Integrated ctDNA Methylation Profiling of P2RX1, CBFA2T3, and CERS4 as Pancreatic Ductal Adenocarcinoma Biomarkers: Identification and Mechanistic Insights

Integrated ctDNA Methylation Profiling of P2RX1, CBFA2T3, and CERS4 as Pancreatic Ductal Adenocarcinoma Biomarkers: Identification and Mechanistic Insights | 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 Research Article Integrated ctDNA Methylation Profiling of P2RX1, CBFA2T3, and CERS4 as Pancreatic Ductal Adenocarcinoma Biomarkers: Identification and Mechanistic Insights Hongsheng Chen, Hongyuan Shen, Xudong Wang, Shaoqing Ju, Weilin Pu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4982255/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) stands as one of the most formidable cancers globally, yet its diagnosis continues to be a pressing challenge, largely owing to the inherent limitations of current diagnostic methodologies. Methods: Utilizing whole-genome bisulfite-sequencing (WGBS), differentially methylated genes in the promoter region were identified from the plasma of PDAC patients. Subsequently, screening of candidate methylation genes, methylation-specific primers and probes for P2RX1 , CBFA2T3 and CERS4 were designed, and a stable multiple quantitative methylation-specific polymerase chain reaction (M-QMSP) system was established to detect the circulating tumor DNA (ctDNA) methylation level of these genes in PDAC. Finally, the disease-driving mechanism of the biomarker CERS4 was explored separately in vitro and in vivo. Results: A total of 112,294 differential methylation regions (DMRs) were precisely identified by WGBS, encompassing 88,210 hypermethylation DMRs and 24,084 hypomethylation DMRs. Notably, three differentially methylated genes, P2RX1 , CBFA2T3 and CERS4 (nominated as P2CC model), were astutely pinpointed as potential plasma methylation biomarkers. The M-QMSP system was established through the prioritization of these genes, which demonstrated that the combined diagnostic prowess of P2CC modelnotably outperformed carbohydrate antigen 19-9 (CA19-9). In PDAC, overexpression of CERS4 has been observed to suppress tumor growth in nude mice xenografts and inhibit cell proliferation and migration. Additionally, CERS4 played a pivotal role in promoting lipid metabolism. Conclusion: Based on WGBS screening and M-QMSP validation, we have determined that the P2CC model can serve as the combined diagnostic biomarkers for PDAC, superior to CA19-9. Moreover, as a tumor suppressor gene, CERS4 regulates the metabolism of sphingolipids. pancreatic ductal adenocarcinoma DNA methylation cell-free DNA P2CC model diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pancreatic cancer is a common malignant tumor of the digestive system 1 . The incidence of pancreatic cancer is escalating year by year, and by 2024, it is anticipated to rank third in terms of mortality among all malignant tumors 2 . The most prevalent form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), accounts for over 90% of pancreatic exocrine malignant tumors 3 . Due to the aggressive nature of PDAC and the absence of characteristic early signs, the majority of patients are diagnosed at advanced stages, with only a mere 10–20% being eligible for surgical removal 4 . Traditionally, the gold standard for the diagnosis of PDAC has been the histological examination of tissue samples procured from surgical or biopsy specimens. However, due to the pancreas's anatomical position as a retroperitoneal organ, the current modalities for obtaining pathological tissue are limited to invasive procedures such as surgical exploration, endoscopic ultrasound guided fine needle aspiration (EUS-FNA), or endoscopic retrograde cholangiopancreatography (ERCP). These methodologies are characterized by their high invasiveness and the inability to be performed repeatedly, thus precluding the capture of dynamic changes in tumor kinetics and drug susceptibility throughout the entire course of the disease progression 5 . As a result, conventional assessments have been unable to capture the evolving dynamics of tumor growth and drug responsiveness as the disease advances. Therefore, the timely identification of cancerous transformations is of utmost importance for preventing and managing this deadly disease. The Food and Drug Administration (FDA) has approved the serum biomarker CA19-9 for monitoring purposes in patients with pancreatic cancer. However, it has a low specificity and is not suitable for the early screening of PDAC 6 . Clinically, there is an urgent need for an effective, non-invasive examination to improve the rate of early detection of pancreatic cancer. In healthy individuals, the majority of cell-free DNA (cfDNA) in plasma originates from the hematopoietic system 7 . Circulating tumor DNA (ctDNA), a type of cfDNA, is derived from tumor cells released into the blood and may be associated with apoptotic and necrotic tumor cells 8 . Employing liquid biopsy to detect ctDNA in blood enables repeated sampling to monitor the tumor's current status and heterogeneity 9 . Research indicates that patients with circulating tumor cells (CTCs) have a poorer overall survival compared to those without CTCs 10 . In PDAC, tumor patients exhibit higher ctDNA levels than healthy controls, suggesting that ctDNA could be an ideal biomarker for pancreatic cancer diagnosis 11 . Epigenetic is the key driver of many cancers, including colorectal cancer, breast cancer, lung cancer, and pancreatic cancer 12 – 14 . Among them, DNA methylation is the most important epigenetic modification. It participates in the transformation from pancreatic intraepithelial neoplasia to pancreatic tumors 15 . By altering the DNA chromatin structure, DNA methylation can either silence or activate tumor suppressor genes or oncogenes 16 , 17 . DNA methylation modifications are not limited to promoter CpG islands but can occur throughout the genome, such as exons, introns, and 3’ UTRs 18 . Therefore, it is necessary to screen and study the DNA methylation modifications of PDAC on a whole-genome scale. Simultaneously, the DNA methylation features between ctDNA and tissue-derived genomic DNA are highly consistent in cancer models 19 , 20 . As a result, tissue-specific methylation markers can be employed for detection 21 , 22 , making the methylation of ctDNA a promising target for the early detection of cancer as a biomarker 23 , 24 . The whole-genome methylation patterns of ctDNA in plasma were analyzed by us, through which we investigated the distribution of differentially methylated genes on chromosomes and gene structural elements in PDAC. Potential methylation biomarkers were screened based on the functions of differentially methylated genes. A reliable method for detecting DNA methylation was established in plasma, offering new opportunities for the early diagnosis and monitoring of PDAC. Materials and Methods Patients and clinical samples The study encompassed the procurement of plasma specimens from a cohort of sixty patients diagnosed with PDAC and thirty healthy individuals who underwent routine health examinations, all of whom were enrolled at the Affiliated Hospital of Nantong University between the months of December 2021 and June 2023( Supplementary Table 1 ). The research was sanctioned by the Ethics Committee of the Affiliated Hospital of Nantong University under the approval number 2021-K129-01, and all participants provided informed consent. DNA extraction and Bisulfite Conversion Peripheral blood samples of 10 mL placed in EDTA anticoagulant tubes, and refrigerated at 4℃ for two low-temperature centrifugations (1600×g and 16000×g) within 2 h. The samples were then immediately stored at -80℃. Fresh frozen tissue samples were stored at -80℃ immediately after surgery. Extract DNA from tissue samples using phenol-chloroform method, and extract cfDNA from plasma using the Blood Plasma cfDNA Extraction Kit (Qiagen, DP339). Store both samples at -20°C. Extract 500ng of DNA from both tissue and plasma samples and modify the DNA with sodium bisulfite according to the EZ DNA Methylation™ Kit (Zymo research, D5001) protocol. This process converts unmethylated cytosine to uracil, while 5-mC remains unchanged, allowing for the differentiation of methylated and unmethylated genes. The final elution volume for tissue samples is 30 µL, while for plasma samples, a volume of 10 µL is necessary to ensure sufficient DNA concentration. The DNA from normal male human brain tissue was fully methylated using M.SssI methyltransferase (Zymo research, D5011) as a positive control for human methylation. ddH2O was used as a negative control. The Bisulfite DNA (BS-DNA) was stored at -20°C. Whole genome bisulfite sequencing (WGBS) library construction and data analysis After satisfactory detection of plasma DNA from 3 cases of PDAC and 3 normal samples, a certain ratio of lambda DNA was added to serve as quality control for Bisulfite conversion. Genomic DNA was randomly fragmented to 200–400 bp using Covaris M220. The fragmented DNA was repaired, A-tailed, and connected with methylation-modified sequencing adapters. Subsequently, Bisulfite treatment occurred, followed by PCR amplification to obtain the final WGBS library. After library construction, we first used Qubit 2.0 for preliminary quantification, diluting the library to 1ng/µL. Then, insert fragment length was detected using Agilent 2100, and the effective concentration of the library was accurately quantified using Q-PCR (library effective concentration > 2nM) to ensure library quality. After library inspection, different libraries were pooled according to effective concentration and target downstream data volume requirements for Illumina HiSeq sequencing. After obtaining the raw sequencing reads, we conducted bioinformatic analysis. Multiple Quantitative Methylation-Specific Polymerase Chain Reaction (M-QMSP) of Sample Target Fragment Based on pre-existing differential methylation sites, determine the region containing differential methylation sites to be validated. The DNA fragment of this region is the target fragment. Design QMSP primers and probes using this sequence as a template to verify the methylation level differences between the case and control groups in this region. The primer design was completed using the methprimer1.0 online software, and Beacon Designer was used for probe design. Using β-actin (ACTβ) as an internal control gene, design primers and probes in a CpG-free region to avoid false negatives 25 , 26 . Primers and probes were synthesized by Sangon Biotech (Shanghai). The QMSP total reaction system is 20 µL, including 0.4 µL of 10 µmol/L upstream and downstream primers, 0.2 µL of 10 µmol/L probe, 2 µL of BS-DNA, 10 µL of AceQ qPCR Probe Master Mix (Vazyme, Q112), 5 µL of ddH 2 O, cycling parameters: 95℃ for 10 min; 95℃ for 15 s, 58℃ for 1 min, a total of 55 cycles. Fluorescence signals were collected at 58℃ using the LightCycler® 96 Instrument. Each sample was repeated three times, and if no curve amplified twice, it was considered invalid. For each gene, the percent of methylated reference (PMR) of each sample was calculated using the following formula: PMR = 2 −ΔΔCt × 100%, and ΔΔCt = [Ct(gene) - Ct(ACTβ)](Sample) - [Ct(gene) - Ct(ACTβ)](M.SssI treated normal DNA) 25 , 27 ( Fig. 1 ) . Cell culture, reagents, and transfection HPDE6-C7, PANC-1, MIAPaCa-2, CFPAC-1, SW1990, BXPC-3, and AsPC-1 cell lines were purchased from the cell bank of Shanghai Academy of Biological Sciences, Chinese Academy of Sciences. The cells were cultured in DMEM medium supplemented with 10% FBS and 1% penicillin streptomycin. The methods of plasmid transfection and lentivirus infection are described in the supplementary methods. Methylation-Specific Polymerase Chain Reaction(MSP) The bisulfite conversion of tissue or cellular DNA, followed by preparing the reaction mixture in the dark. The system is 10 µL, including 0.4 µL of 10µmol/L upstream and downstream primers, 2 µL of BS-DNA, 10 µL of SYBR Green Master Mix, 7.2 µL of ddH 2 O, cycling parameters: 95℃ for 10 min; 95℃ for 15 s, 60℃ for 30 s, 72℃ for 30 s, a total of 36 cycles. RNA isolation and quantitative real-time PCR (qRT-PCR) analysis Total RNA was isolated using Reagent (Vazyme, R401-01). cDNA was prepared using the PureScript II 1st Strand cDNA Synthesis Kit (Vazyme, PR211-01). qRT-PCR analysis was performed using LightCycler® 96 Instrument. Agarose gel electrophoresis Add 50 mL 1×TBE to 1g agarose, heat for 1 min in microwave, then mix with green dye. Pour the agarose added with nucleic acid dye into the inner groove of the rubber plate, insert the comb, and let it stand at room temperature for 30 min. Carefully pull out the comb after the gel solidifies. Add 1×TBE to tank, submerge gel near black pole, run at 120V for 30 min, then image. Immunohistochemical (IHC) The surgically excised PDAC tissue specimens were fixed in 4% paraformaldehyde, dehydrated, embedded in paraffin, and sectioned. The tissue sections or fixed cells were treated with 3% hydrogen peroxide to block endogenous peroxidase activity. Antigen retrieval was performed, followed by blocking of nonspecific binding with 4% normal goat serum (Gibco). Subsequently, the tissue sections were incubated with the primary antibody, and then with goat anti-rabbit IgG conjugated to horseradish peroxidase (HRP) at a dilution of 1:1000. The sections were then stained using 3,3’-diaminobenzidine (DAB) as the chromogenic substrate solution. Western blot Proteins were meticulously extracted from cultured cells utilizing RIPA buffer (Beyotime) at a controlled temperature of 4°C. Subsequently, the extracted proteins were subjected to immunoblotting analysis, employing the corresponding antibodies in conjunction with a protease inhibitor cocktail and phosphatase inhibitor cocktail to ensure the integrity of the proteins. The concentration of the proteins was accurately determined using a BCA Protein Assay Kit (Beyotime). The proteins present in the cell lysates were then meticulously separated by SDS-PAGE, and transferred onto polyvinylidene difluoride membranes (Sangon Biotech). These membranes were subsequently probed with the specified primary antibodies and HRP-conjugated secondary antibodies for further analysis. Wound-Healing Assay Cells are inoculated into a six-well plate, with 5x10 5 cells in each well, and incubated at 37°C in a humidified incubator with 5% CO 2 . Once the cells reach confluence, a straight edge is aligned, and using a 200 µL pipette tip, two perpendicular lines are drawn across the wells, marking the scratch. At 0 h, 12 h, and 24 h, photographs are taken under the microscope to document the healing progression of the scratch assay. Clonogenic cell survival Assay Cells were seeded into a six-well plate at a density of 700 cells per well. After two weeks of culture in complete medium, the cells were fixed with 4% paraformaldehyde and stained with 1mL of crystal violet solution for 15 min. The cells were then observed and photographed under a microscope. Oil Red O staining Cells were seeded in a six-well plate at a density of 5x10 5 cells per well. Following the experimental groups' assignment, either complete medium (basic medium + 10%FBS) or lipid medium (Oleic acid: Palmitic acid = 0.4 mM:0.2 mM) was added to each well. Incubate plates at 37°C, 5% CO 2 for 48 h, then stain with Oil Red O kit (Beyotime, C0157S). Statistics analysis The findings are depicted as mean ± SEM. PMR calculations were executed in Excel 2021. Statistical assessments employed paired t-tests for tissue samples and unpaired t-tests for plasma samples, examining methylation status of both tumor patients and healthy individuals. One-way ANOVA was used to analyze the correlation between DNA methylation and clinical stage. GraphPad Prism 8.0 created scatter plots while SPSS v. 19.0 software drew ROC curves and conducted AUC evaluations. The Youden index = sensitivity + specificity − 1, with the maximum value in the dataset representing the optimal cutoff, indicating equal significance of sensitivity and specificity. P < 0.05 is considered statistically significant. Results Genome-wide ctDNA differential methylation region analysis Utilizing WGBS, the whole-genome methylome of ctDNA was investigated, and 112,294 differentially methylated regions (DMRs) were identified ( Supplementary Fig. 1 ). The clustering heatmap showing the methylation status and differences between the combinations in genes was drawn based on the average methylation level of DMRs (Fig. 2 a). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis were performed on genes overlapping with DMRs across the genome, based on the annotation results of DMRs (Fold change > 2, adj. P < 0.05), revealing associations with metabolic pathways, transcription factor activity, and various tumor signaling pathways ( Figs. 2 b, 2 c ) . To investigate functional genes with opposing expression patterns, the promoter region analysis of differentially methylated genes (DMGs) was scrutinized. The analysis concentrated on the promoter regions of genes with high methylation in tumors (Difference > 0.15, P -value < 0.05) 28 , 29 , leading to the identification of 4,709 genes. Based on the analytical workflow (Fig. 3 ), RNA-seq data from PDAC tissue was obtained from the GEO database, and 1,783 genes with low expression in PDAC tissue were discerned (Log FC<-0.5, P -value < 0.05). By intersecting with the set of high methylation genes in the promoter regions, 153 genes were isolated. Further integration with the GEPIA database unveiled 8 genes with high methylation and low expression: TDH, PLIN4, PLIN5, CBFA2T3, CERS4, P2RX1, FKBP11 , and SLC7A2 . Based on the spatial distribution of DMRs across the genome and their overlap with the promoter region (spanning from 1.5 kb upstream to 0.5 kb downstream of the transcription start site), functional enrichment analysis was conducted for the involved genes. Among the top 15 KEGG and GO analysis categories, only CBFA2T3 , P2RX1 and CERS4 genes were enriched (Figs. 2 d, 2 e and Fig. 3 ). Low expressions of candidate genes in PDAC tissue accompanied with adverse prognosis In contrast to the elevated DMRs, the expression levels of CBFA2T3, P2RX1 and CERS4 genes in PDAC tissues were reduced compared to normal pancreatic tissues, as evident from the GEO database ( Fig. 4 a ) . Upon further examination, a correlation was observed between the decreased expression of these genes and an unfavorable prognosis in PDAC cases ( Fig. 4 b ) . M-QMSP to validate P2RX1, CBFA2T3 , and CERS4 genes as biomarkers for PDAC Considering that ctDNA originates from tumor cell lysis in tissues, a multiplex QMSP system was first established in carcinoma and adjacent tissues ( Fig. 1 and Supplementary Figs. 2a-2c ). Thereafter, M-QMSP was performed on plasma samples from PDAC patients, and the amplification curve was shown in Supplementary Fig. 2d . In comparison to those in normal plasma samples, the P2RX1, CBFA2T3 , and CERS4 genes were significantly hypermethylated in PDAC patients ( Figs. 4 c-e ) , which we nominated it as P2CC model. These findings suggest that the P2CC model may serve as a potential biomarker for PDAC. Diagnostic performance of the P2CC model superior to CA19-9 for PDAC CA19-9 stands as the prevalent biomarker for diagnosing pancreatic cancer. Therefore, to further explore diagnostic efficiency of P2CC model, a comparison of CA19-9 and the P2CC model with clinical staging relevance was conducted ( Supplementary Figs. 4a-4c ). The P2CC model in both malignant and cancer-free samples were examined, and receiver operating characteristic curve (ROC) were generated (Fig. 4 f). P2CC model demonstrated an AUC value of 0.974, a sensitivity of 90%, and a specificity of 100%, exhibiting superior diagnostic efficacy for PDAC compared to CA19-9 ( Table 1 ) . Notably, the hypermethylation status in the promoter regions of the CBFA2T3 and P2RX1 genes showed exceptional diagnostic performance for PDAC, especially in stages IIA and III ( Supplementary Fig. 3a and 3b ), indicating the P2CC model's potential for early detection against PDAC. Briefly, our study demonstrated that the P2CC model possessed a superior capability to distinguish among PDAC patients at different stages and the healthy individuals, greater than CA19-9. Table 1 Diagnostic efficacy of ctDNA methylation markers P2RX1 , CBFA2T3 and CERS4 AUC Sensitivity Specificity CA19-9 0.873 0.883 0.867 P2RX1 0.848 0.617 0.967 CBFA2T3 0.867 0.883 0.733 CERS4 0.867 0.783 0.867 P2RX1 + CBFA2T3 0.924 0.85 0.9 P2RX1 + CERS4 0.96 0.883 0.933 CBFA2T3 + CERS4 0.951 0.883 0.933 P2RX1 + CBFA2T3 + CERS4 0.974 0.9 1 P2RX1 + CBFA2T3 + CERS4 + CA19-9 0.995 0.967 1 The pattern of hypermethylation coupled with reduced expression of CERS4 gene. In order to explore the potential therapeutic target to PDAC, the expression and the function of CERS4 gene were investigated by in vitro and in vivo experiments. Firstly, using UALCAN to assess CERS4 promoter methylation in PDAC, it was found to be significantly elevated in stages I to III compared to both healthy controls and stage IV patients (Fig. 5 a). Subsequently, t leveraging our PDAC cohort, a fully or partially methylated state of CERS4 gene were validated through methylation specific PCR (MSP) (Fig. 5 b). While, the stark reduction in CERS4 protein and mRNA expression levels within PDAC relative to adjacent tissues was found (Figs. 5 c, 5 d). Finally, we further demonstrated that both mRNA and protein expression of the CERS4 gene were markedly attenuated in PDAC cell lines compared to the normal pancreatic ductal epithelial cells (Figs. 5 e, 5 f). The outcomes demonstrated that both mRNA and protein expression of the CERS4 gene were markedly attenuated in PDAC cell lines compared to the normal pancreatic ductal epithelial cells. However, MSP validated that the CERS4 promoter region was entirely methylated in PDAC cell lines (Fig. 5 g). Collectively, these findings indicated that CERS4 expression level was suppressed in the PDAC, potentially due to its DNA hypermethylation. CERS4 as a tumor suppressor gene of PDAC promotes lipid metabolism. Overexpression vectors for CERS4 were constructed and transfected into PANC-1 and MIAPaCa-2 cells( Supplementary Fig. 4) . The proliferation of PANC-1 and MIAPaCa-2 cells was significantly suppressed by CERS4 overexpression, as well as a noticeable decrease in the number of colonies formed and a substantial attenuation in migration capacity compared to those in the control group (Figs. 6 a, 6 b). Moreover, Moreover, the cell-line derived xenograft (CDX) mice model confirmed that the overexpression of CERS4 effectively inhibited tumor growth of PDAC ( Supplementary Figs. 5, 6 ). The fore-mentionded GO findings in Fig. 2 d implied that the location of CERS4 within the cellular compartments was possibly the plasma membrane and nucleus. And KEGG findings in Fig. 2 e hinted that the function of CERS4 may be implicated in the sphingosine lipid metabolism. Based on these, the immunofluorescence analysis assay validated that CERS4 was predominantly localized to the cell membrane and nucleus ( Supplementary Fig. 6 ). Oil red O staining revealed that the lipid metabolism level of CERS4 in PDAC cells was significantly weaker than that in normal control cells, whereas overexpression of CERS4 led to a substantial enhancement in lipid metabolic activity (Fig. 6 c). Together, these indicated that CERS4 as a tumor suppressor gene promoted lipid metabolism of PDAC. Discussion The FDA approved the first cfDNA-driven method in 2015, using real-time PCR for EGFR mutations in NSCLC, marking a milestone in liquid biopsy. Epi proColon, detecting SEPT9 methylation for CRC screening, received FDA approval in 2016 30 . Our previous research suggests that ZFGs' methylation profiles could serve as early colorectal cancer biomarkers, especially with KRAS mutations 31 . We examined PDAC ctDNA methylation patterns using WGBS, revealing significant differences between PDAC patients and healthy subjects. These findings underscore the link between methylation patterns and PDAC. Notably, differentially methylated regions (DMRs) in 'CpG islands' were identified, which are crucial for gene expression regulation 32 . Hypomethylation in these regions may contribute to tumor suppressor gene silencing, promoting cancer growth 33 . The M-QMSP system, analyzing plasma samples, identified significant methylation level discrepancies in P2RX1 , CBFA2T3 , and CERS4 between PDAC patients and controls. Our study correlated gene methylation with tumor staging, showing that this method outperforms CA19-9 in specificity and reproducibility. The combined diagnostic model's AUC and specificity exceeded 0.9, indicating its potential as a specific plasma biomarker for PDAC. Compared to single QMSP, the M-QMSP system allows for the simultaneous quantification of multiple genes, reducing DNA consumption and detection costs 34 , 35 . However, the study's limitation is its small sample size. Future research requires extensive validation across diverse populations to ensure high specificity. P2RX1 , involved in transcription factor networks in pancreatic progenitor cells, is implicated in immune microenvironment formation and PDAC metastasis 36 – 38 . CBFA2T3 , a transcriptional co-repressor, is suggested as a novel epigenetic target for PDAC resistance to chemotherapy 39 – 41 . CERS4 , a sphingolipid metabolic enzyme 42 , has been identified as a tumor suppressor in PDAC for the first time, affecting cell proliferation, migration, and lipid metabolism. In summary, our research has identified P2RX1 , CBFA2T3 , and CERS4 as potential diagnostic biomarkers for PDAC through liquid biopsy, offering a new P2CC model for early detection and monitoring of the disease. Declarations Ethics approval and consent to participate The studies involving human participants were reviewed and approved by the institutional review boards of the Affiliated Hospital of Nantong University. The patients/participants provided their written informed consent to participate in this study. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The work was supported by the National Natural Science Foundation of China (grant No. 81974313, 82272411, 82472364), the Natural Science Foundation of Jiangsu Commission of Health (grant No. M2020065), Jiangsu Provincial Research Hospital (grant No.YJXYY202204), Jiangsu Provincial Medical Key Discipline (grant No.ZDXK202240), Science and Technology Project of Jiangsu Province (grant No.BE2023741). Author Contribution HC and HS conducted molecular and cell biology experiments, performed analyses, interpreted the results, and drafted the manuscript. XW conducted molecular and cell biology experiments. SJ aided in drafting and editing the manuscript and advising on the study. WP aided in drafting and editing the manuscript, aided in in silico experimental designs and supervised the analyses and results interpretation. WD and JZ designed the study, conducted molecular and cell biology experiments, performed analyses, interpreted results, designed the functional experiments, and contributed to the drafting of this manuscript. References Cronin KA, Scott S, Firth AU et al. Annual report to the nation on the status of cancer, part 1: National cancer statistics. Cancer. 2022; 128 (24): 4251–4284. Siegel RL, Giaquinto AN, Jemal A, Cancer statistics. 2024. CA: a Cancer Journal For Clinicians 2024; 74 (1): 12–49. Tempero MA, Malafa MP, Al-Hawary M, et al. Pancreatic Adenocarcinoma, Version 2.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2017;15(8):1028–61. Yang J, Xu R, Wang C, et al. Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review. Cancer Commun (Lond). 2021;41(12):1257–74. Yang Y, Feng Y, Cheng G, et al. Etiological Diagnosis of Linear-Array Endoscopic Ultrasonography in Distal Common Bile Duct Dilatation. Ultrasound Q. 2022;38(2):170–8. Hou J, Li X, Xie K-P. Coupled liquid biopsy and bioinformatics for pancreatic cancer early detection and precision prognostication. Mol Cancer. 2021;20(1):34. Sun K, Jiang P, Chan KCA, et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci U S A. 2015;112(40):E5503–12. Nordgård O, Tjensvoll K, Gilje B, et al. Circulating tumour cells and DNA as liquid biopsies in gastrointestinal cancer. Br J Surg. 2018;105(2):e110–20. Imamura T, Komatsu S, Ichikawa D, et al. Liquid biopsy in patients with pancreatic cancer: Circulating tumor cells and cell-free nucleic acids. World J Gastroenterol. 2016;22(25):5627–41. Xie Z-B, Yao L, Jin C, et al. Circulating tumor cells in pancreatic cancer patients: efficacy in diagnosis and value in prognosis. Discov Med. 2016;22(120):121–8. Qi Z-H, Xu H-X, Zhang S-R, et al. The Significance of Liquid Biopsy in Pancreatic Cancer. J Cancer. 2018;9(18):3417–26. Beaver JA, Jelovac D, Balukrishna S, et al. Detection of cancer DNA in plasma of patients with early-stage breast cancer. Clin Cancer Res. 2014;20(10):2643–50. Eissa MAL, Lerner L, Abdelfatah E, et al. Promoter methylation of ADAMTS1 and BNC1 as potential biomarkers for early detection of pancreatic cancer in blood. Clin Epigenetics. 2019;11(1):59. Abbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature. 2017;545(7655):446–51. Esteller M, Herman JG. Cancer as an epigenetic disease: DNA methylation and chromatin alterations in human tumours. J Pathol. 2002;196(1):1–7. Jones PA, Baylin SB. The fundamental role of epigenetic events in cancer. Nat Rev Genet. 2002;3(6):415–28. Shao K, Pu W, Zhang J, et al. DNA hypermethylation contributes to colorectal cancer metastasis by regulating the binding of CEBPB and TFCP2 to the CPEB1 promoter. Clin Epigenetics. 2021;13(1):89. Chatterjee A, Stockwell PA, Rodger EJ, et al. Genome-wide DNA methylation map of human neutrophils reveals widespread inter-individual epigenetic variation. Sci Rep. 2015;5:17328. Chan KCA, Woo JKS, King A, et al. Analysis of Plasma Epstein-Barr Virus DNA to Screen for Nasopharyngeal Cancer. N Engl J Med. 2017;377(6):513–22. Schwarzenbach H, Hoon DSB, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426–37. Karampini E, McCaughan F. Circulating DNA in solid organ cancers-analysis and clinical application. QJM. 2016;109(4):223–7. Lubotzky A, Zemmour H, Neiman D et al. Liquid biopsy reveals collateral tissue damage in cancer. JCI Insight 2022; 7 (2). Costa-Pinheiro P, Montezuma D, Henrique R, et al. Diagnostic and prognostic epigenetic biomarkers in cancer. Epigenomics. 2015;7(6):1003–15. Hong S-M, Omura N, Vincent A, et al. Genome-wide CpG island profiling of intraductal papillary mucinous neoplasms of the pancreas. Clin Cancer Res. 2012;18(3):700–12. de Vos L, Gevensleben H, Schröck A, et al. Comparison of quantification algorithms for circulating cell-free DNA methylation biomarkers in blood plasma from cancer patients. Clin Epigenetics. 2017;9:125. Salta S, Nunes P, Fontes-Sousa S. A DNA Methylation-Based Test for Breast Cancer Detection in Circulating Cell-Free DNA. J Clin Med. 2018;7:11. Singh A, Gupta S, Badarukhiya JA, et al. Detection of aberrant methylation of HOXA9 and HIC1 through multiplex MethyLight assay in serum DNA for the early detection of epithelial ovarian cancer. Int J Cancer. 2020;147(6):1740–52. Zhu H, Wu L-F, Mo X-B, et al. Rheumatoid arthritis-associated DNA methylation sites in peripheral blood mononuclear cells. Ann Rheum Dis. 2019;78(1):36–42. Izquierdo AG, Boughanem H, Diaz-Lagares A, et al. DNA methylome in visceral adipose tissue can discriminate patients with and without colorectal cancer. Epigenetics. 2022;17(6):665–76. Issa IA, Noureddine M. Colorectal cancer screening: An updated review of the available options. World J Gastroenterol. 2017;23(28):5086–96. Pu W, Qian F, Liu J, et al. Targeted Bisulfite Sequencing Reveals DNA Methylation Changes in Zinc Finger Family Genes Associated With KRAS Mutated Colorectal Cancer. Front Cell Dev Biology. 2021;9:759813. Hidaka H, Higashimoto K, Aoki S, et al. Comprehensive methylation analysis of imprinting-associated differentially methylated regions in colorectal cancer. Clin Epigenetics. 2018;10(1):150. Laplana M, Bieg M, Faltus C, et al. Differentially methylated regions within lung cancer risk loci are enriched in deregulated enhancers. Epigenetics. 2022;17(2):117–32. Olkhov-Mitsel E, Zdravic D, Kron K, et al. Novel multiplex MethyLight protocol for detection of DNA methylation in patient tissues and bodily fluids. Sci Rep. 2014;4:4432. Constâncio V, Nunes SP, Moreira-Barbosa C, et al. Early detection of the major male cancer types in blood-based liquid biopsies using a DNA methylation panel. Clin Epigenetics. 2019;11(1):175. Thompson N, Gésina E, Scheinert P, et al. RNA profiling and chromatin immunoprecipitation-sequencing reveal that PTF1a stabilizes pancreas progenitor identity via the control of MNX1/HLXB9 and a network of other transcription factors. Mol Cell Biol. 2012;32(6):1189–99. Patel S, Fu S, Mastio J, et al. Unique pattern of neutrophil migration and function during tumor progression. Nat Immunol. 2018;19(11):1236–47. Wang X, Hu L-P, Qin W-T, et al. Identification of a subset of immunosuppressive P2RX1-negative neutrophils in pancreatic cancer liver metastasis. Nat Commun. 2021;12(1):174. Davis JN, McGhee L, Meyers S. The ETO (MTG8) gene family. Gene 2003; 303. Rossetti S, Hoogeveen AT, Sacchi N. The MTG proteins: chromatin repression players with a passion for networking. Genomics. 2004;84(1):1–9. Ahmed AA, Marchetti C, Ohnmacht SA, et al. A G-quadruplex-binding compound shows potent activity in human gemcitabine-resistant pancreatic cancer cells. Sci Rep. 2020;10(1):12192. Summers SA. Could Ceramides Become the New Cholesterol? Cell Metab. 2018;27(2):276–80. Additional Declarations No competing interests reported. Supplementary Files Supplementary.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-4982255","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":357109417,"identity":"593d1545-4b05-4a70-81fa-80b97c941f9e","order_by":0,"name":"Hongsheng Chen","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Hongsheng","middleName":"","lastName":"Chen","suffix":""},{"id":357109419,"identity":"cddc2583-2d39-4154-98e4-d3db08efe068","order_by":1,"name":"Hongyuan Shen","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Hongyuan","middleName":"","lastName":"Shen","suffix":""},{"id":357109421,"identity":"cdfa509d-c799-4c91-8223-97fb3efb0070","order_by":2,"name":"Xudong Wang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University, Medical school of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Wang","suffix":""},{"id":357109422,"identity":"3e4b132f-5236-47bb-9939-a55cdd08afa8","order_by":3,"name":"Shaoqing Ju","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University, Medical school of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Shaoqing","middleName":"","lastName":"Ju","suffix":""},{"id":357109424,"identity":"f312bbbe-efc6-4407-bb7d-046dee7f12ad","order_by":4,"name":"Weilin Pu","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Weilin","middleName":"","lastName":"Pu","suffix":""},{"id":357109427,"identity":"51d04daf-9063-45c3-9025-f7a2fa12ee1c","order_by":5,"name":"Jianfeng Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Zhang","suffix":""},{"id":357109428,"identity":"d37d7d57-2632-433e-a80f-bc26251f2094","order_by":6,"name":"Weifeng Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYDCCA4wPH0BYCURrYTY2gKgmQYuZBGla+G4ks1UX/jjMwM+eY8DwcwcRWiSBWm7PSDjMINnzxoCx9wwRWgxu5x+7zQPUYnAjx4CZsY0oLclsxSAt9iRpYQbbIkGsFsn7j5mledLSeSTOPCs42EuMFr4zhxk/89hYy/G3J2988JMYLTDAAyIOkKBhFIyCUTAKRgE+AAA3rzLQBASkFgAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Nantong University, Medical school of Nantong University","correspondingAuthor":true,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-08-27 07:15:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4982255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4982255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66854263,"identity":"2717a3ae-444b-4d4d-97cd-91cfca9ae4a8","added_by":"auto","created_at":"2024-10-17 07:36:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe graphical abstract of Multiple Quantitative Methylation-Specific Polymerase Chain Reaction (M-QMSP).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/0f18755da3d3ae5471a200ab.png"},{"id":66854082,"identity":"f3624035-a7e0-4483-a9ed-92c222ca9ece","added_by":"auto","created_at":"2024-10-17 07:28:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe heatmap and functional enrichment analysis of differentially methylated genes (DMGs) in PDAC. \u003c/strong\u003e(a) The heatmap of differentially methylated genes (DMGs). The horizontal axis represents the comparison groups, and the vertical axis represents the clustering effect of methylation level values, ranging from blue (low) to red (high). (b, c) DMGs across the entire genome are used for the KEGG pathway and GO functional enrichment analysis. (d, e) DMGs located at the promoter region undergo KEGG and GO analysis, with red markers indicating enriched entries for three target genes.\u003c/p\u003e\n\u003cp\u003eb-e All enriched items, characterized Fold-change\u0026gt;2 and adj \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, were identified.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/f1f3f6d83ec4c5142ad93706.png"},{"id":66854084,"identity":"601c1fa7-246f-49a0-bf47-025834bf1006","added_by":"auto","created_at":"2024-10-17 07:28:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flow chart of differentially methylated genes (DMGs) screening from plasma ctDNA in PDAC.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/cff7e8be9e57bef50bbecb91.png"},{"id":66855716,"identity":"80f54206-2ba1-4353-b12a-87c7adce9c74","added_by":"auto","created_at":"2024-10-17 07:44:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":200850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eM-QMSP to validate the P2CC model as a biomarker for PDAC. \u003c/strong\u003e(a) The GEPIA online analysis based on TCGA database reveals the relative expression of \u003cem\u003eP2RX1\u003c/em\u003e, \u003cem\u003eCBFA2T3 \u003c/em\u003eand\u003cem\u003e CERS4 \u003c/em\u003egenes (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). (b) The kaplam-meier plotter website predicts the relationship between the expression levels of three genes and prognosis in tumors. (c-d) The percent of methylated reference (PMR) of \u003cem\u003eP2RX1\u003c/em\u003e, \u003cem\u003eCBFA2T3 \u003c/em\u003eand\u003cem\u003e CERS4\u003c/em\u003e genes in ctDNA from healthy individuals (n=30) and PDAC patients (n=60). (f) Receiver operator characteristic (ROC) curve discriminating PDAC patients from healthy control in plasma. c-d used unpaired t-test. \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003csup\u003e*\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01\u003csup\u003e**\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001\u003csup\u003e****\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/12b1945af2b87b4556914295.png"},{"id":66854264,"identity":"1cd4e9c2-1e8b-44cf-bf00-0990c39cd84d","added_by":"auto","created_at":"2024-10-17 07:36:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":762324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe pattern of hypermethylation coupled with reduced expression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCERS4 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003egene. \u003c/strong\u003e(a) Promoter methylation levels of \u003cem\u003eCERS4 \u003c/em\u003ein PDAC stages I-IV from the UALCAN database. The Beta value indicates level of DNA methylation ranging from 0 (unmethylated) to 1 (fully methylated). (b) MSP detection of \u003cem\u003eCERS4\u003c/em\u003e methylation status in cancer and adjacent non-tumor tissues. (c, d) RT-qPCR and Immunohistochemical analysis of \u003cem\u003eCERS4\u003c/em\u003e expression levels in tumor and adjacent tissues. (e, f) Detection of \u003cem\u003eCERS4\u003c/em\u003e expression levels in cell lines using RT-qPCR and WB. (g) Methylation status analysis of the \u003cem\u003eCERS4\u003c/em\u003e promoter region by MSP in the cells. \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003csup\u003e*\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01\u003csup\u003e**\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/ace4934a467de11a040e61e4.png"},{"id":66854083,"identity":"10242ce2-1530-4a59-b446-4fff3e2a3248","added_by":"auto","created_at":"2024-10-17 07:28:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1274891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCERS4\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e as a tumor suppressor gene of PDAC promotes lipid metabolism.\u003c/strong\u003e (a) Perform a wound-healing assay to validate the effect of \u003cem\u003eCERS4\u003c/em\u003e on cell migration of PDAC. (b) Perform a clonogenic cell survival assay to detect the effect of \u003cem\u003eCERS4\u003c/em\u003e on cell proliferation of PDAC. (c) The lipid staining of MIA-PaCa-2 cell was observed after oil red O staining. Red represents lipid. Purple is the nucleus.\u003cem\u003e P\u003c/em\u003e\u0026lt;0.05\u003csup\u003e*\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01\u003csup\u003e**\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/02265523c11d7fcd039c2951.png"},{"id":76549226,"identity":"99b98701-25b4-4ab6-82d9-75783ca966ea","added_by":"auto","created_at":"2025-02-18 09:47:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4976357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/7f405a16-53c5-4a2f-91ae-e72756608a7b.pdf"},{"id":66854087,"identity":"056eb76e-ca92-4fb5-b985-630ca0bf4847","added_by":"auto","created_at":"2024-10-17 07:28:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4108402,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4982255/v1/d30a52e9527bdc36e7f6d7e8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated ctDNA Methylation Profiling of P2RX1, CBFA2T3, and CERS4 as Pancreatic Ductal Adenocarcinoma Biomarkers: Identification and Mechanistic Insights","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer is a common malignant tumor of the digestive system\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The incidence of pancreatic cancer is escalating year by year, and by 2024, it is anticipated to rank third in terms of mortality among all malignant tumors\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The most prevalent form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), accounts for over 90% of pancreatic exocrine malignant tumors\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Due to the aggressive nature of PDAC and the absence of characteristic early signs, the majority of patients are diagnosed at advanced stages, with only a mere 10\u0026ndash;20% being eligible for surgical removal \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Traditionally, the gold standard for the diagnosis of PDAC has been the histological examination of tissue samples procured from surgical or biopsy specimens. However, due to the pancreas's anatomical position as a retroperitoneal organ, the current modalities for obtaining pathological tissue are limited to invasive procedures such as surgical exploration, endoscopic ultrasound guided fine needle aspiration (EUS-FNA), or endoscopic retrograde cholangiopancreatography (ERCP). These methodologies are characterized by their high invasiveness and the inability to be performed repeatedly, thus precluding the capture of dynamic changes in tumor kinetics and drug susceptibility throughout the entire course of the disease progression\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a result, conventional assessments have been unable to capture the evolving dynamics of tumor growth and drug responsiveness as the disease advances. Therefore, the timely identification of cancerous transformations is of utmost importance for preventing and managing this deadly disease. The Food and Drug Administration (FDA) has approved the serum biomarker CA19-9 for monitoring purposes in patients with pancreatic cancer. However, it has a low specificity and is not suitable for the early screening of PDAC \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Clinically, there is an urgent need for an effective, non-invasive examination to improve the rate of early detection of pancreatic cancer.\u003c/p\u003e \u003cp\u003eIn healthy individuals, the majority of cell-free DNA (cfDNA) in plasma originates from the hematopoietic system\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Circulating tumor DNA (ctDNA), a type of cfDNA, is derived from tumor cells released into the blood and may be associated with apoptotic and necrotic tumor cells\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Employing liquid biopsy to detect ctDNA in blood enables repeated sampling to monitor the tumor's current status and heterogeneity\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Research indicates that patients with circulating tumor cells (CTCs) have a poorer overall survival compared to those without CTCs\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In PDAC, tumor patients exhibit higher ctDNA levels than healthy controls, suggesting that ctDNA could be an ideal biomarker for pancreatic cancer diagnosis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEpigenetic is the key driver of many cancers, including colorectal cancer, breast cancer, lung cancer, and pancreatic cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Among them, DNA methylation is the most important epigenetic modification. It participates in the transformation from pancreatic intraepithelial neoplasia to pancreatic tumors\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. By altering the DNA chromatin structure, DNA methylation can either silence or activate tumor suppressor genes or oncogenes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. DNA methylation modifications are not limited to promoter CpG islands but can occur throughout the genome, such as exons, introns, and 3\u0026rsquo; UTRs\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Therefore, it is necessary to screen and study the DNA methylation modifications of PDAC on a whole-genome scale. Simultaneously, the DNA methylation features between ctDNA and tissue-derived genomic DNA are highly consistent in cancer models\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. As a result, tissue-specific methylation markers can be employed for detection\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, making the methylation of ctDNA a promising target for the early detection of cancer as a biomarker\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The whole-genome methylation patterns of ctDNA in plasma were analyzed by us, through which we investigated the distribution of differentially methylated genes on chromosomes and gene structural elements in PDAC. Potential methylation biomarkers were screened based on the functions of differentially methylated genes. A reliable method for detecting DNA methylation was established in plasma, offering new opportunities for the early diagnosis and monitoring of PDAC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and clinical samples\u003c/h2\u003e \u003cp\u003eThe study encompassed the procurement of plasma specimens from a cohort of sixty patients diagnosed with PDAC and thirty healthy individuals who underwent routine health examinations, all of whom were enrolled at the Affiliated Hospital of Nantong University between the months of December 2021 and June 2023(\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). The research was sanctioned by the Ethics Committee of the Affiliated Hospital of Nantong University under the approval number 2021-K129-01, and all participants provided informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and Bisulfite Conversion\u003c/h2\u003e \u003cp\u003ePeripheral blood samples of 10 mL placed in EDTA anticoagulant tubes, and refrigerated at 4℃ for two low-temperature centrifugations (1600\u0026times;g and 16000\u0026times;g) within 2 h. The samples were then immediately stored at -80℃. Fresh frozen tissue samples were stored at -80℃ immediately after surgery. Extract DNA from tissue samples using phenol-chloroform method, and extract cfDNA from plasma using the Blood Plasma cfDNA Extraction Kit (Qiagen, DP339). Store both samples at -20\u0026deg;C. Extract 500ng of DNA from both tissue and plasma samples and modify the DNA with sodium bisulfite according to the EZ DNA Methylation\u0026trade; Kit (Zymo research, D5001) protocol. This process converts unmethylated cytosine to uracil, while 5-mC remains unchanged, allowing for the differentiation of methylated and unmethylated genes. The final elution volume for tissue samples is 30 \u0026micro;L, while for plasma samples, a volume of 10 \u0026micro;L is necessary to ensure sufficient DNA concentration. The DNA from normal male human brain tissue was fully methylated using M.SssI methyltransferase (Zymo research, D5011) as a positive control for human methylation. ddH2O was used as a negative control. The Bisulfite DNA (BS-DNA) was stored at -20\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eWhole genome bisulfite sequencing (WGBS) library construction and data analysis\u003c/h2\u003e \u003cp\u003eAfter satisfactory detection of plasma DNA from 3 cases of PDAC and 3 normal samples, a certain ratio of lambda DNA was added to serve as quality control for Bisulfite conversion. Genomic DNA was randomly fragmented to 200\u0026ndash;400 bp using Covaris M220. The fragmented DNA was repaired, A-tailed, and connected with methylation-modified sequencing adapters. Subsequently, Bisulfite treatment occurred, followed by PCR amplification to obtain the final WGBS library. After library construction, we first used Qubit 2.0 for preliminary quantification, diluting the library to 1ng/\u0026micro;L. Then, insert fragment length was detected using Agilent 2100, and the effective concentration of the library was accurately quantified using Q-PCR (library effective concentration\u0026thinsp;\u0026gt;\u0026thinsp;2nM) to ensure library quality. After library inspection, different libraries were pooled according to effective concentration and target downstream data volume requirements for Illumina HiSeq sequencing. After obtaining the raw sequencing reads, we conducted bioinformatic analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Quantitative Methylation-Specific Polymerase Chain Reaction (M-QMSP) of Sample Target Fragment\u003c/h2\u003e \u003cp\u003eBased on pre-existing differential methylation sites, determine the region containing differential methylation sites to be validated. The DNA fragment of this region is the target fragment. Design QMSP primers and probes using this sequence as a template to verify the methylation level differences between the case and control groups in this region. The primer design was completed using the methprimer1.0 online software, and Beacon Designer was used for probe design. Using \u003cem\u003eβ-actin\u003c/em\u003e (ACTβ) as an internal control gene, design primers and probes in a CpG-free region to avoid false negatives\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Primers and probes were synthesized by Sangon Biotech (Shanghai). The QMSP total reaction system is 20 \u0026micro;L, including 0.4 \u0026micro;L of 10 \u0026micro;mol/L upstream and downstream primers, 0.2 \u0026micro;L of 10 \u0026micro;mol/L probe, 2 \u0026micro;L of BS-DNA, 10 \u0026micro;L of AceQ qPCR Probe Master Mix (Vazyme, Q112), 5 \u0026micro;L of ddH\u003csub\u003e2\u003c/sub\u003eO, cycling parameters: 95℃ for 10 min; 95℃ for 15 s, 58℃ for 1 min, a total of 55 cycles. Fluorescence signals were collected at 58℃ using the LightCycler\u0026reg; 96 Instrument. Each sample was repeated three times, and if no curve amplified twice, it was considered invalid. For each gene, the percent of methylated reference (PMR) of each sample was calculated using the following formula: PMR\u0026thinsp;=\u0026thinsp;2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e \u0026times; 100%, and ΔΔCt = [Ct(gene) - Ct(ACTβ)](Sample) - [Ct(gene) - Ct(ACTβ)](M.SssI treated normal DNA)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell culture, reagents, and transfection\u003c/h2\u003e \u003cp\u003eHPDE6-C7, PANC-1, MIAPaCa-2, CFPAC-1, SW1990, BXPC-3, and AsPC-1 cell lines were purchased from the cell bank of Shanghai Academy of Biological Sciences, Chinese Academy of Sciences. The cells were cultured in DMEM medium supplemented with 10% FBS and 1% penicillin streptomycin. The methods of plasmid transfection and lentivirus infection are described in the supplementary methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMethylation-Specific Polymerase Chain Reaction(MSP)\u003c/h2\u003e \u003cp\u003eThe bisulfite conversion of tissue or cellular DNA, followed by preparing the reaction mixture in the dark. The system is 10 \u0026micro;L, including 0.4 \u0026micro;L of 10\u0026micro;mol/L upstream and downstream primers, 2 \u0026micro;L of BS-DNA, 10 \u0026micro;L of SYBR Green Master Mix, 7.2 \u0026micro;L of ddH\u003csub\u003e2\u003c/sub\u003eO, cycling parameters: 95℃ for 10 min; 95℃ for 15 s, 60℃ for 30 s, 72℃ for 30 s, a total of 36 cycles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and quantitative real-time PCR (qRT-PCR) analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated using Reagent (Vazyme, R401-01). cDNA was prepared using the\u003c/p\u003e \u003cp\u003ePureScript II 1st Strand cDNA Synthesis Kit (Vazyme, PR211-01). qRT-PCR analysis was\u003c/p\u003e \u003cp\u003eperformed using LightCycler\u0026reg; 96 Instrument.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAgarose gel electrophoresis\u003c/h2\u003e \u003cp\u003eAdd 50 mL 1\u0026times;TBE to 1g agarose, heat for 1 min in microwave, then mix with green dye. Pour the agarose added with nucleic acid dye into the inner groove of the rubber plate, insert the comb, and let it stand at room temperature for 30 min. Carefully pull out the comb after the gel solidifies. Add 1\u0026times;TBE to tank, submerge gel near black pole, run at 120V for 30 min, then image.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical (IHC)\u003c/h2\u003e \u003cp\u003eThe surgically excised PDAC tissue specimens were fixed in 4% paraformaldehyde, dehydrated, embedded in paraffin, and sectioned. The tissue sections or fixed cells were treated with 3% hydrogen peroxide to block endogenous peroxidase activity. Antigen retrieval was performed, followed by blocking of nonspecific binding with 4% normal goat serum (Gibco). Subsequently, the tissue sections were incubated with the primary antibody, and then with goat anti-rabbit IgG conjugated to horseradish peroxidase (HRP) at a dilution of 1:1000. The sections were then stained using 3,3\u0026rsquo;-diaminobenzidine (DAB) as the chromogenic substrate solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot\u003c/h2\u003e \u003cp\u003eProteins were meticulously extracted from cultured cells utilizing RIPA buffer (Beyotime) at a controlled temperature of 4\u0026deg;C. Subsequently, the extracted proteins were subjected to immunoblotting analysis, employing the corresponding antibodies in conjunction with a protease inhibitor cocktail and phosphatase inhibitor cocktail to ensure the integrity of the proteins. The concentration of the proteins was accurately determined using a BCA Protein Assay Kit (Beyotime). The proteins present in the cell lysates were then meticulously separated by SDS-PAGE, and transferred onto polyvinylidene difluoride membranes (Sangon Biotech). These membranes were subsequently probed with the specified primary antibodies and HRP-conjugated secondary antibodies for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWound-Healing Assay\u003c/h2\u003e \u003cp\u003eCells are inoculated into a six-well plate, with 5x10\u003csup\u003e5\u003c/sup\u003e cells in each well, and incubated at 37\u0026deg;C in a humidified incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e. Once the cells reach confluence, a straight edge is aligned, and using a 200 \u0026micro;L pipette tip, two perpendicular lines are drawn across the wells, marking the scratch. At 0 h, 12 h, and 24 h, photographs are taken under the microscope to document the healing progression of the scratch assay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClonogenic cell survival Assay\u003c/h2\u003e \u003cp\u003eCells were seeded into a six-well plate at a density of 700 cells per well. After two weeks of culture in complete medium, the cells were fixed with 4% paraformaldehyde and stained with 1mL of crystal violet solution for 15 min. The cells were then observed and photographed under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOil Red O staining\u003c/h2\u003e \u003cp\u003eCells were seeded in a six-well plate at a density of 5x10\u003csup\u003e5\u003c/sup\u003e cells per well. Following the experimental groups' assignment, either complete medium (basic medium\u0026thinsp;+\u0026thinsp;10%FBS) or lipid medium (Oleic acid: Palmitic acid\u0026thinsp;=\u0026thinsp;0.4 mM:0.2 mM) was added to each well. Incubate plates at 37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e for 48 h, then stain with Oil Red O kit (Beyotime, C0157S).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistics analysis\u003c/h2\u003e \u003cp\u003eThe findings are depicted as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. PMR calculations were executed in Excel 2021. Statistical assessments employed paired t-tests for tissue samples and unpaired t-tests for plasma samples, examining methylation status of both tumor patients and healthy individuals. One-way ANOVA was used to analyze the correlation between DNA methylation and clinical stage. GraphPad Prism 8.0 created scatter plots while SPSS v. 19.0 software drew ROC curves and conducted AUC evaluations. The Youden index\u0026thinsp;=\u0026thinsp;sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1, with the maximum value in the dataset representing the optimal cutoff, indicating equal significance of sensitivity and specificity. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide ctDNA differential methylation region analysis\u003c/h2\u003e \u003cp\u003eUtilizing WGBS, the whole-genome methylome of ctDNA was investigated, and 112,294 differentially methylated regions (DMRs) were identified (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). The clustering heatmap showing the methylation status and differences between the combinations in genes was drawn based on the average methylation level of DMRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis were performed on genes overlapping with DMRs across the genome, based on the annotation results of DMRs (Fold change\u0026thinsp;\u0026gt;\u0026thinsp;2, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), revealing associations with metabolic pathways, transcription factor activity, and various tumor signaling pathways \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. To investigate functional genes with opposing expression patterns, the promoter region analysis of differentially methylated genes (DMGs) was scrutinized. The analysis concentrated on the promoter regions of genes with high methylation in tumors (Difference\u0026thinsp;\u0026gt;\u0026thinsp;0.15, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, leading to the identification of 4,709 genes. Based on the analytical workflow (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), RNA-seq data from PDAC tissue was obtained from the GEO database, and 1,783 genes with low expression in PDAC tissue were discerned (Log FC\u0026lt;-0.5, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By intersecting with the set of high methylation genes in the promoter regions, 153 genes were isolated. Further integration with the GEPIA database unveiled 8 genes with high methylation and low expression: \u003cem\u003eTDH, PLIN4, PLIN5, CBFA2T3, CERS4, P2RX1, FKBP11\u003c/em\u003e, and \u003cem\u003eSLC7A2\u003c/em\u003e. Based on the spatial distribution of DMRs across the genome and their overlap with the promoter region (spanning from 1.5 kb upstream to 0.5 kb downstream of the transcription start site), functional enrichment analysis was conducted for the involved genes. Among the top 15 KEGG and GO analysis categories, only \u003cem\u003eCBFA2T3\u003c/em\u003e, \u003cem\u003eP2RX1\u003c/em\u003e and \u003cem\u003eCERS4\u003c/em\u003e genes were enriched (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLow expressions of candidate genes in PDAC tissue accompanied with adverse prognosis\u003c/h2\u003e \u003cp\u003eIn contrast to the elevated DMRs, the expression levels of \u003cem\u003eCBFA2T3, P2RX1\u003c/em\u003e and \u003cem\u003eCERS4\u003c/em\u003e genes in PDAC tissues were reduced compared to normal pancreatic tissues, as evident from the GEO database \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Upon further examination, a correlation was observed between the decreased expression of these genes and an unfavorable prognosis in PDAC cases \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eM-QMSP to validate\u003c/b\u003e \u003cb\u003eP2RX1, CBFA2T3\u003c/b\u003e, \u003cb\u003eand\u003c/b\u003e \u003cb\u003eCERS4\u003c/b\u003e \u003cb\u003egenes as biomarkers for PDAC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eConsidering that ctDNA originates from tumor cell lysis in tissues, a multiplex QMSP system was first established in carcinoma and adjacent tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eand Supplementary Figs.\u0026nbsp;2a-2c\u003c/b\u003e). Thereafter, M-QMSP was performed on plasma samples from PDAC patients, and the amplification curve was shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;2d\u003c/b\u003e. In comparison to those in normal plasma samples, the \u003cem\u003eP2RX1, CBFA2T3\u003c/em\u003e, and \u003cem\u003eCERS4\u003c/em\u003e genes were significantly hypermethylated in PDAC patients \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec-e\u003cb\u003e)\u003c/b\u003e, which we nominated it as P2CC model. These findings suggest that the P2CC model may serve as a potential biomarker for PDAC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic performance of the P2CC model superior to CA19-9 for PDAC\u003c/h2\u003e \u003cp\u003eCA19-9 stands as the prevalent biomarker for diagnosing pancreatic cancer. Therefore, to further explore diagnostic efficiency of P2CC model, a comparison of CA19-9 and the P2CC model with clinical staging relevance was conducted (\u003cb\u003eSupplementary Figs.\u0026nbsp;4a-4c\u003c/b\u003e). The P2CC model in both malignant and cancer-free samples were examined, and receiver operating characteristic curve (ROC) were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). P2CC model demonstrated an AUC value of 0.974, a sensitivity of 90%, and a specificity of 100%, exhibiting superior diagnostic efficacy for PDAC compared to CA19-9 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Notably, the hypermethylation status in the promoter regions of the \u003cem\u003eCBFA2T3\u003c/em\u003e and \u003cem\u003eP2RX1\u003c/em\u003e genes showed exceptional diagnostic performance for PDAC, especially in stages IIA and III (\u003cb\u003eSupplementary Fig.\u0026nbsp;3a and 3b\u003c/b\u003e), indicating the P2CC model's potential for early detection against PDAC. Briefly, our study demonstrated that the P2CC model possessed a superior capability to distinguish among PDAC patients at different stages and the healthy individuals, greater than CA19-9.\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\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=\"left\" 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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDiagnostic efficacy of ctDNA methylation markers \u003cem\u003eP2RX1\u003c/em\u003e, \u003cem\u003eCBFA2T3\u003c/em\u003e and \u003cem\u003eCERS4\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2RX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCBFA2T3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCERS4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2RX1\u0026thinsp;+\u0026thinsp;CBFA2T3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2RX1\u0026thinsp;+\u0026thinsp;CERS4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCBFA2T3\u0026thinsp;+\u0026thinsp;CERS4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2RX1\u0026thinsp;+\u0026thinsp;CBFA2T3\u0026thinsp;+\u0026thinsp;CERS4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2RX1\u0026thinsp;+\u0026thinsp;CBFA2T3\u0026thinsp;+\u0026thinsp;CERS4\u0026thinsp;+\u003c/em\u003e\u0026thinsp;CA19-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\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\u003e \u003cb\u003eThe pattern of hypermethylation coupled with reduced expression of\u003c/b\u003e \u003cb\u003eCERS4\u003c/b\u003e \u003cb\u003egene.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn order to explore the potential therapeutic target to PDAC, the expression and the function of \u003cem\u003eCERS4\u003c/em\u003e gene were investigated by \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments. Firstly, using UALCAN to assess \u003cem\u003eCERS4\u003c/em\u003e promoter methylation in PDAC, it was found to be significantly elevated in stages I to III compared to both healthy controls and stage IV patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Subsequently, t leveraging our PDAC cohort, a fully or partially methylated state of \u003cem\u003eCERS4\u003c/em\u003e gene were validated through methylation specific PCR (MSP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). While, the stark reduction in \u003cem\u003eCERS4\u003c/em\u003e protein and mRNA expression levels within PDAC relative to adjacent tissues was found (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Finally, we further demonstrated that both mRNA and protein expression of the \u003cem\u003eCERS4\u003c/em\u003e gene were markedly attenuated in PDAC cell lines compared to the normal pancreatic ductal epithelial cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). The outcomes demonstrated that both mRNA and protein expression of the \u003cem\u003eCERS4\u003c/em\u003e gene were markedly attenuated in PDAC cell lines compared to the normal pancreatic ductal epithelial cells. However, MSP validated that the \u003cem\u003eCERS4\u003c/em\u003e promoter region was entirely methylated in PDAC cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). Collectively, these findings indicated that \u003cem\u003eCERS4\u003c/em\u003e expression level was suppressed in the PDAC, potentially due to its DNA hypermethylation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCERS4\u003c/b\u003e \u003cb\u003eas a tumor suppressor gene of PDAC promotes lipid metabolism.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverexpression vectors for \u003cem\u003eCERS4\u003c/em\u003e were constructed and transfected into PANC-1 and MIAPaCa-2 cells(\u003cb\u003eSupplementary Fig.\u0026nbsp;4)\u003c/b\u003e. The proliferation of PANC-1 and MIAPaCa-2 cells was significantly suppressed by \u003cem\u003eCERS4\u003c/em\u003e overexpression, as well as a noticeable decrease in the number of colonies formed and a substantial attenuation in migration capacity compared to those in the control group (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Moreover, Moreover, the cell-line derived xenograft (CDX) mice model confirmed that the overexpression of \u003cem\u003eCERS4\u003c/em\u003e effectively inhibited tumor growth of PDAC (\u003cb\u003eSupplementary Figs.\u0026nbsp;5, 6\u003c/b\u003e). The fore-mentionded GO findings in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed implied that the location of \u003cem\u003eCERS4\u003c/em\u003e within the cellular compartments was possibly the plasma membrane and nucleus. And KEGG findings in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee hinted that the function of \u003cem\u003eCERS4\u003c/em\u003e may be implicated in the sphingosine lipid metabolism. Based on these, the immunofluorescence analysis assay validated that \u003cem\u003eCERS4\u003c/em\u003e was predominantly localized to the cell membrane and nucleus (\u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e). Oil red O staining revealed that the lipid metabolism level of \u003cem\u003eCERS4\u003c/em\u003e in PDAC cells was significantly weaker than that in normal control cells, whereas overexpression of \u003cem\u003eCERS4\u003c/em\u003e led to a substantial enhancement in lipid metabolic activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Together, these indicated that \u003cem\u003eCERS4\u003c/em\u003e as a tumor suppressor gene promoted lipid metabolism of PDAC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe FDA approved the first cfDNA-driven method in 2015, using real-time PCR for EGFR mutations in NSCLC, marking a milestone in liquid biopsy. Epi proColon, detecting SEPT9 methylation for CRC screening, received FDA approval in 2016\u003csup\u003e30\u003c/sup\u003e. Our previous research suggests that ZFGs' methylation profiles could serve as early colorectal cancer biomarkers, especially with KRAS mutations\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe examined PDAC ctDNA methylation patterns using WGBS, revealing significant differences between PDAC patients and healthy subjects. These findings underscore the link between methylation patterns and PDAC. Notably, differentially methylated regions (DMRs) in 'CpG islands' were identified, which are crucial for gene expression regulation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Hypomethylation in these regions may contribute to tumor suppressor gene silencing, promoting cancer growth\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe M-QMSP system, analyzing plasma samples, identified significant methylation level discrepancies in \u003cem\u003eP2RX1\u003c/em\u003e, \u003cem\u003eCBFA2T3\u003c/em\u003e, and \u003cem\u003eCERS4\u003c/em\u003e between PDAC patients and controls. Our study correlated gene methylation with tumor staging, showing that this method outperforms CA19-9 in specificity and reproducibility. The combined diagnostic model's AUC and specificity exceeded 0.9, indicating its potential as a specific plasma biomarker for PDAC.\u003c/p\u003e \u003cp\u003eCompared to single QMSP, the M-QMSP system allows for the simultaneous quantification of multiple genes, reducing DNA consumption and detection costs\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. However, the study's limitation is its small sample size. Future research requires extensive validation across diverse populations to ensure high specificity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eP2RX1\u003c/em\u003e, involved in transcription factor networks in pancreatic progenitor cells, is implicated in immune microenvironment formation and PDAC metastasis\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCBFA2T3\u003c/em\u003e, a transcriptional co-repressor, is suggested as a novel epigenetic target for PDAC resistance to chemotherapy\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCERS4\u003c/em\u003e, a sphingolipid metabolic enzyme\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, has been identified as a tumor suppressor in PDAC for the first time, affecting cell proliferation, migration, and lipid metabolism.\u003c/p\u003e \u003cp\u003eIn summary, our research has identified \u003cem\u003eP2RX1\u003c/em\u003e, \u003cem\u003eCBFA2T3\u003c/em\u003e, and \u003cem\u003eCERS4\u003c/em\u003e as potential diagnostic biomarkers for PDAC through liquid biopsy, offering a new P2CC model for early detection and monitoring of the disease.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e The studies involving human participants were reviewed and approved by the institutional review boards of the Affiliated Hospital of Nantong University. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe work was supported by the National Natural Science Foundation of China (grant No. 81974313, 82272411, 82472364), the Natural Science Foundation of Jiangsu Commission of Health (grant No. M2020065), Jiangsu Provincial Research Hospital (grant No.YJXYY202204), Jiangsu Provincial Medical Key Discipline (grant No.ZDXK202240), Science and Technology Project of Jiangsu Province (grant No.BE2023741).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHC and HS conducted molecular and cell biology experiments, performed analyses, interpreted the results, and drafted the manuscript. XW conducted molecular and cell biology experiments. SJ aided in drafting and editing the manuscript and advising on the study. WP aided in drafting and editing the manuscript, aided in in silico experimental designs and supervised the analyses and results interpretation. WD and JZ designed the study, conducted molecular and cell biology experiments, performed analyses, interpreted results, designed the functional experiments, and contributed to the drafting of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCronin KA, Scott S, Firth AU et al. Annual report to the nation on the status of cancer, part 1: National cancer statistics. Cancer. 2022; 128 (24): 4251\u0026ndash;4284.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A, Cancer statistics. 2024. CA: a Cancer Journal For Clinicians 2024; 74 (1): 12\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTempero MA, Malafa MP, Al-Hawary M, et al. Pancreatic Adenocarcinoma, Version 2.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2017;15(8):1028\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Xu R, Wang C, et al. Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review. Cancer Commun (Lond). 2021;41(12):1257\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Feng Y, Cheng G, et al. Etiological Diagnosis of Linear-Array Endoscopic Ultrasonography in Distal Common Bile Duct Dilatation. Ultrasound Q. 2022;38(2):170\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou J, Li X, Xie K-P. Coupled liquid biopsy and bioinformatics for pancreatic cancer early detection and precision prognostication. Mol Cancer. 2021;20(1):34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun K, Jiang P, Chan KCA, et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci U S A. 2015;112(40):E5503\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordg\u0026aring;rd O, Tjensvoll K, Gilje B, et al. Circulating tumour cells and DNA as liquid biopsies in gastrointestinal cancer. Br J Surg. 2018;105(2):e110\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImamura T, Komatsu S, Ichikawa D, et al. Liquid biopsy in patients with pancreatic cancer: Circulating tumor cells and cell-free nucleic acids. World J Gastroenterol. 2016;22(25):5627\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Z-B, Yao L, Jin C, et al. Circulating tumor cells in pancreatic cancer patients: efficacy in diagnosis and value in prognosis. Discov Med. 2016;22(120):121\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi Z-H, Xu H-X, Zhang S-R, et al. The Significance of Liquid Biopsy in Pancreatic Cancer. J Cancer. 2018;9(18):3417\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaver JA, Jelovac D, Balukrishna S, et al. Detection of cancer DNA in plasma of patients with early-stage breast cancer. Clin Cancer Res. 2014;20(10):2643\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEissa MAL, Lerner L, Abdelfatah E, et al. Promoter methylation of ADAMTS1 and BNC1 as potential biomarkers for early detection of pancreatic cancer in blood. Clin Epigenetics. 2019;11(1):59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature. 2017;545(7655):446\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsteller M, Herman JG. Cancer as an epigenetic disease: DNA methylation and chromatin alterations in human tumours. J Pathol. 2002;196(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones PA, Baylin SB. The fundamental role of epigenetic events in cancer. Nat Rev Genet. 2002;3(6):415\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao K, Pu W, Zhang J, et al. DNA hypermethylation contributes to colorectal cancer metastasis by regulating the binding of CEBPB and TFCP2 to the CPEB1 promoter. Clin Epigenetics. 2021;13(1):89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee A, Stockwell PA, Rodger EJ, et al. Genome-wide DNA methylation map of human neutrophils reveals widespread inter-individual epigenetic variation. Sci Rep. 2015;5:17328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan KCA, Woo JKS, King A, et al. Analysis of Plasma Epstein-Barr Virus DNA to Screen for Nasopharyngeal Cancer. N Engl J Med. 2017;377(6):513\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwarzenbach H, Hoon DSB, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarampini E, McCaughan F. Circulating DNA in solid organ cancers-analysis and clinical application. QJM. 2016;109(4):223\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLubotzky A, Zemmour H, Neiman D et al. Liquid biopsy reveals collateral tissue damage in cancer. JCI Insight 2022; 7 (2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta-Pinheiro P, Montezuma D, Henrique R, et al. Diagnostic and prognostic epigenetic biomarkers in cancer. Epigenomics. 2015;7(6):1003\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong S-M, Omura N, Vincent A, et al. Genome-wide CpG island profiling of intraductal papillary mucinous neoplasms of the pancreas. Clin Cancer Res. 2012;18(3):700\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Vos L, Gevensleben H, Schr\u0026ouml;ck A, et al. Comparison of quantification algorithms for circulating cell-free DNA methylation biomarkers in blood plasma from cancer patients. Clin Epigenetics. 2017;9:125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalta S, Nunes P, Fontes-Sousa S. A DNA Methylation-Based Test for Breast Cancer Detection in Circulating Cell-Free DNA. J Clin Med. 2018;7:11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh A, Gupta S, Badarukhiya JA, et al. Detection of aberrant methylation of HOXA9 and HIC1 through multiplex MethyLight assay in serum DNA for the early detection of epithelial ovarian cancer. Int J Cancer. 2020;147(6):1740\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Wu L-F, Mo X-B, et al. Rheumatoid arthritis-associated DNA methylation sites in peripheral blood mononuclear cells. Ann Rheum Dis. 2019;78(1):36\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzquierdo AG, Boughanem H, Diaz-Lagares A, et al. DNA methylome in visceral adipose tissue can discriminate patients with and without colorectal cancer. Epigenetics. 2022;17(6):665\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIssa IA, Noureddine M. Colorectal cancer screening: An updated review of the available options. World J Gastroenterol. 2017;23(28):5086\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu W, Qian F, Liu J, et al. Targeted Bisulfite Sequencing Reveals DNA Methylation Changes in Zinc Finger Family Genes Associated With KRAS Mutated Colorectal Cancer. Front Cell Dev Biology. 2021;9:759813.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHidaka H, Higashimoto K, Aoki S, et al. Comprehensive methylation analysis of imprinting-associated differentially methylated regions in colorectal cancer. Clin Epigenetics. 2018;10(1):150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaplana M, Bieg M, Faltus C, et al. Differentially methylated regions within lung cancer risk loci are enriched in deregulated enhancers. Epigenetics. 2022;17(2):117\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlkhov-Mitsel E, Zdravic D, Kron K, et al. Novel multiplex MethyLight protocol for detection of DNA methylation in patient tissues and bodily fluids. Sci Rep. 2014;4:4432.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConst\u0026acirc;ncio V, Nunes SP, Moreira-Barbosa C, et al. Early detection of the major male cancer types in blood-based liquid biopsies using a DNA methylation panel. Clin Epigenetics. 2019;11(1):175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson N, G\u0026eacute;sina E, Scheinert P, et al. RNA profiling and chromatin immunoprecipitation-sequencing reveal that PTF1a stabilizes pancreas progenitor identity via the control of MNX1/HLXB9 and a network of other transcription factors. Mol Cell Biol. 2012;32(6):1189\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel S, Fu S, Mastio J, et al. Unique pattern of neutrophil migration and function during tumor progression. Nat Immunol. 2018;19(11):1236\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Hu L-P, Qin W-T, et al. Identification of a subset of immunosuppressive P2RX1-negative neutrophils in pancreatic cancer liver metastasis. Nat Commun. 2021;12(1):174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis JN, McGhee L, Meyers S. The ETO (MTG8) gene family. Gene 2003; 303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossetti S, Hoogeveen AT, Sacchi N. The MTG proteins: chromatin repression players with a passion for networking. Genomics. 2004;84(1):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed AA, Marchetti C, Ohnmacht SA, et al. A G-quadruplex-binding compound shows potent activity in human gemcitabine-resistant pancreatic cancer cells. Sci Rep. 2020;10(1):12192.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSummers SA. Could Ceramides Become the New Cholesterol? Cell Metab. 2018;27(2):276\u0026ndash;80.\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":"pancreatic ductal adenocarcinoma, DNA methylation, cell-free DNA, P2CC model, diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-4982255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4982255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePancreatic ductal adenocarcinoma (PDAC) stands as one of the most formidable cancers globally, yet its diagnosis continues to be a pressing challenge, largely owing to the inherent limitations of current diagnostic methodologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Utilizing whole-genome bisulfite-sequencing (WGBS), differentially methylated genes in the promoter region were identified from the plasma of PDAC patients. Subsequently, screening of candidate methylation genes, methylation-specific primers and probes for \u003cem\u003eP2RX1\u003c/em\u003e, \u003cem\u003eCBFA2T3\u003c/em\u003e and \u003cem\u003eCERS4\u003c/em\u003e were designed, and a stable multiple quantitative methylation-specific polymerase chain reaction (M-QMSP) system was established to detect the circulating tumor DNA (ctDNA) methylation level of these genes in PDAC. Finally, the disease-driving mechanism of the biomarker \u003cem\u003eCERS4\u003c/em\u003e was explored separately in vitro and in vivo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 112,294 differential methylation regions (DMRs) were precisely identified by WGBS, encompassing 88,210 hypermethylation DMRs and 24,084 hypomethylation DMRs. Notably, three differentially methylated genes, \u003cem\u003eP2RX1\u003c/em\u003e, \u003cem\u003eCBFA2T3\u003c/em\u003e and \u003cem\u003eCERS4 \u003c/em\u003e(nominated as P2CC model), were astutely pinpointed as potential plasma methylation biomarkers. The M-QMSP system was established through the prioritization of these genes, which demonstrated that the combined diagnostic prowess of P2CC modelnotably outperformed carbohydrate antigen 19-9 (CA19-9). In PDAC, overexpression of \u003cem\u003eCERS4\u003c/em\u003e has been observed to suppress tumor growth in nude mice xenografts and inhibit cell proliferation and migration. Additionally,\u003cem\u003e CERS4\u003c/em\u003eplayed a pivotal role in promoting lipid metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eBased on WGBS screening and M-QMSP validation, we have determined that the P2CC model can serve as the combined diagnostic biomarkers for PDAC, superior to CA19-9. Moreover, as a tumor suppressor gene, \u003cem\u003eCERS4\u003c/em\u003eregulates the metabolism of sphingolipids.\u003c/p\u003e","manuscriptTitle":"Integrated ctDNA Methylation Profiling of P2RX1, CBFA2T3, and CERS4 as Pancreatic Ductal Adenocarcinoma Biomarkers: Identification and Mechanistic Insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 07:28:45","doi":"10.21203/rs.3.rs-4982255/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":"9c6bef22-0d11-42b2-804c-5de307898f57","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-18T09:38:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 07:28:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4982255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4982255","identity":"rs-4982255","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}