ENPP2 promotes progression and lipid accumulation via AMPK/SREBP1/FAS pathway in chronic lymphocytic leukemia

ENPP2 promotes progression and lipid accumulation via AMPK/SREBP1/FAS pathway in chronic lymphocytic leukemia | 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 ENPP2 promotes progression and lipid accumulation via AMPK/SREBP1/FAS pathway in chronic lymphocytic leukemia Xin Wang, Liyan Lu, Xinting Hu, Yang Han, Hua Wang, Xin Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3431397/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 Disorders of lipid metabolism are critical factors in the progression of chronic lymphocytic leukemia (CLL). The characteristics of lipid metabolism and related regulatory mechanisms of CLL remain unclear. Hence, we identified altered metabolites in CLL patients by lipidomic to investigate aberrant lipid metabolism pathways. Based on the area under the curve value, a combination of three metabolites (PC O-24:2_18:2, PC O-35:3, and LPC 34:3) potentially served as a biomarker for the diagnosis of CLL. Moreover, utilizing integrated lipidomic, transcriptomic, and molecular studies, we reveal that ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) plays a crucial role in regulating oncogenic lipogenesis. ENPP2 expression was significantly elevated in CLL patients compared to normal cells and was validated in an independent cohort. Besides, ENPP2 knockdown and targeted inhibitor PF-8380 treatment exerted an anti-tumor effect by regulating cell viability, proliferation, apoptosis, cell cycle, and enhanced the drug sensitivity to ibrutinib. Mechanistically, ENPP2 inhibited AMP-activated protein kinase (AMPK) phosphorylation and promoted lipogenesis through the sterol regulatory element-binding transcription factor 1 (SREBP-1)/fatty acid synthase (FAS) signaling pathway to promote lipogenesis. Taken together, our findings unravel the lipid metabolism characteristics of CLL, and highlight the potential role of ENPP2 as a novel therapeutic target for CLL treatment. Biological sciences/Cancer/Haematological cancer/Leukaemia/Chronic lymphocytic leukaemia Biological sciences/Cancer/Cancer metabolism Chronic lymphocytic leukemia Lipid metabolism ENPP2 PF-8380 AMPK LPL Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Chronic lymphocytic leukemia (CLL), a malignant B-cell tumor, is the most common form of adult leukemia in western countries[ 1 , 2 ]. As part of plastic and context-dependent metabolic reprogramming triggered by both oncogenic and environmental stimuli, cancer cells and other cell types use a variety of strategies to access lipids in the tumor microenvironment[ 3 , 4 ]. It has been observed that CLL cells could rapidly take up fatty acids to promote their proliferation[ 5 ]. A close association between altered lipid metabolism and pathogenicity is supported. In this context, particular lipid profiles are evolving as distinct biomarkers with diagnostic capabilities. On the other hand, with the development of targeted therapeutic agents, there have been significant improvements in CLL treatment[ 6 – 8 ], but CLL remains currently as a challenging hematologic neoplasm. Discovering innovative therapeutic targets for CLL remain significant imperatives that require attention. Ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), an adipocyte-derived lysophospholipase D, played an extensive role in many metabolisms[ 9 – 11 ]. ENPP2 expression is upregulated in corpulence patients and mice and is associated with insulin resistance and impaired glucose tolerance[ 11 , 12 ]. ENPP2 has been described to be engaged in several solid neoplasms, such as chondrosarcoma[ 13 ], breast cancer[ 14 ], hepatocellular carcinoma[ 15 ], and pancreatic cancer[ 16 ], and has been mentioned in multiple myeloma[ 17 ]. Nevertheless, the effects of ENPP2 inhibition in CLL remain poorly understood. Herein, we integrated lipidomics and transcriptomics to investigate the lipid metabolic features of CLL. In addition, our study was the first investigation on the role of ENPP2 in the tumorigenesis of CLL. The biological processes involved were examined through biological process by loss-of-function and gain-of-function assays, and unraveling the regulatory mechanism in CLL. In conclusion, our results will inform a highlighting CLL treatment strategy. Materials and methods Metabolomics Data Processing Data were analyzed as described previously[ 18 , 19 ]. Details of the methods was provided in the supplementary materials. Fold change (FC) > 2.0 or < 0.5, q value 1 was taken as the screening conditions to obtain significantly different metabolites. By using LipidSearch 4.2, all differential feature ions were annotated. Using the R package (heatmap), a heatmap was created using the annotated differential lipids. Based on the Kyoto encyclopedia of genes and genomes (KEGG) database, a pathway enrichment study was done on LIPEA. 25 enhanced pathways were displayed in a scatter plot. The area under the curve (AUC) was calculated and illustrated using GraphPad Prism 9.0. Transcriptome data analysis RNA extraction was performed on cell samples using RNAiso Plus from TaKaRa, located in Dalian, China. Subsequently, Huada Gene Technology Co. Ltd (Shenzhen, China) analyzed cell samples using the Illumina HiSeq 4000 platform. KEGG pathway analysis was performed on the screened differentially expressed genes (DEGs) using the DAVID v6.8 database ( https://david-d.ncifcrf.gov/ ) to acquire the biological functions of these DEGs. Cell lines and reagents The MEC-1 cell line, a human p53 deleted/mutated CLL cell line, was obtained from the Moores Cancer Center at the University of California, San Diego. The human CLL cell line, EHEB, was derived from American Type Culture Collection (ATCC, Manassas, VA, USA). These cells were cultured in supplemented IMDM, RPMI-1640 medium with 10% heat-inactivated FBS obtained from Gibco, MD, USA, alongside 1% penicillin/streptomycin mixture, 2 mM L-glutamine, and incubated under ideal conditions of 37°C with 5% CO2. Regular screening for mycoplasma infection was conducted on all cells. ENPP2 inhibitor PF-8380 (S8218, Selleck, Shanghai, China) and Ibrutinib (PCI-32765, MCE, Shanghai, China) were soluble in DMSO (Solarbio, Beijing, China). Patient specimens The Medical Ethics Committee of Shandong Provincial Hospital approved for this study, and informed consent was acquired from each patient. The participants in this study were 82 patients diagnosed and treated in the Department of Hematology at Shandong Provincial Hospital, and their blood samples were collected. The criteria for diagnosing CLL were based on the revised International Workshop on Chronic Lymphocytic Leukemia (IWCLL)[ 20 ]. Patients' peripheral blood mononuclear cells (PBMCs) were extracted using the FicollHypaque density gradient method according to previously reported methods[ 21 , 22 ]. RNA isolation and quantitative real-time PCR The purification of total RNA was carried out using RNAiso Plus (TaKaRa, Dalian, China). Reverse transcription was carried out utilizing a reverse transcription kit from the same source. In adherence to the manufacturer's instructions, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted, and the results were analyzed through Light cycler 480 software. Primer sequences were as follows: ENPP2-F: ACTTGTGATGATAAGGTAGAGCCA; ENPP2-R: CTGTAGACCCTTTTGTATGAAGCC; LPL-F: AGTAGCAGAGTCCGTGGCTA; LPL-R: ATTCCTGTTACCGTCCAGCC; GAPDH-F: 5′-GCACCGTCAAGGCTGAGAAC-3′; GAPDH-R: 5′-TGGTGAAGACGCCAGTGGA-3′. Details of the methods was provided in the supplementary materials. Plasmids mediated regulation of ENPP2 The sequences for ENPP2 shRNAS were as follows: shENPP2#1, 5’-GCAGCAAAGTCATGCCTAATA-3’; shENPP2#2, 5’-GCAGTGCTTTATCGGACTAGA-3’. The knockdown plasmids were synthesized by GenePharma (Shanghai, China). GenePharma (Shanghai, China) synthesized and purified corresponding negative control plasmids. The sequence of ENPP2 lvRNA was 5’-CGCAAATGGGCGGTAGGCGTG-3’. The pENTER-ENPP2-Flag/His plasmid was purchased from ViGene Biosciences Inc (Shandong, China). Lipofectamine 3000 reagent (Invitrogen) was used to transiently transfect plasmids into cells. Cell proliferation assays The procedure was performed as described preceding[ 21 , 23 ]. Cell Counting Kit-8 (CCK-8) from Dojindo, Kumamoto, Japan. Details of the methods was provided in the supplementary materials. Analysis of cell apoptosis and cell cycle The procedure was performed as described preceding[ 21 , 23 ]. The reagents used were as follows: Annexin V-PE/7AAD Kit (BD Biosciences, Bedford, MA, USA); PI/RNase Staining Buffer (BD Biosciences, Bedford, MA, USA). Details of the methods was provided in the supplementary materials. Elisa assay Collect the cell supernatant after treating the cells separately and the concentration of LPA was measured using human LPA ELISA Kit (LANSO, China). Western blotting The western blot procedure was performed as described preceding[ 21 , 23 ]. The primary antibodies used were as follows: ENPP2, LPL (Santa Cruz Biotechnology, USA), c-myc, Cyclin D1, CDK4, p21, p27, Bcl-2, Bax, PARP, cle-PARP (Cell Signaling Technology, USA), AMPK, FAS (abcam, USA), α-tubulin, and GAPDH (Zhongshan Goldenbridge, Beijing, China). Secondary antibodies from Zhongshan Goldenbridge, Beijing, China. Details of the methods was provided in the supplementary materials. Triglyceride (TG) assay The quantification of TG content in CLL cells was performed in accordance with the instructions provided by the manufacturer. A triglyceride quantification kit (BC0625, Solarbio, China) was utilized for this purpose. Lipid staining assay Incubate cells with BODIPY 493/503 (HY-W090090, MCE, USA) at 37 degrees for 30 minutes in the incubator, with DAPI (Beyotime, Shanghai, China) for 5 minutes at room temperature and observe under a fluorescent microscope. Co-Immunoprecipitation (Co-IP) assay Lysis of cells with Co-IP lysis solution. The resulting lysate was subjected to centrifugation, and the supernatant was treated with 1–3 ug of primary antibody before being shaken and incubated at 4°C overnight. Subsequently, Protein A/G Agarose (Santa Cruz Biotechnology, USA) were added to the antibody-treated buffer and incubated for 1 hours at 4°C to facilitate antibody binding. PBS wash the beads 3 times before heating at 100°C to denature the proteins. Detection of target proteins using western blotting. Statistical analysis The data in this paper underwent statistical analysis using SPSS 26.0 software and GraphPad Prism 9.0 statistical software. The study presents the mean ± standard deviation (SD) of results obtained from three distinct experiments. Student’s t-test and Mann-Whitney U test were used for direct comparisons, while multigroup comparisons were carried out using one-way ANOVA or two-way ANOVA. The significance threshold was established at * p < 0.05 to declare statistical significance. Results Untargeted metabolomics demonstrate significant differences in lipid metabolites between CLL patients and healthy control To investigate the differences of lipid metabolites in CLL patients and normal subjects, we retained blood supernatants for untargeted metabolomic profiling. In our study, we employed univariate analysis techniques, specifically assessing fold-change and utilizing t-test statistical testing with BH correction to derive q-values. Further to this, we integrated the VIP metric generated from multivariate statistical analysis, PLS-DA (Fig. S1 A-C) . The volcano plot (Fig. 1A) revealed a total of 913 differential feature ions, displaying apparent patterns of both upregulation and downregulation. To pinpoint the pathways with strong differential metabolite enrichment, we conducted a thorough analysis of the annotated results using enrichment analysis techniques. A comprehensive analysis has revealed the annotation of 52 differential metabolites, with 40 indicating an up-regulation and 12 indicating a down-regulation, as evidenced by Fig. 1B . Remarkable variations were observed in the levels of sphingolipids (SP), glycerolipids (GL), glycerophospholipids (GP), and fatty acids (FA) in patients afflicted with CLL, in comparison to their healthy counterparts. To illustrate the expression of the 52 annotated differential metabolites between CLL and healthy control groups, a clustering heatmap was utilized, as depicted in Fig. 1C . Pathway analysis was performed further on the differential metabolites, of which 24 pathways were significantly enriched. The KEGG enrichment scatter plot (Fig. 1D) shows that the metabolites differing between CLL patients and healthy controls were mainly labeled as enriched in glycerolipid metabolism, inositol phosphate metabolism, glycerophospholipid metabolism, ether lipid metabolism, sphingolipid metabolism, metabolic pathways. Then we conducted the comprehensive evaluation of the lipids to explore potential lipid biomarkers for diagnosing CLL. Through this investigation, five metabolites were discovered to have notable diagnostic significance, as evidenced by the top AUC values (Fig. 1E) . Notably, all of the top five lipids exhibited AUC values above 0.93. Especially, PC O-24:2_18:2 had the highest AUC value of 0.965, 95%CI of (0.9006, 1.000). Three of these metabolites were chosen as combinational potential biomarkers for CLL. The model equation established after removing the confounding factor was Y=-2.575 + 2.126*PC O-24:2_18:2 + 3.544*LPC 34:3 + 3.174* PC O-35:3. The area under the curve (AUC) value of these biomarkers was 0.92 (95% CI: 0.764–0.997), which was diagnostically significant. Combined analysis of metabolomics and transcriptomics data The genomic microarray profile GSE50006 was performed for transcriptome analysis. Set the DEGs screening threshold to |log2(fold change) |>0.25, adjusted to p < 0.01. In this study, 539 DEGs were identified and 257 of the pathways were enriched, as depicted in Fig. 2A-B . In our current study, the pathway analysis based on metabolomics and transcriptomics data produced 20 KEGG pathways (Fig. S1 E) . As depicted in Fig. 2D , these pathways cover a variety of metabolic processes, including phosphatidylinositol signaling system, phospholipase D signaling pathway, sphingolipid signaling pathway, choline metabolism in cancer, pathways in cancer, inositol phosphate metabolism, glycerophospholipid metabolism, glycerolipid metabolism, ether lipid metabolism, sphingolipid metabolism, fat digestion and absorption, and regulation of lipolysis in adipocytes. The details are shown in Table 1 . Table 1 Integrating metabolomics and transcriptomics for KEGG pathway enrichment and relevant genes. Pathway P-Value Corrected P-Value Gene Symbol Gap junction 0.038993 0.074212 TUBB6|PDGFD|PRKACB Autophagy - animal 0.00489 0.019897 IGF1R|DDIT4|EIF2AK3|HIF1A|PRKACB Phosphatidylinositol signaling system 0.200611 0.255915 PIK3C2B|DGKG Phospholipase D signaling pathway 0.008727 0.028197 CYTH3|RAPGEF3|PDGFD|DGKG|IGH Sphingolipid signaling pathway 0.260831 0.315672 S1PR5|TNFRSF1A Choline metabolism in cancer 0.001694 0.010158 CHPT1|PDGFD|DGKG|HIF1A|FOS Pathways in cancer 1.37E-05 0.000323 CDKN2B|IGF1R|FOS|GNB4|PRKACB|RXRA|HIF1A|SMAD3|IL6|DLL1|JUP|ARAF|CDK6|IL15|LEF1|MYC Insulin resistance 0.063329 0.104514 IL6|SOCS3|TNFRSF1A Pathogenic Escherichia coli infection 0.369197 0.410993 TUBB6 Inositol phosphate metabolism 0.460569 0.485242 PIK3C2B Metabolic pathways 0.210821 0.263247 PLD4|CD38|GCNT1|GPT2|AASS|NT5E|HACD1|PIK3C2B|MGAT3|RRM2|CHDH|CHPT1|LARGE1|DGKG|CSGALNACT1 Glycerophospholipid metabolism 0.04923 0.086061 PLD4|CHPT1|DGKG Glycerolipid metabolism 0.094317 0.147409 MGAT3|DGKG Ether lipid metabolism 0.008024 0.027446 PLD4|CHPT1|ENPP2 Sphingolipid metabolism 0.363591 0.415781 SGPP2 Fat digestion and absorption 0.048439 0.085311 SCARB1|MGAT3 Vitamin digestion and absorption 0.185855 0.240998 SCARB1 Regulation of lipolysis in adipocytes 0.369197 0.410993 PRKACB Retrograde endocannabinoid signaling 0.036793 0.071173 GNAO1|GABRB2|GNB4|PRKACB Long-term depression 0.015044 0.03859 GNAO1|IGF1R|ARAF The PPI network analysis was performed on all integrated DEGs using the STRING database and was visualized (Fig. S1 F) . Network analysis was performed using the logarithm of the fold change in metabolite levels between control and CLL samples and the differential gene ENPP2 was screened for further study. Overexpression of ENPP2 in CLL cells In the gene databases GSE50006 and GSE31048, which include 376 CLL patients, the expression of ENPP2 was significantly higher than in normal group (Fig. 2G-H) . Based on statistical data from GSE22762, the Kaplan-Meier method observed that exhibiting high levels of ENPP2 expression experienced a considerably diminished overall survival (Fig. 2F) . Additionally, CLL patient specimens were selected for qRT-PCR analysis which demonstrated that the expression level of ENPP2 in CLL specimens was significantly higher when compared to the normal group ( Fig. 2I) . Moreover, compared with B cells from healthy volunteers, ENPP2 mRNA expression in MEC-1 was significantly higher than that in B cells (Fig. S1 F). RNA sequencing analysis for ENPP2 functional enrichment in CLL cells To investigate the attributes of ENPP2, RNA-sequencing was performed on MEC-1 cells transfected with both ShControl and ShENPP2#2. The results of our study, depicted in Fig. 3A , indicate that ENPP2 was concentrated in pathways linked to metabolisms, such as the TCA cycle, ether lipid metabolism, and glycerophospholipid metabolism, through analysis of KEGG. Gene ontology (GO) analysis revealed that ENPP2 is intimately involved in metabolic processes, cellular processes, biological regulation (Fig. 3B) . Gene set enrichment analysis (GSEA) revealed that ENPP2 was primary enriched in glycerolipid metabolism, triacylglycerol, and GTP diphosphate lyase (Fig. 3C-E). Taken together, ENPP2 may promote the occurrence of CLL by regulating lipid metabolic pathways. ENPP2 regulates the proliferation, apoptosis, and cell cycle of CLL cell In order to confirm the results of our bioinformatics analysis, we conducted functional experiments in CLL cells to investigate the role of ENPP2. ENPP2 was successfully silenced by ShENPP2#1 and ShENPP2#2 in MEC-1 and EHEB cells (Fig. 4A) . We determined that downregulation of ENPP2 indirectly inhibited CLL cell proliferation through CCK-8 assays (Fig. 4B) . Furthermore, through Annexin V-PE/7AAD assay, we observed a notable increase in apoptosis of shENPP2 transfected cells (Fig. 4C) . Additionally, we monitored the cell cycle of downregulated ENPP2 cells and found that they exhibited a significant G0/G1 phase block compared to control cells (Fig. 4D). The results highlight that the ENPP2 contributes significantly to the survival of CLL cells through its ability to inhibit apoptosis and facilitate the progression of cells from the G0/G1 phase. To further verify the biological function of ENPP2, we constructed overexpressed plasmids (Fig. S2 A) . In contrast, ENPP2 overexpression promoted cell proliferation, reduced the proportion of apoptotic cells and accelerated the cell cycle (Fig. S2 B-D) . Targeted inhibition of ENPP2 by PF-8380 exerted anti-tumor activity in CLL cells ENPP2 inhibitor PF-8380 inhibited the activity of MEC-1 cells and CLL primary cells in dose-dependent and time-dependent (Fig. 5A-B) . In addition, ibrutinib supplementation with 16 µm or 4µm PF-8380 increased cytotoxicity to CLL cells (Fig. 5C, D) . Moreover, the amount of apoptotic cells increased with the increase of PF-8380 concentration after 24 h flow cytometry analysis of primary CLL cells treated with PF-8380 (Fig. 5E) . Compared with DMSO treatment, PF-8380 also induced the increase of G0/G1 phase cells (Fig. 5F) . Western blotting analysis showed that with the increase of PF-8380 concentration, the levels of cyclin-related proteins, including C-myc, Cyclin D1, CDK4, P21, and P27 (Fig. 5G) and apoptosis-related proteins such as Bcl-2 and Bax changed (Fig. 5H) . Taken together, PF-8380 exerts therapeutic potential by inhibiting CLL cell survival and cell cycle, enhancing apoptosis and chemosensitivity. ENPP2 regulates lipid metabolism in CLL Previous combined metabolomics and transcriptional analyses, as well as RNAseq, provide evidence that ENPP2 may act as a regulatory factor for lipid metabolism in CLL, and holds significance in the lipid metabolic process. To test this hypothesis, bodipy staining (Fig. 6A-B) showed increased lipid accumulation in ENPP2 overexpressed CLL cell lines and significantly reduced lipid accumulation in ENPP2 knockout cells. In addition, quantitative analysis of bodipy staining was performed by flow cytometry, and the results were consistent with the above (Fig. 6C) . Effect of targeted inhibition PF-8380 on lipid metabolism in CLL cells To explore the effect of ENPP2-targeted drug PF-8380 on the lipid metabolism of CLL, we treated CLL cell lines with 16µm and 32µm, respectively. We stained them with bodipy (Fig. 6D, F) . The study findings suggest that as drug concentration increased, the intracellular lipid deposition decreased gradually. The quantitative treatment of bodipy staining by flow cytometry showed the same results as before (Fig. 6E, G) . Besides, we measured the content of TG in the cells treated with the drug. The observed decline in TG content within the cells was found to be directly proportional to the increase in drug concentration (Fig. S3 A) . This trend is congruent with the results obtained via Bodipy staining. Taken together, the ENPP2 targeted inhibitor PF-8380 could alter the disease course by regulating lipid metabolism. ENPP2 functions through the AMPK/SREBP1/FAS pathway We then considered how ENPP2 regulates the process of lipogenesis in CLL. Based on RNA sequencing results, we became attracted to AMP-activated protein kinase (AMPK), which is a central player in metabolism[ 24 ] and negatively correlates with tumor progression and genesis[ 25 , 26 ]. AMPK/SREBP1/FAS pathway is one of the key pathways for intracellular lipogenesis. AMPK regulates the expression of adipogenic genes through the sterol regulatory elements binding transcription factor 1(SREBP1) transcription factor. We examined the protein levels of p-AMPK, AMPK, SREBP1, and fatty acid synthase (FAS) to elucidate the molecular mechanism of ENPP2 involvement in cellular lipid metabolism. The results showed that the AMPK phosphorylated form was significantly increased in ENPP2 knockdown cells compared to control. Moreover, ENPP2 knockdown significantly decreased SREBP1 and FAS proteins. ENPP2 overexpression showed results corresponding to knockdown cells (Fig. 7A) . In addition, we treated MEC-1 with 16µm and 32 µm PF-8380 which showed enhanced AMPK phosphorylation and attenuated SREBP1 and FAS (Fig. 7B) . ENPP2 is a secreted lysophospholipase D that promotes the hydrolysis of extracellular lysophosphatidylcholine (LPC) to lysophosphatidic acid (LPA)[ 27 ]. Therefore, we assayed the LPA content in cell supernatants after drug treatment utilizing ELISA. The results showed that both in the CLL cell line and primary cells from different CLL patients, LPA in the cell supernatant was significantly decreased after treatment with the targeted inhibitor PF-8380 compared to the DMSO control (Fig. 7C) . ENPP2 positively correlated with LPL Lipoprotein lipase (LPL) has been identified as a crucial driver in the metabolic processes of CLL cells by facilitating the absorption of lipoprotein[ 28 – 30 ]. We verified in the GEO database that LPL expression is increased in CLL patients and correlates with poor prognosis (Fig. 7D-F) . Meanwhile, in GEO database, ENPP2 expression exhibited a significant positive correlation with LPL (Spearman: r = 0.3057, p < 0.0001; Spearman: r = 0.3653, p < 0.0001) (Fig. 7G-H) . It is hypothesized that ENPP2 may regulate CLL lipid metabolism through LPL. We transfected ShENPP2 and LvENPP2 into CLL cell lines to detect LPL levels. The results showed a significant positive correlation between LPL and ENPP2 expression, evident through analysis of mRNA and protein levels (Fig. 7I-J) . Subsequently, further Co-IP experiments revealed potential interactions between ENPP2 and LPL in CLL cells (Fig. 7K) . Our results provide evidence that ENPP2 modulates LPL expression in CLL. Taken together, the catalytic function of ENPP2 in CLL tumorigenesis was preliminarily elucidated (Fig. 8) . Discussion In this study, lipidomic analysis suggested differences in lipid metabolites between CLL patients and healthy controls. The correlation between lipid metabolism and CLL has been substantiated. We used an untargeted quantitative metabolomics approach to examine and contrast the distinct serum metabolic profiles of CLL patients and healthy individuals. And then validated the selected metabolites and corresponding pathways by transcriptomic data, thus identifying altered biological processes or metabolic features in CLL patients. 52 differential metabolites and 539 differential genes were defined, and three metabolites (PC O-24:2_18:2, PC O-35:3, LPC 34:3) were selected as biomarkers for CLL diagnosis based on the ROC curve area. Additionally, further transcriptome associative analysis revealed that ENPP2 may has a significant function in lipid metabolism in CLL. However, the function of ENPP2 in CLL metabolism modulation remain considerably nebulous. Our present study represents the role of ENPP2 in the pathogenesis of CLL, which is significantly expressed in CLL patients and predicted poorer survival and prognosis. Further, it was demonstrated that ENPP2 is involved in lipid metabolic pathways in CLL and promote cell survival by AMPK pathway to promote lipid deposition. Cancer cells require metabolic reorganization to improve their value-added and survival rates compared to normally differentiated cells. Previous studies have found that lipid metabolism is important in tumorigenesis, progression and metastasis[ 31 ]. Disturbances in lipid metabolism may induce abnormal gene expression and lead to abnormal signaling pathways[ 32 ]. Altered lipid metabolism is closely related to pathogenic processes and can give rise to distinct disease biomarkers with diagnostic, prognostic, and predictive capabilities. The investigation of the metabolic changes in CLL cells has revealed their ability to store lipids and derive chemical energy from free fatty acids, similar to adipocytes[ 33 ]. Lipid droplet vesicles are present within the cytoplasm of CLL B cells, and upon incubation with free fatty acids, an upsurge in their metabolic rate could be witnessed[ 29 ]. Pallasch’s colleagues have identified significantly elevated levels of lipase-related genes and triglyceride-specific lipase activity in CLL B cells as compared to normal CD5 + B cells. Notably, the inhibition of lipase activity has been shown to increase CLL cell apoptosis[ 34 ]. ENPP2 as an adipose-derived secretory enzyme, controls adipose expansion, a fat brown supply and energy expenditure[ 11 ]. During the recent years, it has been shown that ENPP2 is closely correlated with obesity and disorders of glucolipid metabolism in obese individuals[ 35 ]. It is considered a possible target for the treatment of obesity-related diseases. Adipocyte ENPP2 expression was accompanied by a substantial increase in adipogenesis in individuals exhibiting type II diabetes associated with obesity[ 36 ]. Prior research has demonstrated the potential of ENPP2 as a prognostic biomarker in various cancers[ 37 ], including breast and liver cancer[ 38 , 39 ]. Cholia and his colleagues found that ENPP2 enhances the aggressive potential of glioblastoma[ 40 ]. Through a comprehensive analysis inclusive of RNA sequencing, this study sheds light on the regulatory role of ENPP2 in CLL. Furthermore, this investigation identified ENPP2 as an important biomarker of prognosis in CLL. Our analysis has revealed a dysregulated expression of ENPP2 in CLL, and a strong correlation between elevated ENPP2 expression and patient survival, as demonstrated in GSE22762, suggesting a potential role for ENPP2 in CLL progression. Further validation studies are required to confirm its predictive significance. Our findings indicate that the silencing of ENPP2 results in decreased cell proliferation, enhanced apoptosis, and G0/G1 cell cycle arrest. To elucidate the molecular mechanisms involved in lipid metabolism by ENPP2, we examined the degree of AMPK protein phosphorylation and downstream target gene regulation. AMPK is engaged in energy sensing and homeostasis regulation in vivo, and performs a crucial function in lipid regulation[ 41 ]. AMPK is believed to be fundamental for lipid metabolism through the regulation of fatty acid synthesis and regulation[ 42 , 43 ]. Prior research has demonstrated that AMPK could modulate SREBP1 and FAS, thereby impacting adipogenesis and lipid metabolism[ 24 ]. Coincidentally, ENPP2 has been described as a secreted lysophospholipase D that promotes the hydrolysis of extracellular LPC to LPA. It has been reported that LPA stimulates glucose uptake and regulates AMPK phosphorylation[ 44 – 46 ]. This connection may provide a novel insight into the regulation of lipid metabolism by ENPP2. Our research has demonstrated that the reduction of ENPP2 inhibits lipid accumulation by augmenting AMPK phosphorylation and reducing the level of SREBP1 and FAS. ENPP2 regulation of the AMPK/SREBP1/FAS signaling pathway may be an effective mechanism for anti-lipogenic effects in CLL cells. LPL is an enzyme normally expressed in adipocytes and muscle cells and is essential for the metabolism of free fatty acids[ 47 ]. It has been demonstrated that it is not expressed in normal lymphocytes, but its expression is increased in CLL cells. It has also been meaningfully associated with the prognosis of CLL, and high expression levels of LPL are usually associated with poorer clinical outcomes[ 48 , 49 ]. LPL induces lipoprotein storage in CLL cells and reprograms CLL cells to use lipids as an energy source preferentially. It seems to result in a higher cell survival rate[ 34 , 47 ]. Metabolic reprogramming is initiated as CLL cells increase their demand for energy and metabolites to meet their rapid proliferation and survival[ 2 ]. ENPP2 expression was increased in CLL cells. Consistent with our hypothesis, it has been observed that the downregulation of ENPP2 demonstrates notable anti-leukemic properties and reduced the role of key kinases in the lipid metabolism pathway. In CLL cells, LPL expression was reduced after ENPP2 silencing, whereas LPL expression was enhanced after ENPP2 overexpression, suggesting a positive effect of ENPP2 on LPL expression. We hypothesized that ENPP2 might participate in cellular lipid metabolism by binding to LPL, thus regulating CLL cell growth. Therefore, we elucidated the interaction between ENPP2 and LPL through Co-IP experiments. Our results demonstrate that the aberrant lipid metabolism pathway involved in ENPP2 is involved in the regulation of CLL onset and development. PF-8380 serves as a targeted inhibitor of ENPP2 and has been implicated in the pathogenesis and management of numerous diseases[ 50 ]. Specifically, PF-8380 has been shown to elicit a reduction in tumor vascularity, delay tumor growth, and heighten radiosensitivity in glioblastoma[ 51 ]. In a mouse model of hepatic encephalopathy, PF-8380 has demonstrated the ability to mitigate neuroinflammation and enhance neurological function[ 52 ]. Studies undertaken by D'Souza and colleagues have demonstrated that 24-hour incubation of adipocytes with PF-8380 resulted in increased production of peroxisome proliferator-activated receptor γ and downstream targets consequent to ENPP2 inhibition[ 53 ]. Nevertheless, the role of PF-8380 in the treatment of CLL warrants further exploration. We have demonstrated the antitumor effect of PF-8380 in CLL through in vitro experimentation, which confers a novel avenue for the treatment of this malignancy. Over the past few years, targeted drug therapies have demonstrated remarkable therapeutic effects in CLL[ 54 ]. Although Ibrutinib, a Bruton's tyrosine kinase inhibitor, has displayed impressive efficacy in CLL treatment[ 55 ], its clinical resistance is still a significant challenge. Drug resistance and toxicity lead to poor clinical outcomes[ 56 – 58 ], which could be mitigated through the implementation of combination therapy aimed at reducing the incidence of drug resistance[ 59 ]. In our study, we observed that the ENPP2-targeted inhibitor PF-8380 exhibited positive anti-drug resistance in CLL-targeted drug sensitivities, such as Ibrutinib, thus providing new prospects for clinical chemotherapy resistance. However, it is imperative to further investigate the mechanism of resistance and the clinical implementation of PF-8380 in the treatment of CLL. Conclusions In summary, our investigation has screened differential metabolites of CLL and established a diagnostic model utilizing lipidomic. Furthermore, our results have highlighted the potential of inhibiting ENPP2 to impede the progression of CLL. Specifically, we have observed anti-tumor effects of PF-8380 in CLL, such as hindering cell survival, enhancing cell apoptosis, and blocking the cell cycle. Taken together, our findings suggest that ENPP2 serves as a promising target for targeted therapeutic interventions, potentially paving the way for an innovative approach to treating CLL. Declarations Acknowledgements Not applicable. Author contributions XW and YZ designed the research and revised the manuscript. LL conducted the experiments and wrote the manuscript. XH and YH collected the data. HW, XZ and ZT performed the statistical analyses. All authors read and approved the final manuscript. Funding This study was funded by National Natural Science Foundation (No.82270200, No.82000195, No.82070203, and No.81770210); China Postdoctoral Science Foundation (No. 2022M721981); Key Research and Development Program of Shandong Province (No.2018CXGC1213); Taishan Scholars Program of Shandong Province (No. tspd20230610, tsqn201909184); Translational Research Grant of NCRCH (No.2021WWB02, No.2020ZKMB01); Shandong Provincial Natural Science Foundation (No.ZR2020QH094); Shandong Provincial Engineering Research Center of Lymphoma; Academic Promotion Programme of Shandong First Medical University (No.2019QL018, No. 2020RC007). Ethics approval and consent to participate Written informed consent was gained from all participants based on guidelines of the Declaration of Helsinki. All experiments were approved by the Medical Ethical Committee of Shandong Provincial Hospital. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Scarfo L, Ferreri AJ, Ghia P: Chronic lymphocytic leukaemia. Crit Rev Oncol Hematol 2016, 104:169–182. Nie Y, Yun X, Zhang Y, Wang X: Targeting metabolic reprogramming in chronic lymphocytic leukemia. Exp Hematol Oncol 2022, 11(1):39. Currie E, Schulze A, Zechner R, Walther TC, Farese RV, Jr.: Cellular fatty acid metabolism and cancer. Cell Metab 2013, 18(2):153–161. Martin-Perez M, Urdiroz-Urricelqui U, Bigas C, Benitah SA: The role of lipids in cancer progression and metastasis. Cell Metab 2022, 34(11):1675–1699. Thurgood LA, Best OG, Rowland A, Lower KM, Brooks DA, Kuss BJ: Lipid uptake in chronic lymphocytic leukemia. Exp Hematol 2022, 106:58–67. Smolewski P, Robak T: Current Treatment of Refractory/Relapsed Chronic Lymphocytic Leukemia: A Focus on Novel Drugs. Acta Haematol 2021, 144(4):365–379. Zhang J, Lu X, Li J, Miao Y: Combining BTK inhibitors with BCL2 inhibitors for treating chronic lymphocytic leukemia and mantle cell lymphoma. Biomark Res 2022, 10(1):17. Nguyen PH, Niesen E, Hallek M: New roles for B cell receptor associated kinases: when the B cell is not the target. Leukemia 2019, 33(3):576–587. Koike S, Keino-Masu K, Ohto T, Masu M: The N-terminal hydrophobic sequence of autotaxin (ENPP2) functions as a signal peptide. Genes Cells 2006, 11(2):133–142. Nishimasu H, Ishitani R, Aoki J, Nureki O: A 3D view of autotaxin. Trends Pharmacol Sci 2012, 33(3):138–145. Reeves VL, Trybula JS, Wills RC, Goodpaster BH, Dube JJ, Kienesberger PC, Kershaw EE: Serum Autotaxin/ENPP2 correlates with insulin resistance in older humans with obesity. Obesity (Silver Spring) 2015, 23(12):2371–2376. Rancoule C, Dusaulcy R, Treguer K, Gres S, Attane C, Saulnier-Blache JS: Involvement of autotaxin/lysophosphatidic acid signaling in obesity and impaired glucose homeostasis. Biochimie 2014, 96:140–143. Sioletic S, Czaplinski J, Hu L, Fletcher JA, Fletcher CD, Wagner AJ, Loda M, Demetri GD, Sicinska ET, Snyder EL: c-Jun promotes cell migration and drives expression of the motility factor ENPP2 in soft tissue sarcomas. J Pathol 2014, 234(2):190–202. Drosouni A, Panagopoulou M, Aidinis V, Chatzaki E: Autotaxin in Breast Cancer: Role, Epigenetic Regulation and Clinical Implications. Cancers (Basel) 2022, 14(21). Kaffe E, Magkrioti C, Aidinis V: Deregulated Lysophosphatidic Acid Metabolism and Signaling in Liver Cancer. Cancers (Basel) 2019, 11(11). Quan M, Cui JJ, Feng X, Huang Q: The critical role and potential target of the autotaxin/lysophosphatidate axis in pancreatic cancer. Tumour Biol 2017, 39(3):1010428317694544. Li Y, Zhang L, Xu T, Zhao X, Jiang X, Xiao F, Sun H, Wang L: Aberrant ENPP2 expression promotes tumor progression in multiple myeloma. Leuk Lymphoma 2022, 63(4):963–974. Yu C, Luo X, Zhan X, Hao J, Zhang L, YB LS, Shen C, Dong M: Comparative metabolomics reveals the metabolic variations between two endangered Taxus species (T. fuana and T. yunnanensis) in the Himalayas. BMC Plant Biol 2018, 18(1):197. Li Y, Fang J, Qi X, Lin M, Zhong Y, Sun L, Cui W: Combined Analysis of the Fruit Metabolome and Transcriptome Reveals Candidate Genes Involved in Flavonoid Biosynthesis in Actinidia arguta. Int J Mol Sci 2018, 19(5). Hallek M, Cheson BD, Catovsky D, Caligaris-Cappio F, Dighiero G, Dohner H, Hillmen P, Keating MJ, Montserrat E, Rai KR et al : Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia updating the National Cancer Institute-Working Group 1996 guidelines. Blood 2008, 111(12):5446–5456. Han Y, Hu X, Yun X, Liu J, Yang J, Tian Z, Zhang X, Zhang Y, Wang X: Nucleolar and spindle associated protein 1 enhances chemoresistance through DNA damage repair pathway in chronic lymphocytic leukemia by binding with RAD51. Cell Death Dis 2021, 12(11):1083. Hu X, Han Y, Liu J, Wang H, Tian Z, Zhang X, Zhang Y, Wang X: CTP synthase 2 predicts inferior survival and mediates DNA damage response via interacting with BRCA1 in chronic lymphocytic leukemia. Exp Hematol Oncol 2023, 12(1):6. Zhang Y, Zhou X, Li Y, Xu Y, Lu K, Li P, Wang X: Inhibition of maternal embryonic leucine zipper kinase with OTSSP167 displays potent anti-leukemic effects in chronic lymphocytic leukemia. Oncogene 2018, 37(41):5520–5533. Keerthana CK, Rayginia TP, Shifana SC, Anto NP, Kalimuthu K, Isakov N, Anto RJ: The role of AMPK in cancer metabolism and its impact on the immunomodulation of the tumor microenvironment. Front Immunol 2023, 14:1114582. Avsec D, Jakos Djordjevic AT, Kanduser M, Podgornik H, Skerget M, Mlinaric-Rascan I: Targeting Autophagy Triggers Apoptosis and Complements the Action of Venetoclax in Chronic Lymphocytic Leukemia Cells. Cancers (Basel) 2021, 13(18). Verrelli D, Dallera L, Stendardo M, Monzani S, Pasqualato S, Giorgio M, Pallavi R: Hydroxycitric Acid Inhibits Chronic Myelogenous Leukemia Growth through Activation of AMPK and mTOR Pathway. Nutrients 2022, 14(13). Sah JP, Hao NTT, Han X, Tran TTT, McCarthy S, Oh Y, Yoon JK: Ectonucleotide pyrophosphatase 2 (ENPP2) plays a crucial role in myogenic differentiation through the regulation by WNT/β-Catenin signaling. The International Journal of Biochemistry & Cell Biology 2020, 118. Prieto D, Seija N, Uriepero A, Souto-Padron T, Oliver C, Irigoin V, Guillermo C, Navarrete MA, Ines Landoni A, Dighiero G et al : LPL protein in Chronic Lymphocytic Leukaemia have different origins in Mutated and Unmutated patients. Advances for a new prognostic marker in CLL. Br J Haematol 2018, 182(4):521–525. Rozovski U, Grgurevic S, Bueso-Ramos C, Harris DM, Li P, Liu Z, Wu JY, Jain P, Wierda W, Burger J et al : Aberrant LPL Expression, Driven by STAT3, Mediates Free Fatty Acid Metabolism in CLL Cells. Mol Cancer Res 2015, 13(5):944–953. Wu Z, Gu D, Wang R, Zuo X, Zhu H, Wang L, Lu X, Xia Y, Qin S, Zhang W et al : CircRIC8B regulates the lipid metabolism of chronic lymphocytic leukemia through miR199b-5p/LPL axis. Exp Hematol Oncol 2022, 11(1):51. Long J, Zhang CJ, Zhu N, Du K, Yin YF, Tan X, Liao DF, Qin L: Lipid metabolism and carcinogenesis, cancer development. Am J Cancer Res 2018, 8(5):778–791. Lim SA, Su W, Chapman NM, Chi H: Lipid metabolism in T cell signaling and function. Nat Chem Biol 2022, 18(5):470–481. Rozovski U, Hazan-Halevy I, Barzilai M, Keating MJ, Estrov Z: Metabolism pathways in chronic lymphocytic leukemia. Leuk Lymphoma 2016, 57(4):758–765. Pallasch CP, Schwamb J, Konigs S, Schulz A, Debey S, Kofler D, Schultze JL, Hallek M, Ultsch A, Wendtner CM: Targeting lipid metabolism by the lipoprotein lipase inhibitor orlistat results in apoptosis of B-cell chronic lymphocytic leukemia cells. Leukemia 2008, 22(3):585–592. Chattopadhyay A, Mukherjee P, Sulaiman D, Wang H, Girjalva V, Dorreh N, Jacobs JP, Delk S, Moolenaar WH, Navab M et al : Role of enterocyte Enpp2 and autotaxin in regulating lipopolysaccharide levels, systemic inflammation, and atherosclerosis. Journal of Lipid Research 2023, 64(5). Saulnier-Blache JS: [Secretion and role of autotaxin and lysophosphatidic acid in adipose tissue]. J Soc Biol 2006, 200(1):77–81. Panagopoulou M, Fanidis D, Aidinis V, Chatzaki E: ENPP2 Methylation in Health and Cancer. Int J Mol Sci 2021, 22(21). Panagopoulou M, Drosouni A, Fanidis D, Karaglani M, Balgkouranidou I, Xenidis N, Aidinis V, Chatzaki E: ENPP2 Promoter Methylation Correlates with Decreased Gene Expression in Breast Cancer: Implementation as a Liquid Biopsy Biomarker. Int J Mol Sci 2022, 23(7). Deng W, Chen F, Zhou Z, Huang Y, Lin J, Zhang F, Xiao G, Liu C, Liu C, Xu L: Hepatitis B Virus Promotes Hepatocellular Carcinoma Progression Synergistically With Hepatic Stellate Cells via Facilitating the Expression and Secretion of ENPP2. Front Mol Biosci 2021, 8:745990. Cholia RP, Dhiman M, Kumar R, Mantha AK: Oxidative stress stimulates invasive potential in rat C6 and human U-87 MG glioblastoma cells via activation and cross-talk between PKM2, ENPP2 and APE1 enzymes. Metab Brain Dis 2018, 33(4):1307–1326. Kim MH, Seong JB, Huh JW, Bae YC, Lee HS, Lee DS: Peroxiredoxin 5 ameliorates obesity-induced non-alcoholic fatty liver disease through the regulation of oxidative stress and AMP-activated protein kinase signaling. Redox Biol 2020, 28:101315. Garcia D, Shaw RJ: AMPK: Mechanisms of Cellular Energy Sensing and Restoration of Metabolic Balance. Mol Cell 2017, 66(6):789–800. Janzen NR, Whitfield J, Hoffman NJ: Interactive Roles for AMPK and Glycogen from Cellular Energy Sensing to Exercise Metabolism. Int J Mol Sci 2018, 19(11). Joshi L PI, Bernhart E, Reicher H, Koyani CN, Madl T, Madreiter-Sokolowski C, Koshenov Z, Graier WF, Hallström S, Sattler W.: Lysophosphatidic Acid Induces Aerobic Glycolysis, Lipogenesis, and Increased Amino Acid Uptake in BV-2 Microglia. Int J Mol Sci 2021 Feb 17(22). Chen H LS, Liu X, Yang J, Wang F, Cong X, Chen X: Lysophosphatidic Acid Pretreatment Attenuates Myocardial Ischemia/Reperfusion Injury in the Immature Hearts of Rats. 2017 Mar 21(8:153). Kim EK PJ, Lim S, Choi JW, Kim HS, Seok H, Seo JK, Oh K, Lee DS, Kim KT, Ryu SH, Suh PG: Activation of AMP-activated Protein Kinase Is Essential for Lysophosphatidic Acid-induced Cell Migration in Ovarian Cancer Cells. 2011 Jul 8(286(27)). Prieto D, Oppezzo P: Lipoprotein Lipase Expression in Chronic Lymphocytic Leukemia: New Insights into Leukemic Progression. Molecules 2017, 22(12). Rombout A, Verhasselt B, Philippe J: Lipoprotein lipase in chronic lymphocytic leukemia: function and prognostic implications. Eur J Haematol 2016, 97(5):409–415. McCaw L, Shi Y, Wang G, Li YJ, Spaner DE: Low Density Lipoproteins Amplify Cytokine-signaling in Chronic Lymphocytic Leukemia Cells. EBioMedicine 2017, 15:24–35. Tan Z, Lei H, Guo M, Chen Y, Zhai X: An updated patent review of autotaxin inhibitors (2017-present). Expert Opin Ther Pat 2021, 31(5):421–434. Bhave SR, Dadey DY, Karvas RM, Ferraro DJ, Kotipatruni RP, Jaboin JJ, Hallahan AN, Dewees TA, Linkous AG, Hallahan DE et al : Autotaxin Inhibition with PF-8380 Enhances the Radiosensitivity of Human and Murine Glioblastoma Cell Lines. Front Oncol 2013, 3:236. Roy S, Chakrabarti M, Dasgupta H, Mahale A, Tripathi S, Sharma V, Banerjee M, Kulkarni OP: Inhibition of Autotaxin Ameliorates LPA-Mediated Neuroinflammation and Alleviates Neurological Dysfunction in Acute Hepatic Encephalopathy. ACS Chem Neurosci 2022, 13(19):2829–2841. D'Souza K, Kane DA, Touaibia M, Kershaw EE, Pulinilkunnil T, Kienesberger PC: Autotaxin Is Regulated by Glucose and Insulin in Adipocytes. Endocrinology 2017, 158(4):791–803. Smolej L: Refractory Chronic Lymphocytic Leukemia: A Therapeutic Challenge. Curr Cancer Drug Targets 2016, 16(8):701–709. Skanland SS, Karlsen L, Tasken K: B cell signalling pathways-New targets for precision medicine in chronic lymphocytic leukaemia. Scand J Immunol 2020, 92(5):e12931. Busque L: HSPCs are marathon and not sprint relay athletes. Blood 2017, 129(11):1406–1407. George B, Chowdhury SM, Hart A, Sircar A, Singh SK, Nath UK, Mamgain M, Singhal NK, Sehgal L, Jain N: Ibrutinib Resistance Mechanisms and Treatment Strategies for B-Cell lymphomas. Cancers (Basel) 2020, 12(5). Kaur V, Swami A: Ibrutinib in CLL: a focus on adverse events, resistance, and novel approaches beyond ibrutinib. Ann Hematol 2017, 96(7):1175–1184. Timofeeva N, Gandhi V: Ibrutinib combinations in CLL therapy: scientific rationale and clinical results. Blood Cancer J 2021, 11(4):79. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Supplementaryfigure1.jpg Supplementary figure 1. (A) LC-MS based PCA score plot. (B) PLS-DA score plot (R2 = 0.935, Q2 = 0.679). (C) PLS-DA model alignment test. Two coordinate points on the scoring plot are relatively far apart, indicating a significant difference between the two samples, and vice versa. The oval area represents the 95% confidence interval. (D) Higher expression of ENPP2 mRNA in CLL cell line MEC-1 than in normal CD19 + B cells was detected by qRT-PCR. (E) Venn diagram showing metabolomics and transcriptomics with 20 intersecting genes. (F) All integrated DEGs were analyzed for protein-protein interaction networks using the STRING database. Supplementaryfigure2.jpg Supplementary figure 2. (A) qRT-PCR detection of ENPP2 overexpression efficiency. (B) Proliferation curves of ENPP2 overexpression and control cells. (C) Flow cytometry detection of apoptosis after ENPP2 overexpression.(D) Flow cytometry detection of cycle distribution after ENPP2 overexpression and the relative proportions of cells in different cell cycle phases. Data were shown as the mean ± SD of at least three independent experiments. * p <0.05, ** p <0.01, *** p <0.001. Supplementaryfigure3.jpg Supplementary figure 3. Detection of intracellular triglyceride content. Data were shown as the mean ± SD of at least three independent experiments. * p <0.05, ** p <0.01, *** p <0.001. Supplementarymaterial.docx 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-3431397","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":239661556,"identity":"f4917ea2-1c8c-44fb-b00a-6ac2fb84b4fb","order_by":0,"name":"Xin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDACZiBOMPgvx8befIAELR8qmI35eI4lEG8R44wzzInzJHIUiFNucJz3mDRvG1t6G0MOA8OPim2EtUg286UBtfDktjGcPcDYc+Y2YS38zDxmt3nbJHLbGPsSmBnbiNDCBtFikA5kGBCnBWTLzRlnEhLY2IjVItnMY/7jQ8UBwzYetoSDRPnF4PwZY4MEgwPy8vMfH3zwo4IILSjgAInqR8EoGAWjYBTgAgAeYDWH83C1awAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":239661557,"identity":"2a2cedea-c17f-48ff-b4da-cb67a17d2727","order_by":1,"name":"Liyan Lu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liyan","middleName":"","lastName":"Lu","suffix":""},{"id":239661558,"identity":"6455200c-935a-48bf-a163-d85317336d81","order_by":2,"name":"Xinting Hu","email":"","orcid":"","institution":"Shandong Provincial Hospital, Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinting","middleName":"","lastName":"Hu","suffix":""},{"id":239661559,"identity":"89fb75ba-f98b-402e-bf38-4514c6d3dfe7","order_by":3,"name":"Yang Han","email":"","orcid":"https://orcid.org/0000-0001-9501-7814","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Han","suffix":""},{"id":239661560,"identity":"9b8b8c18-0199-4caa-99d5-d0d50f7a0f85","order_by":4,"name":"Hua Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Wang","suffix":""},{"id":239661561,"identity":"bde13abe-968d-42be-927e-7ae00572f2a5","order_by":5,"name":"Xin Zhang","email":"","orcid":"","institution":"Shandong Provincial Hospital, Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":239661562,"identity":"da46591f-cd70-4782-9b8f-759bb95ad66a","order_by":6,"name":"Zheng Tian","email":"","orcid":"","institution":"Shandong Provincial Hospital, Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Tian","suffix":""},{"id":239661563,"identity":"1aa2c6d0-1b15-4828-ab61-de47f79a057b","order_by":7,"name":"Ya Zhang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-10-11 09:20:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3431397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3431397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44740895,"identity":"10992013-c245-48ac-a985-871c1750bba3","added_by":"auto","created_at":"2023-10-16 23:22:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6311184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGC/LC-MS based multivariate data analysis of serum data between CLL groups and healthy controls.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Differential metabolite volcano map. \u003cstrong\u003e(B)\u003c/strong\u003eIdentification and annotation of metabolites. \u003cstrong\u003e(C)\u003c/strong\u003e Heat map clustering of serum metabolites from the case and healthy control groups according to LC-MS. Significantly upregulated metabolites are shown in red (FC ≥ 1, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), significantly downregulated metabolites are shown in blue (FC ≤ -1, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), and non-significantly different metabolites are shown in gray. \u003cstrong\u003e(D)\u003c/strong\u003eEnrichment analysis of metabolites was performed. The scatter plot shows the most variable metabolic pathways. \u003cstrong\u003e(E)\u003c/strong\u003e \u0026nbsp;ROC curves of metabolic products.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/c2ff7058a11439da81ebfb25.jpg"},{"id":44740559,"identity":"15007996-a7d7-4602-a69a-2a1266d89762","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4256647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined transcriptome and metabolome data analysis revealed abnormal expression of ENPP2 in CLL. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Sequencing data GSE50006 was downloaded from the GEO database with the screening condition |log2(fold change) |\u0026gt;2, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. Significantly up-regulated genes are shown in red, significantly down-regulated genes are shown in blue, and non-significantly different genes are shown in gray. (\u003cstrong\u003eB\u003c/strong\u003e) Enrichment analysis of differential genes was performed. \u003cstrong\u003e(C)\u003c/strong\u003e KEGG enrichment was performed on 20 genes.\u003cstrong\u003e (D)\u003c/strong\u003e Overall survival (OS) curves of CLL patients based on ENPP2 stratified expression of GSE22762.\u003cstrong\u003e (E)-(F) \u003c/strong\u003eENPP2 expression was significantly upregulated in the CLL public database. The analysis was based on GSE5006 and GSE31048 respectively.\u003cstrong\u003e (G)\u003c/strong\u003e ENPP2 mRNA was elevated in CLL progenitor cells compared to normal CD19\u003csup\u003e+\u003c/sup\u003e B cells. Data are shown as the mean ± SD. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/1e0a2ca3492190c900c72af3.jpg"},{"id":44740557,"identity":"6b60440b-df03-4e64-9ed0-b5a447897a79","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4108104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNA-seq analysis of ENPP2 between ShControl and ShENPP2 cells. (A)\u003c/strong\u003e KEGG enrichment analysis.\u003cstrong\u003e (B)\u003c/strong\u003e GO terms analysis of differently expressing genes. (\u003cstrong\u003eC)-(E)\u003c/strong\u003e GSEA analysis of different gene expression correlated with ENPP2 was performed. NES normalized enrichment score.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/a292e43901bd9aab907502d4.jpg"},{"id":44742095,"identity":"74d86393-0655-4a56-af80-659bb0d2022a","added_by":"auto","created_at":"2023-10-16 23:30:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3736523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eENPP2 knockdown restrained the survival of CLL cell lines. (A) \u003c/strong\u003eqRT-PCR assay for knockdown efficiency. \u003cstrong\u003e(B)\u003c/strong\u003e Proliferation curves of ENPP2 knockdown cells and control cells. \u003cstrong\u003e(C)\u003c/strong\u003e Flow cytometry detection of apoptosis after ENPP2 knockdown. \u003cstrong\u003e(D)\u003c/strong\u003e Flow cytometry detection of cycle distribution after ENPP2 knockdown and the relative proportions of cells in different cell cycle phases. Data are shown as the mean ± SD. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/721a85438f36ea2588a7bf2a.jpg"},{"id":44740555,"identity":"237d432b-0ed7-4b0e-ba8a-e8188c716b2c","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4443601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of ENPP2 target inhibitor PF-8380 in CLL cells. (A)\u003c/strong\u003e CCK8 assay to detect the survival rate of CLL cell line MEC-1 treated with different concentrations of PF-8380.\u003cstrong\u003e(B) \u003c/strong\u003eCCK8 assay to detect the survival rate of CLL primary cells treated with different concentrations of PF-8380.\u003cstrong\u003e (C) \u003c/strong\u003eCCK8 assay to detect the survival rate of MEC-1 cells in combination with ibrutinib.\u003cstrong\u003e(D) \u003c/strong\u003eCCK8 assay to detect the cell survival rate of CLL primary cells treated with ibrutinib. \u003cstrong\u003e(E)\u003c/strong\u003e Representative dot plots generated by flow cytometry analysis of PF-8380 groups versus negative control.\u003cstrong\u003e (F)\u003c/strong\u003e Representative results for the cell cycle distributions with PF-8380. \u003cstrong\u003e(G)\u003c/strong\u003e Detection of cycle-associated protein expression levels in MEC-1 cells. \u003cstrong\u003e(H) \u003c/strong\u003eDetection of apoptosis-associated protein expression levels in MEC-1 cells.\u003cstrong\u003e \u003c/strong\u003eData were shown as the mean ± SD of at least three independent experiments. *\u003cem\u003e p\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003e p\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/02d716f28596ffe5822037c7.jpg"},{"id":44740558,"identity":"2e34486a-84e9-4f4b-aa74-23fe0cbd08c6","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8055635,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of ENPP2 knockdown and overexpression on intracellular lipids. Alterations of lipids in cells treated with PF-8380. (A)-(B) \u003c/strong\u003eDetection of intracellular lipid content by Bodipy staining.\u003cstrong\u003e (C) \u003c/strong\u003eQuantification of intracellular lipid content by flow cytometry.\u003cstrong\u003e (D) \u003c/strong\u003eDetection of intracellular lipid content of MEC-1 by Bodipy staining.\u003cstrong\u003e(E) \u003c/strong\u003eQuantification of intracellular lipid content of MEC-1 by flow cytometry.\u003cstrong\u003e(F) \u003c/strong\u003eDetection of intracellular lipid content of EHEB by Bodipy staining.\u003cstrong\u003e(G) \u003c/strong\u003eQuantification of intracellular lipid content of EHEB by flow cytometry.\u003cstrong\u003e \u003c/strong\u003eData were shown as the mean ± SD of at least three independent experiments. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/fe11bcc5b9b1abeb03acb2f2.jpg"},{"id":44740553,"identity":"9d035598-ad19-4790-b75d-224b38bcf9cb","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4399546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eENPP2 regulated AMPK signaling pathway and interacted with LPL. (A)\u003c/strong\u003e Protein expression of ENPP2, p-AMPK, AMPK, SREBP1, FAS.\u003cstrong\u003e (B)\u003c/strong\u003e Protein expression of ENPP2, p-AMPK, AMPK, SREBP1, FAS with PF-8380 treated. \u003cstrong\u003e(C) \u003c/strong\u003eMEC-1, CLL patient cells were treated with 32 μm PF8380 for 24 hours and the cell supernatant LPA levels were measured by ELISA. \u003cstrong\u003e(D) \u003c/strong\u003eLPL was markedly upregulated in CLL public database. Analyses were based on GSE31048.\u003cstrong\u003e(E)\u003c/strong\u003e CLL patients with unmutated IGHV presented high LPL expression (GSE69034).\u003cstrong\u003e(F)\u003c/strong\u003eKaplan–Meier survival curves of CLL patients from GSE22762 with stratified LPL expression. \u003cstrong\u003e(G)-(H) \u003c/strong\u003eCorrelation between ENPP2 and LPL mRNA expression in CLL patients from GSE50006 and GSE31048.\u003cstrong\u003e (I) \u003c/strong\u003eqRT-PCR was performed to detect LPL mRNA content in ENPP2 knockdown and overexpression cells. \u003cstrong\u003e(J) \u003c/strong\u003eWestren Blot assays for the amount of LPL protein in ENPP2 knockdown and overexpression cells. \u003cstrong\u003e(K) \u003c/strong\u003eCo-immunoprecipitation demonstrated that ENPP2 and LPL could be co-precipitated. Data were shown as the mean ± SD of at least three independent experiments. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/b960970b0edb4803c163c1f7.jpg"},{"id":44740893,"identity":"940d19af-b1b0-4e2f-8bfb-f6c21ba90bb6","added_by":"auto","created_at":"2023-10-16 23:22:03","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":59175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic model of ENPP2 mediated lipid metabolism.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/23ea8da45cf3b93f4851d64a.jpg"},{"id":46408811,"identity":"38116bb2-038f-4283-8b95-3a77f04f0411","added_by":"auto","created_at":"2023-11-14 11:16:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1796457,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/54ab8854-3be9-42e2-8499-1a0dda85d25e.pdf"},{"id":44740892,"identity":"9c28dab2-40e4-44e7-9305-a71ed2be5e84","added_by":"auto","created_at":"2023-10-16 23:22:03","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4198960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 1. (A)\u003c/strong\u003e LC-MS based PCA score plot. (\u003cstrong\u003eB\u003c/strong\u003e) PLS-DA score plot (R2 = 0.935, Q2 = 0.679). \u003cstrong\u003e(C)\u003c/strong\u003e PLS-DA model alignment test. Two coordinate points on the scoring plot are relatively far apart, indicating a significant difference between the two samples, and vice versa. The oval area represents the 95% confidence interval. \u003cstrong\u003e(D)\u003c/strong\u003e Higher expression of ENPP2 mRNA in CLL cell line MEC-1 than in normal CD19\u003csup\u003e+\u003c/sup\u003e B cells was detected by qRT-PCR. \u003cstrong\u003e(E)\u003c/strong\u003e Venn diagram showing metabolomics and transcriptomics with 20 intersecting genes.\u003cstrong\u003e (F) \u003c/strong\u003eAll integrated DEGs were analyzed for protein-protein interaction networks using the STRING database.\u003c/p\u003e","description":"","filename":"Supplementaryfigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/d9be4ba8db843235022f8ff3.jpg"},{"id":44740556,"identity":"badc29f0-e954-4214-a85d-2d3a673446c2","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3541844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 2. (A) \u003c/strong\u003eqRT-PCR detection of ENPP2 overexpression efficiency. \u003cstrong\u003e(B)\u003c/strong\u003e Proliferation curves of ENPP2 overexpression and control cells. \u003cstrong\u003e(C)\u003c/strong\u003e Flow cytometry detection of apoptosis after ENPP2 overexpression.\u003cstrong\u003e(D)\u003c/strong\u003e Flow cytometry detection of cycle distribution after ENPP2 overexpression and the relative proportions of cells in different cell cycle phases. Data were shown as the mean ± SD of at least three independent experiments. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003e p\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Supplementaryfigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/db982b6224439b74ea0b3510.jpg"},{"id":44740548,"identity":"78bbd1d8-6ef1-48c4-9697-7acc535f211f","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2451439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 3. \u003c/strong\u003eDetection of intracellular triglyceride content.\u003cstrong\u003e \u003c/strong\u003eData were shown as the mean ± SD of at least three independent experiments. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Supplementaryfigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/f44b64a57d3f3cf06d23c67f.jpg"},{"id":44740549,"identity":"e54f5c46-b7c6-466d-9dcf-f08658a3420c","added_by":"auto","created_at":"2023-10-16 23:14:03","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":150280,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3431397/v1/da05d87e775acba48ff81615.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"ENPP2 promotes progression and lipid accumulation via AMPK/SREBP1/FAS pathway in chronic lymphocytic leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic lymphocytic leukemia (CLL), a malignant B-cell tumor, is the most common form of adult leukemia in western countries[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As part of plastic and context-dependent metabolic reprogramming triggered by both oncogenic and environmental stimuli, cancer cells and other cell types use a variety of strategies to access lipids in the tumor microenvironment[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It has been observed that CLL cells could rapidly take up fatty acids to promote their proliferation[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A close association between altered lipid metabolism and pathogenicity is supported. In this context, particular lipid profiles are evolving as distinct biomarkers with diagnostic capabilities. On the other hand, with the development of targeted therapeutic agents, there have been significant improvements in CLL treatment[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but CLL remains currently as a challenging hematologic neoplasm. Discovering innovative therapeutic targets for CLL remain significant imperatives that require attention.\u003c/p\u003e \u003cp\u003eEctonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), an adipocyte-derived lysophospholipase D, played an extensive role in many metabolisms[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. ENPP2 expression is upregulated in corpulence patients and mice and is associated with insulin resistance and impaired glucose tolerance[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. ENPP2 has been described to be engaged in several solid neoplasms, such as chondrosarcoma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], breast cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], hepatocellular carcinoma[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and pancreatic cancer[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and has been mentioned in multiple myeloma[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, the effects of ENPP2 inhibition in CLL remain poorly understood.\u003c/p\u003e \u003cp\u003eHerein, we integrated lipidomics and transcriptomics to investigate the lipid metabolic features of CLL. In addition, our study was the first investigation on the role of ENPP2 in the tumorigenesis of CLL. The biological processes involved were examined through biological process by loss-of-function and gain-of-function assays, and unraveling the regulatory mechanism in CLL. In conclusion, our results will inform a highlighting CLL treatment strategy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics Data Processing\u003c/h2\u003e \u003cp\u003eData were analyzed as described previously[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Details of the methods was provided in the supplementary materials. Fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2.0 or \u0026lt;\u0026thinsp;0.5, q value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 was taken as the screening conditions to obtain significantly different metabolites. By using LipidSearch 4.2, all differential feature ions were annotated. Using the R package (heatmap), a heatmap was created using the annotated differential lipids. Based on the Kyoto encyclopedia of genes and genomes (KEGG) database, a pathway enrichment study was done on LIPEA. 25 enhanced pathways were displayed in a scatter plot. The area under the curve (AUC) was calculated and illustrated using GraphPad Prism 9.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome data analysis\u003c/h2\u003e \u003cp\u003eRNA extraction was performed on cell samples using RNAiso Plus from TaKaRa, located in Dalian, China. Subsequently, Huada Gene Technology Co. Ltd (Shenzhen, China) analyzed cell samples using the Illumina HiSeq 4000 platform. KEGG pathway analysis was performed on the screened differentially expressed genes (DEGs) using the DAVID v6.8 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david-d.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david-d.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to acquire the biological functions of these DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and reagents\u003c/h2\u003e \u003cp\u003e The MEC-1 cell line, a human p53 deleted/mutated CLL cell line, was obtained from the Moores Cancer Center at the University of California, San Diego. The human CLL cell line, EHEB, was derived from American Type Culture Collection (ATCC, Manassas, VA, USA). These cells were cultured in supplemented IMDM, RPMI-1640 medium with 10% heat-inactivated FBS obtained from Gibco, MD, USA, alongside 1% penicillin/streptomycin mixture, 2 mM L-glutamine, and incubated under ideal conditions of 37\u0026deg;C with 5% CO2. Regular screening for mycoplasma infection was conducted on all cells. ENPP2 inhibitor PF-8380 (S8218, Selleck, Shanghai, China) and Ibrutinib (PCI-32765, MCE, Shanghai, China) were soluble in DMSO (Solarbio, Beijing, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePatient specimens\u003c/h2\u003e \u003cp\u003e The Medical Ethics Committee of Shandong Provincial Hospital approved for this study, and informed consent was acquired from each patient. The participants in this study were 82 patients diagnosed and treated in the Department of Hematology at Shandong Provincial Hospital, and their blood samples were collected. The criteria for diagnosing CLL were based on the revised International Workshop on Chronic Lymphocytic Leukemia (IWCLL)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Patients' peripheral blood mononuclear cells (PBMCs) were extracted using the FicollHypaque density gradient method according to previously reported methods[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and quantitative real-time PCR\u003c/h2\u003e \u003cp\u003eThe purification of total RNA was carried out using RNAiso Plus (TaKaRa, Dalian, China). Reverse transcription was carried out utilizing a reverse transcription kit from the same source. In adherence to the manufacturer's instructions, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted, and the results were analyzed through Light cycler 480 software. Primer sequences were as follows: ENPP2-F: ACTTGTGATGATAAGGTAGAGCCA; ENPP2-R: CTGTAGACCCTTTTGTATGAAGCC; LPL-F: AGTAGCAGAGTCCGTGGCTA; LPL-R: ATTCCTGTTACCGTCCAGCC; GAPDH-F: 5\u0026prime;-GCACCGTCAAGGCTGAGAAC-3\u0026prime;; GAPDH-R: 5\u0026prime;-TGGTGAAGACGCCAGTGGA-3\u0026prime;. Details of the methods was provided in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlasmids mediated regulation of ENPP2\u003c/h2\u003e \u003cp\u003eThe sequences for ENPP2 shRNAS were as follows: shENPP2#1, 5\u0026rsquo;-GCAGCAAAGTCATGCCTAATA-3\u0026rsquo;; shENPP2#2, 5\u0026rsquo;-GCAGTGCTTTATCGGACTAGA-3\u0026rsquo;. The knockdown plasmids were synthesized by GenePharma (Shanghai, China). GenePharma (Shanghai, China) synthesized and purified corresponding negative control plasmids. The sequence of ENPP2 lvRNA was 5\u0026rsquo;-CGCAAATGGGCGGTAGGCGTG-3\u0026rsquo;. The pENTER-ENPP2-Flag/His plasmid was purchased from ViGene Biosciences Inc (Shandong, China). Lipofectamine 3000 reagent (Invitrogen) was used to transiently transfect plasmids into cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation assays\u003c/h2\u003e \u003cp\u003eThe procedure was performed as described preceding[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Cell Counting Kit-8 (CCK-8) from Dojindo, Kumamoto, Japan. Details of the methods was provided in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of cell apoptosis and cell cycle\u003c/h2\u003e \u003cp\u003eThe procedure was performed as described preceding[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The reagents used were as follows: Annexin V-PE/7AAD Kit (BD Biosciences, Bedford, MA, USA); PI/RNase Staining Buffer (BD Biosciences, Bedford, MA, USA). Details of the methods was provided in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eElisa assay\u003c/h2\u003e \u003cp\u003eCollect the cell supernatant after treating the cells separately and the concentration of LPA was measured using human LPA ELISA Kit (LANSO, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eThe western blot procedure was performed as described preceding[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The primary antibodies used were as follows: ENPP2, LPL (Santa Cruz Biotechnology, USA), c-myc, Cyclin D1, CDK4, p21, p27, Bcl-2, Bax, PARP, cle-PARP (Cell Signaling Technology, USA), AMPK, FAS (abcam, USA), α-tubulin, and GAPDH (Zhongshan Goldenbridge, Beijing, China). Secondary antibodies from Zhongshan Goldenbridge, Beijing, China. Details of the methods was provided in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTriglyceride (TG) assay\u003c/h2\u003e \u003cp\u003eThe quantification of TG content in CLL cells was performed in accordance with the instructions provided by the manufacturer. A triglyceride quantification kit (BC0625, Solarbio, China) was utilized for this purpose.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLipid staining assay\u003c/h2\u003e \u003cp\u003eIncubate cells with BODIPY 493/503 (HY-W090090, MCE, USA) at 37 degrees for 30 minutes in the incubator, with DAPI (Beyotime, Shanghai, China) for 5 minutes at room temperature and observe under a fluorescent microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCo-Immunoprecipitation (Co-IP) assay\u003c/h2\u003e \u003cp\u003eLysis of cells with Co-IP lysis solution. The resulting lysate was subjected to centrifugation, and the supernatant was treated with 1\u0026ndash;3 ug of primary antibody before being shaken and incubated at 4\u0026deg;C overnight. Subsequently, Protein A/G Agarose (Santa Cruz Biotechnology, USA) were added to the antibody-treated buffer and incubated for 1 hours at 4\u0026deg;C to facilitate antibody binding. PBS wash the beads 3 times before heating at 100\u0026deg;C to denature the proteins. Detection of target proteins using western blotting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data in this paper underwent statistical analysis using SPSS 26.0 software and GraphPad Prism 9.0 statistical software. The study presents the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) of results obtained from three distinct experiments. Student\u0026rsquo;s t-test and Mann-Whitney U test were used for direct comparisons, while multigroup comparisons were carried out using one-way ANOVA or two-way ANOVA. The significance threshold was established at * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to declare statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eUntargeted metabolomics demonstrate significant differences in lipid metabolites between CLL patients and healthy control\u003c/h2\u003e \u003cp\u003eTo investigate the differences of lipid metabolites in CLL patients and normal subjects, we retained blood supernatants for untargeted metabolomic profiling. In our study, we employed univariate analysis techniques, specifically assessing fold-change and utilizing t-test statistical testing with BH correction to derive q-values. Further to this, we integrated the VIP metric generated from multivariate statistical analysis, PLS-DA \u003cb\u003e(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-C)\u003c/b\u003e. The volcano plot \u003cb\u003e(Fig.\u0026nbsp;1A)\u003c/b\u003e revealed a total of 913 differential feature ions, displaying apparent patterns of both upregulation and downregulation. To pinpoint the pathways with strong differential metabolite enrichment, we conducted a thorough analysis of the annotated results using enrichment analysis techniques. A comprehensive analysis has revealed the annotation of 52 differential metabolites, with 40 indicating an up-regulation and 12 indicating a down-regulation, as evidenced by \u003cb\u003eFig.\u0026nbsp;1B\u003c/b\u003e. Remarkable variations were observed in the levels of sphingolipids (SP), glycerolipids (GL), glycerophospholipids (GP), and fatty acids (FA) in patients afflicted with CLL, in comparison to their healthy counterparts. To illustrate the expression of the 52 annotated differential metabolites between CLL and healthy control groups, a clustering heatmap was utilized, as depicted in \u003cb\u003eFig.\u0026nbsp;1C\u003c/b\u003e. Pathway analysis was performed further on the differential metabolites, of which 24 pathways were significantly enriched. The KEGG enrichment scatter plot \u003cb\u003e(Fig.\u0026nbsp;1D)\u003c/b\u003e shows that the metabolites differing between CLL patients and healthy controls were mainly labeled as enriched in glycerolipid metabolism, inositol phosphate metabolism, glycerophospholipid metabolism, ether lipid metabolism, sphingolipid metabolism, metabolic pathways.\u003c/p\u003e \u003cp\u003eThen we conducted the comprehensive evaluation of the lipids to explore potential lipid biomarkers for diagnosing CLL. Through this investigation, five metabolites were discovered to have notable diagnostic significance, as evidenced by the top AUC values \u003cb\u003e(Fig.\u0026nbsp;1E)\u003c/b\u003e. Notably, all of the top five lipids exhibited AUC values above 0.93. Especially, PC O-24:2_18:2 had the highest AUC value of 0.965, 95%CI of (0.9006, 1.000). Three of these metabolites were chosen as combinational potential biomarkers for CLL. The model equation established after removing the confounding factor was Y=-2.575\u0026thinsp;+\u0026thinsp;2.126*PC O-24:2_18:2\u0026thinsp;+\u0026thinsp;3.544*LPC 34:3\u0026thinsp;+\u0026thinsp;3.174* PC O-35:3. The area under the curve (AUC) value of these biomarkers was 0.92 (95% CI: 0.764\u0026ndash;0.997), which was diagnostically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCombined analysis of metabolomics and transcriptomics data\u003c/h2\u003e \u003cp\u003eThe genomic microarray profile GSE50006 was performed for transcriptome analysis. Set the DEGs screening threshold to |log2(fold change) |\u0026gt;0.25, adjusted to \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01. In this study, 539 DEGs were identified and 257 of the pathways were enriched, as depicted in \u003cb\u003eFig.\u0026nbsp;2A-B\u003c/b\u003e. In our current study, the pathway analysis based on metabolomics and transcriptomics data produced 20 KEGG pathways \u003cb\u003e(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE)\u003c/b\u003e. As depicted in \u003cb\u003eFig.\u0026nbsp;2D\u003c/b\u003e, these pathways cover a variety of metabolic processes, including phosphatidylinositol signaling system, phospholipase D signaling pathway, sphingolipid signaling pathway, choline metabolism in cancer, pathways in cancer, inositol phosphate metabolism, glycerophospholipid metabolism, glycerolipid metabolism, ether lipid metabolism, sphingolipid metabolism, fat digestion and absorption, and regulation of lipolysis in adipocytes. The details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntegrating metabolomics and transcriptomics for KEGG pathway enrichment and relevant genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrected P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGap junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTUBB6|PDGFD|PRKACB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutophagy - animal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIGF1R|DDIT4|EIF2AK3|HIF1A|PRKACB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylinositol signaling system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.200611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.255915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePIK3C2B|DGKG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipase D signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCYTH3|RAPGEF3|PDGFD|DGKG|IGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingolipid signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.260831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.315672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS1PR5|TNFRSF1A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine metabolism in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCHPT1|PDGFD|DGKG|HIF1A|FOS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDKN2B|IGF1R|FOS|GNB4|PRKACB|RXRA|HIF1A|SMAD3|IL6|DLL1|JUP|ARAF|CDK6|IL15|LEF1|MYC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.063329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL6|SOCS3|TNFRSF1A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathogenic Escherichia coli infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.369197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.410993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTUBB6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInositol phosphate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.460569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.485242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePIK3C2B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.210821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePLD4|CD38|GCNT1|GPT2|AASS|NT5E|HACD1|PIK3C2B|MGAT3|RRM2|CHDH|CHPT1|LARGE1|DGKG|CSGALNACT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePLD4|CHPT1|DGKG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.094317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.147409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMGAT3|DGKG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEther lipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePLD4|CHPT1|ENPP2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.363591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.415781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSGPP2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat digestion and absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.085311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCARB1|MGAT3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin digestion and absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.240998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCARB1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulation of lipolysis in adipocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.369197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.410993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePRKACB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetrograde endocannabinoid signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAO1|GABRB2|GNB4|PRKACB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAO1|IGF1R|ARAF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PPI network analysis was performed on all integrated DEGs using the STRING database and was visualized \u003cb\u003e(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF)\u003c/b\u003e. Network analysis was performed using the logarithm of the fold change in metabolite levels between control and CLL samples and the differential gene ENPP2 was screened for further study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eOverexpression of ENPP2 in CLL cells\u003c/h2\u003e \u003cp\u003eIn the gene databases GSE50006 and GSE31048, which include 376 CLL patients, the expression of ENPP2 was significantly higher than in normal group \u003cb\u003e(Fig.\u0026nbsp;2G-H)\u003c/b\u003e. Based on statistical data from GSE22762, the Kaplan-Meier method observed that exhibiting high levels of ENPP2 expression experienced a considerably diminished overall survival \u003cb\u003e(Fig.\u0026nbsp;2F)\u003c/b\u003e. Additionally, CLL patient specimens were selected for qRT-PCR analysis which demonstrated that the expression level of ENPP2 in CLL specimens was significantly higher when compared to the normal group (\u003cb\u003eFig.\u0026nbsp;2I)\u003c/b\u003e. Moreover, compared with B cells from healthy volunteers, ENPP2 mRNA expression in MEC-1 was significantly higher than that in B cells \u003cb\u003e(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing analysis for ENPP2 functional enrichment in CLL cells\u003c/h2\u003e \u003cp\u003eTo investigate the attributes of ENPP2, RNA-sequencing was performed on MEC-1 cells transfected with both ShControl and ShENPP2#2. The results of our study, depicted in \u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e, indicate that ENPP2 was concentrated in pathways linked to metabolisms, such as the TCA cycle, ether lipid metabolism, and glycerophospholipid metabolism, through analysis of KEGG. Gene ontology (GO) analysis revealed that ENPP2 is intimately involved in metabolic processes, cellular processes, biological regulation \u003cb\u003e(Fig.\u0026nbsp;3B)\u003c/b\u003e. Gene set enrichment analysis (GSEA) revealed that ENPP2 was primary enriched in glycerolipid metabolism, triacylglycerol, and GTP diphosphate lyase \u003cb\u003e(Fig.\u0026nbsp;3C-E).\u003c/b\u003e Taken together, ENPP2 may promote the occurrence of CLL by regulating lipid metabolic pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eENPP2 regulates the proliferation, apoptosis, and cell cycle of CLL cell\u003c/h2\u003e \u003cp\u003eIn order to confirm the results of our bioinformatics analysis, we conducted functional experiments in CLL cells to investigate the role of ENPP2. ENPP2 was successfully silenced by ShENPP2#1 and ShENPP2#2 in MEC-1 and EHEB cells \u003cb\u003e(Fig.\u0026nbsp;4A)\u003c/b\u003e. We determined that downregulation of ENPP2 indirectly inhibited CLL cell proliferation through CCK-8 assays \u003cb\u003e(Fig.\u0026nbsp;4B)\u003c/b\u003e. Furthermore, through Annexin V-PE/7AAD assay, we observed a notable increase in apoptosis of shENPP2 transfected cells \u003cb\u003e(Fig.\u0026nbsp;4C)\u003c/b\u003e. Additionally, we monitored the cell cycle of downregulated ENPP2 cells and found that they exhibited a significant G0/G1 phase block compared to control cells \u003cb\u003e(Fig.\u0026nbsp;4D).\u003c/b\u003e The results highlight that the ENPP2 contributes significantly to the survival of CLL cells through its ability to inhibit apoptosis and facilitate the progression of cells from the G0/G1 phase. To further verify the biological function of ENPP2, we constructed overexpressed plasmids \u003cb\u003e(Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA)\u003c/b\u003e. In contrast, ENPP2 overexpression promoted cell proliferation, reduced the proportion of apoptotic cells and accelerated the cell cycle \u003cb\u003e(Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB-D)\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eTargeted inhibition of ENPP2 by PF-8380 exerted anti-tumor activity in CLL cells\u003c/h2\u003e \u003cp\u003eENPP2 inhibitor PF-8380 inhibited the activity of MEC-1 cells and CLL primary cells in dose-dependent and time-dependent \u003cb\u003e(Fig.\u0026nbsp;5A-B)\u003c/b\u003e. In addition, ibrutinib supplementation with 16 \u0026micro;m or 4\u0026micro;m PF-8380 increased cytotoxicity to CLL cells \u003cb\u003e(Fig.\u0026nbsp;5C, D)\u003c/b\u003e. Moreover, the amount of apoptotic cells increased with the increase of PF-8380 concentration after 24 h flow cytometry analysis of primary CLL cells treated with PF-8380 \u003cb\u003e(Fig.\u0026nbsp;5E)\u003c/b\u003e. Compared with DMSO treatment, PF-8380 also induced the increase of G0/G1 phase cells \u003cb\u003e(Fig.\u0026nbsp;5F)\u003c/b\u003e. Western blotting analysis showed that with the increase of PF-8380 concentration, the levels of cyclin-related proteins, including C-myc, Cyclin D1, CDK4, P21, and P27 \u003cb\u003e(Fig.\u0026nbsp;5G)\u003c/b\u003e and apoptosis-related proteins such as Bcl-2 and Bax changed \u003cb\u003e(Fig.\u0026nbsp;5H)\u003c/b\u003e. Taken together, PF-8380 exerts therapeutic potential by inhibiting CLL cell survival and cell cycle, enhancing apoptosis and chemosensitivity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eENPP2 regulates lipid metabolism in CLL\u003c/h2\u003e \u003cp\u003ePrevious combined metabolomics and transcriptional analyses, as well as RNAseq, provide evidence that ENPP2 may act as a regulatory factor for lipid metabolism in CLL, and holds significance in the lipid metabolic process. To test this hypothesis, bodipy staining \u003cb\u003e(Fig.\u0026nbsp;6A-B)\u003c/b\u003e showed increased lipid accumulation in ENPP2 overexpressed CLL cell lines and significantly reduced lipid accumulation in ENPP2 knockout cells. In addition, quantitative analysis of bodipy staining was performed by flow cytometry, and the results were consistent with the above \u003cb\u003e(Fig.\u0026nbsp;6C)\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eEffect of targeted inhibition PF-8380 on lipid metabolism in CLL cells\u003c/h2\u003e \u003cp\u003eTo explore the effect of ENPP2-targeted drug PF-8380 on the lipid metabolism of CLL, we treated CLL cell lines with 16\u0026micro;m and 32\u0026micro;m, respectively. We stained them with bodipy \u003cb\u003e(Fig.\u0026nbsp;6D, F)\u003c/b\u003e. The study findings suggest that as drug concentration increased, the intracellular lipid deposition decreased gradually. The quantitative treatment of bodipy staining by flow cytometry showed the same results as before \u003cb\u003e(Fig.\u0026nbsp;6E, G)\u003c/b\u003e. Besides, we measured the content of TG in the cells treated with the drug. The observed decline in TG content within the cells was found to be directly proportional to the increase in drug concentration \u003cb\u003e(Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA)\u003c/b\u003e. This trend is congruent with the results obtained via Bodipy staining. Taken together, the ENPP2 targeted inhibitor PF-8380 could alter the disease course by regulating lipid metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eENPP2 functions through the AMPK/SREBP1/FAS pathway\u003c/h2\u003e \u003cp\u003eWe then considered how ENPP2 regulates the process of lipogenesis in CLL. Based on RNA sequencing results, we became attracted to AMP-activated protein kinase (AMPK), which is a central player in metabolism[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and negatively correlates with tumor progression and genesis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. AMPK/SREBP1/FAS pathway is one of the key pathways for intracellular lipogenesis. AMPK regulates the expression of adipogenic genes through the sterol regulatory elements binding transcription factor 1(SREBP1) transcription factor. We examined the protein levels of p-AMPK, AMPK, SREBP1, and fatty acid synthase (FAS) to elucidate the molecular mechanism of ENPP2 involvement in cellular lipid metabolism. The results showed that the AMPK phosphorylated form was significantly increased in ENPP2 knockdown cells compared to control. Moreover, ENPP2 knockdown significantly decreased SREBP1 and FAS proteins. ENPP2 overexpression showed results corresponding to knockdown cells \u003cb\u003e(Fig.\u0026nbsp;7A)\u003c/b\u003e. In addition, we treated MEC-1 with 16\u0026micro;m and 32 \u0026micro;m PF-8380 which showed enhanced AMPK phosphorylation and attenuated SREBP1 and FAS \u003cb\u003e(Fig.\u0026nbsp;7B)\u003c/b\u003e. ENPP2 is a secreted lysophospholipase D that promotes the hydrolysis of extracellular lysophosphatidylcholine (LPC) to lysophosphatidic acid (LPA)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, we assayed the LPA content in cell supernatants after drug treatment utilizing ELISA. The results showed that both in the CLL cell line and primary cells from different CLL patients, LPA in the cell supernatant was significantly decreased after treatment with the targeted inhibitor PF-8380 compared to the DMSO control \u003cb\u003e(Fig.\u0026nbsp;7C)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eENPP2 positively correlated with LPL\u003c/h2\u003e \u003cp\u003eLipoprotein lipase (LPL) has been identified as a crucial driver in the metabolic processes of CLL cells by facilitating the absorption of lipoprotein[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We verified in the GEO database that LPL expression is increased in CLL patients and correlates with poor prognosis \u003cb\u003e(Fig.\u0026nbsp;7D-F)\u003c/b\u003e. Meanwhile, in GEO database, ENPP2 expression exhibited a significant positive correlation with LPL (Spearman: r\u0026thinsp;=\u0026thinsp;0.3057, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Spearman: r\u0026thinsp;=\u0026thinsp;0.3653, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(Fig.\u0026nbsp;7G-H)\u003c/b\u003e. It is hypothesized that ENPP2 may regulate CLL lipid metabolism through LPL. We transfected ShENPP2 and LvENPP2 into CLL cell lines to detect LPL levels. The results showed a significant positive correlation between LPL and ENPP2 expression, evident through analysis of mRNA and protein levels \u003cb\u003e(Fig.\u0026nbsp;7I-J)\u003c/b\u003e. Subsequently, further Co-IP experiments revealed potential interactions between ENPP2 and LPL in CLL cells \u003cb\u003e(Fig.\u0026nbsp;7K)\u003c/b\u003e. Our results provide evidence that ENPP2 modulates LPL expression in CLL. Taken together, the catalytic function of ENPP2 in CLL tumorigenesis was preliminarily elucidated \u003cb\u003e(Fig.\u0026nbsp;8)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, lipidomic analysis suggested differences in lipid metabolites between CLL patients and healthy controls. The correlation between lipid metabolism and CLL has been substantiated. We used an untargeted quantitative metabolomics approach to examine and contrast the distinct serum metabolic profiles of CLL patients and healthy individuals. And then validated the selected metabolites and corresponding pathways by transcriptomic data, thus identifying altered biological processes or metabolic features in CLL patients. 52 differential metabolites and 539 differential genes were defined, and three metabolites (PC O-24:2_18:2, PC O-35:3, LPC 34:3) were selected as biomarkers for CLL diagnosis based on the ROC curve area. Additionally, further transcriptome associative analysis revealed that ENPP2 may has a significant function in lipid metabolism in CLL. However, the function of ENPP2 in CLL metabolism modulation remain considerably nebulous. Our present study represents the role of ENPP2 in the pathogenesis of CLL, which is significantly expressed in CLL patients and predicted poorer survival and prognosis. Further, it was demonstrated that ENPP2 is involved in lipid metabolic pathways in CLL and promote cell survival by AMPK pathway to promote lipid deposition.\u003c/p\u003e \u003cp\u003eCancer cells require metabolic reorganization to improve their value-added and survival rates compared to normally differentiated cells. Previous studies have found that lipid metabolism is important in tumorigenesis, progression and metastasis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Disturbances in lipid metabolism may induce abnormal gene expression and lead to abnormal signaling pathways[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Altered lipid metabolism is closely related to pathogenic processes and can give rise to distinct disease biomarkers with diagnostic, prognostic, and predictive capabilities. The investigation of the metabolic changes in CLL cells has revealed their ability to store lipids and derive chemical energy from free fatty acids, similar to adipocytes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Lipid droplet vesicles are present within the cytoplasm of CLL B cells, and upon incubation with free fatty acids, an upsurge in their metabolic rate could be witnessed[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Pallasch\u0026rsquo;s colleagues have identified significantly elevated levels of lipase-related genes and triglyceride-specific lipase activity in CLL B cells as compared to normal CD5\u003csup\u003e+\u003c/sup\u003e B cells. Notably, the inhibition of lipase activity has been shown to increase CLL cell apoptosis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eENPP2 as an adipose-derived secretory enzyme, controls adipose expansion, a fat brown supply and energy expenditure[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. During the recent years, it has been shown that ENPP2 is closely correlated with obesity and disorders of glucolipid metabolism in obese individuals[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It is considered a possible target for the treatment of obesity-related diseases. Adipocyte ENPP2 expression was accompanied by a substantial increase in adipogenesis in individuals exhibiting type II diabetes associated with obesity[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Prior research has demonstrated the potential of ENPP2 as a prognostic biomarker in various cancers[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], including breast and liver cancer[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Cholia and his colleagues found that ENPP2 enhances the aggressive potential of glioblastoma[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Through a comprehensive analysis inclusive of RNA sequencing, this study sheds light on the regulatory role of ENPP2 in CLL. Furthermore, this investigation identified ENPP2 as an important biomarker of prognosis in CLL. Our analysis has revealed a dysregulated expression of ENPP2 in CLL, and a strong correlation between elevated ENPP2 expression and patient survival, as demonstrated in GSE22762, suggesting a potential role for ENPP2 in CLL progression. Further validation studies are required to confirm its predictive significance. Our findings indicate that the silencing of ENPP2 results in decreased cell proliferation, enhanced apoptosis, and G0/G1 cell cycle arrest.\u003c/p\u003e \u003cp\u003eTo elucidate the molecular mechanisms involved in lipid metabolism by ENPP2, we examined the degree of AMPK protein phosphorylation and downstream target gene regulation. AMPK is engaged in energy sensing and homeostasis regulation in vivo, and performs a crucial function in lipid regulation[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. AMPK is believed to be fundamental for lipid metabolism through the regulation of fatty acid synthesis and regulation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Prior research has demonstrated that AMPK could modulate SREBP1 and FAS, thereby impacting adipogenesis and lipid metabolism[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Coincidentally, ENPP2 has been described as a secreted lysophospholipase D that promotes the hydrolysis of extracellular LPC to LPA. It has been reported that LPA stimulates glucose uptake and regulates AMPK phosphorylation[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This connection may provide a novel insight into the regulation of lipid metabolism by ENPP2. Our research has demonstrated that the reduction of ENPP2 inhibits lipid accumulation by augmenting AMPK phosphorylation and reducing the level of SREBP1 and FAS. ENPP2 regulation of the AMPK/SREBP1/FAS signaling pathway may be an effective mechanism for anti-lipogenic effects in CLL cells.\u003c/p\u003e \u003cp\u003eLPL is an enzyme normally expressed in adipocytes and muscle cells and is essential for the metabolism of free fatty acids[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. It has been demonstrated that it is not expressed in normal lymphocytes, but its expression is increased in CLL cells. It has also been meaningfully associated with the prognosis of CLL, and high expression levels of LPL are usually associated with poorer clinical outcomes[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. LPL induces lipoprotein storage in CLL cells and reprograms CLL cells to use lipids as an energy source preferentially. It seems to result in a higher cell survival rate[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Metabolic reprogramming is initiated as CLL cells increase their demand for energy and metabolites to meet their rapid proliferation and survival[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ENPP2 expression was increased in CLL cells. Consistent with our hypothesis, it has been observed that the downregulation of ENPP2 demonstrates notable anti-leukemic properties and reduced the role of key kinases in the lipid metabolism pathway. In CLL cells, LPL expression was reduced after ENPP2 silencing, whereas LPL expression was enhanced after ENPP2 overexpression, suggesting a positive effect of ENPP2 on LPL expression. We hypothesized that ENPP2 might participate in cellular lipid metabolism by binding to LPL, thus regulating CLL cell growth. Therefore, we elucidated the interaction between ENPP2 and LPL through Co-IP experiments. Our results demonstrate that the aberrant lipid metabolism pathway involved in ENPP2 is involved in the regulation of CLL onset and development.\u003c/p\u003e \u003cp\u003ePF-8380 serves as a targeted inhibitor of ENPP2 and has been implicated in the pathogenesis and management of numerous diseases[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Specifically, PF-8380 has been shown to elicit a reduction in tumor vascularity, delay tumor growth, and heighten radiosensitivity in glioblastoma[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In a mouse model of hepatic encephalopathy, PF-8380 has demonstrated the ability to mitigate neuroinflammation and enhance neurological function[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Studies undertaken by D'Souza and colleagues have demonstrated that 24-hour incubation of adipocytes with PF-8380 resulted in increased production of peroxisome proliferator-activated receptor γ and downstream targets consequent to ENPP2 inhibition[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Nevertheless, the role of PF-8380 in the treatment of CLL warrants further exploration. We have demonstrated the antitumor effect of PF-8380 in CLL through in vitro experimentation, which confers a novel avenue for the treatment of this malignancy.\u003c/p\u003e \u003cp\u003eOver the past few years, targeted drug therapies have demonstrated remarkable therapeutic effects in CLL[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Although Ibrutinib, a Bruton's tyrosine kinase inhibitor, has displayed impressive efficacy in CLL treatment[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], its clinical resistance is still a significant challenge. Drug resistance and toxicity lead to poor clinical outcomes[\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], which could be mitigated through the implementation of combination therapy aimed at reducing the incidence of drug resistance[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In our study, we observed that the ENPP2-targeted inhibitor PF-8380 exhibited positive anti-drug resistance in CLL-targeted drug sensitivities, such as Ibrutinib, thus providing new prospects for clinical chemotherapy resistance. However, it is imperative to further investigate the mechanism of resistance and the clinical implementation of PF-8380 in the treatment of CLL.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our investigation has screened differential metabolites of CLL and established a diagnostic model utilizing lipidomic. Furthermore, our results have highlighted the potential of inhibiting ENPP2 to impede the progression of CLL. Specifically, we have observed anti-tumor effects of PF-8380 in CLL, such as hindering cell survival, enhancing cell apoptosis, and blocking the cell cycle. Taken together, our findings suggest that ENPP2 serves as a promising target for targeted therapeutic interventions, potentially paving the way for an innovative approach to treating CLL.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXW and YZ designed the research and revised the manuscript. LL conducted the experiments and wrote the manuscript. XH and YH collected the data. HW, XZ and ZT performed the statistical analyses. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by National Natural Science Foundation (No.82270200, No.82000195, No.82070203, and No.81770210); China Postdoctoral Science Foundation (No. 2022M721981); Key Research and Development Program of Shandong Province (No.2018CXGC1213); Taishan Scholars Program of Shandong Province (No. tspd20230610, tsqn201909184); Translational Research Grant of NCRCH (No.2021WWB02, No.2020ZKMB01); Shandong Provincial Natural Science Foundation (No.ZR2020QH094); Shandong Provincial Engineering Research Center of Lymphoma; Academic Promotion Programme of Shandong First Medical University (No.2019QL018, No. 2020RC007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was gained from all participants based on guidelines of the Declaration of Helsinki. All experiments were approved by the Medical Ethical Committee of Shandong Provincial Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScarfo L, Ferreri AJ, Ghia P: Chronic lymphocytic leukaemia. Crit Rev Oncol Hematol 2016, 104:169\u0026ndash;182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie Y, Yun X, Zhang Y, Wang X: Targeting metabolic reprogramming in chronic lymphocytic leukemia. Exp Hematol Oncol 2022, 11(1):39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurrie E, Schulze A, Zechner R, Walther TC, Farese RV, Jr.: Cellular fatty acid metabolism and cancer. Cell Metab 2013, 18(2):153\u0026ndash;161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin-Perez M, Urdiroz-Urricelqui U, Bigas C, Benitah SA: The role of lipids in cancer progression and metastasis. Cell Metab 2022, 34(11):1675\u0026ndash;1699.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThurgood LA, Best OG, Rowland A, Lower KM, Brooks DA, Kuss BJ: Lipid uptake in chronic lymphocytic leukemia. Exp Hematol 2022, 106:58\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmolewski P, Robak T: Current Treatment of Refractory/Relapsed Chronic Lymphocytic Leukemia: A Focus on Novel Drugs. Acta Haematol 2021, 144(4):365\u0026ndash;379.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Lu X, Li J, Miao Y: Combining BTK inhibitors with BCL2 inhibitors for treating chronic lymphocytic leukemia and mantle cell lymphoma. Biomark Res 2022, 10(1):17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen PH, Niesen E, Hallek M: New roles for B cell receptor associated kinases: when the B cell is not the target. Leukemia 2019, 33(3):576\u0026ndash;587.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoike S, Keino-Masu K, Ohto T, Masu M: The N-terminal hydrophobic sequence of autotaxin (ENPP2) functions as a signal peptide. Genes Cells 2006, 11(2):133\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishimasu H, Ishitani R, Aoki J, Nureki O: A 3D view of autotaxin. Trends Pharmacol Sci 2012, 33(3):138\u0026ndash;145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReeves VL, Trybula JS, Wills RC, Goodpaster BH, Dube JJ, Kienesberger PC, Kershaw EE: Serum Autotaxin/ENPP2 correlates with insulin resistance in older humans with obesity. Obesity (Silver Spring) 2015, 23(12):2371\u0026ndash;2376.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRancoule C, Dusaulcy R, Treguer K, Gres S, Attane C, Saulnier-Blache JS: Involvement of autotaxin/lysophosphatidic acid signaling in obesity and impaired glucose homeostasis. Biochimie 2014, 96:140\u0026ndash;143.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSioletic S, Czaplinski J, Hu L, Fletcher JA, Fletcher CD, Wagner AJ, Loda M, Demetri GD, Sicinska ET, Snyder EL: c-Jun promotes cell migration and drives expression of the motility factor ENPP2 in soft tissue sarcomas. J Pathol 2014, 234(2):190\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrosouni A, Panagopoulou M, Aidinis V, Chatzaki E: Autotaxin in Breast Cancer: Role, Epigenetic Regulation and Clinical Implications. \u003cem\u003eCancers (Basel)\u003c/em\u003e 2022, 14(21).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaffe E, Magkrioti C, Aidinis V: Deregulated Lysophosphatidic Acid Metabolism and Signaling in Liver Cancer. Cancers (Basel) 2019, 11(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuan M, Cui JJ, Feng X, Huang Q: The critical role and potential target of the autotaxin/lysophosphatidate axis in pancreatic cancer. Tumour Biol 2017, 39(3):1010428317694544.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhang L, Xu T, Zhao X, Jiang X, Xiao F, Sun H, Wang L: Aberrant ENPP2 expression promotes tumor progression in multiple myeloma. Leuk Lymphoma 2022, 63(4):963\u0026ndash;974.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu C, Luo X, Zhan X, Hao J, Zhang L, YB LS, Shen C, Dong M: Comparative metabolomics reveals the metabolic variations between two endangered Taxus species (T. fuana and T. yunnanensis) in the Himalayas. BMC Plant Biol 2018, 18(1):197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Fang J, Qi X, Lin M, Zhong Y, Sun L, Cui W: Combined Analysis of the Fruit Metabolome and Transcriptome Reveals Candidate Genes Involved in Flavonoid Biosynthesis in Actinidia arguta. Int J Mol Sci 2018, 19(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallek M, Cheson BD, Catovsky D, Caligaris-Cappio F, Dighiero G, Dohner H, Hillmen P, Keating MJ, Montserrat E, Rai KR \u003cem\u003eet al\u003c/em\u003e: Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia updating the National Cancer Institute-Working Group 1996 guidelines. \u003cem\u003eBlood\u003c/em\u003e 2008, 111(12):5446\u0026ndash;5456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Hu X, Yun X, Liu J, Yang J, Tian Z, Zhang X, Zhang Y, Wang X: Nucleolar and spindle associated protein 1 enhances chemoresistance through DNA damage repair pathway in chronic lymphocytic leukemia by binding with RAD51. Cell Death Dis 2021, 12(11):1083.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, Han Y, Liu J, Wang H, Tian Z, Zhang X, Zhang Y, Wang X: CTP synthase 2 predicts inferior survival and mediates DNA damage response via interacting with BRCA1 in chronic lymphocytic leukemia. Exp Hematol Oncol 2023, 12(1):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhou X, Li Y, Xu Y, Lu K, Li P, Wang X: Inhibition of maternal embryonic leucine zipper kinase with OTSSP167 displays potent anti-leukemic effects in chronic lymphocytic leukemia. Oncogene 2018, 37(41):5520\u0026ndash;5533.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeerthana CK, Rayginia TP, Shifana SC, Anto NP, Kalimuthu K, Isakov N, Anto RJ: The role of AMPK in cancer metabolism and its impact on the immunomodulation of the tumor microenvironment. Front Immunol 2023, 14:1114582.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvsec D, Jakos Djordjevic AT, Kanduser M, Podgornik H, Skerget M, Mlinaric-Rascan I: Targeting Autophagy Triggers Apoptosis and Complements the Action of Venetoclax in Chronic Lymphocytic Leukemia Cells. \u003cem\u003eCancers (Basel)\u003c/em\u003e 2021, 13(18).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerrelli D, Dallera L, Stendardo M, Monzani S, Pasqualato S, Giorgio M, Pallavi R: Hydroxycitric Acid Inhibits Chronic Myelogenous Leukemia Growth through Activation of AMPK and mTOR Pathway. Nutrients 2022, 14(13).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSah JP, Hao NTT, Han X, Tran TTT, McCarthy S, Oh Y, Yoon JK: Ectonucleotide pyrophosphatase 2 (ENPP2) plays a crucial role in myogenic differentiation through the regulation by WNT/β-Catenin signaling. The International Journal of Biochemistry \u0026amp; Cell Biology 2020, 118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrieto D, Seija N, Uriepero A, Souto-Padron T, Oliver C, Irigoin V, Guillermo C, Navarrete MA, Ines Landoni A, Dighiero G \u003cem\u003eet al\u003c/em\u003e: LPL protein in Chronic Lymphocytic Leukaemia have different origins in Mutated and Unmutated patients. Advances for a new prognostic marker in CLL. Br J Haematol 2018, 182(4):521\u0026ndash;525.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRozovski U, Grgurevic S, Bueso-Ramos C, Harris DM, Li P, Liu Z, Wu JY, Jain P, Wierda W, Burger J \u003cem\u003eet al\u003c/em\u003e: Aberrant LPL Expression, Driven by STAT3, Mediates Free Fatty Acid Metabolism in CLL Cells. Mol Cancer Res 2015, 13(5):944\u0026ndash;953.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Z, Gu D, Wang R, Zuo X, Zhu H, Wang L, Lu X, Xia Y, Qin S, Zhang W \u003cem\u003eet al\u003c/em\u003e: CircRIC8B regulates the lipid metabolism of chronic lymphocytic leukemia through miR199b-5p/LPL axis. Exp Hematol Oncol 2022, 11(1):51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong J, Zhang CJ, Zhu N, Du K, Yin YF, Tan X, Liao DF, Qin L: Lipid metabolism and carcinogenesis, cancer development. Am J Cancer Res 2018, 8(5):778\u0026ndash;791.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim SA, Su W, Chapman NM, Chi H: Lipid metabolism in T cell signaling and function. Nat Chem Biol 2022, 18(5):470\u0026ndash;481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRozovski U, Hazan-Halevy I, Barzilai M, Keating MJ, Estrov Z: Metabolism pathways in chronic lymphocytic leukemia. Leuk Lymphoma 2016, 57(4):758\u0026ndash;765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePallasch CP, Schwamb J, Konigs S, Schulz A, Debey S, Kofler D, Schultze JL, Hallek M, Ultsch A, Wendtner CM: Targeting lipid metabolism by the lipoprotein lipase inhibitor orlistat results in apoptosis of B-cell chronic lymphocytic leukemia cells. Leukemia 2008, 22(3):585\u0026ndash;592.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChattopadhyay A, Mukherjee P, Sulaiman D, Wang H, Girjalva V, Dorreh N, Jacobs JP, Delk S, Moolenaar WH, Navab M \u003cem\u003eet al\u003c/em\u003e: Role of enterocyte Enpp2 and autotaxin in regulating lipopolysaccharide levels, systemic inflammation, and atherosclerosis. Journal of Lipid Research 2023, 64(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaulnier-Blache JS: [Secretion and role of autotaxin and lysophosphatidic acid in adipose tissue]. J Soc Biol 2006, 200(1):77\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagopoulou M, Fanidis D, Aidinis V, Chatzaki E: ENPP2 Methylation in Health and Cancer. Int J Mol Sci 2021, 22(21).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagopoulou M, Drosouni A, Fanidis D, Karaglani M, Balgkouranidou I, Xenidis N, Aidinis V, Chatzaki E: ENPP2 Promoter Methylation Correlates with Decreased Gene Expression in Breast Cancer: Implementation as a Liquid Biopsy Biomarker. Int J Mol Sci 2022, 23(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng W, Chen F, Zhou Z, Huang Y, Lin J, Zhang F, Xiao G, Liu C, Liu C, Xu L: Hepatitis B Virus Promotes Hepatocellular Carcinoma Progression Synergistically With Hepatic Stellate Cells via Facilitating the Expression and Secretion of ENPP2. Front Mol Biosci 2021, 8:745990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCholia RP, Dhiman M, Kumar R, Mantha AK: Oxidative stress stimulates invasive potential in rat C6 and human U-87 MG glioblastoma cells via activation and cross-talk between PKM2, ENPP2 and APE1 enzymes. Metab Brain Dis 2018, 33(4):1307\u0026ndash;1326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim MH, Seong JB, Huh JW, Bae YC, Lee HS, Lee DS: Peroxiredoxin 5 ameliorates obesity-induced non-alcoholic fatty liver disease through the regulation of oxidative stress and AMP-activated protein kinase signaling. Redox Biol 2020, 28:101315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia D, Shaw RJ: AMPK: Mechanisms of Cellular Energy Sensing and Restoration of Metabolic Balance. Mol Cell 2017, 66(6):789\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanzen NR, Whitfield J, Hoffman NJ: Interactive Roles for AMPK and Glycogen from Cellular Energy Sensing to Exercise Metabolism. Int J Mol Sci 2018, 19(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi L PI, Bernhart E, Reicher H, Koyani CN, Madl T, Madreiter-Sokolowski C, Koshenov Z, Graier WF, Hallstr\u0026ouml;m S, Sattler W.: Lysophosphatidic Acid Induces Aerobic Glycolysis, Lipogenesis, and Increased Amino Acid Uptake in BV-2 Microglia. Int J Mol Sci 2021 Feb 17(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H LS, Liu X, Yang J, Wang F, Cong X, Chen X: Lysophosphatidic Acid Pretreatment Attenuates Myocardial Ischemia/Reperfusion Injury in the Immature Hearts of Rats. 2017 Mar 21(8:153).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim EK PJ, Lim S, Choi JW, Kim HS, Seok H, Seo JK, Oh K, Lee DS, Kim KT, Ryu SH, Suh PG: Activation of AMP-activated Protein Kinase Is Essential for Lysophosphatidic Acid-induced Cell Migration in Ovarian Cancer Cells. 2011 Jul 8(286(27)).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrieto D, Oppezzo P: Lipoprotein Lipase Expression in Chronic Lymphocytic Leukemia: New Insights into Leukemic Progression. Molecules 2017, 22(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRombout A, Verhasselt B, Philippe J: Lipoprotein lipase in chronic lymphocytic leukemia: function and prognostic implications. Eur J Haematol 2016, 97(5):409\u0026ndash;415.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCaw L, Shi Y, Wang G, Li YJ, Spaner DE: Low Density Lipoproteins Amplify Cytokine-signaling in Chronic Lymphocytic Leukemia Cells. EBioMedicine 2017, 15:24\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan Z, Lei H, Guo M, Chen Y, Zhai X: An updated patent review of autotaxin inhibitors (2017-present). Expert Opin Ther Pat 2021, 31(5):421\u0026ndash;434.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhave SR, Dadey DY, Karvas RM, Ferraro DJ, Kotipatruni RP, Jaboin JJ, Hallahan AN, Dewees TA, Linkous AG, Hallahan DE \u003cem\u003eet al\u003c/em\u003e: Autotaxin Inhibition with PF-8380 Enhances the Radiosensitivity of Human and Murine Glioblastoma Cell Lines. Front Oncol 2013, 3:236.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy S, Chakrabarti M, Dasgupta H, Mahale A, Tripathi S, Sharma V, Banerjee M, Kulkarni OP: Inhibition of Autotaxin Ameliorates LPA-Mediated Neuroinflammation and Alleviates Neurological Dysfunction in Acute Hepatic Encephalopathy. ACS Chem Neurosci 2022, 13(19):2829\u0026ndash;2841.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Souza K, Kane DA, Touaibia M, Kershaw EE, Pulinilkunnil T, Kienesberger PC: Autotaxin Is Regulated by Glucose and Insulin in Adipocytes. Endocrinology 2017, 158(4):791\u0026ndash;803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmolej L: Refractory Chronic Lymphocytic Leukemia: A Therapeutic Challenge. Curr Cancer Drug Targets 2016, 16(8):701\u0026ndash;709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkanland SS, Karlsen L, Tasken K: B cell signalling pathways-New targets for precision medicine in chronic lymphocytic leukaemia. Scand J Immunol 2020, 92(5):e12931.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusque L: HSPCs are marathon and not sprint relay athletes. Blood 2017, 129(11):1406\u0026ndash;1407.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge B, Chowdhury SM, Hart A, Sircar A, Singh SK, Nath UK, Mamgain M, Singhal NK, Sehgal L, Jain N: Ibrutinib Resistance Mechanisms and Treatment Strategies for B-Cell lymphomas. Cancers (Basel) 2020, 12(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur V, Swami A: Ibrutinib in CLL: a focus on adverse events, resistance, and novel approaches beyond ibrutinib. Ann Hematol 2017, 96(7):1175\u0026ndash;1184.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimofeeva N, Gandhi V: Ibrutinib combinations in CLL therapy: scientific rationale and clinical results. Blood Cancer J 2021, 11(4):79.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Chronic lymphocytic leukemia, Lipid metabolism, ENPP2, PF-8380, AMPK, LPL","lastPublishedDoi":"10.21203/rs.3.rs-3431397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3431397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDisorders of lipid metabolism are critical factors in the progression of chronic lymphocytic leukemia (CLL). The characteristics of lipid metabolism and related regulatory mechanisms of CLL remain unclear. Hence, we identified altered metabolites in CLL patients by lipidomic to investigate aberrant lipid metabolism pathways. Based on the area under the curve value, a combination of three metabolites (PC O-24:2_18:2, PC O-35:3, and LPC 34:3) potentially served as a biomarker for the diagnosis of CLL. Moreover, utilizing integrated lipidomic, transcriptomic, and molecular studies, we reveal that ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) plays a crucial role in regulating oncogenic lipogenesis. ENPP2 expression was significantly elevated in CLL patients compared to normal cells and was validated in an independent cohort. Besides, ENPP2 knockdown and targeted inhibitor PF-8380 treatment exerted an anti-tumor effect by regulating cell viability, proliferation, apoptosis, cell cycle, and enhanced the drug sensitivity to ibrutinib. Mechanistically, ENPP2 inhibited AMP-activated protein kinase (AMPK) phosphorylation and promoted lipogenesis through the sterol regulatory element-binding transcription factor 1 (SREBP-1)/fatty acid synthase (FAS) signaling pathway to promote lipogenesis. Taken together, our findings unravel the lipid metabolism characteristics of CLL, and highlight the potential role of ENPP2 as a novel therapeutic target for CLL treatment.\u003c/p\u003e","manuscriptTitle":"ENPP2 promotes progression and lipid accumulation via AMPK/SREBP1/FAS pathway in chronic lymphocytic leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-16 23:13:57","doi":"10.21203/rs.3.rs-3431397/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":"d9ae0b9e-1bfb-4998-b636-870c38446a5b","owner":[],"postedDate":"October 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25338818,"name":"Biological sciences/Cancer/Haematological cancer/Leukaemia/Chronic lymphocytic leukaemia"},{"id":25338819,"name":"Biological sciences/Cancer/Cancer metabolism"}],"tags":[],"updatedAt":"2023-11-14T11:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-16 23:13:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3431397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3431397","identity":"rs-3431397","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}