The ratio of monocyte to apolipoprotein A1 is an independent predictor of hepatocellular carcinoma: a retrospective study

The ratio of monocyte to apolipoprotein A1 is an independent predictor of hepatocellular carcinoma: a retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The ratio of monocyte to apolipoprotein A1 is an independent predictor of hepatocellular carcinoma: a retrospective study Cheng Su, Xuefeng Zhu, Zhongyuan Lin, Fu Wei, Ling Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6705868/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 The ratio of monocyte to apolipoprotein A1 (MAR) is a new diagnostic indicator of some chronic diseases, but there have been no reports on hepatocellular carcinoma (HCC). This study aimed to explore the predictive value of MAR in HCC. A total of 270 HCC patients, 540 hepatitis B patients and 540 healthy volunteers were included in this study. Laboratory data were retrospectively collected from electronic medical records. We found that the MAR level was the highest in HCC, followed by hepatitis B disease, and the lowest in healthy volunteers. It was found to be an independent risk factor for HCC, which is also an indicator for distinguishing HCC from hepatitis B disease. The MAR cut-off value for distinguishing HCC from hepatitis B was 0.53, while the cut-off value in the HCC and healthy volunteer groups was 0.62. An increase in MAR levels showed that the risk of HCC was 6.001 times higher than that in healthy volunteers. MAR was correlated with indicators such as lymphocytes, carcinoembryonic antigen, and albumin. The results of this study showed that MAR is a new indicator for the differential diagnosis of HCC and hepatitis B, and is an independent risk factor for HCC. Biological sciences/Cancer Biological sciences/Cancer/Gastrointestinal cancer Health sciences/Diseases/Cancer hepatocellular carcinoma hepatitis B the ratio of monocyte to apolipoprotein A1 monocyte apolipoprotein A1 Figures Figure 1 Figure 2 Introduction Liver cancer is one of the most common malignant tumors of the digestive tract worldwide, with a 5-year survival of 18% 1 . Hepatocellular Carcinoma (HCC) accounts for 90% of the cases 2 . According to the latest statistics, approximately 50–80% of HCC cases worldwide are caused by chronic hepatitis B virus (HBV) infection 3 . HBV infection can promote hepatocellular carcinogenesis through direct or indirect mechanisms. The liver microenvironment changes through HBV infection-induced chronic inflammation, the interaction between the virus and innate and adaptive immune cells, which helps the virus evade immune surveillance and promote disease evolution from inflammation to tumor formation. The early stage of HCC is insidious, and most patients are already in the late stage when symptoms appear. Therefore, early screening and diagnosis of HCC are of great clinical significance for the development of suitable treatment programs, and determine more reliable hematological predictors that are closely related to the characteristics of HCC. About 15% of the global cancer burden is attributable to infectious agents, and inflammation is a major component of these chronic infections. The relationship between cancer-related chronic inflammation, dyslipidemia, and malignant tumors has attracted much attention nowadays 4 , 5 . Additionally, chronic inflammation is closely related to the occurrence and development of tumors 6 . Inflammation provides a good environment for oncogenesis. Tumour-associated macrophages (TAM) are a major component of the infiltrate of most, if not all, tumours 7 . TAM are a subpopulation of monocytes that originates in the circulating blood and is activated around the tumor due to the release of tumor chemokines 8 . In recent years, inflammatory markers such as monocyte count have been found to be associated with HCC risk. Monocytes (M) are the innate immune cells of the mononuclear phagocyte system in chronic inflammation, and have become an important regulator of cancer development and progression 9 . The presence of lipids in the human body mainly comes from the diet and the synthesis of liver cells. The change of the local microenvironment of tumor may cause lipid metabolism disorder 10 . ApoA1 is the main apolipoprotein in HDL and the largest component of ApoA family, which plays a key role in blood lipids 11 . It has been shown that dyslipidemia can increase the risk of tumor and is closely related to the prognosis and development of diseases, such as prostate cancer, colorectal cancer and HCC 12 – 14 . MAR, obtained from the ratio of M to ApoA1, is a new diagnostic indicator of some chronic diseases in recent years, such as type-2 diabetes and breast cancer 15 , 16 , but there have been no reports on HCC. As the liver is the main organ for metabolism, we are curious whether MAR indicators are meaningful in the diagnosis of HCC. Therefore, the purpose of this study is to explore the clinical diagnostic value of MAR in HCC. We retrospectively collected the hematological test results of 270 HCC patients, 540 hepatitis B patients and 540 healthy controls, using statistical analysis software to analyze the clinical diagnostic value of MAR in HCC. Patients and Methods Patients HCC and HBV patients hospitalized in the Department of Gastroenterology of the People's Hospital of Guangxi Zhuang Autonomous Region from January 1, 2020 to November 30, 2024 were selected as the research objects. Baseline data, laboratory test indicators, and clinical history were retrospectively collected. The inclusion criteria were as follows: 1) Complete clinical data; 2) HCC patients who met the diagnostic criteria of the 2018 Chinese Society of Clinical Oncology (CSCO) Guidelines for the Diagnosis and Treatment of HCC; 3) No anticancer treatment; 4) Patients with hepatitis B diagnosed in accordance with the 2019 Guidelines for the Prevention and Treatment of Chronic Hepatitis B. Exclusion criteria: 1) Patients with liver metastases or secondary tumors; 2) Simultaneously suffering from other tumors; 3) Patients with a history of major organ transplantation or other serious illnesses; 4) Merge hepatitis A, C, E virus or other viral infections such as HIV; 5) Those with unclear diagnosis. Healthy individuals without liver cancer and hepatitis B who were examined at the physical examination center of our hospital at the same time were selected as the control group. This study was approved by the Ethics Committee of our hospital and met the ethical requirements specified by the Ethics Committee (KY-IIT-2025-46), and has obtained informed consent from the subjects (or guardians). Methods Laboratory indicators Fasting venous blood was collected from patients in the morning and placed in EDTA-K2 anticoagulant tubes (2 ml) and procoagulant tubes (5 ml). Laboratory parameters such as lymphocytes (L), platelets (PLT), monocytes (M), and ApoA1 were tested by Sysmex (XN9000 automatic blood analyzer). Serum carcinoembryonic antigen (CEA) levels were measured by Roche cobas 801 automatic chemiluminescence immunoassay analyzer. Apolipoprotein B (ApoB), albumin (ALB) and alkaline phosphatase (ALP) were determined by Beckman coulter AU5800 automatic biochemical analyzer. Alpha-fetoprotein (AFP) levels were measured by Beckman coulter UniCel Dxl 800 Access immune analyzer. These instruments were used after daily quality control inspections in strict accordance with the operation. PLR is the ratio of platelet to lymphocyte; LAR is the ratio of lymphocyte to ApoA1; LMR is the ratio of lymphocyte to monocyte; PAR is the ratio of platelet to ApoA1; BAR is the ratio of ApoB to ApoA1; MAR is the ratio of monocyte to ApoA1. Statistical Analysis In this study, the HCC group was matched with hepatitis B patients and healthy individuals by 1:2 propensity score matching (PSM) method using R software. There were no significant differences in age between groups. SPSS 26.0 software (IBM corp, Armonk, NY) was used for data analysis. Measurement data with normal distribution were represented as mean ± SD, non-normal distribution were expressed as median (P 25 , P 75 ), and the Kruskal-Wallis H-test was used for comparison between groups. The truncation value, area under the curve (AUC) and Youden index of each significant variable were calculated using the receiver operating characteristic curve (ROC). Univariate and multivariate logistic regression analyses were used to analyze risk factors for HCC. A correlation heatmap was used to analyze the correlation between each index and MAR, and the correlation coefficient r value was calculated. A two-tailed P value of no more than 0.05 was considered statistically significant. Furthermore, ROCs were generated using GraphPad Prism 8 software (GraphPad Software, San Diego, CA, USA). Results Information about the subjects was retrospectively collected using a medical electronic recording system. A total of 270 HCC patients, 570 hepatitis B patients and 570 healthy controls were enrolled in this study. Their ages were mainly concentrated around 57 years old. Difference Analysis of Three Groups Comparison of hematological indicators among the HCC, hepatitis B, and healthy volunteer groups showed statistically significant differences in PLT ( P < 0.001), L ( P < 0.001), M ( P < 0.001), CEA ( P < 0.001), ALB ( P < 0.001), ApoA1 ( P < 0.001), ApoB ( P < 0.001), ALP ( P < 0.001), PLR ( P < 0.001), PAR ( P < 0.001), BAR ( P < 0.001), LAR ( P < 0.001), LMR ( P < 0.001), and MAR ( P < 0.001) (Table 1). Subsequently, the comparison between the two groups showed that PLT, L, M, ALB, ApoA1, ApoB, PLR, PAR, BAR, LAR and LMR were notably different between healthy volunteers and patients with hepatitis B ( P < 0.05). There were significant differences in PLT, L, M, CEA, ALB, ApoA1, ApoB, ALP, BAR, LAR, LMR and MAR levels between healthy volunteers and HCC groups ( P < 0.05). Moreover, there were significant difference in L, M, CEA, ALB, ApoA1, ALP, PLR, PAR, BAR, LMR and MAR between the hepatitis B group and HCC groups ( P < 0.05). The MAR level was highest in the HCC group (0.92), followed by hepatitis B group (0.38) and the healthy volunteer group (0.36), as shown in Table 1. Table 1. Comparison of Hematological Parameters Among Patients with Hepatocellular Carcinoma, Those with hepatitis B patients and Healthy Volunteers. Group Healthy Volunteers hepatitis B patients Hepatocellular Carcinoma H P N 540 540 270 Age (years) 57(50, 66) 58(50, 65) 57(50, 66) 0.233 0.890 PLT (×10 9 /L) 263(224, 305) 172(101, 224) a 151(98, 226) b 406.897 0.000 L (×10 9 /L) 2.20(1.76,2.62) 1.52(1.05, 1.94) a 1.06(0.76, 1.51) bc 413.560 0.000 M (×10 9 /L) 0.50(0.40, 0.60) 0.42(0.32, 0.57) a 0.63(0.43, 0.95) bc 121.623 0.000 CEA ( μ g/L) 1.88(1.41, 2.57) 2.04(1.20, 3.69) 2.36(1.52, 3.99) bc 22.494 0.000 AFP ( μ g/L) 2.96(2.28, 3.95) 2.91(1.81, 6.33) 7.25(0.96, 95.91) 3.493 0.174 ALB(g/L) 45.00(43.10, 47.42) 37.10(30.60, 40.30) a 31.00(27.00, 35.45) bc 772.356 0.000 ApoA1 (g/L) 1.31(1.20, 1.46) 1.10(0.88, 1.32) a 0.75(0.53, 1.03) bc 420.352 0.000 ApoB (g/L) 0.98(0.85, 1.12) 0.85(0.67, 1.05) a 0.83(0.63, 1.05) b 69.593 0.000 ALP (U/L) 75(63, 89) 75(60, 104) 126(96, 213) bc 246.483 0.000 PLR 121.05(97.82, 151.85) 108.37(76.06, 142.97) a 137.98(89.66, 216.80) c 48.371 0.000 PAR 196.97(166.60, 240.89) 152.18(103.88, 207.09) a 205.54(113.11, 356.39) c 99.343 0.000 BAR 0.74(0.61, 0.86) 0.79(0.61, 1.00) a 1.07(0.77, 1.62) bc 137.446 0.000 LAR 1.65(1.30, 2.03) 1.38(0.97, 1.88) a 1.52(0.93, 2.19) b 35.553 0.000 LMR 4.40(3.48, 5.60) 3.37(2.26, 4.86) a 1.61(1.06, 2.58) bc 386.867 0.000 MAR 0.36(0.29, 0.47) 0.38(0.26, 0.60) a 0.92(0.47, 1.60) bc 232.113 0.000 Notes: a Healthy Volunteers VS hepatitis B patients, P < 0.05; b Healthy Volunteers VS Hepatocellular Carcinoma, P < 0.05; c hepatitis B patients VS Hepatocellular Carcinoma, P < 0.05. P value is analyzed by Kruskal-Wallis H-test. Abbreviations: AFP, Alpha-fetoprotein; ALB, albumin; ApoA1, apolipoprotein A1; ApoB, Apolipoprotein B; ALP, alkaline phosphatase; BAR, ApoB to ApoA1; CEA, carcinoembryonic antigen; L, lymphocytes; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; M, Monocytes; MAR, monocyte to ApoA1; PLT, platelet; PLR, platelet to lymphocyte; PAR, platelet to ApoA1. Calculation of Cut-off Value of MAR ROCs were drawn, and the cut-off values and AUC for NLR, PLR, NAR, PAR, LAR, LMR, and MAR were calculated (Table 2). The MAR cut-off value for distinguishing HCC from hepatitis B was 0.53, and the AUC was 0.762, while the cut-off value in the HCC and healthy volunteer groups was 0.62, and the AUC was 0.829 (Figure 1). Table 2. Identification of Optimal Cut-off Values for Different Predictive Factors Based on the ROC Curve. Group HCC VS HV HBV VS HV HCC VS HBV Parameter Cut off AUC Youden Cut off AUC Youden Cut off AUC Youden PLR 174.46 0.562 0.217 75.82 0.60 0.187 129.73 0.624 0.239 PAR 327.27 0.510 0.238 150.98 0.68 0.345 229.10 0.624 0.273 BAR 0.97 0.757 0.476 0.97 0.56 0.145 0.98 0.683 0.334 LAR 3.08 0.455 0.130 1.22 0.61 0.214 1.71 0.543 0.115 LMR 3.05 0.904 0.685 3.40 0.67 0.284 2.20 0.786 0.441 MAR 0.62 0.829 0.574 0.57 0.54 0.160 0.53 0.762 0.421 Notes: The truncation value, AUC and Youden index of each valuable variable were calculated using the receiver operating characteristic curve. Abbreviations: BAR, ApoB to ApoA1; HCC, Hepatocellular Carcinoma; HV, Healthy Volunteers; HBV, hepatitis B patients; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; MAR, monocyte to ApoA1; PLR, platelet to lymphocyte; PAR, platelet to ApoA1. Logistic Regression Analysis to Determine Predictive Factors As shown in Table 3, 4, univariate and multivariate logistic regression analyses showed that MAR was an indicator to distinguish HCC from hepatitis B disease and healthy people, as well as an independent risk factor for HCC. The increase in MAR level demonstrated that the risk of HCC was 6.001 times higher than that of the healthy volunteers ( P < 0.001). Table 3. The Predictive Factors Identified by Univariate Logistic Regression. Group HCC VS HV HBV VS HV HCC VS HBV Parameter OR(95%CI) P OR(95%CI) P OR(95%CI) P PLT 0.986(0.984, 0.988) 0.000 0.986(0.984, 0.988) 0.000 1.000(0.999, 1.002) 0.654 L 0.217(0.170, 0.276) 0.000 1.001(0.997, 1.006) 0.526 0.417(0.324, 0.538) 0.000 M 12.834(7.237, 22.760) 0.000 0.363(0.201, 0.656) 0.001 18.539(10.203, 33.686) 0.000 CEA 1.108(1.042, 1.178) 0.001 1.107(1.040, 1.177) 0.001 1.001(0.998, 1.004) 0.502 ALB 0.579(0.547, 0.612) 0.000 0.642(0.609, 0.676) 0.000 0.901(0.880, 0.924) 0.000 ApoA1 0.004(0.002, 0.008) 0.000 0.052(0.032, 0.084) 0.000 0.103(0.065, 0.162) 0.000 ApoB 0.371(0.221, 0.622) 0.000 0.302(0.197, 0.462) 0.000 1.147(0.745, 1.767) 0.532 ALP 1.025(1.021, 1.029) 0.000 1.010(1.006, 1.014) 0.000 1.015(1.012, 1.018) 0.000 PLR 1.004(1.003, 1.006) 0.000 0.999(0.997, 1.000) 0.097 1.004(1.003, 1.006) 0.000 PAR 1.002(1.001, 1.003) 0.000 0.998(0.996, 0.999) 0.000 1.003(1.002, 1.004) 0.000 BAR 5.539(3.974, 8.085) 0.000 4.092(2.829, 5.920) 0.000 1.346(1.171, 1.547) 0.000 LAR 1.051(0.977, 1.130) 0.180 1.052(0.979, 1.132) 0.168 0.999(0.993, 1.004) 0.625 LMR 0.386(0.340, 0.440) 0.000 1.001(0.999, 1.002) 0.520 0.499(0.440,0.567) 0.000 MAR 13.229(8.216, 21.303) 0.000 6.573(4.146, 10.421) 0.000 1.981(1.630, 2.409) 0.000 Abbreviations: ALB, albumin; ApoA1, apolipoprotein A1; ApoB, Apolipoprotein B; ALP, alkaline phosphatase; BAR, ApoB to ApoA1; CEA, carcinoembryonic antigen; HCC, Hepatocellular Carcinoma; HV, Healthy Volunteers; HBV, hepatitis B patients; L, lymphocytes; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; M, Monocytes; MAR, monocyte to ApoA1; PLT, platelet; PLR, platelet to lymphocyte; PAR, platelet to ApoA1. Table 4. The Predictive Factors Identified by Multivariate Logistic Regression. Group HCC VS HV HBV VS HV HCC VS HBV Parameter OR(95%CI) P OR(95%CI) P OR(95%CI) P PAR 0.995(0.994, 0.997) 0.000 0.991(0.989, 0.992) 0.000 1.001(1.000, 1.003) 0.015 BAR 6.411(3.844, 10.692) 0.000 10.875(6.576, 17.984) 0.000 0.799(0.669, 0.954) 0.013 LMR 0.466(0.398, 0.545) 0.000 1.000(0.998, 1.003) 0.696 0.615(0.529, 0.715) 0.000 MAR 6.001(2.432, 14.811) 0.000 2.492(1.297, 4.788) 0.006 3.941(1.869, 8.307) 0.000 Abbreviations: BAR, ApoB to ApoA1; HCC, Hepatocellular Carcinoma; HV, Healthy Volunteers; HBV, hepatitis B patients; LMR, lymphocyte to monocyte; MAR, monocyte to ApoA1; PAR, platelet to ApoA1. Relationship Between MAR and Other Laboratory Indicators of HCC As shown in Figure 2, MAR was correlated with L (r = -0.075, P < 0.05), CEA (r = 0.171, P < 0.001), ALB (r = -0.445, P < 0.001), ALP (r = 0.340, P < 0.001), PAR (r = 0.463, P < 0.001), BAR (r = 0.554, P < 0.001), LAR (r = 0.485, P < 0.001), LMR (r = -0.653, P < 0.001). Discussion In this study, the venous blood indicators of HCC, hepatitis B and healthy volunteers were analyzed, and it was found that the level of MAR was the highest in the HCC group, followed by the hepatitis B group, and the lowest among the healthy volunteers, suggesting that MAR can be used for the auxiliary differential diagnosis of hepatitis B and HCC. It is speculated that the difference of MAR ratio in HCC and hepatitis B diseases is mediated by abnormal blood lipid and chronic inflammation in patients. ApoA1 as an indispensable component of HDL, inhibits monocyte chemotaxis and recruitment and was shown to be involved in inflammatory reactions, and ApoA1/HDL can be activated and participate in antitumor activities in mature immune systems 17, 18 . It has been reported that ApoA1 could potently suppress tumor growth and metastasis in vivo and improve survival in mouse tumor models 19, 20 . Mononuclear cells are the main inflammatory cells in tumor stroma. It has been reported that the increase in activated monocytes (human leukocyte antigen HLA-DR high CD68 + cells) in the liver is related to disease progression 21 . Chemokine (C-C motif) ligand 15 (CCL15) recruits CCR1 + CD14 + monocytes to the edge of HCC tissue. High expression of CCL15 is associated with poor clinical prognosis. CCR1 + CD14 + monocytes suppress antitumor immunity, facilitate tumor metastasis and promote tumor cell proliferation and invasion 22 . Correlation analysis reveals correlations between MAR and L, CEA, ALB, ALP, PAR, BAR, LAR and LMR. Lymphocytes play a pivotal role in immunosurveillance and immune editing, and lymphocyte infiltration in the tumor microenvironment (TME) contributes to the immunologic anticancer reaction 23, 24 . The presence of tumor-infiltrating lymphocytes is associated with improved survival in various cancers. Conversely, low lymphocyte counts and failure to infiltrate the tumor lead to inferior survival 25, 26 . The LMR is the ratio of lymphocytes to monocytes. Various investigations have shown that decreased pretreatment LMR has an unfavorable impact on OS in cancer patients among various tumor subgroups 27 , including HCC 28, 29 . PAR is the ratio of platelet to ApoA1. Platelets facilitate tumor progression by supporting cancer stem cells, inducing angiogenesis, sustaining cell proliferation, and evading immune surveillance 30 . In addition platelets may represent a potential therapeutic target, and indeed, sorafenib is a standard of care in HCC treatment directly targeting pathways mediated by VEGF and platelet[1]derived growth factor (PDGF) 31 . Moreover, previous investigations highlighted the inhibiting effect on tumor growth exerted by antiplatelet therapies (aspirin, warfarin, and cyclo-oxygenase inhibitors), even in HCC 32, 33 . Overall, these indicators are all related to inflammation, and inflammation-based scores may be convenient, easily obtained, low-cost, and reliable biomarkers with diagnostic and prognostic significance for HCC. Using univariate logistic regression analysis, it was found that MAR is an independent risk factor of HCC, and the increased MAR level indicated that the risk of HCC was 13.229 times higher than that of healthy volunteers. Multivariate logistic regression analysis revealed that the risk of HCC was 6.001 times higher than that of healthy volunteers. This ratio is higher than that reported for breast cancer (3.733 times) 16 and Type 2 diabetes mellitus (2.26 times) 15 , suggesting that MAR indicator may have good clinical application prospects. Wang et al 15 believed that MAR was a risk factor for type-2 diabetes metabolic syndrome. The MAR level of diabetes patients with metabolic syndrome was higher than that of patients without metabolic syndrome. Moreover, Lin et al 16 found that MAR is a new indicator for the differential diagnosis of benign and malignant breast diseases, and is an independent risk factor for breast cancer. High MAR is closely related to late stages of breast cancer, deeper tumor invasion, and distant metastasis. The results of this study showed that high MAR was an independent risk factor for HCC, with a cutoff value of 0.62, high MAR was a risk factor for HCC. In addition, MAR level was the highest in the HCC (0.92), followed by hepatitis B disease (0.38), and the lowest in healthy volunteer (0.36), which may serve as an indicator for predicting the progression from hepatitis B to HCC. This study has some limitations. This was a retrospective study without follow-up, and it could not directly reflect the association between MAR and HCC. The significance of MAR in the survival prognosis of patients with HCC requires confirmation in a large-scale, prospective cohort study. Moreover, the subjects included in this study were local residents, and the optimal MAR cut-off values may not be applicable to other races. Conclusions The results of this study showed that MAR is a new indicator for the differential diagnosis of HCC and hepatitis B disease, and is an independent risk factor for HCC. To our knowledge, this is the first study to investigate the clinical value of MAR in HCC in China. It is hoped that more research in the future will explore the exact pathogenic mechanism of HCC, provide a reference for the diagnosis and treatment of HCC, and contribute to the improved quality of life of patients. Abbreviations ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; ALB, albumin; ALP, alkaline phosphatase; AFP, alpha-fetoprotein; AUC, area under the curve; BAR, ApoB to ApoA1; CEA, carcinoembryonic antigen; HCC, hepatocellular carcinoma; HBV, hepatitis B virus; L, lymphocytes; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; M, monocytes; MAR, monocyte to ApoA1; PLT, platelet; PLR, platelet to lymphocyte; PAR, platelet to ApoA1; ROC, receiver operating characteristic curve; TAM, tumour-associated macrophages. Declarations Competing interests The authors declare no competing interests. Ethical approval The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of the People’s Hospital of Guangxi Zhuang Autonomous Region (KY-IIT-2025-46). Written informed consent was obtained from individual or guardian participants. Author Contribution Study concept and design (C.S., X.F.Z, Z.Y.L, F.W., L.L.), acquisition of data (C.S., X.F.Z), analysis of data (F.W.), drafting of the manuscript (C.S., X.F.Z, Z.Y.L, F.W.), administrative support (C.S., L.L.), and study supervision (L.L.). All authors had the opportunity to review and comment on the manuscript before publication and shared the final responsibility for the decision to submit it for publication. Acknowledgement We would like to thank the patients, investigators, and site staff for their participation in the study. We also appreciate the funding from the Guangxi Province Natural Science Foundation (grant number: 2023GXNSFBA026107) and People’s Hospital of GuangXi Autonomous Region of China (grant number: QN2021-43). Data Availability The datasets generated and analyzed during the present study are available from the corresponding author upon reasonable request (Ling Li; [email protected] ). References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Cancer J. Clin. 71 , 209–249 (2021). Koshy, A. Evolving Global Etiology of Hepatocellular Carcinoma (HCC): Insights and Trends for 2024. J. CLIN. EXP. HEPATOL. 15 , 102406 (2025). Sartorius, K. et al. The Regulatory Role of MicroRNA in Hepatitis-B Virus-Associated Hepatocellular Carcinoma (HBV-HCC) Pathogenesis. CELLS-BASEL 8 , 1504 (2019). Pansy, K. et al. Immune Regulatory Processes of the Tumor Microenvironment under Malignant Conditions. Int. J. Mol. 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1","display":"","copyAsset":false,"role":"figure","size":200012,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of MAR in HCC patients, hepatitis B patients (HBV) and Healthy Volunteers (HV).\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6705868/v1/463c932aa11f19554da3a8d7.jpg"},{"id":94398603,"identity":"da3d53d3-2897-4beb-9afa-d392fcde2a2f","added_by":"auto","created_at":"2025-10-27 13:57:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1182167,"visible":true,"origin":"","legend":"\u003cp\u003ecorrelation heatmap of Selected Variables.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6705868/v1/0ebaed10e1b11d8b8e968688.jpg"},{"id":97900114,"identity":"161cfc21-6b8c-473c-9a4b-7fa1a8ad5978","added_by":"auto","created_at":"2025-12-10 15:45:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2121116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6705868/v1/a45bbe3d-2d7b-4ff6-98aa-54f6c09e7d91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The ratio of monocyte to apolipoprotein A1 is an independent predictor of hepatocellular carcinoma: a retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer is one of the most common malignant tumors of the digestive tract worldwide, with a 5-year survival of 18%\u003csup\u003e1\u003c/sup\u003e. Hepatocellular Carcinoma (HCC) accounts for 90% of the cases\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. According to the latest statistics, approximately 50\u0026ndash;80% of HCC cases worldwide are caused by chronic hepatitis B virus (HBV) infection\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. HBV infection can promote hepatocellular carcinogenesis through direct or indirect mechanisms. The liver microenvironment changes through HBV infection-induced chronic inflammation, the interaction between the virus and innate and adaptive immune cells, which helps the virus evade immune surveillance and promote disease evolution from inflammation to tumor formation. The early stage of HCC is insidious, and most patients are already in the late stage when symptoms appear. Therefore, early screening and diagnosis of HCC are of great clinical significance for the development of suitable treatment programs, and determine more reliable hematological predictors that are closely related to the characteristics of HCC.\u003c/p\u003e\u003cp\u003eAbout 15% of the global cancer burden is attributable to infectious agents, and inflammation is a major component of these chronic infections. The relationship between cancer-related chronic inflammation, dyslipidemia, and malignant tumors has attracted much attention nowadays\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Additionally, chronic inflammation is closely related to the occurrence and development of tumors\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Inflammation provides a good environment for oncogenesis. Tumour-associated macrophages (TAM) are a major component of the infiltrate of most, if not all, tumours\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. TAM are a subpopulation of monocytes that originates in the circulating blood and is activated around the tumor due to the release of tumor chemokines\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In recent years, inflammatory markers such as monocyte count have been found to be associated with HCC risk. Monocytes (M) are the innate immune cells of the mononuclear phagocyte system in chronic inflammation, and have become an important regulator of cancer development and progression\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe presence of lipids in the human body mainly comes from the diet and the synthesis of liver cells. The change of the local microenvironment of tumor may cause lipid metabolism disorder\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. ApoA1 is the main apolipoprotein in HDL and the largest component of ApoA family, which plays a key role in blood lipids\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. It has been shown that dyslipidemia can increase the risk of tumor and is closely related to the prognosis and development of diseases, such as prostate cancer, colorectal cancer and HCC\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. MAR, obtained from the ratio of M to ApoA1, is a new diagnostic indicator of some chronic diseases in recent years, such as type-2 diabetes and breast cancer\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, but there have been no reports on HCC.\u003c/p\u003e\u003cp\u003eAs the liver is the main organ for metabolism, we are curious whether MAR indicators are meaningful in the diagnosis of HCC. Therefore, the purpose of this study is to explore the clinical diagnostic value of MAR in HCC. We retrospectively collected the hematological test results of 270 HCC patients, 540 hepatitis B patients and 540 healthy controls, using statistical analysis software to analyze the clinical diagnostic value of MAR in HCC.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCC and HBV patients hospitalized in the Department of Gastroenterology of the People\u0026apos;s Hospital of Guangxi Zhuang Autonomous Region from January 1, 2020 to November 30, 2024 were selected as the research objects. Baseline data, laboratory test indicators, and clinical history were retrospectively collected. The inclusion criteria were as follows: 1) Complete clinical data; 2) HCC patients who met the diagnostic criteria of the 2018 Chinese Society of Clinical Oncology (CSCO) Guidelines for the Diagnosis and Treatment of HCC; 3) No anticancer treatment; 4) Patients with hepatitis B diagnosed in accordance with the 2019 Guidelines for the Prevention and Treatment of Chronic Hepatitis B. Exclusion criteria: 1) Patients with liver metastases or secondary tumors; 2) Simultaneously suffering from other tumors; 3) Patients with a history of major organ transplantation or other serious illnesses; 4) Merge hepatitis A, C, E virus or other viral infections such as HIV; 5) Those with unclear diagnosis. Healthy individuals without liver cancer and hepatitis B who were examined at the physical examination center of our hospital at the same time were selected as the control group. This study was approved by the Ethics Committee of our hospital and met the ethical requirements specified by the Ethics Committee (KY-IIT-2025-46), and has obtained informed consent from the subjects (or guardians).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory indicators\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFasting venous blood was collected from patients in the morning and placed in EDTA-K2 anticoagulant tubes (2 ml) and procoagulant tubes (5 ml). Laboratory parameters such as lymphocytes (L), platelets (PLT), monocytes (M), and ApoA1 were tested by Sysmex (XN9000 automatic blood analyzer). Serum carcinoembryonic antigen (CEA) levels were measured by Roche cobas 801 automatic chemiluminescence immunoassay analyzer. Apolipoprotein B (ApoB), albumin (ALB) and alkaline phosphatase (ALP) were determined by Beckman coulter AU5800 automatic biochemical analyzer. Alpha-fetoprotein (AFP) levels were measured by Beckman coulter UniCel Dxl 800 Access immune analyzer. These instruments were used after daily quality control inspections in strict accordance with the operation. PLR is the ratio of platelet to lymphocyte; LAR is the ratio of lymphocyte to ApoA1; LMR is the ratio of lymphocyte to monocyte; PAR is the ratio of platelet to ApoA1; BAR is the ratio of ApoB to ApoA1; MAR is the ratio of monocyte to ApoA1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, the HCC group was matched with hepatitis B patients and healthy individuals by 1:2 propensity score matching (PSM) method using R software. There were no significant differences in age between groups. SPSS 26.0 software (IBM corp, Armonk, NY) was used for data analysis. Measurement data with normal distribution were represented as mean \u0026plusmn; SD, non-normal distribution were expressed as median (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e), and the Kruskal-Wallis H-test was used for comparison between groups. The truncation value, area under the curve (AUC) and Youden index of each significant variable were calculated using the receiver operating characteristic curve (ROC). Univariate and multivariate logistic regression analyses were used to analyze risk factors for HCC. A correlation heatmap was used to analyze the correlation between each index and MAR, and the correlation coefficient r value was calculated. A two-tailed \u003cem\u003eP\u003c/em\u003e value of no more than 0.05 was considered statistically significant. Furthermore, ROCs were generated using GraphPad Prism 8 software (GraphPad Software, San Diego, CA, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eInformation about the subjects was retrospectively collected using a medical electronic recording system. A total of 270 HCC patients, 570 hepatitis B patients and 570 healthy controls were enrolled in this study. Their ages were mainly concentrated around 57 years old.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifference Analysis of Three Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison of hematological indicators among the HCC, hepatitis B, and healthy volunteer groups showed statistically significant differences in PLT (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), L (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), M (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), CEA (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), ALB (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), ApoA1 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), ApoB (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), ALP (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), PLR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), PAR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), BAR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), LAR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), LMR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and MAR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Table 1). Subsequently, the comparison between the two groups showed that PLT, L, M, ALB, ApoA1, ApoB, PLR, PAR, BAR, LAR and LMR were notably different between healthy volunteers and patients with hepatitis B (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). There were significant differences in PLT, L, M, CEA, ALB, ApoA1, ApoB, ALP, BAR, LAR, LMR and MAR levels between healthy volunteers and HCC groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). Moreover, there were significant difference in L, M, CEA, ALB, ApoA1, ALP, PLR, PAR, BAR, LMR and MAR between the hepatitis B group and HCC groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). The MAR level was highest in the HCC group (0.92), followed by hepatitis B group (0.38) and the healthy volunteer group (0.36), as shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Comparison of Hematological Parameters Among Patients with Hepatocellular Carcinoma, Those with hepatitis B patients and Healthy Volunteers.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"685\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003eHealthy Volunteers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003ehepatitis B patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003eHepatocellular Carcinoma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e57(50, 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e58(50, 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e57(50, 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e263(224, 305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e172(101, 224)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e151(98, 226)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e406.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eL (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e2.20(1.76,2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e1.52(1.05, 1.94)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e1.06(0.76, 1.51)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e413.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eM (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e0.50(0.40, 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e0.42(0.32, 0.57)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e0.63(0.43, 0.95)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e121.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eCEA (\u003cem\u003e\u0026mu;\u003c/em\u003eg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e1.88(1.41, 2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e2.04(1.20, 3.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e2.36(1.52, 3.99)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e22.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eAFP (\u003cem\u003e\u0026mu;\u003c/em\u003eg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e2.96(2.28, 3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e2.91(1.81, 6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e7.25(0.96, 95.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e3.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eALB(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e45.00(43.10, 47.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e37.10(30.60, 40.30)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e31.00(27.00, 35.45)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e772.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eApoA1 (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e1.31(1.20, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e1.10(0.88, 1.32)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e0.75(0.53, 1.03)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e420.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eApoB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e0.98(0.85, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e0.85(0.67, 1.05)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e0.83(0.63, 1.05)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e69.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e75(63, 89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e75(60, 104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e126(96, 213)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e246.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e121.05(97.82, 151.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e108.37(76.06, 142.97)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e137.98(89.66, 216.80)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e48.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003ePAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e196.97(166.60, 240.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e152.18(103.88, 207.09)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e205.54(113.11, 356.39)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e99.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eBAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e0.74(0.61, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e0.79(0.61, 1.00)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e1.07(0.77, 1.62)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e137.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eLAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e1.65(1.30, 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e1.38(0.97, 1.88)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e1.52(0.93, 2.19)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e35.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e4.40(3.48, 5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e3.37(2.26, 4.86)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e1.61(1.06, 2.58)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e386.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4503%;\"\u003e\n \u003cp\u003eMAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5146%;\"\u003e\n \u003cp\u003e0.36(0.29, 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2456%;\"\u003e\n \u003cp\u003e0.38(0.26, 0.60)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e0.92(0.47, 1.60)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0877%;\"\u003e\n \u003cp\u003e232.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.89474%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: \u003csup\u003ea\u0026nbsp;\u003c/sup\u003eHealthy Volunteers \u003csub\u003eVS\u003c/sub\u003e hepatitis B patients, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; \u003csup\u003eb\u0026nbsp;\u003c/sup\u003eHealthy Volunteers \u003csub\u003eVS\u003c/sub\u003e Hepatocellular Carcinoma, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; \u003csup\u003ec\u0026nbsp;\u003c/sup\u003ehepatitis B patients \u003csub\u003eVS\u003c/sub\u003e Hepatocellular Carcinoma, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05. \u003cem\u003eP\u003c/em\u003e value is analyzed by Kruskal-Wallis H-test.\u003c/p\u003e\n\u003cp\u003eAbbreviations: AFP, Alpha-fetoprotein; ALB, albumin; ApoA1, apolipoprotein A1; ApoB, Apolipoprotein B; ALP, alkaline phosphatase; BAR, ApoB to ApoA1; CEA, carcinoembryonic antigen; L, lymphocytes; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; M, Monocytes; MAR, monocyte to ApoA1; PLT, platelet; PLR, platelet to lymphocyte; PAR, platelet to ApoA1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of Cut-off Value of MAR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROCs were drawn, and the cut-off values and AUC for NLR, PLR, NAR, PAR, LAR, LMR, and MAR were calculated (Table 2). The MAR cut-off value for distinguishing HCC from hepatitis B was 0.53, and the AUC was 0.762, while the cut-off value in the HCC and healthy volunteer groups was 0.62, and the AUC was 0.829 (Figure 1).\u003c/p\u003e\n\u003cp\u003eTable 2. Identification of Optimal Cut-off Values for Different Predictive Factors Based on the ROC Curve.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 30.0971%;\"\u003e\n \u003cp\u003eHCC \u003csub\u003eVS\u003c/sub\u003e HV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 30.0971%;\"\u003e\n \u003cp\u003eHBV \u003csub\u003eVS\u003c/sub\u003e HV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 30.0971%;\"\u003e\n \u003cp\u003eHCC \u003csub\u003eVS\u003c/sub\u003e HBV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eCut off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003eYouden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eCut off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003eYouden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eCut off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.54693%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8414%;\"\u003e\n \u003cp\u003eYouden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e174.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e75.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e129.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.54693%;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8414%;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003ePAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e327.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e150.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e229.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.54693%;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8414%;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eBAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.54693%;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8414%;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eLAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.54693%;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8414%;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.54693%;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8414%;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003eMAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.38511%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0032%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70874%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.54693%;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8414%;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: The truncation value, AUC and Youden index of each valuable variable were calculated using the receiver operating characteristic curve.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BAR, ApoB to ApoA1; HCC, Hepatocellular Carcinoma; HV, Healthy Volunteers; HBV, hepatitis B patients; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; MAR, monocyte to ApoA1; PLR, platelet to lymphocyte; PAR, platelet to ApoA1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic Regression Analysis to Determine Predictive Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3, 4, univariate and multivariate logistic regression analyses showed that MAR was an indicator to distinguish HCC from hepatitis B disease and healthy people, as well as an independent risk factor for HCC. The increase in MAR level demonstrated that the risk of HCC was 6.001 times higher than that of the healthy volunteers (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTable 3. The Predictive Factors Identified by Univariate Logistic Regression.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"651\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eHCC \u003csub\u003eVS\u003c/sub\u003e HV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003eHBV \u003csub\u003eVS\u003c/sub\u003e HV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eHCC \u003csub\u003eVS\u003c/sub\u003e HBV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.986(0.984, 0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.986(0.984, 0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.000(0.999, 1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.217(0.170, 0.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.001(0.997, 1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.417(0.324, 0.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e12.834(7.237, 22.760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.363(0.201, 0.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e18.539(10.203, 33.686)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.108(1.042, 1.178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.107(1.040, 1.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.001(0.998, 1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.579(0.547, 0.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.642(0.609, 0.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.901(0.880, 0.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eApoA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.004(0.002, 0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.052(0.032, 0.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.103(0.065, 0.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eApoB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.371(0.221, 0.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.302(0.197, 0.462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.147(0.745, 1.767)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.025(1.021, 1.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.010(1.006, 1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.015(1.012, 1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.004(1.003, 1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.999(0.997, 1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.004(1.003, 1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.002(1.001, 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.998(0.996, 0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.003(1.002, 1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eBAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5.539(3.974, 8.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e4.092(2.829, 5.920)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.346(1.171, 1.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eLAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.051(0.977, 1.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.052(0.979, 1.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.999(0.993, 1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.386(0.340, 0.440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.001(0.999, 1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.499(0.440,0.567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eMAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e13.229(8.216, 21.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e6.573(4.146, 10.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.981(1.630, 2.409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ALB, albumin; ApoA1, apolipoprotein A1; ApoB, Apolipoprotein B; ALP, alkaline phosphatase; BAR, ApoB to ApoA1; CEA, carcinoembryonic antigen; HCC, Hepatocellular Carcinoma; HV, Healthy Volunteers; HBV, hepatitis B patients; L, lymphocytes; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; M, Monocytes; MAR, monocyte to ApoA1; PLT, platelet; PLR, platelet to lymphocyte; PAR, platelet to ApoA1.\u003c/p\u003e\n\u003cp\u003eTable 4. The Predictive Factors Identified by Multivariate Logistic Regression.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eHCC \u003csub\u003eVS\u003c/sub\u003e HV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eHBV \u003csub\u003eVS\u003c/sub\u003e HV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHCC \u003csub\u003eVS\u003c/sub\u003e HBV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.995(0.994, 0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.991(0.989, 0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.001(1.000, 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eBAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e6.411(3.844, 10.692)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e10.875(6.576, 17.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.799(0.669, 0.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.466(0.398, 0.545)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.000(0.998, 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.615(0.529, 0.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e6.001(2.432, 14.811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e2.492(1.297, 4.788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3.941(1.869, 8.307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BAR, ApoB to ApoA1; HCC, Hepatocellular Carcinoma; HV, Healthy Volunteers; HBV, hepatitis B patients; LMR, lymphocyte to monocyte; MAR, monocyte to ApoA1; PAR, platelet to ApoA1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship Between MAR and Other Laboratory Indicators of HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, MAR was correlated with L (r = -0.075, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), CEA (r = 0.171, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), ALB (r = -0.445, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), ALP (r = 0.340, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), PAR (r = 0.463, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), BAR (r = 0.554, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), LAR (r = 0.485, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), LMR (r = -0.653, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the venous blood indicators of HCC, hepatitis B and healthy volunteers were analyzed, and it was found that the level of MAR was the highest in the HCC group, followed by the hepatitis B group, and the lowest among the healthy volunteers, suggesting that MAR can be used for the auxiliary differential diagnosis of hepatitis B and HCC. It is speculated that the difference of MAR ratio in HCC and hepatitis B diseases is mediated by abnormal blood lipid and chronic inflammation in patients.\u003c/p\u003e\n\u003cp\u003eApoA1 as an indispensable component of HDL, inhibits monocyte chemotaxis and recruitment and was shown to be involved in inflammatory reactions, and ApoA1/HDL can be activated and participate in antitumor activities in mature immune systems\u003csup\u003e17, 18\u003c/sup\u003e. It has been reported that ApoA1 could potently suppress tumor growth and metastasis in vivo and improve survival in mouse tumor models\u003csup\u003e19, 20\u003c/sup\u003e. Mononuclear cells are the main inflammatory cells in tumor stroma. It has been reported that the increase in activated monocytes (human leukocyte antigen HLA-DR\u003csup\u003ehigh\u003c/sup\u003eCD68\u003csup\u003e+\u003c/sup\u003e cells) in the liver is related to disease progression\u003csup\u003e21\u003c/sup\u003e. Chemokine (C-C motif) ligand 15 (CCL15) recruits CCR1\u003csup\u003e+\u003c/sup\u003eCD14\u003csup\u003e+\u003c/sup\u003e monocytes to the edge of HCC tissue. High expression of CCL15 is associated with poor clinical prognosis. CCR1\u003csup\u003e+\u003c/sup\u003eCD14\u003csup\u003e+\u003c/sup\u003e monocytes suppress antitumor immunity, facilitate tumor metastasis and promote tumor cell proliferation and invasion\u003csup\u003e22\u003c/sup\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis reveals correlations between MAR and L, CEA, ALB, ALP, PAR, BAR, LAR and LMR. Lymphocytes play a pivotal role in immunosurveillance and immune editing, and lymphocyte infiltration in the tumor microenvironment (TME) contributes to the immunologic anticancer reaction\u003csup\u003e23, 24\u003c/sup\u003e. The presence of tumor-infiltrating lymphocytes is associated with improved survival in various cancers. Conversely, low lymphocyte counts and failure to infiltrate the tumor lead to inferior survival\u003csup\u003e25, 26\u003c/sup\u003e. The LMR is the ratio of lymphocytes to monocytes. Various investigations have shown that decreased pretreatment LMR has an unfavorable impact on OS in cancer patients among various tumor subgroups\u003csup\u003e27\u003c/sup\u003e, including HCC\u003csup\u003e28, 29\u003c/sup\u003e. PAR is the ratio of platelet to ApoA1. Platelets facilitate tumor progression by supporting cancer stem cells, inducing angiogenesis, sustaining cell proliferation, and evading immune surveillance\u003csup\u003e30\u003c/sup\u003e. In addition platelets may represent a potential therapeutic target, and indeed, sorafenib is a standard of care in HCC treatment directly targeting pathways mediated by VEGF and platelet[1]derived growth factor (PDGF)\u003csup\u003e31\u003c/sup\u003e. Moreover, previous investigations highlighted the inhibiting effect on tumor growth exerted by antiplatelet therapies (aspirin, warfarin, and cyclo-oxygenase inhibitors), even in HCC\u003csup\u003e32, 33\u003c/sup\u003e. Overall, these indicators are all related to inflammation, and inflammation-based scores may be convenient, easily obtained, low-cost, and reliable biomarkers with diagnostic and prognostic significance for HCC. Using univariate logistic regression analysis, it was found that MAR is an independent risk factor of HCC, and the increased MAR level indicated that the risk of HCC was 13.229 times higher than that of healthy volunteers. Multivariate logistic regression analysis revealed that the risk of HCC was 6.001 times higher than that of healthy volunteers. This ratio is higher than that reported for breast cancer (3.733 times)\u003csup\u003e16\u003c/sup\u003e and Type 2 diabetes mellitus (2.26 times)\u003csup\u003e15\u003c/sup\u003e, suggesting that MAR indicator may have good clinical application prospects.\u003c/p\u003e\n\u003cp\u003eWang et al\u003csup\u003e15\u003c/sup\u003e believed that MAR was a risk factor for type-2 diabetes metabolic syndrome. The MAR level of diabetes patients with metabolic syndrome was higher than that of patients without metabolic syndrome. Moreover,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLin et al\u003csup\u003e16\u003c/sup\u003e found that MAR is a new indicator for the differential diagnosis of benign and malignant breast diseases, and is an independent risk factor for breast cancer. High MAR is closely related to late stages of breast cancer, deeper tumor invasion, and distant metastasis. The results of this study showed that high MAR was an independent risk factor for HCC, with a cutoff value of 0.62, high MAR was a risk factor for HCC. In addition, MAR level was the highest in the HCC (0.92), followed by hepatitis B disease (0.38), and the lowest in healthy volunteer (0.36), which may serve as an indicator for predicting the progression from hepatitis B to HCC.\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. This was a retrospective study without follow-up, and it could not directly reflect the association between MAR and HCC. The significance of MAR in the survival prognosis of patients with HCC requires confirmation in a large-scale, prospective cohort study. Moreover, the subjects included in this study were local residents, and the optimal MAR cut-off values may \u0026nbsp;not be applicable to other races.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results of this study showed that MAR is a new indicator for the differential diagnosis of HCC and hepatitis B disease, and is an independent risk factor for HCC. To our knowledge, this is the first study to investigate the clinical value of MAR in HCC in China. It is hoped that more research in the future will explore the exact pathogenic mechanism of HCC, provide a reference for the diagnosis and treatment of HCC, and contribute to the improved quality of life of patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eApoA1, apolipoprotein A1; ApoB, apolipoprotein B; ALB, albumin; ALP, alkaline phosphatase; AFP, alpha-fetoprotein; AUC, area under the curve; BAR, ApoB to ApoA1; CEA, carcinoembryonic antigen; HCC, hepatocellular carcinoma; HBV, hepatitis B virus; L, lymphocytes; LAR, lymphocyte to ApoA1; LMR, lymphocyte to monocyte; M, monocytes; MAR, monocyte to ApoA1; PLT, platelet; PLR, platelet to lymphocyte; PAR, platelet to ApoA1; ROC, receiver operating characteristic curve; TAM, tumour-associated macrophages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003e The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of the People\u0026rsquo;s Hospital of Guangxi Zhuang Autonomous Region (KY-IIT-2025-46). Written informed consent was obtained from individual or guardian participants.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy concept and design (C.S., X.F.Z, Z.Y.L, F.W., L.L.), acquisition of data (C.S., X.F.Z), analysis of data (F.W.), drafting of the manuscript (C.S., X.F.Z, Z.Y.L, F.W.), administrative support (C.S., L.L.), and study supervision (L.L.). All authors had the opportunity to review and comment on the manuscript before publication and shared the final responsibility for the decision to submit it for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the patients, investigators, and site staff for their participation in the study. We also appreciate the funding from the Guangxi Province Natural Science Foundation (grant number: 2023GXNSFBA026107) and People\u0026rsquo;s Hospital of GuangXi Autonomous Region of China (grant number: QN2021-43).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the present study are available from the corresponding author upon reasonable request (Ling Li; [email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCancer J. Clin.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 209\u0026ndash;249 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoshy, A. Evolving Global Etiology of Hepatocellular Carcinoma (HCC): Insights and Trends for 2024. \u003cem\u003eJ. CLIN. EXP. 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CANCER\u003c/em\u003e. \u003cb\u003e122\u003c/b\u003e, 1765\u0026ndash;1773 (2008).\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":"hepatocellular carcinoma, hepatitis B, the ratio of monocyte to apolipoprotein A1, monocyte, apolipoprotein A1","lastPublishedDoi":"10.21203/rs.3.rs-6705868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6705868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ratio of monocyte to apolipoprotein A1 (MAR) is a new diagnostic indicator of some chronic diseases, but there have been no reports on hepatocellular carcinoma (HCC). This study aimed to explore the predictive value of MAR in HCC. A total of 270 HCC patients, 540 hepatitis B patients and 540 healthy volunteers were included in this study. Laboratory data were retrospectively collected from electronic medical records. We found that the MAR level was the highest in HCC, followed by hepatitis B disease, and the lowest in healthy volunteers. It was found to be an independent risk factor for HCC, which is also an indicator for distinguishing HCC from hepatitis B disease. The MAR cut-off value for distinguishing HCC from hepatitis B was 0.53, while the cut-off value in the HCC and healthy volunteer groups was 0.62. An increase in MAR levels showed that the risk of HCC was 6.001 times higher than that in healthy volunteers. MAR was correlated with indicators such as lymphocytes, carcinoembryonic antigen, and albumin. The results of this study showed that MAR is a new indicator for the differential diagnosis of HCC and hepatitis B, and is an independent risk factor for HCC.\u003c/p\u003e","manuscriptTitle":"The ratio of monocyte to apolipoprotein A1 is an independent predictor of hepatocellular carcinoma: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 00:59:05","doi":"10.21203/rs.3.rs-6705868/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":"734f623c-ccf0-4150-9c4f-c4812f07717a","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56779907,"name":"Biological sciences/Cancer"},{"id":56779908,"name":"Biological sciences/Cancer/Gastrointestinal cancer"},{"id":56779909,"name":"Health sciences/Diseases/Cancer"}],"tags":[],"updatedAt":"2025-12-10T04:54:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 00:59:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6705868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6705868","identity":"rs-6705868","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}