Serum CEA, CA199, NSE and Lipid Profile to Predict the Occurrence, Pathological Grade and Metastasis of Pancreatic Neuroendocrine Tumor

Serum CEA, CA199, NSE and Lipid Profile to Predict the Occurrence, Pathological Grade and Metastasis of Pancreatic Neuroendocrine Tumor | 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 Serum CEA, CA199, NSE and Lipid Profile to Predict the Occurrence, Pathological Grade and Metastasis of Pancreatic Neuroendocrine Tumor Jingjing Zhang, Xiaofeng Jia, Yanling Xu, Feiyu Lu, Jinxing Bao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6794382/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 Objective This study aims to explore the diagnostic value of CEA, CA199, NSE, and biochemical lipid metabolism indexes for pNET. Methods A total of 175 patients with pNET as experimental group and 88 relatively healthy patients as control group were enrolled in the study. CEA, CA199, NSE, TC, HDL-c and LDL-c level of blood, along with BMI, were collected from both groups and subjected to statistical analysis. A logistic regression analysis model was constructed, and the ROC curve was utilized to evaluate the efficacy of individual indicators with statistically significant differences, as well as the combined detection model, in predicting the occurrence, pathological grading, and metastasis of pNET. Results The levels of CEA, NSE, and LDL-c in the experimental group were higher than control group, whereas the level of HDL-c was lower than that in the control group. Among patients with pNET of varying pathological grades, statistically significant differences were observed in the occurrence of metastasis and the levels of CEA, CA199, NSE, and LDL-c. Patients with high-grade pNET exhibited elevated levels of CEA, CA199, NSE, TC, and LDL-c compared to those with low-grade pNET, and were more prone to metastasis. The number of male patients with metastatic pNET exceeded that of female patients. Furthermore, the levels of CEA, CA199, and NSE were higher in metastatic cases compared to non-metastatic cases. These differences are statistical significant. An increase in NSE levels and a decrease in HDL-c levels were identified as risk factors for the development of pNET. The AUCs for NSE and HDL-c were 0.899 and 0.666, respectively, both of which were lower than the AUC (0.911) of the combined diagnostic model. Additionally, elevated levels of CA199 and NSE were found as risk factors for both high-grade and metastatic pNET. The AUCs for CA199 and NSE in predicting high-grade pNET were 0.728 and 0.645, respectively, again lower than the AUC (0.765) of the combined diagnostic model. The AUCs for CA199 and NSE in predicting pNET metastasis were 0.693 and 0.651, respectively, while the AUC (0.689) of the combined diagnostic model did not demonstrate superiority over the individual models. Conclusion In conclusion, an increase in blood NSE level serves as a significant predictive indicator for the occurrence, pathological grading, and metastasis of pNET. While an increase in blood HDL-c level can predict the occurrence of pNET, it does not serve as a predictor for pathological grading or metastasis. Pancreatic Neuroendocrine Tumor Lipid Profile Tumor marker Occurrence Figures Figure 1 Figure 2 Figure 3 Introduction Neuroendocrine tumor (NET) arises from neuroendocrine cells, which are distributed throughout the body and exhibit both neural and endocrine functions [ 1 ] . These cells are capable of secreting a variety of hormones and peptide substances that regulate physiological functions. NET exhibit significant heterogeneity, with tumors located in different anatomical sites displaying variations in biological behavior and clinical manifestations [ 2 ] . NET are commonly found in the digestive tract (including the stomach, intestines, and pancreas) and lungs, seldom in liver, breast and other organs [ 3 – 6 ] . Among these, pancreatic neuroendocrine tumor (pNET) represent a relatively rare subset, accounting for approximately 1–2% of all pancreatic tumors [ 7 ] . In recent years, advancements in imaging technology and pathological diagnostic techniques have led to an increased incidence of pNET [ 8 – 10 ] . The malignancy degree of pNET varies. Some tumors exhibit slow growth, while others demonstrate higher aggressiveness. According to the classification standards set by the World Health Organization, pNET are categorized into three grades: G1, G2, and G3 [ 11 ] . The proliferation index, measured by the Ki-67 index, is typically less than 3% for G1-grade tumors, ranges from 3–20% for G2-grade tumors, and exceeds 20% for G3-grade tumors [ 3 ] . Grade G1 and G2 tumors are classified as well-differentiated low-grade neuroendocrine tumors (L-pNET), characterized by a relatively low degree of malignancy and a favorable prognosis; in contrast, grade G3 tumors are classified as poorly differentiated high-grade neuroendocrine tumors (H-pNET), exhibiting a higher degree of malignancy and a poorer prognosis [ 12 , 13 ] . PNETs are known to have a propensity for metastasis, with common metastatic sites including the liver, lymph nodes, and lungs [ 14 , 15 ] . The 5-year survival rate for pNET varies according to the pathological stage: G1 tumors may achieve a survival rate exceeding 90%, G2 tumors typically exhibit a survival rate of 60–80%, while G3 tumors generally have a survival rate of less than 50% [ 13 ] . Early diagnosis and surgical resection can improve prognosis. For advanced-stage patients who are not candidates for surgical intervention, comprehensive treatment modalities such as targeted therapy, chemotherapy, and peptide receptor radionuclide therapy can extend survival and improve quality of life [ 16 – 18 ] . Consequently, early diagnosis, accurate grading, and personalized treatment strategies are essential for improving patient outcomes. This study primarily investigates the diagnostic utility of tumor markers, including carcinoembryonic antigen (CEA), carbohydrate antigen (CA199), neuron-specific enolase (NSE), and biochemical indicators such as triglycerides, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), and body mass index (BMI) to predict the occurrence, pathological stage and metastasis of pNET. Materials and Methods 1.1 General Information A total of 175 patients diagnosed with pNET were admitted to Jiangsu Provincial People's Hospital from January 2019 to December 2024, constituting the experimental group. In contrast, the control group was comprised of 88 relatively healthy individuals who visited the hospital between January 2023 and December 2024. Inclusion criteria for the experimental group: Patients must have been diagnosed with pNET through pathological examination, with complete pathological data available; the primary tumor site must be the pancreas; comprehensive data regarding gender, age, BMI, and blood levels of CEA, CA199, NSE, triglycerides, TC, HDL-c, and LDL-c at the time of diagnosis must be provided. Exclusion criteria for the experimental group: Patients with incomplete pathological data, tumors originating from other sites and metastasizing to the pancreas and individuals with incomplete data on gender, age, BMI, or blood markers as specified above were excluded. Inclusion criteria for the control group: Individuals with no tumors and without chronic diseases such as hypertension, hyperlipidemia, diabetes, or coronary heart disease were included; complete data regarding gender, age, BMI, and blood levels of CEA, CA199, NSE, triglycerides, TC, HDL-c, and LDL-c were also required. Exclusion criteria for the control group: Individuals suffering from any tumor or with chronic diseases such as hypertension, hyperlipidemia, diabetes, or coronary heart disease were excluded; those with incomplete information regarding gender, age, BMI, or blood markers were not included. 1.2 Methods Collect data of the experimental and control group, including gender, age, BMI, pathological grade, whether there is metastasis and blood levels of CEA, CA199, NSE, triglyceride, TC, HDL-c, LDL-c at the time of diagnosis. According to relevant diagnostic criteria, the pathological grades of pNET are divided into three types: G1, G2, and G3. 1.3 Statistical processing Use SPSS22.0 software for data analysis. Count data are expressed in terms of the number of cases and percentages, and inter-group comparisons are performed using the χ 2 test. After normal test, the measurement data with normal distribution is expressed as average±standard deviation (x±s), and the comparison between two groups is performed using the independent sample t-test. The measurement data not conforming to normal distribution is expressed as median (first quartile, third quartile) [M (P25, P75)], and the comparison between two groups is performed using the Mann-Whitney U test. Binary logistic regression is used to analyze the factors affecting the occurrence, grading and metastasis of pNET, and a combined diagnostic model is established. The receiver operating characteristic (ROC) curve is drawn to evaluate the diagnostic value of single-item and combined model. P < 0.05 is considered statistical difference. Results 1.1 General characteristics of pNET patients Among 175 pNET patients, the age range is 21-89 years [57.5 (P51.0, P62.5)]. There are 94 males and 81 females; 51 cases of G1 grade, 99 cases of G2 grade and 25 cases of G3 grade; 121 cases without metastasis and 74 cases with metastasis. 1.2 Comparison of each parameter between the experimental and control group There is no statistical difference in gender, age, BMI, CA199, TC and triglyceride levels between the experimental and control group. The CEA, NSE and LDL-c levels in the experimental group are higher than those in the control group, and the HDL-c level is lower than that in the control group. The differences are statistically significant (Table 1). 1.3 Comparison of each parameter among pNET patients with different pathological grades There are statistical differences in CEA, CA199, NSE, LDL-c levels and whether metastasis or not among pNET patients with different pathological grades. While there is no statistically significant difference in gender, age, BMI, HDL-c, and triglyceride levels (Table 3). When pairwise comparison is done between G1, G2 and G3 pNET patients, whether metastasis occurs show differences between each pair. The higher the pathological grade, the more likely metastasis occurs. The CEA and CA199 levels of G3 are higher than that of G1 and G2. The NSE, TC, and LDL-c levels of G3 are higher than that of G1. The levels of CEA, CA199, NSE, TC, and LDL-c in patients with high-grade pNET are higher than those with low-grade pNET, and they are more likely to develop metastasis (Table 4). 1.4 Comparison of each parameter between metastatic and non-metastatic pNET In pNET patients with metastasis, the number of male patients is more than that of female, and the levels of CEA, CA199, and NSE are higher than those without metastasis (Table 5). 1.5 Binary Logistic regression analysis and ROC curve drawing Taking whether suffering from pNET, whether it is a high-grade pNET, and whether metastasis occurs as dependent variable assignments (yes = 1, no = 0), and taking the indicators with statistical significance in univariate analysis as independent variable assignments (all input with original values), perform binary Logistic regression analysis and draw ROC curves. 1.5.1 Diagnostic prediction model for pNET patients Increased NSE and decreased HDL-c levels are risk factors for pNET patients. The combined diagnostic model is Y= 0.487´NSE-1.296´HDL-c-5.203. The AUCs of NSE and HDL-c are 0.899 and 0.666 respectively, both lower than the AUC (0.911) of the combined diagnostic model(Table 6) (Figure 1). 1.5.2 Grading prediction model for pNET patients Increased CA199 and NSE levels are risk factors for high-grade pNET patients. The combined diagnostic model is Y= 0.004´CA199+0.046´NSE-3.332. The AUCs of CA199 and NSE were 0.728 and 0.645 respectively, both lower than the AUC (0.765) of the combined diagnostic model(Table 6) (Figure 2). 1.5.3 Metastasis prediction model for pNET patients Increase CA199 and NSE levels are risk factors for pNET patients with metastasis. The combined diagnostic model is Y= 0.003´CA199+ 0.061´NSE-2.349. The AUCs of CA199 and NSE were 0.693 and 0.651 respectively. The AUC (0.689) of the combined diagnostic model does not show superiority over the individual models(Table 6) (Figure 3). Discussion PNET represents a relatively rare yet highly heterogeneous group of neoplasms. Their pathogenesis is intricate, involving multiple molecular and metabolic pathways [19-26] . Recent studies have increasingly highlighted the role of abnormal lipid metabolism in tumorigenesis and progression. Particularly in pNET, research focused on lipid metabolism has provided novel insights into their biological behavior. Lipid metabolism is a crucial process for cellular energy supply and biomembrane synthesis, encompassing the synthesis, degradation, and transport of various lipid substances, including triglycerides, cholesterol, and phospholipids. Evidence suggests that the lipid metabolism levels in pNET patients often exhibit significant alterations, which may be associated with the heightened lipid synthesis necessary for tumor cell proliferation, the augmented inflammatory response and oxidative stress within the tumor microenvironment, and the increased lipid utilization by tumor cells [27-29] . These abnormalities in lipid metabolism are not only linked to the onset of pNET but may also influence tumor grade, metastasis, and prognosis. Research by Gu and et al indicated that in patients with pNET, the expression of high-density lipoprotein binding protein mRNA is down-regulated in pNET tissue compared to peritumoral tissue, and survival analyses revealed that pNET patients with elevated low-density lipoprotein receptor levels have an increased risk of poor survival outcomes [29] . Chen and his colleagues’ research revealed a significant correlation between serum HDL-c levels and the malignant behavior of pNET, particularly concerning tumor grading and lymph node metastasis [30] . Furthermore, their investigations indicated that a high non-HDL-c/HDL-c ratio was associated with high-grade or poorly differentiated pNET [31] . These findings underscore the important research value of lipid metabolism molecules, particularly HDL-c, in the context of pNET. This suggests that the combined assessment of biochemical indicators and lipid metabolism molecules may enhance the diagnostic and predictive efficacy for pNET. Our study analyzed blood tumor markers and biochemical lipid metabolism indicators in 175 patients with pNET and 88 healthy controls, aiming to explore the diagnostic value of CEA, CA199, NSE, and lipid metabolism indicators in relation to the diagnosis, pathological grading, and metastasis prediction of pNET. The results demonstrated that NSE and HDL-c possess significant predictive value for the occurrence of pNET, while CA199 and NSE also play critical roles in tumor grading and metastasis prediction. These findings provided novel insights for the early diagnosis and assessment of pNET. Notably, this study found that NSE levels were significantly elevated in patients with pNET and were closely linked to tumor grading and metastasis. As a neuron-specific marker, NSE typically exhibits high expression in NET. The findings of this study further validate the diagnostic value of NSE in pNET, particularly in high-grade and metastatic tumors, where the increase in NSE levels is especially pronounced. This indicates that NSE can serve not only as a diagnostic marker for pNET but also as a means to evaluate the malignancy and metastasis risk of tumors. Furthermore, the decrease in HDL-c levels is significantly correlated with the occurrence of pNET. As a crucial index of lipid metabolism, the reduction in HDL-c levels may be associated with changes in the tumor microenvironment. Although the role of HDL-c in predicting tumor grading and metastasis is limited, its predictive value regarding the occurrence of pNET should not be overlooked. This finding suggests that abnormal lipid metabolism may contribute to the onset and progression of pNET. Future research should further investigate the molecular mechanisms linking lipid metabolism to tumor occurrence. Additionally, the level of CA199 is significantly elevated in high-grade and metastatic pNET, indicating that CA199 possesses clinical applicability in assessing the malignancy and metastasis risk of tumors. While the diagnostic value of CA199 in pancreatic cancer is well-established, this study further affirms its predictive role in pNET, particularly in diagnosis and high-grade tumors. Through the construction of a joint diagnostic model, it was observed that the AUC value for the combined detection of multiple indicators was significantly higher than that of individual indicators. This finding indicates that combined detection can enhance the diagnostic and predictive efficacy of pNET. Consequently, in clinical practice, the combined application of NSE, CA199, and lipid metabolism indicators should be considered to improve the early diagnosis rate of pNET and the accuracy of disease assessment. In conclusion, NSE, CA199, and HDL-c hold significant clinical value in the diagnosis, grading, and metastasis prediction of pNET. Future studies should further investigate the molecular mechanisms of these markers in the occurrence and development of pNET and validate their clinical applicability in larger sample sizes. Declarations Ethics approval and consent to participate All procedures were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1964 and later versions. All protocols were approved by The First Affiliated Hospital with Nanjing Medical University. And informed consent was obtained from all subjects. Clinical trial number Not applicable. Acknowledgments Not Applicable. Conflict of Interest Statement The authors have no conflicts of interest to declare. Funding Sources No funding was obtained for this project. Author Contributions Mujie Ye designed the analysis, reviewed the clinical data. Jingjing Zhang and Xiaofeng Jia performed statistical analysis, and drafted the manuscript. Yanling Xu, Jinxing Bao, and Feiyu Lu treated patients and collected the clinical data. Mujie Ye and Jingjing Zhang revised the manuscript. All authors read and approved the final manuscript. Data Availability Statement The datasets used and analysed during the current study available from the corresponding author on reasonable request. References Liu Y, Yang X, Wang Y, et al. Efficacy and safety of surufatinib in the treatment of patients with neuroendocrine tumor: a real-world study in Chinese population[J]. BMC Cancer. 2024;24(1):1342. Kamboj S, Guerra-Bauman F, Mahmud H, et al. Neuroendocrine Neoplasms[J] Prim Care. 2024;51(3):549–60. La Rosa S, Uccella S. Classification of neuroendocrine neoplasms: lights and shadows[J]. Reviews Endocr metabolic disorders. 2021;22(3):527–38. Samady KA, Madadi-Sanjani O, Abolzadeh A et al. Primary hepatic neuroendocrine neoplasms of children, a systematic review[J]. J Neuroendocrinol, 2025:e13495. Sen T, Dotsu Y, Corbett V, et al. Pulmonary neuroendocrine neoplasms: the molecular landscape, therapeutic challenges, and diagnosis and management strategies[J]. Lancet Oncol. 2025;26(1):e13–33. Jiang L, Pan X, Lang Z. Neuroendocrine Neoplasms of the Breast: Current Insights and Future Directions[J]. Cancer Rep (Hoboken). 2024;7(11):e70059. Asmundo L, Rizzetto F, Blake M et al. Advancements in Neuroendocrine Neoplasms: Imaging and Future Frontiers[J]. J Clin Med, 2024,13(11). Nordstrand MA, Lea D, Soreide JA. Incidence of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): An updated systematic review of population-based reports from 2010 to 2023[J]. J Neuroendocrinol, 2025:e70001. Uhlig J, Nie J, Gibson J, et al. Epidemiology, treatment and outcomes of gastroenteropancreatic neuroendocrine neoplasms[J]. Sci Rep. 2024;14(1):30536. Xu Z, Wang L, Dai S, et al. Epidemiologic Trends of and Factors Associated With Overall Survival for Patients With Gastroenteropancreatic Neuroendocrine Tumors in the United States[J]. JAMA Netw Open. 2021;4(9):e2124750. Rindi G, Mete O, Uccella S, et al. Overview of the 2022 WHO Classification of Neuroendocrine Neoplasms[J]. Endocr Pathol. 2022;33(1):115–54. Joseph NM, Umetsu SE, Kim GE, et al. Progression of Low-Grade Neuroendocrine Tumors (NET) to High-Grade Neoplasms Harboring the NEC-Like Co-alteration of RB1 and TP53[J]. Endocr Pathol. 2024;35(4):325–37. Bourdeleau P, Pokossy-Epee J, Hentic O et al. Temporal increase in Ki-67 index in patients with pancreatic neuroendocrine tumours[J]. Endocr Relat Cancer, 2025,32(3). Lu F, Ye M, Shen Y, et al. Hypoxic tumor-derived exosomal miR-4488 induces macrophage M2 polarization to promote liver metastasis of pancreatic neuroendocrine neoplasm through RTN3/FABP5 mediated fatty acid oxidation[J]. Int J Biol Sci. 2024;20(8):3201–18. Jiang Y, Dong YH, Zhao SW, et al. Multiregion WES of metastatic pancreatic neuroendocrine tumors revealed heterogeneity in genomic alterations, immune microenvironment and evolutionary patterns[J]. Cell Commun Signal. 2024;22(1):164. Arrivi G, Fazio N, Tafuto S, et al. The efficacy of streptozotocin in managing pancreatic neuroendocrine neoplasms - A systematic review[J]. Cancer Treat Rev. 2025;134:102899. Nair G, Black M, Baer K et al. Treatment Patterns of Pancreatic Neuroendocrine Tumor (pNET) Patients at Two Canadian Cancer Centres[J]. Curr Oncol, 2025,32(2). Tacelli M, Partelli S, Falconi M, et al. Pancreatic Neuroendocrine Neoplasms: Classification and Novel Role of Endoscopic Ultrasound in Diagnosis and Treatment Personalization[J]. United European Gastroenterol J; 2024. Alexandraki KI, Papadimitriou E, Spyroglou A, et al. Immunohistochemical expression of ephrin receptors in neuroendocrine neoplasms: a case-series of gastroenteropancreatic neuroendocrine neoplasms and a systematic review of the literature[J]. Endocrine. 2025;87(3):1323–32. Ye M, Lu F, Gu D, et al. Hypoxia exosome derived CEACAM5 promotes tumor-associated macrophages M2 polarization to accelerate pancreatic neuroendocrine tumors metastasis via MMP9[J]. FASEB J. 2024;38(13):e23762. Ye Z, Zhou Y, Hu Y, et al. Single-cell sequencing reveals the heterogeneity of pancreatic neuroendocrine tumors under genomic instability and histological grading[J]. iScience. 2024;27(9):110836. Xu J, Lou X, Wang F, et al. MEN1 Deficiency-Driven Activation of the beta-Catenin-MGMT Axis Promotes Pancreatic Neuroendocrine Tumor Growth and Confers Temozolomide Resistance[J]. Adv Sci (Weinh). 2024;11(35):e2308417. Ye Z, Li Q, Hu Y, et al. The stromal microenvironment endows pancreatic neuroendocrine tumors with spatially specific invasive and metastatic phenotypes[J]. Cancer Lett. 2024;588:216769. Padwal MK, Parghane RV, Chakraborty A et al. Systemic Cancer Hallmarks as Novel Markers Associated with Progression-free Survival in Gastroenteropancreatic Neuroendocrine Tumor Patients Undergoing PRRT[J]. Neuroendocrinology, 2024:1–18. Chen Z, Lin S, Liang F, et al. The prognostic and therapeutic value of the tumor microenvironment and immune checkpoints in pancreatic neuroendocrine neoplasms[J]. Sci Rep. 2024;14(1):24669. Liu Y, Liu P, Duan S, et al. CTCF enhances pancreatic cancer progression via FLG-AS1-dependent epigenetic regulation and macrophage polarization[J]. Cell Death Differ; 2024. Chen J, Ye M, Gu D, et al. FTO-induced APOE promotes the malignant progression of pancreatic neuroendocrine neoplasms through FASN-mediated lipid metabolism[J]. Int J Biol Sci. 2025;21(4):1478–96. Ye M, Xu L, Lu F, et al. Hypoxia drives CBR4 down-regulation promotes gastroenteropancreatic neuroendocrine tumors via activation mammalian target of rapamycin mediated by fatty acid synthase[J]. J Cell Commun Signal. 2024;18(3):e12041. Gu D, Ye M, Zhu G, et al. Hypoxia upregulating ACSS2 enhances lipid metabolism reprogramming through HMGCS1 mediated PI3K/AKT/mTOR pathway to promote the progression of pancreatic neuroendocrine neoplasms[J]. J Transl Med. 2024;22(1):93. Gu D, Xue B, Zhu G, et al. Serum Lipid Level in Evaluating Chinese Pancreatic Neuroendocrine Neoplasms: A Retrospective Study[J]. Exp Clin Endocrinol Diabetes. 2024;132(2):98–106. Quan L, Liu Y, Cui W, et al. The associations between serum high-density lipoprotein cholesterol levels and malignant behavior in pancreatic neuroendocrine neoplasms[J]. Lipids Health Dis. 2022;21(1):58. Zhou H, Zhu Y, Qin B, et al. The association between non-HDL cholesterol and high-grade pancreatic neuroendocrine neoplasms[J]. Endocrine. 2024;86(2):584–91. Tables Table 1 to 6 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.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-6794382","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469683136,"identity":"c781a058-6cf9-418b-8610-9248fc4d4944","order_by":0,"name":"Jingjing Zhang","email":"","orcid":"","institution":"Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Zhang","suffix":""},{"id":469683137,"identity":"b729a039-f8c0-4e90-8660-b726fa46b369","order_by":1,"name":"Xiaofeng Jia","email":"","orcid":"","institution":"Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Jia","suffix":""},{"id":469683138,"identity":"0c8b2f8c-8f06-4a00-ab8e-03035fba71ec","order_by":2,"name":"Yanling Xu","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University, Institute of Neuroendocrine Tumor of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanling","middleName":"","lastName":"Xu","suffix":""},{"id":469683140,"identity":"d949ef31-dbd0-46aa-ac13-037a8d1ebddd","order_by":3,"name":"Feiyu Lu","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University, Institute of Neuroendocrine Tumor of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feiyu","middleName":"","lastName":"Lu","suffix":""},{"id":469683142,"identity":"91de6484-a4c2-4136-9727-7868beda3975","order_by":4,"name":"Jinxing Bao","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University, Institute of Neuroendocrine Tumor of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinxing","middleName":"","lastName":"Bao","suffix":""},{"id":469683144,"identity":"d951459b-5419-49d3-a136-d83505bb0dde","order_by":5,"name":"Mujie Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3RMQrCMBTG8VcE6xDpJhFBTyBUCuJQ8CqvCE5RBMELCJlyAIuX8AgpD3QR54KL4izErYOCOrnZjIL5zd+fkATAcX4TagDeDvyl1qawT+KoqbZJlir7k8ZJmIuIalWLbXeFZ32ThJALQ8CgEzT096SfI2appKmnrhuaDaCXrrE8obqkecWfbGjFAMOjTfKQlEgQJ2JV28ST40TVBFgm+xNm6hBHnG3D1yNzi7vsxMgUC94e7pYXY4q4E7RKEgCG4H2+g5fN33wNcLcZOo7j/K0nrqhR1E/EJvkAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University, Institute of Neuroendocrine Tumor of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mujie","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-06-01 07:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6794382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6794382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84665205,"identity":"a16a2b80-37d2-46b7-9fed-a09196a7c529","added_by":"auto","created_at":"2025-06-16 05:35:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23936,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of NSE, HDL-c and NSE combined HDL-c to predict pNETs occurence\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6794382/v1/73f1c2c228a643beee53a134.png"},{"id":84666182,"identity":"703c705a-6e50-4779-a941-9a8449419a69","added_by":"auto","created_at":"2025-06-16 05:43:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17686,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of CA199, NSE, and CA199 combined NSE to predict pNETs grade\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6794382/v1/b1aba8fa6a903cca1d72f7f3.png"},{"id":84666183,"identity":"c515ef25-100a-48d3-89c0-31a41b4a89fd","added_by":"auto","created_at":"2025-06-16 05:43:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18291,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of CA199, NSE, and CA199 combine\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6794382/v1/f579f1b9518e5652c8784b6f.png"},{"id":87504204,"identity":"393e316e-5285-4181-b90a-3427e47f5632","added_by":"auto","created_at":"2025-07-24 14:24:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":737047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6794382/v1/953fcc6a-1dd5-4e53-8432-33dcc5aadbdc.pdf"},{"id":84665202,"identity":"91aa034c-97cf-4fec-b66a-109f1697e61b","added_by":"auto","created_at":"2025-06-16 05:35:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57009,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6794382/v1/629b25e32476cb4f4de941b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum CEA, CA199, NSE and Lipid Profile to Predict the Occurrence, Pathological Grade and Metastasis of Pancreatic Neuroendocrine Tumor","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeuroendocrine tumor (NET) arises from neuroendocrine cells, which are distributed throughout the body and exhibit both neural and endocrine functions\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. These cells are capable of secreting a variety of hormones and peptide substances that regulate physiological functions. NET exhibit significant heterogeneity, with tumors located in different anatomical sites displaying variations in biological behavior and clinical manifestations\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. NET are commonly found in the digestive tract (including the stomach, intestines, and pancreas) and lungs, seldom in liver, breast and other organs\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Among these, pancreatic neuroendocrine tumor (pNET) represent a relatively rare subset, accounting for approximately 1\u0026ndash;2% of all pancreatic tumors\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, advancements in imaging technology and pathological diagnostic techniques have led to an increased incidence of pNET\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The malignancy degree of pNET varies. Some tumors exhibit slow growth, while others demonstrate higher aggressiveness. According to the classification standards set by the World Health Organization, pNET are categorized into three grades: G1, G2, and G3\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The proliferation index, measured by the Ki-67 index, is typically less than 3% for G1-grade tumors, ranges from 3\u0026ndash;20% for G2-grade tumors, and exceeds 20% for G3-grade tumors\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Grade G1 and G2 tumors are classified as well-differentiated low-grade neuroendocrine tumors (L-pNET), characterized by a relatively low degree of malignancy and a favorable prognosis; in contrast, grade G3 tumors are classified as poorly differentiated high-grade neuroendocrine tumors (H-pNET), exhibiting a higher degree of malignancy and a poorer prognosis\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePNETs are known to have a propensity for metastasis, with common metastatic sites including the liver, lymph nodes, and lungs\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The 5-year survival rate for pNET varies according to the pathological stage: G1 tumors may achieve a survival rate exceeding 90%, G2 tumors typically exhibit a survival rate of 60\u0026ndash;80%, while G3 tumors generally have a survival rate of less than 50%\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Early diagnosis and surgical resection can improve prognosis. For advanced-stage patients who are not candidates for surgical intervention, comprehensive treatment modalities such as targeted therapy, chemotherapy, and peptide receptor radionuclide therapy can extend survival and improve quality of life\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsequently, early diagnosis, accurate grading, and personalized treatment strategies are essential for improving patient outcomes. This study primarily investigates the diagnostic utility of tumor markers, including carcinoembryonic antigen (CEA), carbohydrate antigen (CA199), neuron-specific enolase (NSE), and biochemical indicators such as triglycerides, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), and body mass index (BMI) to predict the occurrence, pathological stage and metastasis of pNET.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 General Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 175 patients diagnosed with pNET were admitted to Jiangsu Provincial People\u0026apos;s Hospital from January 2019 to December 2024, constituting the experimental group. In contrast, the control group was comprised of 88 relatively healthy individuals who visited the hospital between January 2023 and December 2024. Inclusion criteria for the experimental group: Patients must have been diagnosed with pNET through pathological examination, with complete pathological data available; the primary tumor site must be the pancreas; comprehensive data regarding gender, age, BMI, and blood levels of CEA, CA199, NSE, triglycerides, TC, HDL-c, and LDL-c at the time of diagnosis must be provided. Exclusion criteria for the experimental group: Patients with incomplete pathological data, tumors originating from other sites and metastasizing to the pancreas and individuals with incomplete data on gender, age, BMI, or blood markers as specified above were excluded. Inclusion criteria for the control group: Individuals with no tumors and without chronic diseases such as hypertension, hyperlipidemia, diabetes, or coronary heart disease were included; complete data regarding gender, age, BMI, and blood levels of CEA, CA199, NSE, triglycerides, TC, HDL-c, and LDL-c were also required. Exclusion criteria for the control group: Individuals suffering from any tumor or with chronic diseases such as hypertension, hyperlipidemia, diabetes, or coronary heart disease were excluded; those with incomplete information regarding gender, age, BMI, or blood markers were not included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollect data of the experimental and control group, including gender, age, BMI, pathological grade, whether there is metastasis and blood levels of CEA, CA199, NSE, triglyceride, TC, HDL-c, LDL-c at the time of diagnosis. According to relevant diagnostic criteria, the pathological grades of pNET are divided into three types: G1, G2, and G3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Statistical processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse SPSS22.0 software for data analysis. Count data are expressed in terms of the number of cases and percentages, and inter-group comparisons are performed using the \u0026chi;\u003csup\u003e2\u003c/sup\u003e test. After normal test, the measurement data with normal distribution is expressed as average\u0026plusmn;standard deviation (x\u0026plusmn;s), and the comparison between two groups is performed using the independent sample t-test. \u0026nbsp;The measurement data not conforming to normal distribution is expressed as median (first quartile, third quartile) [M (P25, P75)], and the comparison between two groups is performed using the Mann-Whitney U test. Binary logistic regression is used to analyze the factors affecting the occurrence, grading and metastasis of pNET, and a combined diagnostic model is established. The receiver operating characteristic (ROC) curve is drawn to evaluate the diagnostic value of single-item and combined model. P \u0026lt; 0.05 is considered statistical difference. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.1 General characteristics of pNET patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 175 pNET patients, the age range is 21-89 years [57.5 (P51.0, P62.5)]. \u0026nbsp;There are 94 males and 81 females; 51 cases of G1 grade, 99 cases of G2 grade and 25 cases of G3 grade; 121 cases without metastasis and 74 cases with metastasis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Comparison of each parameter between the experimental and control group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no statistical difference in gender, age, BMI, CA199, TC and triglyceride levels between the experimental and control group. The CEA, NSE and LDL-c levels in the experimental group are higher than those in the control group, and the HDL-c level is lower than that in the control group. The differences are statistically significant (Table 1). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Comparison of each parameter among pNET patients with different pathological grades\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are statistical differences in CEA, CA199, NSE, LDL-c levels and whether metastasis or not among pNET patients with different pathological grades. While there is no statistically significant difference in gender, age, BMI, HDL-c, and triglyceride levels (Table 3). When pairwise comparison is done between G1, G2 and G3 pNET patients, whether metastasis occurs show differences between each pair. The higher the pathological grade, the more likely metastasis occurs. The CEA and CA199 levels of G3 are higher than that of G1 and G2. The NSE, TC, and LDL-c levels of G3 are higher than that of G1. The levels of CEA, CA199, NSE, TC, and LDL-c in patients with high-grade pNET are higher than those with low-grade pNET, and they are more likely to develop metastasis (Table 4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Comparison of each parameter between metastatic and non-metastatic pNET\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn pNET patients with metastasis, the number of male patients is more than that of female, and the levels of CEA, CA199, and NSE are higher than those without metastasis (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Binary Logistic regression analysis and ROC curve drawing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaking whether suffering from pNET, whether it is a high-grade pNET, and whether metastasis occurs as dependent variable assignments (yes = 1, no = 0), and taking the indicators with statistical significance in univariate analysis as independent variable assignments (all input with original values), perform binary Logistic regression analysis and draw ROC curves. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5.1 Diagnostic prediction model for pNET patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncreased NSE and decreased HDL-c levels are risk factors for pNET patients. The combined diagnostic model is Y= 0.487\u0026acute;NSE-1.296\u0026acute;HDL-c-5.203. The AUCs of NSE and HDL-c are 0.899 and 0.666 respectively, both lower than the AUC (0.911) of the combined diagnostic model(Table 6) (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5.2 Grading prediction model for pNET patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncreased CA199 and NSE levels are risk factors for high-grade pNET patients. The combined diagnostic model is Y= 0.004\u0026acute;CA199+0.046\u0026acute;NSE-3.332. The AUCs of CA199 and NSE were 0.728 and 0.645 respectively, both lower than the AUC (0.765) of the combined diagnostic model(Table 6) (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5.3 Metastasis prediction model for pNET patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncrease CA199 and NSE levels are risk factors for pNET patients with metastasis. \u0026nbsp;The combined diagnostic model is Y= 0.003\u0026acute;CA199+ 0.061\u0026acute;NSE-2.349. The AUCs of CA199 and NSE were 0.693 and 0.651 respectively. The AUC (0.689) of the combined diagnostic model does not show superiority over the individual models(Table 6) (Figure 3). \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePNET represents a relatively rare yet highly heterogeneous group of neoplasms. Their pathogenesis is intricate, involving multiple molecular and metabolic pathways\u003csup\u003e[19-26]\u003c/sup\u003e. Recent studies have increasingly highlighted the role of abnormal lipid metabolism in tumorigenesis and progression. Particularly in pNET, research focused on lipid metabolism has provided novel insights into their biological behavior. Lipid metabolism is a crucial process for cellular energy supply and biomembrane synthesis, encompassing the synthesis, degradation, and transport of various lipid substances, including triglycerides, cholesterol, and phospholipids. Evidence suggests that the lipid metabolism levels in pNET patients often exhibit significant alterations, which may be associated with the heightened lipid synthesis necessary for tumor cell proliferation, the augmented inflammatory response and oxidative stress within the tumor microenvironment, and the increased lipid utilization by tumor cells\u003csup\u003e[27-29]\u003c/sup\u003e. \u0026nbsp;These abnormalities in lipid metabolism are not only linked to the onset of pNET but may also influence tumor grade, metastasis, and prognosis.\u003c/p\u003e\n\u003cp\u003eResearch by Gu and et al indicated that in patients with pNET, the expression of \u0026nbsp; high-density lipoprotein binding protein mRNA is down-regulated in pNET tissue compared to peritumoral tissue, and survival analyses revealed that pNET patients with elevated low-density lipoprotein receptor levels have an increased risk of poor survival outcomes\u003csup\u003e[29]\u003c/sup\u003e. Chen and his colleagues\u0026rsquo; research revealed a significant correlation between serum HDL-c levels and the malignant behavior of pNET, particularly concerning tumor grading and lymph node metastasis\u003csup\u003e[30]\u003c/sup\u003e. Furthermore, their investigations indicated that a high non-HDL-c/HDL-c ratio was associated with high-grade or poorly differentiated pNET\u003csup\u003e[31]\u003c/sup\u003e. These findings underscore the important research value of lipid metabolism molecules, particularly HDL-c, in the context of pNET. This suggests that the combined assessment of biochemical indicators and lipid metabolism molecules may enhance the diagnostic and predictive efficacy for pNET.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study analyzed blood tumor markers and biochemical lipid metabolism indicators in 175 patients with pNET and 88 healthy controls, aiming to explore the diagnostic value of CEA, CA199, NSE, and lipid metabolism indicators in relation to the diagnosis, pathological grading, and metastasis prediction of pNET. The results demonstrated that NSE and HDL-c possess significant predictive value for the occurrence of pNET, while CA199 and NSE also play critical roles in tumor grading and metastasis prediction. These findings provided novel insights for the early diagnosis and assessment of pNET. Notably, this study found that NSE levels were significantly elevated in patients with pNET and were closely linked to tumor grading and metastasis. As a neuron-specific marker, NSE typically exhibits high expression in NET. The findings of this study further validate the diagnostic value of NSE in pNET, particularly in high-grade and metastatic tumors, where the increase in NSE levels is especially pronounced. This indicates that NSE can serve not only as a diagnostic marker for pNET but also as a means to evaluate the malignancy and metastasis risk of tumors. Furthermore, the decrease in HDL-c levels is significantly correlated with the occurrence of pNET. As a crucial index of lipid metabolism, the reduction in HDL-c levels may be associated with changes in the tumor microenvironment. Although the role of HDL-c in predicting tumor grading and metastasis is limited, its predictive value regarding the occurrence of pNET should not be overlooked. This finding suggests that abnormal lipid metabolism may contribute to the onset and progression of pNET. Future research should further investigate the molecular mechanisms linking lipid metabolism to tumor occurrence. Additionally, the level of CA199 is significantly elevated in high-grade and metastatic pNET, indicating that CA199 possesses clinical applicability in assessing the malignancy and metastasis risk of tumors. While the diagnostic value of CA199 in pancreatic cancer is well-established, this study further affirms its predictive role in pNET, particularly in diagnosis and high-grade tumors. Through the construction of a joint diagnostic model, it was observed that the AUC value for the combined detection of multiple indicators was significantly higher than that of individual indicators. This finding indicates that combined detection can enhance the diagnostic and predictive efficacy of pNET. Consequently, in clinical practice, the combined application of NSE, CA199, and lipid metabolism indicators should be considered to improve the early diagnosis rate of pNET and the accuracy of disease assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, NSE, CA199, and HDL-c hold significant clinical value in the diagnosis, grading, and metastasis prediction of pNET. Future studies should further investigate the molecular mechanisms of these markers in the occurrence and development of pNET and validate their clinical applicability in larger sample sizes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1964 and later versions. All protocols were approved by The First Affiliated Hospital with Nanjing Medical University. And informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was obtained for this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMujie Ye designed the analysis, reviewed the clinical data. Jingjing Zhang and Xiaofeng Jia performed statistical analysis, and drafted the manuscript. Yanling Xu, Jinxing Bao, and Feiyu Lu treated patients and collected the clinical data. Mujie Ye and Jingjing Zhang revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Yang X, Wang Y, et al. Efficacy and safety of surufatinib in the treatment of patients with neuroendocrine tumor: a real-world study in Chinese population[J]. BMC Cancer. 2024;24(1):1342.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamboj S, Guerra-Bauman F, Mahmud H, et al. Neuroendocrine Neoplasms[J] Prim Care. 2024;51(3):549\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Rosa S, Uccella S. Classification of neuroendocrine neoplasms: lights and shadows[J]. Reviews Endocr metabolic disorders. 2021;22(3):527\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamady KA, Madadi-Sanjani O, Abolzadeh A et al. Primary hepatic neuroendocrine neoplasms of children, a systematic review[J]. J Neuroendocrinol, 2025:e13495.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSen T, Dotsu Y, Corbett V, et al. Pulmonary neuroendocrine neoplasms: the molecular landscape, therapeutic challenges, and diagnosis and management strategies[J]. Lancet Oncol. 2025;26(1):e13\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang L, Pan X, Lang Z. Neuroendocrine Neoplasms of the Breast: Current Insights and Future Directions[J]. Cancer Rep (Hoboken). 2024;7(11):e70059.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsmundo L, Rizzetto F, Blake M et al. Advancements in Neuroendocrine Neoplasms: Imaging and Future Frontiers[J]. J Clin Med, 2024,13(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordstrand MA, Lea D, Soreide JA. Incidence of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): An updated systematic review of population-based reports from 2010 to 2023[J]. J Neuroendocrinol, 2025:e70001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUhlig J, Nie J, Gibson J, et al. Epidemiology, treatment and outcomes of gastroenteropancreatic neuroendocrine neoplasms[J]. Sci Rep. 2024;14(1):30536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Wang L, Dai S, et al. Epidemiologic Trends of and Factors Associated With Overall Survival for Patients With Gastroenteropancreatic Neuroendocrine Tumors in the United States[J]. JAMA Netw Open. 2021;4(9):e2124750.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRindi G, Mete O, Uccella S, et al. Overview of the 2022 WHO Classification of Neuroendocrine Neoplasms[J]. Endocr Pathol. 2022;33(1):115\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoseph NM, Umetsu SE, Kim GE, et al. Progression of Low-Grade Neuroendocrine Tumors (NET) to High-Grade Neoplasms Harboring the NEC-Like Co-alteration of RB1 and TP53[J]. Endocr Pathol. 2024;35(4):325\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourdeleau P, Pokossy-Epee J, Hentic O et al. Temporal increase in Ki-67 index in patients with pancreatic neuroendocrine tumours[J]. Endocr Relat Cancer, 2025,32(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu F, Ye M, Shen Y, et al. Hypoxic tumor-derived exosomal miR-4488 induces macrophage M2 polarization to promote liver metastasis of pancreatic neuroendocrine neoplasm through RTN3/FABP5 mediated fatty acid oxidation[J]. Int J Biol Sci. 2024;20(8):3201\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y, Dong YH, Zhao SW, et al. Multiregion WES of metastatic pancreatic neuroendocrine tumors revealed heterogeneity in genomic alterations, immune microenvironment and evolutionary patterns[J]. Cell Commun Signal. 2024;22(1):164.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArrivi G, Fazio N, Tafuto S, et al. The efficacy of streptozotocin in managing pancreatic neuroendocrine neoplasms - A systematic review[J]. Cancer Treat Rev. 2025;134:102899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair G, Black M, Baer K et al. Treatment Patterns of Pancreatic Neuroendocrine Tumor (pNET) Patients at Two Canadian Cancer Centres[J]. Curr Oncol, 2025,32(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTacelli M, Partelli S, Falconi M, et al. Pancreatic Neuroendocrine Neoplasms: Classification and Novel Role of Endoscopic Ultrasound in Diagnosis and Treatment Personalization[J]. United European Gastroenterol J; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexandraki KI, Papadimitriou E, Spyroglou A, et al. Immunohistochemical expression of ephrin receptors in neuroendocrine neoplasms: a case-series of gastroenteropancreatic neuroendocrine neoplasms and a systematic review of the literature[J]. Endocrine. 2025;87(3):1323\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe M, Lu F, Gu D, et al. Hypoxia exosome derived CEACAM5 promotes tumor-associated macrophages M2 polarization to accelerate pancreatic neuroendocrine tumors metastasis via MMP9[J]. FASEB J. 2024;38(13):e23762.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Z, Zhou Y, Hu Y, et al. Single-cell sequencing reveals the heterogeneity of pancreatic neuroendocrine tumors under genomic instability and histological grading[J]. iScience. 2024;27(9):110836.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Lou X, Wang F, et al. MEN1 Deficiency-Driven Activation of the beta-Catenin-MGMT Axis Promotes Pancreatic Neuroendocrine Tumor Growth and Confers Temozolomide Resistance[J]. Adv Sci (Weinh). 2024;11(35):e2308417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Z, Li Q, Hu Y, et al. The stromal microenvironment endows pancreatic neuroendocrine tumors with spatially specific invasive and metastatic phenotypes[J]. Cancer Lett. 2024;588:216769.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePadwal MK, Parghane RV, Chakraborty A et al. Systemic Cancer Hallmarks as Novel Markers Associated with Progression-free Survival in Gastroenteropancreatic Neuroendocrine Tumor Patients Undergoing PRRT[J]. Neuroendocrinology, 2024:1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Lin S, Liang F, et al. The prognostic and therapeutic value of the tumor microenvironment and immune checkpoints in pancreatic neuroendocrine neoplasms[J]. Sci Rep. 2024;14(1):24669.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Liu P, Duan S, et al. CTCF enhances pancreatic cancer progression via FLG-AS1-dependent epigenetic regulation and macrophage polarization[J]. Cell Death Differ; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Ye M, Gu D, et al. FTO-induced APOE promotes the malignant progression of pancreatic neuroendocrine neoplasms through FASN-mediated lipid metabolism[J]. Int J Biol Sci. 2025;21(4):1478\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe M, Xu L, Lu F, et al. Hypoxia drives CBR4 down-regulation promotes gastroenteropancreatic neuroendocrine tumors via activation mammalian target of rapamycin mediated by fatty acid synthase[J]. J Cell Commun Signal. 2024;18(3):e12041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu D, Ye M, Zhu G, et al. Hypoxia upregulating ACSS2 enhances lipid metabolism reprogramming through HMGCS1 mediated PI3K/AKT/mTOR pathway to promote the progression of pancreatic neuroendocrine neoplasms[J]. J Transl Med. 2024;22(1):93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu D, Xue B, Zhu G, et al. Serum Lipid Level in Evaluating Chinese Pancreatic Neuroendocrine Neoplasms: A Retrospective Study[J]. Exp Clin Endocrinol Diabetes. 2024;132(2):98\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuan L, Liu Y, Cui W, et al. The associations between serum high-density lipoprotein cholesterol levels and malignant behavior in pancreatic neuroendocrine neoplasms[J]. Lipids Health Dis. 2022;21(1):58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou H, Zhu Y, Qin B, et al. The association between non-HDL cholesterol and high-grade pancreatic neuroendocrine neoplasms[J]. Endocrine. 2024;86(2):584\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 6 are available in the Supplementary Files section.\u003c/p\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 Neuroendocrine Tumor, Lipid Profile, Tumor marker, Occurrence","lastPublishedDoi":"10.21203/rs.3.rs-6794382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6794382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to explore the diagnostic value of CEA, CA199, NSE, and biochemical lipid metabolism indexes for pNET.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 175 patients with pNET as experimental group and 88 relatively healthy patients as control group were enrolled in the study. CEA, CA199, NSE, TC, HDL-c and LDL-c level of blood, along with BMI, were collected from both groups and subjected to statistical analysis. A logistic regression analysis model was constructed, and the ROC curve was utilized to evaluate the efficacy of individual indicators with statistically significant differences, as well as the combined detection model, in predicting the occurrence, pathological grading, and metastasis of pNET.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe levels of CEA, NSE, and LDL-c in the experimental group were higher than control group, whereas the level of HDL-c was lower than that in the control group. Among patients with pNET of varying pathological grades, statistically significant differences were observed in the occurrence of metastasis and the levels of CEA, CA199, NSE, and LDL-c. Patients with high-grade pNET exhibited elevated levels of CEA, CA199, NSE, TC, and LDL-c compared to those with low-grade pNET, and were more prone to metastasis. The number of male patients with metastatic pNET exceeded that of female patients. Furthermore, the levels of CEA, CA199, and NSE were higher in metastatic cases compared to non-metastatic cases. These differences are statistical significant. An increase in NSE levels and a decrease in HDL-c levels were identified as risk factors for the development of pNET. The AUCs for NSE and HDL-c were 0.899 and 0.666, respectively, both of which were lower than the AUC (0.911) of the combined diagnostic model. Additionally, elevated levels of CA199 and NSE were found as risk factors for both high-grade and metastatic pNET. The AUCs for CA199 and NSE in predicting high-grade pNET were 0.728 and 0.645, respectively, again lower than the AUC (0.765) of the combined diagnostic model. The AUCs for CA199 and NSE in predicting pNET metastasis were 0.693 and 0.651, respectively, while the AUC (0.689) of the combined diagnostic model did not demonstrate superiority over the individual models.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, an increase in blood NSE level serves as a significant predictive indicator for the occurrence, pathological grading, and metastasis of pNET. While an increase in blood HDL-c level can predict the occurrence of pNET, it does not serve as a predictor for pathological grading or metastasis.\u003c/p\u003e","manuscriptTitle":"Serum CEA, CA199, NSE and Lipid Profile to Predict the Occurrence, Pathological Grade and Metastasis of Pancreatic Neuroendocrine Tumor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 05:35:45","doi":"10.21203/rs.3.rs-6794382/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":"13531900-2c58-4e64-8916-e0a4d1df6127","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-24T14:23:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 05:35:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6794382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6794382","identity":"rs-6794382","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}