The mechanism of CPT1A involved in hepatocellular carcinoma growth and Bufalin regulates malignant behavior of hepatocellular carcinoma via CPT1A

doi:10.21203/rs.3.rs-4901559/v1

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Research Article

The mechanism of CPT1A involved in hepatocellular carcinoma growth and Bufalin regulates malignant behavior of hepatocellular carcinoma via CPT1A

https://doi.org/10.21203/rs.3.rs-4901559/v1

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Objective

Cinobufacini exhibits significant anti-cancer effects on various malignant tumors, particularly demonstrating outstanding efficacy against hepatocarcinoma. The anti-tumor effects of Cinobufacini primarily manifest as inhibition of tumor cell proliferation, cell cycle arrest, and modulation of immune responses. Bufalin, the most potent active component in Cinobufacini, requires further exploration of its anti-tumor mechanisms. We aim to elucidate the potential mechanisms of Bufalin in treating hepatocarcinoma through experimental research guided by proteomic clues.

Materials and Methods

In this study, Bufalin was employed to target human hepatocellular carcinoma cell line HepG2. Quantitative proteomic analysis using tandem mass tag (TMT) was conducted to explore differentially expressed proteins (DEPs) before and after Bufalin treatment. The bioinformatics analysis of DEPs was performed using hierarchical clustering, volcano plots, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). The PPARα/CPT1A pathway was selected for further analysis. Immunohistochemistry was performed on postoperative liver cancer tissues collected from 91 liver cancer patients to analyze the correlation between relevant DEPs, differentially expressed protein CPT1A, and hepatocellular carcinoma prognosis, as well as the expression differences of CPT1A in cancer tissue and adjacent tissue. Western blot, qRT-PCR, scratch assay, transwell invasion assay, Oil Red O staining, ATP analysis, and other in vitro experiments were conducted to further identify the mechanism of Bufalin in treating hepatocarcinoma. Furthermore, in vivo experiments in nude mice were carried out to validate the reversal of Sorafenib resistance in hepatocarcinoma by Bufalin through CPT1A.

Results

TMT labeling quantitative proteomic analysis revealed significant differences in protein expression before and after Bufalin treatment in the HepG2 cells. A total of 835 proteins showed significant differences between the comparison groups, with 373 proteins upregulated and 462 proteins downregulated. GO analysis indicated that the DEPs were mainly associated with cellular processes, metabolic processes, and biological regulation. KEGG pathway analysis showed that DEPs were primarily related to lysosomes, complement and coagulation cascades, extracellular matrix (ECM)-receptor interaction, cholesterol metabolism, and the PPAR signaling pathway. Among these, the PPARα/CPT1A pathway may be a crucial pathway for Bufalin in hepatocellular carcinoma. Clinical significance of CPT1A was elucidated in postoperative tissues from hepatocarcinoma patients, with high CPT1A expression affecting tumor prognosis. Further analysis and validation of the PPARα/CPT1A fatty acid oxidation pathway revealed that Bufalin could downregulate the expression of the PPARα/CPT1A pathway, inhibit the proliferation of liver cancer cells, reduce their migration and invasion capabilities, and attenuate their fatty acid oxidation. Moreover, it demonstrated that Bufalin could reverse Sorafenib resistance in hepatocarcinoma by modulating CPT1A in vivo.

Conclusion

1. CPT1A is an adverse prognostic factor for hepatocarcinoma.

2. Downregulation of CPT1A can inhibit the growth of hepatocellular carcinoma cells.

3. Bufalin can intervene in tumor growth and suppress fatty acid oxidation in hepatocarcinoma by regulating CPT1A expression, which may be one of the mechanisms by which Bufalin inhibits liver cancer growth.

4. Bufalin can reverse Sorafenib resistance by modulating CPT1A in hepatocellular carcinoma.

Hepatocellular carcinoma

Bufalin

CPT1A

Fatty acid oxidation

Primary liver carcinoma (PLC), commonly referred to as liver cancer, ranks fourth in global malignant tumor mortality, as reported by latest global cancer data in 2020 ^{[1, 2]} posing a significant threat to human health. The primary types of PLC include hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, and mixed hepatocellular-cholangiocarcinoma. Among these, HCC is the most common pathological type, accounting for 75%-85% ^[3]. Currently, HCC is characterized by insidious onset, rapid progression, and a lack of effective treatment options in clinical practice. Standard treatments for HCC, represented by Sorafenib, offer limited survival extension (with a median survival increase of only 3 months). Immunotherapies, represented by immune checkpoint inhibitors (ICIs), also demonstrate unsatisfactory efficacy. Exploring effective and low-toxicity treatments for HCC holds urgent clinical significance and profound theoretical value.

Cinobufacini has demonstrated significant anti-cancer effects on various solid tumors, particularly highlighting its efficacy against HCC. The anti-tumor effects of Cinobufacini ^[4–9] primarily include inhibiting tumor cell proliferation, migration, and invasion; blocking the cell cycle; and regulating immune responses. These anti-tumor activities mainly depend on bufadienolides ^[10] a class of ester compounds primarily composed of Bufalin, Resibufogenin, and Cinobufagin. Among them, Bufalin is the active ingredient with the highest anti-cancer activity in the current study, however, its antitumor mechanism needs to be deeply analyzed.

Proteomic analysis relies on high-precision mass spectrometry equipment and high-quality quantitative proteomics techniques. Proteomics, focusing on the proteome, systematically and qualitatively analyzes the quantity and function of all proteins within cells using specific techniques. It provides qualitative and quantitative analyses of specific protein structures and relative abundances, systematically describing the structural and functional information of proteins within cells under different stages or conditions. This approach reveals cellular responses to various stimuli or drugs. TCM encompasses a wide range of types, with complex mechanisms of action of their active ingredients. However, with the advancement of proteomics, the comprehensive and systematic analysis of drug mechanisms aligns with the characteristic multi-target synergistic effects of TCM, providing new insights into the mechanisms of TCM in treating diseases. Currently, proteomic techniques have been applied to the study of the mechanisms of action of TCM and have made substantial breakthroughs. Proteomic technology has been applied in the study of the mechanisms of action of some TCMs or TCM monomers on tumors, viral infections and other diseases^[11–13]. The application of proteomics plays a crucial role in the study of the mechanisms of action and targets of TCMs.

To further explore the mechanism by which Bufalin inhibits HCC, we employed tandem mass tag (TMT) labeling quantitative proteomic technology. We found significant differences in protein expression before and after Bufalin treatment, with the biological processes primarily involving metabolic processes. Functional enrichment analysis of differentially expressed proteins using Gene Ontology (GO) revealed their involvement in lipid metabolism processes. Furthermore, pathway enrichment analysis of differentially expressed proteins using the Kyoto Encyclopedia of Genes and Genomes (KEGG) identified the peroxisome proliferation-activated receptor alpha (PPARα)/ carnitine palmitoyl transferase 1A (CPT1A) fatty acid oxidation pathway as one of the significantly enriched pathways. Meanwhile, PPARα and CPT1A are key factors in the fatty acid oxidation pathway as well.

PPARα participates in various metabolic processes in the liver, including fatty acid oxidation (FAO), fatty acid elongation and degradation, and bile acid metabolism. CPT1 is located on the outer membrane of mitochondria and serves as a key enzyme in FAO. It is mainly divided into three isoforms: CPT1A, CPT1B, and CPT1C ^[14]. Among them, CPT1A is predominantly expressed in the liver and is the most important subtype catalyzing FAO^{[15, 16]}. CPT1B, commonly referred to as the muscle subtype, is highly expressed in skeletal muscle, heart, and brown adipose tissue^[17]. CPT1C is primarily expressed in the brain and does not play a significant role in FAO but may affect neuronal oxidative metabolism^{[18, 19]}. PPARα acts as an upstream factor of CPT1A and can influence fatty acid oxidation by regulating the expression of CPT1A, thereby maintaining cellular energy requirements and lipid metabolism homeostasis.

Research has shown that compared to normal cells, tumor cells possess characteristics of unlimited proliferation, with more fatty acids transported into mitochondria, enhancing FAO and tumor nutritional adaptation. Aberrantly active FAO is a crucial metabolic feature of tumor cells ^[20]. The PPARα/CPT1A pathway is closely associated with cellular FAO and plays a promotive role in the occurrence and development of tumors. As the center of lipid metabolism in the body, the liver often experiences lipid metabolism abnormalities during the development of HCC. In recent years, tumor lipid metabolism has become a research hotspot, with a focus on lipid synthesis, while research on fatty acid oxidation in HCC remains limited.

Sorafenib, an orally administered multi-kinase inhibitor, is a classic drug used to treat HCC. It acts by inhibiting the receptor tyrosine kinase activity of serine-threonine kinases (Raf-1, B-Raf), vascular endothelial growth factor receptors (VEGFRs), and platelet-derived growth factor receptor beta (PDGFR-β)^[21]. Since Sorafenib inhibits VEGFR and thereby blocks tumor neovascularization, it is inferred that metabolic reprogramming may occur during HCC treatment. Due to the reduction in tumor blood supply, the level of lipid metabolism may increase. Further research is needed.

Therefore, this study utilized TMT labeling quantitative proteomic technology to identify proteins associated with Bufalin intervention in HCC. Through bioinformatics analysis of differentially expressed proteins (DEPs) involved in signaling pathways, we aimed to gain insights into the role of CPT1A in the progression and invasion of HCC. By exploring the intervention of Bufalin on CPT1A and analyzing the malignant behavior of HCC, as well as its impact on Sorafenib resistance, this study aims to provide a scientific basis for molecular diagnosis and the development of drugs with new mechanisms for HCC.

2.1. Reagents

Bufalin, and the Cell Counting Kit-8 assay (CCK-8), were both purchased from ApexBio Technology (Houston, TX, USA), Anti-CPT1A antibodies and Anti-PPARα antibodies for Western Blotting analysis were provided by Abcam (Cambridge, UK), Upstream and downstream primers of CPT1A and PPARα for Polymerase Chain Reaction (PCR) were provided by Sangon Biotech Co. (Shanghai, China).

2.2. Cell lines

Human HCC cell lines (HepG2, Huh7 and Hep3B) were purchased from Procell (Wuhan, China). HepG2 cell line and Huh7 cell line were cultured in Gibco™ Dulbecco’s Modified Eagle Medium (DMEM), Hep3B cell line were cultured in Gibco™ Modified Eagle Medium (MEM) with non-essential amino acid (Solarbio, Beijing,China). All media were added with 10% fetal bovine serum (Thermo Fisher Scientific, Waltham, MA, USA), 100 µg/ml streptomycin, and 100 µg/ml penicillin. Cultures were maintained in a humidified incubator with 5% CO₂ at 37℃.

2.3. TMT Labeling Quantitative Proteomic Analysis

2.3.1 Experimental Method of TMT Labeling Quantitative Proteomics

2.3.1.1 Sample Collection and Processing

Experimental Group: HepG2 human HCC cells were cultured until reaching approximately 80% confluence. After treatment with 50nM Bufalin for 48 hours, cells were digested with 0.25% trypsin, centrifuged, and placed on ice for subsequent experimental steps.

Control Group: HepG2 cells were cultured simultaneously with the experimental group and treated with Bufalin for 48 hours. After treatment, cells were digested with 0.25% trypsin, centrifuged, and placed on ice for subsequent experimental steps.

Three pairs of samples were collected in total.

2.3.1.2 Protein Extraction and Peptide Digestion

Proteins in the samples were extracted using the SDT (4% SDS, 100mM Tris-HCl pH 7.6, 0.1M DTT) lysis method, followed by protein quantification using the BCA method. An appropriate amount of protein from each sample was subjected to trypsin digestion using the Filter-aided proteome preparation (FASP) method. Peptides were desalted using a C18 Cartridge, followed by lyophilization. The dried peptides were reconstituted in 40µL of 0.1% formic acid solution, and peptide quantification was performed (OD280).

2.3.1.3 TMT Labeling

100µg of peptides from each sample were labeled according to the instructions provided in the TMT labeling kit. There were two experimental groups, each containing three replicate samples, totaling six samples.

2.3.1.4 Chromatographic Fractionation

Reversed-Phase (RP) Fractionation: After labeling, peptides from each group were mixed in equal amounts and fractionated using the High pH RP Peptide Fractionation Kit. Initially, the column was equilibrated with acetonitrile and 0.1% trifluoroacetic acid (TFA). Then, the mixed labeled peptide samples were loaded onto the column, followed by desalting with centrifugation at low speed and elution of the bound peptides using a concentration gradient of high pH acetonitrile solution. After each fraction was eluted, peptides were vacuum-dried, reconstituted with 12µL of 0.1% FA, and then lyophilized. The peptide concentration was determined (OD280).

2.3.1.5 Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) Data Acquisition

Each sample was separated using the nano-flow rate HPLC liquid phase system Easy nLC. Buffer A was 0.1% formic acid aqueous solution, and buffer B was 0.1% formic acid acetonitrile aqueous solution (acetonitrile content was 84%). The chromatographic column was balanced with 95% buffer A, and the sample was loaded onto the sample column (Thermo Scientific Acclaim PepMap100, 100µm*2cm, nanoViper C18) by automatic injector and separated through the analysis column (Thermo scientific EASY column, 10cm, ID75µm, 3µm, C18-A2) at a flow rate of 300 nL/min.

The Q-Exactive mass spectrometer was used for mass spectrometric analysis after chromatographic separation of the samples. The detection mode was positive ionization, with a mass scan range of 300–1800 m/z, a resolution of 70,000 at 200 m/z for MS1, AGC (Automatic gain control) target of 1e6, maximum IT of 50ms, and dynamic exclusion time of 60.0s. Peptide and its fragment mass-to-charge ratios were collected as follows: 20 fragment spectra were collected after each full scan, MS2 Activation Type was HCD, isolation window was 2m/z, MS2 resolution was 17,500 at 200 m/z, Normalized Collision Energy was 30eV, and Underfill was 0.1%.

2.3.1.6 Protein Identification and Quantitative Analysis

The raw data of mass spectrometric analysis were in RAW format, Mascot 2.2 and Proteome Discoverer 1.4 software were used for database search identification and quantitative analysis. The identification and quantification parameters are listed in Table 1.

Table 1

Identification and Quantification Parameters Table
Item	Parameters
Enzyme	Trypsin
Maximum Allowed Missed Cleavages	2
Primary Ion Mass Tolerance	± 20ppm
Secondary Ion Mass Tolerance	0.1Da
Fixed Modifications	Carbamidomethyl C)TMT 6/10/16 plex (N-term),TMT6/10/16 plex (K)
Variable Modifications	Oxidation (M)
Protein Sequence Database Used for Searching	Swissprot_Homo_sapiens_20395_20210106
Database Pattern Used for Calculating FDR	Decoy
Filter Criteria for Reliable Proteins	≤ 0.01
Protein Quantification Method	Based on median quantification value of unique peptides
Experimental Data Correction Method	Based on median quantification value of proteins

2.3.2 Bioinformatics Analysis Methods

2.3.2.1 Protein Clustering Analysis

The quantitative information of the target protein set was normalized to the (-1,1) range. Using the Complexheatmap R package (R Version 3.4), both sample and protein expression levels were classified dimensionally (distance algorithm: Euclidean, linkage method: Average linkage), and a hierarchical clustering heatmap was generated.

2.3.2.2 Subcellular Localization Analysis

The subcellular localization was predicted using the CELLO method (http://cello.life.nctu.edu.tw/). This method employs a multi-class support vector machine (SVM) approach to machine learning, modeling known subcellular localization information of protein sequence data from public databases to predict the subcellular localization information of the proteins under investigation.

2.3.2.3 Protein Domain Analysis

The Pfam database was utilized for protein domain analysis. Pfam is a collection of protein families, each represented by multiple sequence alignments and hidden Markov models. The database contains domain information. Specifically, the InterProScan software package was used to functionally characterize sequences using an integrated approach from the InterPro database, thereby obtaining domain annotation information for target protein sequences in the Pfam database.

2.3.2.4 GO Functional Annotation

The Blast2GO tool was employed for GO annotation of the target protein set, involving sequence alignment, GO term extraction, GO annotation, and supplementary annotation using InterProScan.

2.3.2.5 KEGG Pathway Annotation

The KAAS (KEGG Automatic Annotation Server) software was utilized for KEGG pathway annotation of the target protein set.

2.3.2.6 Enrichment Analysis

Fisher's exact test was employed to compare the distribution of various GO categories (or KEGG pathways) in the target protein set and the overall protein set, conducting enrichment analysis for GO annotation or KEGG pathways annotation of the target protein set.

2.4 Cell Transfection

Table 2

shRNA sequence of CPT1A and NC used in the present experiment.
Primer	Species
CPT1A-sh1	Homo	Forward:GATCCGCAGAGGATCCTGGACAATACTTCAAGAGAGTATTGTCCAGGATCCTCTGCTTTTTTG Reverse:AATTCAAAAAAGCAGAGGATCCTGGACAATACTCTCTTGAAGTATTGTCCAGGATCCTCTGCG
CPT1A-sh2	Homo	Forward:GATCCGGTGGTTTGACAAGTCGTTCATTCAAGAGATGAACGACTTGTCAAACCACCTTTTTTG Reverse:AATTCAAAAAAGGTGGTTTGACAAGTCGTTCATCTCTTGAATGAACGACTTGTCAAACCACCG
NC	Homo	Forward:GATCCGTTCTCCGAACGTGTCACGTTTCAAGAGAACGTGACACGTTCGGAGAACTTTTTTG Reverse:AATTCAAAAAAGTTCTCCGAACGTGTCACGTTCTCTTGAAACGTGACACGTTCGGAGAACG

CPT1A shRNA transductions

TRCN0000036279 (CPTsh1) and TRCN0000036281 (CPTsh2) CPT1A shRNAs and the nontargeting control SHC002 were purchased from Functional Genomics Core. Lentiviral transduction and selection were performed according to Sigma’s MISSION protocol but using lentiviral packing plasmids pCMV-R8.74psPAX2 and VSV-G/ pMD2.G and transfection reagent TransIT-LT1 (Mirius).

Short hairpin RNAs(shRNAs) for CPT1A (GenePharma, Suzhou, China) are listed in Table 2.

2.5 Cell viability assay

Cell viability was tested using the Cell Counting Kit-8 assay. Transfected HepG2, Huh7 and Hep3B cells were cultured in 96-well plates overnight. And HepG2, Huh7 and Hep 3B cells were cultured in 96-well plates overnight, the medium was then changed to fresh medium containing a series of bufalin concentrations. After a certain time, the cells were cultured with 10µl of CCK-8 solution for 2h and OD values were measured at 450nm.

2.6 Scratch assay

Transfected HepG2, Huh7 and Hep3B cells were cultured in the Petri dish were taken for the studies. And utilizing cells in the logarithmic growth phase in the Petri dish, the medium was then changed to fresh medium containing a series of bufalin concentrations. 100µL sterile tip was used for scratching. Photomicrographs were recorded under fluorescence inverted microscopy at 0h and then after a certain time.

2.7 HepG2 cell migration and invasion capability transwell assay

Transfected HepG2, Huh7 and Hep3B cells were trypsin-digested and counted. HCC cell lines HepG2 and Huh7 treated with the drug for 48 hours and Hep3B treated with the drug for 24 hours were trypsin-digested and counted. Then spread on the transwell’s upper room (the invasion chamber was inserted beforehand with 200µl of matrigel). For 15 minutes, the cells were fixed in 10% methanol. The cells were then stained with 0.1% crystal violet and then after 30 minutes the gel matrix and cells from the inside of the penetration well were obtained fluorescence inverted microscopy was taken and the number was counted.

2.8 Western blotting

Cellular whole protein extracts (40µg) were separated on 10% sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) gels and transferred to polyvinylidene difluoride (PVDF) membranes (EMD Millipore, Worcester, MA, USA). After blocking with 8% milk (BD Biosciences), protein bands were probed with the corresponding primary antibodies overnight, and followed by three TBST washes. Protein bands were then incubated with HRP-conjugated secondary antibodies for 2h at room temperature and visualized by electrochemiluminescence (ECL) and scanning of grayscale intensity was done by ImageJ software. Protein concentrations were determined using the BCA Protein Assay Kit (Beyotime, Shanghai, China).

2.9 Quantitative real-time PCR analysis

RNA was extracted by Trizol (Cowin Bio., Jiangsu, China). Aliquots (1µg) of total RNA were reverse transcribed using the EvoM-MLV RT kit with gDNA clean (Agbio, Hunan, China). Quantitative real-time PCR (qRT-PCR) was conducted using an Applied Biosystems StepOnePlus Real-Time PCR System (Bioer technology) with the Premix Pro TaqHS qPCR kit (Agbio, Hunan, China) to quantitate mRNA levels. Expression levels were normalized by β-Actin RNA levels. Primers (Sangon Biotech Co., Shanghai, China)) are listed in Table 3.

Table 3

Primer sequence used in the present experiment.
Primer	Species	Sequence (5′-3′)
PPARα	Homo	Forward: TAAGGTCTGTCTCCTCTGAACT Reverse: ATGTATGCGGCTGGTAAGTAG
CPT1A	Homo	Forward: GAGCACGGCAAGATGAG Reverse: GCAGCGATGTCTGGAAG
β-actin	Homo	Forward: CCTGGCACCCAGCACAAT Reverse: GGGCCGGACTCGTCATAC

2.10 Oil red O staining

The cells prepared according to the conditions were placed in the six-well plate overnight, followed by steps using the oil red O staining kit (Solarbio, Beijing, China), adding distilled water, observing and photographing with an inverted microscope, and then calculating the area ratio with ImageJ.

2.11 ATP content detection

The prepared cells were placed in the six-well plate overnight. When the cell state was stable, 200µl of cell lysate was added to each well, and the cell suspension was centrifuged to obtain the supernatant. Follow the steps with the ATP content test kit (Beyotime, Shanghai, China) and save the data.

2.12 Immunohistochemistry (IHC)

In this study, approved by the First Affiliated Hospital of Dalian Medical University, 91 pathologically confirmed HCC postoperative specimens were collected from 2019 to 2020 and complete follow-up data were provided. The tissue sections were incubated with anti-CPT1A antibody (diluted 1:200) or anti-PPARα antibody (diluted 1:200) in wet boxes at 4℃. Pathological grading was performed according to the Edmondson-Steiner grading system^[22]. Immunostaining levels were graded as previously described ^[10].

2.13 Xenograft production in nude mice

4- to 6-week-old male athymic nude mice were purchased from Liaoning Changsheng Biotechnology Co.. The tumor cell lines were pretreated, and the experiment was divided into 4 groups according to the pretreatment conditions: control group, drug resistant group, shRNA-transfected drug resistant group, drug resistance with Bufalin group. All groups of mice were injected subcutaneously with 100µl liquid, and the tumor cell dose was 4x10⁶ per milliliter. Drug resistant group: Sorafenib 30mg/kg daily gavage. ShRNA transfected drug resistant group: Sorafenib 30mg/kg daily gavage. Drug resistance with Bufalin group: Bufalin 1mg/kg, intraperitoneal injection daily; Sorafenib 30mg/kg daily. The drug was administered continuously for 28 days.

2.14 Statistical Analysis

The data in this study have been conducted in 3 independent experiments under the same conditions and are expressed as mean ± standard error. Results Statistical analysis was performed by GraphPad prism 9.0 software. Multivariate analysis of variance was used to compare the above three groups, and the ratio of western blot protein band gray value, cell migration and invasion area and oil red O staining area was statistically analyzed by ImageJ software. p < 0.05 was considered as a significant statistical difference.

3.1 Bufalin's Effect on Human HCC Cells Analyzed by TMT Labeling Quantitative Proteomics

3.1.1 Protein Identification, Quantification Results, and Differential Protein Analysis

3.1.1.1 Protein Identification and Quantification Results Analysis

In this experiment, mass spectrometry analysis was performed on Bufalin-treated groups and blank control groups (3 pairs in total). A total of 569,059 secondary spectra were obtained, with 93,534 spectra identified as effective. The spectrum utilization rate was 16.43%. A total of 40,475 peptides were identified, including 73,128 specific peptide segments. Ultimately, 5,250 proteins were identified, of which 5,213 contained quantitative information, constituting the quantifiable proteins. The detailed experimental results are summarized as follows (Fig.1A).

In the two groups with equal labeling (Bufalin-treated and blank control groups), the abundance ratio of most proteins was close to 1 (Fig.1B). The distribution of identified protein relative molecular weights is as follows (Fig.1C), indicating that the molecular weights of the 5,250 identified proteins are mainly ranged from 10 to 130kDa, demonstrating a favorable distribution. The distribution of peptide sequence lengths for the 5,250 identified proteins is depicted (Fig.1D), with the majority of peptide lengths falling between 7-20 amino acids, showing a decreasing trend with increasing peptide length. These indicate that the abundance ratio of identified proteins, relative molecular weight and distribution, and peptide length distribution meet quality control requirements.

This experiment employed a Q-Exactive mass spectrometer with high-quality precision and resolution. During data acquisition, good quality deviation was maintained, and MS1 and MS2 spectra were finally obtained. As shown in the MS1 spectrum (Fig.1E), the mass deviation of all 5,250 identified peptides were mainly within 10ppm, indicating accurate and reliable identification results. Subsequently, the MS2 spectrum data were analyzed with the MASCOT analysis tool, and the MS2 spectrum scores were obtained. The MS2 spectra (Fig.1F) demonstrated that the MS2 MASCOT scores were more favorable, with approximately 61.29% of peptide segments scoring above 20, and a median peptide score of 25.21. A false discovery rate (FDR) ≤ 0.01 was used as the screening criterion in qualitative analysis work, combined with the excellent distribution of peptide scores obtained, further illustrating the high quality of the MS experimental data.

3.1.1.2 Differential Protein Analysis Results

The experiment was divided into two comparison groups, the Bufalin-treated group and the blank control group. Proteins with a fold change (FC) > 1.1 or < 0.909 and a p-value<0.05 were considered significantly DEPs. In the Bufalin treatment group, 835 proteins exhibited significant differences, with 373 upregulated and 462 downregulated proteins (Fig.2A). The DEPs were visualized in a volcano plot (Fig.2B). Cluster analysis demonstrated the mechanism of Bufalin in HCC treatment by differential protein expression levels (Fig.2C). The list of significantly DEPs after Bufalin treatment is provided below (Table 4).

3.1.2. Bioinformatics Analysis

In order to further elucidate the functions and characteristics of DEPs, bioinformatics analysis was conducted, including subcellular structural localization, Gene Ontology (GO) functional analysis, and KEGG pathway enrichment analysis.

3.1.2.1 Subcellular Localization

Subcellular organelles such as the nucleus and mitochondria serve as crucial sites for protein functionality. Analyzing the subcellular localization of DEPs can help to further explore the functions proteins play in cells. The subcellular structural prediction software CELLO was used to perform subcellular localization analysis on all differential proteins (Fig.2D). Among the 835 DEPs in the Bufalin treatment group (with some proteins localized in multiple organelles), the majority were localized in the nucleus (337 proteins), cytoplasm (273 proteins), plasma membrane (140 proteins), and mitochondria (124 proteins), indicating diverse biological functions exerted by these proteins.

3.1.2.2 GO Analysis

To comprehensively understand the functions, localizations, and biological pathways of DEPs in organisms, GO annotation was conducted on the proteins (Fig.2E). Among the 835 DEPs, they were mainly involved in: (1) Biological processes including cellular processes (752 proteins), metabolic processes (614 proteins), biological regulation (558 proteins), regulation of biological processes (520 proteins), and response to stimulus (445 proteins); (2) At the molecular function level, the DEPs primarily functioned in binding (648 proteins), catalytic activity (376 proteins), and molecular function regulation (87 proteins); (3) Regarding cellular components, the DEPs were predominantly concentrated in cells (795 proteins), cell parts (794 proteins), organelles (729 proteins), organelle parts (581 proteins), and membranes (473 proteins).

Fisher's exact test was employed to perform GO functional enrichment analysis on the 835 DEPs. By comparing the GO annotation results of all DEPs with those identified in the control group, the significance of differences was determined using Fisher's exact test (p<0.05). The functional enrichment of DEPs was visualized using bubble plots under the three major categories of GO. The DEPs were mainly involved in important biological processes such as platelet degranulation, acute inflammatory response, cell secretion, and lipid metabolic processes (Fig.2F; Fig.2G).

The molecular functions of the DEPs primarily included signaling receptor binding and protease binding (Fig.2F; Fig.2H).

Regarding the significant changes in protein localization, the DEPs were primarily located on the cell surface, in the ECM, in collagen-containing ECM, and in the plasma membrane (Fig.2F; Fig.2I).

3.1.2.3 KEGG Pathway Enrichment Analysis

The 835 DEPs were annotated and analyzed using the KEGG pathway database to comprehensively and systematically interpret biological processes, disease mechanisms, and drug action mechanisms from the perspective of protein coordination. The figure below (Fig.2J) lists the top 20 pathways with the highest number of DEPs. Furthermore, Fisher's exact test was employed to perform KEGG pathway enrichment analysis on the DEPs (Fig.2K). The results indicate significant changes in pathways such as lysosome, phagosome, cholesterol metabolism, and the PPAR signaling pathway.

3.2. Mechanisms of CPT1A involved in the growth of HCC

3.2.1 The Expression Level of CPT1A Influences the Prognosis of HCC Patients, and CPT1A Expression Level Correlates with Pathological Features in Patients.

We collected paraffin-embedded specimens from 91 postoperative HCC patients with complete follow-up data treated at the First Affiliated Hospital of Dalian Medical University from 2019 to 2020. Immunohistochemistry was employed to detect the expression of CPT1A in the specimens. We analyzed the expression characteristics of CPT1A in cancerous and adjacent non-cancerous tissues and conducted correlation analysis between CPT1A expression levels and various clinicopathological parameters (such as gender, age, pathological grade, TNM stage) as well as disease-free survival (DFS).

Statistical analysis using the chi-square test revealed a correlation between the expression level of CPT1A and the pathological grade and TNM stage of patients (p<0.05, Table 5).

Immunohistochemical analysis of CPT1A expression in specimens showed that CPT1A was mainly expressed in the cytoplasm, with high expression in cancerous tissues and low expression or no expression in adjacent non-tumor tissues (Fig.3A).

Using SPSS 25.0 software, Kaplan-Meier analysis and Log-Rank test were conducted for univariate survival analysis. From the survival curve, it was observed that patients with high expression of CPT1A had a significantly poorer prognosis compared to those with low expression of CPT1A (p<0.05, Fig.3A).

Furthermore, we compared human HCC cell lines HepG2, Huh7, Hep3B, and human hepatic cell line MIHA Cell. Western blotting results showed (Fig.3C) that the protein expression level of CPT1A in human HCC cell lines HepG2, Huh7, and Hep3B was higher than that in human hepatic cell line MIHA Cell. RT-qPCR results (Fig.3D) indicated that the RNA level of CPT1A in human HCC cell lines HepG2, Huh7, and Hep3B was higher than that in human hepatic cell line MIHA Cell.

Table 5 Correlation Analysis of CPT1A Expression with Clinical Characteristics

3.2.2 Impact of CPT1A on the biological behavior of human HCC cell lines HepG2, Huh7, and Hep3B

To investigate the impact of downregulating CPT1A on the malignant behavior of liver cancer, we employed lentiviral vectors to separately infect human HCC cell lines HepG2, Huh7, and Hep3B, thereby establishing stable CPT1A knockdown cell lines. The knockdown efficiency of CPT1A in the three human HCC cell lines was assessed using RT-qPCR and western blot experiments. It was observed that the mRNA and protein expression levels of CPT1A in the experimental group were significantly lower than those in the control group, and these differences in expression were statistically significant (p<0.05, Fig.4A-B).

Research has shown that fatty acid oxidation plays a crucial role in various biological behaviors of tumor cells, and inhibiting the process of fatty acid oxidation can affect the development of tumor cells. In this experiment, the impact of knocking down CPT1A on the viability of human HCC cell lines was assessed using CCK-8 cell viability assay. The results revealed that compared to the control group, knocking down CPT1A resulted in decreased cell viability in all three cell lines, with statistically significant differences (p<0.05, Fig.4B).

The effect of knocking down CPT1A on the migration ability of human HCC cell lines was analyzed using scratch assays. Compared to their respective control groups, the migration ability of the three cell lines decreased with shCPT1A-1 and shCPT1A-2, indicating that knocking down CPT1A led to a decrease in migration ability in all three cell lines, with statistically significant differences (p<0.05, Fig.4D).

Transwell assays were conducted to analyze the effect of knocking down CPT1A on the migration ability of human HCC cell lines. Compared to their respective control groups, the migration ability of the three cell lines decreased with shCPT1A-1 and shCPT1A-2, indicating that knocking down CPT1A led to a decrease in invasive ability in all three cell lines, with statistically significant differences (p<0.05, Fig.4E).

3.2.3 Impact of CPT1A on cellular fatty acid oxidation in human HCC cell lines HepG2, Huh7, and Hep3B

CPT1A is a key enzyme in the process of fatty acid oxidation. To further validate the impact of CPT1A knockout on fatty acid oxidation, this experiment utilized Oil Red O staining to analyze the effect of CPT1A knockdown on intracellular lipid deposition in human HCC cell lines. Compared to their respective control groups, shCPT1A-1 and shCPT1A-2 exhibited increased lipid deposition in all three cell lines, indicating that knocking down CPT1A led to increased cellular lipid accumulation, with statistically significant differences (p<0.05, Fig.5A).

Fatty acids provide twice as much ATP as carbohydrates, and fatty acid oxidation can provide energy for the occurrence and development of tumors. In this experiment, the impact of knocking down CPT1A on ATP levels in human HCC cell lines was analyzed using an ATP assay kit. Compared to their respective control groups, shCPT1A-1 and shCPT1A-2 exhibited increased lipid deposition in all three cell lines, indicating that knocking down CPT1A resulted in decreased ATP levels, with statistically significant differences (p<0.05, Fig.5A).

3.3 Bufalin regulation of CPT1A expression interferes with HCC growth

3.3.1 Effects of graded doses of Bufalin on malignant cell biological behavior in human HCC cell lines HepG2, Huh7, and Hep3B

Bufalin, a TCM monomer, has been shown to inhibit the development of various tumors. In this experiment, the impact of Bufalin on inhibiting the malignant biological behavior of HCC was investigated. Initially, Bufalin was diluted with Dimethylsulfoxide (DMSO) into different dose gradients, and human HCC cell lines HepG2, Huh7, and Hep3B were treated with Bufalin at different doses. CCK-8 assay results (Fig.6A) revealed that compared to the blank control group, the viability of HepG2, Huh7, and Hep3B cells treated with Bufalin decreased, and the degree of inhibition of cell viability increased with the increasing dose of Bufalin. Thus, we concluded that Bufalin inhibits the proliferation of HCC cells in a dose-dependent manner.

Treatment with different doses of Bufalin was carried out on HepG2 and Huh7 cells for 24h, 48h and 72h; and on Hep3B cells for 12h, 24h, and 48h. The results showed that compared to treatment for 24h and 48h, the inhibitory effect of Bufalin on cell viability was most significant after 72h in HepG2 and Huh7 cells (p<0.05, statistically significant); compared to treatment for 12h and 24h, the inhibitory effect of Bufalin on cell viability was most significant after 48h in Hep3B cells (p<0.05, statistically significant). Additionally, as the duration of Bufalin treatment increased, the inhibitory effect on the viability of HepG2, Huh7, and Hep3B cells became more pronounced, indicating a time-dependent effect of Bufalin inhibition.

In HepG2 and Huh7 cells, Bufalin did not exhibit significant tumor suppression at 24h of drug action, but significant inhibition of proliferation was observed in both cell lines after 48h of treatment. In Hep3B cells, significant inhibition of proliferation was observed after 24h of treatment with Bufalin. The half maximal inhibitory concentration (IC₅₀) values for Bufalin treatment in HepG2 cells at 24h, 48h, and 72h were 244.5nM, 73.44nM, and 23.62nM, respectively; in Huh7 cells, the IC₅₀ values at 24h, 48h, and 72h were 82.64nM, 56.02nM, and 26.83nM, respectively; in Hep3B cells, the IC₅₀ values at 12h, 24h, and 48h were 123.8nM, 59.34nM, and 12.73nM, respectively.

From the above observations, it can be concluded that Bufalin inhibits the proliferation of HCC cells and exhibits dose-time dependence. For HepG2 and Huh7 cells, the optimal treatment time for Bufalin is 48h; for Hep3B cells, the optimal treatment time is 24h. Furthermore, based on the IC₅₀ values, the high, medium, and low doses for subsequent experiments were determined.

We utilized 0nM, 25nM, 50nM, and 100nM doses of Bufalin to treat HepG2 cells for 24h, 48h, and 72h; 0nM, 25nM, 50nM, and 75nM doses of Bufalin to treat Huh7 cells for 24h, 48h, and 72h; and 0nM, 25nM, 50nM, and 75nM doses of Bufalin to treat Hep3B cells for 12h, 24h, and 48h. Bufalin significantly reduced cell migration rates and inhibited the inhibitory effect dose-dependently when compared with the DMSO solvent control group in all groups respectively (Fig.6B).

HepG2 cells were treated with 0nM, 25nM, 50nM, and 100nM Bufalin for 48 hours; Huh7 cells were treated with 0nM, 25nM, 50nM, and 75nM Bufalin for 48 hours; Hep3B cells were treated with 0nM, 25nM, 50nM, and 75Nm Bufalin for 24 hours, and then transwell assay was performed. The results revealed that Bufalin significantly reduced the cell invasion rate in different dose gradients compared with the DMSO solvent control group, and the inhibitory effect was dose-dependent (Fig.6C).

3.3.2. Effect of Bufalin on fatty acid oxidation in human HCC cell lines HepG2, Huh7, and Hep3B

According to the mass spectrometry results suggesting Bufalin's influence on fatty acid oxidation in liver cancer cells, we treated the three cell lines with graded doses of Bufalin and analyzed its effects on cellular lipid deposition. HepG2 cells were treated with Bufalin at doses of 0nM, 25nM, 50nM, and 100nM for 48 hours; Huh7 cells were treated with Bufalin at doses of 0nM, 25nM, 50nM, and 75nM for 48 hours; Hep3B cells were treated with Bufalin at doses of 0nM, 25nM, 50nM, and 75nM for 24 hours. The groups exhibited increased cellular lipid deposition with increasing drug doses in all three cell lines compared to the DMSO solvent control group, showing a dose-dependent effect (p<0.05, Fig.7A).

Furthermore, based on the mass spectrometry results suggesting Bufalin's impact on fatty acid oxidation in HCC cells, we utilized graded doses of Bufalin to act on each of the three cell lines and analyzed the effects on intracellular ATP levels after the action. HepG2 cells were treated with Bufalin at doses of 0nM, 25nM, 50nM, and 100nM for 48 hours; Huh7 cells were treated with Bufalin at doses of 0nM, 25nM, 50nM, and 75nM for 48 hours; Hep3B cells were treated with Bufalin at doses of 0nM, 25nM, 50nM, and 75nM for 24 hours. The intracellular ATP levels of the three cell lines were reduced in all groups compared to the DMSO solvent control group, showing a decreasing trend with the increase of the drug dose (p<0.05, Fig.7B).

Moreover, CPT1A is a key enzyme in the process of fatty acid oxidation, regulating the rate of fatty acid oxidation, while PPARα is an upstream regulatory protein of CPT1A. To further verify Bufalin's inhibitory effect on fatty acid oxidation, we assessed the mRNA expression levels of the fatty acid oxidation-related genes PPARα and CPT1A in the three cell lines using RT-qPCR after treatment with graded doses of Bufalin. It was found that the mRNA expression levels of PPARα and CPT1A were significantly reduced in all three cell lines after the action of graded doses of Bufalin, showing a decreasing trend with the increase of the drug dose (p<0.05, Fig.7C).

Additionally, western blot analysis was performed to assess the protein expression levels of the fatty acid oxidation-related genes PPARα and CPT1A in the three cell lines after treatment with graded doses of Bufalin. It was found that the expression levels of PPARα and CPT1A proteins were reduced in all three cell lines after the action of graded doses of Bufalin, exhibiting a decreasing trend with the increase of drug dose (p<0.05, Fig.7A).

3.3.3 Effect of Bufalin and CPT1A agonist on the biological behavior of malignant cells from human HCC cell lines

To further demonstrate that Bufalin affects the migratory ability of HCC cells through CPT1A, we applied Bufalin, CPT1A agonist, or a combination of the two to HCC cells, and analyzed the migratory ability of the cells in each group by a scratch assay. A dose of 12.5μM of CPT1A agonist (BEC2) was selected based on literature review and applied alone or in combination with Bufalin at a dose of 50nM to HCC cells. The results showed (Fig.8A) that both Bufalin alone significantly inhibited cell migration compared to DMSO solvent control group (p<0.05); both CPT1A agonist alone significantly enhanced cell migration compared to DMSO solvent control group (p<0.05); and the combination of Bufalin and CPT1A agonist group reduced cell migration compared to the CPT1A agonist alone group (p<0.05).Specifically, for HepG2 cells, the migration rate at 48h was 17.91% vs. 46.18% for Bufalin+CPT1A agonist group vs. CPT1A agonist alone group, respectively (p<0.01); for Huh7 cells, the migration rate at 48h was 31.19% vs. 52.14% for Bufalin+CPT1A agonist group vs. CPT1A agonist alone group (p<0.001); for Hep3B cells, the migration rates at 48h was 24.47% vs. 74.57% for Bufalin+CPT1A agonist group vs. CPT1A agonist alone group (p<0.05).

HCC cells were treated with a 12.5μM dose of CPT1A agonist and a 50nM dose of Bufalin separately or in combination, and the transwell assays were performed. HepG2, Huh7, and Hep3B cells were observed at 72h, 72h, and 48h, respectively. The results showed (Fig.8B) that, compared with the DMSO solvent control group, Bufalin alone inhibited cell invasion, while CPT1A agonist alone enhanced cell invasion.

For the three cell lines, the cell invasion ability was attenuated in the combination group of Bufalin and CPT1A agonist compared to the CPT1A agonist group (p<0.05). Specifically, for HepG2 cells, the 48h invasion rate was 68.31% vs. 174.81% in Bufalin+CPT1A agonist group vs. CPT1A agonist group (p<0.001); for Huh7 cells, the 48h invasion rate was 70.89% vs. 182.68% in Bufalin+CPT1A agonist group vs. CPT1A agonist group (p<0.05); for Hep3B cells, the 48h invasion rate was 99.48% vs. 236.04% in Bufalin+CPT1A agonist group vs. CPT1A agonist group (p<0.05).

3.3.4 Effect of Bufalin and CPT1A agonist on fatty acid oxidation in human HCC cell lines

HCC cells were treated with a 12.5μM dose of CPT1A agonist BEC2 and a 50nM dose of Bufalin separately or in combination, and Oil Red O staining was performed after HepG2, Huh7, and Hep3B cells were treated with the drugs for 48h, 48h, and 24h respectively. The results showed (Fig.8C) that, when compared to the DMSO solvent control group, Bufalin alone caused an increase in intracellular lipid deposition, while CPT1A agonist alone decreased intracellular lipid deposition. The degree of intracellular lipid deposition in the Bufalin+CPT1A agonist combination group was stronger than that in the CPT1A agonist group alone, and lower than that in the Bufalin group alone, with statistical significance (p<0.05).

HCC cells were treated with a 12.5μM dose of CPT1A agonist BEC2 and a 50nM dose of Bufalin separately or in combination, and HepG2, Huh7, and Hep3B were detected by using the ATP kit after 48h, 48h, and 24h of drug action, respectively. The results showed (Fig.8D) that, when compared to the DMSO solvent control group, Bufalin alone resulted in a decrease in intracellular ATP levels, while CPT1A agonist alone resulted in an increase in intracellular ATP levels. The degree of intracellular ATP levels in the Bufalin+CPT1A agonist combination group was lower than that in the CPT1A agonist alone group, and higher than that in the Bufalin alone group, with statistical significance (p<0.05).

To further validate the inhibitory effect of Bufalin on fatty acid oxidation through CPT1A, HCC cells were treated with a 12.5μM dose of CPT1A agonist and a 50nM dose of Bufalin, respectively or in combination, and after HepG2, Huh7, and Hep3B were drug-active for 48h, 48h, and 24h, respectively. Subsequently, we evaluated the expression levels of the fatty acid oxidation-related gene CPT1A mRNA and protein in these cell lines by RT-qPCR and western blot analysis. The results showed (Fig.8E-F) that, compared to the DMSO solvent control group, Bufalin alone decreased the expression of CPT1A, while CPT1A agonist alone increased the expression of CPT1A. And the combination of Bufalin and CPT1A agonist resulted in weaker expression of cellular CPT1A in the cells than that of CPT1A agonist alone group, with statistical significance (p<0.05).

3.3.5 Effect of Bufalin and CPT1A inhibitor on the biological behavior of malignant cells from human HCC cell lines

Etomoxir, an irreversible inhibitor of CPT1A, has been used in the treatment of heart diseases and diabetes, and it is widely employed as a fatty acid oxidation inhibitor in various studies. Recent research has shown promising effects of the CPT1A inhibitor Etomoxir in inhibiting tumor proliferation in cancer treatment studies^{[23, 24]}. However, its severe adverse effects have hindered its widespread application in clinical. Our study found that Bufalin has the capability to inhibit CPT1A. Therefore, in this section, we compared Bufalin with Etomoxir, and assessed their effects on cell migration ability through scratch assays. We selected a dose of 200μM for the CPT1A inhibitor Etomoxir based on literature review and treated HCC cells with a 50nM dose of Bufalin alone or in combination with Etomoxir. The results showed as follow (Fig.9A). For all three cell lines, either Bufalin alone or Etomoxir alone inhibited cell migration compared to DMSO solvent control group (p<0.05). For all three cell lines, the combination of Bufalin and Etomoxir showed stronger inhibition of cell migration compared to the Etomoxir alone group (p<0.05). For HepG2, the 72h migration rate was 11.17% vs. 19.10% for the Bufalin+Etomoxir group vs. the Etomoxir group, respectively (p<0.01). For Huh7, the 72h migration rate was 12.56% vs. 32.55% for the Bufalin+Etomoxir group vs. the Etomoxir group, respectively (p<0.001). For Hep3B, the 48h migration rate was 26.93% vs. 42.54%for the Bufalin+Etomoxir group vs. the Etomoxir group, respectively (p<0.05).

HCC cells were treated with a 200μM dose of the CPT1A inhibitor Etomoxir and a 50nM dose of Bufalin separately or in combination, and transwell assays were performed, with HepG2, Huh7, and Hep3B cells observed at 72h, 72h, and 48h, respectively, and the results were showed as follows (Fig.9B). For all three cell lines, either Bufalin alone or Etomoxir alone inhibited cell invasion compared to the DMSO solvent control group (p<0.05). For all three cell lines, the combination of Bufalin and Etomoxir showed stronger inhibition of cell invasion compared to the Etomoxir alone group, with statistical significance (p<0.05). For HepG2, the 72h invasion rate was 33.65% vs. 64.59%for for the Bufalin+Etomoxir group vs. the Etomoxir group, respectively (p<0.001). For Huh7, the 72h invasion rate was 23.92% vs. 53.89% for (p<0.05). For He 3B, the 48h invasion rate was 38.06% vs. 59.64% for the Bufalin+Etomoxir group vs. the Etomoxir group, respectively (p<0.05).

3.3.6 Effect of Bufalin and CPT1A inhibitor on fatty acid oxidation in human HCC cell lines

HCC cells were treated with a 200μM dose of CPT1A inhibitor Etomoxir and a 50nM dose of Bufalin separately or in combination, and Oil Red O staining was performed after HepG2, Huh7, and Hep3B cells were treated with the drugs for 48h, 48h, and 24h respectively. The results showed as follows (Fig.9C). For all three cell lines, either Bufalin alone or Etomoxir alone increased intracellular lipid deposition compared to the DMSO solvent control group (p<0.05). For all three cell lines, the combination of Bufalin and Etomoxir resulted in more significant intracellular lipid deposition compared to the Etomoxir alone group, with a statistically significant difference at p<0.05.

HCC cells were treated with a 200μM dose of the CPT1A inhibitor Etomoxir and a 50nM dose of Bufalin separately or in combination, and ATP assay was performed, with HepG2, Huh7, and Hep3B cells observed at 72h, 72h, and 48h, respectively, and the results were presented as follows (Fig.9D).

For all three cell lines, either Bufalin alone or Etomoxir alone resulted in decreased intracellular ATP levels compared to the DMSO solvent control group, with statistical significance at p<0.05.

For HepG2, the decrease in intracellular ATP levels was slightly more pronounced in the Bufalin and Etomoxir combination group compared to the Etomoxir alone group (p<0.05). For Huh7 and Hep3B, the decrease in intracellular ATP levels was more significant in the Bufalin and Etomoxir combination group compared to the Etomoxir alone group, with a statistically significant difference at p<0.05.

To further validate the inhibitory effect of Bufalin on fatty acid oxidation through CPT1A, HCC cells were treated with a 200μM dose of CPT1A inhibitor Etomoxir and a 50nM dose of Bufalin, respectively or in combination, and after HepG2, Huh7, and Hep3B were drug-active for 48h, 48h, and 24h, respectively. Subsequently, we examined the expression levels of the fatty acid oxidation-related gene CPT1A mRNA and protein in these cell lines by RT-qPCR as well as western blot analysis. The results indicated (Fig.8E-F) that, for all three cell lines, either Bufalin alone or Etomoxir alone resulted in decreased expression levels of intracellular CPT1A compared to the DMSO solvent control group, with statistical significance at p<0.001. Besides, the decrease in intracellular CPT1A expression levels was more pronounced in the Bufalin and Etomoxir combination group compared to the Etomoxir alone group, with a statistically significant difference at p<0.001.

3.4 Bufalin intervenes in Sorafenib resistance in HCC via CPT1A

Sorafenib, a classic therapy for HCC, is a multi-kinase inhibitor targeting various signaling pathways. However, its anti-angiogenic effects can lead to hypoxia and nutrient deprivation in HCC cells, triggering metabolic reprogramming in tumors. Furthermore, current research suggests significant potential in targeting tumor metabolism pathways or combining common drugs for cancer treatment from different perspectives^[25] In this section, we determined the IC₅₀ of Sorafenib in HCC cell lines using the CCK-8 assay and established Sorafenib-resistant cell lines (Fig.10A). The reversal index (RI) of HepG2, Huh7, and Hep3B cell lines to Sorafenib were 3.17, 2.37, and 2.53, respectively. Additionally, we evaluated the reversal fold (RF) of Bufalin on Sorafenib resistance in HCC cells using the CCK-8 assay. The RF was calculated as the IC₅₀ of the resistant group divided by the IC₅₀ of the resistant group treated with Bufalin, resulting in RF of 2.40, 1.65, and 1.66, respectively.

Using the CCK-8 assay, we compared the differences in cell viability among HCC cell lines, Sorafenib-resistant HCC cell lines, and Sorafenib-resistant cells treated with Bufalin. The results demonstrated that Bufalin treatment decreased the viability of Sorafenib-resistant HCC cells (Fig.10B). RT-qPCR and western blot analysis further revealed that Bufalin downregulated the expression of CPT1A in Sorafenib-resistant HCC cells (Fig.10C-D).

3.4.1 Effects of Bufalin on subcutaneous tumor growth, body weight, and tumor volume

We established stable HepG2 Sorafenib-resistant cells with knocked down CPT1A (Fig.11A) and conducted subcutaneous tumor experiments in nude mice. The mice were divided into four groups: blank control group, Sorafenib-resistant group (SR), shCPT1A-Sorafenib-resistant group (shCPT1A-SR), and Sorafenib-resistant group treated with Bufalin (SR+B).

There were no statistically significant differences in body weight among the four groups before and on the first day after treatment (p>0.05, Fig.11B). Similarly, there were no statistically significant differences in tumor volume among the four groups before treatment (p>0.05, Fig.11B). However, on the 27th day after treatment, there were statistically significant differences in tumor volume (p<0.05, Fig.11B). On the 27th day after treatment, the tumor volumes of the shCPT1A-SR group and the SR+B group were both lower than those of the SR group and the control group, with statistically significant differences (p<0.05, Fig.11C).

3.4.2 Effect of Bufalin on the relative expression of CPT1A mRNA and protein in subcutaneous implant tumors of nude mice

The results of RT-qPCR showed that the tumor volumes of the shCPT1A-SR group and the SR+B group were both lower than those of the SR group and the control group, with statistically significant differences (p<0.05, Fig.11D).

Cinobufacini exerts a significant anti-cancer effect on various solid tumors, including HCC, owing to its multi-targeted, efficient, and low-toxicity properties. Currently, research suggests that Bufalin is the most potent active ingredient in the anti-tumor activity of Cinobufacini. However, the anti-tumor mechanism of Bufalin requires further investigation.

As we know, most of the functional information of the genome is ultimately executed by the proteome. Proteomics, which focuses on the study of the proteome, employs mass spectrometry analysis with high quality and precision to qualitatively and quantitatively analyze proteins within cells. This approach enables the characterization of specific protein structures and relative abundances, providing a systematic and comprehensive description of the structure and functional information of cellular proteins. Proteomics reveals how cells respond to different stimuli or drugs, thereby shedding light on disease diagnosis, prognosis, and the mechanisms of response to various stimuli and drugs^[26-29]. Currently, proteomic techniques have been applied to the study of the mechanism of action of TCM, achieving substantial progress^[30]. For example, previous studies have used quantitative proteomics to investigate the mechanism of action of dendrobium phenol on lung cancer H460 cells, revealing its ability to intervene in the PI3K/AKT pathway and induce tumor cell apoptosis^[11]. Another study applied proteomics technology to study the mechanism of action of eleutheroside B1 on lung cancer cell line A549^[12]; in addition, Guggulsterone acted on colorectal cancer cell line HCT116, evodiamine acted on thyroid cancer cell line ARO, and Atractylodes lancea acted on cholangiocarcinoma cell line CL-6, etc., and all of these studies have analyzed differential proteins and functional pathways before and after treatment by proteomic techniques^[31-33]. In this study, we utilized TMT-labeled quantitative proteomics to identify Bufalin-related proteins in HCC and used bioinformatics analysis to elucidate the signaling pathways regulated by significantly different proteins before and after Bufalin treatment, so as to elucidate the mechanism of inhibition of HCC by Bufalin at molecular level. To comprehensively and systematically explain biological processes, disease mechanisms, and drug action mechanisms from the perspective of protein coordination, we annotated and enriched 266 differential proteins in the Bufalin treatment group using KEGG pathway analysis. The results revealed significant changes in important pathways such as complement and coagulation cascades, ECM-receptor interaction, ABC transporters, PPAR signaling pathway, and cholesterol metabolism. These findings suggest that Bufalin may affect the development of HCC by interfering with the protein expression of different pathways, providing a basis for further analyzing the anti-tumor mechanism of Bufalin. Notably, we found that fatty acid metabolism-related proteins enriched in the PPAR signaling pathway, such as PPARs

(PPARα、PPARβ、PPARγ), CPT, FATP, FABP, LCAD, and ACO, were downregulated. These significantly DEPs were selected, indicating that Bufalin may inhibit the development of HCC by regulating the expression of key factors in these pathways, laying the foundation for subsequent experiments.

Currently, research suggests that CPT1A is an important oncogene. Shi et al. ^[34] found that the expression of CPT1A is significantly correlated with the prognosis of acute leukemia patients, and those with high expression of CPT1A had a poorer prognosis. Xiong et al. ^[35] confirmed in their study on breast cancer that CPT1A can promote lymphangiogenesis by regulating vascular endothelial growth factors (VEGFs). Additionally, another study^[36] discovered that downregulating RNA expression of CPT1A enhanced the radiosensitivity of nasopharyngeal carcinoma. However, there is limited research on the effects of CPT1A on HCC. In our study, we employed immunohistochemistry to stain HCC tissues and their adjacent tissues for CPT1A. We found that CPT1A is more abundant in cancer tissues compared to adjacent tissues, and patients with high expression of CPT1A have a poorer prognosis than those with lower expression. To further validate the biological effects of CPT1A on lHCC cells, we constructed stable CPT1A-knockdown human HCC cell lines. We observed that knocking down CPT1A resulted in decreased cell viability, migration, and invasion capabilities of HCC cells.

CPT1 is a key enzyme in the process of fatty acid oxidation, consisting of three isoforms: CPT1A, CPT1B, and CPT1C. Among them, CPT1A plays a major role in fatty acid oxidation and regulates the efficiency of fatty acid oxidation. We infer that the promotion of tumor cell growth by CPT1A may depend on the promotion of fatty acid oxidation. Therefore, we further analyzed the effects of knocking down CPT1A on two important indicators of fatty acid oxidation in HCC cells (lipid deposition and ATP levels). Our results revealed that, knocking down CPT1A led to significantly increased lipid deposition and decreased ATP levels in HCC cells compared to the control group. This part of the study demonstrates that knocking down CPT1A can inhibit fatty acid oxidation in HCC cells, thereby reducing ATP supply to liver cancer cells, which may be a reason for the inhibition of cell growth in HCC.

The liver, as the primary organ for lipid metabolism in the body, is associated with abnormal lipid metabolism during the development of liver cancer. In China, hepatitis B virus infection is prevalent, and studies have confirmed^[37] that HBsAg can upregulate lipid metabolism and promote lipid synthesis. Current research indicates that active lipid metabolism occurs in both precancerous lesions and tumors^[38] . In HCC tissues, rapid proliferation of cancer cells occurs, accompanied by relatively weak angiogenesis within the tumor. Additionally, some patients experience reduced blood supply due to certain treatments, leading to hypoxia and nutrient deprivation in HCC cells. Reprogramming of lipid metabolism is an important mechanism for HCC cells to sustain proliferation, invasion, and migration.

This study found that different doses of Bufalin significantly influence fatty acid oxidation in HCC cells. It was observed that Bufalin induces significant lipid deposition and decreases intracellular ATP levels in HCC cells. Moreover, with increasing doses of Bufalin, there is more lipid deposition in HCC cells, accompanied by a significant decrease in intracellular ATP levels. Additionally, experiments revealed that the addition of Bufalin to CPT1A agonist-activated HCC cells could reduce the fatty acid oxidizing ability of HCC cells. So far, there is no literature on the effect of Bufalin (or Cinobufacini) in tumor cell fatty acid oxidation. Subsequently, we used RT-qPCR to verify that Bufalin can inhibit the expression of key genes in the PPAR pathway, including PPARα and CPT1A mRNA, in a dose-dependent manner. Western blot analysis further confirmed that Bufalin can inhibit the protein expression of PPARα and CPT1A, and the protein expression tended to decrease with increasing Bufalin dose. Additionally, experiments showed that the addition of Bufalin to CPT1A agonist-activated HCC cells could reduce the protein expression of PPARα and CPT1A. There is no relevant literature on the effect of Bufalin (or Cinobufacini) on the PPARα-CPT1A pathway in tumor cells.

Etomoxir, a CPT1A inhibitor, has been used in the treatment of cardiovascular diseases and diabetes, and is currently widely employed as an inhibitor of fatty acid oxidation in various studies. Recent research has shown promising effects of Etomoxir in inhibiting tumor proliferation in cancer treatment^{[23, 24]}. Additionally, Etomoxir has been found to enhance the radiosensitivity of gliomas ^[39], and studies have demonstrated that the combination of Etomoxir with Sorafenib enhances the anti-HCC effects of Sorafenib^[40]. However, due to the irreversible inhibition of CPT1A by Etomoxir, its extensive side effects ^[41] have limited its further application in the clinic. Bufalin, the main active component of Cinobufacini, has been used safely in clinical anti-tumor applications for many years. This study found that Bufalin has a similar ability to inhibit CPT1A as Etomoxir, providing a basis for the clinical substitution of Cinobufacini for Etomoxir against fatty acid oxidation in tumor cells.

For HCC, some treatments, such as transarterial chemoembolization (TACE) and multikinase inhibitors like Sorafenib, both can inhibit angiogenesis, causing HCC cells to frequently exist in a state of hypoxia and nutrient deprivation, which triggers metabolic reprogramming of the tumor. Studies have shown that resistance to Sorafenib is often accompanied by lipid metabolism reprogramming ^[42]. This study, conducted using nude mice with orthotopic tumor implants, found that Bufalin can reverse Sorafenib resistance in HCC by inhibiting CPT1A. Combining the Chinese medical preparation Cinobufacini with Sorafenib may provide a new therapeutic strategy to improve the prognosis and prolong the survival of cancer patients.

Currently, there is a lack of effective treatment options for HCC in clinical practice, highlighting the profound significance of exploring therapeutics that are both highly efficient and low in toxicity. TCM holds potential therapeutic value in regulating bodily constitution and combating tumors. Bufalin, as a crucial component of Cinobufacini, exhibits the ability to inhibit fatty acid oxidation in HCCs while exerting anti-tumor effects, offering a novel strategy for the clinical treatment of HCC.

All data in this study were accessible upon reasonable requirement.

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University.

Consent for publication

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. The work described is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Funding

This research was sponsored by Beijing Science And Technology Innovation Medical Development Foundation (KC2023-JX-0288-FQ17), a horizontal program hosted by Liu Ying.

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to thank laboratory of integrative medicine in the First Affiliated Hospital of Dalian Medical University for providing the experimental site for this work. This research was sponsored by Beijing Science And Technology Innovation Medical Development Foundation (KC2023-JX-0288-FQ17).

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The mechanism of CPT1A involved in hepatocellular carcinoma growth and Bufalin regulates malignant behavior of hepatocellular carcinoma via CPT1A

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Reagents

2.2. Cell lines

2.3. TMT Labeling Quantitative Proteomic Analysis

2.3.1 Experimental Method of TMT Labeling Quantitative Proteomics

2.3.1.1 Sample Collection and Processing

2.3.1.2 Protein Extraction and Peptide Digestion

2.3.1.3 TMT Labeling

2.3.1.4 Chromatographic Fractionation

2.3.1.5 Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) Data Acquisition

2.3.1.6 Protein Identification and Quantitative Analysis

2.3.2 Bioinformatics Analysis Methods

2.3.2.1 Protein Clustering Analysis

2.3.2.2 Subcellular Localization Analysis

2.3.2.3 Protein Domain Analysis

2.3.2.4 GO Functional Annotation

2.3.2.5 KEGG Pathway Annotation

2.3.2.6 Enrichment Analysis

2.4 Cell Transfection

2.5 Cell viability assay

2.6 Scratch assay

2.7 HepG2 cell migration and invasion capability transwell assay

2.8 Western blotting

2.9 Quantitative real-time PCR analysis

2.10 Oil red O staining

2.11 ATP content detection

2.12 Immunohistochemistry (IHC)

2.13 Xenograft production in nude mice

2.14 Statistical Analysis

3. Results

Discussion

Conclusion

Declarations

References

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