EGF/APOC1/CPT1A Axis: A Novel Pathway in Gastric Cancer Metabolism and Therapeutic Targeting

doi:10.21203/rs.3.rs-4832902/v1

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EGF/APOC1/CPT1A Axis: A Novel Pathway in Gastric Cancer Metabolism and Therapeutic Targeting

https://doi.org/10.21203/rs.3.rs-4832902/v1

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Gastric cancer (GC) relies heavily on the reprogramming of lipid metabolism for energy, growth, and survival. Apolipoprotein C-I (APOC1) is implicated in the reprogramming of lipid metabolic processes in GC, yet the precise molecular mechanisms underlying this association remain incompletely understood. In this study, we identified APOC1 as a key player in GC metabolism and progression. Our findings show that APOC1 is upregulated in GC tissues and cells compared with controls and is correlated with poor prognosis. Furthermore, the overexpression of APOC1 promoted proliferation and migration, whereas the silencing of APOC1 led to decreased tumor growth and metastasis. Moreover, APOC1 was found to augment fatty acid oxidation (FAO) through its interaction with and regulation of carnitine palmitoyltransferase 1A (CPT1A). Disruption of APOC1 resulted in metabolic dysregulation, leading to mitochondrial oxidative stress. Conversely, the upregulation of APOC1 in GC cells promoted the catabolism of long-chain fatty acids, thereby facilitating tumor growth and migration. Mechanistically, APOC1 was shown to be regulated by epidermal growth factor (EGF), and its expression was directly targeted at the transcriptional level by AP-2α binding to its promoter region. Our research reveals a novel association between APOC1 and lipid metabolism, highlighting the EGF/APOC1/CPT1A axis as crucial factors in the progression of GC and potential targets for therapeutic interventions.

Biological sciences/Cancer/Gastrointestinal cancer/Gastric cancer

Biological sciences/Cancer/Cancer metabolism

APOC1

gastric cancer

CPT1A

fatty acid oxidation

lipid metabolic reprogramming

Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide^[1]. In a study on the global burden of GC, obesity and overweight were identified as risk factors for gastric carcinogenesis^[1]. Meanwhile, the infection and eradication of Helicobacter pylori, a potent carcinogen of GC, are associated with elevated serum lipid levels^{[2, 3]}. There has been evidence that statins, which lower serum lipid levels, can have antiproliferative, proapoptotic, anti-angiogenic, and immunomodulating effects in patients with GC^[4], and long-term statin treatment (≥ 2 years) decreases the risk of GC^[5]. These studies highlight the fact that lipid metabolic dysfunction is critical for tumorigenesis and the development of GC^[6]. Further studies investigating the molecular mechanisms underlying lipid metabolism in GC tumorigenesis may provide promising therapeutic targets for GC.

Fatty acid oxidation (FAO) is a significant component of lipid metabolic reprogramming and is essential for the acquisition of energy by malignant tumor cells^[7]. Recent research has highlighted the critical role of abnormal FAO in the initiation and progression of gastric, colorectal, breast and prostate cancers^{[8, 9, 10]}. In GC, specific targets and pathways drive FAO to increase stemness, cancer metastasis, and resistance to chemotherapy^{[11, 12]}. Notably, as a key enzyme in FAO, carnitine palmitoyltransferase 1A (CPT1A) has been shown to play a crucial role in promoting cell growth and metastasis in GC^{[13, 14]}, implying that targeting FAO and the key enzyme CPT1A as a promise method for regulating lipid metabolism. However, the underlying mechanisms of CPT1A GC lipid metabolism remains unclear. Therefore, investigating the regulatory mechanisms of abnormal FAO and targeting its metabolic pathways may present a novel strategy for curbing GC progression.

Apolipoprotein C1 (APOC1) is a member of the plasma apolipoprotein family and is known for its significant role in the transport and metabolism of high-density lipoprotein and very low-density lipoprotein. Studies have indicated that APOC1 inhibits cholesteryl ester transfer protein in plasma, potentially impacting serum lipid levels in patients with cancer. An increasing number of studies have demonstrated the proinflammatory effects of APOC1 on the progression of atherosclerosis and diabetic nephropathy^{[15, 16, 17]}. In the context of human cancers, APOC1 has emerged as a promising biomarker for prognosis and a potential therapeutic target in hepatocellular carcinoma^{[18, 19, 20]}. A recent study utilizing single-cell RNA sequencing demonstrated that the inhibition of APOC1 can enhance the efficacy of anti-PD1 immunotherapy by inducing M1 macrophage polarization^[18]. Another study reported that the silencing of APOC1 effectively suppresses the MAPK/ERK kinase pathway and NF-κB, thereby inhibiting breast cancer metastasis^[21]. In GC, APOC1 is activated by noncoding RNA to stimulate proliferation^[22]. Despite its significant impact on cancer development and progression, the specific functional roles and mechanisms through which APOC1 promotes GC progression via the reprogramming of lipid metabolism remain poorly understood.

In this study, we elucidated a significant role of APOC1 in the regulation of fatty acid metabolism in GC, leading to enhanced growth and metastasis. High expression of APOC1 is associated with poor prognosis in GC patients. Through functional assays, transcriptomics, and metabolomics, we demonstrated that APOC1 plays a crucial role in promoting GC metabolism, growth and survival. Additionally, we found that APOC1 was transactivated by AP-2α upon epidermal growth factor (EGF) stimulation, and that targeting the EGF/APOC1/CPT1A axis could be a promising therapeutic strategy for GC.

2.1 Data and resources

We obtained the RNA-seq data shown in Fig. 1A, B and E from the TCGA database, which contains 415 GC tissues and 34 normal tissues from the UALCAN website (https://ualcan.path.uab.edu/). The RNA-seq data are listed in Supplementary Table 1. The RNA-seq data shown in Fig. 1C were obtained from the TCGA and GETx databases, which contain 211 GC tissues and 408 normal tissues from the GEPIA website (http://gepia.cancer-pku.cn/). Supplementary Fig. 1A data were obtained from the CCLE database (https://depmap.org/portal/gene/APOC1). Kaplan–Meier survival curves for 875 patients with GC were obtained from the Kaplan–Meier Plotter website (https://kmplot.com/analysis/index.php?p=service). The prediction of protein interaction was performed by AlphaFold 3 (https://alphafoldserver.com/).

2.2 Cell lines and cell culture

A human normal gastric epithelial cell line (GES-1) and gastric cancer cell lines (AGS, MKN-28, MKN-45, HGC-27 and MFC) were obtained from the Cell Type Bank of the Chinese Academy of Sciences and Shanghai Institute of Biochemistry and Cell Biology. The cell lines were cultured in RPMI-1640, DMEM or DF12 supplemented with 10% FBS. All the cell culture experiments were performed in an incubator at 37°C under 5% CO₂.

2.3 Cell line transfection

The overexpression and shRNA plasmids were designed by Youbio Biotech (China). Plasmid transfection was performed using Lipofectamine 2000 Reagent (Invitrogen, California, USA) and 1 µg of plasmid according to the manufacturer’s instructions. The shRNA sequences used are listed in Supplementary Table 2.

2.4 Viral transduction

The constructed overexpression and silencing plasmids were transfected into 293T cells together with the packaging plasmids psPAX and PMD2.G at a ratio of 4:3:1 using the transfection reagent Lipofectamine 2000 (Invitrogen). The supernatants were collected and filtered through a 0.45 µm filter membrane after 72 h. The cells were plated in 6-well plates at 1 × 10⁶ cells per well and transfected with the collected lentivirus particles. The transfected cells were then selected with puromycin for 2 weeks. The establishment of stable cell lines was verified by western blotting.

2.5 Real-time quantitative polymerase chain reaction (real-time qPCR) and PCR array

Real-time qPCR was performed with SYBR Green using a previously described protocol^[23]. Human GAPDH was used as the internal control, and the relative expression of mRNA was calculated using the 2^−△△Ct method. The primer sequences are listed in Supplementary Table 2. A human oxidative stress PCR array (Wcgene® Biotech, China) was used according to the manufacturer’s instructions. The genes differentially expressed are listed in Supplementary Table 3.

2.6 Western blot analysis

Protein was extracted from cells using RIPA buffer, and western blotting was performed as described previously^[23]. The following primary antibodies were used: APOC1 (ab198288, Abcam, UK); CPT1A (15184-1-AP, Proteintech, Chicago, USA); AP-2 (13019-3-AP, Proteintech); EGFR (A11351, ABclonal, China); and phospho-EGFR-Y1068 (AP0301, ABclonal).

2.7 Coimmunoprecipitation (Co-IP) and mass spectrometry (MS)

For co-IP, lysates of 1×10⁷ MKN-28 cells were immunoprecipitated with IP buffer containing IP antibody-coupled agarose beads, and protein-protein complexes were subsequently subjected to western blot and MS assays. MS was performed with a Shanghai bioprofile (China) to detect potential interacting proteins. Here, IgG was used as a negative control. The following primary antibodies were used: APOC1 (ab198288, Abcam) and CPT1A (15184-1-AP, Proteintech).

2.8 Immunohistochemical (IHC) staining

A total of 20 formalin-fixed, paraffin-embedded GC tissues and corresponding adjacent nontumor tissues were purchased from Shanghai Outdo Biotechnology (China). IHC and IHC scoring were performed essentially as described previously^[23]. The following primary antibodies were used: APOC1 (ab198288, Abcam), APOC1 (ab189866, Abcam), and Ki-67 (28074-1-AP, Proteintech).

2.9 Cell Counting Kit-8 (CCK-8)

A 1000-well cell suspension was seeded in a 96-well plate and incubated for 8 h, after which the CCK-8 (Beyotime Biotechnology, China) reagent (1:10) was added, and the mixture was further incubated for 2 h at 37°C. The absorbance at 450 nm was measured using a microplate reader.

2.10 Colony formation assays

A total of 1000 cells were plated in 6-well plates and incubated for 2 weeks at 37°C, with media replenishment every 3–7 days. The cells were then washed with PBS, fixed with 4% paraformaldehyde and stained with 1% crystal violet for 30 minutes. Colonies were counted manually.

2.11 Proliferation assay

The cells were plated in 24-well plates at 2×10⁴ cells/well. After 24 h, cell proliferation was assessed using an EdU Apollo In Vitro Kit (C10310, RiboBio, China) per the manufacturer’s instructions. In brief, the cells were incubated with EdU reagent for 3 h, after which the supernatant was discarded and the cells were fixed in 4% paraformaldehyde for 20 min and washed. The cell membranes were then permeabilized in 0.2% Triton X-100 for 20 min, after which a fluorescent probe was added. A fluorescence microscope (Leica, Germany) was used for detection.

2.12 Transwell migration assay

The cells were digested with trypsin, washed twice and resuspended in serum-free basal medium. The cells in the lower chamber were suspended in RPMI-1640 (0.6 ml) supplemented with 10% FBS, whereas a 2×10⁴ cell suspension with serum-free basal medium was added to the upper chamber. The cells were then incubated at 37°C for 24 h, after which the cells were fixed with 4% paraformaldehyde and stained with 1% crystal violet for 30 minutes. Excess dye and water were removed from the cells, and then different fields were randomly selected and counted under a microscope.

2.13 Wound healing assay

The cells were seeded at a density of 5×10⁵ cells/well in 6-well plates. A wound was created in each well using a P200 tip. Images were taken at 0 h and 48 h using a bright-field microscope at 4× magnification.

2.14 Mitochondrial reactive oxygen species (ROS) measurement

For determination of ROS levels in mitochondria, MKN-28 cells were seeded into 6-well plates with 3 ml of culture medium. 24 h later, the cells were stained with MitoSOX™ Red (5 µM in PBS; S0061S, Beyotime, China) for 20 min. Finally, the nuclei were counterstained with DAPI. The data were collected using a fluorescence microscope (Leica).

2.15 Evaluation of the mitochondrial membrane potential

To evaluate the mitochondrial membrane potential, MKN-28 cells were washed with PBS and stained with JC-1 (5 µg/mL, C2006, Beyotime) at 37°C for 30 min to detect changes in the mitochondrial membrane potential. Then, the cells were washed with PBS three times and observed under a fluorescence microscope (excitation wavelength: 475 nm; green emission: 530 nm; red emission: 590 nm).

2.16 Fatty acid oxidation assay

A fatty acid oxidation kit (ab217602, Abcam) and an extracellular O₂ consumption kit (ab197243, Abcam) were used for FAO analysis according to the manufacturer’s protocol. Briefly, cells were seeded in 96-well plates at a density of 1×10⁵ cells/well. After incubation overnight, the medium was replaced. Every group was divided into two subgroups, and etomoxir (40 µM) was added to block FAO or was not added. After 20 min of incubation, 10 µL of extracellular O₂ consumption reagent and 2 drops of high-sensitivity mineral oil (preheated at 37°C) were added to each well. The fluorescence was measured with a plate reader at 4 min intervals for 60 min at excitation/emission = 380/650 nm. The relative FAO rate was calculated as the (fluorescence intensity (total) − fluorescence intensity (etomoxir)) / time. At least three replicates were included for each measurement.

2.17 Immunofluorescence

For immunofluorescence colocalization, MKN-28 cells were cultured on confocal dishes for 24 h. MKN-28 cells were incubated with APOC1 antibody (ab198288, Abcam) and CPT1A antibody (66039-1-Ig, Proteintech) at 4°C overnight after fixation with 4% paraformaldehyde and permeabilization with 0.2% Triton X-100. Then, the cells were incubated with goat anti-rabbit-conjugated antibodies (A0408, Beyotime) and anti-mouse-conjugated antibodies (A0473, Beyotime) at room temperature for 30 min in the dark. Finally, the nuclei were counterstained with DAPI. Images were taken using a confocal laser scanning microscope (Olympus, Japan).

2.18 Dual-luciferase reporter assay

Primers were designed from the genomic DNA to clone the required promoter fragments and mutant sequences, and then the sequences were inserted into the luciferase reporter plasmid (pGL3-basic). The target and control plasmids were prepared and purified for later use. The reporter gene plasmid and transcription factor expression plasmid were transfected into MKN-28 cells. All experimental groups of cells were transfected with pRL-TK plasmids as an internal control. Luciferase activities were detected with a Dual-Luciferase Reporter Assay System (Promega, E1910, Massachusetts, USA). The relative luciferase intensity is shown as the numerical value of the firefly luciferase activity divided by the Renilla luciferase activity. The sequences of the primers used for the luciferase reporter assay are listed in Supplementary Table 2.

2.19 Animal experiments

All animal procedures were approved by the Experimental Animal Center, Capital Medical University (approval number M-2019–037) and carried out in accordance with the guidelines of the Capital Medical University Animal Care Committee. Four-week-old female nude mice were divided into 4 groups and subcutaneously inoculated with 1×10⁶ stable APOC1-knockdown, APOC1-overexpressing or control MFC cells in the lateral flank. The mice were sacrificed 2 weeks later, and the tumors were harvested.

For the drug validation experiments, four-week-old female nude mice were divided into 5 groups and subcutaneously inoculated with 1×10⁶ stable APOC1-overexpressing or control MFC cells in the lateral flank. Three days after inoculation, gefitinib (10 mg/kg) alone, etomoxir (40 mg/kg) alone or both were injected intraperitoneally every day for one week. The control mice received vehicle. The mice were monitored every two days, tumor growth was measured using a Vernier caliper over time, and the tumors were removed after one week of treatment. The tumor volume was calculated as (L×W²)/2, where length (L) is the longest dimension and width (W) is the shortest dimension. Tumor weight was also measured. The mice were sacrificed 10 days later, and the tumors were harvested.

For the tumor metastasis model, 1×10⁶ stable APOC1-knockdown or APOC1-overexpressing MFC cells were injected into the tail veins of nude mice, the mice were sacrificed 2 weeks later, and the lungs were harvested.

2.20 Statistical analysis

All the experiments were performed at least three times. Statistical analysis was performed using GraphPad Prism 7.02 software. The experimental data are expressed as the mean ± standard deviation (x ± SD).

3.1 The expression of APOC1 is significantly upregulated in GC

To assess the significance of lipid metabolism in the progression of GC, we initially examined the highly differentially expressed genes (DEGs) in GC from TCGA datasets, and the top 25 significant DEGs are shown in Fig. 1A. Survival analysis revealed that 22 of these DEGs were associated with unfavorable outcomes, and three of them (APOC1, SFRP4, ETV4) were associated with Helicobacter pylori infection status in GC (Fig. 1E and Supplementary Fig. 1B). Due to the close relationship between abnormal lipid metabolism and Helicobacter pylori pathogenesis and GC progression^{[24, 25]}, the apolipoprotein C1 (APOC1) was chose for Further examination. The TCGA data showed a marked increase in APOC1 expression in the GC samples when compared with the normal control (Fig. 1B and 1C). Notably, elevated levels of APOC1 were strongly correlated with cancer stage, nodal metastasis status, and poor overall survival (Fig. 1D and 1E). RNA-seq data from the CCLE database revealed the expression pattern in 29 types of GC cell lines (Supplementary Fig. 1A). We then determined APOC1 expression levels in gastric epithelial cells and GC cell lines using real-time qPCR and western blotting. As illustrated in Fig. 1F and 1G, APOC1 expression was increased in various GC cell lines compared with that in gastric epithelial cells. Additionally, we conducted an analysis of APOC1 protein expression in gastric tissue microarrays consisting of 20 pairs of GC tissues and normal gastric tissues, revealing the predominant cytoplasmic localization of APOC1 (Fig. 1H). The IHC score indicated significantly increased APOC1 expression in GC tissues (Fig. 1H). These data suggest a strong association between elevated APOC1 expression and tumor progression in patients with GC.

3.2 Overexpression of APOC1 promotes GC cell proliferation and migration

To explore the biological role of APOC1 in GC, a short hairpin RNA (shRNA) targeting APOC1 was designed and utilized. AGS and MKN-28 cells were transfected with control shRNA or APOC1-shRNA, resulting in effective suppression of APOC1 expression in both cell lines after 48 h of treatment. APOC1 plasmids were transfected into AGS and MKN-28 cells to elevate APOC1 expression levels (Fig. 2A and 2B). CCK-8 assays revealed that APOC1 knockdown significantly inhibited GC cell proliferation, whereas APOC1 overexpression increased cell proliferation (Fig. 2C). Furthermore, the findings from the EdU and colony formation assays revealed that ectopic expression of APOC1 notably enhanced the proliferation of GC cells, while knockdown of APOC1 inhibited cell proliferation (Fig. 2D and 2E).

Given the correlation between APOC1 levels and lymphatic metastasis, we further prompted exploration into the potential impact of APOC1 on cell migration and wound healing (Fig. 1E). Transwell migration assays revealed that APOC1 knockdown significantly inhibited cell migration (Fig. 2F). APOC1 overexpression significantly promoted the migration of AGS and MKN-28 cells (Fig. 2F). Wound healing assays demonstrated that silencing APOC1 suppressed migration (Fig. 2G), whereas overexpressing APOC1 promoted migration (Fig. 2G). These findings suggest that APOC1 plays a crucial role in promoting the proliferation and migration of GC cells.

3.3 Upregulation of Apoc1 promotes the growth and metastasis of GC cells in vivo

To investigate the impact of APOC1 on the proliferation of GCs in vivo, we performed cell-based xenograft experiments. GC MFC cell lines with stable knockdown or overexpression of Apoc1, along with their respective control groups, were established and subcutaneously inoculated into 4-week-old BALB/c14 nude mice. The knockdown of Apoc1 markedly inhibited the growth of tumors, whereas its overexpression accelerated tumor growth in GC (Fig. 3A and 3B). Accordingly, IHC staining of GC tissues further revealed a decrease in Ki-67-positive cells in Apoc1-silenced xenografts but an increase in Ki-67-positive cells in Apoc1-overexpressing xenografts (Fig. 3C).

To determine the impact of Apoc1 on in vivo metastasis, stable MFC cell lines with either APOC1 deficiency or APOC1 overexpression, along with their respective control counterparts, were administered intravenously to nude mice. Analysis revealed a notable reduction in metastatic foci in the lungs of mice injected with Apoc1-deficient GC cells compared with those of control cell-injected mice, whereas Apoc1 overexpression led to an increase in metastatic foci in vivo (Fig. 3D-F). These results suggest that APOC1 is significantly associated with GC growth and metastasis in vivo.

3.4. APOC1 regulates fatty acid catabolism and mitochondrial oxidative stress

To determine the involvement of APOC1 in tumor metabolism within the context of GC, transcriptome and untargeted metabolomic analyses of cells overexpressing APOC1 were performed (Fig. 4A). The metabolomic analysis demonstrated a reduction in long-chain fatty acids as a consequence of APOC1 overexpression, suggesting a potential connection to fatty acid metabolism (Fig. 4B). Subsequent examination of the transcriptomic data confirmed the impact of APOC1 on fatty acid metabolism, revealing the upregulation of genes associated with both lipid synthesis and catabolism (Fig. 4C). These findings collectively shed light on the underlying mechanism of fatty acid catabolism.

Furthermore, analysis of the transcriptome and oxidative stress PCR array data revealed decreases in the expression levels of genes associated with oxidative stress, including prostaglandin-endoperoxide synthase 2 (PTGS2), following APOC1 overexpression (Fig. 4D). Given that the breakdown of long-chain fatty acids predominantly takes place in mitochondria, it was postulated that APOC1, a key player in this process, could impact mitochondrial oxidative stress. To assess the extent of oxidative stress and its impact on mitochondrial function, measurements of mitochondrial ROS levels were conducted alongside a mitochondrial depolarization assay. The knockdown of APOC1 in MKN-28 cells resulted in a significant increase in mitochondrial oxidative stress and a decrease in mitochondrial function. Conversely, upregulation of APOC1 expression conferred protection to mitochondria against oxidative stress (Fig. 4E and 4F). These findings suggest a crucial role for APOC1 in the catabolism of long-chain fatty acids and mitochondrial oxidative stress in GC cells.

3.5 APOC1 promotes GC fatty acid oxidation and progression via interaction with CPT1A

To elucidate the molecular mechanisms underlying the involvement of APOC1 in lipid metabolism regulation, co-IP and mass spectrometry assays were conducted, revealing interactions between APOC1 and 117 proteins (Fig. 5A). Our study focused on specific proteins, including CPT1A, TECR, LDHA, FASN, MACT, PTGES3, and HACD3, and their interactions with 309 lipid metabolism proteins (Fig. 5A). CPT1A, a crucial enzyme in FAO, validated our initial assumption. We investigated the impact of APOC1 on CPT1A expression. Real-time qPCR and western blotting analyses revealed that APOC1 positively modulated the protein level of CPT1A but had no effect on its transcription (Fig. 5B and 5C). Subsequently, co-IP assays confirmed the interaction between APOC1 and CPT1A (Fig. 5D), while immunofluorescence assays confirmed their colocalization (Fig. 5E). The site prediction of interaction between APOC1 and CPT1A showed that there are two potential binding sites, LYS86-GLU45 and THR93-LYS56 (Fig. 5F). These results provide evidence of the interaction between APOC1 and CPT1A.

Next, the western blotting results suggested that the upregulation of CPT1A expression in APOC1-overexpressing MKN-28 cells was counteracted by the introduction of a CPT1A-knockdown plasmid (Fig. 5G). Conversely, the suppression of CPT1A expression in APOC1-knockdown MKN-28 cells was reversed upon CPT1A overexpression (Fig. 5G). These results suggest that CPT1A may serve as a crucial downstream target regulated by APOC1. Following this, we conducted functional rescued experiments and found that CPT1A overexpression mitigated the suppressive effects of APOC1 knockdown in transwell and colony formation assays (Fig. 5H and 5I). Additionally, FAO assays indicated that the stimulatory impact of APOC1 overexpression on FAO could be counteracted by CPT1A knockdown, and vice versa (Fig. 5J). Furthermore, using mitochondrial ROS and mitochondrial membrane potential measurements, we found that silencing APOC1 exacerbated mitochondrial oxidative stress and compromised mitochondrial function in MKN-28 cells, whereas CPT1A overexpression alleviated this impairment (Fig. 5K and 5L). These results suggest that APOC1 facilitates FAO and progression in GC through CPT1A.

3.6 EGF stimulation upregulates APOC1 expression via the transcription factor AP-2α

APOC1 were elevated following EGF stimulation in MKN-28 cells at both transcription and protein levels, indicating that EGF might regulates the transcriptional activity of APOC1 (Fig. 6A). Bioinformatics analysis (PROMO) was then utilized to identify potential transcription factors, and the top 5 transcription factors associated with the EGFR signaling pathway were chosen for the design of siRNAs. Consistently, knockdown of AP-2α resulted in decreased APOC1 expression at both the protein and mRNA levels (Fig. 6B). Furthermore, Jasper analysis of the APOC1 promoter identified nine potential AP-2α binding sites. Truncation experiments evaluated using a dual-luciferase reporter assay demonstrated that AP-2α transcriptionally activated APOC1 in a promoter-dependent manner (Fig. 6C). In addition, mutagenesis experiments revealed that mutation of binding site 1 resulted in the inhibition of the transactivation of APOC1 by AP-2α (Fig. 6D). These findings demonstrate that APOC1 is under the regulatory control of EGF and is directly influenced at the transcriptional level through the binding of AP-2α to its promoter region.

3.7 Targeting the EGF/APOC1/CPT1A axis inhibits GC progression in vivo

To validate the impact of the EGF/APOC1/CPT1A axis on the malignant progression of GC, cell-based xenograft studies were conducted utilizing targeted therapies. Nude mice were randomly assigned to five experimental groups: control, APOC1 overexpression, gefitinib treatment, etomoxir treatment, and combination therapy with gefitinib and etomoxir. Tumor growth was significantly inhibited in all treatment groups, with the combination therapy demonstrating the most pronounced effect (Fig. 6E-G). IHC staining of tumor tissues indicated a reduction in Ki-67-positive cells across the treatment groups (Fig. 6H). These results substantiate the efficacy of targeting the EGF/APOC1/CPT1A axis in mitigating tumor growth in GC.

Lipid metabolic reprogramming is critically involved in the regulation of gastric cancer (GC) development; however, the molecular mechanisms governing this process and their implications for disease progression remain inadequately understood. In this study, we elucidated the role of apolipoprotein C1 (APOC1) in augmenting fatty acid oxidation (FAO) and protecting cells from oxidative stress through its interaction with carnitine palmitoyltransferase 1A (CPT1A). Additionally, the epidermal growth factor (EGF)/APOC1/CPT1A axis was identified as a pivotal therapeutic target. These findings offer novel insights into the interplay between lipid metabolism and stress responses in gastric cancer, highlighting two potential targets for future therapeutic interventions.

APOC1 plays a significant role in the regulation of lipid metabolism in plasma and remains a subject of limited research in the contexts of atherosclerosis and diabetes^[26]. APOC1 functions as an inhibitor of lipoprotein lipase activity and modulates lipid metabolism by binding to lipoprotein lipase to restrict its breakdown of triacylglycerol. APOC1 influences lipoprotein metabolism, particularly the interaction between high-density lipoprotein and low-density lipoprotein. Additionally, APOC1 may participate in antioxidant processes and mitigate the detrimental effects of oxidative stress on the vascular endothelium. Furthermore, APOC1 exhibits phospholipase inhibitory activity, which could be significant in various physiological processes. The pathophysiological functions of APOC1 in various tumors have been a subject of research interest, particularly in establishing the associations between macrophage polarization and signaling pathways^[18,21]. Despite this focus, the role of APOC1 in gastric cancer remains poorly understood, with limited knowledge on its involvement in the reprogramming of lipid metabolism in cancer. This study represents the first attempt to elucidate the molecular mechanisms underlying the role of APOC1 in lipid metabolism reprogramming in any cancer type. The untargeted metabolomic analysis conducted in this study identified that dysregulated APOC1 levels impacted various lipid metabolites, such as fatty acyls, sterol lipids, sphingolipids and glycerophospholipids, in gastric cancer. Furthermore, protein‒protein interactions between APOC1 and several enzymes related to lipid metabolism were confirmed. Moreover, transcriptomic findings indicated that upregulation of APOC1 resulted in increased expression levels of genes associated with lipid synthesis and breakdown. Collectively, these results suggest that APOC1 plays roles in diverse aspects of lipid metabolic reprogramming, revealing that APOC1, a previously unappreciated but important target, influences lipid metabolic reprogramming in GC.

Fatty acids play a crucial role in the energy metabolism of cancer cells, with FAO in mitochondria yielding significantly more ATP than does glucose oxidation^[27]. The enzyme CPT1A, which is responsible for the rate-limiting step of FAO, has been recognized as a key factor in tumor development and a promising target for immunotherapy. The inhibition of CPT1A has been shown to enhance the antitumor immune response and induce ferroptosis in lung cancer^[28]. Moreover, a novel covalent inhibitor of CPT1A has demonstrated therapeutic potential in colorectal cancer through the modulation of FAO^[9]. In this study, we revealed that the interaction between CPT1A and APOC1 accelerates FAO and promotes GC progression. APOC1 positively regulates the expression of CPT1A, and depletion of CPT1A restores APOC1-mediated FAO and biological functions in GC. These results underscore the significance of CPT1A and FAO. Therefore, molecules that enhance FAO, such as APOC1 and CPT1A, should be further explored as potential therapeutic targets in oncology, particularly for tumors with lipid-rich microenvironments such as those found in the stomach.

The EGFR signaling pathway participates in the regulation of tumor cell growth, proliferation, and migration^[29]. Despite that, EGFR-targeted therapies did not improve survival rates in patients with GC during a phase III trial^[30,31]. However, recent studies have uncovered a connection between EGFR-targeted therapies and lipid metabolism, providing valuable insights into this area^[32,33,34]. Our research indicates that the activation of EGFR signaling leads to increased expression of APOC1 through the transcription factor AP-2α. Moreover, findings from in vivo experiments investigating the EGF/APOC1/CPT1A axis mechanism indicate that the synergistic effect of combining gefitinib, a tyrosine kinase inhibitor of EGFR, with etomoxir, a CPT1A inhibitor, is superior to monotherapy in treating GC. The discovery of EGF-associated lipid metabolic pathways in GC underscores the metabolic changes associated with cancer progression and reveals potential therapeutic targets.

In summary, our study identified the first link between APOC1 and CPT1A, which is involved in FAO in GC. Importantly, the critical roles of APOC1 and the EGF/APOC1/CPT1A axis indicate that they are major players in GC growth and progression, warranting additional research into their potential as therapeutic targets.

gastric cancer

APOC1

apolipoprotein C-I

CPT1A

carnitine palmitoyltransferase 1A

EGF

epidermal growth factor

EGFR

epidermal growth factor receptor

IHC

immunohistochemistry

PTGS2

prostaglandin-endoperoxide synthase 2

Co-IP

coimmunoprecipitation

FAO

fatty acid oxidation.

Acknowledgments: This study was supported by combined grants from the National Natural Science Foundation of China (No.82203700, No.82070575), National Key Research and Development Program of China (2023YFC2507400), Capital Clinical Diagnosis and Treatment Technology Research and Application Project of China (Z221100007422018), National Postdoctoral Program for Innovative Talents (BX20220216), Postdoctoral Research Foundation of China (2022M720100) and Research Foundation of Beijing Friendship Hospital (YYQCJH20223, YYZZ202216).

Author Contributions: XJ, SYY and JYS performed the experiments. XJ, GYC and SYY assisted in immunohistochemistry staining and animal experiments. XJ and JYS gave assistance in conceiving experiments and analyzing data. STZ, STZ,ZZ, ANZ, GPZ, ZZ and SFL gave assistance in collecting tissues samples. LP, FD and XJ designed studies and wrote the paper.

Conflicts of interest statement:

This manuscript is our original and unpublished work and has not been submitted to any other journals for review. All authors have read and approved the manuscript for submission to your journal. The authors declare no competing financial interests.

Ethics statement: All animal procedures were approved by the Experimental Animal Center, Capital Medical University (approval number M-2019–037) and carried out in accordance with the guidelines of the Capital Medical University Animal Care Committee.

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EGF/APOC1/CPT1A Axis: A Novel Pathway in Gastric Cancer Metabolism and Therapeutic Targeting

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Abstract

Figures

1. Introduction

2. Methods and materials

2.1 Data and resources

2.2 Cell lines and cell culture

2.3 Cell line transfection

2.4 Viral transduction

2.5 Real-time quantitative polymerase chain reaction (real-time qPCR) and PCR array

2.6 Western blot analysis

2.7 Coimmunoprecipitation (Co-IP) and mass spectrometry (MS)

2.8 Immunohistochemical (IHC) staining

2.9 Cell Counting Kit-8 (CCK-8)

2.10 Colony formation assays

2.11 Proliferation assay

2.12 Transwell migration assay

2.13 Wound healing assay

2.14 Mitochondrial reactive oxygen species (ROS) measurement

2.15 Evaluation of the mitochondrial membrane potential

2.16 Fatty acid oxidation assay

2.17 Immunofluorescence

2.18 Dual-luciferase reporter assay

2.19 Animal experiments

2.20 Statistical analysis

3. Results

3.1 The expression of APOC1 is significantly upregulated in GC

3.2 Overexpression of APOC1 promotes GC cell proliferation and migration

3.3 Upregulation of Apoc1 promotes the growth and metastasis of GC cells in vivo

3.4. APOC1 regulates fatty acid catabolism and mitochondrial oxidative stress

3.5 APOC1 promotes GC fatty acid oxidation and progression via interaction with CPT1A

3.6 EGF stimulation upregulates APOC1 expression via the transcription factor AP-2α

3.7 Targeting the EGF/APOC1/CPT1A axis inhibits GC progression in vivo

4. Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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