Exploring the mechanism of Naringenin in the treatment of hepatocellular carcinoma based on mRNA sequencing and experimental validation

Exploring the mechanism of Naringenin in the treatment of hepatocellular carcinoma based on mRNA sequencing and experimental validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the mechanism of Naringenin in the treatment of hepatocellular carcinoma based on mRNA sequencing and experimental validation Zheng Zhang, Haoran Wang, Xin Liu, Meng He, Yujia Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5403136/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Background Hepatocellular carcinoma (HCC) ranks among the top three causes of cancer-related mortality globally and is associated with a relatively low five-year overall survival rate. Naringenin has demonstrated significant inhibitory effects on various neoplasms; however, the mechanisms of action and potential molecular targets of naringenin in the context of HCC remain to be elucidated. Methods Cellular proliferation in cancer cells was quantified using the Cell Counting Kit-8 (CCK-8) assay. Wound healing and transwell tests were employed to evaluate the migratory and invasive capabilities of the cells, respectively. Apoptosis was evaluated using Hoechst staining to visualize nuclear changes and flow cytometry to quantify apoptotic populations. Following mRNA sequencing, we integrated the TCGA database with known naringenin-related targets to identify overlapping genes, which were subsequently subjected to clinical significance analysis. The expression of these genes was confirmed at the protein and mRNA levels using Western blot (WB) and quantitative PCR (qPCR), respectively. In vivo experiments were conducted using an MHCC-97H xenograft model in nude mice, with histopathological examination of tumor sections performed using hematoxylin and eosin (H&E) staining. Results In vitro, naringenin demonstrated a potent inhibitory effect on the proliferation, invasion, and migration of MHCC-97H and Huh7 cells while exhibiting a pronounced pro-apoptotic impact on both cell lines. mRNA sequencing results revealed significant differential gene expression. Utilizing Venn diagrams, we identified key genes, including IGFBP3, PGF, CA9, AKR1C3, KLK1, and CHRNA7. We implicated signaling pathways such as the "Wnt signaling pathway" and "MAPK signaling pathway" as potentially critical in naringenin's anti-HCC activity. The clinical significance analysis revealed that CA9 and AKR1C3 were identified as autonomous prognostic variables for hepatocellular carcinoma (HCC), a conclusion supported by molecular docking investigations. The therapeutic promise of naringenin was further supported by its considerable reduction in tumor weight and volume shown in animal trials. Conclusion This study shows that naringenin may regulate signaling pathways by targeting a series of genes: IGFBP3, PGF, CA9, AKR1C3, KLK1, and CHRNA7, resulting in the inhibition of tumor cell proliferation and metastasis, alongside the promotion of apoptosis. Biological sciences/Cancer/Cancer therapy Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Drug discovery/Biomarkers/Diagnostic markers Naringenin Hepatocellular carcinoma (HCC) Molecular docking mRNA-seq Subcutaneous Xenograft tumor model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction The most common primary malignant tumor of the liver is hepatocellular carcinoma (HCC) 1 . Based on the GLOBOCAN 2020 figures, hepatocellular carcinoma (HCC) was the sixth most often diagnosed cancer and the third most significant contributor to cancer-related fatalities worldwide in 2020. It was reported that there were around 906,000 new cases of HCC and 830,000 deaths 2 . Furthermore, there is evidence to suggest that liver cancer was the second leading cause of cancer-related deaths in China in 2022, with 316,500 deaths reported 3 . Despite continuous improvements in treatment modalities, including surgery, targeted therapies, and immune checkpoint inhibitors (ICI), the objective response rate in HCC patients remains at approximately 30%, and the three-year overall survival rate (OS) is still below 50% 4 . Hence, it is imperative to investigate novel treatment agents and mechanisms in order to enhance the prognosis of patients with hepatocellular carcinoma (HCC). In recent decades, Phytochemicals are attracting increasing attention and focus in cancer treatment and chemoprevention due to the characteristics of easy accessibility, high safety margins, and relatively low costs associated with natural dietary compounds 5 . According to reports, there are more than 3000 plant species that have been found to possess anticancer properties. Thus far, around 30 substances originating from plants have been extracted and evaluated in clinical studies for the purpose of treating cancer. 6 Naringenin, a flavonoid compound, is predominantly present in the form of glycosidic elements derived from the hydrolysis of naringin or narirutin. This bioactive phytochemical is abundantly found in grapefruits, lemons, oranges, and tomatoes. It is acclaimed for its pharmacological benefits, including anti-inflammatory, antioxidant, neuroprotective, hepatoprotective, and anticancer properties. 7 Substantial evidence underscores naringenin's potent anticancer activity; for instance, it can significantly inhibit the migration and lung metastasis of breast cancer cells by reducing the release of TGF-β1. 8 Additionally, naringenin impedes the progression of epithelial ovarian cancer by suppressing the expression of the PI3K pathway and fostering a healthy gut microbiota. 9 However, the intricate molecular mechanisms underpinning naringenin's antitumor effects remain to be fully elucidated, particularly in the context of hepatocellular carcinoma. The advent of high-throughput mRNA sequencing (mRNA-seq) has revolutionized our capacity to analyze gene expression changes across the entire genome, offering an unprecedented view of the transcriptomic landscape in response to therapeutic agents. 10 This technique has yielded valuable insights into the molecular mechanisms of drug action and has facilitated the identification of potential biomarkers and therapeutic targets. The present study aims to employ mRNA sequencing coupled with experimental validation to pinpoint the targets of naringenin in the treatment of hepatocellular carcinoma (HCC), systematically evaluating the mechanisms by which naringenin exerts its effects on HCC. Moreover, we assessed the clinical relevance and distribution of central targets in HCC and conducted cellular experiments on MHCC-97H and HUH7 cell lines to confirm their inhibitory actions and the expression of central target genes. Ultimately, our in vitro experiments substantiated that naringenin can inhibit the proliferation and metastasis of hepatocellular carcinoma cells. This research lays the groundwork for a more profound understanding of the molecular underpinnings of liver cancer pathogenesis and the identification of novel drug targets, potentially leading to more effective treatments for patients with liver cancer. Materials and methods 1.1 Cell Culture and Reagent . The human hepatocellular carcinoma (HCC) cell lines of HUH7 and HMCC-97H were acquired from Wuhan Punosai Life Science and Technology Co., Ltd (Priscilla). To provide ideal growth conditions, the cell lines were grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% Fetal Bovine Serum (FBS) and 1% penicillin-streptomycin. A temperature of 37ºC was maintained for the cells in an environment consisting of 5% CO2 and 95% air. The culture media underwent systematically replaced on an alternate day. 1.2 The Structure and pharmacological properties data of Naringenin The information regarding the structure (Fig. 1 ) and pharmacological properties of Naringenin was acquired from the DrugBank Database and Analysis Platform 11 , using the search term "Naringenin". The naringin utilized in these experiments was procured from MedChemExpress, bearing the respective product identification number HY-N0100. 1.3 CCK-8 assay The cytotoxic impact of naringenin on HUH7 and MHCC-97H hepatic carcinoma cell lines was ascertained via the colorimetric CCK-8 assay. The study was divided into three groups: (1) experimental groups treated with naringenin at concentrations of 80 µM, 160 µM, 240 µM, and 320 µM; (2) a negative control group containing cell suspension in complete medium; and (3) a blank control group with complete medium alone (no cells or drugs). Each group comprised six replicate wells. After 24 hours of initial incubation, the old medium was aspirated using a pipette. For experimental groups, 100 µL of fresh medium containing the designated naringenin concentrations was added to each well. The 96-well plate was then incubated at 37°C under 5% CO₂ for 24, 48, or 72 hours. Following incubation, the medium was removed, and 100 µL of basal medium supplemented with 10% CCK-8 reagent (Cell Counting Kit-8, Dojindo Laboratories) was carefully added to each well to avoid bubble formation. The plate was further incubated at 37°C for 1 hour. Post-incubation, the optical density at 450 nm was quantitatively assessed utilizing an ultra-microplate reader (SpectraMax; Molecular Devices Corp, Sunnyvale, CA, USA). To affirm the precision and the reproducibility of the experimental results, the assay was performed in triplicate and iterated on three separate occasions. The resultant data were normalized and presented as a percentage relative to the untreated control group. 1.4 Transwell cell migration and invasion assay The Boyden chamber was employed for the transwell migration and invasion studies. In the upper chamber, a total of 5 × 104-1 x 105 cells were cultured in 200 µL of serum-free DMEM. Naringenin was added to the upper chamber in a gradient manner(80 µM, 160 µM, 240 µM, and 320 µM), while the lower wells were supplemented with 500 µL of medium containing 10% FBS. In the case of invasion experiments, Matrigel was applied to the bottom of the small chamber using a procedure similar to that employed for the migration experiments. After an incubation period of 20–24 hours, the cells were fixed, washed with PBS, and stained with crystal violet. The assessment of hepatocellular carcinoma cell migration and invasion was conducted utilizing an inverted light microscope. The quantification of migrating or infiltrated cells was conducted by enumerating the cells within 10 individually chosen fields at a magnification of ×100. 1.5 Hoechst Initially, cells were cultured in a 24-well plate until they attained the requisite confluency. Following the initial treatment, the cells were exposed to naringenin at graded concentrations of 80 µM, 160 µM, 240 µM, and 320 µM, and subsequently incubated for 24 hours under standard culture conditions (37°C, 5% CO₂).After incubation, cells were subjected to a phosphate-buffered saline (PBS) wash. To facilitate effective staining, 500 µL of Hoechst 33342 solution was dispensed into each well, ensuring uniform coverage of the cellular monolayer. The cells were then incubated at ambient temperature and shielded from light for 30 minutes. Post-incubation, the staining solution was carefully aspirated, and the cells were washed thrice with PBS, with each wash extending for 5–10 minutes. The cells were subsequently filtered and imaged at 100x magnification in three arbitrarily selected fields. The process of capturing images was then followed by analyzing them using ImageJ software for further assessment and interpretation of the data. 1.6 Flow cytometry The experimental protocol involved culturing cells until they reached 70–80% confluency. Following the initial treatment, cells were treated with naringenin at graded concentrations (80 µM, 160 µM, 240 µM, and 320 µM) and incubated under standard conditions (37°C, 5% CO₂) for 24 hours. Cells were harvested by trypsinization (using trypsin without EDTA), followed by centrifugation (300 × g, 5 min) to remove the supernatant. To ensure complete supernatant removal, cells were washed three times with ice-cold PBS and subjected to additional centrifugation (300 × g, 5 min). The cell pellet was resuspended in 500 µl of binding buffer (Annexin V Binding Buffer, BD Biosciences) for subsequent apoptosis analysis. Apoptosis was assessed using an Annexin V-FITC/PI Apoptosis Detection Kit (Sigma-Aldrich, Cat# APOAF) according to the manufacturer’s protocol. Briefly, 5 µl of Annexin V-FITC and 5 µl of propidium iodide (PI) were added to 500 µl of cell suspension, followed by gentle mixing. After incubation at room temperature for 15 min in the dark, samples were analyzed by flow cytometry to distinguish viable, early apoptotic, and late apoptotic/necrotic cell populations. 1.7 Transcriptome Analysis of MHCC-97H Cells Cells were cultivated to an optimal density and treated with naringenin at an IC50 of 182.2 µM for 24 hours, designated as the NG group. Cells that did not undergo any treatment were referred to as the NC group. Following treatment, the culture medium was aspirated, and the cells were washed one to two times with 1X PBS to ensure complete removal of the medium. TRIzol reagent was then applied to lyse the cells, with pipetting performed repeatedly to achieve a clear, non-viscous lysate. The cell lysate was subsequently transferred into RNase-free cryovials with screw caps. After a 5-minute rest at room temperature, the samples were stored at -80°C and transported on dry ice. Independent observations were conducted on all samples, with sextuplicate determinations. A differential gene expression analysis was performed to investigate the probable activities and underlying biological mechanisms of differently expressed genes. The objective was to identify candidate genes that are linked with cell proliferation, migration, and apoptosis, which are impacted by NG therapy. 1.8 Screening of Differential Genes We conducted a comparative analysis of the expression profiles between the treatment group and the control group using R software (version 4.3.1). For each gene, we performed differential expression analysis using the edgeR Bioconductor software package 12 . We identified differentially expressed genes (DEGs) using the thresholds of |log2(fold change [FC]) | > 1 and a p-value < 0.05. In total, we identified 4498 DEGs. Heatmaps and volcano plots were generated using ggplot2 packages in R software. The same methodology was applied for the differential expression analysis of the LIHC expression profiles in the TCGA database. ( https://portal.gdc.cancer.gov/ ) 1.9 Enrichment analysis for differential genes Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to elucidate the functional pathways implicated by the genes differentially expressed after pharmacological intervention using the clusterProfiler package R 13 . The threshold for significance in the enrichment analyses was established at a q-value and a p-value of 0.05. The results were then represented graphically as a bar plot to facilitate the visualization of the analyzed data. 2.0 Sequencing Differential Genes Pathway Map Analysis We performed visual analysis using the KEGG Mapper tool 14 on the KEGG 15 website ( https://www.kegg.jp/ ) to demonstrate the complex relationships of genes that are expressed differently within key pathways. This methodology enables us to visually depict genes' spatial arrangement and interconnectedness throughout different pathways, thus clarifying the intricacy and importance of gene regulatory networks. 2.1 Collection of Naringenin-related target genes After conducting a search for "Naringenin" on The Chinese Medicine System Pharmacology Database and Analysis Platform(TCMSP) 16 , we collected the corresponding targets. Due to the limited number of targets available in the TCMSP database, we supplemented our search with SwissTarget Prediction 12 and DrugBank databases. The results from the three databases were combined after removing duplicates and standardized using the UniProt database 17 for gene names. Our analysis identified a total of 131 Naringenin-related targets. 2.2 Analysis of clinical value We harnessed the data available in The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ) to evaluate the expression levels of pivotal genes. We generated Kaplan-Meier survival curves through the Xiantao Academic platform, which interfaces with TCGA data. These curves facilitated the comparison of overall survival (OS) differences. Our analysis was thorough, with the exclusion of extraneous data. Based on the expression profiles of the core genes of interest, patients were categorized into two cohorts: high-expression and low-expression. To validate the prognostic utility of these genes, The R packages pROC and ggplot2 were utilized to generate Receiver Operating Characteristic (ROC) curves, facilitating patient diagnosis evaluation. Univariate and multivariate Cox regression analyses were applied to the TCGA data for these genes, and a prognostic nomogram was established for those genes that were achieved. The multivariate analysis yielded a statistically significant result (P-value < 0.05). 2.3 RNA extraction and quantitative real-time PCR (qRT-PCR) TransZol Up (Cat#ET111, Beijing Full-Style Gold Bio-Technology Co., Ltd., Beijing, China) was used to extract total RNA from MHCC-97H cells within the negative control (NC) and treatment groups (NG), following the manufacturer's directions. The concentration and purity of the isolated RNA were quantified using a BioSpec-nano spectrophotometer (Shimadzu Co., Ltd., Japan), accepting only samples with an optical density (OD) ratio of A260/A280 between 1.8 and 2.0, which were subsequently preserved at -80°C for later analysis. Reverse transcription was carried out with commercially sourced cDNA synthesis kits, following a protocol of incubation at 37°C for 15 minutes and a subsequent inactivation at 85°C for 5 seconds, with the reaction mixture thereafter being chilled to 4°C on a specialized reverse transcription apparatus. The primer sequences employed are delineated in Table 1 . The experiment required the CFX-96 Real-Time PCR Detection System (Bio-Rad Labs, Hercules, California, United States) for quantitative real-time PCR (qRT-PCR). The protocol employed a 2* SYBR Green qPCR Master Mix (Cat#SM143, Severn Innovation Biotechnology Ltd., Beijing, China). The experimental procedure involved an initial denaturation stage at 95°C for of 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 10 seconds and subsequent annealing at 60°C for 30 seconds. Relative quantification of gene expression was computed via the 2^-ΔΔCT method, and to ensure the reproducibility of results, all samples were analyzed in triplicate. Table 1 Univariate and multivariate Cox regression analyses are used to assess the impact of key targets on patient prognosis. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value IGFBP3 373 Low 187 Reference Reference High 186 1.670 (1.178–2.368) 0.004 1.441 (0.986–2.105) 0.059 CHRNA7 373 Low 186 Reference High 187 1.286 (0.910–1.817) 0.154 PGF 373 Low 186 Reference Reference High 187 1.780 (1.251–2.533) 0.001 1.295 (0.878–1.909) 0.193 CA9 373 Low 187 Reference Reference High 186 1.966 (1.382–2.797) < 0.001 1.493 (1.008–2.213) 0.046 KLK1 373 Low 187 Reference High 186 1.385 (0.979–1.960) 0.066 AKR1C3 373 Low 187 Reference Reference High 186 1.630 (1.150–2.310) 0.006 1.544 (1.077–2.214) 0.018 2.4 Western blot analysis We extracted proteins from HUH7 and MHCC-97H cells using cold radioimmunoprecipitation assay lysis buffer (RIPA, Pierce). Total protein was quantified using the BCA assay kit (P0012, Beyotime Bio, Beijing, China). Subsequently, the proteins underwent separation through the utilization of sodium dodecyl sulfate-polyacrylamide gel electrophoresis, followed by their transfer onto polyvinylidene difluoride membranes. The membrane-bound proteins were blocked with rapid blocking solution for 1 hour and then incubated overnight at 4°C with primary antibodies against CHRNA7, IGFBP3, PGF, CA9, KLK1, AKR1C3 (all diluted 1:1000), and β-Actin (diluted 1:1000). The appropriate secondary antibodies (diluted 1:10000) were then added and incubated for 2 hours. The protein bands were quantified using the BeyoECL Plus Moon system (P0018, Beyotime Bio, Beijing, China) and analyzed with the software ImageJ (Bethesda, MD, USA) after washing the membrane twice with TBST. 2.5 Molecular Docking of Key Targets Genes AutoDock Vina utilizes a multivariate gradient-based optimization algorithm during local refinement to enhance computational precision in ligand-receptor docking. Systematic virtual screening was performed through molecular docking simulations between six target proteins (retrieved from PDB) and ligands, with binding affinities (kcal/mol) quantifying interaction strengths - values ≤-5.0 kcal/mol indicating high-affinity conformations. Hesperidin's 2D structure was acquired from PubChem 18 . The six most thermodynamically stable complexes (lowest ΔG values) underwent PyMOL-based structural analysis to map hydrogen bonds and hydrophobic interactions at binding interfaces 19 . This integrated approach combining quantitative scoring with structural visualization enables atomic-level characterization of molecular recognition mechanisms. 2.6 Animal experiments All murine-oriented investigations were carried out in adherence to the guidelines set forth for the care and use of laboratory animals at Harbin Medical University, China, and subsequently sanctioned by the Institutional Animal Care and Use Committee (IACUC) of Harbin Medical University. The present research report is compliant with the stipulations proffered by the ARRIVE guidelines. Female athymic nude mice, aged four weeks, were bought from Beijing Weitonglihua Experimental Animal Technology Co., Ltd. The Harbin Medical University Ethics Committee granted approval for all experimental protocols (Permission Number: YJSDW2023-282). Each mouse received an injection into the right forelimb with 8 × 10^6 MHCC-97H cells. Upon the tumors reaching a volume of 30 mm^3, the mice were allocated into two cohorts, each comprising four animals. The treatment group was administered naringenin intraperitoneally (i.p.) at a dosage of 50 mg/kg for a duration of 20 days, while the control group received an equivalent volume of phosphate-buffered saline (PBS) via the same route. Body weight and tumor volume were monitored biweekly post-tumor detection. Tumor volume (V) was gauged with calipers and calculated employing the formula: V = (length × width^2) / 2 20 . The study was concluded on day 35 with the humane euthanasia(Cervical dislocation method) of the mice and subsequent tumor excision for histopathological analysis. 2.7 Statistical analysis All quantitative statistical analyses in this study were conducted using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). To assess diagnostic potential, receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) along with the 95% confidence interval (CI) were calculated. The optimal cutoff was determined by the Youden Index, where the sum of sensitivity and specificity reached its maximum. Survival analysis was performed using the Kaplan–Meier method to plot the overall survival curve, and both univariate and multivariate Cox proportional hazards models were utilized to identify prognostic factors for survival time. Moreover, to evaluate cell proliferation, migration, and invasion, three independent experiments were conducted, with the results reported as mean ± standard deviation (S.D.). For all comparative analyses, a p-value of less than 0.05 was considered statistically significant. Quantitative analysis of protein levels was carried out using ImageJ software (National Institutes of Health, Bethesda, MD, USA). Comparisons between two groups were made using an unpaired Student's t-test. In this study, "ns" denotes a lack of statistical significance. Based on the p-value, levels of significance were deemed slight, moderate, and strong at p < 0.05, p < 0.01, and p < 0.001, respectively. Results 3.1 Naringenin significantly inhibits the proliferation of cancer cells in vitro To determine the inhibitory effect of naringin on hepatocellular carcinoma (HCC) and normal hepatic cells in vitro, we evaluated the cytotoxic effects of naringin on one normal cell line (WRL) and two tumor cell lines (HUH7, MHCC-97H). For the normal cell line, low concentrations (80 µM, 160 µM) and short-term treatment (24H) did not significantly affect cell viability. Naringin's growth inhibition in the cancer cell lines (HUH7, MHCC-97H) exhibited a distinct reliance on dose and time. Also, a comparison indicated that the adverse effects of naringin were more prominent in cancer cells than in normal cells (Fig. 2 a-c). To evaluate the effects of naringin on the three cell lines, we constructed time-dependent and dose-dependent curves. The IC50 values at 24 hours were determined by analyzing the dose-response viability curves. The IC50 values for MHCC-97H and HUH7 were 182.2 µM and 165.7 µM, respectively. Based on the results shown in Figs. 2 d and e, a concentration of 180 µM naringin induced a 24-hour treatment of the MHCC-97H cell line in subsequent sequencing experiments. 3.2 Naringenin inhibits cancer cell migration and invasion in vitro. The process of tumor cell migration is of paramount importance in the development and advancement of hepatocellular carcinoma. To investigate the effects of naringenin on cell migration and invasion, we utilized the scratch test and transwell experiment. Our study revealed a significant inhibition of cell migration and invasion with varying concentrations of naringenin (Fig. 3 .a-h). In MHCC-97H cells, the migration inhibitory rates were 47.4% and 57.9% for 80µM and 160µM naringenin, respectively. Similarly, in HUH7 cells, the migration inhibitory rates were 43.6% and 55% for 80µM and 160µM naringenin, respectively. The results obtained from the transwell assay were consistent with these findings, which demonstrated migration inhibitory rates of 28% and 44.5% for 80µM and 160µM naringenin in MHCC-97H cells and 40% and 52% in HUH7 cells, respectively. 3.3 Naringenin induces apoptosis in cancer cells in vitro. 3.3.1 Hoechst 33258 staining Hoechst 33258 is a cell membrane-permeable fluorescent dye that can infiltrate living cells. Upon interaction with double-helical DNA, it emits a characteristic blue fluorescence. During apoptosis, nuclei stained with Hoechst 33258 exhibit intensified fluorescence alongside morphological alterations indicative of apoptosis, including chromatin condensation and nuclear fragmentation. Our findings reveal a dose-dependent augmentation in apoptotic rates correlating with increasing concentrations of naringenin(Fig. 4 a). Specifically, after 24-hour exposure to 160 µM naringenin, MHCC-97H, and HUH7 cell lines displayed apoptotic rates of 10% and 8%, respectively, marking a statistically significant elevation from the untreated control group. The ratios of naringenin concentration increased to 30% for MHCC-97H cells and 27% for HUH7 cells at a concentration of 320 µM (Fig. 4 b-c). The data presented in this study highlight a consistent upward trend in apoptosis as naringenin concentrations increase. 3.3.2 Flow cytometry To ascertain cellular apoptosis with higher precision and to distinguish apoptotic from necrotic cells, flow cytometry was employed. The analysis delineated a dose-dependent decrement in the population of viable cells concomitant with an increment in both the early and late stages of apoptosis (Fig. 5 a-b). The incidence of mechanically induced necrosis did not exceed 4%. Upon quantification of the total apoptotic fraction, it was discerned that post 24-hour treatment with 160 µM naringenin, approximately 8.9% of the MHCC-97H cohort underwent apoptosis, a figure that stands in significant contrast to the 3.25% observed in the control group. In parallel, HUH7 cells manifested an apoptotic rate of 6.48%, again significantly divergent from the control value of 3.7%. For cells exposed to 320 µM naringenin, the apoptotic rates were approximately 24.7% and 22.28%, respectively. Comparative analysis of the results obtained through flow cytometry and morphological examination revealed that under identical conditions, the apoptotic rate of cells treated with 160 µM naringenin for 24 hours was consistent. 3.4 Differential expression analysis and identification of key genes In our quest to identify potential novel targets of naringenin in hepatocellular carcinoma, we initially conducted transcriptome sequencing and identified 4390 differentially expressed genes. Subsequently, we performed differential analysis on the LIHC dataset from TCGA, revealing 4498 differentially expressed genes (Fig. 6 a-b). Following this, we employed screening techniques using The Chinese Medicine System Pharmacology Database and Analysis Platform(TCMSP), SwissTarget, and DrugBank databases, identifying 131 naringenin-related target gene. Finally, utilizing a Venn diagram 21 , we pinpointed overlapping genes as our key targets for further investigation(Fig. 6 c). 3.5 Functional Annotation and the anti-HCC Comprehensive Pathway Analysis of Naringenin To elucidate the regulatory mechanisms underlying the differential expression of genes and their impact on biological metabolic processes, we conducted functional enrichment analyses on the annotated genes. This approach enabled us to extract more profound functional insights. The analyses culminated in the generation of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) 22 enrichment diagram(Fig. 7 a-b), highlighting the top 30 enriched terms and pathways, respectively. Within the GO categories, the foremost 10 terms included response to fatty acid, positive regulation of DNA-binding transcription factor activity, cellular response to fatty acid, organic anion transport, carboxylic acid transport, organic acid transport, Wnt signaling pathway, cell-cell signaling via Wnt, regionalization, and epithelial tube morphogenesis. The leading 10 KEGG pathways were the Wnt signaling pathway, Phosphatidylinositol signaling system, Inositol phosphate metabolism, MAPK signaling pathway, Sphingolipid metabolism, Amino sugar and nucleotide sugar metabolism, Hepatocellular carcinoma, Neurotrophin signaling pathway, Human papillomavirus infection, and Axon guidance. The functional enrichment analyses have uncovered potential therapeutic targets, delineating the pathways through which naringenin may modulate hepatocellular carcinoma cells. Notably, the Wnt and MAPK signaling pathways emerged as the most prominent pathways implicated in the therapeutic effects of naringenin (Fig. 7 c-d). 3.6 Clinical value of key genes in HCC To verify the role of key targets in hepatocellular carcinoma, based on the data in the TCGA database, we compared the expression levels of key targets in normal liver tissue and hepatocellular carcinoma. As shown in the Figure.8a, AKR1C3, PGF, CA9, and KLK1 expression levels in hepatocellular carcinoma tissue were significantly higher than in normal liver tissue. In the sequencing results, the expression levels of CA9, KLK1, and AKR1C3 were significantly reduced, further indicating that these genes are the key targets of naringenin. To assess the relationship between clinical prognosis and these key target genes, we utilized Xiantao to construct Kaplan-Meier survival curves for survival analysis. The results indicated that patients with high expression of key targets had significantly lower overall survival (OS) Compared to those with low expression of key targets (p < 0.01), suggesting a close correlation between these key targets and patient survival rate(Fig. 8 b). Furthermore, to predict the 1-year, 2-year, and 3-year survival rates, we plotted time-dependent survival ROC curves for all key targets and found that all the AUC values were greater than 0.5(Fig. 8 c). These findings demonstrate that the identified key targets possess effective prognostic value 3.7 Cox Regression Analysis and Nomogram A univariate Cox regression analysis was first conducted to determine the potential independence of the six chosen main targets as predictive variables for overall survival (OS) in hepatocellular carcinoma (Table.1). The study found a notable association between IGFBP3, PGF, CA9, AKR1C3, and OS. Afterward, we conducted multivariate Cox regression analysis for the important targets that had p-values more than 0.05 in the univariate Cox regression study. Among them, CA9 and AKR1C3 were identified as independent prognostic factors for HCC. A nomogram is useful for generating individual probabilities of clinical events by integrating various prognostic and determinant variables. In our study, we constructed a nomogram based on age, gender, tumor stage, pathological stage, CA9, and AKR1C3. The results, as shown in the Figure.9, demonstrated that the nomogram had time-dependent AUC values consistently exceeding 0.6 for predicting OS within 5 years. Additionally, the nomogram exhibited relatively good consistency with observed values in predicting survival outcomes at 1, 3, and 5 years. In conclusion, the nomogram based on CA9 and AKR1C3 can effectively predict both short-term and long-term OS in HCC. This prediction model provides valuable support for clinical diagnosis, treatment, and management. 3.8 Validation of key target gene expression by PCR Validation of gene expression levels for 6 key target genes was performed using qRT-PCR(Fig. 10 ). The results revealed a general consistency between the expression trends observed in qRT-PCR and RNA-seq data, indicating a high reliability of these key target genes in transcriptional regulation within signal transduction pathways. However, it is important to note that one gene (CA9) exhibited a discordant expression trend compared to the RNA-seq results. This discrepancy may be attributed to the inherent nature of RNA-seq as a tool for large-scale screening of overall gene expression trends, which cannot guarantee a perfect concordance with qRT-PCR results for every individual gene. 3.9 Validation of key target gene expression by WB To validate the consistency of protein expression levels of key target genes with RNA-seq results, Western blot (WB) experiments were conducted. The results demonstrated that the protein expression trends of key target genes, as verified by WB, largely agreed with the RNA-seq data, as shown in the Fig. 11 . This observed concordance further supports the pivotal roles of these key target genes in signal transduction pathways, providing crucial insights for a deeper understanding of their functions and regulatory mechanisms. These findings offer valuable clues for investigating this signaling pathway's regulatory mechanisms and potential therapeutic targets. 4.0 Molecular docking To further investigate the interaction between naringenin and the key targets, we utilized the CB-Dock2 online analysis tool for molecular docking. According to relevant literature, a binding energy of less than − 5 kcal/mol between a compound and a protein is considered indicative of a stable binding. The results revealed that naringenin exhibited a binding energy of less than − 5 kcal/mol with all the proteins, suggesting a strong binding affinity between naringenin and the key targets. Interestingly, the best binding effects were observed for AKR1C3 and CA9, with binding energies of -9.5 kcal/mol and − 8.5 kcal/mol, respectively, which is consistent with the results of clinical prognostic analysis. These findings further support the notion that naringenin has a strong binding affinity with the key targets, particularly AKR1C3 and CA9. The binding affinity and binding details of hesperidin with these target genes are presented in Fig. 12 . The key interacting parameters between hesperidin and target amino acid residues are summarized in Table 2 . Table 2 The BE of 6 key targets and Naringenin Medicines Protein PDB ID BE naringenin AKR1C3 1s1p - 9.6 kcal/mol naringenin CA9 3iai - 8.0kcal/mol naringenin CHRNA7 3sq6 - 6.8 kcal/mol naringenin KLK1 1spj - 8.2 kcal/mol naringenin IGFBP3 7wgr - 7.4 kcal/mol naringenin PGF 1fzv - 6.3 kcal/mol 5.1Validation of naringenin against HCC in animal models To assess the in vivo anticancer activity of naringenin, a robust subcutaneous xenograft tumor model was created using the MHCC-97H cell line (Fig. 13 a). The tumor was visible ten days after inoculation. Naringenin was injected intraperitoneally once a day from the 15th day until the 35th day. During this period, there were no differences in body weight between the treated group and the untreated group. It was clearly observed that the tumor volume of the untreated group increased significantly (Fig. 13 b). On the 36th day, the mice were put to death, and the tumors were removed for morphological and histological investigations. The treated group observed reduced tumor weight and volume compared to the untreated group. HE pictures revealed a lower amount of necrotic tissue in the treated group compared to the untreated group. In addition, they exhibited normal characteristics. The mice's liver, spleen, and kidneys in both the treated and untreated groups were normal (Fig. 13 c-e) Discussion Hepatocellular carcinoma (HCC) is a prevalent form of malignancy on a global scale, exhibiting an annual mortality rate increase of roughly 2–3% 23 . This alarming trend necessitates the exploration of innovative and more efficacious therapeutic approaches. Natural products represent a vast reservoir of compounds for drug discovery 24 , among which flavonoids have garnered particular attention since the 1990s for their anticancer properties 25 . Naringenin, a prevalent flavonoid, possesses substantial anticancer potential. Our study is designed to identify potential targets and pathways of naringenin in the treatment of HCC through mRNA sequencing, experimental validation, and molecular docking. Naringenin is a naturally occurring citrus flavonoid known for its significant anticancer activity and minimal toxicity 25 . It exhibits less cytotoxicity against the normal liver cell line WRL compared to other cancer cell lines, a finding corroborated in ovarian cancer models 9 . Moreover, comparative analyses of liver, spleen, and kidney tissues from mice treated with naringenin showed no statistically significant differences, further affirming its safety profile. Previous studies have demonstrated the efficacy of naringenin in inhibiting proliferation and inducing apoptosis in human liver cancer cell lines such as HepG2 26 . Furthermore, it has been demonstrated to demonstrate anticancer properties by suppressing the expression of PKC and EGF, thereby inhibiting the MAPK and PI3K/Akt signaling pathways in liver cancer cells triggered by TPA 27 . However, the precise anticancer targets of naringenin and its clinical value remain unclear. In this work, for the first time, we utilized sequencing data combined with the extensive gene information from the TCGA database to identify a set of core target genes, including IGFBP3, PGF, CA9, KLK1, CHRNA7, and AKR1C3. These genes not only respond to naringenin but also show clinical relevance to HCC. Subsequent KEGG pathway analysis suggested that the WNT, MAPK, and P53 signaling pathways might be integral to naringenin's anti-HCC mechanism. Our findings indicate that the expression of target genes in tumor tissues differs significantly from that in normal tissues. Moreover, PGF, CA9, KLK1, and IGFBP3 demonstrated diagnostic value for overall survival, with CA9 and AKR1C3 identified as independent prognostic factors for HCC. In addition, molecular docking validation confirmed the strong interaction between naringenin and these target genes. Cellular experiments further validated that naringenin inhibits the viability and invasiveness of HCC cells in a time- and dose-dependent manner. Mechanistically, these effects may be mediated by the modulation of CA9, IGFBP3, AKR1C3, CHRNA7, and KLK1 by naringenin. Moreover, the suppression of CHRNA7 expression has been proven to increase the sensitivity of cancer cells to chemotherapeutic drugs 28 . A study conducted by Mina Afrashteh Nour et al. revealed that reducing the expression of α7nAChR greatly improves the ability of resistant HepG2 and Hepa 1–6 cells to respond to SOR treatment. This demonstrates the crucial role of α7-nAChR in determining the drug sensitivity of HCC cells 29 . p53 upregulates the transcription of insulin-like growth factor-binding protein 3 (IGFBP3), which acts as the main transporter of insulin-like growth factors (IGF) in the bloodstream 30 . Our Hoechst staining and flow cytometry results revealed a marked anti-apoptotic effect of naringenin, likely related to the upregulation of IGFBP3 and activation of the P53 signaling pathway. Aldehyde ketone reductase family member AKR1C3 plays a pivotal role in tumor progression and metastasis. Research findings have shown a notable increase in the expression of AKR1C3 in hepatocellular carcinoma (HCC), which subsequently facilitates the proliferation and spread of tumors by activating the NF-κB signaling pathway 31 . Intriguingly, our sequencing results revealed a marked downregulation of AKR1C3, suggesting that naringenin may exert its inhibitory effects on the NF-κB pathway through the modulation of AKR1C3 expression—a potentially novel finding that warrants further investigation. The discoveries made in this study pave the way for the development of naringenin as a targeted therapeutic agent and provide new insights into its mechanisms of anticancer activity. Nonetheless, this research has its limitations. Although the clinical significance analysis is derived from a robust database, it remains retrospective and hypothesis-generating. Prospective clinical studies are required to confirm the therapeutic potential of naringenin and establish its efficacy and safety in the treatment of liver cancer. While our molecular docking analysis provided valuable insights into the potential interactions between naringenin and its putative targets (AKR1C3 and CA9), several limitations inherent to computational approaches must be acknowledged. First, docking simulations rely on static protein structures (e.g., PDB: 1s1p for AKR1C3), which may not fully capture the dynamic conformational changes occurring in vivo during ligand binding. Second, scoring functions used to estimate binding affinities (e.g., binding energies of -9.5 kcal/mol for AKR1C3) are approximations and may not reflect true thermodynamic stability. Furthermore, the transition from in vitro discoveries to clinical application is fraught with complexity. While our study delineates the effects of naringenin at the cellular level, in vivo dynamics, including metabolism, bioavailability, and interaction with the tumor microenvironment, require further clarification. Conclusions The comprehensive research findings indicate that naringenin effectively inhibits the proliferation of hepatocellular carcinoma cells in vitro and significantly impedes the growth of HCC tumors in vivo. Utilizing an array of scientific methodologies, including mRNA sequencing, experimental validation, and molecular docking, this study has substantiated the potential of naringenin as an efficacious multi-targeted agent against hepatocellular carcinoma. Notably, no adverse effects were observed in either in vitro or in vivo experiments, corroborating the inherent safety profile of naringenin as a natural constituent of dietary plants. This research provides a solid scientific rationale for the pharmacological mechanism of naringenin in the treatment of hepatocellular carcinoma and lays a foundational framework for future clinical translational studies. Declarations Funding This work was supported by the National Natural Science Foundation of China (Grant Nos. 82373111, 81972724，81901576). Acknowledgements All animal-related research undertaken in this study adheres strictly to the regulations laid out by the ARRIVE guidelines ((https://arriveguidelines.org). ). All experimental procedures have undergone meticulous ethical review, ensuring the minimization of any animal discomfort and distress at each stage Disclosure The authors have declared no conflicts of interest for this work Data availability All data generated or analyzed during this study is included in this published article and its supplementary information files. The data that support the findings of this study are available from the corresponding author, [Zheng Zhang], upon reasonable request. References Hepatocellular carcinoma. Nat. reviews Disease primers 7 , 7 (2021). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Cancer J. Clin. 71 , 209–249 (2021). Han, B. et al., Cancer incidence and mortality in China, 2022. J. Natl. Cancer Cent. (2024). Llovet, J. M. et al. Molecular pathogenesis and systemic therapies for hepatocellular carcinoma. Nat. cancer . 3 , 386–401 (2022). Haque, A., Brazeau, D. & Amin, A. R. Perspectives on natural compounds in chemoprevention and treatment of cancer: an update with new promising compounds. European journal of cancer (Oxford, England : 149, 165–183 (2021). (1990). Gali-Muhtasib, H., Hmadi, R., Kareh, M., Tohme, R. & Darwiche, N. Cell death mechanisms of plant-derived anticancer drugs: beyond apoptosis. Apoptosis: Int. J. Program. cell. death . 20 , 1531–1562 (2015). Motallebi, M. et al. A potential flavonoid phytochemical for cancer therapy. Life Sci. 305 , 120752 (2022). Naringenin. Zhang, F. et al. Naringenin prevents TGF-β1 secretion from breast cancer and suppresses pulmonary metastasis by inhibiting PKC activation. Breast cancer research: BCR . 18 , 38 (2016). Lin, C. et al. Naringenin suppresses epithelial ovarian cancer by inhibiting proliferation and modulating gut microbiota. Phytomedicine: Int. J. phytotherapy phytopharmacology . 106 , 154401 (2022). Oliver, D. J., Nikolau, B. & Wurtele, E. S. Functional genomics: high-throughput mRNA, protein, and metabolite analyses. Metab. Eng. 4 , 98–106 (2002). Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46 , D1074–d1082 (2018). Daina, A., Michielin, O. & Zoete, V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 47 , W357–w364 (2019). Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. (Cambridge (Mass)) . 2 , 100141 (2021). Okuda, S. et al. KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res. 36 , W423–426 (2008). Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 , D672–d677 (2025). Ru, J. et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 6 , 13 (2014). UniProt. the universal protein knowledgebase in 2021. Nucleic Acids Res. 49 , D480–d489 (2021). Wang, Y. et al. PubChem's BioAssay Database. Nucleic Acids Res. 40 , D400–412 (2012). Liu, H., Jin, Y. & Ding, H. MDBuilder: a PyMOL plugin for the preparation of molecular dynamics simulations. Brief. Bioinform 24 (2023). Viswanath, P. et al. A Novel Method for Quantifying Total Thoracic Tumor Burden in Mice. Neoplasia (New York N Y) . 20 , 975–984 (2018). Chen, H. & Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12 , 35 (2011). Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28 , 27–30 (2000). Wang, W. & Wei, C. Advances in the early diagnosis of hepatocellular carcinoma. Genes Dis. 7 , 308–319 (2020). Harvey, A. L., Edrada-Ebel, R. & Quinn, R. J. The re-emergence of natural products for drug discovery in the genomics era. Nat. Rev. Drug Discov. 14 , 111–129 (2015). Vissenaekens, H. et al. Flavonoids and cellular stress: a complex interplay affecting human health. Crit. Rev. Food Sci. Nutr. 62 , 8535–8566 (2022). Zhang, M. et al., Naringenin Induces HepG2 Cell Apoptosis via ROS-Mediated JAK-2/STAT-3 Signaling Pathways. Molecules 28 (2023). Yen, H. R., Liu, C. J. & Yeh, C. C. Naringenin suppresses TPA-induced tumor invasion by suppressing multiple signal transduction pathways in human hepatocellular carcinoma cells. Chemico-Biol. Interact. 235 , 1–9 (2015). Hajiasgharzadeh, K. et al. Small interfering RNA targeting alpha7 nicotinic acetylcholine receptor sensitizes hepatocellular carcinoma cells to sorafenib. Life Sci. 244 , 117332 (2020). Nour, M. A., Kheradmand, F., Rasmi, Y. & Baradaran, B. Alpha7 nicotinic acetylcholine receptor expression in Sorafenib-resistant Hepatocellular carcinoma cells. Med. Oncol. (Northwood Lond. Engl.) . 39 , 165 (2022). Li, X. et al. Elevated expression of WSB2 degrades p53 and activates the IGFBP3-AKT-mTOR-dependent pathway to drive hepatocellular carcinoma. Exp. Mol. Med. 56 , 177–191 (2024). Zhou, Q. et al. A Positive Feedback Loop of AKR1C3-Mediated Activation of NF-κB and STAT3 Facilitates Proliferation and Metastasis in Hepatocellular Carcinoma. Cancer Res. 81 , 1361–1374 (2021). Additional Declarations No competing interests reported. Supplementary Files geneexpression.xls WB.pdf Summaryofintersectinggenes.xlsx Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 May, 2025 Editor assigned by journal 01 May, 2025 Editor invited by journal 28 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers invited by journal 18 Apr, 2025 Submission checks completed at journal 05 Apr, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5403136","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444909135,"identity":"e17a69ee-e81c-4112-9b0f-89e25231377e","order_by":0,"name":"Zheng Zhang","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Zhang","suffix":""},{"id":444909136,"identity":"e560a287-5ac0-408e-8336-c6cb5bbebc5b","order_by":1,"name":"Haoran Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Wang","suffix":""},{"id":444909137,"identity":"e1567534-57d5-4377-b7a3-0f16e3bb9a4b","order_by":2,"name":"Xin Liu","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":444909138,"identity":"ef3810b5-2035-49d2-9eb5-789f5a3b357a","order_by":3,"name":"Meng He","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"He","suffix":""},{"id":444909139,"identity":"f3893cc2-a5ad-4427-bcdd-00329f32176b","order_by":4,"name":"Yujia Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACPmYQySPBwC/BwAYROkBACxtMi+QMorXAGAY3iNbCzntMmkfGQt74du+zRzfbGOT4biQwfi7A6zC+NGkeHgnDbXeOmxvntjEYS95IYJaegVcLjxlIC+O2G2ls0kAtiRtuJAAFidBiv3kGREs90VoSN0hAtCQYEKHF2HIOj0TyjBtp7MY55yQMZ5552CyNTws//xnDG2976mz7gQ57nFNmI893PPngZ3xagIBFgrEHzpEAYsYG/BoYGJg/MPwgpGYUjIJRMApGNAAAUTk71+mPONsAAAAASUVORK5CYII=","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yujia","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-11-06 13:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5403136/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5403136/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-09013-y","type":"published","date":"2025-07-02T15:58:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81035824,"identity":"9226f19e-a885-4297-9fad-d60e5e4b12d4","added_by":"auto","created_at":"2025-04-21 12:10:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8017,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular structure of Naringenin (molecular mass :272.25)\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/b70f7aac511f28301cc90ee8.jpg"},{"id":81035889,"identity":"2b6f1568-de67-4c79-be0b-b2d3ed7bd911","added_by":"auto","created_at":"2025-04-21 12:10:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159138,"visible":true,"origin":"","legend":"\u003cp\u003eThe effects of naringenin on cell proliferation. (A) 24 h, (B) 48 h, and (C) 72 h cell viability of MHCC-97H HUH7 WRL treated with a serial concentration of Naringenin. (C-D) Dose-dependent activity curves at 24 hours. The information is presented as the average ± standard deviation of values obtained from experiments conducted in triplicate. A significance level of 0.05, 0.01, and 0.001 was observed when comparing the ctrl groups.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/27939403a128a69c78026bc4.jpg"},{"id":81036645,"identity":"16eac85b-c6bb-4fe3-aa90-ef8a4a20bb1d","added_by":"auto","created_at":"2025-04-21 12:26:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":338434,"visible":true,"origin":"","legend":"\u003cp\u003evitro naringenin has been identified to impede cell migration and invasion. A visual depiction of the outcomes obtained from the wound healing assay (A-D) and Transwell assay (E-H) was employed to assess cellular migration. The mean ± standard deviation of experimental values obtained in triplicate expresses all data. The p-value is 0.001 when compared to the control.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/b31332eb32da2924831299e8.jpg"},{"id":81036647,"identity":"38552ee3-ec36-40fc-8c8c-55597d7774dc","added_by":"auto","created_at":"2025-04-21 12:26:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":176688,"visible":true,"origin":"","legend":"\u003cp\u003eApoptosis was observed through Hoechst 33258 staining. (A) The number of apoptotic cells increased progressively with the drug dosage. Cells treated with varying dosages demonstrated notable morphological alterations indicative of apoptosis, such as chromatin condensation and nuclear disintegration. (B, C) Statistical results of apoptosis in 97H and HUH7 cells. Error bars represent standard deviation. Compared to the control group, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/82b6f01e993f76ed73822045.jpg"},{"id":81035835,"identity":"2481468f-394c-4b49-8147-8a80f954a10c","added_by":"auto","created_at":"2025-04-21 12:10:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94741,"visible":true,"origin":"","legend":"\u003cp\u003eApoptosis in MHCC-97H and HUH7 cells after treatment with naringenin. (A) The flow cytometry technique was employed to detect and evaluate the various stages of apoptosis in MHCC-97H and HUH7 cells. Necrototic, late apoptotic, early apoptotic, and healthy cells are represented by cells located in the Q1, Q2, Q3, and Q4 quadrants, respectively. (B) The ratios among healthy and apoptotic cells were evaluated using the outcomes of the apoptosis stage detection. The results are displayed as the average ± standard deviation, with each sample being measured three times separately. The standard deviation is represented by error bars. The indicated significant differences are *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and #p \u0026lt; 0.0001 (or ****p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/e7dd60669faaef1b7312dbe0.jpg"},{"id":81036918,"identity":"1add0c26-7fb0-4cbc-8122-9db5a8990e3b","added_by":"auto","created_at":"2025-04-21 12:34:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":77142,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of differentially expressed genes and target genes. (A) Volcano plot of sequencing results. (B) TCGA volcano plot. （C）TCGA Differentially Expressed Genes, Sequencing Differentially Expressed Genes, and Naringenin Drug Target Venn Diagram.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/b6950ffcd990b6cffdbcff0d.jpg"},{"id":81036642,"identity":"1f6dfe8a-592c-4d4f-9050-9c4ccad9c6f5","added_by":"auto","created_at":"2025-04-21 12:26:49","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":475249,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment Analysis of Related Genes (A, B) The top 30 significantly enriched GO and KEGG pathway terms for the 4390 intersecting target differentially expressed genes (P\u0026lt;0.05). The integrated pathways map of Naringenin in an OS setting. (C) WNT signaling pathway\u003csup\u003e14\u003c/sup\u003e (D) MAPK signaling pathway\u003csup\u003e14\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/7cc3f42243aaf7af109f5f1b.jpg"},{"id":81035834,"identity":"79b7e2fb-7c7e-4b1f-954f-56975195f5c2","added_by":"auto","created_at":"2025-04-21 12:10:49","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":546683,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression levels and diagnostic value of key targets of Naringenin. (A) The expression levels of central targets in normal bone and liver tissues compared to HCC tissues. (B)Survival curves for OS (overall survival) in HCC patients with high (red) and low (blue) expression of key target genes (P \u0026lt; 0.05). (C) ROC (Receiver Operating Characteristic) curves confirming the accuracy of key targets expression in diagnosis and prognosis prediction (AUC = 1.000).\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/c34bc26a1ab088f63c1bb19a.jpg"},{"id":81035830,"identity":"97721d96-dcf1-45da-aa53-f335cff1f286","added_by":"auto","created_at":"2025-04-21 12:10:49","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":179525,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for assessing the probability of HCC based on the TCGA cohort. (A) Nomograms for predicting the probabilities of 1-year, 3-year, and 5-year overall survival (OS). (B, C) Time-dependent AUC curves. (D–F) Calibration curves for the nomograms predicting the probabilities of OS at (D) 1 year, (E) 3 years, and (F) 5 years.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/3147897928acd840ca721cfd.jpg"},{"id":81036128,"identity":"8db69a04-4bb2-4341-89dd-547f3c194d36","added_by":"auto","created_at":"2025-04-21 12:18:49","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":70435,"visible":true,"origin":"","legend":"\u003cp\u003eThe qRT-PCR technique was applied to evaluate the quantitative expression of specific important targets about the internal reference gene. The qRT-PCR data is reported as the mean ± standard deviation, with a sample size of n = 3. The statistical analysis reveals significant differences at the following levels: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/bb0db8b8b35a7f35f19a5eb2.jpg"},{"id":81036147,"identity":"74796c11-c21d-4d6c-88f6-484014712697","added_by":"auto","created_at":"2025-04-21 12:18:50","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":90722,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of the expression of central target proteins detected by Western blotting (A). All data (B) are presented as the mean ± SD of triplicate experiments. *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001 compared to the control group.\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/284c7d44a6be18a4e50161eb.jpg"},{"id":81035843,"identity":"58311a48-57f5-41df-b5e4-ac2ec805acd2","added_by":"auto","created_at":"2025-04-21 12:10:49","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":142855,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results for Naringenin and key targets.\u003c/p\u003e\n\u003cp\u003e(A)Naringenin-AKR1C3(PDBID:1s1p).(B)Naringenin-CA9(PDB-ID:3iai).(C)Naringenin-IGFBP3(PDB-ID:7wqr). (D)Naringenin-PGF(PDB-ID:1fzv). (E)Naringenin-KLK1(PDB-ID:1spj) (F)Naringenin-CHRNA7(PDB-ID:3sq6).\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/15c1e8947cedd5515ed1276c.jpg"},{"id":81036145,"identity":"97a52217-d828-4140-a04d-5623baf2b611","added_by":"auto","created_at":"2025-04-21 12:18:50","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":670932,"visible":true,"origin":"","legend":"\u003cp\u003eNaringenin inhibited tumor growth in vivo. Diagram of the Experimental Workflow for In Vivo Studies (A). Nude Mouse and Tumor Imaging(B). Tumor size(C). Tumor weight(D). Representative H\u0026amp;E is staining images of the tumor, liver, kidney, and spleen. Scale bar = 100μm. The data are presented as the mean ± ST. The control group exhibited p-values of *P\u0026lt;0.05, **P\u0026lt;0.01, and ***P 0.001, respectively.\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/ee4ab82dd9dc51377a9ef05c.jpg"},{"id":86179723,"identity":"b23e33b1-0637-4ed6-a3c4-3209649d293a","added_by":"auto","created_at":"2025-07-07 16:18:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4373019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/e1c15202-c7f8-46fb-97fd-edf99c9bd4c1.pdf"},{"id":81036146,"identity":"c8025079-64ac-4bfb-a296-c017d57838c2","added_by":"auto","created_at":"2025-04-21 12:18:50","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11897498,"visible":true,"origin":"","legend":"","description":"","filename":"geneexpression.xls","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/6b84e06bad5c0ff2fa55bfbc.xls"},{"id":81036163,"identity":"517de9df-29e5-4fea-810e-1b1ddf37c371","added_by":"auto","created_at":"2025-04-21 12:18:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21477811,"visible":true,"origin":"","legend":"","description":"","filename":"WB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/cffc5dcd493daa7405c6e071.pdf"},{"id":81036133,"identity":"64b2489a-3903-4a8a-85c4-99b8278bb9bb","added_by":"auto","created_at":"2025-04-21 12:18:49","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":420080,"visible":true,"origin":"","legend":"","description":"","filename":"Summaryofintersectinggenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5403136/v1/e2982c81c256c3ab51dba79e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the mechanism of Naringenin in the treatment of hepatocellular carcinoma based on mRNA sequencing and experimental validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe most common primary malignant tumor of the liver is hepatocellular carcinoma (HCC)\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003c/sup\u003e. Based on the GLOBOCAN 2020 figures, hepatocellular carcinoma (HCC) was the sixth most often diagnosed cancer and the third most significant contributor to cancer-related fatalities worldwide in 2020. It was reported that there were around 906,000 new cases of HCC and 830,000 deaths\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Furthermore, there is evidence to suggest that liver cancer was the second leading cause of cancer-related deaths in China in 2022, with 316,500 deaths reported \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite continuous improvements in treatment modalities, including surgery, targeted therapies, and immune checkpoint inhibitors (ICI), the objective response rate in HCC patients remains at approximately 30%, and the three-year overall survival rate (OS) is still below 50% \u003csup\u003e4\u003c/sup\u003e. Hence, it is imperative to investigate novel treatment agents and mechanisms in order to enhance the prognosis of patients with hepatocellular carcinoma (HCC).\u003c/p\u003e \u003cp\u003eIn recent decades, Phytochemicals are attracting increasing attention and focus in cancer treatment and chemoprevention due to the characteristics of easy accessibility, high safety margins, and relatively low costs associated with natural dietary compounds\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. According to reports, there are more than 3000 plant species that have been found to possess anticancer properties. Thus far, around 30 substances originating from plants have been extracted and evaluated in clinical studies for the purpose of treating cancer.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNaringenin, a flavonoid compound, is predominantly present in the form of glycosidic elements derived from the hydrolysis of naringin or narirutin. This bioactive phytochemical is abundantly found in grapefruits, lemons, oranges, and tomatoes. It is acclaimed for its pharmacological benefits, including anti-inflammatory, antioxidant, neuroprotective, hepatoprotective, and anticancer properties.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Substantial evidence underscores naringenin's potent anticancer activity; for instance, it can significantly inhibit the migration and lung metastasis of breast cancer cells by reducing the release of TGF-β1.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Additionally, naringenin impedes the progression of epithelial ovarian cancer by suppressing the expression of the PI3K pathway and fostering a healthy gut microbiota.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, the intricate molecular mechanisms underpinning naringenin's antitumor effects remain to be fully elucidated, particularly in the context of hepatocellular carcinoma.\u003c/p\u003e \u003cp\u003eThe advent of high-throughput mRNA sequencing (mRNA-seq) has revolutionized our capacity to analyze gene expression changes across the entire genome, offering an unprecedented view of the transcriptomic landscape in response to therapeutic agents.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e This technique has yielded valuable insights into the molecular mechanisms of drug action and has facilitated the identification of potential biomarkers and therapeutic targets. The present study aims to employ mRNA sequencing coupled with experimental validation to pinpoint the targets of naringenin in the treatment of hepatocellular carcinoma (HCC), systematically evaluating the mechanisms by which naringenin exerts its effects on HCC. Moreover, we assessed the clinical relevance and distribution of central targets in HCC and conducted cellular experiments on MHCC-97H and HUH7 cell lines to confirm their inhibitory actions and the expression of central target genes. Ultimately, our in vitro experiments substantiated that naringenin can inhibit the proliferation and metastasis of hepatocellular carcinoma cells. This research lays the groundwork for a more profound understanding of the molecular underpinnings of liver cancer pathogenesis and the identification of novel drug targets, potentially leading to more effective treatments for patients with liver cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e1.1\u0026emsp;Cell Culture and Reagent\u003c/b\u003e.\u003c/h2\u003e \u003cp\u003eThe human hepatocellular carcinoma (HCC) cell lines of HUH7 and HMCC-97H were acquired from Wuhan Punosai Life Science and Technology Co., Ltd (Priscilla). To provide ideal growth conditions, the cell lines were grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% Fetal Bovine Serum (FBS) and 1% penicillin-streptomycin. A temperature of 37\u0026ordm;C was maintained for the cells in an environment consisting of 5% CO2 and 95% air. The culture media underwent systematically replaced on an alternate day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2 The Structure and pharmacological properties data of Naringenin\u003c/h2\u003e \u003cp\u003eThe information regarding the structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and pharmacological properties of Naringenin was acquired from the DrugBank Database and Analysis Platform\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, using the search term \"Naringenin\". The naringin utilized in these experiments was procured from MedChemExpress, bearing the respective product identification number HY-N0100.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3 CCK-8 assay\u003c/h2\u003e \u003cp\u003eThe cytotoxic impact of naringenin on HUH7 and MHCC-97H hepatic carcinoma cell lines was ascertained via the colorimetric CCK-8 assay. The study was divided into three groups: (1) experimental groups treated with naringenin at concentrations of 80 \u0026micro;M, 160 \u0026micro;M, 240 \u0026micro;M, and 320 \u0026micro;M; (2) a negative control group containing cell suspension in complete medium; and (3) a blank control group with complete medium alone (no cells or drugs). Each group comprised six replicate wells. After 24 hours of initial incubation, the old medium was aspirated using a pipette. For experimental groups, 100 \u0026micro;L of fresh medium containing the designated naringenin concentrations was added to each well. The 96-well plate was then incubated at 37\u0026deg;C under 5% CO₂ for 24, 48, or 72 hours. Following incubation, the medium was removed, and 100 \u0026micro;L of basal medium supplemented with 10% CCK-8 reagent (Cell Counting Kit-8, Dojindo Laboratories) was carefully added to each well to avoid bubble formation. The plate was further incubated at 37\u0026deg;C for 1 hour. Post-incubation, the optical density at 450 nm was quantitatively assessed utilizing an ultra-microplate reader (SpectraMax; Molecular Devices Corp, Sunnyvale, CA, USA). To affirm the precision and the reproducibility of the experimental results, the assay was performed in triplicate and iterated on three separate occasions. The resultant data were normalized and presented as a percentage relative to the untreated control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Transwell cell migration and invasion assay\u003c/h2\u003e \u003cp\u003eThe Boyden chamber was employed for the transwell migration and invasion studies. In the upper chamber, a total of 5 \u0026times; 104-1 x 105 cells were cultured in 200 \u0026micro;L of serum-free DMEM. Naringenin was added to the upper chamber in a gradient manner(80 \u0026micro;M, 160 \u0026micro;M, 240 \u0026micro;M, and 320 \u0026micro;M), while the lower wells were supplemented with 500 \u0026micro;L of medium containing 10% FBS. In the case of invasion experiments, Matrigel was applied to the bottom of the small chamber using a procedure similar to that employed for the migration experiments. After an incubation period of 20\u0026ndash;24 hours, the cells were fixed, washed with PBS, and stained with crystal violet. The assessment of hepatocellular carcinoma cell migration and invasion was conducted utilizing an inverted light microscope. The quantification of migrating or infiltrated cells was conducted by enumerating the cells within 10 individually chosen fields at a magnification of \u0026times;100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Hoechst\u003c/h2\u003e \u003cp\u003eInitially, cells were cultured in a 24-well plate until they attained the requisite confluency. Following the initial treatment, the cells were exposed to naringenin at graded concentrations of 80 \u0026micro;M, 160 \u0026micro;M, 240 \u0026micro;M, and 320 \u0026micro;M, and subsequently incubated for 24 hours under standard culture conditions (37\u0026deg;C, 5% CO₂).After incubation, cells were subjected to a phosphate-buffered saline (PBS) wash. To facilitate effective staining, 500 \u0026micro;L of Hoechst 33342 solution was dispensed into each well, ensuring uniform coverage of the cellular monolayer. The cells were then incubated at ambient temperature and shielded from light for 30 minutes. Post-incubation, the staining solution was carefully aspirated, and the cells were washed thrice with PBS, with each wash extending for 5\u0026ndash;10 minutes. The cells were subsequently filtered and imaged at 100x magnification in three arbitrarily selected fields. The process of capturing images was then followed by analyzing them using ImageJ software for further assessment and interpretation of the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Flow cytometry\u003c/h2\u003e \u003cp\u003eThe experimental protocol involved culturing cells until they reached 70\u0026ndash;80% confluency. Following the initial treatment, cells were treated with naringenin at graded concentrations (80 \u0026micro;M, 160 \u0026micro;M, 240 \u0026micro;M, and 320 \u0026micro;M) and incubated under standard conditions (37\u0026deg;C, 5% CO₂) for 24 hours. Cells were harvested by trypsinization (using trypsin without EDTA), followed by centrifugation (300 \u0026times; g, 5 min) to remove the supernatant. To ensure complete supernatant removal, cells were washed three times with ice-cold PBS and subjected to additional centrifugation (300 \u0026times; g, 5 min). The cell pellet was resuspended in 500 \u0026micro;l of binding buffer (Annexin V Binding Buffer, BD Biosciences) for subsequent apoptosis analysis. Apoptosis was assessed using an Annexin V-FITC/PI Apoptosis Detection Kit (Sigma-Aldrich, Cat# APOAF) according to the manufacturer\u0026rsquo;s protocol. Briefly, 5 \u0026micro;l of Annexin V-FITC and 5 \u0026micro;l of propidium iodide (PI) were added to 500 \u0026micro;l of cell suspension, followed by gentle mixing. After incubation at room temperature for 15 min in the dark, samples were analyzed by flow cytometry to distinguish viable, early apoptotic, and late apoptotic/necrotic cell populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1.7 Transcriptome Analysis of MHCC-97H Cells\u003c/h2\u003e \u003cp\u003eCells were cultivated to an optimal density and treated with naringenin at an IC50 of 182.2 \u0026micro;M for 24 hours, designated as the NG group. Cells that did not undergo any treatment were referred to as the NC group. Following treatment, the culture medium was aspirated, and the cells were washed one to two times with 1X PBS to ensure complete removal of the medium. TRIzol reagent was then applied to lyse the cells, with pipetting performed repeatedly to achieve a clear, non-viscous lysate. The cell lysate was subsequently transferred into RNase-free cryovials with screw caps. After a 5-minute rest at room temperature, the samples were stored at -80\u0026deg;C and transported on dry ice. Independent observations were conducted on all samples, with sextuplicate determinations. A differential gene expression analysis was performed to investigate the probable activities and underlying biological mechanisms of differently expressed genes. The objective was to identify candidate genes that are linked with cell proliferation, migration, and apoptosis, which are impacted by NG therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1.8 Screening of Differential Genes\u003c/h2\u003e \u003cp\u003eWe conducted a comparative analysis of the expression profiles between the treatment group and the control group using R software (version 4.3.1). For each gene, we performed differential expression analysis using the edgeR Bioconductor software package\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. We identified differentially expressed genes (DEGs) using the thresholds of |log2(fold change [FC]) | \u0026gt; 1 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In total, we identified 4498 DEGs. Heatmaps and volcano plots were generated using ggplot2 packages in R software. The same methodology was applied for the differential expression analysis of the LIHC expression profiles in the TCGA database. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e1.9 Enrichment analysis for differential genes\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to elucidate the functional pathways implicated by the genes differentially expressed after pharmacological intervention using the clusterProfiler package R\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The threshold for significance in the enrichment analyses was established at a q-value and a p-value of 0.05. The results were then represented graphically as a bar plot to facilitate the visualization of the analyzed data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.0 Sequencing Differential Genes Pathway Map Analysis\u003c/h3\u003e\n\u003cp\u003eWe performed visual analysis using the KEGG Mapper tool\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e on the KEGG\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to demonstrate the complex relationships of genes that are expressed differently within key pathways. This methodology enables us to visually depict genes' spatial arrangement and interconnectedness throughout different pathways, thus clarifying the intricacy and importance of gene regulatory networks.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Collection of Naringenin-related target genes\u003c/h2\u003e \u003cp\u003eAfter conducting a search for \"Naringenin\" on The Chinese Medicine System Pharmacology Database and Analysis Platform(TCMSP)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, we collected the corresponding targets. Due to the limited number of targets available in the TCMSP database, we supplemented our search with SwissTarget Prediction\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and DrugBank databases. The results from the three databases were combined after removing duplicates and standardized using the UniProt database \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003efor gene names. Our analysis identified a total of 131 Naringenin-related targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of clinical value\u003c/h2\u003e \u003cp\u003eWe harnessed the data available in The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to evaluate the expression levels of pivotal genes. We generated Kaplan-Meier survival curves through the Xiantao Academic platform, which interfaces with TCGA data. These curves facilitated the comparison of overall survival (OS) differences. Our analysis was thorough, with the exclusion of extraneous data. Based on the expression profiles of the core genes of interest, patients were categorized into two cohorts: high-expression and low-expression. To validate the prognostic utility of these genes, The R packages pROC and ggplot2 were utilized to generate Receiver Operating Characteristic (ROC) curves, facilitating patient diagnosis evaluation. Univariate and multivariate Cox regression analyses were applied to the TCGA data for these genes, and a prognostic nomogram was established for those genes that were achieved. The multivariate analysis yielded a statistically significant result (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.3 RNA extraction and quantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTransZol Up (Cat#ET111, Beijing Full-Style Gold Bio-Technology Co., Ltd., Beijing, China) was used to extract total RNA from MHCC-97H cells within the negative control (NC) and treatment groups (NG), following the manufacturer's directions. The concentration and purity of the isolated RNA were quantified using a BioSpec-nano spectrophotometer (Shimadzu Co., Ltd., Japan), accepting only samples with an optical density (OD) ratio of A260/A280 between 1.8 and 2.0, which were subsequently preserved at -80\u0026deg;C for later analysis. Reverse transcription was carried out with commercially sourced cDNA synthesis kits, following a protocol of incubation at 37\u0026deg;C for 15 minutes and a subsequent inactivation at 85\u0026deg;C for 5 seconds, with the reaction mixture thereafter being chilled to 4\u0026deg;C on a specialized reverse transcription apparatus. The primer sequences employed are delineated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The experiment required the CFX-96 Real-Time PCR Detection System (Bio-Rad Labs, Hercules, California, United States) for quantitative real-time PCR (qRT-PCR). The protocol employed a 2* SYBR Green qPCR Master Mix (Cat#SM143, Severn Innovation Biotechnology Ltd., Beijing, China). The experimental procedure involved an initial denaturation stage at 95\u0026deg;C for of 95\u0026deg;C for 30 seconds, followed by 40 cycles of denaturation at 95\u0026deg;C for 10 seconds and subsequent annealing at 60\u0026deg;C for 30 seconds. Relative quantification of gene expression was computed via the 2^-ΔΔCT method, and to ensure the reproducibility of results, all samples were analyzed in triplicate.\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\u003eUnivariate and multivariate Cox regression analyses are used to assess the impact of key targets on patient prognosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGFBP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.670 (1.178\u0026ndash;2.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.441 (0.986\u0026ndash;2.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHRNA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.286 (0.910\u0026ndash;1.817)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.780 (1.251\u0026ndash;2.533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.295 (0.878\u0026ndash;1.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.966 (1.382\u0026ndash;2.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.493 (1.008\u0026ndash;2.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.385 (0.979\u0026ndash;1.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKR1C3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.630 (1.150\u0026ndash;2.310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.544 (1.077\u0026ndash;2.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Western blot analysis\u003c/h2\u003e \u003cp\u003eWe extracted proteins from HUH7 and MHCC-97H cells using cold radioimmunoprecipitation assay lysis buffer (RIPA, Pierce). Total protein was quantified using the BCA assay kit (P0012, Beyotime Bio, Beijing, China). Subsequently, the proteins underwent separation through the utilization of sodium dodecyl sulfate-polyacrylamide gel electrophoresis, followed by their transfer onto polyvinylidene difluoride membranes. The membrane-bound proteins were blocked with rapid blocking solution for 1 hour and then incubated overnight at 4\u0026deg;C with primary antibodies against CHRNA7, IGFBP3, PGF, CA9, KLK1, AKR1C3 (all diluted 1:1000), and β-Actin (diluted 1:1000). The appropriate secondary antibodies (diluted 1:10000) were then added and incubated for 2 hours. The protein bands were quantified using the BeyoECL Plus Moon system (P0018, Beyotime Bio, Beijing, China) and analyzed with the software ImageJ (Bethesda, MD, USA) after washing the membrane twice with TBST.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Molecular Docking of Key Targets Genes\u003c/h2\u003e \u003cp\u003eAutoDock Vina utilizes a multivariate gradient-based optimization algorithm during local refinement to enhance computational precision in ligand-receptor docking. Systematic virtual screening was performed through molecular docking simulations between six target proteins (retrieved from PDB) and ligands, with binding affinities (kcal/mol) quantifying interaction strengths - values \u0026le;-5.0 kcal/mol indicating high-affinity conformations. Hesperidin's 2D structure was acquired from PubChem\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The six most thermodynamically stable complexes (lowest ΔG values) underwent PyMOL-based structural analysis to map hydrogen bonds and hydrophobic interactions at binding interfaces\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This integrated approach combining quantitative scoring with structural visualization enables atomic-level characterization of molecular recognition mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Animal experiments\u003c/h2\u003e \u003cp\u003e All murine-oriented investigations were carried out in adherence to the guidelines set forth for the care and use of laboratory animals at Harbin Medical University, China, and subsequently sanctioned by the Institutional Animal Care and Use Committee (IACUC) of Harbin Medical University. The present research report is compliant with the stipulations proffered by the ARRIVE guidelines. Female athymic nude mice, aged four weeks, were bought from Beijing Weitonglihua Experimental Animal Technology Co., Ltd. The Harbin Medical University Ethics Committee granted approval for all experimental protocols (Permission Number: YJSDW2023-282). Each mouse received an injection into the right forelimb with 8 \u0026times; 10^6 MHCC-97H cells. Upon the tumors reaching a volume of 30 mm^3, the mice were allocated into two cohorts, each comprising four animals. The treatment group was administered naringenin intraperitoneally (i.p.) at a dosage of 50 mg/kg for a duration of 20 days, while the control group received an equivalent volume of phosphate-buffered saline (PBS) via the same route. Body weight and tumor volume were monitored biweekly post-tumor detection. Tumor volume (V) was gauged with calipers and calculated employing the formula: V = (length \u0026times; width^2) / 2\u003csup\u003e20\u003c/sup\u003e. The study was concluded on day 35 with the humane euthanasia(Cervical dislocation method) of the mice and subsequent tumor excision for histopathological analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll quantitative statistical analyses in this study were conducted using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). To assess diagnostic potential, receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) along with the 95% confidence interval (CI) were calculated. The optimal cutoff was determined by the Youden Index, where the sum of sensitivity and specificity reached its maximum. Survival analysis was performed using the Kaplan\u0026ndash;Meier method to plot the overall survival curve, and both univariate and multivariate Cox proportional hazards models were utilized to identify prognostic factors for survival time. Moreover, to evaluate cell proliferation, migration, and invasion, three independent experiments were conducted, with the results reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (S.D.). For all comparative analyses, a p-value of less than 0.05 was considered statistically significant. Quantitative analysis of protein levels was carried out using ImageJ software (National Institutes of Health, Bethesda, MD, USA). Comparisons between two groups were made using an unpaired Student's t-test. In this study, \"ns\" denotes a lack of statistical significance. Based on the p-value, levels of significance were deemed slight, moderate, and strong at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Naringenin significantly inhibits the proliferation of cancer cells in vitro\u003c/h2\u003e \u003cp\u003eTo determine the inhibitory effect of naringin on hepatocellular carcinoma (HCC) and normal hepatic cells in vitro, we evaluated the cytotoxic effects of naringin on one normal cell line (WRL) and two tumor cell lines (HUH7, MHCC-97H). For the normal cell line, low concentrations (80 \u0026micro;M, 160 \u0026micro;M) and short-term treatment (24H) did not significantly affect cell viability. Naringin's growth inhibition in the cancer cell lines (HUH7, MHCC-97H) exhibited a distinct reliance on dose and time. Also, a comparison indicated that the adverse effects of naringin were more prominent in cancer cells than in normal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c). To evaluate the effects of naringin on the three cell lines, we constructed time-dependent and dose-dependent curves. The IC50 values at 24 hours were determined by analyzing the dose-response viability curves. The IC50 values for MHCC-97H and HUH7 were 182.2 \u0026micro;M and 165.7 \u0026micro;M, respectively. Based on the results shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed and e, a concentration of 180 \u0026micro;M naringin induced a 24-hour treatment of the MHCC-97H cell line in subsequent sequencing experiments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Naringenin inhibits cancer cell migration and invasion in vitro.\u003c/h2\u003e \u003cp\u003eThe process of tumor cell migration is of paramount importance in the development and advancement of hepatocellular carcinoma. To investigate the effects of naringenin on cell migration and invasion, we utilized the scratch test and transwell experiment. Our study revealed a significant inhibition of cell migration and invasion with varying concentrations of naringenin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.a-h). In MHCC-97H cells, the migration inhibitory rates were 47.4% and 57.9% for 80\u0026micro;M and 160\u0026micro;M naringenin, respectively. Similarly, in HUH7 cells, the migration inhibitory rates were 43.6% and 55% for 80\u0026micro;M and 160\u0026micro;M naringenin, respectively. The results obtained from the transwell assay were consistent with these findings, which demonstrated migration inhibitory rates of 28% and 44.5% for 80\u0026micro;M and 160\u0026micro;M naringenin in MHCC-97H cells and 40% and 52% in HUH7 cells, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Naringenin induces apoptosis in cancer cells in vitro.\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Hoechst 33258 staining\u003c/h2\u003e \u003cp\u003eHoechst 33258 is a cell membrane-permeable fluorescent dye that can infiltrate living cells. Upon interaction with double-helical DNA, it emits a characteristic blue fluorescence. During apoptosis, nuclei stained with Hoechst 33258 exhibit intensified fluorescence alongside morphological alterations indicative of apoptosis, including chromatin condensation and nuclear fragmentation. Our findings reveal a dose-dependent augmentation in apoptotic rates correlating with increasing concentrations of naringenin(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Specifically, after 24-hour exposure to 160 \u0026micro;M naringenin, MHCC-97H, and HUH7 cell lines displayed apoptotic rates of 10% and 8%, respectively, marking a statistically significant elevation from the untreated control group. The ratios of naringenin concentration increased to 30% for MHCC-97H cells and 27% for HUH7 cells at a concentration of 320 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c). The data presented in this study highlight a consistent upward trend in apoptosis as naringenin concentrations increase.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Flow cytometry\u003c/h2\u003e \u003cp\u003eTo ascertain cellular apoptosis with higher precision and to distinguish apoptotic from necrotic cells, flow cytometry was employed. The analysis delineated a dose-dependent decrement in the population of viable cells concomitant with an increment in both the early and late stages of apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b). The incidence of mechanically induced necrosis did not exceed 4%. Upon quantification of the total apoptotic fraction, it was discerned that post 24-hour treatment with 160 \u0026micro;M naringenin, approximately 8.9% of the MHCC-97H cohort underwent apoptosis, a figure that stands in significant contrast to the 3.25% observed in the control group. In parallel, HUH7 cells manifested an apoptotic rate of 6.48%, again significantly divergent from the control value of 3.7%. For cells exposed to 320 \u0026micro;M naringenin, the apoptotic rates were approximately 24.7% and 22.28%, respectively. Comparative analysis of the results obtained through flow cytometry and morphological examination revealed that under identical conditions, the apoptotic rate of cells treated with 160 \u0026micro;M naringenin for 24 hours was consistent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Differential expression analysis and identification of key genes\u003c/h2\u003e \u003cp\u003eIn our quest to identify potential novel targets of naringenin in hepatocellular carcinoma, we initially conducted transcriptome sequencing and identified 4390 differentially expressed genes. Subsequently, we performed differential analysis on the LIHC dataset from TCGA, revealing 4498 differentially expressed genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-b). Following this, we employed screening techniques using The Chinese Medicine System Pharmacology Database and Analysis Platform(TCMSP), SwissTarget, and DrugBank databases, identifying 131 naringenin-related target gene. Finally, utilizing a Venn diagram\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, we pinpointed overlapping genes as our key targets for further investigation(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Functional Annotation and the anti-HCC Comprehensive Pathway Analysis of Naringenin\u003c/h2\u003e \u003cp\u003eTo elucidate the regulatory mechanisms underlying the differential expression of genes and their impact on biological metabolic processes, we conducted functional enrichment analyses on the annotated genes. This approach enabled us to extract more profound functional insights. The analyses culminated in the generation of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e enrichment diagram(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-b), highlighting the top 30 enriched terms and pathways, respectively. Within the GO categories, the foremost 10 terms included response to fatty acid, positive regulation of DNA-binding transcription factor activity, cellular response to fatty acid, organic anion transport, carboxylic acid transport, organic acid transport, Wnt signaling pathway, cell-cell signaling via Wnt, regionalization, and epithelial tube morphogenesis. The leading 10 KEGG pathways were the Wnt signaling pathway, Phosphatidylinositol signaling system, Inositol phosphate metabolism, MAPK signaling pathway, Sphingolipid metabolism, Amino sugar and nucleotide sugar metabolism, Hepatocellular carcinoma, Neurotrophin signaling pathway, Human papillomavirus infection, and Axon guidance. The functional enrichment analyses have uncovered potential therapeutic targets, delineating the pathways through which naringenin may modulate hepatocellular carcinoma cells. Notably, the Wnt and MAPK signaling pathways emerged as the most prominent pathways implicated in the therapeutic effects of naringenin (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec-d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Clinical value of key genes in HCC\u003c/h2\u003e \u003cp\u003eTo verify the role of key targets in hepatocellular carcinoma, based on the data in the TCGA database, we compared the expression levels of key targets in normal liver tissue and hepatocellular carcinoma. As shown in the Figure.8a, AKR1C3, PGF, CA9, and KLK1 expression levels in hepatocellular carcinoma tissue were significantly higher than in normal liver tissue. In the sequencing results, the expression levels of CA9, KLK1, and AKR1C3 were significantly reduced, further indicating that these genes are the key targets of naringenin. To assess the relationship between clinical prognosis and these key target genes, we utilized Xiantao to construct Kaplan-Meier survival curves for survival analysis. The results indicated that patients with high expression of key targets had significantly lower overall survival (OS) Compared to those with low expression of key targets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting a close correlation between these key targets and patient survival rate(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Furthermore, to predict the 1-year, 2-year, and 3-year survival rates, we plotted time-dependent survival ROC curves for all key targets and found that all the AUC values were greater than 0.5(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). These findings demonstrate that the identified key targets possess effective prognostic value\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Cox Regression Analysis and Nomogram\u003c/h2\u003e \u003cp\u003eA univariate Cox regression analysis was first conducted to determine the potential independence of the six chosen main targets as predictive variables for overall survival (OS) in hepatocellular carcinoma (Table.1). The study found a notable association between IGFBP3, PGF, CA9, AKR1C3, and OS. Afterward, we conducted multivariate Cox regression analysis for the important targets that had p-values more than 0.05 in the univariate Cox regression study. Among them, CA9 and AKR1C3 were identified as independent prognostic factors for HCC. A nomogram is useful for generating individual probabilities of clinical events by integrating various prognostic and determinant variables. In our study, we constructed a nomogram based on age, gender, tumor stage, pathological stage, CA9, and AKR1C3. The results, as shown in the Figure.9, demonstrated that the nomogram had time-dependent AUC values consistently exceeding 0.6 for predicting OS within 5 years. Additionally, the nomogram exhibited relatively good consistency with observed values in predicting survival outcomes at 1, 3, and 5 years.\u003c/p\u003e \u003cp\u003eIn conclusion, the nomogram based on CA9 and AKR1C3 can effectively predict both short-term and long-term OS in HCC. This prediction model provides valuable support for clinical diagnosis, treatment, and management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Validation of key target gene expression by PCR\u003c/h2\u003e \u003cp\u003eValidation of gene expression levels for 6 key target genes was performed using qRT-PCR(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The results revealed a general consistency between the expression trends observed in qRT-PCR and RNA-seq data, indicating a high reliability of these key target genes in transcriptional regulation within signal transduction pathways. However, it is important to note that one gene (CA9) exhibited a discordant expression trend compared to the RNA-seq results. This discrepancy may be attributed to the inherent nature of RNA-seq as a tool for large-scale screening of overall gene expression trends, which cannot guarantee a perfect concordance with qRT-PCR results for every individual gene.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Validation of key target gene expression by WB\u003c/h2\u003e \u003cp\u003eTo validate the consistency of protein expression levels of key target genes with RNA-seq results, Western blot (WB) experiments were conducted. The results demonstrated that the protein expression trends of key target genes, as verified by WB, largely agreed with the RNA-seq data, as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. This observed concordance further supports the pivotal roles of these key target genes in signal transduction pathways, providing crucial insights for a deeper understanding of their functions and regulatory mechanisms. These findings offer valuable clues for investigating this signaling pathway's regulatory mechanisms and potential therapeutic targets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4.0 Molecular docking\u003c/h3\u003e\n\u003cp\u003eTo further investigate the interaction between naringenin and the key targets, we utilized the CB-Dock2 online analysis tool for molecular docking. According to relevant literature, a binding energy of less than \u0026minus;\u0026thinsp;5 kcal/mol between a compound and a protein is considered indicative of a stable binding. The results revealed that naringenin exhibited a binding energy of less than \u0026minus;\u0026thinsp;5 kcal/mol with all the proteins, suggesting a strong binding affinity between naringenin and the key targets. Interestingly, the best binding effects were observed for AKR1C3 and CA9, with binding energies of -9.5 kcal/mol and \u0026minus;\u0026thinsp;8.5 kcal/mol, respectively, which is consistent with the results of clinical prognostic analysis. These findings further support the notion that naringenin has a strong binding affinity with the key targets, particularly AKR1C3 and CA9. The binding affinity and binding details of hesperidin with these target genes are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. The key interacting parameters between hesperidin and target amino acid residues are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\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 BE of 6 key targets and Naringenin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicines\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKR1C3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1s1p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- 9.6 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCA9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3iai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- 8.0kcal/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHRNA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3sq6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- 6.8 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKLK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1spj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- 8.2 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIGFBP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7wgr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- 7.4 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1fzv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- 6.3 kcal/mol\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=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e5.1Validation of naringenin against HCC in animal models\u003c/h2\u003e \u003cp\u003eTo assess the in vivo anticancer activity of naringenin, a robust subcutaneous xenograft tumor model was created using the MHCC-97H cell line (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). The tumor was visible ten days after inoculation. Naringenin was injected intraperitoneally once a day from the 15th day until the 35th day. During this period, there were no differences in body weight between the treated group and the untreated group. It was clearly observed that the tumor volume of the untreated group increased significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eb). On the 36th day, the mice were put to death, and the tumors were removed for morphological and histological investigations. The treated group observed reduced tumor weight and volume compared to the untreated group. HE pictures revealed a lower amount of necrotic tissue in the treated group compared to the untreated group. In addition, they exhibited normal characteristics. The mice's liver, spleen, and kidneys in both the treated and untreated groups were normal (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ec-e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is a prevalent form of malignancy on a global scale, exhibiting an annual mortality rate increase of roughly 2\u0026ndash;3% \u003csup\u003e23\u003c/sup\u003e. This alarming trend necessitates the exploration of innovative and more efficacious therapeutic approaches. Natural products represent a vast reservoir of compounds for drug discovery \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, among which flavonoids have garnered particular attention since the 1990s for their anticancer properties\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Naringenin, a prevalent flavonoid, possesses substantial anticancer potential. Our study is designed to identify potential targets and pathways of naringenin in the treatment of HCC through mRNA sequencing, experimental validation, and molecular docking.\u003c/p\u003e \u003cp\u003eNaringenin is a naturally occurring citrus flavonoid known for its significant anticancer activity and minimal toxicity\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It exhibits less cytotoxicity against the normal liver cell line WRL compared to other cancer cell lines, a finding corroborated in ovarian cancer models \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, comparative analyses of liver, spleen, and kidney tissues from mice treated with naringenin showed no statistically significant differences, further affirming its safety profile.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated the efficacy of naringenin in inhibiting proliferation and inducing apoptosis in human liver cancer cell lines such as HepG2 \u003csup\u003e26\u003c/sup\u003e. Furthermore, it has been demonstrated to demonstrate anticancer properties by suppressing the expression of PKC and EGF, thereby inhibiting the MAPK and PI3K/Akt signaling pathways in liver cancer cells triggered by TPA \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, the precise anticancer targets of naringenin and its clinical value remain unclear. In this work, for the first time, we utilized sequencing data combined with the extensive gene information from the TCGA database to identify a set of core target genes, including IGFBP3, PGF, CA9, KLK1, CHRNA7, and AKR1C3. These genes not only respond to naringenin but also show clinical relevance to HCC. Subsequent KEGG pathway analysis suggested that the WNT, MAPK, and P53 signaling pathways might be integral to naringenin's anti-HCC mechanism. Our findings indicate that the expression of target genes in tumor tissues differs significantly from that in normal tissues. Moreover, PGF, CA9, KLK1, and IGFBP3 demonstrated diagnostic value for overall survival, with CA9 and AKR1C3 identified as independent prognostic factors for HCC. In addition, molecular docking validation confirmed the strong interaction between naringenin and these target genes.\u003c/p\u003e \u003cp\u003eCellular experiments further validated that naringenin inhibits the viability and invasiveness of HCC cells in a time- and dose-dependent manner. Mechanistically, these effects may be mediated by the modulation of CA9, IGFBP3, AKR1C3, CHRNA7, and KLK1 by naringenin. Moreover, the suppression of CHRNA7 expression has been proven to increase the sensitivity of cancer cells to chemotherapeutic drugs \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. A study conducted by Mina Afrashteh Nour et al. revealed that reducing the expression of α7nAChR greatly improves the ability of resistant HepG2 and Hepa 1\u0026ndash;6 cells to respond to SOR treatment. This demonstrates the crucial role of α7-nAChR in determining the drug sensitivity of HCC cells \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. p53 upregulates the transcription of insulin-like growth factor-binding protein 3 (IGFBP3), which acts as the main transporter of insulin-like growth factors (IGF) in the bloodstream \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Our Hoechst staining and flow cytometry results revealed a marked anti-apoptotic effect of naringenin, likely related to the upregulation of IGFBP3 and activation of the P53 signaling pathway. Aldehyde ketone reductase family member AKR1C3 plays a pivotal role in tumor progression and metastasis. Research findings have shown a notable increase in the expression of AKR1C3 in hepatocellular carcinoma (HCC), which subsequently facilitates the proliferation and spread of tumors by activating the NF-κB signaling pathway \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Intriguingly, our sequencing results revealed a marked downregulation of AKR1C3, suggesting that naringenin may exert its inhibitory effects on the NF-κB pathway through the modulation of AKR1C3 expression\u0026mdash;a potentially novel finding that warrants further investigation.\u003c/p\u003e \u003cp\u003eThe discoveries made in this study pave the way for the development of naringenin as a targeted therapeutic agent and provide new insights into its mechanisms of anticancer activity. Nonetheless, this research has its limitations. Although the clinical significance analysis is derived from a robust database, it remains retrospective and hypothesis-generating. Prospective clinical studies are required to confirm the therapeutic potential of naringenin and establish its efficacy and safety in the treatment of liver cancer. While our molecular docking analysis provided valuable insights into the potential interactions between naringenin and its putative targets (AKR1C3 and CA9), several limitations inherent to computational approaches must be acknowledged. First, docking simulations rely on static protein structures (e.g., PDB: 1s1p for AKR1C3), which may not fully capture the dynamic conformational changes occurring in vivo during ligand binding. Second, scoring functions used to estimate binding affinities (e.g., binding energies of -9.5 kcal/mol for AKR1C3) are approximations and may not reflect true thermodynamic stability. Furthermore, the transition from in vitro discoveries to clinical application is fraught with complexity. While our study delineates the effects of naringenin at the cellular level, in vivo dynamics, including metabolism, bioavailability, and interaction with the tumor microenvironment, require further clarification.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe comprehensive research findings indicate that naringenin effectively inhibits the proliferation of hepatocellular carcinoma cells in vitro and significantly impedes the growth of HCC tumors in vivo. Utilizing an array of scientific methodologies, including mRNA sequencing, experimental validation, and molecular docking, this study has substantiated the potential of naringenin as an efficacious multi-targeted agent against hepatocellular carcinoma. Notably, no adverse effects were observed in either in vitro or in vivo experiments, corroborating the inherent safety profile of naringenin as a natural constituent of dietary plants. This research provides a solid scientific rationale for the pharmacological mechanism of naringenin in the treatment of hepatocellular carcinoma and lays a foundational framework for future clinical translational studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant Nos. 82373111, 81972724，81901576).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal-related research undertaken in this study adheres strictly to the regulations laid out by the ARRIVE guidelines ((https://arriveguidelines.org). ). All experimental procedures have undergone meticulous ethical review, ensuring the minimization of any animal discomfort and distress at each stage\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflicts of interest for this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study is included in this published article and its supplementary information files. The data that support the findings of this study are available from the corresponding author, [Zheng Zhang], upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHepatocellular carcinoma. \u003cem\u003eNat. reviews Disease primers\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 7 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCancer J. Clin.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 209\u0026ndash;249 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, B. et al., \u003cem\u003eCancer incidence and mortality in China, 2022. J. Natl. Cancer Cent.\u003c/em\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlovet, J. M. et al. Molecular pathogenesis and systemic therapies for hepatocellular carcinoma. \u003cem\u003eNat. cancer\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 386\u0026ndash;401 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaque, A., Brazeau, D. \u0026amp; Amin, A. R. Perspectives on natural compounds in chemoprevention and treatment of cancer: an update with new promising compounds. \u003cem\u003eEuropean journal of cancer (Oxford, England\u003c/em\u003e: 149, 165\u0026ndash;183 (2021). (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGali-Muhtasib, H., Hmadi, R., Kareh, M., Tohme, R. \u0026amp; Darwiche, N. Cell death mechanisms of plant-derived anticancer drugs: beyond apoptosis. \u003cem\u003eApoptosis: Int. J. Program. cell. death\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 1531\u0026ndash;1562 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotallebi, M. et al. A potential flavonoid phytochemical for cancer therapy. \u003cem\u003eLife Sci.\u003c/em\u003e \u003cb\u003e305\u003c/b\u003e, 120752 (2022). Naringenin.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, F. et al. Naringenin prevents TGF-β1 secretion from breast cancer and suppresses pulmonary metastasis by inhibiting PKC activation. \u003cem\u003eBreast cancer research: BCR\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 38 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, C. et al. Naringenin suppresses epithelial ovarian cancer by inhibiting proliferation and modulating gut microbiota. \u003cem\u003ePhytomedicine: Int. J. phytotherapy phytopharmacology\u003c/em\u003e. \u003cb\u003e106\u003c/b\u003e, 154401 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver, D. J., Nikolau, B. \u0026amp; Wurtele, E. S. Functional genomics: high-throughput mRNA, protein, and metabolite analyses. \u003cem\u003eMetab. Eng.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 98\u0026ndash;106 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e, D1074\u0026ndash;d1082 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina, A., Michielin, O. \u0026amp; Zoete, V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, W357\u0026ndash;w364 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. \u003cem\u003eInnov. (Cambridge (Mass))\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e, 100141 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkuda, S. et al. KEGG Atlas mapping for global analysis of metabolic pathways. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, W423\u0026ndash;426 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. \u0026amp; Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, D672\u0026ndash;d677 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRu, J. et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. \u003cem\u003eJ. Cheminform.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 13 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniProt. the universal protein knowledgebase in 2021. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, D480\u0026ndash;d489 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. et al. PubChem's BioAssay Database. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, D400\u0026ndash;412 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, H., Jin, Y. \u0026amp; Ding, H. MDBuilder: a PyMOL plugin for the preparation of molecular dynamics simulations. \u003cem\u003eBrief. Bioinform\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViswanath, P. et al. A Novel Method for Quantifying Total Thoracic Tumor Burden in Mice. \u003cem\u003eNeoplasia (New York N Y)\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 975\u0026ndash;984 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, H. \u0026amp; Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 35 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M. \u0026amp; Goto, S. KEGG: kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 27\u0026ndash;30 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, W. \u0026amp; Wei, C. Advances in the early diagnosis of hepatocellular carcinoma. \u003cem\u003eGenes Dis.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 308\u0026ndash;319 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvey, A. L., Edrada-Ebel, R. \u0026amp; Quinn, R. J. The re-emergence of natural products for drug discovery in the genomics era. \u003cem\u003eNat. Rev. Drug Discov.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 111\u0026ndash;129 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVissenaekens, H. et al. Flavonoids and cellular stress: a complex interplay affecting human health. \u003cem\u003eCrit. Rev. Food Sci. Nutr.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e, 8535\u0026ndash;8566 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, M. et al., \u003cem\u003eNaringenin Induces HepG2 Cell Apoptosis via ROS-Mediated JAK-2/STAT-3 Signaling Pathways. Molecules\u003c/em\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e28\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYen, H. R., Liu, C. J. \u0026amp; Yeh, C. C. Naringenin suppresses TPA-induced tumor invasion by suppressing multiple signal transduction pathways in human hepatocellular carcinoma cells. \u003cem\u003eChemico-Biol. Interact.\u003c/em\u003e \u003cb\u003e235\u003c/b\u003e, 1\u0026ndash;9 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajiasgharzadeh, K. et al. Small interfering RNA targeting alpha7 nicotinic acetylcholine receptor sensitizes hepatocellular carcinoma cells to sorafenib. \u003cem\u003eLife Sci.\u003c/em\u003e \u003cb\u003e244\u003c/b\u003e, 117332 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNour, M. A., Kheradmand, F., Rasmi, Y. \u0026amp; Baradaran, B. Alpha7 nicotinic acetylcholine receptor expression in Sorafenib-resistant Hepatocellular carcinoma cells. \u003cem\u003eMed. Oncol. (Northwood Lond. Engl.)\u003c/em\u003e. \u003cb\u003e39\u003c/b\u003e, 165 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X. et al. Elevated expression of WSB2 degrades p53 and activates the IGFBP3-AKT-mTOR-dependent pathway to drive hepatocellular carcinoma. \u003cem\u003eExp. Mol. Med.\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e, 177\u0026ndash;191 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Q. et al. A Positive Feedback Loop of AKR1C3-Mediated Activation of NF-κB and STAT3 Facilitates Proliferation and Metastasis in Hepatocellular Carcinoma. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e, 1361\u0026ndash;1374 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Naringenin Hepatocellular carcinoma (HCC) Molecular docking mRNA-seq Subcutaneous Xenograft tumor model","lastPublishedDoi":"10.21203/rs.3.rs-5403136/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5403136/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) ranks among the top three causes of cancer-related mortality globally and is associated with a relatively low five-year overall survival rate. Naringenin has demonstrated significant inhibitory effects on various neoplasms; however, the mechanisms of action and potential molecular targets of naringenin in the context of HCC remain to be elucidated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCellular proliferation in cancer cells was quantified using the Cell Counting Kit-8 (CCK-8) assay. Wound healing and transwell tests were employed to evaluate the migratory and invasive capabilities of the cells, respectively. Apoptosis was evaluated using Hoechst staining to visualize nuclear changes and flow cytometry to quantify apoptotic populations. Following mRNA sequencing, we integrated the TCGA database with known naringenin-related targets to identify overlapping genes, which were subsequently subjected to clinical significance analysis. The expression of these genes was confirmed at the protein and mRNA levels using Western blot (WB) and quantitative PCR (qPCR), respectively. In vivo experiments were conducted using an MHCC-97H xenograft model in nude mice, with histopathological examination of tumor sections performed using hematoxylin and eosin (H\u0026amp;E) staining.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn vitro, naringenin demonstrated a potent inhibitory effect on the proliferation, invasion, and migration of MHCC-97H and Huh7 cells while exhibiting a pronounced pro-apoptotic impact on both cell lines. mRNA sequencing results revealed significant differential gene expression. Utilizing Venn diagrams, we identified key genes, including IGFBP3, PGF, CA9, AKR1C3, KLK1, and CHRNA7. We implicated signaling pathways such as the \"Wnt signaling pathway\" and \"MAPK signaling pathway\" as potentially critical in naringenin's anti-HCC activity. The clinical significance analysis revealed that CA9 and AKR1C3 were identified as autonomous prognostic variables for hepatocellular carcinoma (HCC), a conclusion supported by molecular docking investigations. The therapeutic promise of naringenin was further supported by its considerable reduction in tumor weight and volume shown in animal trials.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study shows that naringenin may regulate signaling pathways by targeting a series of genes: IGFBP3, PGF, CA9, AKR1C3, KLK1, and CHRNA7, resulting in the inhibition of tumor cell proliferation and metastasis, alongside the promotion of apoptosis.\u003c/p\u003e","manuscriptTitle":"Exploring the mechanism of Naringenin in the treatment of hepatocellular carcinoma based on mRNA sequencing and experimental validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 12:10:44","doi":"10.21203/rs.3.rs-5403136/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-02T07:00:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-01T12:48:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T05:18:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-18T15:22:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112216353516303334050186104354531723881","date":"2025-04-18T14:28:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-18T14:23:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-05T14:25:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-27T14:52:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9eb6db45-8268-4a89-becf-f117c67180a0","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47371379,"name":"Biological sciences/Cancer/Cancer therapy"},{"id":47371380,"name":"Biological sciences/Computational biology and bioinformatics/Data mining"},{"id":47371381,"name":"Biological sciences/Drug discovery/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2025-07-07T16:09:20+00:00","versionOfRecord":{"articleIdentity":"rs-5403136","link":"https://doi.org/10.1038/s41598-025-09013-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-02 15:58:52","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-04-21 12:10:44","video":"","vorDoi":"10.1038/s41598-025-09013-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-09013-y","workflowStages":[]},"version":"v1","identity":"rs-5403136","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5403136","identity":"rs-5403136","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}