9c77409264ebc04fabae59f.png"},{"id":97271488,"identity":"035efc40-1e03-4f86-9069-290b4bf0c31d","added_by":"auto","created_at":"2025-12-02 15:02:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":508869,"visible":true,"origin":"","legend":"\u003cp\u003eCDT1 binds PRKD1, COX7A2, and FYB1.\u003cstrong\u003e(A-C) \u003c/strong\u003eCDT1 binding peak genes of FYB1, COX7A2 and PRKD1. IGV-sashimi plot showing the peaks reads and binding sites across mRNA, the green and red panels represent the position of peaks. Reads distribution of bound gene is plotted in the up panel and the transcripts of each gene are shown below.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/42a6e539a2b22291da23b881.png"},{"id":97369225,"identity":"2300c0cb-84c1-4181-af5f-9e674cda862c","added_by":"auto","created_at":"2025-12-03 16:23:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":855344,"visible":true,"origin":"","legend":"\u003cp\u003eCDT1 binds and regulates DNA damage repair related genes expression in Huh7 cells. \u003cstrong\u003e(A)\u003c/strong\u003eVenn diagram showing the overlapped genes between peak genes and DEGs. \u003cstrong\u003e(B)\u003c/strong\u003eScatter plot exhibiting the most enriched GO biological process results of overlap genes. \u003cstrong\u003e(C)\u003c/strong\u003eBar plot showing the expression pattern and statistical difference of DEGs from RNA sequencing. Error bars represent mean ± SEM. ***P-value \u0026lt; 0.001. \u003cstrong\u003e(D)\u003c/strong\u003eCDT1 binding peak genes of FBXL4. IGV-sashimi plot showing the peaks reads and binding sites across mRNA, the green and red panels represent the position of peaks. Reads distribution of bound gene is plotted in the up panel and the transcripts of each gene are shown below. \u003cstrong\u003e(E)\u003c/strong\u003eCDT1 binding peak genes of DROSHA. IGV-sashimi plot showing the peaks reads and binding sites across mRNA, the green and red panels represent the position of peaks. Reads distribution of bound gene is plotted in the up panel and the transcripts of each gene are shown below. \u003cstrong\u003e(F)\u003c/strong\u003eCDT1 binding peak genes of WDR77. IGV-sashimi plot showing the peaks reads and binding sites across mRNA, the green and red panels represent the position of peaks. Reads distribution of bound gene is plotted in the up panel and the transcripts of each gene are shown below.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/2936fa6b5b97e785d1d3b160.png"},{"id":97271490,"identity":"87506a1b-ab9a-46fd-9b9b-f7d78c65c81b","added_by":"auto","created_at":"2025-12-02 15:02:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":403147,"visible":true,"origin":"","legend":"\u003cp\u003eCDT1 binds to and regulates immune-related genes expression in Huh7 cells. (A)Venn diagram showing the overlapped genes between overlapped genes from figure4A and immune genes. (B)Bar plot showing the expression pattern and statistical difference of DEGs from RNA sequencing. Error bars represent mean ± SEM. ***P-value \u0026lt; 0.001.(C-E) CDT1 binding peak genes of PIK3R3, SEMA3D and CHUK. IGV-sashimi plot showing the peaks reads and binding sites across mRNA, the green and red panels represent the position of peaks. Reads distribution of bound gene is plotted in the up panel and the transcripts of each gene are shown below.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/99cf516b037e18bac74197c9.png"},{"id":97372974,"identity":"515b8d89-a57d-49d5-b1ab-be2788cedbfc","added_by":"auto","created_at":"2025-12-03 16:33:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5078941,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/d80d510e-23d9-471b-9c8c-af42d3844111.pdf"},{"id":97369062,"identity":"3a7c6590-70c8-4934-8847-3f77227918c2","added_by":"auto","created_at":"2025-12-03 16:23:34","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9906,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/ccf446d26e85f3a28a776e3b.xlsx"},{"id":97271485,"identity":"a56e4f12-c7ee-4170-aeab-1c5dfb8330ad","added_by":"auto","created_at":"2025-12-02 15:02:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":138717,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8024873/v1/e2866142f632461293e686e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CDT1-Mediated Regulation of DNA Damage Repair and Immune-Related Target Genes Underlies Hepatocellular Carcinoma Development","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related mortality worldwide, with an estimated 906,000 new cases and 830,000 deaths in 2022 alone, underscoring the urgent need for deeper insights into its molecular drivers\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.This aggressive malignancy, often arising from chronic liver diseases such as viral hepatitis and metabolic-associated steatotic liver disease (MASLD), is characterized by high genomic instability and dysregulated DNA repair mechanisms that fuel tumor progression and therapeutic resistance\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Amid these complexities, chromatin licensing and DNA replication factor 1 (CDT1), a key regulator of DNA replication initiation, emerges as a potential oncogenic player, yet its precise contributions to HCC pathogenesis remain incompletely understood.\u003c/p\u003e\u003cp\u003eHCC, the predominant primary liver cancer (80\u0026ndash;90% of cases), has rising incidence driven by risk factors such as hepatitis B/C, alcohol use, and non-alcoholic fatty liver disease\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Genomic instability, driven by aberrant DNA damage repair (DDR) pathways, plays a central role in HCC development, enabling accumulation of mutations that promote oncogenesis and evasion of cellular safeguards\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Key DDR pathways, such as homologous recombination (HR), base excision repair (BER), and nucleotide excision repair (NER), are frequently altered in HCC, leading to enhanced tumor proliferation and poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Recent studies have highlighted mutations in DDR genes as predictors of clinical outcomes, with high tumor mutation burden (TMB) correlating with advanced disease stages in HCC patients\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the tumor microenvironment in HCC involves intricate immune interactions, where dysregulated immune-related genes influence inflammation, immune evasion, and response to therapies like checkpoint inhibitors\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Immune signatures, including genes involved in cytokine signaling and antigen presentation, have been associated with HCC progression and survival, emphasizing the interplay between genomic alterations and immune modulation\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Central to maintaining genomic fidelity is the process of DNA replication licensing, which ensures that DNA is duplicated exactly once per cell cycle\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. CDT1, a core component of the pre-replication complex, facilitates the loading of the minichromosome maintenance (MCM) helicase onto chromatin, a step essential for replication fork assembly\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Tight regulation of CDT1, through mechanisms like ubiquitination and degradation during S-phase, prevents re-replication and associated DNA damage\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Dysregulation of CDT1 has been implicated in various cancers, where its overexpression induces genomic instability, activates DNA damage checkpoints, and promotes tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In HCC specifically, elevated CDT1 expression correlates with tumor aggressiveness and has been observed across histological subtypes and grades\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. High CDT1 levels are linked to poorer overall survival, positioning it as a prognostic biomarker in HCC cohorts\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Moreover, CDT1's influence extends beyond replication, potentially modulating gene expression networks involved in repair and immune responses, as suggested by its interactions with chromatin remodeling factors\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Interdisciplinary approaches integrating genomics and immunology have revealed that such regulators contribute to HCC's heterogeneity, impacting therapeutic strategies and societal burdens through increased healthcare demands in high-prevalence regions\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite extensive studies on DDR pathways in HCC, gaps remain in elucidating how regulators like CDT1 integrate replication licensing with repair mechanisms\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Current research highlights DDR gene mutations as drivers of HCC heterogeneity, but often overlooks the upstream transcriptional control exerted by licensing factors, leading to incomplete models of genomic instability\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. For instance, while CDT1's role in preventing re-replication is well-established, its direct binding to genomic loci and regulation of DDR gene expression in cancer contexts remain underexplored, particularly in HCC where chemotherapy resistance is a major challenge\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Limitations in existing studies include reliance on overexpression models that fail to capture endogenous regulatory dynamics, and a lack of integrated analyses combining transcriptomics with chromatin immunoprecipitation to map CDT1's targets\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Additionally, the intersection of CDT1 with immune-related pathways in HCC is poorly characterized, despite evidence that immune gene signatures predict prognosis and immunotherapy response\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. These deficiencies hinder the development of targeted therapies, as unresolved issues in CDT1-mediated gene regulation may exacerbate tumor immune evasion and repair proficiency, widening disparities in patient outcomes across diverse populations.\u003c/p\u003e\u003cp\u003eThis study aims to elucidate CDT1's regulatory functions in HCC by examining its impact on DNA damage, gene expression, and chromatin binding in Huh7 cells. Specifically, we hypothesize that CDT1 promotes HCC development by directly binding to and modulating the expression of genes involved in DNA damage repair and immune responses, thereby maintaining genomic stability and facilitating tumor progression.The objectives of this study are as follows: first, establish a CDT1 knockdown model to assess its effects on DNA damage markers such as γ-H2AX; second, perform RNA sequencing to identify differentially expressed genes and enriched pathways related to replication and repair; third, conduct chromatin immunoprecipitation sequencing (ChIP-seq) to map CDT1 binding sites and overlap them with regulated genes; and finally, integrate these datasets to highlight CDT1's contributions to immune-related targets, with implications for prognosis and therapy.By addressing these aims, this work seeks to uncover actionable insights into HCC pathogenesis. It emphasizes practical applications, including novel biomarkers for early detection and strategies to enhance immunotherapy efficacy, ultimately advancing interdisciplinary efforts in oncology and public health.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLentivirus information\u003c/h2\u003e\u003cp\u003eAll lentiviral shRNA constructs were obtained from Gemma (Suzhou, China). The LV-3 (pGLVH1/GFP\u0026thinsp;+\u0026thinsp;Puro) lentiviral vector served as the backbone. A non-targeting control shRNA (shNegative; sense, 5\u0026prime;-TTCTCCGAACGTGTCACGT-3\u0026prime;) and an shRNA targeting CDT1 (5\u0026prime;-GGCCAGAAGATAAAGAAATCC-3\u0026prime;) were incorporated.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCell culture and transfections\u003c/h3\u003e\n\u003cp\u003eHuh7 cells (CL-0120; Procell Life Science \u0026amp; Technology Co., Ltd., China) were maintained at 37℃ in a humidified atmosphere containing 5% CO₂. Cultures were grown in DMEM complete medium (PM150210; Procell Life Science \u0026amp; Technology Co., Ltd., China) supplemented with 10% fetal bovine serum (FBS; 10091148; Gibco, China), 100 \u0026micro;g/mL streptomycin, and 100 U/mL penicillin (SV30010; Hyclone, USA). Cells were infected with lentivirus at MOI\u0026thinsp;=\u0026thinsp;100. Stable cell lines were generated by selection with 0.3 \u0026micro;g/mL puromycin, after which cells were harvested for RT-qPCR and Western blotting analyses.\u003c/p\u003e\n\u003ch3\u003eAssessment of gene expression\u003c/h3\u003e\n\u003cp\u003ecDNA was synthesized using a reverse transcription kit (R323-01, Vazyme, China) on a T100 thermocycler (Bio-Rad, USA) with the following program: 42\u0026deg;C for 5 min, 37\u0026deg;C for 15 min, and 85\u0026deg;C for 5 s. Subsequently, qPCR was conducted on an ABI QuantStudio 5 instrument with an initial denaturation at 95\u0026deg;C for 10 min, followed by 40 cycles of denaturation at 95\u0026deg;C for 15 s and annealing/extension at 60\u0026deg;C for 1 min. Each sample was analyzed in three technical replicates. Transcript levels were normalized to GAPDH (glyceraldehyde-3-phosphate dehydrogenase), and mRNA abundance was quantified using the 2^\u0026minus;ΔΔCT method (Livak and Schmittgen, 2001). Primer sequences for quantitative (q)PCR are provided in Additional file 1.\u003c/p\u003e\n\u003ch3\u003eWestern Blot\u003c/h3\u003e\n\u003cp\u003eHuh7 cells were lysed on ice in RIPA buffer (PR20001, Proteintech, China) supplemented with a protease inhibitor cocktail (4693116001, Sigma, USA) for 30 minutes. Lysates were mixed with protein loading buffer (P1040, Solarbio, China), boiled for 10 minutes, separated by 10% SDS-PAGE, and transferred to PVDF membranes (0.45 \u0026micro;m; ISEQ00010, Millipore, USA). Membranes were blocked for 1 hour at room temperature and incubated overnight at 4\u0026deg;C with primary antibodies against CDT1 (rabbit, 1:1,000; 14382-1-AP, Proteintech, China) and GAPDH (mouse, 1:50,000; 60004-1-Ig, Proteintech, China). Proteins were detected using horseradish peroxidase-conjugated secondary antibodies (anti-rabbit, 1:10,000, SA00001-2, Proteintech, China; anti-mouse, 1:10,000, AS003, ABclonal, China) for 45 minutes at room temperature, with signals visualized by chemiluminescence using an enhanced ECL reagent (P0018FM, Beyotime, China).\u003c/p\u003e\n\u003ch3\u003eImmunofluorescence\u003c/h3\u003e\n\u003cp\u003eDNA damage was assessed using a γ-H2A histone family member X (γ-H2AX) immunofluorescence kit (C2035S; Beyotime Institute of Biotechnology, China) in Huh7 cells subjected to CDT1 knockdown or control treatment following 24 h of incubation at 37\u0026deg;C. Cells were fixed in 4% paraformaldehyde (Beijing Solarbio Science \u0026amp; Technology Co., Ltd.) for 10 min at room temperature and rinsed three times with PBS. After blocking with the kit-supplied blocking buffer (C2035S-3; Beyotime, China), samples were incubated overnight at 4\u0026deg;C with 50 \u0026micro;L of primary antibody against γ-H2AX (C2035S; Beyotime, China) and washed three times with PBST. Subsequently, cells were incubated for 1 h at room temperature with the secondary antibody (C2035S-5; Beyotime, China), followed by three TBST washes at room temperature. Nuclear counterstaining was performed with 1 mL DAPI per well for 5 min at room temperature, after which cells were rinsed with TBST. Fluorescent signals were acquired on a BX53 immunofluorescence microscope (Olympus Corporation).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRNA extraction and sequencing\u003c/h2\u003e\u003cp\u003eTotal RNA was treated with RQ1 DNase (Promega) to eliminate residual DNA. RNA concentration and purity were assessed by measuring the A260/A280 ratio at 260/280 nm on a SmartSpec Plus spectrophotometer (Bio-Rad), and integrity was verified by electrophoresis on 1.5% agarose gels.\u003c/p\u003e\u003cp\u003eFor each sample, 1 \u0026micro;g of DNase-treated total RNA was used to generate directional RNA-seq libraries with the VAHTS\u0026reg; Universal V8 RNA-seq Library Prep Kit for Illumina (N605). mRNA was enriched with VAHTS mRNA Capture Beads (Vazyme, N401) or, alternatively, ribosomal RNA was removed using the Ribo-off\u0026trade; rRNA Depletion Kit (Vazyme, N406-01). The resulting RNA fragments were converted to double-stranded cDNA; after end repair and A-tailing, adapters from the VAHTS RNA Multiplex Oligos Set 1 for Illumina (N323) were ligated. Ligated products were subsequently PCR-amplified, purified, quantified, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C pending sequencing. Because the second cDNA strand incorporates dUTP, it is not amplified during PCR, thereby preserving strand specificity.\u003c/p\u003e\u003cp\u003eFor high-throughput sequencing, libraries prepared according to the manufacturer\u0026rsquo;s instructions were loaded onto an Illumina NovaSeq 6000 platform to obtain 150-nt paired-end reads.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRNA-Seq Raw Data Clean and Alignment\u003c/h3\u003e\n\u003cp\u003eRaw reads containing more than two ambiguous nucleotides (N) were discarded at the outset. Adapter sequences and low-quality bases were then trimmed from the remaining reads using the FASTX-Toolkit (v0.0.13), and any reads shorter than 16 nt after trimming were removed. The resulting clean reads were aligned to the GRCh38.p13 human reference genome with HISAT2, permitting up to four mismatches\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Only uniquely mapped reads were retained for gene-level read counting and for calculation of FPKM (fragments per kilobase of transcript per million fragments mapped) values\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eDifferentially Expressed Genes (DEG) analysis\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were identified using the DESeq2 package from Bioconductor in R\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Statistical significance was defined as an adjusted P value (after multiple-testing correction)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 together with a fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;0.5.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAlternative splicing analysis\u003c/h2\u003e\u003cp\u003eAlternative splicing events (ASEs) and regulated alternative splicing events (RASEs) between samples were delineated and quantified with the ABLas pipeline as previously described\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In ABLas, detection relied on splice-junction reads and encompassed ten ASE categories, including exon skipping (ES), alternative 5\u0026prime;splice site (A5SS), alternative 3\u0026prime;splice site (A3SS), mutually exclusive exons (MXE), mutually exclusive 5\u0026prime;UTRs (5pMXE), mutually exclusive 3\u0026prime;UTRs (3pMXE), cassette exon, A3SS\u0026amp;ES, and A5SS\u0026amp;ES.\u003c/p\u003e\u003cp\u003eTo identify CDT1-regulated ASEs, alterations in splicing ratios were assessed using Student\u0026rsquo;s t-test; events meeting a P-value threshold corresponding to a 5% false discovery rate were designated as CDT1-regulated ASEs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCDT1 ChIP-seq\u003c/h2\u003e\u003cp\u003eChromatin immunoprecipitation (ChIP) assays were performed by Wuhan Ruixing Biotechnology Co., Ltd. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rxbio.cc\u003c/span\u003e\u003cspan address=\"http://www.rxbio.cc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Approximately 6 \u0026times; 10^7 cells were cross-linked in 1% formaldehyde for 10 min, and the reaction was quenched with 0.125 M glycine for 5 min. Cross-linked cells were lysed in lysis buffer (1\u0026times; PBS, 0.1% SDS, 0.5% NP-40, 0.5% sodium deoxycholate) and sonicated (10 W; 10 s on/10 s off for 10 min) to generate DNA fragments of ~\u0026thinsp;200\u0026ndash;1000 bp. Ten percent of the IP volume was reserved as Input. For immunoprecipitation, protein\u0026ndash;DNA complexes were captured with 50 \u0026micro;L ChIP-grade Protein A/G magnetic beads (26162, Invitrogen, USA) conjugated to 10 \u0026micro;g anti-CDT1 antibody (#8064S, CST, USA) or control IgG (AC005, ABclonal, China) for 2 h at 4\u0026deg;C. Beads were then washed twice sequentially with LOW buffer (1\u0026times; PBS, 0.1% SDS, 0.5% sodium deoxycholate, 0.5% NP-40), HIGH buffer (5\u0026times; PBS, 0.1% SDS, 0.5% sodium deoxycholate, 0.5% NP-40), LiCl buffer (100 mM LiCl, 100 mM Tris-HCl, pH 7.4, 0.5% sodium deoxycholate, 0.5% NP-40), and TE buffer (10 mM Tris-HCl, pH 8.0, 0.1 mM EDTA). DNA\u0026ndash;protein complexes were eluted with elution buffer (100 mM NaHCO₃, 1% SDS) at 65\u0026deg;C and 1,000 rpm for 1 h; one-fifth of the eluate was used for WB analysis. Cross-links in Input and IP samples were reversed by overnight incubation at 65\u0026deg;C in the presence of NaCl. Following sequential RNase A (EN0531, Thermo Scientific, USA) and proteinase K (B600169-0002, Sangon Biotech, China) treatments, DNA was purified using phenol:chloroform:isoamyl alcohol extraction (pH\u0026thinsp;\u0026gt;\u0026thinsp;7.8) (p1011, Solarbio, China). Libraries were prepared with the VAHTS Universal DNA Library Prep Kit for Illumina V3 (ND607, Vazyme, China) according to the manufacturer\u0026rsquo;s instructions; PCR products of 200\u0026ndash;500 bp were enriched, quantified, and sequenced on a NovaSeq 6000 (Illumina, USA) using the PE150 mode.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eChIP-seq data Analysis\u003c/h2\u003e\u003cp\u003eReads were aligned to the GRCh38 reference genome with Bowtie2\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, and only uniquely mapped reads were retained for downstream analyses. CDT1-binding peaks were identified using Model-based Analysis for ChIP-seq (MACS, version 1.4) \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e with input (non-immunoprecipitated) samples serving as background. Genomic feature assignment and visualization\u0026mdash;including peak distributions relative to TSS and global binding profiles\u0026mdash;were carried out with DeepTools\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Enriched sequence motifs within peaks were interrogated using HOMER (Hypergeometric Optimization of Motif EnRichment)\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e\u003cp\u003eFunctional categories of genes associated with the identified peaks (target genes) were determined by annotating Gene Ontology (GO) terms and KEGG pathways using the KOBAS 2.0 server\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Enrichment of each term was assessed through a hypergeometric test, with false discovery rate (FDR) control applied using the Benjamini-Hochberg procedure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGSEA analysis\u003c/h2\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA) is an analytical approach used to assess genome-wide expression profile data, enabling the identification of functional enrichment by comparing gene expression with predefined gene sets. A gene set consists of genes that share common features such as localization, pathways, or functions. GSEA was performed using the clusterProfiler package (version 4.6.2)\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. The fold change in gene expression between the Mets and Primary groups was calculated, and a gene list was created based on the magnitude of |log2FC|. Subsequently, GSEA-based enrichment analysis of Gene Ontology (GO) biological processes was carried out.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll plots, including pattern diagrams and stacked bar charts, were generated in R (v 4.2.3) using RStudio. Data are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). Statistical comparisons between two groups were performed using Student\u0026rsquo;s t-test.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eCDT1 knockdown promotes DNA damage in Huh7 cells\u003c/h2\u003e\u003cp\u003eTo elucidate the role of CDT1 in hepatocellular carcinoma, lentiviral shRNA was employed to establish a stable CDT1 knockdown model in Huh7 cells. Quantitative RT-PCR analysis demonstrated a significant reduction in CDT1 mRNA levels in shCDT1 cells by approximately 90% compared to negative control (NC) cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Western blot analysis confirmed a significant reduction in CDT1 protein expression in shCDT1 cells, with stable GAPDH levels verifying the specificity and efficiency of the knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The impact of CDT1 knockdown on DNA damage was evaluated using γ-H2AX immunofluorescence staining, a marker of DNA double-strand breaks. Compared to NC cells, shCDT1 cells displayed a significant increase in γ-H2AX-positive foci, with DAPI staining employed to visualize nuclear morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Quantitative analysis revealed that the proportion of γ-H2AX-positive cells in shCDT1 cells increased significantly to approximately 20%, compared to 5% in NC cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These findings suggest that CDT1 knockdown exacerbates DNA damage in Huh7 cells, supporting a protective role for CDT1 in maintaining genomic integrity. To correlate cellular observations with clinical relevance, CDT1 expression was analyzed using the UALCAN database within the TCGA dataset. CDT1 expression was significantly upregulated in HCC tumor tissues compared to normal liver tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This overexpression was evident across various histological subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and tumor grades (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Kaplan-Meier survival analysis demonstrated that elevated CDT1 expression was associated with poorer overall survival in HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Immunohistochemical analysis from the Human Protein Atlas (HPA) confirmed elevated CDT1 protein levels in HCC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These findings collectively support the role of CDT1 as a potential oncogenic factor in HCC progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eCDT1 regulates gene expression in Huh7 cells\u003c/h2\u003e\u003cp\u003eTo examine the effects of CDT1 knockdown on the Huh7 cell transcriptome, we conducted RNA sequencing on control (NC) and CDT1-knockdown (shCDT1) Huh7 cells. PCA showed distinct separation between the NC and shCDT1 groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), indicating that CDT1 knockdown substantially altered global gene expression. Differential expression analysis identified 2,739 upregulated and 1,842 downregulated genes in shCDT1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These differentially expressed genes exhibited distinct expression patterns between the groups, as confirmed by heatmap visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). GO enrichment analysis showed that upregulated genes were primarily enriched in pathways involving extracellular matrix organization, negative regulation of angiogenesis, blood-brain barrier transport, collagen fibril organization, cholesterol homeostasis, negative regulation of axon extension in axon guidance, brown fat cell differentiation, glycolysis, angiogenesis, and sodium ion transmembrane transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, red bars). These pathway alterations suggest that CDT1 knockdown profoundly affects the extracellular environment and energy metabolism. Conversely, downregulated genes were mainly enriched in DNA replication processes, such as DNA unwinding, homologous recombination repair of double-strand breaks, DNA replication initiation, break-induced replication repair, DNA repair, spliceosome-mediated mRNA splicing, rRNA processing, protein targeting to mitochondria, and replication fork processing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, blue bars). These gene changes indicate that CDT1 knockdown disrupts DNA replication and repair mechanisms, thereby compromising genomic stability. We also identified several differentially expressed genes (e.g., upregulated PGM1 and downregulated AUNIP, FEN1, RAD51AP1, SFPQ, MCM8, RAD51, and MCM7) linked to cancer progression, with their expression changes potentially playing key roles in hepatocellular carcinoma (HCC) development (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). These findings underscore CDT1's essential role in maintaining DNA integrity and cellular homeostasis. GSEA using the clusterProfiler package confirmed these results, revealing significant suppression of DNA damage repair pathways (e.g., DNA repair, replication, and recombination) in shCDT1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This evidence demonstrates that CDT1 regulates DNA integrity and repair in HCC cells while also maintaining cellular homeostasis and gene expression programs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCDT1 selectively binds DNA in Huh7 cells\u003c/h2\u003e\u003cp\u003eThe CDT1 protein is expressed in Huh7 cells and can be successfully immunoprecipitated using anti-CDT1 antibody, while GAPDH serves as the loading control, indicating that the IP experiment was successful (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). ChIP-seq analysis of CDT1 shows that the binding peaks are primarily distributed in the intergenic and intronic regions of the genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Further displaying the motifs obtained from two experiments, the most significant motif in the IP1 vs Input1 group is \"AATGAGG\", and the most significant motif in the IP2 vs Input2 group is \"TTAAGCAA\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). GO functional enrichment of genes associated with IP1 vs Input1 binding peaks is primarily enriched in pathways such as detection of chemical stimulus involved in sensory perception of smell, G-protein coupled receptor signaling pathway, muscle contraction, response to vitamin D, vascular endothelial growth factor receptor signaling pathway, olfaction, regulation of presynaptic membrane potential, smooth muscle contraction, negative regulation of endothelial cell apoptotic process, and negative regulation of Rho protein signal transduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). GO functional enrichment of genes associated with IP2 vs Input2 binding peaks is primarily enriched in pathways such as detection of chemical stimulus involved in sensory perception of smell, G-protein coupled receptor signaling pathway, sensory perception of chemical stimulus, positive regulation of nitric oxide biosynthetic process, olfaction, stimulatory C-type lectin receptor signaling pathway, detection of chemical stimulus involved in sensory perception of bitter taste, biological process, neuron migration, and protein transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Overlap analysis of CDT1 binding peaks obtained between the two experimental replicates reveals 4437 overlapping binding peaks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Among them, PRKD1, COX7A2, and FYB1 have been reported to be associated with cancer and warrant attention (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCDT1 binds and regulates DNA damage repair related genes expression in Huh7 cells\u003c/h2\u003e\u003cp\u003eIntegrated analysis of differentially expressed genes obtained from CDT1 RNA-seq data and genes associated with peaks appearing in the two ChIP-seq experiments (union of the two IP experiment results) reveals a total of 328 overlapping genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The 328 overlapping genes are enriched in GO-BP pathways including: signal transduction, regulation of transcription by RNA polymerase II, positive regulation of cell proliferation, xenobiotic metabolic process, negative regulation of neuron apoptotic process, response to external stimulus, extracellular matrix organization, negative regulation of transcription by RNA polymerase II, regulation of cell migration, Golgi organization, ubiquitin-dependent protein catabolic process, negative regulation of gene expression, positive regulation of gene expression, positive regulation of transcription, DNA-templated, regulation of transcription, DNA-templated, regulation of gene expression, and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Among them, HNRNPD, FBXL4, and DROSHA have been reported to be associated with DNA damage repair and cancer, warranting attention (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-F). These findings indicate that CDT1 influences the expression of genes related to DNA damage repair through binding, thereby promoting the occurrence and development of liver cancer. These research results indicate that CDT1 influences the expression of genes related to DNA damage repair through binding, thereby promoting the occurrence and development of liver cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eDisplay of Results for CDT1-Regulated Differential Expression of Immune-Related Target Genes\u003c/h2\u003e\u003cp\u003eWe investigated the regulatory role of CDT1 on immune-related target genes and their differential expression using chromatin immunoprecipitation sequencing data. A Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) reveals a total of 307 overlapping genes between the peak genes identified in our dataset and immune-related genes, emphasizing the significant involvement of CDT1 in regulating immune gene expression. Further validation of specific genes (SEMA3D, CHUK, and PIK3R3) was performed using FPKM (Fragments Per Kilobase of exon per Million fragments mapped) expression data (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The results demonstrate that knockdown of CDT1 (shCDT1) significantly reduced the expression of these genes compared to the negative control (NC), suggesting that CDT1 is an important regulator of immune-related genes. Specifically, SEMA3D, CHUK, and PIK3R3 exhibited a marked decrease in FPKM values in the shCDT1 group, indicating a repression of their transcriptional activity. ChIP-seq tracks for PIK3R3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), SEMA3D (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), and CHUK (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE) further support these findings. For each gene, the analysis of peaks across different conditions (IP1_peak, IP2_peak, and input) shows that the binding of CDT1 at these genomic loci is notably reduced in the shCDT1 samples, aligning with the observed decrease in gene expression. The detailed ChIP-seq profiles for these genes illustrate the regions of CDT1 binding, which correlate with the reduced expression observed in the knockdown condition. Together, these data suggest that CDT1 plays a crucial role in modulating the expression of immune-related genes, and its knockdown leads to differential regulation of key immune pathways.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we set out to clarify the role of CDT1 in hepatocellular carcinoma, particularly how this DNA replication licensing factor interfaces with DNA damage repair mechanisms and immune pathways. Our findings reveal that CDT1 is markedly upregulated in HCC tissues and cell lines, correlating with advanced tumor stage and poorer patient survival. Functionally, CDT1 knockdown in HCC cells led to an accumulation of DNA double-strand breaks (as evidenced by γ-H2AX foci) and broad transcriptomic changes affecting both DDR and immune-related genes. Moreover, ChIP-seq mapping of CDT1 chromatin occupancy uncovered its association with regulatory regions of key DDR and immune modulators (such as HNRNPD, DROSHA, FBXL4, SEMA3D, CHUK, and PIK3R3), suggesting a direct influence of CDT1 on genes that preserve genomic stability and shape antitumor immune responses. These data support a model in which CDT1 functions as a pivotal oncogenic driver coupling replication stress tolerance with immune pathway modulation in HCC.\u003c/p\u003e\u003cp\u003eA central finding of our work is the link between CDT1 dysregulation and genomic instability in liver cancer cells. As a licensing factor, CDT1 ensures that DNA replication origins fire once per cell cycle; its overexpression or misregulation can induce re-replication and stall forks, triggering DNA damage checkpoints\u003csup\u003e[\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Consistent with this, we observed that silencing CDT1 causes a surge in DNA damage markers, implicating CDT1 in maintaining replication fidelity under stress. This result aligns with prior studies in other malignancies showing that aberrant CDT1 activity leads to DNA damage accumulation and chromosomal instability\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. For example, Petropoulos et al.demonstrated that enforced CDT1 overexpression in colorectal cells drives overlicensing of origins and DNA breaks, promoting tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Likewise, Kanellou et al.reported that cells rapidly degrade CDT1 in response to DNA damage, underscoring a tight coupling between the licensing machinery and the DDR\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Our observations build on these mechanisms: when CDT1 is depleted, HCC cells appear unable to properly execute DNA repair programs, as shown by downregulation of homologous recombination and checkpoint genes in our RNA-seq.\u0026nbsp;This loss of DDR capacity likely exacerbates genomic instability, creating a permissive environment for malignant progression. Notably, we found CDT1 knockdown cells had reduced expression of canonical repair factors (e.g. RAD51 and FEN1 in our data), echoing the notion that replication licensing proteins support tumor cell survival by sustaining DNA repair proficiency. Together, these findings suggest a mechanistic model wherein CDT1 safeguards genome integrity in cancer cells by orchestrating a network of DDR genes; when CDT1 is absent, replication stress goes unmitigated, leading to DNA lesions that can trigger cell-cycle arrest or apoptosis if not tolerated.\u003c/p\u003e\u003cp\u003eIntriguingly, our study also uncovers a link between CDT1 and the tumor immune microenvironment. We identified hundreds of genes with immune-related functions whose expression depended on CDT1, including cytokine regulators and antigen presentation molecules. This indicates that aberrant CDT1 activity might contribute to immune evasion in HCC by rewiring immune signaling pathways. One notable CDT1 target is CHUK (encoding IKKα), a kinase that activates NF-κB; NF-κB can be stimulated by DNA damage and drives expression of inflammatory and immune-suppressive genes\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. In line with this, previous work has shown that DNA damage can elicit innate immune signaling: for instance, ATM-dependent activation of the cGAS/STING pathway and NF-κB occurs in response to nuclear DNA breaks\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Li and Chen described how unrepaired DNA can act as a danger signal to trigger inflammation via STING, linking genomic instability to immune activation\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Our data extend this concept by implying that CDT1 helps control such crosstalk. We speculate that when CDT1 is high, it supports efficient DNA repair and may limit the leakage of DNA fragments that activate innate immunity. Conversely, CDT1 loss leads to excess DNA damage, which could stimulate pathways like STING and alter cytokine profiles in the tumor. The downregulation of SEMA3D and PIK3R3 upon CDT1 knockdown is also noteworthy\u0026mdash;these genes are implicated in modulating immune cell migration and PI3K signaling, respectively, both of which can influence immune surveillance in the tumor milieu. Semaphorin-3D, for example, has been reported to shape the infiltration of myeloid cells and T cells in certain cancers, thereby affecting anti-tumor immunity. Although CDT1 is primarily known as a replication factor, our ChIP-seq finding that it localizes to regulatory DNA near immune genes raises the possibility of a broader role in transcriptional control. One hypothesis is that CDT1, perhaps through interactions with chromatin modifiers or specific DNA elements at origins, might co-regulate genes that determine the immunogenicity of tumor cells. This novel insight into a replication factor influencing immune pathways distinguishes our study from prior investigations that focused only on cell-intrinsic cell cycle effects of CDT1.\u003c/p\u003e\u003cp\u003eOur results are largely consistent with and build upon existing literature. Previous studies have documented CDT1 overexpression in numerous cancers and its association with aggressive behavior. In HCC, Karavias et al. first reported that high CDT1 levels correlate with poor patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, a finding we confirm using both TCGA data and our clinical samples. A more recent comprehensive analysis by Cai et al. likewise identified CDT1 as part of a prognostic gene signature in HCC and noted its positive correlation with tumor proliferation markers and certain immune cell infiltrates\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.Those studies, however, were primarily descriptive or correlative. Our work advances the field by providing mechanistic evidence for how CDT1 might drive HCC progression: through direct regulation of gene networks that maintain genome stability and modulate immune responses. In breast cancer, overexpression of CDT1 and its partner CDC6 has been linked to worse outcomes and genomic instability, mirroring the pattern seen in liver tumors\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e.Mughal et al. have highlighted that replication licensing proteins (including CDT1) are \u0026ldquo;saints and sinners\u0026rdquo; \u0026ndash; essential for normal cell division yet, when deregulated, capable of instigating malignant transformation via replication stress\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Our data in HCC align with this duality: CDT1 appears to be a double-edged sword, necessary for replication but dangerous when in excess. Notably, we also observed a potential connection to the Th1/Th2 balance in the immune microenvironment. Prior research shows that many cancers, including HCC, exhibit a shift toward a Th2-dominant immune response that facilitates tumor immune escape\u003csup\u003e[\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. In the study by Cai et al., CDT1 expression was positively associated with Th2 cell infiltration in HCC\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Our finding that CDT1 knockdown alters immune gene expression (e.g., downregulating cytokine signaling components) provides a possible explanation: by sustaining DNA integrity and controlling immune genes, high CDT1 might help tumors maintain an immunosuppressive environment. This integration of cell cycle and immune regulation is a novel aspect of CDT1\u0026rsquo;s role that, to our knowledge, has not been previously reported in HCC. We note, however, a contrasting point: while high CDT1 generally portends worse outcomes (likely due to more aggressive tumor growth), the concomitant increase in DNA damage from CDT1 loss could in theory make cells more visible to the immune system. This paradox will require further investigation, but it echoes the concept that inducing genomic instability in cancer cells can sometimes provoke anti-tumor immunity if the immune system is capable of recognizing the resulting neoantigens or damage-associated signals\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom a clinical and translational perspective, our work underscores the value of CDT1 as both a prognostic biomarker and a candidate therapeutic target in HCC. Consistent with previous bioinformatics analyses\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, we found that elevated CDT1 expression identifies HCC patients with significantly worse survival, independent of other factors. This suggests that measuring CDT1 could help stratify patients and inform prognosis. More provocatively, targeting the replication licensing apparatus may offer a new treatment avenue. Cancer cells often operate under heightened replication stress and are remarkably dependent on licensing factors to complete DNA synthesis\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Inhibiting CDT1 could tip this balance, selectively killing rapidly dividing tumor cells by precipitating lethal re-replication or fork collapse, while sparing normal cells that have stricter licensing controls\u003csup\u003e[\u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan additionalcitationids=\"CR59 CR60 CR61 CR62 CR63 CR64 CR65 CR66\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. Indeed, a recent study identified a small-molecule inhibitor of the CDT1\u0026ndash;Geminin complex that induced DNA damage and apoptosis preferentially in cancer cells, essentially by exploiting the addiction of cancer cells to CDT1-driven replication programs\u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e .Our findings provide a strong rationale to pursue similar strategies in HCC. If CDT1 inhibitors were developed or repurposed, they could not only suppress tumor proliferation but also potentially augment immunotherapy. There is growing evidence that combining DDR-targeting agents with immune checkpoint blockade can produce synergistic effects, as DNA-damaging treatments can increase tumor immunogenicity and T-cell infiltration\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. For example, Sen et al. showed that inhibiting ATR (a key replication stress kinase) in tumors activates STING-dependent interferon responses and enhances the efficacy of anti-PD-1 therapy, even in cancers without classical DDR deficiencies\u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e. By analogy, a CDT1-targeted therapy that elevates intratumoral DNA damage might similarly trigger innate immune sensing and improve responses to immunotherapy in HCC. This dual impact\u0026mdash;stalling the cell cycle and boosting immune visibility\u0026mdash;positions CDT1 as an attractive therapeutic target at the nexus of genomic stability and immune surveillance. Of course, any such interventions must be approached cautiously given the essential role of CDT1 in normal cells; partial inhibition or transient targeting in combination with immunotherapy might be needed to achieve a therapeutic window.\u003c/p\u003e\u003cp\u003eDespite the strengths of our study, including integrated genomics and chromatin profiling, we acknowledge several limitations. First, our experimental findings were derived from in vitro models (primarily the Huh7 HCC cell line). While Huh7 provided a convenient system to dissect molecular mechanisms, cell lines cannot fully recapitulate the complexity of HCC tumors in patients. Factors like 3D tumor architecture, liver microenvironment, and immune cell interactions are absent in monoculture. This limitation means that the downstream consequences of CDT1 knockdown on immune pathways were inferred from gene expression changes rather than directly observed in an immune-competent context. Future studies should employ in vivo models, such as HCC xenografts or genetically engineered mice, to confirm that altering CDT1 levels impacts tumor growth and immune evasion under physiological conditions. Second, our ChIP-seq data suggest CDT1 binds to certain gene loci, but the resolution and nature of this binding remain to be clarified. It is unusual for a licensing factor without known DNA-binding motifs to act as a classical transcription factor. One possibility is that CDT1 is recruited to sites of DNA damage or specific chromatin regions through interaction with other proteins. Indeed, prior reports hint that replication factors can localize to damage sites as part of emergency response networks\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e .However, the ChIP-seq peaks we identified at genes like CHUK or PIK3R3 could reflect indirect associations (e.g., CDT1 being present at a nearby replication origin). Additional experiments, such as chromatin interaction analyses or CUT\u0026amp;RUN assays, are needed to validate direct binding and rule out artifacts. Third, while we demonstrated correlations between CDT1 and immune gene signatures, we did not directly test functional immune outcomes. For example, does knocking down CDT1 in tumor cells alter their recognition or killing by cytotoxic T cells or natural killer cells? Answering such questions would require co-culture experiments with immune cells or syngeneic immunocompetent animal models. Finally, our study focused on a subset of identified targets and pathways; HCC is a genetically heterogeneous disease and CDT1 likely cooperates with other oncogenic alterations (like TP53 mutations or Myc activation) that were not the primary focus here. These caveats underscore that our proposed model of \u0026ldquo;CDT1-regulated DDR and immune modulation\u0026rdquo; should be further refined with broader validation across diverse HCC samples and in the context of combination treatments.In conclusion, our work provides new insights into how a core DNA replication factor, CDT1, drives HCC progression by simultaneously bolstering DNA repair processes and altering tumor immune pathways. We have shown that CDT1 overexpression is a hallmark of aggressive HCC and that its depletion unleashes replication stress, leading to DNA damage and changes in immune gene expression. These findings illuminate a previously underappreciated liaison between the cell cycle machinery and the immune microenvironment in liver cancer. From a scientific standpoint, the study contributes to a growing body of literature that connects genomic instability with immune regulation in cancer evolution\u003csup\u003e[\u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e.From a clinical standpoint, it highlights CDT1 as a promising biomarker for patient stratification and a potential therapeutic vulnerability. Targeting CDT1 or its associated licensing proteins could not only curb HCC cell proliferation but also render tumors more susceptible to immune attack, thereby enhancing the efficacy of existing treatments such as immunotherapy. Looking ahead, we envision that these findings will spur further research into replication licensing inhibitors in HCC, investigations into the immunogenic effects of inducing replication stress, and ultimately the development of combined modality therapies that exploit the link between aberrant DNA replication and anti-tumor immunity. Such strategies hold promise to improve outcomes for HCC patients, a group that urgently needs new options in the face of this deadly disease.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates the critical role of CDT1 in hepatocellular carcinoma pathogenesis, emphasizing its dual contributions to DNA damage repair and regulation of immune-related gene expression. Our findings demonstrate that CDT1 knockdown exacerbates DNA damage, as indicated by increased γ-H2AX foci, and impairs DNA damage response pathways, including homologous recombination and DNA repair. Additionally, CDT1 selectively binds to genomic regions associated with key cancer progression genes, including HNRNPD, FBXL4, and DROSHA, while regulating immune-related genes such as SEMA3D, CHUK, and PIK3R3. These findings establish CDT1 as a key oncogenic driver in HCC, modulating both genomic stability and immune evasion mechanisms. Given its regulatory role in these processes, CDT1 emerges as a potential prognostic biomarker and therapeutic target to enhance immunotherapy efficacy in HCC. Future research should investigate CDT1-targeted strategies to attenuate HCC progression and address challenges associated with tumor heterogeneity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the TCGA, UALCAN, and HPA databases for providing their valuable platforms and the researchers who contributed the datasets. The funder of this study had no involvement in the study design, data collection, data analysis, data interpretation, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eThe study was conceived by ZW and YW. LM and HX planned and supervised data collection. BLand JL performed the data analyses. YW drafted the initial manuscript. ZW contributed to manuscript writing and revision. All authors discussed the work, approved the final manuscript, and consented to publication.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China (NSFC; No. 82160466) and the Graduate Research and Practice Innovation Project of Qinghai University (No. 2025-GPKY-8).\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study involved only cell-based (in vitro) experiments and did not include clinical research or animal studies. The data used were obtained from public databases, including TCGA, UALCAN, and HPA. Because the work relies entirely on publicly available, open-source datasets, no issues related to participant privacy or animal use arose and ethics approval was not required. The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBRAY, F. \u0026amp; LAVERSANNE, M. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. \u003cem\u003eCancer J. 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Immunother Cancer\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1136/jitc-2023-007716\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2023-007716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, CDT1, DNA damage repair, Immune modulation, Genomic instability, Tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-8024873/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8024873/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHepatocellular carcinoma (HCC) progression is tightly driven by genomic instability and dysregulated DNA damage repair (DDR) pathways. The replication licensing factor Chromatin Licensing and DNA Replication Factor 1 (CDT1) is frequently overexpressed in HCC tissues; however, the molecular mechanisms by which CDT1 coordinates DDR programs and modulates tumor immune microenvironment remain poorly elucidated. This study aimed to systematically characterize CDT1-centered regulatory networks in HCC, delineate their dual impacts on DDR machinery and immune-related signaling pathways, and validate the clinical translational value of CDT1 as a potential biomarker or therapeutic target.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA stable CDT1 knockdown (CDT1-KD) cell model was established in Huh7 HCC cells using lentiviral short hairpin RNA (shRNA), with silencing efficiency verified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot analysis. DNA double-strand breaks (DSBs) were quantified via γ-H2AX immunofluorescence staining. RNA sequencing (RNA-seq) was performed to profile CDT1-dependent transcriptional alterations, followed by differential expression analysis using DESeq2, Gene Ontology (GO) functional annotation, and Gene Set Enrichment Analysis (GSEA). Chromatin immunoprecipitation sequencing (ChIP-seq) was employed to map genome-wide CDT1 chromatin occupancy, which was further integrated with the transcriptome data to prioritize direct CDT1 target genes. The involvement of CDT1 in immune regulation was investigated by intersecting CDT1-bound and CDT1-regulated genes with manually curated immune-related gene sets. Finally, the clinical relevance of CDT1 expression in HCC was analyzed using public databases, including The Cancer Genome Atlas (TCGA), UALCAN, and the Human Protein Atlas (HPA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCDT1 silencing significantly increased the number of γ-H2AX foci in Huh7 cells, indicating exacerbated accumulation of DNA DSBs. RNA-seq analysis identified 4,581 CDT1-dependent differentially expressed genes (DEGs), including 2,739 upregulated and 1,842 downregulated transcripts. Functional enrichment analysis of downregulated DEGs revealed significant enrichment in biological processes related to DNA replication initiation, homologous recombination (HR), and broader DDR cascades\u0026mdash;consistent with GSEA results showing suppressed activity of DDR-related gene sets in CDT1-KD cells. ChIP-seq data demonstrated that CDT1 primarily binds to intergenic and intronic regions of the genome; integration with RNA-seq data identified 328 CDT1-bound DEGs, which were enriched in signal transduction and cell proliferation pathways (e.g., HNRNPD, FBXL4, DROSHA). Intersection with immune gene catalogs yielded 307 immune-related CDT1 target genes; notably, CDT1 knockdown led to reduced expression of key immune regulators such as SEMA3D, CHUK, and PIK3R3, implicating CDT1 in the modulation of HCC immune signaling. Clinically, CDT1 expression was significantly upregulated in HCC tissues compared to adjacent non-tumor tissues, and high CDT1 expression was independently associated with poorer overall survival in HCC patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCDT1 functions as a pivotal oncogenic regulator in HCC by coupling maintenance of DDR proficiency with control of immune-related gene expression. Its dual roles in sustaining DNA replication/repair programs and shaping the tumor immune microenvironment highlight CDT1 as a promising prognostic biomarker and a potential therapeutic target for HCC. Targeting CDT1 may not only disrupt tumor genomic stability but also enhance responsiveness to immunotherapy, thereby addressing the challenge of tumor heterogeneity in HCC treatment.\u003c/p\u003e","manuscriptTitle":"CDT1-Mediated Regulation of DNA Damage Repair and Immune-Related Target Genes Underlies Hepatocellular Carcinoma Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 15:02:30","doi":"10.21203/rs.3.rs-8024873/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-09T10:25:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T05:08:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T00:42:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302860044847306860487898643146835129464","date":"2025-12-15T11:40:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199895777125173070057498305630758431730","date":"2025-12-13T02:28:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T14:41:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50557228665532251830025898058406522977","date":"2025-12-01T10:35:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T00:38:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T06:48:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-12T07:01:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-08T12:23:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-08T12:19:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc39837d-f22f-4e86-a549-869b5b606561","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58902794,"name":"Health sciences/Biomarkers"},{"id":58902795,"name":"Biological sciences/Cancer"},{"id":58902796,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":58902797,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-17T08:38:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 15:02:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8024873","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8024873","identity":"rs-8024873","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}