Integrated network pharmacology and experimental validation reveal Coreopsis tinctoria Total Flavonoids (CTFs) as potential therapeutic agents against hepatocellular carcinoma

Abstract

Objective: To validate the anti-hepatocellular carcinoma (HCC) efficacy of Coreopsis tinctoria Total Flavonoids (CTFs) and explore its underlying mechanisms using a comprehensive approach integrating network pharmacology and experimental verification, thereby supporting its potential as a multi-target therapeutic agent for liver cancer. Methods: Potential targets of CTFs were retrieved from Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database, while HCC-related targets were collected from GeneCards, OMIM, and DrugBank. Common targets were identified using VENNY2.1, and protein-protein interaction (PPI) networks were constructed via STRING. Functional Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using DAVID. A “CTFs-HCC-target-pathway” network was built with Cytoscape to identify key components and core targets. Molecular docking was performed using Autodock Vina. The Differential expression of key targets between HCC and normal tissues was visualized using boxplots, and prognostic relevance was evaluated by Kaplan-Meier survival analysis. In vitro assays, including CCK-8, live/dead staining, colony formation, flow cytometry, qPCR, were used to evaluate proliferation, viability, reactive oxygen species (ROS) levels, cell cycle distribution, and gene expression. A zebrafish xenograft model was established to determine the minimum toxic concentration (MTC) and evaluate tumor inhibition through fluorescence imaging and HE staining. Results: Network analysis identified 27 bioactive components and 318 putative targets of CTFs, with 32 associated with HCC. Core targets included Caspase-3, P53, MAPK1, Bcl-2 and Bax, primarily interacting with quercetin, (-)-Epigallocatechin (EGCG), fisetin, acacetin, luteolin, and kaempferol. Molecular docking confirmed strong binding affinities between these compounds and core targets. Pro-apoptotic genes (Bax, Caspase-3, P53) were upregulated in HCC tissues, and low expression of Bax/Caspase-3 correlated with poor survival. CTFs treatment further enhanced expression of Bax, p53 and Caspase-3, suppressed Bcl-2 while increased the Bax/Bcl-2 ratio. In vitro, CTFs inhibited HepG2 proliferation, promoted LO2 growth, induced ROS production, G₂/M and S-phase arrest and apoptosis. In vivo, CTFs significantly suppressed tumor growth in zebrafish xenografts. Conclusion: CTFs exert anti-HCC effects through multi-target regulation of apoptosis-related genes and multiple signaling pathways, effectively inhibiting cancer cell proliferation.

Introduction

Hepatocellular carcinoma (HCC) constitutes a significant global health burden, accounting for approximately 90% of primary liver cancers and ranking as the third leading cause of cancer-related mortality worldwide. The clinical management of HCC remains highly challenging due to its aggressive biological behavior, manifested by post-treatment recurrence rates exceeding 70% within 5 years, rapid metastatic progression, and inherent resistance to conventional chemotherapy regimens. Although molecular-targeted agents including sorafenib and lenvatinib have improved patient outcomes, their therapeutic efficacy remains limited by dose- dependent toxicities, acquired resistance, and eventual treatment failure [1-3]. These limitations underscore the urgent need for developing innovative anti-HCC agents with enhanced tumor specificity and reduced systemic toxicity.

Natural products derived from plants have been extensively studied for their chemical diversity and broad physiological activities, establishing them as valuable sources for nutritional, medicinal, and pharmaceutical applications. In oncology drug discovery, these compounds have re-emerged as promising candidates due to their multi-target mechanisms, cost-effectiveness, favorable safety profiles, and low potential for resistance development, collectively highlighting their therapeutic value [4-6]. Among these bioactive compounds, flavonoids have attracted considerable scientific interest for their diverse pharmacological properties, encompassing potent antioxidant, anti-inflammatory, and antitumor activities. Coreopsis tinctoria Nutt. (C. tinctoria), a medicinal plant native to high-altitude regions, is particularly rich in flavonoids and polyphenolic constituents. Traditionally, C. tinctoria has been used for metabolic regulation in diabetes and hyperlipidemia; however, recent studies have revealed its notable antitumor properties [7]. Despite these promising findings, the anti-HCC potential of C. tinctoria flavonoids (CTFs) remains underexplored. The specific bioactive constituents responsible for its anti-tumor effects and their molecular targets remain to be systematically elucidated, representing a critical research gap in current phytopharmacology research [8-12].

Motivated by the urgent need for novel HCC therapies with improved safety and efficacy profiles, this study aimed to comprehensively investigate the anti-HCC properties of CTFs through an integrative research strategy. Although CTFs have demonstrated diverse pharmacological activities, their therapeutic efficacy against HCC remains poorly characterized. To address this gap, we employed a network pharmacology-based framework to systematically elucidate the “CTFs-HCC-targets-pathways” interactions, thereby providing a systems-level understanding of the molecular mechanisms underlying CTFs’ anti-HCC actions. Furthermore, this approach was substantiated by experimental validation through a tripartite methodology encompassing predictive modeling, pharmacodynamic evaluation, and mechanistic investigation in both in vitro and in vivo settings. By bridging the holistic principles of traditional medicine with modern computational biology, our strategy provides a powerful platform for deciphering the multi-target therapeutic mechanisms of CTFs. Collectively, this study offers a comprehensive assessment of CTFs’ therapeutic potential and highlights their clinical translational value as a novel multi-target, natural product-derived treatment strategy capable of overcoming the inherent limitations of current single-target therapies for HCC.

Material and methods

Materials and instruments

Total flavonoids from Coreopsis tinctoria Nutt. (production license number: QS650114020007) were obtained from Xinjiang Coreopsis Tinctoria Biological Technology Co., Ltd. Zebrafish were purchased from Huantai Biological Technology Co., Ltd. and maintained in accordance with the standards of the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC International; Accreditation Number: 001458). All animal experiments were reviewed and approved by the Laboratory Animal Ethics Committee of Xinjiang Medical University (Ethics Review No.: IACUC-2024228-120). The Animal Use License number is SYXK (Zhe) 2022-0004.

Network pharmacology analysis of the effects of CTFs on HCC

Prediction of active compound targets and screening of disease-related targets for CTFs

Based on a comprehensive literature review [9], bioactive compounds of CTFs were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database using selection criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, followed by target prediction and validation. HCC targets were collected from DisGeNET, OMIM, DrugBank databases using “hepatocellular carcinoma” or “liver cancer” as search terms. All retrieved targets were subsequently deduplicated and normalized for downstream analysis.

Identification of common targets and protein-protein interactions between HCC and CTFs

Common targets shared between CTFs and HCC were identified and imported into the STRING database to construct a protein-protein interaction (PPI) network. The network was analyzed using Cytoscape 3.8.2, and core (hub) targets were identified based on three topological parameters: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC). Nodes with parameter values exceeding the median for all three indices were designated as hub targets. A “target-compound” reverse screening strategy was then applied, performing a reverse VLOOKUP from the hub targets to corresponding key compounds of CTFs, thereby enabling rapid identification of candidate bioactive molecules specifically interacting with the core targets.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

The potential targets were imported into the DAVID 6.8 database for GO and KEGG pathway enrichment analyses, selecting “OFFICE_GENE_SYMBOL” and “Homo sapiens” as parameters. The top 10 GO terms and top 20 KEGG pathways were visualized using the Bioinformatics online platform (https://www. bioinformatics.com.cn/). In the visualized bubble plots, bubble size represents the number of genes involved in each pathway, and color intensity represents the level of enrichment significance.

Construction of the “component-Target-HCC-Pathway” network

An integrated “CTFs-target-pathway-HCC” network was constructed using Cytoscape 3.8.2 to visualize and analyze the interactions among bioactive compounds, corresponding protein targets, signaling pathways, and disease nodes.

Molecular docking of active compounds with key targets

To further verify the reliability of the target prediction results, hub targets from the “component-target-HCC-pathway” network were validated through molecular docking (AutoDock Vina) to evaluate the binding affinities between key bioactive compounds and their respective targets.

Comparative analysis of gene expression profiles and normal liver tissues and survival analysis

To investigate the differential expression of Key targets between and normal liver tissues and to evaluate their prognostic relevance, gene expression data were analyzed using the GEPIA2 database (http://gepia2.cancer-pku.cn/). HCC datasets were obtained from The Cancer Genome Atlas (TCGA) and normal liver tissue data from the Genotype-Tissue Expression (GTEx) database. Liver hepatocellular carcinoma (LIHC) was selected as the cancer type, and gene expression levels were compared between tumor and normal samples using boxplots. Kaplan-Meier (K-M) survival curves were generated to evaluate the association between gene expression levels and overall survival, with P < 0.01 considered statistically significant.

Experimental verification of CTFs effects on HCC

CCK-8 assay

The human hepatocellular carcinoma cell line HepG2 and normal human liver cell line L02 (Purose, China) were utilized and cultured according to the instructions. Cells were seeded into 96-well plates at 5×10³ cells/well and incubated for 24 hours (h). Subsequently, cells were treated with various concentrations of CTFs (50, 100, 150 µg/mL) for 48 h at 37°C. Following incubation, 10 µL of CCK-8 regent (Cytoskeleton, Beijing) was added to each well, followed by incubation for 2h at 37°C. The optical density (OD) at 450 nm was measured using a microplate reader. Cell viability (%) = OD (experimental group)/OD (control group) ×100%. Each experiment was performed in triplicate.

Assessment of CTFs effects on HepG2 cell viability

HepG2 cells were seeded into 6-well plate at 1×10⁵ cells/well and treated with CTFs (50, 100, 150 µg/mL) for 48 h. Cell viability was assessed using a dual-staining assay with Calcein-AM (for live cells) and propidium iodide (PI, for dead cells) (Elabscience, China). After staining at 37°C for 15 min in the dark, live and dead cells were observed and quantified under a fluorescence microscope.

Colony formation assay

To evaluate the effect of CTFs on the clonogenic potential of HepG2 cells, HepG2 cells were seeded into 6-well plates at 1×10⁵ cells/well and and allowed to adhere for 24 h. Cells were then treated with CTFs (50, 100, 150 µg/mL) for 24 h, after which the medium was replaced with fresh complete medium. The cells were cultured for 14 days under standard conditions, with medium renewal every 72 h. Colonies (> 50 cells) were fixed, stained with 0.5%crystal violet (Elabscience, China), and quantified manually under light microscopy, with representative images captured for documentation.

Hoechst staining

HepG2 cells were seeded in 6-well plates at 3×10⁵ cells/well and incubated for 24 h. Cells were then treated with varying concentrations of CTFs (50, 100, 150 µg/mL) for 24 h, followed by staining with Hoechst solution (10 μg/mL; Salarbio, Beijing, China) at 37°C for 10 min. Nuclear morphology was examined under a fluorescence microscope, and fluorescence intensity was quantified using ImageJ.

Assessment of CTFs-Induced ROS production in HepG2 cells

HepG2 cells were seeded in 6-well plates at 3×10⁵ cells/well and incubated for 24 h, followed by treatment with varying concentrations of CTFs (50, 100, 150 µg/mL) for 24 h. After treatment, cells were incubated with ROS detection reagent (10 μg/mL; Salarbio, Beijing, China) at 37°C for 10 min in the dark. Intracellular ROS levels were immediately quantified under a fluorescence microscopy (excitation/emission: 488/525 nm), and fluorescence intensity was analyzed using ImageJ.

Cell cycle analysis of CTFs-treated HepG2 cells by flow cytometry

For cell cycle analysis, HepG2 cells were seeded in 6-well plates at 3×10⁵ cells/well and cultured for 48 h. Cells were then treated with varying concentrations of CTFs (50, 100, 150 µg/mL) for 48 h, followed by fixation in chilled 70% ethanol at 4°C overnight. Fixed cells were treated with RNaseA (100 μg/mL) at 37°C for 30 min and stained with PI (50 μg/mL; Elabscience, China) at 4°C. Cell cycle distribution was analyzed using flow cytometry (BD FACSCanto™ II/FlowJo).

Quantitative Real-time PCR (qRT-PCR) analysis of gene expression

Total RNA was extracted from HepG2 cells using an RNA isolation Kit with Spin Column (Omega, USA). Complementary DNA (cDNA) was synthesized, and qRT-PCR was performed using the SYBR Green qRT-PCR Kit (Quantinova, Germany) on a LightCycler 96 Real-Time PCR system. Each sample was analyzed in triplicate. Relative gene expression levels were calculated using the 2^-ΔΔCt method, with primer sequences listed in Table 1.

Table 1.

PCR primer sequences

Gene	Primer sequence (5’→3’)
Bax	Forward: ACCAAGCTGAGCGAGTGTC
	Reverse: TGTCCACGGCGGCAATCATC
Bcl-2	Forward: GAGTGGGATGCGGGAGATGTG
	Reverse: CGGGATGCGGGATGG
Caspase-3	Forward: ATGGAAGCGAATCAATGGACTCTG
	Reverse: TCTGAATGTTTCCCTGAGGTTTGC
P53	Forward: ATGAGCCGCCTGAGGTTGG
	Reverse: CAGTGTGATGATGGTGAGGATGG
β-actin	Forward: GGCGGCACCACCATGTACCCT
	Reverse: AGGGGCCGGACTCGTCATACT

Effect of CTFs on tumor proliferation in a zebrafish xenograft model

Determination of the Minimum Toxic Concentration (MTC) of CTFs

HepG2 cells were fluorescently labeled with CM-DiI red fluorescence dye (Thermo Fisher Scientific, USA). Approximately 200 labeled HepG2 cells were microinjected into zebrafish embryos (AB strain, 2 days post-fertilization, dpf) to establish xenograft models. After CTFs treatment at 35°C for 48 h, mortality and teratogenicity were recorded to determine MTC.

Evaluation of CTFs effects on tumor proliferation

Zebrafish receiving MTC-adjusted CTFs were maintained at 35°C for 48 h. Tumor fluorescence signals were captured using NIS-Elements D 3.20 imaging software in 10 zebrafish per group. Tumor inhibition rate (IR) was calculated using the formula: (1- mean tumor volume of treated group/mean tumor volume of control group) ×100%.

H&E staining of tumor tissue

Zebrafish larvae were euthanized by immersion in tricaine methanesulfonate (MS-222) prior to fixation. Then, they were fixed in 10% neutral buffered formaldehyde (Servicebio, Wuhan) for 24 h, dehydrated, and embedded in paraffin (Servicebio, Wuhan, China). Tissue sections (4 μm) were stained with hematoxylin and eosin (H&E) following standard histological procedures. Tumor morphology and cellular architecture were examined under a light microscope.

Statistical analysis

All statistical analyses were performed using SPSS 23.0 (IBM Corp., USA) and GraphPad Prism 9.0 (USA). Experimental data from three independent assays were presented as mean ± standard deviation (SD). For normally distributed data, comparisons between two groups were performed using Student’s t-tests, while multiple group comparisons were analyzed by one-way ANOVA followed by Dunnett’s post hoc test. P values < 0. 05 indicated statistically significant differences.

Results and analysis

Network pharmacological analysis of CTFs in HCC

Target screening for CTFs and HCC

Systematic analysis of the TCMSP identified 27 bioactive compounds derived from CTFs, mapping to 318 potential therapeutic targets (Figure 1A). Concurrently, comprehensive searches of the DrugBank, OMIM, and DisGeNET databases yielded 486 molecular targets associated with HCC. Comparative analysis revealed 70 shared molecular targets between CTFs and HCC, suggesting potential pharmacological interactions and therapeutic mechanisms underlying CTFs-mediated anti-HCC activity.

Figure 1.

Screening of hub genes from common targets and PPI network analysis. A: Venny diagram showing the overlap between the predicted targets of CTFs and HCC-related targets; B: PPI network illustrating the intersections among the intersecting targets. Notes: HCC, Hepatocellular Carcinoma; CTFs, Coreopsis tinctoria Flavonoids; PPI, Protein-Protein Interaction.

Identification of shared targets and PPI network analysis

PPI analysis conducted using the STRING database demonstrated functional convergence among the 70 shared targets, forming an integrated network containing 269 nodes (Figure 1B). This network structure highlights complex and interconnected nature of CTFs-mediated modulation of HCC-related molecular pathways.

The PPI network was subsequently imported into Cytoscape 3.8.2 for topological analysis. Based on the selection criteria of DC ≥ 80.00, BC ≥ 16.42, CC ≥ 0.71, a total of 32 high-confidence core targets were identified as key mediators of CTFs’ therapeutic effects against HCC. Reverse pharmacopoeia mapping of these core targets revealed 15 key bioactive compounds in CTFs with putative anti-HCC activity, including quercetin, epigallocatechin gallate (EGCG), luteolin, diosmetin, fisetin, kaempferol, tricin, acacetin, nobiletin, (-)-epicatechin, isorhamnetin, glycitein, galangin, astilbin, and baicalein) (Table 2).

Table 2.

Core targets associated with the anti-HCC activity of CTFs

Numbers	Targets	DC	BC	CC	Compound name
1	TP53	132.00	230.45	0.99	Acacetin, EGCG, Baicalein, Fisetin, Luteolin, Nobiletin, Quercetin
2	MYC	118.00	88.82	0.89	Quercetin
3	AKT1	118.00	100.46	0.89	EGCG, Baicalein, Fisetin, kaempferol, Luteolin, Quercetin
4	CTNNB1	114.00	110.56	0.87	Fisetin
5	JUN	114.00	64.56	0.87	EGCG, Fisetin, kaempferol, Luteolin, Nobiletin, Quercetin
6	STAT3	112.00	49.54	0.86	EGCG
7	HIF1A	112.00	64.31	0.86	EGCG, Baicalein, Quercetin
8	EGFR	112.00	82.13	0.86	EGCG, Fisetin, Luteolin, Quercetin
9	Caspas-3	110.00	61.66	0.85	Acacetin, EGCG, Baicalein, Fisetin, kaempferol, Luteolin, Quercetin, Bioflavonoid
10	TNF	110.00	77.13	0.85	EGCG, Astilbin, Fisetin, kaempferol, Luteolin, Quercetin
11	IL6	110.00	68.89	0.85	EGCG, Fisetin, Luteolin, Quercetin, Bioflavonoid
12	PTEN	106.00	60.25	0.83	Quercetin
13	MAPK3	106.00	72.94	0.83	EGCG
14	CCND1	106.00	52.85	0.83	EGCG, Fisetin, Luteolin, Quercetin
15	ESR1	104.00	92.77	0.82	EGCG, Cianidanol, Glycitein, Isorhamnetin
16	IL1B	102.00	39.56	0.81	Fisetin, Quercetin, Bioflavonoid
17	PPARG	98.00	62.20	0.79	EGCG, Fisetin, Galangin, Glycitein, Isorhamnetin
18	MMP9	96.00	31.20	0.78	Baicalein, Luteolin, Nobiletin, Quercetin
19	Caspase-8	96.00	27.78	0.78	Acacetin, EGCG, Fisetin, Quercetin
20	PTGS2	96.00	33.61	0.78	Acacetin, Diosmetin, Fisetin, Galangin, Glycitein, Genkwanin, Hesperetin, Hydroxygenkwanin, Isorhamnet, Baicalein, Cianidanol
21	MAPK8	94.00	21.93	0.77	EGCG, kaempferol, Nobiletin
22	ERBB2	94.00	24.61	0.77	EGCG, Luteolin, Quercetin
23	CXCL8	94.00	30.65	0.77	Fisetin, Quercetin , Bioflavonoid
24	CDH1	92.00	29.16	0.76	EGCG
25	CDKN2A	86.00	24.42	0.74	Quercetin
26	MCL1	86.00	26.06	0.74	Fisetin, Luteolin
27	MDM2	86.00	29.35	0.74	EGCG, Luteolin
28	CDKN1A	84.00	22.98	0.73	Acacetin, EGCG, Fisetin, Luteolin, Quercetin
29	MMP2	84.00	20.71	0.73	EGCG, Luteolin, Quercetin
30	TLR4	82.00	21.60	0.72	EGCG
31	IL10	82.00	11.40	0.72	EGCG, Astilbin, Luteolin, Quercetin
32	MAPK1	80.00	16.42	0.71	EGCG, Luteolin, Quercetin

Notes: DC, Degree Centrality; BC, Betweenness Centrality; CC, Closeness Centrality; EGCG, Epigallocatechin Gallate; HCC, Hepatocellular Carcinoma; CTFs, Coreopsis tinctoria Flavonoids.

GO and KEGG pathway analysis

GO enrichment analysis was performed using the DAVID bioinformatics database to characterize the functional distribution of the 32 core targets, yielding 718 significantly enriched GO terms. Within the biological process (BP) category, 575 entries were identified, primarily involving the regulation of apoptotic process (positive/negative), negative regulation of cell proliferation, and positive regulation of gene expression. The cellular component (CC) category comprised 51 GO entries, including enrichment in macromolecular complexes, mitochondrial components, and chromatin organization. The molecular function (MF) category contained 99 entries, primarily associated with enzyme-substrate interactions, transcription factor activity, and sequence-specific DNA recognition activities. The top 10 enriched terms (P < 0.05) in the BP, CC and MF categories are shown in Figure 2A.

Figure 2.

Results of GO and KEGG enrichment analysis. A: Bubble map of GO analysis; B: Bubble map of KEGG analysis; C: KEGG pathway. Notes: GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

KEGG pathway enrichment analysis elucidated the mechanistic landscape of CTFs-mediated regulation in HCC, revealing three major clusters of molecular pathways: (1) Core oncogenic pathways, including cell cycle regulation, PI3K-Akt signaling, p53 tumor suppressor activation, and TGF-β-mediated differentiation; (2) Early hepatocarcinogenesis pathways, characterized by MAPK cascade and Wnt/β-catenin signaling; and (3) Tumor microenvironment modulation, involving AGE-RAGE interactions, IL-17-mediated inflammation, and TNF-α signaling. The 20 most statistically significant pathways (P < 0.05) are shown in Figure 2B, 2C.

Construction of the “CTFs-HCC-target-pathway” network

Enrichment analysis of CTFs-derived bioactive components and their corresponding targets revealed the principal signaling pathways involved in HCC modulation. Using Cytoscape 3.8.2, an integrated “CTFs-HCC-target-pathway” network was constructed and visualized (Figure 3). This comprehensive network demonstrated the complex interactions among 27 active components in CTFs (e.g., EGCG, quercetin, luteolin, fisetin, and kaempferol), and multiple key regulatory targets, including Caspase-3, P53, AKT1, MAPK3, JUN, BCL-2, Bax, MAPK1, IL6, TNF, Caspase-8, and MMP9. The observed interaction patterns support a polypharmacological mechanism of CTFs-mediated anticancer effects through multi-component and multi-target mechanisms.

Figure 3.

CTFs-HCC-targets-pathway network. Octagon: CTFs; Hexagon: CTFs compound; Triangle: target; Diamond: HCC; Parallelogram: pathway; The size decreases with the connectivity value decreases, and the color becomes lighter with the connectivity value decreases. Notes: HCC, Hepatocellular Carcinoma; CTFs, Coreopsis tinctoria Flavonoids.

Molecular docking of key targets

Network topology analysis of the “CTFs-HCC-target-pathway” network identified highly connected nodes (degree ≥ 12), for which the corresponding active compounds of CTFs were subjected to reverse molecular docking. The binding affinity threshold was set at -7.0 kcal/mol with lower energy values indicating stronger molecular interactions, as illustrated in Figure 4 and Table 3. The molecular docking results confirmed that the active components of CTFs exhibited strong binding affinities with key HCC target proteins, thereby validating the reliability of the predicted targets.

Figure 4.

Molecular docking results. A: Caspase3-Acacetin; B: Bcl-2-Kaempferol; C: P53-Quercetin; D: AKT1-EGCG; Yellow: compound-hydrogen bond; Dark blue: potential target residue that binds to the compound.

Table 3.

Molecular docking results

Compound	Binding energy (Kcal/mol)
	Caspase-3	TP53	AKT1	Bcl-2	Bax	Mapk1
Quercetin	-6.39	-6.92	-6.74	-5.86	-4.69	-6.44
EGCG	-4.54	-5.74	-4.29	-5.43	-4.72	-5.03
Fisetin	-6.52	-6.89	-6.84	-7.59	-6.54	-6.74
Acacetin	-4.72	-7.22	-5.97	-5.96	-6.56	-7.62
Luteolin	-6.15	-6.76	-5.48	-5.48	-5.67	-6.9
Kaempferol	-5.44	-5.21	-6.15	-4.45	-5.85	-5.42

Notes: EGCG, Epigallocatechin Gallate.

Comparative analysis of gene expression profiles and survival analysis

Boxplots generated by GEPIA2 database demonstrated upregulated expression of target genes (P53, Bcl-2, Bax and Caspase-3) in HCC tissues, compared to normal liver tissues (Figure 5A). Notably, the pro-apoptotic gene Bax exhibited the most pronounced increase (P < 0.01), followed by Caspase-3 and P53. While the anti-apoptotic gene Bcl-2 was similarly upregulated, its expression increase was less prominent than that of Bax (Figure 5B). This differential regulatory pattern suggests that the relatively weak upregulation of Bcl-2 may be insufficient to counteract the strong pro-apoptotic signals, potentially leading to tumor progression through dysregulated apoptosis.

Figure 5.

Genomic alterations and clinical relevance of key targets in HCC. A: Boxplot comparing genes expression between HCC and normal liver tissues; B: Matrix plot illustrating multiple genes comparison; C: Survival analysis. Notes: HCC, Hepatocellular Carcinoma.

Survival analysis further revealed distinct prognostic implications of these apoptosis-related genes in HCC (Figure 5C). Bax showed significant survival curve separation; patients with the low-expression group demonstrating poorer overall survival and a 70% higher mortality risk (HR=1.7) (P=0.0037), indicating that Bax deficiency may attenuate apoptotic activity and worsen prognosis. Bcl-2 showed an overall protective trend (HR=0.96), with overlapping curves during the initial 18 months, followed by worse survival in the high-expression group from 18-54 months, potentially indicating that excessive apoptosis inhibition may promote tumor persistence. Interestingly, after 54 months, survival declined in the low-expression group, possibly due to loss of Bcl-2’s compensatory protective effects. Caspase-3 expression was associated with a 30% increase in mortality risk (HR=1.3), consistent with reduced apoptotic efficiency in the low-expression subgroup. In contrast, P53 showed nearly overlapping survival curves (HR=1.0, NS), indicating its involvement in HCC regulation may be dose-independent.

Experimental validation of CTFs in HCC models

CTFs exhibited differential effects on LO2 and HepG2 cell proliferation

To assess the selective anti-cancer activity of CTFs, both LO2 normal hepatocytes and HepG2 cells were treated with increasing concentrations of CTFs. In LO2 cells, CTFs treatment significantly enhanced cell viability (P < 0.05), suggesting potential hepatoprotective effects (Figure 6A). Covertly, CCK-8 assay revealed distinct concentration-dependent inhibitory effect of CTFs on HepG2 cell proliferation (P < 0.05) (Figure 6B). This differential pharmacological response highlights the dual therapeutic potential of CTFs, functioning as hepatocyte-protective agents and selective HCC growth inhibitor, suggesting their possible role in primary prevention among high-risk populations, adjuvant combination therapy, and prognosis improvement in HCC. Nevertheless, further mechanistic exploration and clinical validation are warranted.

Figure 6.

Effects of CTFs on cell viability of HepG2 cells. A: Effect of CTFs on the survival of HepG2 cells; B: Effect of CTFs on the survival of Lo2 cells; C: Effect of CTFs on the colony formation efficiency of HepG2 cells, ×100; D: Effects of CTFs on cell viability of HepG2 cells, ×100. *Compared with control group, P < 0.05. Notes: CTFs, Coreopsis tinctoria Flavonoids.

CTFs-mediated suppression of HepG2 cell proliferation

The clonogenic potential of HepG2 cells following CTFs treatment was quantitatively assessed using a standardized colony formation assay. Compared to untreated controls, CTFs exposure resulted in a significant, concentration-dependent reduction in the number of viable cell colonies (P < 0.05) (Figure 6C).

CTFs induced cell death in HepG2

To assess the cytotoxic effects of CTFs on HepG2 cells, live/dead fluorescence staining revealed that untreated control cells exhibited minimal PI fluorescence, indicating low baseline cytotoxicity. In contrast, CTFs treatment produced a dose-dependent increase in PI fluorescence intensity, signifying elevated levels of cell death (Figure 6D).

CTFs induced apoptotic nuclear morphological changes

Hoechst 33342 staining revealed that control HepG2 cells maintained regular, faintly-stained nuclei, whereas CTFs treatment for 24 hours elicited characteristic apoptotic morphology, evidenced by chromatin condensation, nuclear shrinkage, fragmentation, and enhanced bright blue fluorescence (Figure 7A).

Figure 7.

Effects of CTFs on apoptosis, ROS generation, and cell cycle progression in HepG2 cells. A: Effects of CTFs on apoptosis in HepG2 cells, ×200; B: Effects of CTFs on ROS content in HepG2 cells, ×100; C: Effects of CTFs on cell cycle distribution of HepG2 cells. *P < 0.05, compared with control group. Notes: CTFs, Coreopsis tinctoria Flavonoids; ROS, Reactive Oxygen Species.

CTFs induced ROS accumulation in HepG2 cells

To assess the effect of CTFs on intracellular ROS levels, HepG2 cells were treated with increasing concentrations of CTFs for 24 h, followed by incubation with the fluorescent probe DCFH-DA. Quantitative fluorescence microscopy demonstrated a dose-dependent elevation of ROS production in CTFs-treated HepG2 cells compared with untreated controls (Figure 7B). These results demonstrate that CTFs enhance oxidative stress in HepG2 cells in a dose-responsive manner.

CTFs induced cell cycle arrest in HepG2 cells

Flow cytometric analysis demonstrated that CTFs treatment markedly altered the cell cycle distribution of HepG2 cells relative to untreated controls. Specifically, CTFs treatment led to a marked reduction in the proportion of G0/G1 phase cells, accompanied by increases in both S- and G2/M- phase fractions (Figure 7C).

Effects of CTFs on the mRNA expression of key apoptotic regulators in HepG2 cells

qRT-PCR analysis revealed that CTFs treatment significantly upregulated the expression of pro-apoptotic genes (Bax, Caspase-3, and P53), while downregulating anti-apoptotic Bcl2 mRNA expression in HepG2 cells compared with untreated controls (P < 0.05, Figure 8). Notably, the Bax/Bcl2 ratio was significantly increased (P < 0.05), confirming activation of the intrinsic apoptotic pathway.

Figure 8.

Effect of CTFs on mRNA expression of apoptosis-related gene in HepG2 cells. *P < 0.05, compared with control group. Notes: CTFs, Coreopsis tinctoria Flavonoids.

Determination of the MTC of CTFs in zebrafish tumor models

Toxicity assessment of CTFs-treated zebrafish larvae is documented in Table 4. Exposure to 62.5 µg/mL CTFs produced no observable morphological abnormalities or mortality, whereas treatment with 125 µg/mL CTFs induced significant developmental deformities compared with the control group. Based on these findings, CTFs concentrations of 15.6, 31.2, and 62.5 µg/mL were selected for subsequent investigations.

Table 4.

Dose optimization study of CTFs for suppressing tumor growth in xenograft models (n=30)

Group	Drug concentration (µg/mL)	Death count (per fish)	Mortality rate (%)	Phenotype
Normal control group	-	0	0	No significant abnormalities were observed
Model control group	-	0	0	No significant abnormalities were observed
CTFs Group	7. 81	0	0	No significant deviation from the model control group
	15. 60	0	0	No significant deviation from the model control group
	31. 20	0	0	No significant deviation from the model control group
	62. 50	0	0	No significant deviation from the model control group
	125. 00	0	0	Aggravated in comparison to the model control group

Notes: CTFs, Coreopsis tinctoria Flavonoids.

Confocal laser scanning microscopy performed after 48 hours of drug exposure revealed a concentration-dependent attenuation in fluorescence signal intensity in zebrafish xenografts (Figure 9A, 9B). This inverse correlation between CTFs concentration and fluorescence emission provides compelling evidence for the capacity of CTFs to suppress tumor cell proliferation in the zebrafish model.

Figure 9.

In vivo study of CTFs’ effects on tumor proliferation and growth. A: Fluorescence intensity map of HepG2 treated with CTFs, ×100; B: Fluorescence intensity quantification in HepG2 cells; C: Zebrafish tumor growth inhibition rate; D: HE staining of tumor tissue (X100 for upper panel, X400 for middle panel and X1000 for lower panel). *P < 0.05, compared with control group. Notes: CTFs, Coreopsis tinctoria Flavonoids.

Quantitative analysis further demonstrated a positive correlation between CTFs concentration and tumor growth suppression efficacy (Figure 9C). The concentration-dependent enhancement of tumor inhibition rate substantiates that CTFs significantly impair HepG2 cell tumorigenicity following CTFs administration.

H&E staining of tumor tissue in zebrafish

H&E staining was employed to observe the histopathological changes in zebrafish tumor tissues (Figure 9D). In the control group, tumor cells displayed compact organization with uniform spatial distribution, characterized by intensely basophilic chromatin condensation and high nuclear-to-cytoplasmic ratios. Conversely, CTFs-treated tumors exhibited disrupted architectural integrity, with irregular cellular arrangement with decreased nuclear-to-cytoplasmic ratios and prominent cytoplasmic vacuolization. These cytological alterations provide morphological evidence supporting the necrotic potential of CTFs in tumor cells.

Discussion

Extensive evidence confirms that bioactive natural compounds can modulate critical oncogenic processes, including cell proliferation, inhibition, cell cycle arrest, apoptosis induction, and angiogenesis suppression [13]. Nevertheless, the clinical translation of numerous natural compounds faces limitations due to incomplete elucidation of their molecular mechanisms. Our research focused on CTFs, a class of flavonoid that have shown potent anti-tumor activity across multiple cancer types in preclinical models [14,15]. The therapeutic efficacy of CTFs against HCC remains poorly characterized. To address this gap, we employed an innovative network pharmacology approach that combined bioinformatics and systems biology to (1) map compound-target-pathway networks, and (2) experimentally validate these predictions through cellular and molecular assays. This strategy bridges the holistic principles of traditional medicine with contemporary computational biology [16,17], offering a powerful framework to elucidate multi-target therapeutic mechanisms of CTFs in HCC.

Our analysis identified 27 bioactive components in CTFs [9], and network pharmacology screening revealed 70 putative molecular targets associated with HCC. Subsequent PPI network analysis, employing the median degree as a topological threshold, delineated 32 core targets, including P53, MYC, AKT1, CTNNB1, JUN, STAT3, HIF1A, EGFR, and Caspase-3. Subsequent construction of “CTFs-HCC-target-pathway” network revealed key CTFs components - Quercetin, EGCG, fisetin, acacetin, luteolin, kaempferol - that exhibited strong interaction with key apoptosis-related targets (e.g., Caspase-3, P53, Bcl-2. Bax, AKT1, and MAPK1).

Boxplot analysis demonstrated that pro-apoptotic genes (Bax and Caspase-3) were significantly upregulated in HCC tissues, whereas the anti-apoptotic Bcl-2 exhibited moderate increase, suggesting an imbalance between apoptotic and survival signaling that may contribute to apoptosis resistance in cancer cells. The altered P53 expression pattern may reflect underlying mutational or functional abnormalities, which are frequently implicated in HCC pathogenesis. Collectively, these findings provide crucial insights into apoptotic dysregulation underlying HCC and highlight several potential therapeutic targets for CTFs-mediated intervention.

The survival analysis found that low Bax and Caspase-3 levels was significantly associated with worse HCC survival, whereas Bcl-2 exhibited dual, time-dependent prognostic effects, and p53 expression showed no significant correlation with prognosis. Elevated Bax expression was predictive of favorable prognosis and enhanced responsiveness to apoptosis-inducing therapies. Cancer cells evade immune elimination by overexpressing anti-apoptotic proteins like Bcl-2, which block cytochrome C release and prevents caspase activation. Consequently, targeting Bcl-2 family members has opened new avenues in precision oncology, though drug resistance and toxicity remain major obstacles [18,19]. Building upon these insights, molecular docking analyses demonstrated that CTFs manifest multi-target and multi-pathway anti-HCC activity, with the active CTFs components showing good binding affinities to several key regulatory proteins involved in apoptosis. Consistent with the computational predictions, q-PCR validation in HepG2 cells revealed that CTFs treatment significantly modulated apoptotic gene expression, characterized by upregulation of pro-apoptotic molecules (Bax, P53 and Caspase-3) and suppression of anti-apoptotic Bcl-2. This coordinated regulation led to a pronounced elevation in the Bax/Bcl 2 ratio, suggesting a shift toward pro-apoptotic signaling. Collectively, CTFs therapy demonstrates potential in improving unfavorable prognosis of HCC.

Notably, quercetin modulates multiple oncogenic pathways such as PI3K/AKT/mTOR, MAPK/ERK, NF-κB, JAK/STAT and demonstrates synergistic activity with deferoxamine in cervical cancer by reducing cellular proliferation, promoting ROS- and P53-mediated cell cycle arrest and apoptosis [20-22]. Multiple studies have shown that EGCG can exert pro-oxidant effects, inducing an increase in ROS levels, ultimately leading to tumor cell apoptosis and helping to reduce tumor size. The low concentration of ROS can protect the body from carcinogenic interference by inducing the endogenous antioxidant system. EGCG can inhibit the cancer cell cycle at all four stages, affecting the expression of relevant inflammatory signaling molecules, such as upregulating TNF-α protein, down regulating HO-1protein, and reducing IL-10 levels, thereby blocking cells in the G2/M phase and ultimately causing HCC apoptosis [23-27]. Extensive evidence confirms the broad-spectrum antitumor properties of the major bioactive constituents in CTFs [9,14,15]. Consistent with these findings, our GO and KEGG enrichment analyses revealed that the anti-tumor effects of CTFs are closely associated with the regulation of cell proliferation and apoptotic processes. Specifically, KEGG pathway enrichment analysis highlighted the involvement of multiple oncogenic signaling cascades, including the P53, TGF-β, Wnt, and MAPK signaling pathway in mediating CTFs-regulated processes of hepatic neoplastic cell proliferation, differentiation, and cell cycle control.

Both in vitro and in vivo experiments showed that CTFs effectively inhibit the cell proliferation and induce cell death in HCC models. Cell cycle arrest represents a fundamental mechanism underlying the suppression of tumor cell proliferation, with block at two critical phases (G1/S and G2/M) directly leading to abnormal proliferation of tumor cells. Therefore, identifying and targeting these key targets can provide new insights for cancer treatment and prevention [28]. Excessive ROS concentrations mediate growth-inhibitory effects by suppressing Rb phosphorylation, inhibiting DNA synthesis, and blocking S-phase progression. ROS can also modulate cell cycle proliferation through the G2/M checkpoint, where elevated ROS levels inhibit the proliferation of normal cells but promote the growth of tumor cells [29]. ROS serve as critical modulators of various apoptotic pathways. In the context of the pathway, ROS facilitate programmed cell death through regulation of Bcl-2 family proteins such as Bax. Moreover, high ROS induce direct oxidative modification of key cysteine residues within the P53 protein, enhancing its stability and transcriptional activation of downstream pro-apoptotic genes [30-32]. Our experimental evidence demonstrates that CTFs treatment of HepG2 cells results in ROS accumulation, accompanied by cell cycle arrest at both G2/M and S phases. These findings suggest that CTFs exert their anti-HCC effects by increasing intracellular ROS generation and disrupting cell cycle progression.

The P53 signaling pathway plays a pivotal role in tumorigenesis and cancer therapy, making Bcl2 family-targeted agents as a major focus of contemporary oncology research. The transcription factor P53 is activated in response to cellular stress stimulitation, including DNA damage, oxidative stress, and hypoxia, and coordinates the transcription of downstream genes that govern cell cycle arrest, DNA repair, or apoptosis. exerts its apoptotic function through dual regulatory pathways. At the transcriptional level, activated P53 directly upregulates pro-apoptotic genes (Bax, PUMA, NOXA), while concurrently suppressing anti-apoptotic Bcl-2 expression. This shift in Bax/Bcl-2 ration promotes mitochondrial outer membrane permeabilization (MOMP) and loss of mitochondrial membrane potential. These mitochondrial perturbations trigger the release of cytochrome c into the cytoplasm, which in turn activates the caspase cascade, culminating in Caspase-3-mediated apoptosis [33,34]. In this study, CTF treatment in HepG2 cells induced substantial ROS accumulation, which subsequently activates p53-mediated transcriptional repression of Bcl-2 and facilitates Bax translocation to the mitochondria. These events are collectively contribute to Caspase-3 activation and the induction of apoptosis.

Conclusion

In summary, evidence from network pharmacology predictions and experimental validation demonstrates that the principal bioactive components of CTFs exert anti-HCC activity through multi-target and multi-pathway regulation. Their antitumor efficacy primarily stems from the coordinated regulation of cell proliferation and apoptosis. Elucidation of these molecular pathways not only establishes a theoretical foundation for CTFs-mediated HCC intervention, but also facilitates the rational development of novel pharmacological agents. Furthermore, such research carries substantial socioeconomic implications, contributing to the sustainable utilization of the unique medicinal plant resources of Xinjiang.

However, this study has several limitations. While our phenotypic findings provide preliminary evidence for the anticancer effects of CTFs, the apoptotic signaling networks involved are highly complex and require further mechanistic elucidation. Future studies should validate the specific roles of the identified targets using genetic knockout/knockdown approaches, pathway-specific inhibitors, and protein-level expression and functional rescue experiments

To comprehensively evaluate therapeutic potential of CTFs, future studies should prioritize in-depth mechanistic investigations, alongside systematic assessment of pharmacokinetics, safety, and potential synergistic effects with existing chemotherapeutic or molecular-targeted agents. Additionally, integrated metabolomics should be employed to systematically characterize the influence of CTFs on cancer-associated metabolic reprogramming.

Disclosure of conflict of interest

None.