ATP citrate lyase (ACLY) promotes the occurrence of gastric cancer through regulating lipid metabolism

Introduction

Gastric cancer (GC) is the fifth most common cancer and the third most common cause of cancer death worldwide, with high mortality rates and medical burdens (1). The incidence and mortality of GC in China rank third in the world (2). Metabolic reprogramming, in which tumor cells undergo metabolic changes to meet energy demands, is a hallmark of cancer (3). The metabolic signatures of cancer cells include alterations in glycolysis, mitochondrial respiration, fatty acid/lipid and amino acid metabolism (4). Among these alterations, alterations in lipid metabolism are the most prominent metabolic alterations in cancer (5).

Biological lipids, which originate from the condensation process of ketoacyl subunits or isoprene subunits, belong to a class of small molecular compounds that are sparingly soluble in water but highly soluble in organic solvents (6). The biological functions of lipids in the body include providing energy storage, serving as signaling molecules, and serving as structural components of cell membranes; however, excessive lipids promote tumorigenesis and increase the transfer capacity of tumor cells (5). The major changes in lipid metabolism in cancer cells are the high synthesis rate of fatty acids and cholesterol and the reduction in fatty acid degradation, thereby supporting the proliferation and metastasis of cancer cells. Unlike normal cells, tumor cells primarily synthesize fatty acids de novo to meet their rapid growth and proliferation needs (7). Acetyl-CoA is an important precursor for the synthesis of fatty acids. It is produced mainly by the cleavage of citrate by ATP citrate lyase (ACLY) in the tricarboxylic acid cycle (8), which is further transformed into fatty acids by acetyl-CoA carboxylase and fatty acid synthase to provide synthetic materials for tumor cells (9,10). Unlike mitochondrial enzymes such as KCAC, which operate across the mitochondrial membrane, ACLY functions primarily in the cytosol, utilizing citrate exported from mitochondria to generate acetyl-CoA required for anabolic processes. In addition, the levels of several key enzymes involved in de novo fatty acid synthesis, such as acetyl-CoA carboxylase and fatty acid synthase, are abnormally elevated in many cancers (7). ACLY is a key enzyme in the process of lipid metabolism in tumors. Abnormal expression and activation of ACLY have been widely reported in a variety of cancers (9). Therefore, it is essential to study the effects of ACLY on GC through lipid metabolism. We present this article in accordance with the MDAR and TRIPOD reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-814/rc).

Methods

Data sources and processing

The literature data used in this study were obtained from PubMed https://pubmed.ncbi.nlm.nih.gov/). “Lipid AND gastric cancer” was searched in PubMed on November 4, 2024, the species was humans, the language was English, and the time span was nearly 5years. A total of 557 articles were retrieved and exported in PubMed. Expression data were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov) to download and organize TCGA-stomach adenocarcinoma (STAD; gastric adenocarcinoma) project STAR process RNAseq data and extract the transcripts per million (TPM) format of the data. A total of 407 samples were included in the analysis after excluding normal cases and those lacking clinical information. For statistical analysis, either the Wilcoxon rank sum test or the Kruskal-Wallis test was applied, depending on the specific research design and data characteristics. This study used R software (4.2.1) analysis and visualization (11).

Bibliometric analysis methods

VOSviewer is a free Java-based software developed by the Science and Technology Research Center of Leiden University in The Netherlands in 2009. It has a strong drawing ability and is suitable for processing big data (12). VOSviewer is used to analyze keywords. Bibliometrix is a literature statistical tool that relies on R software. This tool is used to analyze the annual growth trends.

Correlations between ACLY expression and clinicopathological features in GC patients

The expression levels of ACLY in GC and normal tissues were compared. The TCGA database was used to analyze the correlation between different clinical characteristics of patients with STAD and ACLY expression levels. The clinical characteristics investigated included age, sex, race, Helicobacter pylori infection status, tumor (T) stage, node (N) stage, metastasis (M) stage, pathological stage, and overall survival (OS).

Cells culture

GES-1 cells (normal gastric epithelial cell line) were bought from Wuhan Sunncell Biotechnology Co., Ltd. (SNL-304, Wuhan, China), HGC-27 cells (human gastric cancer cell line) were bought from Wuhan Pricella Biotechnology Co., Ltd. (CL-0107, Wuhan, China), MKN-45 cells were bought from Seven Innovation Biological Technology Co., Ltd. (AC121, Beijing, China), AGS cells were bought from Applied Biological Materials (abm) Inc. (T9923, Zhenjiang, China). HGC-27 cells and GES-1 cells were cultured in RPMI-1640 medium (Gibco, Beijing, China) with 10% fetal bovine serum (FBS; Gibco, Beijing, China). All the cells were cultured in a 37 ℃, 5% CO₂ incubator and passaged every 2–3 days.

Western blot

Protein inside cells or exosomes were collected in RIPA lysis buffer (R0010, Solarbio, Beijing, China). Protein concentration was determined by ultraviolet spectrophotometry at 280 nm, based on the absorbance of aromatic amino acids. After separation by 8% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, the proteins were transferred to polyvinylidene fluoride (PVDF) membranes. The membrane was blocked with rapid blocking solution at room temperature for 30 minutes and incubated with primary antibodies overnight at 4 °C. The primary antibodies were diluted as follows: ACLY (1:1,400; ET1609-37; RRID: AB_3069847, HUABIO, Hangzhou, China), β-actin (1:5,000; 20536-1-AP; RRID: AB_10700003, Proteintech, Wuhan, China). The membrane was incubated with the HRP-conjugated anti-rabbit secondary antibody (1:10,000; SA0001-2; RRID: AB_2722564, Proteintech) for 30 minutes at room temperature. The protein bands were visualized using the ECL kit (BL520B; Beijing, China) and analyzed using ImageJ software.

Prognosis of GC patients and its correlation with ACLY

All independent clinicopathological prognostic factors were selected from the Cox regression analysis to construct a nomogram to assess the probability of OS at 1, 3, and 5 years in patients with STAD. The accuracy of the nomogram was verified by comparing the predicted probability of the line plot with the actual probability observed through the calibration curve. Overlapping reference lines indicate that the model is accurate. Receiver operating characteristic (ROC) curves, time-dependent ROC curves, and Kaplan-Meier (K-M) survival curves of GC patient samples in the TCGA database were plotted. Pearson correlation analysis was used to screen the genes coexpressed with ACLY in GC.

Correlation and gene set enrichment analysis

For the TCGA data collected to analyze the correlation between ACLY and GC, the screening conditions were |log fold change (FC)| ≥1.5 and P<0.05. Genome Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the significantly differentially expressed genes were performed via R language.

Correlations between lipid metabolism and ACLY in GC

ACLY, lipid metabolism, and GC-related gene data were derived from GeneCards (https://www.genecards.org/). Genes with GC-related gene relevance scores greater than 50, ACLY-related gene relevance scores greater than 1, and lipid metabolism-related gene relevance scores greater than 30 were selected for analysis. The overlapping genes among these three categories were identified and further analyzed.

Experimental verification: determination of fatty acid content in GC cells after 2-furoic acid intervention

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to analyze the non-targeted lipidomics of HGC-27 cells treated with 20 µM 2-furoic acid for 48 hours furoate and the substances in HGC-27 cells, and visualization of the heatmap was carried out without data conversion, normalization of pairs, Euclidean distance clustering, or column clustering. Chromatographic analysis was carried out with a Thermo Scientific U3000 fast liquid chromatograph and reversed-phase chromatography column. Mass spectrometry was performed with the use of a quadrupole orbital ion-trap mass spectrometer (Q Exactive^TM) equipped with a thermoelectrospray ion source. Xcalibur 2.2 SP1.48 software was used to control the liquid-mass system and collect the data. CAS for 2-furoic acid: 88-14-2 (Sigma-Aldrich, Beijing, China).

Statistical analysis

All statistical analyses were performed using R software (version 4.2.1). Continuous variables between two groups were compared using the Wilcoxon rank sum test, and comparisons among multiple groups were conducted with the Kruskal-Wallis test. Survival analyses were carried out using the Kaplan-Meier method with log-rank tests and Cox proportional hazards regression models to identify independent prognostic factors. The predictive performance of the prognostic model was evaluated by ROC curves, time-dependent ROC curves, and calibration plots. Pearson correlation analysis was used to assess associations between gene expression levels. A two-sided P value <0.05 was considered statistically significant.

Ethical statement

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Results

Bibliometric results

Annual growth trend: as shown in Figure 1, the number of relevant references increased annually (Figure 1).

Figure 1 Annual growth trend graph.

Keywords co-occurrence result analysis: there were 2813 keywords, and a total of 55 keywords appeared at least 15 times. Unrelated terms such as “humans”, “aimals”, “male” and “female” were removed. The most common type of cancer was GC (557 times), followed by cell lines (373 times), tumors (373 times), and cell proliferation (77 times) (Figure 2).

Figure 2 Keyword co-occurrence analysis.

The thicker the connection between the nodes of the keyword co-occurrence network graph, the more frequently two keywords appear together. Keywords are divided into 4 clusters. The first group was the largest cluster with 17 co-occurrence terms, which were mainly composed of GC, lipid metabolism, biomarkers, prognosis, etc. The second group had 15 co-occurrence terms, including cell line, tumor, cell proliferation, and apoptosis. The third group had 10 co-occurrence terms, which were mainly composed of cancer, ferroptosis, antioxidants, and Helicobacter pylori. The fourth group had four co-occurrence terms: adenocarcinoma, fatty acids, immunotherapy, and the tumor microenvironment.

Based on the bibliometric analysis, lipid metabolism has emerged as a key research focus in GC. Among the enzymes involved, ACLY, a central regulator of de novo lipid synthesis, has gained increasing attention due to its potential role in tumor progression. Therefore, we next examined the expression and clinical significance of ACLY in GC.

Correlations between the expression level of ACLY in GC and the clinicopathological features of GC patients

To investigate the expression of ACLY in GC and its clinical relevance, RNA-seq data and corresponding clinical information were obtained from TCGA database. ACLY expression was compared between tumor and normal tissues, and correlations with clinical parameters such as tumor stage were analyzed. The results revealed that the expression of ACLY was significantly higher in GC tissues than in normal tissues (*, P<0.001, Figure 3A). Further subgroup analyses showed that ACLY expression did not differ significantly by gender (Figure 3B) or age (Figure 3C). However, higher ACLY expression was associated with race (*, P<0.05, Figure 3D), pathological T stage (*, P<0.05, Figure 3E), N stage (*, P<0.05, Figure 3F), and M stage (*, P<0.05, Figure 3G). In addition, ACLY expression was related to Helicobacter pylori infection (*, P<0.05, Figure 3H), pathological stage (*, P<0.05, Figure 3I), and OS events (*, P<0.05, Figure 3J). These data suggest that ACLY is upregulated in gastric adenocarcinoma and correlated with multiple clinical characteristics.

Figure 3 Expression levels of ACLY in the tumor tissues of patients with different clinical features. (A) Expression levels of ACLY in normal and tumor tissues from the TCGA database. (B-J) ACLY expression levels in tumor tissues of patients with different clinical features in the TCGA database: (B) gender, (C) age, (D) race, (E) T stage, (F) N stage, (G) M stage, (H) H. pylori infection, (I) pathological stage, and (J) OS event. *, P<0.05; ***, P<0.001, respectively. ACLY, ATP citrate lyase; H. pylori, Helicobacter pylori; M, metastasis; N, node; OS, overall survival; T, tumor; TCGA, The Cancer Genome Atlas; TPM, transcripts per million.

ACLY protein expression in GC cells

This study examines the protein expression levels of ACLY in HGC-27 cells and GES-1 cells. The expression of ACLY was significantly higher in HGC-27 cells compared to GES-1 cells (Figure 4).

Figure 4 ACLY protein expression in GES-1, HGC-27, AGS and MKN-45 cells detected by western blot. *, P<0.05; **, P<0.01. ACLY, ATP citrate lyase.

High independent risk factors and the prognosis of gastric adenocarcinoma

Univariate and multivariate Cox risk regression analyses were used to determine the independent prognostic factors of gastric adenocarcinoma in terms of age, gender, T stage, N stage and M stage. Age [hazard ratio (HR) =0.551; 95% confidence interval (CI): 0.383–0.793; P=0.001], N stage (HR =1.691; 95% CI: 1.086–2.634; P=0.02), M stage (HR =2.386; 95% CI: 1.310–4.344; P=0.004) were independent prognostic factors (Table 1), and the forest plot was drawn according to the results (Figure 5A). These independent prognostic factors were used to construct a prognostic nomogram and a prognostic calibration curve (Figure 5B,5C). The prognostic nomogram revealed the relationships between three clinicopathological variables (age, N stage, and M stage) and the 1-, 3-, and 5-year OS probabilities. The calibration curve was consistent with the results and predictions of the prognostic nomogram.

Figure 5 Construction of prognostic line graphs. (A) Forest plots of independent risk factors. (B) Nomogram for predicting OS at 1, 3, and 5 years. (C) Calibration curves for 1, 3, and 5 years. CI, confidence interval; CR, complete response; HR, hazard ratio; M, metastasis; N, node; OS, overall survival; PD, progressive disease; PR, partial response; SD, stable disease; T, tumor.

Role of ACLY in the prognosis of GC

To explore the prognostic value of ACLY in GC, we analyzed OS data of GC patients from the TCGA database, stratified by ACLY expression levels. ROC curves revealed that the area under the curve (AUC) =0.882, indicating that the accuracy of ACLY in predicting outcomes for the diagnosis of GC is relatively high, and the OS 1-, 3-, and 5-year time-dependent AUCs are 0.502, 0.525 and 0.598, respectively (Figure 6A,6B). The K-M survival curve revealed that the survival rate of patients with high ACLY expression was lower than that of patients with low ACLY expression, but not statistically significant, P=0.08 (Figure 6C). These data indicate that ACLY is upregulated in GC and is associated with poor prognosis. Candidate genes associated with ACLY and gastric adenocarcinoma were initially retrieved from the GeneCards database based on relevance scores. Subsequently, Pearson’s correlation analysis was performed to assess the relationship between these genes and ACLY expression levels. The 10 genes with the highest correlation coefficients—KLHL11, LIG3, EFTUD2, DNAJC7, PSME3, DHX8, RFWD3, URB2, SNRNP200, and DHX9—were identified for further analysis, and their expression profiles were visualized using a heatmap. The results revealed that the ACLY expression group had relatively high convergence (Figure 6D). Although no statistically significant difference was observed, patients with high ACLY expression showed a hazard ratio greater than 1, suggesting a potential association with poorer prognosis.

Figure 6 Prognostic relationship between ACLY and gastric cancer. (A) The ROC curve of ACLY. (B) Time-dependent ROC curve of ACLY. (C) K-M survival curves of patients with different ACLY expression levels. (D) Coexpression heatmap of ACLY and the top 10 genes related to gastric cancer. ACLY, ATP citrate lyase; AUC, area under the curve; CI, confidence interval; FPR, false positive rate; HR, hazard ratio; ROC, receiver operating characteristic; TPM, transcripts per million; TPR, true positive rate.

Enrichment analysis of differentially expressed genes

Differential expression analysis of high and low expression of ACLY in STAD was performed (Figure 7A). A total of 404 genes from the GO and KEGG pathways were studied via R language (Figure 7B). The ACLY-coexpressed genes were involved in 151 biological processes, 101 cellular components, 61 molecular functions and 46 KEGG pathways. Bubble plots show the top 3 categories of information, namely, biological process, cellular component, molecular function and KEGG. GO term annotation revealed that these genes were involved mainly in epithelial cell differentiation, keratinocyte differentiation and keratinization. Keratinocyte envelope, intermediate filament protein cytoskeleton, keratin filament, endopeptidase activity, serine endopeptidase inhibitors, and structural components of the skin epidermis. KEGG pathway analysis revealed that these genes were involved mainly in protein digestion and absorption, pancreatic secretion, fat digestion and absorption signaling pathways.

Figure 7 Functional clustering of ACLY-related genes. (A) Volcano plot showing DEGs between 2-furoic acid-treated HGC-27 cells and untreated control cells. (B) GO and KEGG enrichment analysis of the identified DEGs. ACLY, ATP citrate lyase; BP, biological process; CC, cellular component; DEGs, differentially expressed genes; GO, Genome Ontology; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Venn diagram and gene correlation results

There were 354 differentially expressed genes between GC and ACLY-related genes, 292 differentially expressed genes between GC and lipid metabolism, and 290 differentially expressed genes between ACLY-related genes and lipid metabolism. There were 3 differentially expressed genes in common among the three groups, namely, TP53, AKT1, and PPARG (Figure 8A). The correlation coefficient was calculated by Spearman’s coefficient: ACLY exhibits a weak positive correlation with TP53 (Figure 8B), a strong positive correlation with AKT1 (Figure 8C), and an extremely weak positive correlation with PPARG (Figure 8D). Compared with those in normal tissues, the levels of TP53 (P<0.001), AKT1 (P<0.01) and PPARG (P<0.001) were significantly increased in GC tissues (Figure 8E-8G).

Figure 8 Relationship between ACLY and lipid metabolism-related genes in gastric cancer. (A) Venn diagram of ACLY, lipid metabolism and gastric cancer. (B) Correlation between TP53 and ACLY. (C) Correlation between AKT1 and ACLY. (D) Correlation between PPARG and ACLY. (E) Expression of TP53 in normal and gastric cancer tissues. (F) Expression of PPARG in normal and gastric cancer tissues. (G) Expression of AKT1 in normal and gastric cancer tissues. **, P<0.01; ***, P<0.001, respectively. ACLY, ATP citrate lyase; TPM, transcripts per million.

Changes in the cellular fatty acid content after 2-furoic acid intervention

The fatty acid content decreased after 2-furoic acid intervention, as shown in Figure 9. Group 1 was the fatty acid extracted from HGC-27 cells after 2-furoic acid intervention, and Group 2 was the fatty acid extracted from HGC-27 cells. Treatment with 2-furoic acid significantly reduced the fatty acid content in GC cells, indicating that ACLY inhibition affects lipid biosynthesis and may contribute to the anti-cancer effect.

Figure 9 Fatty acid content in gastric cancer cells treated with 2-furoic acid. Group 1 was the fatty acid extracted from HGC-27 cells after 2-furoic acid intervention. Group 2 was the fatty acid extracted from HGC-27 cells.

This study revealed that ACLY is highly expressed in GC and is associated with tumor progression. Bioinformatics analysis highlighted its link to lipid metabolism and cancer-related pathways. Functional experiments confirmed that ACLY inhibition by 2-furoic acid suppresses lipid synthesis in GC cells. These findings suggest that ACLY plays a key role in tumor metabolism and may be a promising therapeutic target.

Discussion

GC is one of the most common malignant tumors in the world and has high morbidity and mortality rates (13). The incidence and mortality of GC rank third among all cancer types in China, and new GC cases and GC-related deaths account for approximately 44.0% and 48.6% in the world, respectively. Evident regional differences exist in GC incidence and mortality, and the annual number of new cases and deaths is increasing rapidly in some developing regions (14). In tumor cells, reprogramming of lipid metabolism regulates tumor progression and patient prognosis by changing cellular energy metabolism, cell membrane and signal molecule synthesis, and gene expression (15). ACLY is a key enzyme in the process of lipid metabolism in tumors. Studies have shown that it is related to the advanced stage and prognosis of gastric adenocarcinoma (9). Therefore, studying the effects of ACLY on GC through lipid metabolism is highly important.

Bibliometrics is a branch of informatics in which quantitative and qualitative analyses of literature are conducted by studying literature systems and bibliometric characteristics. Through bibliometric analysis, researchers can deeply understand the current situation and development trends of the research field and provide directions and ideas for future research (16). This study uses the bibliometric method, which is based on the PubMed database, and uses VOSviewer and Bibliometrix to analyze the annual growth trends and keywords of relevant literature visually. In the past 5 years, a total of 567 articles have been published in this field, and the number of publications has increased, indicating that the study of lipid metabolism in GC has gradually increased in number and is playing an increasingly important role in scientific research. In this study, keyword co-occurrence analysis was used to identify the main directions and hotspots in this field and to reveal the development and changes in its content. The word with the highest frequency in this field was “gastric cancer”, which appeared 362 times, which is consistent with the theme. Other keywords in the top 5 terms were “cell line”, “tumor”, “cell proliferation”, “lipid metabolism”, “gene expression regulation”, and “neoplastic”. Lipids are essential for maintaining cytoskeletal structure, storage and energy production and participate in the transduction of many important signaling pathways (17,18). Lipid metabolic reprogramming is one of the hallmarks of cancer progression (19). The analysis of keywords in this study revealed that lipid metabolism has an impact on the prognosis of patients with GC, which is a hot topic for researchers. Lipid metabolism is one of the three main types of energy metabolism in cells. The growth of cancer cells depends on de novo lipid synthesis and exogenous fatty acid uptake; 90% of the fatty acids required by tumor cells are derived from de novo lipid synthesis, and ACLY is of great research value as a key enzyme in de novo lipid synthesis (20).

Studies have indicated that the expression level of ACLY in cancer tissues is of great reference value for tumor clinical staging. In hepatocellular carcinoma, Sun et al. (21) demonstrated through data mining analysis that ACLY was significantly upregulated in tumor tissues compared to adjacent normal tissues, and its high expression was positively correlated with advanced tumor stage and vascular invasion. These findings suggest that ACLY may serve as a potential biomarker for diagnosis and disease progression monitoring in liver cancer. In this study, we used data from the TCGA database and reported that the expression of ACLY was increased in GC tissues compared with normal tissues and was related to race, T stage, Helicobacter pylori infection and OS events, indicating that ACLY may be related to the growth of GC cells. This study examined the protein expression levels of ACLY in HGC-27 cells and GES-1 cells. As shown in Figure 4, the expression of ACLY was significantly higher in HGC-27 cells compared to GES-1 cells. This result supports the bioinformatics analysis findings that ACLY is upregulated in GC tissues and is associated with tumor progression. Univariate and multivariate Cox regression analyses, prognostic nomograms and prognostic calibration curves revealed that age, N stage and M stage were independent prognostic factors for GC. In this study, ROC curves, time-dependent ROC curves and K-M survival curves were used to analyze the prognostic role of ACLY in GC. Although no significant difference in OS was observed between the high and low ACLY expression groups (P>0.05), the hazard ratio exceeded 1, as shown in Figure 6, suggesting a potential trend toward poorer prognosis with high ACLY expression. The results showed that ACLY was a reliable prognostic indicator for GC. To further investigate the biological function of ACLY in GC, we performed functional enrichment of ACLY-related genes. These results suggest that ACLY may have an effect on cell differentiation and structure, signal transduction, and enzyme activity in GC.

For further analysis of which genes in GC may have synergistic effects with ACLY, the genes related to ACLY were analyzed, and the top 10 genes related to ACLY were used to construct a co-expression heatmap. The results revealed that these genes were more similar to those in the ACLY high-expression group. A Venn diagram was used to analyze the genes common to GC, ACLY and lipid metabolism, and it was found that TP53, AKT1 and PPARG were related to ACLY and highly expressed in GC tissues. TP53 is a major participant in cancer formation and an important tumor suppressor gene (22). AKT1 is a serine/threonine protein kinase that plays a central role in regulating cell survival, growth, and metabolism. Aberrant activation of AKT1 is common in multiple cancer types and has been strongly associated with chemotherapy resistance (23). Mechanistically, sustained AKT1 signaling promotes cell survival by inhibiting pro-apoptotic pathways and enhancing DNA repair, thereby reducing the efficacy of cytotoxic agents. This chemoresistance mechanism underscores the therapeutic potential of targeting the AKT1 pathway in tumors with dysregulated AKT1 activity. PPARG is an important regulator involved in lipid metabolism. It can directly regulate adipocyte differentiation and the expression of lipid metabolism-related genes and promote lipid uptake and lipogenesis by increasing insulin sensitivity and adiponectin release (24-26). The associations of these three genes with ACLY suggest that these three genes may affect the development of GC by regulating the expression or activity of ACLY.

In summary, ACLY can affect the process of GC development, which is related to lipid metabolism. To verify this process, this study used 2-furoic acid to intervene in GC cells. 2-furoic acid, also known as furanic acid, has been shown to specifically inhibit the activity of ACLY and a lipid-lowering effect (27). To date, there is no published evidence indicating that 2-furoic acid exerts anti-cancer activity or directly affects cancer cell proliferation. In this study, 2-furoic acid was used exclusively as an experimental agent to inhibit ACLY, in order to explore the functional impact of ACLY on lipid metabolism in GC cells. Furthermore, ACLY generates acetyl-CoA from citrate, and acetyl-CoA is converted into malonyl-CoA by acetyl-CoA carboxylase (ACC), which participates in fatty acid synthesis. 5-tetradecyloxy-2-furoic acid (TOFA) is a conformational inhibitor of acetyl-CoA carboxylase that can effectively inhibit ACC and reduce lipid accumulation in cancer cells (28). Recent studies have demonstrated that TOFA, as an acetyl-CoA carboxylase inhibitor, induces caspase-3 activation and apoptosis (29). The experiment used liquid chromatography mass spectrometry to analyze the changes in fatty acid content in GC cells after 2-furoic acid intervention inhibited ACLY activity. The results revealed that the fatty acid content decreased after 2-furoic acid intervention, indicating that ACLY is crucial for synthesizing fatty acids in GC cells. Therefore, TOFA affects the related metabolic processes induced by ACLY and inhibits cancer cells, suggesting that ACLY may be a potential target for cancer treatment. Therefore, by regulating the activity of ACLY, fatty acid synthesis and other related metabolic processes in cancer cells can be affected, providing a new approach for cancer treatment.

Conclusions

This study demonstrates the potential of ACLY as a prognostic biomarker for GC and the critical role of ACLY in regulating lipid metabolism through bibliometric analysis, bioinformatics investigations, and experimental validation. ACLY may co-regulate lipid metabolism with Lipid metabolism-related genes in GC, thereby influencing the initiation and progression of GC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR and TRIPOD reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-814/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-814/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-814/prf

Funding: This work was supported by the National Natural Science Foundation of China (No. 82405236 to P.C.) and Hebei Province University Science and Technology Research Project (No. BJK2024183 to J.S.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-814/coif). J.S. reports the funding from Hebei Province University Science and Technology Research Project (No. BJK2024183). P.C. reports the funding from the National Natural Science Foundation of China (No. 82405236). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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