HepG2 cells (cat. no. HB-8065; American Type Culture Collection) were cultured in Dulbecco's modified Eagle's medium (Gibco; Thermo Fisher Scientific, Inc.) containing 10% fetal calf serum [Serana (WA) Pty, Ltd.], 100 U/ml penicillin and 100 µg/ml streptomycin (cat. no. C0222; Beyotime Institute of Biotechnology). Transfection of HepG2 cells was performed using Lipofectamine^® 2000 (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. Cells were transfected at ~80% confluence, with 5 µg plasmid/dish, The plasmid was incubated with Lipofectamine for 10 min, then the DNA-lipid complex was added to the cells and incubated for 48 h at 37°C. FAXDC2 overexpression was achieved by inserting the coding sequence of human FAXDC2 (Accession no. NM_032385) into the pCMV-HA vector (Agilent Technologies, Inc.) to generate pCMV-HA-FAXDC2. Empty pCMV-HA vector was used as a negative control for overexpression. GDP was diluted with medium before being used to treat the cells for 48 h. The GDP concentrations were 20, 40, 80, 160 and 200 µM. The cells were treated and cultured at 37°C and 5% CO₂.

Total RNA was extracted from cells utilizing the FastPure Complex Tissue/Cell Total RNA Isolation Kit (cat. no. RC113-01; Vazyme Biotech Co., Ltd.), according to the manufacturer's protocol. Subsequently, cDNA synthesis was achieved by employing the HiScript II 1st Strand cDNA Synthesis Kit (+gDNA wiper) (cat. no. R212-01; Vazyme Biotech Co., Ltd.), again according to the manufacturer's guidelines. qPCR was performed using SYBR Premix Ex Taq II (Takara Bio, Inc.), according to the manufacturer's protocol. The qPCR cycling conditions were as follows: Initial denaturation at 50°C for 2 min; followed by a second denaturation at 95°C for 5 min; and then 40 cycles of amplification, each consisting of denaturation at 95°C for 15 sec and annealing/extension at 60°C for 40 sec. The primers employed in the PCR analysis were sourced from TsingKe Biological Technology. For quantification purposes, the expression levels of GAPDH served as an internal reference, and the relative expression levels of FAXDC2 were determined using the 2^−ΔΔCq method (24). The primer sequences were as follows: FAXDC2 forward, 5′-GGCTGCTGACTACATTTGAAGG-3′ and reverse, 5′-TCAACCACCAATAGAAGCCCA-3′; GAPDH forward, 5′-TGCACCACCAACTGCTTAGC-3′ and reverse 5′-GGCATGGACTGTGGTCATGAG-3′. Each RT-qPCR analysis was repeated three times.

The culture medium was removed from the HepG2 cells transfected with FAXDC2 overexpression vector or empty vector, and the plate was washed 2 or 3 times with pre-cooled PBS. Subsequently, the supernatant was discarded, trypsin was added for digestion and the cells were washed a further 2 or 3 times with PBS. The cells were then transferred to a centrifuge tube at 4°C and were centrifuged at 4°C, 1,200 × g for 2 min, after which the supernatant was discarded, and the cells were collected and placed into a 1.5-ml microcentrifuge tube. The cells were frozen in liquid nitrogen for 15 min and then prepared for analysis by liquid chromatography-mass spectrometry (LC-MS). The LC-MS detection and subsequent steps were all performed by Novogene Co. Ltd.

Raw data from the LC-MS analysis underwent preprocessing with the Compound Discoverer 3.3 data processing software (https://www.thermofisher.cn/order/catalog/product/OPTON-31056), followed by metabolite identification through alignment with high-resolution tandem mass spectrometry databases including mzCloud (beta.mzcloud.org), mzVault and the MassList primary database (both Thermo Fisher Scientific, Inc.). Using the relative quantitative values of the metabolites, Pearson correlation coefficients were computed among quality control samples (25). Partial least squares discrimination analysis was then applied, utilizing partial least squares regression (26) to model the relationships between metabolite expression levels and sample categories, enabling the classification and prediction of sample types. Principal component analysis was performed with the logarithmic transformation and standardization of data using MetaX (version: 1.4.17) (27). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted using the KEGG database (https://www.genome.jp/kegg/pathway.html), with categorization of the biological metabolic pathways into the seven primary KEGG groups: Metabolism, Genetic Information Processing, Environmental Information Processing, Cellular Processes, Organismal Systems, Human Diseases and Drug Development. The Human Metabolome Database (HMDB; http://hmdb.ca/metabolites) was employed to elucidate the biological roles, disease associations, chemical reactions, metabolic pathways and other pertinent details of human metabolites.

The AlphaFold3 server (28) (https://www.alphafoldserver.com) was used to predict if there is likely to be any interaction between GDP and FAXDC2.

The human GDP content detection ELISA kit was purchased from Tianjin Ruichuang Biotechnology Co., Ltd. (cat. no. RC-E110643A). The culture medium was removed from cultured cells, and the cells were washed with PBS and then treated with cell lysis solution. After centrifugation at 4°C, 16,260 × g for 5 min, ELISA of the supernatant was performed. Briefly, standard and sample wells were set up, then horseradish peroxidase (HRP)-labeled detection antibodies were added to the wells and incubated in a water bath at 37°C for 60 min. The liquid was then discarded and the wells were washed five times with washing solution. After allowing to stand for 1 min, Substrates A and B were added, the wells were incubated at 37°C in the dark for 15 min, and then stop solution was added. The optical density (OD) of each well was measured using a microplate reader, and the GDP content of the sample was calculated from a standard curve.

Cell viability was assessed using the CCK-8 assay (Beyotime Institute of Biotechnology). At 24, 48 and 72 h after GDP treatment, 20 µl CCK-8 reagent was added to each well, and the cells were incubated in a humidified CO₂ incubator at 37°C for 1 h. The OD of the culture medium was then measured as the absorbance at a wavelength of 450 nm using a microplate reader (Bio-Rad Laboratories, Inc.).

Cell invasion assays were performed in 24-well plates with a 8-µm chamber insert in each well (Corning, Inc.). To evaluate invasion, Matrigel was defrosted at 4°C and layered on an insertion membrane to form a matrix barrier. The membrane was then placed in a 37°C incubator for 30 min to solidify, and 1×10⁵ cells were seeded in the upper chamber after suspension in serum-free medium. Then, 800 µl medium containing 10% FBS was added to the lower chamber and the chamber was carefully placed into a 24-well plate and cultured in an incubator at 37°C for 48 h. Cells that adhered to the underlying membrane were fixed and stained using a mixture of 0.1% crystal violet and 20% methanol for 30 min at room temperature. Images of the cells were then captured and the cells were counted under an inverted microscope (Nikon Eclipse; Nikon Corporation). Each experimental group consisted of three biological replicates.

Total cellular proteins were extracted from cells with RIPA lysis buffer (Wuhan Elabscience Biotechnology Co., Ltd.), and protein concentrations were quantified using the BCA assay. Proteins (20 µg protein/lane) were then loaded onto 10% polyacrylamide gels and electrophoresis was performed at a constant voltage of 100 V to separate the protein bands. Protein transfer was performed at a constant current of 300 mA; transfer was to a PVDF membrane that had been activated with methanol for 30 sec before use. Subsequently, the PVDF membrane was blocked by soaking in 5% skimmed milk in Tris-buffered saline-0.05% Tween 20 (TBST) for 1.5 h at room temperature and the excess milk was washed away with TBST. The PVDF membrane was then incubated with primary antibodies (1:1,000 dilution) for 90 min at room temperature and washed three times with TBST (5 min/wash). Subsequently, the PVDF membrane was incubated at room temperature for 90 min with the secondary antibody (1:5,000 dilution) and then washed three times with TBST (5 min/wash). Super-sensitive ECL chemoluminescent substrate (cat. no. BL520A; Biosharp Life Sciences) was used for film imaging. Semi-quantitative analysis was performed using ImageJ 1.52a software (http://imagej.org; National Institutes of Health). Anti-phosphorylated-p44/42 MAPK (ERK1/2) (cat. no. 4370), anti-p44/42 MAPK (ERK1/2) (cat. no. 4695) and anti-cyclin-dependent kinase 4 (CDK4; 12790) were purchased from Cell Signaling Technology, Inc.; anti-c-Myc protein (cat. no. TA0358), anti-EGFR (cat. no. T55112), anti-cyclin D1 protein (cat. no. TC52046) and anti-tubulin protein (cat. no. M30109) antibodies were purchased from Abmart (Shanghai) Co., Ltd. HRP-conjugated goat anti-mouse IgG and anti-rabbit IgG secondary antibodies were purchased from CoWin Biosciences (1:5,000; cat. nos. CW0102 and CW0103).

Statistical analysis was performed using GraphPad Prism 8.0 software (GraphPad; Dotmatics). Data are presented as the mean ± standard deviation. The data of the GDP experiment were verified to conform to a normal distribution. Differences between two groups were evaluated using unpaired Student's t-test. CCK-8 assay data were analyzed by one-way ANOVA followed by Dunnett's post hoc tests. P<0.05 was considered to indicate a statistically significant difference.

Our previous study showed that FAXDC2 inhibits the proliferation and invasion of HepG2 cells (23). To elucidate the regulatory mechanisms of FAXDC2 in lipid metabolism and substance synthesis, given its classification as a member of the fatty acid hydroxylase superfamily, it was hypothesized that perturbations in the expression of FAXDC2 could potentially modulate the metabolic landscape of HepG2 cells, a widely used cell line in hepatic metabolism research. Consequently, a comprehensive, non-targeted metabolomics analysis was performed, in which the metabolic profiles of HepG2 cells subjected to overexpression of FAXDC2 were compared with those of HepG2 cells transfected with an empty vector control. This approach aimed to identify specific metabolic alterations that may be attributed to the modulation of FAXDC2 expression levels. To validate the experimental paradigm, RT-qPCR analysis was initially conducted on HepG2 cells subjected to overexpression of FAXDC2 in comparison with those subjected to transfection with empty vector control. The analysis confirmed the successful overexpression of FAXDC2 (Fig. 1A). In addition, a principal component analysis was performed, which identified a statistically significant disparity in the vector direction of the principal components between the experimental and control groups (Fig. 1B), indicative of distinct metabolic profiles between the two cohorts. Through the enrichment and annotation of differential metabolites in the HMDB, it was revealed that ‘lipids and lipid-like molecules’ were most enriched after the overexpression of FAXDC2 (Fig. 1C), which is consistent with the biological characteristics of FAXDC2. Through KEGG pathway enrichment analysis, it was observed that the differential metabolites detected after the overexpression of FAXDC2 were mainly enriched in pathways associated with metabolism (Fig. 1D), which indicated that the differential expression of FAXDC2 induces changes in metabolism-related pathways in HepG2 cells. Subsequently, a primary HMDB classification of the differential metabolites in HepG2 cells following the overexpression of FAXDC2 was performed. This revealed that the most prevalent category of these metabolites was ‘lipids and lipid-like molecules’ (Fig. 1E). This aligns well with the established biological function of FAXDC2 in lipid metabolism and biosynthesis.

The correlations among differential metabolites in the FAXDC2-overexpressing cells and their interactions within the metabolic network were investigated. The top 20 ranked differential metabolites are displayed from smallest to largest significance level (P-value) in Fig. 2A. It was observed that GDP was positively correlated with malic acid, uridine diphosphate (UDP) and uridine 5′-diphosphoglucuronic acid (UDPGA), and negatively correlated with other differential metabolites. The relationships between these metabolites are visually represented by a chord diagram (Fig. 2B). The commonalities or similar properties of the three positively correlated metabolites primarily lies in their roles in cellular metabolism. They serve as crucial metabolic intermediates participating in various biochemical pathways, particularly those associated with energy production, synthesis and conversion processes (29,30). Subsequently, a z-score plot was constructed, which revealed that thymidine 5′-diphosphate, malic acid, daidzein, D-glucarate, UDP and its derivative UDPGA exhibited significantly increased levels in the FAXDC2-overexpressing cells compared with those in the control group transfected with empty vector (Fig. 3A).

Further analysis was conducted on the differential metabolites after FAXDC2 overexpression based on their changes in abundance, as depicted in a volcano plot (Fig. 3B). The metabolomics analysis revealed that 527 differential metabolites were collectively enriched, among which 71 exhibited significantly increased or decreased abundance. The 71 differential metabolites are illustrated in a heatmap (Fig. 3C).

A volcano plot of differential metabolites was generated by comparing the contribution values of differential metabolites between the FAXDC2 overexpression group and the empty vector control group. Variable importance in projection (VIP) values were calculated, which estimate the contribution or importance of metabolites in the model (Fig. 3D). The top three differential metabolites with increased abundance were pantetheine, thymidine 5′-diphosphate and GDP, with GDP having the highest VIP value. These findings indicate that the differential metabolite GDP may provide the greatest contribution to the differences between the FAXDC2 overexpression group and the empty vector control group.

Several differential metabolites identified in the untargeted metabolomics analysis were screened. These included GDP, daidzein and 2-deoxyglucose-6-phosphate, with a focus on changes in GDP levels. GDP plays an important role in cellular energy metabolism and signal transduction. GTP is a key energy transfer molecule, similar to ATP, which provides energy for various biochemical reactions, including fatty acid synthesis and degradation. In certain critical enzyme-catalyzed reactions, GDP is converted to guanosine triphosphate (GTP), a process often accompanied by the release or absorption of energy (31).

In HepG2 cells overexpressing FAXDC2, the GDP levels were found to be higher than those in cells transfected with empty vector (Fig. 4A). Additionally, KEGG pathway enrichment analysis revealed that the differential metabolites were most enriched in the ‘purine metabolism’ pathway. It was also noted that GDP was the only differential metabolite enriched in ‘Ras signaling pathway’ and ‘Rap1 signaling pathway’ (Fig. 4B).

A KEGG regulatory network diagram of the differential metabolites was then generated (Fig. 4C), which indicated a particular focus on GDP. Therefore, a KEGG regulatory network diagram specific for GDP was generated (Fig. 4D). It can be observed that GDP, as an input or known compound, may regulate the Ras signaling pathway. Since Ras is a crucial component of the MAPK/ERK signaling transduction pathway, changes in Ras may impact this pathway. This demonstrates the regulatory effect of FAXDC2 on the MAPK/ERK signaling transduction pathway, consistent with our previous study (23).

Given the observation that the overexpression of FAXDC2 elicits alterations in the GDP content of HepG2 cells at the metabolic level, the advanced protein structure prediction tool, AlphaFold3 (28), was employed to model the three-dimensional structures of FAXDC2 and GDP (Fig. 5). This predicted a direct interaction between FAXDC2 and GDP, thereby suggesting a potential mechanistic link. Consequently, it was hypothesized that GDP may mediate the inhibitory effect of FAXDC2 on HepG2 cells, which may serve as a basis for understanding the molecular mechanisms underlying this process.

Based on the findings of the metabolomics analysis and the absence of direct reports on the inhibitory effects of GDP on tumors in previous studies, the present study aimed to directly validate the effects of GDP on the HepG2 cell line. The GDP concentrations used were selected with reference to those used in the study by Traut (32). Initially, ELISA experiments were conducted to detect the GDP content of HepG2 cells following treatment with 200 µM GDP (Fig. 6A). It was observed that the intracellular GDP content increased by ~35% after treatment with 200 µM GDP compared with that in cells without GDP treatment.

The proliferation of HepG2 cells was then evaluated using the CCK-8 assay. The results indicated that a significant reduction in the viability of HepG2 cells occurred following treatment with 40–200 µM GDP compared with that in the control group (Fig. 6). Moreover, the inhibitory effect of GDP gradually intensified as its concentration increased, suggesting a concentration-dependent effect of GDP on the inhibition of HepG2 cell activity. Additionally, as shown in Fig. 6B-G, the inhibitory effect of GDP on HepG2 cells increased over time, compared with that in the control group. Specifically, following GDP treatment, the IC₅₀ values decreased from 4,483 µM at 24 h to 950.2 µM at 48 h, and further to 357.5 µM at 72 h.

A 200 µM concentration of GDP was selected for use in subsequent experiments. The viability of HepG2 cells treated with 200 µM GDP for 24, 48 and 72 h was measured, as shown in Fig. 6H and I. The viability of HepG2 cells was significantly decreased after treatment with 200 µM GDP at each time point. These results indicate that GDP inhibits the proliferation of HepG2 cells.

To comprehensively evaluate the inhibitory effects of GDP on liver cancer, a Transwell assay was performed to assess the invasive ability of HepG2 cells after treatment with 200 µM GDP (Fig. 7). The number of HepG2 cells passing through the Transwell membrane significantly decreased following treatment with 200 µM GDP compared with that of untreated cells, indicating a significant inhibition of the invasive ability of HepG2 cells by GDP.

To explore the impact of increased GDP levels on the signaling pathways within HepG2 cells, guided by metabolomic insights, key signaling molecules in the Ras/ERK pathway and several proteins associated with cell proliferation were examined via western blot analysis (Fig. 8). The results demonstrated that after the treatment of HepG2 cells with 200 µM GDP, the expression levels of ERK, EGFR, c-Myc and the cell cycle-related proteins, CDK4 and cyclin D1, were all significantly downregulated. These findings suggest that an increase in GDP levels may inhibit the Ras/ERK signaling pathway.