Icaritin suppresses CAD-mediated liver cancer development by targeting miR-18b-5p in a xenograft mouse model

Icaritin suppresses CAD-mediated liver cancer development by targeting miR-18b-5p in a xenograft mouse model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Icaritin suppresses CAD-mediated liver cancer development by targeting miR-18b-5p in a xenograft mouse model Di Wu, Tian mi, Xue Tang, Yiming Jia, Tao Guo, Guoqiang Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7606192/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Medical Oncology → Version 1 posted 7 You are reading this latest preprint version Abstract Cancer cells show abnormal nucleotide metabolism and prefer the de novo synthesis pathway. As the key enzymes, Carbamoyl-phosphate synthetase 2 (CAD) is overactivated in cancer and promotes pyrimidine de novo synthesis, supplying cancer cells with DNA and RNA biosynthesis precursors. Therefore, the development of drugs targeting CAD might inhibit cancer progression and transformation. Icaritin (ICT) is an isoprenoid flavonoid derivative with a wide range of anticancer activities, however, the mechanism of ICT in regulating pyrimidine biosynthesis in cancer remains unclear. MicroRNAs are involved in carcinogenesis by regulating the expression of target genes, and ICT has been shown to regulate the expression of miRNAs leading to suppressing cancer progression. Using both human normal hepatocytes and liver cancer cells, we found that CAD expression was significantly elevated in cancer cells. Interestingly, although ICT treatment reduced CAD protein levels in liver cancer cells, it increased CAD transcriptional activity. Dual-luciferase reporter assays confirmed miR-18b-5p as a direct regulator of CAD. By transfecting miR-18b-5p mimics or inhibitors, we showed ICT upregulates miR-18b-5p to suppress CAD, inhibiting liver cancer cell proliferation, migration, and colony formation. Furthermore, in a human liver cancer xenograft mouse model, ICT treatment markedly reduced tumor growth and decreased Ki-67 expression, consistent with the in vitro results, CAD protein expression was downregulated, while its mRNA level was upregulated, further supporting a post-transcriptional regulatory mechanism. Overall, ICT plays an anti-liver cancer role by increasing miR-18b-5p at the post-transcriptional level to inhibit CAD expression, thereby interfering with the de novo synthesis of pyrimidine and development of liver cancer. CAD miR-18b-5p Liver cancer Icaritin De novo synthesis of pyrimidine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Primary liver cancer ranks as the sixth most common malignant tumor worldwide in terms of incidence and the third leading cause of cancer-related mortality, characterized by its insidious onset and high potential for metastasis [ 1 ]. In China, it holds the fourth position in incidence and the second in mortality [ 2 ]. Current clinical treatments face significant limitations, including issues such as drug resistance and short duration [ 3 ]. Consequently, a deeper understanding of the pathogenesis of liver cancer is crucial, and there is a critical need to develop more scientifically effective targeted anticancer drugs that offer better tolerability and safety. As the fundamental building blocks of nucleic acids, nucleotides are ubiquitously distributed throughout the body and play diverse biological roles. Cancer cells preferentially utilize the nucleotide de novo synthesis to rapidly produce the precursors necessary for DNA and RNA, thereby maintaining their growth and stability. CAD is a hexameric enzyme composed of 243 kDa polypeptide chains [ 4 ], functions as a multifunctional enzyme that catalyzes the first three rate-limiting steps in the de novo synthesis of pyrimidine nucleotides, which is overexpressed in cancer cells, ensuring a high flux of pyrimidines essential for their proliferation [ 5 ]. CAD is closely linked to accelerated carcinogenesis and reduced survival rates, making it a potential biomarker for the early recurrence of liver cancer [ 6 ] and an indicator for poor prognosis in patients with glioblastoma [ 7 ]. Moreover, the elevated expression of CAD activates the Notch signaling pathway, which subsequently enhances c-Myc-mediated glycolysis, leading to increased chemoresistance in gastric cancer cells [ 8 ]. Collectively, CAD serves as a significant risk indicator in malignant tumors, playing a crucial role in promoting tumor growth. Consequently, targeting CAD could represent an effective strategy in chemotherapy. It is estimated that microRNAs (miRNAs) can regulate more than 60% of human protein-coding genes [ 9 ], playing pivotal roles in modulating cellular processes such as metabolism, proliferation, differentiation, and apoptosis. miRNAs exert their regulatory effects by binding to complementary sequences within the 3' untranslated region (3' UTR) of target mRNAs, leading to either mRNA degradation or translational repression [ 10 – 12 ]. Accumulating evidence has shown that miRNAs are frequently dysregulated in various cancers, where they can function as either oncogenes or tumor suppressors, thereby influencing tumor development and progression [ 13 ]. For instance, studies have revealed that miR-10b is overexpressed in liver cancer tissues, and enhances the migratory and invasive capabilities of hepatocellular carcinoma (HCC) cells [ 14 ]. Similarly, miR-122 has been shown to target PKM2, a key enzyme in glycolysis, reducing lactate production and suppressing aerobic glycolysis in liver cancer cells, thereby disrupting their energy supply [ 15 ]. Additionally, the inhibition of miR-542-3p has been linked to the overactivation of the transforming growth factor β (TGFβ)/Smad pathway, which promotes epithelial-mesenchymal transition (EMT) and contributes to poor prognosis [ 16 ]. These findings underscore the critical involvement of miRNAs in various mechanisms driving cancer proliferation and metastasis, including the disruption of normal cell cycle regulation. By modulating miRNA expression and targeting specific genes, it is possible to inhibit cancer cell proliferation and impede tumor progression, highlighting their potential as therapeutic targets. ICT is a bioactive compound purified from Epimedium . It belongs to the class of isoprenoid flavonoid derivatives and serves as a metabolite of Icariin (ICA) [ 17 ]. Current pharmacological research has revealed that ICT possesses a wide range of significant biological effects, including anti-inflammatory, immunomodulatory, neuroprotective, anti-osteoporotic, and anti-tumor properties [ 18 – 20 ]. In the context of anti-tumor activity, ICT has demonstrated notable inhibitory effects on various cancers including colon cancer [ 21 ], renal cell carcinoma [ 22 ], prostate cancer [ 23 ], and non-small cell lung cancer [ 24 ]. Additionally, ICT has been shown to modulate tumor cell metabolism and regulate the progression of liver cancer through regulating specific miRNAs, including miR-299-5p [ 25 ], miR-620, miR-1236 [ 26 ]. Our previous studies have demonstrated that ICT suppresses glycolysis in liver cancer cells by regulating the ROS/P38 MAPK/P53 signaling pathway [ 27 ]. Nucleotide metabolism plays a critical in tumor development and progression [ 28 ]. However, the specific mechanisms by which ICT regulates pyrimidine metabolism remain poorly understood. In this study, we performed a series of in vitro and in vivo experiments to study whether ICT inhibits liver cancer progression by regulating the de novo synthesis of pyrimidine, thereby providing a robust theoretical and experimental foundation for the potential clinical application of ICT in the precision treatment of liver cancer. Materials and methods Reagents ICT (purity>98%, SML0551, Sigma-Aldrich, St. Louis, MO, USA) was dissolved in dimethyl sulfoxide (DMSO, D5879, Sigma-Aldrich, St. Louis, MO, USA) to prepare a stock solution and stored at − 20℃. The working solution was diluted with the growth medium before each use. Antibodies against CAD (1:500, AF4715) and β-actin (1:1000, AF7018) were purchased from Affinity Bioscience (Cincinnati, OH, USA). The Ki-67 (1:10000, 27309-1-AP) antibody was purchased from Proteintech (Chicago, USA). Cell Line and Cell Culture The human liver cancer cell lines HepG2 and LM3, the human normal immortalized liver cell line MIHA, and the human renal epithelial cell line HEK-293T were obtained from the Cell Center of the Chinese Academy of Sciences (Shanghai, China). All cell lines were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM, MA0212, MeilunBio, Dalian, China) supplemented with 10% fetal bovine serum (FBS, 04-001-1A, BI, Herzliya, Israel) and 1% penicillin/streptomycin (P1400, Solarbio, Beijing, China) at 37℃ in a humidified atmosphere with 5% CO 2 . Cell transfection The miR-18b-5p mimic (B04002), miR-18b-5p inhibitor (B03001), and their corresponding blank/negative controls, miR-NC (B01001) and inhibitor-NC (B04003) were purchased from GenePharma (Suzhou, China), with their sequences detailed in Table 1 . For transfection experiments, HepG2 and LM3 cells in the logarithmic growth phase were seeded in six-well plates and cultured until reaching approximately 60% confluence. Transfection was performed using siRNA Transfection Reagent (sc-29528, Santa Cruz, Dallas, TX, USA) according to the manufacturer’s protocol, with miR-18b-5p mimic/miR-NC or miR-18b-5p inhibitor/inhibitor-NC complexes. Following a 6-hour transfection period, cells were treated with either vehicle or ICT for 24 h before being harvested for subsequent qRT-PCR and Western blot analyses. Table 1 The Oligo sequences for transfection Oligo name Sequences(5′-3′) miR-18b-5p mimic sense UAAGGUGCAUCUAGUGCAGUUAG miR-18b-5p mimic antisense AACUGCACUAGAUGCACCUUAUU miRNA mimic NC sense UUCUCCGAACGUGUCACGUTT miRNA mimic NC antisense ACGUGACACGUUCGGAGAATT miR-18b-5p inhibitor CUAACUGCACUAGAUGCACCUUA miRNA inhibitor NC CAGUACUUUUGUGUAGUACAA Western Blot Twenty-four hours post ICT treatment, cells were harvested and lysed in RIPA buffer (P0013C, Solarbio, Beijing, China) supplemented with PMSF protease inhibitor (P0100, Solarbio, Beijing, China). Protein concentrations were determined using a BCA assay kit (E112, Vazyme, Nanjing, China). Equal amounts of whole-cell lysates (50 µg) were loaded and separated by 6% SDS-PAGE and then transferred onto polyvinylidene difluoride (PVDF) membranes. Protein molecular weights were evaluated using a pre-stained protein ladder (PageRuler Plus, 26634, Thermo Fisher Scientific, MA, USA), and membranes were cropped according to the molecular weights of target proteins. Membranes were blocked and sequentially incubated with primary and secondary antibodies. Protein bands were visualized using Maxilumin™-WB Pico Chemiluminescent Substrate Reagent (WB001, Baizhi, Beijing, China) and imaged with a ChemiDoc imaging system (Tanon, 4600 SF, Shanghai, China). Quantitative analysis was performed using ImageJ software, with target protein band intensities normalized to β-actin expression levels. qRT-PCR Total RNA was isolated from HepG2 and LM3 cells treated with ICT or vehicle for 24 h using TRIzol reagent (DP424, TIANGEN, Beijing, China). cDNA synthesis was performed using HiScript II First Strand cDNA Synthesis Kit (R223-01, Vazyme, Nanjing, China) for mRNA analysis and the miRNA 1st Strand cDNA Synthesis Kit (MR101-01, Vazyme, Nanjing, China) for miRNA detection. Quantitative PCR was carried out using SYBR Green Master Mix (Q711-02, Vazyme, Nanjing, China) for mRNA quantification and miRNA Universal SYBR qPCR Master Mix (MQ101-01, Vazyme, Nanjing, China) for miRNA analysis on a Lightcycler 96 system (Roche, Basel, Switzerland). The thermal cycling conditions were set as 40 cycles under 56℃ for 30 s, 95℃ for 30 s, and 72℃ for 30 s. Relative gene expression levels were calculated using the 2 −∆∆CT equation, with U6 snRNA and β-actin serving as internal controls for miRNA and mRNA normalization, respectively. All primer sequences, synthesized by Sangon (Shanghai, China), are listed in Table 2 . Table 2 The primer sequences for qRT-PCR Gene name Sequences(5′-3′) CAD Forward: AGTGGTGTTTCAAACCGGCAT Reverse: CAGAGGATAGGTGAGCACTAAGA β-actin Forward: CATGTACGTTGCTATCCAGGC Reverse: CTCCTTAATGTCACGCACGAT miR-18b-5p Forward: TAAGGTGCATCTAGTGCAGTTAG Reverse: AGTGCAGGGTCCGAGGTATT U6 Forward: CTCGCTTCGGCAGCACA Reverse: AACGCTTCACGAATTTGCGT Cell Counting Kit-8 assay Following successful transfection, cells were seeded in 96-well plates at a density of 5 × 10 3 cells/well in 100 µL culture medium, with six replicate wells for each condition. After 24 h of incubation, cells were treated with either ICT or vehicle for an additional 24 h. Cell viability was assessed using the CCK8 kit (CA1210, Solarbio, Beijing, China), where the CCK8 reagent was diluted 1:9 in DMEM medium. Subsequently, 100 µL of the mixture was added to each well, followed by incubation at 37°C for 1.5 h. Absorbance was measured at 450 nm using a microplate reader (Synergy H1, BioTek, Winooski, VT, USA). Colony Formation Assay Following a 6-hour transfection, HepG2 and LM3 cells were trypsinized and replaced with 2 mL of suspension containing 4×10 3 HepG2 cells or 2×10 3 LM3 cells per well in six-well plates, respectively. After 24 h of incubation, the cells were treated with either ICT or vehicle for an additional 24 h. The treatment was withdrawn, and the cells were washed with PBS every three days. After 10 days of culture, the cells were stained with 0.1% crystal violet aqueous solution (G1063, Solarbio, Beijing, China) for 20 min. The stained colonies were photographed, and the colony formation rate was calculated. Scratch-Wound Assay The six-well plate was initially labeled on the back using a marker pen to indicate the position. Following the completion of cell transfection, 2 mL of cell suspension containing 7×10 5 HepG2 and LM3 cells was seeded into each well of the six-well plate. After 24 h, a scratch was made in the cell monolayer using a pipette tip. The cells were then treated with either ICT or DMSO diluted in 1% FBS cell culture medium. Cell migration was monitored and recorded at 0, 24, and 48 h using an inverted microscope (D7500, Nikon, Japan). Dual-Luciferase Reporter assay To investigate the direct interaction between miR-18b-5p and the 3′UTR of CAD, we initially constructed the luciferase reporter vector GP-miRGlO (GenePharma, Suzhou, China) containing either the wild-type or mutated binding site of CAD 3′UTR (Fig. S1 ). The CAD sequence was cloned into the multiple cloning site of the GP-miRGlO reporter vector. 293T cells were seeded in 24-well plates and co-transfected with 1 µg of the luciferase reporter construct and 5 µL of either miR-18b-5p mimic or negative control (NC) mimic using GP-transfect-Mate reagent (G04008, GenePharma, Suzhou, China). Six hours post-transfection, the culture medium was replaced with fresh medium. Cells were harvested 24 h after transfection for luciferase activity measurement using the Dual Luciferase Reporter Assay System (E1910, Promega, Madison, USA). Firefly luciferase activity was normalized to Renilla luciferase activity for each sample, and the relative luciferase activity was calculated by normalizing the FLU/RLU ratio of each group to that of the plasmid + NC mimic control group. Mouse Models and Treatments Five-week-old male BALB/c nude mice (Beijing Huafukang Bioscience Co., Ltd., SCXK (Beijing) 2019-0008) were housed in SPF conditions and acclimatized for one week. All procedures were approved by the Animal Ethics Committee of Hebei University (IACUC-202324SR). HepG2 cells (1×10 7 cells in 100 µL PBS) were subcutaneously injected into the right flank of each mouse. After 7 days, mice were randomly divided into two groups (n = 6): ICT treatment (10 mg/kg) and vehicle control, administered intraperitoneally every other day. Tumor size and body weight were measured every three days. After three weeks, mice were inhaled excess CO 2 and died by cervical dislocation, tumor were collected, and tumor volume (V) was calculated as V=(length×width 2 )/2, and weighed, photographed for analysis. This study was carried out in accordance with guidance on the operation of the Animals (Scientific Procedures) Act 1986 and associated guidelines, EU Directive 2010/63 for the protection of animals used for scientific purposes. Hematoxylin and eosin (H&E) staining Tumor specimens were fixed in 4% paraformaldehyde, dehydrated through an ethanol series, and embedded in paraffin. Serial sections of 4 µm thickness were prepared using a microtome. Following deparaffinization and rehydration, tissue sections were stained with hematoxylin and eosin (H&E) using a commercial staining kit (G1120, Solarbio, Beijing, China). After mounting with neutral balsam, the stained sections were digitally scanned using a high-throughput pathology slide scanner (Pannoramic MIDI Ⅱ, 3DHISTECH, Budapest, Hungary) for histological analysis and documentation. Immunohistochemistry Deparaffinized tumor sections underwent antigen retrieval in preheated Tris-EDTA buffer (PH = 9.0, BL617A, Biosharp, Beijing, China) at 95℃ for 25 min. Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide (ANNJET, Shandong, China) for 10 min at room temperature. After blocking with 5% BSA for 30 min, sections were incubated with primary antibody at room temperature for 1.5 h, followed by the corresponding secondary antibody for 30 min. Immunoreactivity was visualized using a DAB kit (ZLI-9017, ZSGB-BIO, Beijing, China) for 2 min. The stained sections were scanned using a digital pathology slide scanner, and protein expression patterns were analyzed based on staining intensity and cellular localization. Statistical Analyses All statistical analyses were performed using GraphPad Prism 9 software (GraphPad Software, San Diego, CA), with data from at least three independent replicates. Continuous variables following normal distribution were expressed as mean ± standard deviation (SD). For comparisons between two groups, two-tailed Student’s t-tests were applied. Multiple group comparisons were analyzed using one-way analysis of variance (ANOVA) with appropriate post-hoc tests. Non-parametric tests were employed for data that did not meet the assumption of normal distribution. p < 0.05 was considered statistically significant for all analyses. Results ICT may inhibit the highly expressed CAD in human liver cancer cells at the post-transcriptional level To investigate the clinical relevance of CAD in liver cancer, we first analyzed its expression in human liver cancer and normal tissues using the UALCAN ( https://ualcan.path.uab.edu/ ) and GEPIA ( http://gepia.cancer-pku.cn/ ) databases. The results showed that both mRNA and protein levels of CAD were significantly upregulated in liver cancer tissues compared to normal controls (Fig. S2A, B). Furthermore, patients with high CAD expression exhibited shorter overall survival than those with low CAD expression, and CAD expression levels correlated positively with tumor malignancy grade (Fig. S2C, D). To validate these findings experimentally, we examined CAD expression in the human normal liver cell line MIHA and the liver cancer cell lines LM3 and HepG2. Consistent with the bioinformatics data, both mRNA and protein levels of CAD were markedly elevated in the liver cancer cell lines (Fig. 1 A-C). These results suggest that CAD may play a critical role in liver cancer progression and could serve as a potential prognostic marker for poor clinical outcomes. After 24 h of treatment with the optimal concentration of 20 µM ICT (previously determined as the effective dose for HepG2 and LM3 cells [ 27 ]), Western blot analysis revealed a reduction in CAD protein expression in both cell lines (Fig. 1 D, E and 1 G, H). Interestingly, qRT-PCR results showed an upregulation of CAD mRNA levels under the same conditions (Fig. 1 F and I). This discrepancy between transcriptional and translational regulation prompted us to investigate whether ICT modulates CAD at the post-transcriptional, translational, or post-translational level. We hypothesized that intermediate molecules, such as RNA-binding protein or miRNAs, might be involved in this regulatory mechanism. CAD is a direct target of miR-18b-5p MiRNAs are well-established post-transcriptional regulators of gene expression [ 29 ]. Given the observed discordance between CAD mRNA regulation and protein downregulation following ICT treatment, we hypothesized that ICT may modulate CAD expression through miRNA-mediated mechanisms. We used miRbase ( https://mirbase.org/ ) and StarBase ( http://starbase.sysu.edu.cn/ ) to identify candidate miRNAs targeting CAD. The Venn diagram analysis revealed two potential CAD-targeting miRNAs: hsa_miR-18a- 5p and hsa_miR-18b-5p (Fig. 2 A). Analysis of liver cancer tissue expression patterns using the UALCAN database showed differential expression of these miRNAs (Fig. 2 B, C). Based on our earlier findings that ICT increases CAD mRNA while decreasing its protein expression, we postulated that ICT might upregulate miRNAs that suppress CAD translation. We therefore focused on miR-18b-5p for further investigation. Target prediction analysis identified potential binding sites between miR-18b-5p and the CAD 3`UTR (Fig. 2 D). This interaction was verified using a dual-luciferase reporter assay (Fig. 2 E), confirming CAD as a direct target of miR-18b-5p. These results demonstrate that miR-18b-5p may exert a potential tumor-suppressive role in liver cancer via direct binding to CAD mRNA to inhibit its translation, providing a mechanistic explanation for the post-transcriptional regulation of CAD by ICT. ICT inhibits CAD expression in liver cancer cells by upregulating miR-18b-5p To investigate whether CAD is a direct target of miR-18b-5p and whether ICT inhibits CAD expression through miR-18b-5p, we first treated HepG2 and LM3 cells with ICT. As expected, ICT treatment significantly increased miR-18b-5p expression in both cell lines (Fig. 3 A, D). Next, we transfected HepG2 and LM3 cells with either a miR-18b-5p mimic or inhibitor to modulate miR-18b-5p levels. Transfection efficiency was confirmed, which showed a marked increase in miR-18b-5p expression with the mimic (Fig. 3 B, E) and a decrease with the inhibitor (Fig. 3 C, F). To assess the regulatory relationship between miR-18b-5p and CAD, we treated the transfected cells with or without ICT and analyzed CAD expression. In HepG2 cells, miR-18b-5p mimic transfection downregulated CAD expression compared to the miR-NC group, and this suppression was further enhanced by ICT co-treatment (Fig. 3 G, I). Conversely, transfection with the miR-18b-5p inhibitor increased CAD expression when compared to the inhibitor NC group, and this effect was reversed by ICT treatment (Fig. 3 H, J). Similar results were also observed in LM3 cells (Fig. 3 K-L and 3 M-N). These findings demonstrate that miR-18b-5p negatively regulates CAD expression and ICT regulates CAD expression through miR-18b-5p upregulation, confirming CAD as a functional target of miR-18b-5p in liver cancer cells. ICT inhibits proliferation, clonogenicity, and migration of liver cancer cells via miR-18b-5p To elucidate the role of miR-18b-5p in liver cancer cell growth and ICT-mediated regulation, we performed functional assays in both HepG2 and LM3 cells following miR-18b-5p modulation. Transfection with miR-18b-5p mimic significantly reduced cell proliferation, with enhanced suppression observed upon ICT co-treatment (Fig. 4 A, C). Conversely, miR-18b-5p inhibitor promoted proliferation, which ICT treatment effectively reversed (Fig. 4 B, D). Clonogenic assays revealed parallel effects: miR-18b-5p overexpression diminished colony formation, with further reduction following ICT combination, while miR-18b-5p inhibitor enhanced clonogenicity, an effect similarly reversed by ICT (Fig. 4 E-G for HepG2, Fig. 4 H-J for LM3). Scratch tests confirmed these trends, demonstrating that transfection with miR-18b-5p mimic inhibited cell motility, with amplified suppression by ICT (Fig. 5 A, C for HepG2, Fig. 5 E, G for LM3), whereas miR-18b-5p inhibitor increased migration capacity (Fig. 5 B, D for HepG2, Fig. 5 F, H for LM3). All the above results demonstrated that transfection of miR-18b-5p inhibited the proliferative activity, clone formation ability, and migration ability of liver cancer HepG2 and LM3 cells, and this series of biological behaviors of liver cancer cells could be further significantly inhibited by simultaneous treatment with ICT. ICT suppresses xenograft tumor growth in mice by upregulating miR-18b-5p to inhibit CAD expression To validate whether the miR-18b-5p-mediated inhibition of CAD expression observed in vitro could similarly suppress liver cancer progression in vivo , we established a liver cancer xenograft model (Fig. 6 A). Following three weeks of ICT treatment, tumor tissues were collected for analysis. ICT treatment significantly inhibited tumor growth, as evidenced by reduced tissue size, weight, and volume (Fig. 6 B-D), while showing no significant effect on mouse body weight (Fig. 6 E), demonstrating no overt toxicity. Immunohistochemical analysis showed decreased Ki-67 positivity in ICT-treated tumors, and HE staining confirmed the inhibitory effect of ICT on tumor cell proliferation (Fig. 6 F, G). Further analysis of tumor tissue extracts revealed that ICT treatment significantly downregulated CAD protein expression (Fig. 6 H, I), while increasing both CAD mRNA and miR-18b-5p levels (Fig. 6 J, K). These results demonstrate that the mechanism observed in vitro, where ICT post-transcriptionally suppresses CAD protein expression through miR-18b-5p upregulation, is conserved in vivo, providing compelling evidence for the consistency of this regulatory pathway across experimental systems. Discussion Metabolic reprogramming represents a fundamental hallmark of cancer progression, with dysregulated growth signals driving tumor cells to acquire nutrients and biosynthetic precursors through metabolic adaptation [ 30 ]. In this study, we identified ICT, a naturally occurring compound derived from the Epimedium genus , as a potent inhibitor of liver cancer growth through targeting pyrimidine de novo synthesis. Firstly, we found that CAD, the first key enzyme in pyrimidine de novo synthesis. Our comprehensive analysis revealed that CAD – the rate-limiting enzyme in pyrimidine synthesis, was significantly overexpressed in clinical liver cancer specimens and strongly correlated with poor patient prognosis. This clinical observation was corroborated by in vitro studies demonstrating elevated CAD expression at both mRNA and protein levels in liver cancer cell lines compared to normal liver cells. Intriguingly, ICT treatment induced a paradoxical regulatory pattern, significantly upregulating CAD mRNA while concurrently reducing CAD protein expression, suggesting potential post-transcriptional regulation. This discrepancy prompted us to investigate potential mechanisms involving mRNA stability modulation, translational repression, or protein degradation pathways, with particular focus on non-coding RNAs given their established role in post-transcriptional gene regulation [ 31 ]. These findings collectively demonstrate that ICT exerts its anti-tumor effects by specifically targeting the pyrimidine synthesis pathway through complex regulation of CAD expression, highlighting the therapeutic potential of metabolic intervention in liver cancer treatment. We hypothesized that ICT regulates CAD expression post-transcriptionally via miRNA-mediated mechanisms. Bioinformatics analysis identified two candidate miRNAs - miR-18a-5p and miR-18b-5p that were differentially expressed in liver cancer tissues. Although miR-18a-5p is a well-characterized microRNA [ 31 – 33 ], its role in liver cancer remained unclear, prompting its selection for further study. Using dual-luciferase reporter assays, we confirmed CAD as a direct target of miR-18b-5p. Subsequent experiments revealed that ICT significantly upregulated miR-18b-5p in liver cancer cells. Functional validation demonstrated that miR-18b-5p overexpression reduced CAD protein levels, while its inhibition increased them; notably, combining miR-18b-5p mimic with ICT treatment synergistically enhanced CAD expression. Given CAD’s critical role in dNTP and NTP synthesis – essential for energy metabolism and gene expression – its inhibition would be expected to impair cancer cell proliferation and metastasis [ 34 , 35 ]. Indeed, CCK8, scratch, and colony formation assays confirmed that miR-18b-5p mimics inhibited proliferation, migration, and clonogenicity in liver cancer cells, with ICT treatment amplifying these effects. Conversely, miR-18b-5p inhibitor reversed these phenotypes. Collectively, these results establish that ICT suppresses liver cancer progression by upregulating miR-18b-5p to target CAD, thereby disrupting nucleotide metabolism and impairing malignant behaviors. To validate our in vitro findings, we established a xenograft liver cancer model in immunodeficient nude mice. ICT treatment significantly suppressed tumor growth compared to control, while exhibiting no notable effects on body weight, demonstrating both efficacy and biosafety. Terminal analysis revealed that ICT-treated tumors showed reduced cellular proliferation, as confirmed by HE staining and Ki-67 immunohistochemistry. Molecular characterization of tumor tissues demonstrated that ICT consistently upregulated miR-18b-5p while downregulating CAD protein expression in vivo , despite increased CAD mRNA levels, mirroring our in vitro observations. These results conclusively demonstrate that ICT exerts its anti-tumor effects through a conserved miR-18b-5p/CAD regulatory axis across both cellular and animal models, providing comprehensive evidence for its therapeutic potential in liver cancer treatment. Altogether, this study systematically demonstrates that ICT inhibits liver cancer progression by upregulating miR-18b-5p to suppress CAD, the rate-limiting enzyme in pyrimidine de novo synthesis, at both cellular and overall levels. Our finding establishes that CAD as a direct target of miR-18b-5p; and ICT’s ability to restore miR-18b-5p expression in liver cancer cells; and consequent disruption of pyrimidine synthesis leading to tumor suppression. However, several important questions remain: first, whether ICT regulates miR-18b-5p directly or through intermediate factors requires clarification; second, while CAD is identified as a primary target, the comprehensive impact on downstream pyrimidine metabolites needs metabolomic characterization; third, clinical validation across diverse liver cancer subtypes is essential given tumor heterogeneity. Therefore, future directions will include: clinical correlation studies using patient-derived tissues; metabolomic profiling of pyrimidine pathway intermediates; and investigation of potential ceRNA networks involving circRNA/lncRNA in miR-18b-5p regulation. These studies will refine our understanding of ICT’s molecular mechanisms and facilitate its development as a promising therapeutic strategy for liver cancer prevention and treatment. Declarations Author contributions Di Wu contributed to conceptualization, data curation, methodology, performed the experiments, formal analysis and writing–original draft. Tian Mi contribute to conceptualization and performed the experiments. Xue Tang contributed to formal analysis and performed the experiments. Yiming Jia performed some experiments; Tao Guo and Guoqiang Zhou contributed to methodology, supervision and administered the project; Wenjuan Li supervised funding acquisition, methodology, administered the project, writing–review and editing. All the authors reviewed and approved the manuscript. Funding This work was supported by Hebei Provincial Natural Science Foundation of China (Grant No. C2023201073, H2022201017), the Central Guidance on Local Science and Technology Development Fund of Hebei Province (Grant No. 226Z2402G), the “Three Three Three Talents Program” of Hebei Province (Grant No. C20221016), the Natural Science Interdisciplinary Research Program of Hebei University (Grant No. DXK202210), College Students’ Innovation and Entrepreneurship Training Program of Hebei University (Grant No. DC2025530). Data availability Data will be made available on request. Conflict of interest The authors declare no competing financial interest. References Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021;71:209-249. Qiu H, Cao S, and Xu R. 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12:20:49","extension":"png","order_by":45,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1134117,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/c3e8c2c00eb8981176a2e9dc.png"},{"id":96247663,"identity":"7be66ed1-2378-4420-b30a-0eccbdfd9774","added_by":"auto","created_at":"2025-11-19 07:27:39","extension":"xml","order_by":46,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105258,"visible":true,"origin":"","legend":"","description":"","filename":"16dcc61c967844ca8117230a867957751structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/36a3117bfa189b83eee74dcf.xml"},{"id":96249695,"identity":"5a736943-501f-47ed-8c0b-4ed515ddb856","added_by":"auto","created_at":"2025-11-19 07:36:01","extension":"html","order_by":47,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116136,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/9876e95f573208be82b37dfb.html"},{"id":96085028,"identity":"e450f612-8699-4b8b-9aaf-79085db9aa9a","added_by":"auto","created_at":"2025-11-17 12:20:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":870392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCAD expression levels in liver cancer vs. normal cells and the effect of ICT on CAD at mRNA and protein levels.\u003c/strong\u003e (A) CAD protein levels of CAD in liver cancer cell lines (HepG2 and LM3) compared to the normal liver cell line MIHA. (B) Quantification of Figure A. (C) CAD mRNA levels in l HepG2 and LM3 versus MIHA. **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 vs MIHA. ICT downregulated CAD protein expression of in (D) HepG2 and (G) LM3 cells. (E, H) Densitometric quantification of Figure D, G. ICT upregulated CAD mRNA levels of in (F) HepG2 and (I) LM3 cells, *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 vs Control.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/1a4f9ddc3e0b8e8473c922b0.jpg"},{"id":96246766,"identity":"cffca655-03f8-411f-a03e-19657b9d49d4","added_by":"auto","created_at":"2025-11-19 07:26:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1590478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of miR-18b-5p as a direct regulator of CAD.\u003c/strong\u003e (A) Venn diagram analysis of predicted CAD-targeting miRNAs from miRbase and StarBase databases. Expression analysis of (B) miR-18a-5p and (C) miR-18b-5p in human liver cancer versus normal tissues from the UALCAN database, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 vs Normal. (D) Predicted binding site of miR-18b-5p in the CAD 3`UTR and corresponding mutant sequence used for validation. (E) Dual-luciferase reporter assay employed to discover the regulatory relationship between miR-18b-5p and CAD, *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 vs miR-NC.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/852ea02bfc585fc4095bcc55.jpg"},{"id":96085029,"identity":"2587ed7a-03b5-4900-9cf7-d7c09bcc9d75","added_by":"auto","created_at":"2025-11-17 12:20:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3106246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eICT upregulates miR-18b-5p to post-transcriptionally inhibit CAD protein expression.\u003c/strong\u003e ICT increased miR-18b-5p expression in (A) HepG2 and (D) LM3 cells, *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control. Transfection efficiency of the miR-18b-5p mimic in (B) HepG2 and (E) LM3 cells, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 vs miR-NC. Transfection efficiency of the miR-18b-5p inhibitor in (C) HepG2 and (F) LM3 cells, *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs inhibitor-NC. CAD protein expression was assessed after transfection with the miR-18b-5p mimic alone or in combination with ICT treatment in (G) HepG2 and (I) LM3 cells, transfection with the miR-18b-5p inhibitor alone or in combination with ICT treatment in (H) HepG2 and (J) LM3 cells. Statistical analysis: (I) and (M) data from (G) and (K), *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-NC; \u003csup\u003e∆\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-18b-5p mimic; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs ICT+miR-NC. (J) and (N) data from (H) and (L), *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+ inhibitor-NC; \u003csup\u003e∆\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-18b-5p inhibitor; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs ICT+inhibitor-NC.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/e0523539f82134a12c0f101b.jpg"},{"id":96085031,"identity":"5d858b8e-2f35-447c-bb1b-466eb66368ee","added_by":"auto","created_at":"2025-11-17 12:20:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3452200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of ICT-induced upregulation of miR-18b-5p on proliferation and colony formation in HepG2 and LM3 cells. \u003c/strong\u003e(A) (E upper panel) (F) In HepG2 cells, transfection with miR-18b-5p mimic alone or combined with ICT treatment significantly affected proliferation and colony formation, similarly, (C) (H upper panel) (I) in LM3 cells, *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-NC; \u003csup\u003e∆\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-18b-5p mimic;\u003csup\u003e #\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs ICT+miR-NC. (B) (E lower panel) (G) In HepG2 cells, transfection with miR-18b-5p inhibitor alone or combined with ICT treatment altered proliferation and colony formation, and (D) (H lower panel) (J) in LM3 cells (×40), *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+ inhibitor-NC; \u003csup\u003e∆\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-18b-5p inhibitor; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs ICT+inhibitor-NC.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/69059d50032d793954cdb387.jpg"},{"id":96249611,"identity":"00aee31e-d7da-482a-8b4e-0860186d6ea4","added_by":"auto","created_at":"2025-11-19 07:35:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5473221,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of ICT-induced upregulation of miR-18b-5p on the migratory ability of HepG2 and LM3 cells. \u003c/strong\u003e(A) (C) In HepG2 cells, transfection with miR-18b-5p mimic alone or in combination with ICT treatment altered cell migration, similarly, (E) (G) in LM3 cells (×10), *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-NC; \u003csup\u003e∆\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-18b-5p mimic;\u003csup\u003e #\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs ICT+miR-NC. (B) (D) In HepG2 cells, transfection with miR-18b-5p inhibitor alone or in combined with ICT treatment affected migration, and (F) (H) in LM3 cells (×10), *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+ inhibitor-NC; \u003csup\u003e∆\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control+miR-18b-5p inhibitor; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs ICT+inhibitor-NC.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/8dc899edbe6454af228b53d0.jpg"},{"id":96247017,"identity":"2a365811-ac94-4d90-a9f4-ba6f05b3fe2a","added_by":"auto","created_at":"2025-11-19 07:27:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3789468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eantitumor effects of ICT in a liver cancer xenograft model. \u003c/strong\u003e(A) Experimental timeline showing tumor implantation and treatment protocol. (B) (C) (D) ICT treatment significantly inhibited tumor growth, as evidenced by tumor size, weight, and tumor volume progression compared to controls. (E) Body weight monitoring of ICT treatment. (F) Representative images of ki-67 immunohistochemistry (upper panel) and HE staining (lower panel) of tumor tissues in Control and ICT-treated mice (400×). (G) Quantitative analysis of ki-67 positivity rates. (H) Effect of ICT on CAD protein expression. (I) Quantitative analysis of Figure H. Effect of ICT on (J) CAD mRNA and (K) miR-18b-5p levels in tumor tissues compared with controls. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs Control.\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/f26b7867a880abdd337b2343.jpg"},{"id":99172308,"identity":"0d6373f5-c5bd-4047-bb65-9b7c13979592","added_by":"auto","created_at":"2025-12-29 16:07:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19293414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/5b911187-43e7-40b3-b9b2-b03e45500611.pdf"},{"id":96085036,"identity":"a1c4526c-7b64-4fa5-83c3-20129de7aaef","added_by":"auto","created_at":"2025-11-17 12:20:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":813019,"visible":true,"origin":"","legend":"","description":"","filename":"Supplymentarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7606192/v1/3934c41b24c6920662d339f6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Icaritin suppresses CAD-mediated liver cancer development by targeting miR-18b-5p in a xenograft mouse model","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary liver cancer ranks as the sixth most common malignant tumor worldwide in terms of incidence and the third leading cause of cancer-related mortality, characterized by its insidious onset and high potential for metastasis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, it holds the fourth position in incidence and the second in mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current clinical treatments face significant limitations, including issues such as drug resistance and short duration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, a deeper understanding of the pathogenesis of liver cancer is crucial, and there is a critical need to develop more scientifically effective targeted anticancer drugs that offer better tolerability and safety.\u003c/p\u003e\u003cp\u003eAs the fundamental building blocks of nucleic acids, nucleotides are ubiquitously distributed throughout the body and play diverse biological roles. Cancer cells preferentially utilize the nucleotide \u003cem\u003ede novo\u003c/em\u003e synthesis to rapidly produce the precursors necessary for DNA and RNA, thereby maintaining their growth and stability. CAD is a hexameric enzyme composed of 243 kDa polypeptide chains [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], functions as a multifunctional enzyme that catalyzes the first three rate-limiting steps in the \u003cem\u003ede novo\u003c/em\u003e synthesis of pyrimidine nucleotides, which is overexpressed in cancer cells, ensuring a high flux of pyrimidines essential for their proliferation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. CAD is closely linked to accelerated carcinogenesis and reduced survival rates, making it a potential biomarker for the early recurrence of liver cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and an indicator for poor prognosis in patients with glioblastoma [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, the elevated expression of CAD activates the Notch signaling pathway, which subsequently enhances c-Myc-mediated glycolysis, leading to increased chemoresistance in gastric cancer cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Collectively, CAD serves as a significant risk indicator in malignant tumors, playing a crucial role in promoting tumor growth. Consequently, targeting CAD could represent an effective strategy in chemotherapy.\u003c/p\u003e\u003cp\u003eIt is estimated that microRNAs (miRNAs) can regulate more than 60% of human protein-coding genes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], playing pivotal roles in modulating cellular processes such as metabolism, proliferation, differentiation, and apoptosis. miRNAs exert their regulatory effects by binding to complementary sequences within the 3' untranslated region (3' UTR) of target mRNAs, leading to either mRNA degradation or translational repression [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Accumulating evidence has shown that miRNAs are frequently dysregulated in various cancers, where they can function as either oncogenes or tumor suppressors, thereby influencing tumor development and progression [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For instance, studies have revealed that miR-10b is overexpressed in liver cancer tissues, and enhances the migratory and invasive capabilities of hepatocellular carcinoma (HCC) cells [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, miR-122 has been shown to target PKM2, a key enzyme in glycolysis, reducing lactate production and suppressing aerobic glycolysis in liver cancer cells, thereby disrupting their energy supply [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, the inhibition of miR-542-3p has been linked to the overactivation of the transforming growth factor β (TGFβ)/Smad pathway, which promotes epithelial-mesenchymal transition (EMT) and contributes to poor prognosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings underscore the critical involvement of miRNAs in various mechanisms driving cancer proliferation and metastasis, including the disruption of normal cell cycle regulation. By modulating miRNA expression and targeting specific genes, it is possible to inhibit cancer cell proliferation and impede tumor progression, highlighting their potential as therapeutic targets.\u003c/p\u003e\u003cp\u003eICT is a bioactive compound purified from \u003cem\u003eEpimedium\u003c/em\u003e. It belongs to the class of isoprenoid flavonoid derivatives and serves as a metabolite of Icariin (ICA) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Current pharmacological research has revealed that ICT possesses a wide range of significant biological effects, including anti-inflammatory, immunomodulatory, neuroprotective, anti-osteoporotic, and anti-tumor properties [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the context of anti-tumor activity, ICT has demonstrated notable inhibitory effects on various cancers including colon cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], renal cell carcinoma [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], prostate cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and non-small cell lung cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, ICT has been shown to modulate tumor cell metabolism and regulate the progression of liver cancer through regulating specific miRNAs, including miR-299-5p [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], miR-620, miR-1236 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our previous studies have demonstrated that ICT suppresses glycolysis in liver cancer cells by regulating the ROS/P38 MAPK/P53 signaling pathway [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nucleotide metabolism plays a critical in tumor development and progression [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the specific mechanisms by which ICT regulates pyrimidine metabolism remain poorly understood. In this study, we performed a series of \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments to study whether ICT inhibits liver cancer progression by regulating the \u003cem\u003ede novo\u003c/em\u003e synthesis of pyrimidine, thereby providing a robust theoretical and experimental foundation for the potential clinical application of ICT in the precision treatment of liver cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eReagents\u003c/h2\u003e\u003cp\u003eICT (purity\u0026gt;98%, SML0551, Sigma-Aldrich, St. Louis, MO, USA) was dissolved in dimethyl sulfoxide (DMSO, D5879, Sigma-Aldrich, St. Louis, MO, USA) to prepare a stock solution and stored at \u0026minus;\u0026thinsp;20℃. The working solution was diluted with the growth medium before each use. Antibodies against CAD (1:500, AF4715) and β-actin (1:1000, AF7018) were purchased from Affinity Bioscience (Cincinnati, OH, USA). The Ki-67 (1:10000, 27309-1-AP) antibody was purchased from Proteintech (Chicago, USA).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCell Line and Cell Culture\u003c/h3\u003e\n\u003cp\u003eThe human liver cancer cell lines HepG2 and LM3, the human normal immortalized liver cell line MIHA, and the human renal epithelial cell line HEK-293T were obtained from the Cell Center of the Chinese Academy of Sciences (Shanghai, China). All cell lines were cultured in high-glucose Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM, MA0212, MeilunBio, Dalian, China) supplemented with 10% fetal bovine serum (FBS, 04-001-1A, BI, Herzliya, Israel) and 1% penicillin/streptomycin (P1400, Solarbio, Beijing, China) at 37℃ in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003ch3\u003eCell transfection\u003c/h3\u003e\n\u003cp\u003eThe miR-18b-5p mimic (B04002), miR-18b-5p inhibitor (B03001), and their corresponding blank/negative controls, miR-NC (B01001) and inhibitor-NC (B04003) were purchased from GenePharma (Suzhou, China), with their sequences detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For transfection experiments, HepG2 and LM3 cells in the logarithmic growth phase were seeded in six-well plates and cultured until reaching approximately 60% confluence. Transfection was performed using siRNA Transfection Reagent (sc-29528, Santa Cruz, Dallas, TX, USA) according to the manufacturer\u0026rsquo;s protocol, with miR-18b-5p mimic/miR-NC or miR-18b-5p inhibitor/inhibitor-NC complexes. Following a 6-hour transfection period, cells were treated with either vehicle or ICT for 24 h before being harvested for subsequent qRT-PCR and Western blot analyses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Oligo sequences for transfection\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOligo name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSequences(5\u0026prime;-3\u0026prime;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-18b-5p mimic sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUAAGGUGCAUCUAGUGCAGUUAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-18b-5p mimic antisense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAACUGCACUAGAUGCACCUUAUU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiRNA mimic NC sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUUCUCCGAACGUGUCACGUTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiRNA mimic NC antisense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACGUGACACGUUCGGAGAATT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-18b-5p inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCUAACUGCACUAGAUGCACCUUA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiRNA inhibitor NC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCAGUACUUUUGUGUAGUACAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eWestern Blot\u003c/h3\u003e\n\u003cp\u003eTwenty-four hours post ICT treatment, cells were harvested and lysed in RIPA buffer (P0013C, Solarbio, Beijing, China) supplemented with PMSF protease inhibitor (P0100, Solarbio, Beijing, China). Protein concentrations were determined using a BCA assay kit (E112, Vazyme, Nanjing, China). Equal amounts of whole-cell lysates (50 \u0026micro;g) were loaded and separated by 6% SDS-PAGE and then transferred onto polyvinylidene difluoride (PVDF) membranes. Protein molecular weights were evaluated using a pre-stained protein ladder (PageRuler Plus, 26634, Thermo Fisher Scientific, MA, USA), and membranes were cropped according to the molecular weights of target proteins. Membranes were blocked and sequentially incubated with primary and secondary antibodies. Protein bands were visualized using Maxilumin\u0026trade;-WB Pico Chemiluminescent Substrate Reagent (WB001, Baizhi, Beijing, China) and imaged with a ChemiDoc imaging system (Tanon, 4600 SF, Shanghai, China). Quantitative analysis was performed using ImageJ software, with target protein band intensities normalized to β-actin expression levels.\u003c/p\u003e\n\u003ch3\u003eqRT-PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated from HepG2 and LM3 cells treated with ICT or vehicle for 24 h using TRIzol reagent (DP424, TIANGEN, Beijing, China). cDNA synthesis was performed using HiScript II First Strand cDNA Synthesis Kit (R223-01, Vazyme, Nanjing, China) for mRNA analysis and the miRNA 1st Strand cDNA Synthesis Kit (MR101-01, Vazyme, Nanjing, China) for miRNA detection. Quantitative PCR was carried out using SYBR Green Master Mix (Q711-02, Vazyme, Nanjing, China) for mRNA quantification and miRNA Universal SYBR qPCR Master Mix (MQ101-01, Vazyme, Nanjing, China) for miRNA analysis on a Lightcycler 96 system (Roche, Basel, Switzerland). The thermal cycling conditions were set as 40 cycles under 56℃ for 30 s, 95℃ for 30 s, and 72℃ for 30 s. Relative gene expression levels were calculated using the 2\u003csup\u003e\u0026minus;∆∆CT\u003c/sup\u003e equation, with U6 snRNA and β-actin serving as internal controls for miRNA and mRNA normalization, respectively. All primer sequences, synthesized by Sangon (Shanghai, China), are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe primer sequences for qRT-PCR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSequences(5\u0026prime;-3\u0026prime;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: AGTGGTGTTTCAAACCGGCAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: CAGAGGATAGGTGAGCACTAAGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eβ-actin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: CATGTACGTTGCTATCCAGGC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: CTCCTTAATGTCACGCACGAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003emiR-18b-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: TAAGGTGCATCTAGTGCAGTTAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: AGTGCAGGGTCCGAGGTATT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eU6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: CTCGCTTCGGCAGCACA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: AACGCTTCACGAATTTGCGT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCell Counting Kit-8 assay\u003c/h2\u003e\u003cp\u003eFollowing successful transfection, cells were seeded in 96-well plates at a density of 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well in 100 \u0026micro;L culture medium, with six replicate wells for each condition. After 24 h of incubation, cells were treated with either ICT or vehicle for an additional 24 h. Cell viability was assessed using the CCK8 kit (CA1210, Solarbio, Beijing, China), where the CCK8 reagent was diluted 1:9 in DMEM medium. Subsequently, 100 \u0026micro;L of the mixture was added to each well, followed by incubation at 37\u0026deg;C for 1.5 h. Absorbance was measured at 450 nm using a microplate reader (Synergy H1, BioTek, Winooski, VT, USA).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eColony Formation Assay\u003c/h3\u003e\n\u003cp\u003eFollowing a 6-hour transfection, HepG2 and LM3 cells were trypsinized and replaced with 2 mL of suspension containing 4\u0026times;10\u003csup\u003e3\u003c/sup\u003e HepG2 cells or 2\u0026times;10\u003csup\u003e3\u003c/sup\u003e LM3 cells per well in six-well plates, respectively. After 24 h of incubation, the cells were treated with either ICT or vehicle for an additional 24 h. The treatment was withdrawn, and the cells were washed with PBS every three days. After 10 days of culture, the cells were stained with 0.1% crystal violet aqueous solution (G1063, Solarbio, Beijing, China) for 20 min. The stained colonies were photographed, and the colony formation rate was calculated.\u003c/p\u003e\n\u003ch3\u003eScratch-Wound Assay\u003c/h3\u003e\n\u003cp\u003eThe six-well plate was initially labeled on the back using a marker pen to indicate the position. Following the completion of cell transfection, 2 mL of cell suspension containing 7\u0026times;10\u003csup\u003e5\u003c/sup\u003e HepG2 and LM3 cells was seeded into each well of the six-well plate. After 24 h, a scratch was made in the cell monolayer using a pipette tip. The cells were then treated with either ICT or DMSO diluted in 1% FBS cell culture medium. Cell migration was monitored and recorded at 0, 24, and 48 h using an inverted microscope (D7500, Nikon, Japan).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDual-Luciferase Reporter assay\u003c/h2\u003e\u003cp\u003eTo investigate the direct interaction between miR-18b-5p and the 3\u0026prime;UTR of CAD, we initially constructed the luciferase reporter vector GP-miRGlO (GenePharma, Suzhou, China) containing either the wild-type or mutated binding site of CAD 3\u0026prime;UTR (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The CAD sequence was cloned into the multiple cloning site of the GP-miRGlO reporter vector. 293T cells were seeded in 24-well plates and co-transfected with 1 \u0026micro;g of the luciferase reporter construct and 5 \u0026micro;L of either miR-18b-5p mimic or negative control (NC) mimic using GP-transfect-Mate reagent (G04008, GenePharma, Suzhou, China). Six hours post-transfection, the culture medium was replaced with fresh medium. Cells were harvested 24 h after transfection for luciferase activity measurement using the Dual Luciferase Reporter Assay System (E1910, Promega, Madison, USA). Firefly luciferase activity was normalized to Renilla luciferase activity for each sample, and the relative luciferase activity was calculated by normalizing the FLU/RLU ratio of each group to that of the plasmid\u0026thinsp;+\u0026thinsp;NC mimic control group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMouse Models and Treatments\u003c/h2\u003e\u003cp\u003eFive-week-old male BALB/c nude mice (Beijing Huafukang Bioscience Co., Ltd., SCXK (Beijing) 2019-0008) were housed in SPF conditions and acclimatized for one week. All procedures were approved by the Animal Ethics Committee of Hebei University (IACUC-202324SR). HepG2 cells (1\u0026times;10\u003csup\u003e7\u003c/sup\u003e cells in 100 \u0026micro;L PBS) were subcutaneously injected into the right flank of each mouse. After 7 days, mice were randomly divided into two groups (n\u0026thinsp;=\u0026thinsp;6): ICT treatment (10 mg/kg) and vehicle control, administered intraperitoneally every other day. Tumor size and body weight were measured every three days. After three weeks, mice were inhaled excess CO\u003csub\u003e2\u003c/sub\u003e and died by cervical dislocation, tumor were collected, and tumor volume (V) was calculated as V=(length\u0026times;width\u003csup\u003e2\u003c/sup\u003e)/2, and weighed, photographed for analysis. This study was carried out in accordance with guidance on the operation of the Animals (Scientific Procedures) Act 1986 and associated guidelines, EU Directive 2010/63 for the protection of animals used for scientific purposes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHematoxylin and eosin (H\u0026amp;E) staining\u003c/h2\u003e\u003cp\u003eTumor specimens were fixed in 4% paraformaldehyde, dehydrated through an ethanol series, and embedded in paraffin. Serial sections of 4 \u0026micro;m thickness were prepared using a microtome. Following deparaffinization and rehydration, tissue sections were stained with hematoxylin and eosin (H\u0026amp;E) using a commercial staining kit (G1120, Solarbio, Beijing, China). After mounting with neutral balsam, the stained sections were digitally scanned using a high-throughput pathology slide scanner (Pannoramic MIDI Ⅱ, 3DHISTECH, Budapest, Hungary) for histological analysis and documentation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eImmunohistochemistry\u003c/h2\u003e\u003cp\u003eDeparaffinized tumor sections underwent antigen retrieval in preheated Tris-EDTA buffer (PH\u0026thinsp;=\u0026thinsp;9.0, BL617A, Biosharp, Beijing, China) at 95℃ for 25 min. Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide (ANNJET, Shandong, China) for 10 min at room temperature. After blocking with 5% BSA for 30 min, sections were incubated with primary antibody at room temperature for 1.5 h, followed by the corresponding secondary antibody for 30 min. Immunoreactivity was visualized using a DAB kit (ZLI-9017, ZSGB-BIO, Beijing, China) for 2 min. The stained sections were scanned using a digital pathology slide scanner, and protein expression patterns were analyzed based on staining intensity and cellular localization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analyses\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using GraphPad Prism 9 software (GraphPad Software, San Diego, CA), with data from at least three independent replicates. Continuous variables following normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). For comparisons between two groups, two-tailed Student\u0026rsquo;s t-tests were applied. Multiple group comparisons were analyzed using one-way analysis of variance (ANOVA) with appropriate post-hoc tests. Non-parametric tests were employed for data that did not meet the assumption of normal distribution. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eICT may inhibit the highly expressed CAD in human liver cancer cells at the post-transcriptional level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the clinical relevance of CAD in liver cancer, we first analyzed its expression in human liver cancer and normal tissues using the UALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e and GEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e databases. The results showed that both mRNA and protein levels of CAD were significantly upregulated in liver cancer tissues compared to normal controls (Fig. S2A, B). Furthermore, patients with high CAD expression exhibited shorter overall survival than those with low CAD expression, and CAD expression levels correlated positively with tumor malignancy grade (Fig. S2C, D). To validate these findings experimentally, we examined CAD expression in the human normal liver cell line MIHA and the liver cancer cell lines LM3 and HepG2. Consistent with the bioinformatics data, both mRNA and protein levels of CAD were markedly elevated in the liver cancer cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C). These results suggest that CAD may play a critical role in liver cancer progression and could serve as a potential prognostic marker for poor clinical outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter 24 h of treatment with the optimal concentration of 20 \u0026micro;M ICT (previously determined as the effective dose for HepG2 and LM3 cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]), Western blot analysis revealed a reduction in CAD protein expression in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, E and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, H). Interestingly, qRT-PCR results showed an upregulation of CAD mRNA levels under the same conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and I). This discrepancy between transcriptional and translational regulation prompted us to investigate whether ICT modulates CAD at the post-transcriptional, translational, or post-translational level. We hypothesized that intermediate molecules, such as RNA-binding protein or miRNAs, might be involved in this regulatory mechanism.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCAD is a direct target of miR-18b-5p\u003c/h2\u003e\u003cp\u003eMiRNAs are well-established post-transcriptional regulators of gene expression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Given the observed discordance between CAD mRNA regulation and protein downregulation following ICT treatment, we hypothesized that ICT may modulate CAD expression through miRNA-mediated mechanisms. We used miRbase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirbase.org/\u003c/span\u003e\u003cspan address=\"https://mirbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e and StarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/\u003c/span\u003e\u003cspan address=\"http://starbase.sysu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e to identify candidate miRNAs targeting CAD. The Venn diagram analysis revealed two potential CAD-targeting miRNAs: hsa_miR-18a- 5p and hsa_miR-18b-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Analysis of liver cancer tissue expression patterns using the UALCAN database showed differential expression of these miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). Based on our earlier findings that ICT increases CAD mRNA while decreasing its protein expression, we postulated that ICT might upregulate miRNAs that suppress CAD translation. We therefore focused on miR-18b-5p for further investigation. Target prediction analysis identified potential binding sites between miR-18b-5p and the CAD 3`UTR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This interaction was verified using a dual-luciferase reporter assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), confirming CAD as a direct target of miR-18b-5p. These results demonstrate that miR-18b-5p may exert a potential tumor-suppressive role in liver cancer via direct binding to CAD mRNA to inhibit its translation, providing a mechanistic explanation for the post-transcriptional regulation of CAD by ICT.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eICT inhibits CAD expression in liver cancer cells by upregulating miR-18b-5p\u003c/h2\u003e\u003cp\u003eTo investigate whether CAD is a direct target of miR-18b-5p and whether ICT inhibits CAD expression through miR-18b-5p, we first treated HepG2 and LM3 cells with ICT. As expected, ICT treatment significantly increased miR-18b-5p expression in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, D). Next, we transfected HepG2 and LM3 cells with either a miR-18b-5p mimic or inhibitor to modulate miR-18b-5p levels. Transfection efficiency was confirmed, which showed a marked increase in miR-18b-5p expression with the mimic (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, E) and a decrease with the inhibitor (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, F). To assess the regulatory relationship between miR-18b-5p and CAD, we treated the transfected cells with or without ICT and analyzed CAD expression. In HepG2 cells, miR-18b-5p mimic transfection downregulated CAD expression compared to the miR-NC group, and this suppression was further enhanced by ICT co-treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, I). Conversely, transfection with the miR-18b-5p inhibitor increased CAD expression when compared to the inhibitor NC group, and this effect was reversed by ICT treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eH, J). Similar results were also observed in LM3 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eK-L and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eM-N). These findings demonstrate that miR-18b-5p negatively regulates CAD expression and ICT regulates CAD expression through miR-18b-5p upregulation, confirming CAD as a functional target of miR-18b-5p in liver cancer cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eICT inhibits proliferation, clonogenicity, and migration of liver cancer cells via miR-18b-5p\u003c/h2\u003e\u003cp\u003eTo elucidate the role of miR-18b-5p in liver cancer cell growth and ICT-mediated regulation, we performed functional assays in both HepG2 and LM3 cells following miR-18b-5p modulation. Transfection with miR-18b-5p mimic significantly reduced cell proliferation, with enhanced suppression observed upon ICT co-treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, C). Conversely, miR-18b-5p inhibitor promoted proliferation, which ICT treatment effectively reversed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, D). Clonogenic assays revealed parallel effects: miR-18b-5p overexpression diminished colony formation, with further reduction following ICT combination, while miR-18b-5p inhibitor enhanced clonogenicity, an effect similarly reversed by ICT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-G for HepG2, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eH-J for LM3). Scratch tests confirmed these trends, demonstrating that transfection with miR-18b-5p mimic inhibited cell motility, with amplified suppression by ICT (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, C for HepG2, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, G for LM3), whereas miR-18b-5p inhibitor increased migration capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, D for HepG2, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eF, H for LM3). All the above results demonstrated that transfection of miR-18b-5p inhibited the proliferative activity, clone formation ability, and migration ability of liver cancer HepG2 and LM3 cells, and this series of biological behaviors of liver cancer cells could be further significantly inhibited by simultaneous treatment with ICT.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eICT suppresses xenograft tumor growth in mice by upregulating miR-18b-5p to inhibit CAD expression\u003c/h2\u003e\u003cp\u003eTo validate whether the miR-18b-5p-mediated inhibition of CAD expression observed in vitro could similarly suppress liver cancer progression \u003cem\u003ein vivo\u003c/em\u003e, we established a liver cancer xenograft model (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Following three weeks of ICT treatment, tumor tissues were collected for analysis. ICT treatment significantly inhibited tumor growth, as evidenced by reduced tissue size, weight, and volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-D), while showing no significant effect on mouse body weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), demonstrating no overt toxicity. Immunohistochemical analysis showed decreased Ki-67 positivity in ICT-treated tumors, and HE staining confirmed the inhibitory effect of ICT on tumor cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, G). Further analysis of tumor tissue extracts revealed that ICT treatment significantly downregulated CAD protein expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eH, I), while increasing both CAD mRNA and miR-18b-5p levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ, K). These results demonstrate that the mechanism observed in vitro, where ICT post-transcriptionally suppresses CAD protein expression through miR-18b-5p upregulation, is conserved in vivo, providing compelling evidence for the consistency of this regulatory pathway across experimental systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetabolic reprogramming represents a fundamental hallmark of cancer progression, with dysregulated growth signals driving tumor cells to acquire nutrients and biosynthetic precursors through metabolic adaptation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, we identified ICT, a naturally occurring compound derived from the \u003cem\u003eEpimedium genus\u003c/em\u003e, as a potent inhibitor of liver cancer growth through targeting pyrimidine \u003cem\u003ede novo\u003c/em\u003e synthesis. Firstly, we found that CAD, the first key enzyme in pyrimidine \u003cem\u003ede novo\u003c/em\u003e synthesis. Our comprehensive analysis revealed that CAD \u0026ndash; the rate-limiting enzyme in pyrimidine synthesis, was significantly overexpressed in clinical liver cancer specimens and strongly correlated with poor patient prognosis. This clinical observation was corroborated by in vitro studies demonstrating elevated CAD expression at both mRNA and protein levels in liver cancer cell lines compared to normal liver cells. Intriguingly, ICT treatment induced a paradoxical regulatory pattern, significantly upregulating CAD mRNA while concurrently reducing CAD protein expression, suggesting potential post-transcriptional regulation. This discrepancy prompted us to investigate potential mechanisms involving mRNA stability modulation, translational repression, or protein degradation pathways, with particular focus on non-coding RNAs given their established role in post-transcriptional gene regulation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings collectively demonstrate that ICT exerts its anti-tumor effects by specifically targeting the pyrimidine synthesis pathway through complex regulation of CAD expression, highlighting the therapeutic potential of metabolic intervention in liver cancer treatment.\u003c/p\u003e\u003cp\u003eWe hypothesized that ICT regulates CAD expression post-transcriptionally via miRNA-mediated mechanisms. Bioinformatics analysis identified two candidate miRNAs - miR-18a-5p and miR-18b-5p that were differentially expressed in liver cancer tissues. Although miR-18a-5p is a well-characterized microRNA [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], its role in liver cancer remained unclear, prompting its selection for further study. Using dual-luciferase reporter assays, we confirmed CAD as a direct target of miR-18b-5p. Subsequent experiments revealed that ICT significantly upregulated miR-18b-5p in liver cancer cells. Functional validation demonstrated that miR-18b-5p overexpression reduced CAD protein levels, while its inhibition increased them; notably, combining miR-18b-5p mimic with ICT treatment synergistically enhanced CAD expression. Given CAD\u0026rsquo;s critical role in dNTP and NTP synthesis \u0026ndash; essential for energy metabolism and gene expression \u0026ndash; its inhibition would be expected to impair cancer cell proliferation and metastasis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Indeed, CCK8, scratch, and colony formation assays confirmed that miR-18b-5p mimics inhibited proliferation, migration, and clonogenicity in liver cancer cells, with ICT treatment amplifying these effects. Conversely, miR-18b-5p inhibitor reversed these phenotypes. Collectively, these results establish that ICT suppresses liver cancer progression by upregulating miR-18b-5p to target CAD, thereby disrupting nucleotide metabolism and impairing malignant behaviors.\u003c/p\u003e\u003cp\u003eTo validate our in vitro findings, we established a xenograft liver cancer model in immunodeficient nude mice. ICT treatment significantly suppressed tumor growth compared to control, while exhibiting no notable effects on body weight, demonstrating both efficacy and biosafety. Terminal analysis revealed that ICT-treated tumors showed reduced cellular proliferation, as confirmed by HE staining and Ki-67 immunohistochemistry. Molecular characterization of tumor tissues demonstrated that ICT consistently upregulated miR-18b-5p while downregulating CAD protein expression \u003cem\u003ein vivo\u003c/em\u003e, despite increased CAD mRNA levels, mirroring our in vitro observations. These results conclusively demonstrate that ICT exerts its anti-tumor effects through a conserved miR-18b-5p/CAD regulatory axis across both cellular and animal models, providing comprehensive evidence for its therapeutic potential in liver cancer treatment.\u003c/p\u003e\u003cp\u003eAltogether, this study systematically demonstrates that ICT inhibits liver cancer progression by upregulating miR-18b-5p to suppress CAD, the rate-limiting enzyme in pyrimidine \u003cem\u003ede novo\u003c/em\u003e synthesis, at both cellular and overall levels. Our finding establishes that CAD as a direct target of miR-18b-5p; and ICT\u0026rsquo;s ability to restore miR-18b-5p expression in liver cancer cells; and consequent disruption of pyrimidine synthesis leading to tumor suppression. However, several important questions remain: first, whether ICT regulates miR-18b-5p directly or through intermediate factors requires clarification; second, while CAD is identified as a primary target, the comprehensive impact on downstream pyrimidine metabolites needs metabolomic characterization; third, clinical validation across diverse liver cancer subtypes is essential given tumor heterogeneity. Therefore, future directions will include: clinical correlation studies using patient-derived tissues; metabolomic profiling of pyrimidine pathway intermediates; and investigation of potential ceRNA networks involving circRNA/lncRNA in miR-18b-5p regulation. These studies will refine our understanding of ICT\u0026rsquo;s molecular mechanisms and facilitate its development as a promising therapeutic strategy for liver cancer prevention and treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eDi Wu contributed to conceptualization, data curation, methodology, performed the experiments, formal analysis and writing\u0026ndash;original draft. Tian Mi contribute to conceptualization and performed the experiments. Xue Tang contributed to formal analysis and performed the experiments. Yiming Jia performed some experiments; Tao Guo and Guoqiang Zhou contributed to methodology, supervision and administered the project; Wenjuan Li supervised funding acquisition, methodology, administered the project, writing\u0026ndash;review and editing. All the authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by Hebei Provincial Natural Science Foundation of China (Grant No. C2023201073, H2022201017), the Central Guidance on Local Science and Technology Development Fund of Hebei Province (Grant No. 226Z2402G), the\u0026nbsp;\u0026ldquo;Three Three Three Talents Program\u0026rdquo; of Hebei Province (Grant No. C20221016), the Natural Science Interdisciplinary Research Program of Hebei University (Grant No. DXK202210), College Students\u0026rsquo; Innovation and Entrepreneurship Training Program of Hebei University (Grant No. DC2025530).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel R L, et al. 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Serum Exosomal MicroRNA, miR-10b-5p, as a Potential Diagnostic Biomarker for Early-Stage Hepatocellular Carcinoma. Journal of clinical medicine. 2020;9:281.\u003c/li\u003e\n\u003cli\u003eQuan B, Liu W, Yao F, et al. LINC02362/hsa-miR-18a-5p/FDX1 axis suppresses proliferation and drives cuproptosis and oxaliplatin sensitivity of hepatocellular carcinoma. American journal of cancer research. 2023;13:5590-5609.\u003c/li\u003e\n\u003cli\u003eBuj R, and Aird K M. Deoxyribonucleotide Triphosphate Metabolism in Cancer and Metabolic Disease. Frontiers in endocrinology. 2018;9:177.\u003c/li\u003e\n\u003cli\u003eHelleday T, and Rudd S G. Targeting the DNA damage response and repair in cancer through nucleotide metabolism. Molecular oncology. 2022;16:3792-3810.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"medical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"medo","sideBox":"Learn more about [Medical Oncology](https://www.springer.com/journal/12032)","snPcode":"12032","submissionUrl":"https://submission.nature.com/new-submission/12032/3","title":"Medical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CAD, miR-18b-5p, Liver cancer, Icaritin, De novo synthesis of pyrimidine","lastPublishedDoi":"10.21203/rs.3.rs-7606192/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7606192/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCancer cells show abnormal nucleotide metabolism and prefer the \u003cem\u003ede novo\u003c/em\u003e synthesis pathway. As the key enzymes, Carbamoyl-phosphate synthetase 2 (CAD) is overactivated in cancer and promotes pyrimidine \u003cem\u003ede novo\u003c/em\u003esynthesis, supplying cancer cells with DNA and RNA biosynthesis precursors. Therefore, the development of drugs targeting CAD might inhibit cancer progression and transformation. Icaritin (ICT) is an isoprenoid flavonoid derivative with a wide range of anticancer activities, however, the mechanism of ICT in regulating pyrimidine biosynthesis in cancer remains unclear. MicroRNAs are involved in carcinogenesis by regulating the expression of target genes, and ICT has been shown to regulate the expression of miRNAs leading to suppressing cancer progression. Using both human normal hepatocytes and liver cancer cells, we found that CAD expression was significantly elevated in cancer cells. Interestingly, although ICT treatment reduced CAD protein levels in liver cancer cells, it increased CAD transcriptional activity. Dual-luciferase reporter assays confirmed miR-18b-5p as a direct regulator of CAD. By transfecting miR-18b-5p mimics or inhibitors, we showed ICT upregulates miR-18b-5p to suppress CAD, inhibiting liver cancer cell proliferation, migration, and colony formation. Furthermore, in a human liver cancer xenograft mouse model, ICT treatment markedly reduced tumor growth and decreased Ki-67 expression, consistent with the in vitro results, CAD protein expression was downregulated, while its mRNA level was upregulated, further supporting a post-transcriptional regulatory mechanism. Overall, ICT plays an anti-liver cancer role by increasing miR-18b-5p at the post-transcriptional level to inhibit CAD expression, thereby interfering with the \u003cem\u003ede novo \u003c/em\u003esynthesis of pyrimidine and development of liver cancer.\u003c/p\u003e","manuscriptTitle":"Icaritin suppresses CAD-mediated liver cancer development by targeting miR-18b-5p in a xenograft mouse model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 12:20:43","doi":"10.21203/rs.3.rs-7606192/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-29T17:46:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T07:16:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68368431830817931984218099456140030868","date":"2025-11-06T04:34:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T04:17:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T13:09:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T13:08:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Medical Oncology","date":"2025-09-13T09:08:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"medical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"medo","sideBox":"Learn more about [Medical Oncology](https://www.springer.com/journal/12032)","snPcode":"12032","submissionUrl":"https://submission.nature.com/new-submission/12032/3","title":"Medical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"09e97bd1-a12b-4cd6-95f2-7db59180307e","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:01:05+00:00","versionOfRecord":{"articleIdentity":"rs-7606192","link":"https://doi.org/10.1007/s12032-025-03211-4","journal":{"identity":"medical-oncology","isVorOnly":false,"title":"Medical Oncology"},"publishedOn":"2025-12-26 15:57:42","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-11-17 12:20:43","video":"","vorDoi":"10.1007/s12032-025-03211-4","vorDoiUrl":"https://doi.org/10.1007/s12032-025-03211-4","workflowStages":[]},"version":"v1","identity":"rs-7606192","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7606192","identity":"rs-7606192","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}