Crontonylation regulatory factor DPF2 promotes the occurrence of hepatocellular carcinoma by regulating glycosphingolipid metabolism

Crontonylation regulatory factor DPF2 promotes the occurrence of hepatocellular carcinoma by regulating glycosphingolipid metabolism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Crontonylation regulatory factor DPF2 promotes the occurrence of hepatocellular carcinoma by regulating glycosphingolipid metabolism Lintao Dong, Jingping Hu, Fang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6627218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related death worldwide, characterized by poor prognosis and limited therapeutic options. Emerging evidence indicates that both lipid metabolism and post-translational modifications, particularly lysine crotonylation (Kcr), play critical roles in tumor progression. Methods In this study, we employed Mendelian randomization, generalized summary-data-based Mendelian randomization, and transcriptomic analyses to explore the causal roles of Kcr regulatory genes in HCC. Results Among 16 Kcr regulators, DPF2 was identified as significantly associated with increased HCC risk. Mediation analysis further revealed that DPF2 may promote HCC development by downregulating glycosyl-N-behenoyl-sphingadienine (d18:2/22:0), a glycosphingolipid with tumor-suppressive properties, accounting for 13.5% of its effect.Functional enrichment and gene set variation analysis demonstrated that DPF2 expression was linked to lipid metabolic processes, histone modification pathways, and inflammatory responses. Although some associations did not meet strict FDR correction thresholds, the findings were consistent with transcriptomic validation and previous literature, indicating potential biological relevance. Conclusion Overall, this study provides novel evidence supporting the role of DPF2 in the lipid metabolism–Kcr axis of HCC and suggests its value as a potential biomarker or therapeutic target. Further in vivo and in vitro experiments are needed to elucidate the underlying mechanisms. Biological sciences/Cancer/Gastrointestinal cancer Biological sciences/Molecular biology/Post translational modifications Biological sciences/Molecular biology/Transcriptomics DPF2 Crotoylation Hepatocellular carcinoma Glycosphingolipid Multi-omics integrative analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Hepatocellular carcinoma (HCC) accounts for approximately 75–85% of primary liver cancers. According to the latest 2025 Cancer Statistics from the United States, an estimated 42,240 new cases of HCC and intrahepatic cholangiocarcinoma are expected in the country by year's end. Despite ongoing improvements in liver cancer treatment strategies, significant challenges remain. For example, the prognosis of HCC is still poor, with a 5-year survival rate of only 22%. Therefore, there is an urgent need to further explore its molecular mechanism in depth to seek more effective treatment methods 1 . Lipids, as key components of cell membranes, signaling pathways, and energy storage, play a vital role in maintaining cellular functions and physiological balance through their metabolic homeostasis. In recent years, the influence of abnormal lipid metabolism on the occurrence and development of HCC has been emphasized by researchers 2 . For instance, the imbalance of lipid metabolism creates favorable conditions for the occurrence and development of tumor cells, promoting their rapid proliferation, invasion and metastasis. Key factors such as SREBP-1, SCD and FASN are upregulated in HCC, causing the imbalance of lipid metabolism and thereby driving the malignant progression of the tumor 3 – 6 . Therefore, targeting lipid metabolism-related pathways is expected to become a new strategy for the treatment of HCC. In addition to abnormal metabolic levels, post-translational modifications (PTMs) also play a critical role in HCC. PTMs are important epigenetic mechanisms that can affect cell development, differentiation and the occurrence of diseases by regulating protein function, stability and localization. Crotonylation (Kcr), as a novel type of PTMs, has been confirmed not only to exist on histones but also to be widely involved in the modification of non-histones and various biological processes since its first discovery in 2011 7 . In recent years, studies have suggested that a variety of regulatory proteins of Kcr are closely related to tumor-associated genes and may play a key role in the occurrence and development of cancer 8 . The known regulatory factors of Kcr include: ACADS, ACOX1, ACOX2, ACOX3, ACSS2, DPF2, GCDH, HDAC1, HDAC3, HDAC7, KAT2B, SIRT1, SIRT2, SIRT3, SIRT6, and YEATS2 9 . Zhai et al. discovered that Double PHD Fingers 2 (DPF2) can recognize and bind to the lactoylation modification on lysine 14 of histone H3, thereby promoting the transcription of related genes in cervical cancer cells and further enhancing the tumor cells' proliferation and invasion capabilities 10 . Kcr and lactoylation are both acylation modifications on lysine residues and can both regulate gene transcription. Although a large number of studies have explored the relationship between Kcr and various cancers, DPF2, as a reader of Kcr modifications, can also recognize the Kcr site of H3K14. The specific molecular mechanism of DPF2 Kcr modification in HCC is not yet clear and requires further in-depth research. Although Yang et al. have found that DPF2 is highly expressed in HCC and is associated with poor prognosis, there is a lack of exploration of the functional mechanism of DPF2 11 。 Observational studies have certain limitations, and their results are often influenced by confounding factors and reverse causal relationships. Mendelian randomization (MR) analysis can reduce the interference of confounding factors, thereby determining the relationship between exposure and outcome at the genetic level 12 . Compared with traditional MR methods, Generalized Summary-data-based Mendelian Randomization (GSMR) can exclude single nucleotide polymorphisms (SNPs) with pleiotropic effects through the Heterogeneity In Dependent Instruments (HEIDI) test, thereby further improving the accuracy of the analysis 13 . Therefore, in this study, we employ both MR and GSMR methods to systematically assess the potential causal relationships among Kcr genes, lipid metabolism, and the pathogenesis of HCC. Combined with transcriptomic validation, our aim is to provide a novel theoretical basis for elucidating the mechanisms underlying DPF2's role in HCC. 2 Materials and methods 2.1 Research design All data used in this study were derived from published literature and had been approved by the ethical review committees of the relevant research institutions. Therefore, this study does not require additional ethical approval. The research methods followed the STROBE-MR checklist (provided in the supplementary materials). All experimental results are described in detail in the text and supplementary materials. We selected 16 regulatory genes of Kcr as the exposure and patients with HCC from the FinnGen database as the outcome. After identifying Kcr regulatory genes that were significantly associated with HCC, we performed the Steiger directionality test and found no evidence of reverse causality.Subsequently, we selected 1,400 plasma metabolites as potential mediating factors to explore how regulatory genes of Kcr participate in the development and progression of HCC through these plasma metabolites. We then calculate the mediation proportions ( Fig. 1 ).GSMR analysis and transcriptomic data were used to further validate the findings. 2.2 eQTL dataset The eQTL data for all genes in this study were obtained from the eQTLGen database ( https://www.eqtlgen.org/cis-eqtls.html ). All data are cis-eQTLs derived from blood samples 14 . These genes were intersected with 16 Kcr regulatory genes, resulting in 16 Kcr regulators with corresponding cis-eQTL data. 2.3 Outcome dataset The outcome data for MR were obtained from the FinnGen database (Release 11, https://www.finngen.fi/en ). This dataset includes 609 HCC cases and 345,118 controls, all of European ancestry 15 .The GSE14520 and GSE25097 datasets were obtained from the Gene Expression Omnibus database and originate from large-scale gene expression profiling studies based on the Affymetrix platform (GPL3921). These datasets include tumor and paired non-tumor tissue samples from HCC patients 16 – 34 . The HCC data are provided by The Cancer Genome Atlas (TCGA) project and include high-throughput RNA sequencing data and clinical information from HCC patients collected across multiple centers. 2.4 MR And GSMR analysis We use MR Egger, Weighted median and Inverse variance weighted to explore the relationship among regulatory genes of Kcr, plasma metabolites and HCC. Single nucleotide polymorphisms (SNPs) reaching genome-wide significance (P < 5 × 10⁻⁸) were selected as instrumental variables (IVs) for Kcr regulatory genes and plasma metabolites.Then, these SNPs were clustered based on linkage disequilibrium (window size = 10,000 kb and r² < 0.001). Linkage disequilibrium estimates were derived from the 1000 Genomes Project using European ancestry samples. We used Cochran's Q statistic to assess the heterogeneity among IVs and applied the MR-Egger intercept method and MR-PRESSO to detect bias that might be caused by horizontal pleiotropy. Finally, leave-one-out analyses were conducted to evaluate the robustness of the MR results. In the GSMR analysis, genome-wide significant SNPs (P < 5 × 10⁻⁸) for each gut microbiota trait were selected using LD clumping (r² < 0.05). European samples from Phase 3 of the 1000 Genomes Project were used as the LD reference panel. The HEIDI outlier method was then applied to exclude IVs with potential pleiotropic effects, using a P-value threshold of 0.01. 2.5 Transcriptome analysis In this study, the differential expression of DPF2 and its correlation with clinical characteristics were analyzed using an unpaired t-test. In the survival analysis, the P-value of the Kaplan–Meier (KM) curve was calculated using the chi-square distribution. For GO and KEGG pathway enrichment analysis, we first used Pearson correlation to select the top 500 genes most strongly correlated with DPF2 expression. These genes were then uploaded to the DAVID database for functional annotation ( https://david.ncifcrf.gov/tools.jsp ). The Gene Set Variation Analysis(GSVA) heatmap was generated using Pearson correlation to evaluate the relationship between DPF2 and each pathway. All of our above analyses were conducted using "TwoSampleMR", "foreach", "gsmr2", "GSVA", "ggpubr", "survminer", "survival" and "ggplot2" from R version 4.3.1 for data visualization and analysis. 3 Results 3.1 MR and GSMR In the analysis of the relationship between 16 regulatory genes of Kcr and HCC, the IVW method revealed that DPF2 was significantly positively associated with the risk of HCC onset (OR = 1.3403, 95% CI: 1.0425–1.7232, P = 0.022). Subsequently, the GSMR method was used for supplementary verification, and the results were consistent with the IVW analysis (OR = 1.3292, 95% CI: 1.0339–1.7090, P = 0.026) (Fig. 2 A). In the further analysis, a total of 50 plasma metabolites were identified as significantly associated with the risk of HCC (Supplementary Table S3 ).Based on this, we evaluated the associations between DPF2 and the 50 plasma metabolites. The results revealed that DPF2 was correlated with 10 of these metabolites. After eliminating unnamed metabolites and metabolites with insignificant associations (Supplementary Table S3 ), it was finally determined that DPF2 was significantly negatively correlated with Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) (OR = 0.9171, 95% CI: 0.8439–0.9967, P = 0.041). Supplementary verification was performed using the GSMR method, and the results were consistent with the IVW analysis (OR = 0.9184, 95% CI: 0.8535–0.9881, P = 0.022) (Fig. 2 A). Then we calculated the mediating ratio. It was found that Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) accounted for 13.5% in the process of influencing DPF2's participation in the occurrence and development of HCC (β-all = 0.29, β-dir = 0.25, mediating effect ratio = 13.5%, β1x - β2 = 0.039). Furthermore, the F statistics of all exposed SNPs in this study were greater than 10 (in Supplementary Table S1 ). 3.2 Sensitivity analysis We used Cochran's Q test and funnel plot to test the heterogeneity of the results of the mediation analysis. It was observed that there was no heterogeneity or asymmetry in the causal relationship among these SNPs (Supplementary Table S2 and Supplementary Fig. 1). We also tested for horizontal pleiotropy using the MR-Egger intercept, which showed no horizontal pleiotropy (Supplementary Table S2 ). The influence of each SNP on the overall causal estimation was assessed through a leave-one-out analysis (Supplementary Figure S2 ).We also detected using Steiger filtering that there was no reverse causal relationship between DPF2 and HCC, or between Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) and HCC (Supplementary Table S1 ). 3.3 Difference analysis In both the GSE14520 and GSE25097 cohorts, a high expression level of DPF2 was observed in tumor tissues (Fig. 2 B- 2 C), which was consistent with the results of the MR and GSMR analyses. 3.4 Correlation with clinical characteristics We analyzed the relationship between DPF2 expression in the TCGA database and the clinical information of HCC patients. As shown in Fig. 3 A, the survival rate and survival time of patients in the high-expression group of DPF2 were both lower than those in the low-expression group. Furthermore, the expression level of DPF2 was upregulated in both Tumor Grade III–IV and Tumor Stage III–IV (Fig. 3 B–C). 3.5 Functional analysis We conducted enrichment analysis on DPF2 using the GO and KEGG databases. The results of the GO biological process (BP) analysis showed enrichment in the steroid metabolic process and response to ethanol (Fig. 4 A).In Fig. 4 B, enrichment of cellular components such as the plasma membrane, external side of plasma membrane, cell surface, and extracellular region is highly relevant to glycosphingolipid metabolism, as glycosphingolipids are mainly localized to the outer leaflet of the plasma membrane and play essential roles in cell signaling in hepatocellular carcinoma. The GO molecular function (MF) enrichment analysis showed monooxygenase activity and oxidoreductase activity (Fig. 4 C). The KEGG pathway analysis revealed enrichment in arachidonic acid metabolism and retinol metabolism (Fig. 4 D). These enrichment analysis results suggested that DPF2 may be correlated with lipid metabolism regulation and the formation of chronic inflammation in HCC. 3.6 GSVA analysis We further conducted GSVA, as shown in Fig. 5 . The GSVA results indicated that the expression of DPF2 was negatively correlated with the glycosphingolipid biosynthetic process, negative regulation of cholesterol efflux and cholesterol metabolic processes. In addition, DPF2 was positively correlated with pathways related to histone modification, histone H3K9 modification, peptidyl lysine modification, HCC. 3.7 FDR result In the MR IVW algorithm, the FDR result did not reach significance. In the GSE14520 dataset, there were a total of 13,045 genes, and P < 3.833 × 10⁻⁶ was the significance threshold of FDR. In the GSE25097 dataset, there were a total of 18,076 genes, and P < 2.766 × 10⁻⁶ was the significance threshold of FDR. In the TCGA dataset, there were a total of 59,427 genes, and the significance threshold of FDR was P < 8.411 × 10⁻⁷. The results of the differential analysis showed that the p-value for DPF2 in GSE14520 was 1.11 × 10⁻³⁴, and in GSE25097 it was 1.29 × 10⁻²²⁷. Both met the significance criteria of FDR. In the TCGA dataset, the correlation p-values between the expression level of DPF2 and both tumor grade and tumor stage were 0.0007, which did not meet the significance criteria of FDR. Furthermore, survival analysis in the TCGA cohort revealed that the overall survival (OS) of the DPF2 high-expression group was poor, with a p-value of 9.74 × 10⁻⁶, which also failed to meet the significance criteria of FDR. The specific p-value results of GO, KEGG, and GSVA analyses were detailed in Supplementary Table S4 . 4 Discussion These findings suggest that DPF2 may contribute to the development and progression of HCC, at least in part, by downregulating the protective metabolite glycosyl-N-behenoyl-sphingadienine (d18:2/22:0). Mediation analysis showed that glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) accounted for 13.5% of the effect through which DPF2 promotes HCC development and progression. DPF2 is a histone modification reader that contains a double PHD finger domain and is primarily localized in the nucleus, where it recognizes specific histone marks to regulate downstream gene transcription 10 . The DPF domain selectively recognizes crotonylated H3K14, with a binding affinity to Kcr that is 4–8 times higher than that to Kac (acetylated lysine) 35 .Kcr is dynamically regulated by the balance between writer enzymes (e.g., p300/CBP) and eraser enzymes (e.g., HDAC1/HDAC3), and typically occurs at gene promoters and enhancers, using crotonyl-CoA as a substrate and catalyzed by crotonyltransferases. Since its identification as a novel histone mark in 2011, Kcr has remained a focus of epigenetic research 9 . Kcr was initially regarded as a specific epigenetic marker related to gender.With the development of proteomics technology, it has been further discovered to exist in microorganisms and animals. With further research by scholars, it has been found that Kcr is closely related to diseases such as HIV, acute kidney injury, IgA nephropathy, and hypertrophic cardiomyopathy 8 . It is worth noting that a study in 2023 identified through LC-MS that the crotonylated substrate targeted by p300 has potential carcinogenicity, and a large number of non-histone proteins have also been found to be involved in the tumorigenesis process 36 . Given that histone Kcr catalyzed by E1A binding protein p300 stimulates transcription more strongly than histone lysine acetylation (Kac), Byrne et al. knocked out p300 and found that 32 regulatory proteins of Kcr were associated with cancer genes 37 . Mu et al. found that the regulatory gene of Kcr BEX2 was upregulated in lung adenocarcinoma, and the Kcr modification of BEX2 at the K59 site could enhance mitochondrial autophagy, thereby inhibiting apoptosis induced by chemotherapeutic drugs 38 . Xu et al. also found that the level of histone Kcr was positively correlated with the malignancy degree in prostate cancer 39 . Regarding the relationship between Kcr levels and HCC, Wan et al. down-regulated HDAC1 and HDAC3 through the histone deacetylase (HDAC) inhibitor -TSA and found that the total Kcr level in HCC cells increased, which is consistent with our analysis results of the functional pathway for DPF2 40 . Zhang et al. further demonstrated that hypoxia downregulated the expression of Histone Deacetylase 6, resulting in an increase in the Kcr levels of Lamin A at the K265 and K270 sites, and thereby promoting the proliferation of HCC cells 41 . Furthermore, Kcr can also promote cell invasion through the SEPT2-K74-P85α-AKT signaling pathway 42 . However, regarding the relationship between the regulatory gene of Kcr DPF2 and HCC, there are still relatively few studies. The relationship between DPF2 and HCC at the phenotypic level is found in our research to be consistent with that of Yang et al. The demand for lipids in HCC cells is significantly higher than that in other cancer cells. Lipid synthesis is primarily regulated by the sterol regulatory element-binding protein (SREBP) family. Specifically, SREBP-1 is the key regulator of fatty acid synthesis and is activated by the insulin-PI3K/Akt/mTOR signaling pathway, while SREBP-2 mainly controls cholesterol synthesis 43 , 44 . Abnormal activation of SREBP-1 not only promotes fatty acid synthesis, but also contributes to the occurrence and progression of HCC. Inhibiting SREBP-1 can effectively reduce liver lipid levels and decrease the risk of HCC occurrence 45 . Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) is a typical glycosphingolipid, composed of a sphingosine backbone, a 22-carbon fatty acid chain, and a glycosyl group. It is widely involved in the stability of cell membranes and signal transduction. Hung et al. found that the glycosphingolipid subtypes SSEA3, Globo H, and SSEA4 are upregulated in HCC, and their high expression is significantly associated with poor patient prognosis 46 . Interestingly, Guan et al. found that when breast cancer cells were treated with the glucosylceramide (GlcCer) synthase inhibitor EtDO-P4, which inhibits the synthesis of all glycosphingolipids (GSLs) derived from GlcCer, the adhesion and movement capabilities of the breast cancer cells were enhanced 47 . Cumin’s team also found that knocking out endogenous E-cadherin in ovarian cancer cells induces epithelial-mesenchymal transition (EMT) and results in a decrease in the levels of Globo-series glycosphingolipids. The above results indicate that the regulation of glycosphingolipids on HCC is bidirectional. It is worth noting that Zheng's team has for the first time revealed the functional role of the metabolic enzyme Kcr in the progression of pancreatic cancer, and at the same time found that regulatory proteins of Kcr are closely related to lipid metabolism, especially the process of sphingolipid metabolism 48 . Our MR study indicates that the Kcr modification of DPF2 could reduce the level of Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0), thereby promoting the occurrence and development of HCC. Subsequently, the transcriptome data also verify the above conjecture. However, research on Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) remains limited. Given that many biological pathways, such as inflammation, oxidative stress, and apoptosis, are similar in different tissues and organs, insights gained from studying glycosphingolipids in other cancers can potentially inform the protective mechanisms of Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) in HCC. Therefore, we hypothesize that DPF2 may regulate specific enzymes or factors modified by Kcr, thereby increasing the Kcr level in hepatoma cells. This could accelerate the degradation of Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) or activate pathways related to fatty acid β-oxidation or sphingolipid metabolism. By reducing the biosynthetic substrate of Glycosyl-N-behenoyl-sphingadienine, this process may disrupt cellular homeostasis, promote inflammation, epithelial-mesenchymal transition (EMT), cell migration and invasion, and ultimately accelerate the occurrence and progression of HCC. This study has the following advantages. Our research is one of the few that links the relationship of DPF2 in HCC to Kcr. In addition, a considerable number of 1,400 plasma metabolites are used as mediating factors to explore the role of lipid metabolites in the above process. Therefore, our results may provide new biomarkers for the early diagnosis or treatment of HCC. Moreover, MR, GSMR and transcriptomic data are used for mutual verification, further enhancing the causal inference ability and reliability of the study. However, this study still has certain limitations. The samples selected in the study are Finnish patients. Although we hope to provide some clues through this study to guide the current situation of the Chinese population, the applicability of the results may be affected by population differences. As Chinese scholars, we should subsequently collect samples from domestic HCC patients and conduct whole-genome sequencing. Although some results failed to pass the strict threshold of FDR correction, we still believe that the observation results may have biological significance. For example, although the statistical significance does not reach the preset threshold, these results are consistent with the support of our existing literature or experimental observations and may represent potentially important associations. In fact, in many practical biomedical studies, extremely strict statistical thresholds may sometimes deprive researchers of the sensitivity needed to detect potential new discoveries, especially in research fields with complex mechanisms and multiple potential interfering factors. Furthermore, the proportion of the mediating effect is only 13.5%, indicating that this pathway can only explain part of the role of DPF2 in promoting the occurrence and development of HCC. Among them, the mediating factors that account for a relatively large proportion still need to be further explored. Most importantly, the bioinformatics results can only suggest potential molecular mechanisms and cannot completely replace in vivo or in vitro biological experiments. Therefore, in our subsequent research, we will conduct knockout or overexpression experiments of DPF2 in hepatoma cell lines, and use specific antibodies to detect changes in the overall Kcr level through Western blot. Additionally, we will use Liquid Chromatography–Mass Spectrometry to detect changes in Glycosyl-N-behenoyl-sphingadienine levels in hepatoma cells with knocked-out or overexpressed DPF2, as well as alterations in cell proliferation, migration, and invasion abilities. Abbreviations Hepatocellular carcinoma (HCC) Sterol regulatory element-binding protein (SREBP) Stearoyl-CoA desaturase (SCD) Fatty acid synthase (FASN) Post-translational modifications (PTMs) Lysine crotonylation (Kcr) Mendelian randomization (MR) Generalized summary-data-based Mendelian randomization (GSMR) Single nucleotide polymorphisms (SNPs) Heterogeneity in dependent instruments (HEIDI) Expression quantitative trait locus (eQTL) The Cancer Genome Atlas (TCGA) Gene Expression Omnibus (GEO) Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG) Gene set variation analysis (GSVA) Kaplan–Meier (KM) Inverse variance weighted (IVW) Instrumental variables (IVs) Linkage disequilibrium (LD) Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) Epithelial–mesenchymal transition (EMT) Liquid chromatography–mass spectrometry (LC–MS) Declarations Data Availability Statement The datasets used in this study are accessible through online repositories. We are deeply appreciative of all participants and researchers who shared these valuable datasets. If others wish to request access to the data used in this study, please contact [email protected] . Author Contributions Lintao Dong,Jingping Hu:Writing original draft,Methodology,Formal analysis, Conceptualization. Fang Wang*:Writing – review & editing, Conceptualization. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 82460563, project title: The mechanism of PFKFB4 inhibition of SIRT2-mediated ketone body degradation regulating Rela/ZZ modification in promoting chemoresistance in colorectal cancer). Acknowledgments The data for the exposure and outcome in this study were obtained from the GWAS database, and both have received ethical approval and participant informed consent. We appreciate all the participants and investigators for sharing these data. Conflict of interest The authors declare that they have no competing interests. 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Gene signatures derived from a c-MET-driven liver cancer mouse model predict survival of patients with hepatocellular carcinoma. PLoS One . 2011;6(9):e24582 Xiong X, Panchenko T, Yang S et al. Selective recognition of histone crotonylation by double PHD fingers of MOZ and DPF2. Nat Chem Biol . 2016;12(12):1111-1118 Yin X, Zhang H, Wei Z et al. Large-Scale Identification of Lysine Crotonylation Reveals Its Potential Role in Oral Squamous Cell Carcinoma. Cancer Manag Res . 2023;15:1165-1179 Huang H, Wang D, Zhao Y. Quantitative Crotonylome Analysis Expands the Roles of p300 in the Regulation of Lysine Crotonylation Pathway. Proteomics . 2018;18(15):e1700230 Mu N, Wang Y, Li X et al. Crotonylated BEX2 interacts with NDP52 and enhances mitophagy to modulate chemotherapeutic agent-induced apoptosis in non-small-cell lung cancer cells. Cell Death Dis . 2023;14(9):645 Xu X, Zhu X, Liu F et al. The effects of histone crotonylation and bromodomain protein 4 on prostate cancer cell lines. Transl Androl Urol . 2021;10(2):900-914 Hrabeta J, Stiborova M, Adam V, Kizek R, Eckschlager T. Histone deacetylase inhibitors in cancer therapy. A review. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub . 2014;158(2):161-9 Zhang D, Tang J, Xu Y et al. Global crotonylome reveals hypoxia-mediated lamin A crotonylation regulated by HDAC6 in liver cancer. Cell Death Dis . 2022;13(8):717 Zhang X, Liu Z, Zhang Y et al. SEPT2 crotonylation promotes metastasis and recurrence in hepatocellular carcinoma and is associated with poor survival. Cell Biosci . 2023;13(1):63 Du D, Liu C, Qin M et al. Metabolic dysregulation and emerging therapeutical targets for hepatocellular carcinoma. Acta Pharm Sin B . 2022;12(2):558-580 Shimano H. Sterol regulatory element-binding protein family as global regulators of lipid synthetic genes in energy metabolism. Vitam Horm . 2002;65:167-94 Yin F, Feng F, Wang L et al. SREBP-1 inhibitor Betulin enhances the antitumor effect of Sorafenib on hepatocellular carcinoma via restricting cellular glycolytic activity. Cell Death Dis . 2019;10(9):672 Hung T, Huang Y, Yeh C et al. High expression of embryonic stem cell marker SSEA3 confers poor prognosis and promotes epithelial mesenchymal transition in hepatocellular carcinoma. Biomed J . 2024;47(2):100612 Guan F, Handa K, Hakomori S. Specific glycosphingolipids mediate epithelial-to-mesenchymal transition of human and mouse epithelial cell lines. Proc Natl Acad Sci U S A . 2009;106(18):7461-6 Zheng Y, Zhu L, Qin Z et al. Modulation of cellular metabolism by protein crotonylation regulates pancreatic cancer progression. Cell Rep . 2023;42(7):112666 Additional Declarations No competing interests reported. Supplementary Files STROBEMRchecklistfillable.docx SupplementaryFigure.docx LegendsforSupplementaryTables.docx SupplementaryTable.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6627218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463865531,"identity":"e94b3463-bd95-40e8-a427-0d70ca625160","order_by":0,"name":"Lintao Dong","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lintao","middleName":"","lastName":"Dong","suffix":""},{"id":463865532,"identity":"26d97db3-7f23-41e3-acc4-f32db79d571a","order_by":1,"name":"Jingping Hu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingping","middleName":"","lastName":"Hu","suffix":""},{"id":463865533,"identity":"b04efa7d-b694-4756-8beb-94c1306a0a68","order_by":2,"name":"Fang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYLCCCiDmZ29sfvDBwMaOOC1ngFiy5/AxwxkFacnEazG4kZYgzfPhEGMDIdUGx88efnGg5o7dhjNnDIxtDA4wM7AfProBr5YzeWkWB449S555vMfgcY7BHT4GnrS0G/i0mB3IMTP+wHY4mQ9kS47BM2YGCR4z/FrOvzEzOPDvcDLDjRwDaQuDw4wNBLXcyDF+cLDtsJ0AyPsMxGixv/HGjOFg3+EEcCD3GKQlsxHyi2R/jvGHA98O24Oj8scfGzt+9sPH8GoBAjYJIJHYAOcSUA4CzB9ADiRC4SgYBaNgFIxUAAAGW1jE3evz7AAAAABJRU5ErkJggg==","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-09 09:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6627218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6627218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83778665,"identity":"8e0734ed-ca8c-4963-83f3-244f06c1af70","added_by":"auto","created_at":"2025-06-02 14:41:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160927,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrams illustrating the causal associations examined in this study. The β-all was decomposed into: (i) the indirect effect (β₁ × β₂), where β₁ represents the effect of DPF2 on plasma metabolites and β₂ represents the effect of plasma metabolites on HCC; and (ii) the direct effect（β-dir）, calculated as β-all – (β₁ × β₂). The mediated proportion was defined as the ratio of the indirect effect to the total effect.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/2e59c2ba82352e6c764bf039.png"},{"id":83778815,"identity":"1df04a1d-332c-4ec3-b0bb-f9b8c1d99a25","added_by":"auto","created_at":"2025-06-02 14:49:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":792014,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization and gene expression analysis of DPF2 in HCC.\u003c/p\u003e\n\u003cp\u003e(A) Forest plot of MR and GSMR analyses showing the causal relationships among DPF2, glycosyl–N–behenoyl–sphingadienine (d18:2/22:0), and hepatocellular carcinoma. Significant associations (p \u0026lt; 0.05) are marked in blue;(B-C) Box plots showing DPF2 expression levels in normal and tumor tissues from the GSE14520 (B) and GSE25097 (C) datasets.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/9406d077841673c7e3bbebbc.png"},{"id":83779365,"identity":"670ff26f-e48e-42d7-81ad-b9c1ba250e32","added_by":"auto","created_at":"2025-06-02 14:57:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":598331,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between DPF2 expression and clinical characteristics of HCC in TCGA cohort.\u003c/p\u003e\n\u003cp\u003e(A) Kaplan–Meier overall survival (OS) curve comparing high (H) and low (L) DPF2 expression groups in HCC patients from TCGA;(B) Box plot showing the expression of DPF2 in HCC samples with different tumor grades (I–II vs. III–IV);(C) Box plot showing the expression of DPF2 in HCC samples with different tumor stages (stage 0–II vs. stage III–IV).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/454c8b7abea34656902e6998.png"},{"id":83778674,"identity":"590a753f-3a46-47d4-b9bf-c53c334ffb5d","added_by":"auto","created_at":"2025-06-02 14:41:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":919543,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of genes significantly correlated with DPF2 expression in the GSE14520 dataset.\u003c/p\u003e\n\u003cp\u003e(A) Gene Ontology (GO) enrichment analysis for Biological Process (BP) terms;(B) GO enrichment analysis for Cellular Component (CC) terms;(C) GO enrichment analysis for Molecular Function (MF) terms;(D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis;The bar color indicates the P-value, and the x-axis represents the number of enriched genes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/aba5a1438e193f98003d999d.png"},{"id":83778672,"identity":"29867643-df88-4ef9-b8db-f319ebebd1c2","added_by":"auto","created_at":"2025-06-02 14:41:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":479373,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA of pathways associated with DPF2 expression.\u003c/p\u003e\n\u003cp\u003eThe heatmap (left) displays the enrichment scores of selected pathways across samples, where red indicates higher pathway activity and blue indicates lower activity. The bar plot (right) shows Pearson correlation coefficients between DPF2 expression and each pathway (blue bars), and the statistical significance (–log₁₀ p-values) of the associations (orange dots).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/7645e58ad37b5a54357e2ad4.png"},{"id":91403723,"identity":"32cdf2f3-a6cd-4954-b951-87af3ab05204","added_by":"auto","created_at":"2025-09-16 07:30:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3775916,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/411ec991-f21e-4b46-a5a9-c2f1c49675e7.pdf"},{"id":83778666,"identity":"81acccce-97c2-4206-8891-5906ed96e84a","added_by":"auto","created_at":"2025-06-02 14:41:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":41058,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEMRchecklistfillable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/bed948800d516ee60e2ff39f.docx"},{"id":83778676,"identity":"50790ffb-9368-4e40-a2a2-2e16203e98be","added_by":"auto","created_at":"2025-06-02 14:41:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2909088,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/98ec2ebc32841e8efc7c4455.docx"},{"id":83778668,"identity":"e2b7b45f-a5ca-4190-898c-dc7df127c671","added_by":"auto","created_at":"2025-06-02 14:41:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10984,"visible":true,"origin":"","legend":"","description":"","filename":"LegendsforSupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/e01c6b79303fc891e0dfc879.docx"},{"id":83778675,"identity":"ac1f1fbb-ed3c-4e7f-96d9-948e68782b7b","added_by":"auto","created_at":"2025-06-02 14:41:55","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":406572,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6627218/v1/3dd73317a1da419a5de94372.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Crontonylation regulatory factor DPF2 promotes the occurrence of hepatocellular carcinoma by regulating glycosphingolipid metabolism","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) accounts for approximately 75\u0026ndash;85% of primary liver cancers. According to the latest 2025 Cancer Statistics from the United States, an estimated 42,240 new cases of HCC and intrahepatic cholangiocarcinoma are expected in the country by year's end. Despite ongoing improvements in liver cancer treatment strategies, significant challenges remain. For example, the prognosis of HCC is still poor, with a 5-year survival rate of only 22%. Therefore, there is an urgent need to further explore its molecular mechanism in depth to seek more effective treatment methods\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Lipids, as key components of cell membranes, signaling pathways, and energy storage, play a vital role in maintaining cellular functions and physiological balance through their metabolic homeostasis. In recent years, the influence of abnormal lipid metabolism on the occurrence and development of HCC has been emphasized by researchers\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For instance, the imbalance of lipid metabolism creates favorable conditions for the occurrence and development of tumor cells, promoting their rapid proliferation, invasion and metastasis. Key factors such as SREBP-1, SCD and FASN are upregulated in HCC, causing the imbalance of lipid metabolism and thereby driving the malignant progression of the tumor \u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, targeting lipid metabolism-related pathways is expected to become a new strategy for the treatment of HCC.\u003c/p\u003e \u003cp\u003eIn addition to abnormal metabolic levels, post-translational modifications (PTMs) also play a critical role in HCC. PTMs are important epigenetic mechanisms that can affect cell development, differentiation and the occurrence of diseases by regulating protein function, stability and localization. Crotonylation (Kcr), as a novel type of PTMs, has been confirmed not only to exist on histones but also to be widely involved in the modification of non-histones and various biological processes since its first discovery in 2011\u003csup\u003e7\u003c/sup\u003e. In recent years, studies have suggested that a variety of regulatory proteins of Kcr are closely related to tumor-associated genes and may play a key role in the occurrence and development of cancer\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The known regulatory factors of Kcr include: ACADS, ACOX1, ACOX2, ACOX3, ACSS2, DPF2, GCDH, HDAC1, HDAC3, HDAC7, KAT2B, SIRT1, SIRT2, SIRT3, SIRT6, and YEATS2\u003csup\u003e9\u003c/sup\u003e. Zhai et al. discovered that Double PHD Fingers 2 (DPF2) can recognize and bind to the lactoylation modification on lysine 14 of histone H3, thereby promoting the transcription of related genes in cervical cancer cells and further enhancing the tumor cells' proliferation and invasion capabilities\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Kcr and lactoylation are both acylation modifications on lysine residues and can both regulate gene transcription. Although a large number of studies have explored the relationship between Kcr and various cancers, DPF2, as a reader of Kcr modifications, can also recognize the Kcr site of H3K14. The specific molecular mechanism of DPF2 Kcr modification in HCC is not yet clear and requires further in-depth research. Although Yang et al. have found that DPF2 is highly expressed in HCC and is associated with poor prognosis, there is a lack of exploration of the functional mechanism of DPF2\u003csup\u003e11\u003c/sup\u003e。\u003c/p\u003e \u003cp\u003eObservational studies have certain limitations, and their results are often influenced by confounding factors and reverse causal relationships. Mendelian randomization (MR) analysis can reduce the interference of confounding factors, thereby determining the relationship between exposure and outcome at the genetic level\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Compared with traditional MR methods, Generalized Summary-data-based Mendelian Randomization (GSMR) can exclude single nucleotide polymorphisms (SNPs) with pleiotropic effects through the Heterogeneity In Dependent Instruments (HEIDI) test, thereby further improving the accuracy of the analysis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Therefore, in this study, we employ both MR and GSMR methods to systematically assess the potential causal relationships among Kcr genes, lipid metabolism, and the pathogenesis of HCC. Combined with transcriptomic validation, our aim is to provide a novel theoretical basis for elucidating the mechanisms underlying DPF2's role in HCC.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research design\u003c/h2\u003e \u003cp\u003eAll data used in this study were derived from published literature and had been approved by the ethical review committees of the relevant research institutions. Therefore, this study does not require additional ethical approval. The research methods followed the STROBE-MR checklist (provided in the supplementary materials). All experimental results are described in detail in the text and supplementary materials. We selected 16 regulatory genes of Kcr as the exposure and patients with HCC from the FinnGen database as the outcome. After identifying Kcr regulatory genes that were significantly associated with HCC, we performed the Steiger directionality test and found no evidence of reverse causality.Subsequently, we selected 1,400 plasma metabolites as potential mediating factors to explore how regulatory genes of Kcr participate in the development and progression of HCC through these plasma metabolites. We then calculate the mediation proportions ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).GSMR analysis and transcriptomic data were used to further validate the findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 eQTL dataset\u003c/h2\u003e \u003cp\u003eThe eQTL data for all genes in this study were obtained from the eQTLGen database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org/cis-eqtls.html\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org/cis-eqtls.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All data are cis-eQTLs derived from blood samples\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These genes were intersected with 16 Kcr regulatory genes, resulting in 16 Kcr regulators with corresponding cis-eQTL data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcome dataset\u003c/h2\u003e \u003cp\u003eThe outcome data for MR were obtained from the FinnGen database (Release 11, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset includes 609 HCC cases and 345,118 controls, all of European ancestry\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.The GSE14520 and GSE25097 datasets were obtained from the Gene Expression Omnibus database and originate from large-scale gene expression profiling studies based on the Affymetrix platform (GPL3921). These datasets include tumor and paired non-tumor tissue samples from HCC patients\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The HCC data are provided by The Cancer Genome Atlas (TCGA) project and include high-throughput RNA sequencing data and clinical information from HCC patients collected across multiple centers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 MR And GSMR analysis\u003c/h2\u003e \u003cp\u003eWe use MR Egger, Weighted median and Inverse variance weighted to explore the relationship among regulatory genes of Kcr, plasma metabolites and HCC. Single nucleotide polymorphisms (SNPs) reaching genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸) were selected as instrumental variables (IVs) for Kcr regulatory genes and plasma metabolites.Then, these SNPs were clustered based on linkage disequilibrium (window size\u0026thinsp;=\u0026thinsp;10,000 kb and r\u0026sup2; \u0026lt; 0.001). Linkage disequilibrium estimates were derived from the 1000 Genomes Project using European ancestry samples. We used Cochran's Q statistic to assess the heterogeneity among IVs and applied the MR-Egger intercept method and MR-PRESSO to detect bias that might be caused by horizontal pleiotropy. Finally, leave-one-out analyses were conducted to evaluate the robustness of the MR results. In the GSMR analysis, genome-wide significant SNPs (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸) for each gut microbiota trait were selected using LD clumping (r\u0026sup2; \u0026lt; 0.05). European samples from Phase 3 of the 1000 Genomes Project were used as the LD reference panel. The HEIDI outlier method was then applied to exclude IVs with potential pleiotropic effects, using a P-value threshold of 0.01.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transcriptome analysis\u003c/h2\u003e \u003cp\u003eIn this study, the differential expression of DPF2 and its correlation with clinical characteristics were analyzed using an unpaired t-test. In the survival analysis, the P-value of the Kaplan\u0026ndash;Meier (KM) curve was calculated using the chi-square distribution. For GO and KEGG pathway enrichment analysis, we first used Pearson correlation to select the top 500 genes most strongly correlated with DPF2 expression. These genes were then uploaded to the DAVID database for functional annotation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/tools.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/tools.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Gene Set Variation Analysis(GSVA) heatmap was generated using Pearson correlation to evaluate the relationship between DPF2 and each pathway. All of our above analyses were conducted using \"TwoSampleMR\", \"foreach\", \"gsmr2\", \"GSVA\", \"ggpubr\", \"survminer\", \"survival\" and \"ggplot2\" from R version 4.3.1 for data visualization and analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 MR and GSMR\u003c/h2\u003e \u003cp\u003eIn the analysis of the relationship between 16 regulatory genes of Kcr and HCC, the IVW method revealed that DPF2 was significantly positively associated with the risk of HCC onset (OR\u0026thinsp;=\u0026thinsp;1.3403, 95% CI: 1.0425\u0026ndash;1.7232, P\u0026thinsp;=\u0026thinsp;0.022). Subsequently, the GSMR method was used for supplementary verification, and the results were consistent with the IVW analysis (OR\u0026thinsp;=\u0026thinsp;1.3292, 95% CI: 1.0339\u0026ndash;1.7090, P\u0026thinsp;=\u0026thinsp;0.026) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the further analysis, a total of 50 plasma metabolites were identified as significantly associated with the risk of HCC (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).Based on this, we evaluated the associations between DPF2 and the 50 plasma metabolites. The results revealed that DPF2 was correlated with 10 of these metabolites. After eliminating unnamed metabolites and metabolites with insignificant associations (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), it was finally determined that DPF2 was significantly negatively correlated with Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) (OR\u0026thinsp;=\u0026thinsp;0.9171, 95% CI: 0.8439\u0026ndash;0.9967, P\u0026thinsp;=\u0026thinsp;0.041). Supplementary verification was performed using the GSMR method, and the results were consistent with the IVW analysis (OR\u0026thinsp;=\u0026thinsp;0.9184, 95% CI: 0.8535\u0026ndash;0.9881, P\u0026thinsp;=\u0026thinsp;0.022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Then we calculated the mediating ratio. It was found that Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) accounted for 13.5% in the process of influencing DPF2's participation in the occurrence and development of HCC (β-all =\u0026thinsp;0.29, β-dir\u0026thinsp;=\u0026thinsp;0.25, mediating effect ratio\u0026thinsp;=\u0026thinsp;13.5%, β1x - β2\u0026thinsp;=\u0026thinsp;0.039). Furthermore, the F statistics of all exposed SNPs in this study were greater than 10 (in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eWe used Cochran's Q test and funnel plot to test the heterogeneity of the results of the mediation analysis. It was observed that there was no heterogeneity or asymmetry in the causal relationship among these SNPs (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Supplementary Fig.\u0026nbsp;1). We also tested for horizontal pleiotropy using the MR-Egger intercept, which showed no horizontal pleiotropy (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The influence of each SNP on the overall causal estimation was assessed through a leave-one-out analysis (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).We also detected using Steiger filtering that there was no reverse causal relationship between DPF2 and HCC, or between Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) and HCC (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Difference analysis\u003c/h2\u003e \u003cp\u003eIn both the GSE14520 and GSE25097 cohorts, a high expression level of DPF2 was observed in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), which was consistent with the results of the MR and GSMR analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation with clinical characteristics\u003c/h2\u003e \u003cp\u003eWe analyzed the relationship between DPF2 expression in the TCGA database and the clinical information of HCC patients. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the survival rate and survival time of patients in the high-expression group of DPF2 were both lower than those in the low-expression group. Furthermore, the expression level of DPF2 was upregulated in both Tumor Grade III\u0026ndash;IV and Tumor Stage III\u0026ndash;IV (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026ndash;C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Functional analysis\u003c/h2\u003e \u003cp\u003eWe conducted enrichment analysis on DPF2 using the GO and KEGG databases. The results of the GO biological process (BP) analysis showed enrichment in the steroid metabolic process and response to ethanol (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, enrichment of cellular components such as the plasma membrane, external side of plasma membrane, cell surface, and extracellular region is highly relevant to glycosphingolipid metabolism, as glycosphingolipids are mainly localized to the outer leaflet of the plasma membrane and play essential roles in cell signaling in hepatocellular carcinoma. The GO molecular function (MF) enrichment analysis showed monooxygenase activity and oxidoreductase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The KEGG pathway analysis revealed enrichment in arachidonic acid metabolism and retinol metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These enrichment analysis results suggested that DPF2 may be correlated with lipid metabolism regulation and the formation of chronic inflammation in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 GSVA analysis\u003c/h2\u003e \u003cp\u003eWe further conducted GSVA, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The GSVA results indicated that the expression of DPF2 was negatively correlated with the glycosphingolipid biosynthetic process, negative regulation of cholesterol efflux and cholesterol metabolic processes. In addition, DPF2 was positively correlated with pathways related to histone modification, histone H3K9 modification, peptidyl lysine modification, HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7 FDR result\u003c/h2\u003e \u003cp\u003eIn the MR IVW algorithm, the FDR result did not reach significance. In the GSE14520 dataset, there were a total of 13,045 genes, and P\u0026thinsp;\u0026lt;\u0026thinsp;3.833 \u0026times; 10⁻⁶ was the significance threshold of FDR. In the GSE25097 dataset, there were a total of 18,076 genes, and P\u0026thinsp;\u0026lt;\u0026thinsp;2.766 \u0026times; 10⁻⁶ was the significance threshold of FDR. In the TCGA dataset, there were a total of 59,427 genes, and the significance threshold of FDR was P\u0026thinsp;\u0026lt;\u0026thinsp;8.411 \u0026times; 10⁻⁷. The results of the differential analysis showed that the p-value for DPF2 in GSE14520 was 1.11 \u0026times; 10⁻\u0026sup3;⁴, and in GSE25097 it was 1.29 \u0026times; 10⁻\u0026sup2;\u0026sup2;⁷. Both met the significance criteria of FDR. In the TCGA dataset, the correlation p-values between the expression level of DPF2 and both tumor grade and tumor stage were 0.0007, which did not meet the significance criteria of FDR. Furthermore, survival analysis in the TCGA cohort revealed that the overall survival (OS) of the DPF2 high-expression group was poor, with a p-value of 9.74 \u0026times; 10⁻⁶, which also failed to meet the significance criteria of FDR. The specific p-value results of GO, KEGG, and GSVA analyses were detailed in Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThese findings suggest that DPF2 may contribute to the development and progression of HCC, at least in part, by downregulating the protective metabolite glycosyl-N-behenoyl-sphingadienine (d18:2/22:0). Mediation analysis showed that glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) accounted for 13.5% of the effect through which DPF2 promotes HCC development and progression. DPF2 is a histone modification reader that contains a double PHD finger domain and is primarily localized in the nucleus, where it recognizes specific histone marks to regulate downstream gene transcription\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The DPF domain selectively recognizes crotonylated H3K14, with a binding affinity to Kcr that is 4\u0026ndash;8 times higher than that to Kac (acetylated lysine)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.Kcr is dynamically regulated by the balance between writer enzymes (e.g., p300/CBP) and eraser enzymes (e.g., HDAC1/HDAC3), and typically occurs at gene promoters and enhancers, using crotonyl-CoA as a substrate and catalyzed by crotonyltransferases. Since its identification as a novel histone mark in 2011, Kcr has remained a focus of epigenetic research\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Kcr was initially regarded as a specific epigenetic marker related to gender.With the development of proteomics technology, it has been further discovered to exist in microorganisms and animals. With further research by scholars, it has been found that Kcr is closely related to diseases such as HIV, acute kidney injury, IgA nephropathy, and hypertrophic cardiomyopathy\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It is worth noting that a study in 2023 identified through LC-MS that the crotonylated substrate targeted by p300 has potential carcinogenicity, and a large number of non-histone proteins have also been found to be involved in the tumorigenesis process\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Given that histone Kcr catalyzed by E1A binding protein p300 stimulates transcription more strongly than histone lysine acetylation (Kac), Byrne et al. knocked out p300 and found that 32 regulatory proteins of Kcr were associated with cancer genes\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Mu et al. found that the regulatory gene of Kcr BEX2 was upregulated in lung adenocarcinoma, and the Kcr modification of BEX2 at the K59 site could enhance mitochondrial autophagy, thereby inhibiting apoptosis induced by chemotherapeutic drugs\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Xu et al. also found that the level of histone Kcr was positively correlated with the malignancy degree in prostate cancer\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Regarding the relationship between Kcr levels and HCC, Wan et al. down-regulated HDAC1 and HDAC3 through the histone deacetylase (HDAC) inhibitor -TSA and found that the total Kcr level in HCC cells increased, which is consistent with our analysis results of the functional pathway for DPF2\u003csup\u003e40\u003c/sup\u003e. Zhang et al. further demonstrated that hypoxia downregulated the expression of Histone Deacetylase 6, resulting in an increase in the Kcr levels of Lamin A at the K265 and K270 sites, and thereby promoting the proliferation of HCC cells\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Furthermore, Kcr can also promote cell invasion through the SEPT2-K74-P85α-AKT signaling pathway\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, regarding the relationship between the regulatory gene of Kcr DPF2 and HCC, there are still relatively few studies. The relationship between DPF2 and HCC at the phenotypic level is found in our research to be consistent with that of Yang et al.\u003c/p\u003e \u003cp\u003eThe demand for lipids in HCC cells is significantly higher than that in other cancer cells. Lipid synthesis is primarily regulated by the sterol regulatory element-binding protein (SREBP) family. Specifically, SREBP-1 is the key regulator of fatty acid synthesis and is activated by the insulin-PI3K/Akt/mTOR signaling pathway, while SREBP-2 mainly controls cholesterol synthesis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Abnormal activation of SREBP-1 not only promotes fatty acid synthesis, but also contributes to the occurrence and progression of HCC. Inhibiting SREBP-1 can effectively reduce liver lipid levels and decrease the risk of HCC occurrence\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) is a typical glycosphingolipid, composed of a sphingosine backbone, a 22-carbon fatty acid chain, and a glycosyl group. It is widely involved in the stability of cell membranes and signal transduction. Hung et al. found that the glycosphingolipid subtypes SSEA3, Globo H, and SSEA4 are upregulated in HCC, and their high expression is significantly associated with poor patient prognosis\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Interestingly, Guan et al. found that when breast cancer cells were treated with the glucosylceramide (GlcCer) synthase inhibitor EtDO-P4, which inhibits the synthesis of all glycosphingolipids (GSLs) derived from GlcCer, the adhesion and movement capabilities of the breast cancer cells were enhanced\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Cumin\u0026rsquo;s team also found that knocking out endogenous E-cadherin in ovarian cancer cells induces epithelial-mesenchymal transition (EMT) and results in a decrease in the levels of Globo-series glycosphingolipids. The above results indicate that the regulation of glycosphingolipids on HCC is bidirectional.\u003c/p\u003e \u003cp\u003eIt is worth noting that Zheng's team has for the first time revealed the functional role of the metabolic enzyme Kcr in the progression of pancreatic cancer, and at the same time found that regulatory proteins of Kcr are closely related to lipid metabolism, especially the process of sphingolipid metabolism\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Our MR study indicates that the Kcr modification of DPF2 could reduce the level of Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0), thereby promoting the occurrence and development of HCC. Subsequently, the transcriptome data also verify the above conjecture. However, research on Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) remains limited. Given that many biological pathways, such as inflammation, oxidative stress, and apoptosis, are similar in different tissues and organs, insights gained from studying glycosphingolipids in other cancers can potentially inform the protective mechanisms of Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) in HCC. Therefore, we hypothesize that DPF2 may regulate specific enzymes or factors modified by Kcr, thereby increasing the Kcr level in hepatoma cells. This could accelerate the degradation of Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0) or activate pathways related to fatty acid β-oxidation or sphingolipid metabolism. By reducing the biosynthetic substrate of Glycosyl-N-behenoyl-sphingadienine, this process may disrupt cellular homeostasis, promote inflammation, epithelial-mesenchymal transition (EMT), cell migration and invasion, and ultimately accelerate the occurrence and progression of HCC.\u003c/p\u003e \u003cp\u003eThis study has the following advantages. Our research is one of the few that links the relationship of DPF2 in HCC to Kcr. In addition, a considerable number of 1,400 plasma metabolites are used as mediating factors to explore the role of lipid metabolites in the above process. Therefore, our results may provide new biomarkers for the early diagnosis or treatment of HCC. Moreover, MR, GSMR and transcriptomic data are used for mutual verification, further enhancing the causal inference ability and reliability of the study. However, this study still has certain limitations. The samples selected in the study are Finnish patients. Although we hope to provide some clues through this study to guide the current situation of the Chinese population, the applicability of the results may be affected by population differences. As Chinese scholars, we should subsequently collect samples from domestic HCC patients and conduct whole-genome sequencing. Although some results failed to pass the strict threshold of FDR correction, we still believe that the observation results may have biological significance. For example, although the statistical significance does not reach the preset threshold, these results are consistent with the support of our existing literature or experimental observations and may represent potentially important associations. In fact, in many practical biomedical studies, extremely strict statistical thresholds may sometimes deprive researchers of the sensitivity needed to detect potential new discoveries, especially in research fields with complex mechanisms and multiple potential interfering factors. Furthermore, the proportion of the mediating effect is only 13.5%, indicating that this pathway can only explain part of the role of DPF2 in promoting the occurrence and development of HCC. Among them, the mediating factors that account for a relatively large proportion still need to be further explored. Most importantly, the bioinformatics results can only suggest potential molecular mechanisms and cannot completely replace in vivo or in vitro biological experiments. Therefore, in our subsequent research, we will conduct knockout or overexpression experiments of DPF2 in hepatoma cell lines, and use specific antibodies to detect changes in the overall Kcr level through Western blot. Additionally, we will use Liquid Chromatography\u0026ndash;Mass Spectrometry to detect changes in Glycosyl-N-behenoyl-sphingadienine levels in hepatoma cells with knocked-out or overexpressed DPF2, as well as alterations in cell proliferation, migration, and invasion abilities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHepatocellular carcinoma (HCC)\u003c/p\u003e\n\u003cp\u003eSterol regulatory element-binding protein (SREBP)\u003c/p\u003e\n\u003cp\u003eStearoyl-CoA desaturase (SCD)\u003c/p\u003e\n\u003cp\u003eFatty acid synthase (FASN)\u003c/p\u003e\n\u003cp\u003ePost-translational modifications (PTMs)\u003c/p\u003e\n\u003cp\u003eLysine crotonylation (Kcr)\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR)\u003c/p\u003e\n\u003cp\u003eGeneralized summary-data-based Mendelian randomization (GSMR)\u003c/p\u003e\n\u003cp\u003eSingle nucleotide polymorphisms (SNPs)\u003c/p\u003e\n\u003cp\u003eHeterogeneity in dependent instruments (HEIDI)\u003c/p\u003e\n\u003cp\u003eExpression quantitative trait locus (eQTL)\u003c/p\u003e\n\u003cp\u003eThe Cancer Genome Atlas (TCGA)\u003c/p\u003e\n\u003cp\u003eGene Expression Omnibus (GEO)\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO)\u003c/p\u003e\n\u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG)\u003c/p\u003e\n\u003cp\u003eGene set variation analysis (GSVA)\u003c/p\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier (KM)\u003c/p\u003e\n\u003cp\u003eInverse variance weighted (IVW)\u003c/p\u003e\n\u003cp\u003eInstrumental variables (IVs)\u003c/p\u003e\n\u003cp\u003eLinkage disequilibrium (LD)\u003c/p\u003e\n\u003cp\u003eMendelian randomization pleiotropy residual sum and outlier (MR-PRESSO)\u003c/p\u003e\n\u003cp\u003eEpithelial\u0026ndash;mesenchymal transition (EMT)\u003c/p\u003e\n\u003cp\u003eLiquid chromatography\u0026ndash;mass spectrometry (LC\u0026ndash;MS)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are accessible through online repositories. We are deeply appreciative of all participants and researchers who shared these valuable datasets. If others wish to request access to the data used in this study, please contact [email protected].\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eLintao Dong,Jingping Hu:Writing original draft,Methodology,Formal analysis, Conceptualization.\u003c/p\u003e\n\u003cp\u003eFang Wang*:Writing \u0026ndash; review \u0026amp; editing, Conceptualization.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82460563, project title: The mechanism of PFKFB4 inhibition of SIRT2-mediated ketone body degradation regulating Rela/ZZ modification in promoting chemoresistance in colorectal cancer).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data for the exposure and outcome in this study were obtained from the GWAS database, and both have received ethical approval and participant informed consent. We appreciate all the participants and investigators for sharing these data.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2025;75(1):10-45\u003c/li\u003e\n\u003cli\u003eCheng K, Cai N, Zhu J et al. Tumor-associated macrophages in liver cancer: From mechanisms to therapy. \u003cem\u003eCancer Commun (Lond)\u003c/em\u003e. 2022;42(11):1112-1140\u003c/li\u003e\n\u003cli\u003eZhao Q, Lin X, Wang G. Targeting SREBP-1-Mediated Lipogenesis as Potential Strategies for Cancer. \u003cem\u003eFront Oncol\u003c/em\u003e. 2022;12:952371\u003c/li\u003e\n\u003cli\u003eCheng X, Li J, Guo D. 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Emerging evidence indicates that both lipid metabolism and post-translational modifications, particularly lysine crotonylation (Kcr), play critical roles in tumor progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, we employed Mendelian randomization, generalized summary-data-based Mendelian randomization, and transcriptomic analyses to explore the causal roles of Kcr regulatory genes in HCC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong 16 Kcr regulators, DPF2 was identified as significantly associated with increased HCC risk. Mediation analysis further revealed that DPF2 may promote HCC development by downregulating glycosyl-N-behenoyl-sphingadienine (d18:2/22:0), a glycosphingolipid with tumor-suppressive properties, accounting for 13.5% of its effect.Functional enrichment and gene set variation analysis demonstrated that DPF2 expression was linked to lipid metabolic processes, histone modification pathways, and inflammatory responses. Although some associations did not meet strict FDR correction thresholds, the findings were consistent with transcriptomic validation and previous literature, indicating potential biological relevance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, this study provides novel evidence supporting the role of DPF2 in the lipid metabolism\u0026ndash;Kcr axis of HCC and suggests its value as a potential biomarker or therapeutic target. Further in vivo and in vitro experiments are needed to elucidate the underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"Crontonylation regulatory factor DPF2 promotes the occurrence of hepatocellular carcinoma by regulating glycosphingolipid metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 14:41:50","doi":"10.21203/rs.3.rs-6627218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22110a30-60c3-45ae-9883-9ec09a82d256","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49255513,"name":"Biological sciences/Cancer/Gastrointestinal cancer"},{"id":49255514,"name":"Biological sciences/Molecular biology/Post translational modifications"},{"id":49255515,"name":"Biological sciences/Molecular biology/Transcriptomics"}],"tags":[],"updatedAt":"2025-09-16T05:24:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 14:41:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6627218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6627218","identity":"rs-6627218","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}