Application of ceramide as marker in predicting risk of capillary invasion of hepatocellular carcinoma

Abstract

The invention belongs to the technical field of biomedicine, and particularly relates to application of ceramide as a marker in prediction of hepatocellular carcinoma microvascular invasion risk. The invention finds that the preoperative plasma ceramide CER (d18: 1 / 16: 0) level can be used for predicting the MVI of HBV-HCC patients and evaluating the postoperative prognosis of the patients for the first time. The invention provides a preoperative non-invasive detection method based on plasma ceramide CER (d18: 1 / 16: 0), and trauma of traditional histopathology detection is avoided. According to the method, the occurrence risk of the MVI can be effectively reduced by simply detecting a blood sample of a patient. According to the method, the MVI risk of a patient is evaluated through a multi-factor prediction model, and a basis is provided for personalized treatment. The method is not only suitable for preoperative MVI risk assessment, but also can assist in making postoperative follow-up visit and treatment plans, and helps to reduce the postoperative recurrence rate and improve the survival rate of patients.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology, and specifically relates to the application of ceramide as a marker in predicting the risk of microvascular invasion of hepatocellular carcinoma. Background Art

[0002] Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, and the patient's condition is often complicated, especially in the context of hepatitis B virus (HBV) infection. The postoperative recurrence rate of patients with HCC is high, among which microvascular invasion (MVI) is an important pathological factor leading to postoperative recurrence and poor prognosis. The detection of MVI usually relies on histopathological analysis of postoperative resection specimens, which is currently the only reliable diagnostic method. However, this method is invasive and can only be performed after surgery, and cannot provide relevant information before surgery. This limitation poses many challenges to patients with liver cancer in preoperative risk assessment, treatment decisions, and postoperative follow-up.

[0003] Although existing imaging examinations (such as CT and MRI) can provide basic information about tumors, they have low sensitivity and specificity in predicting MVI. At the same time, conventional serum markers (such as alpha-fetoprotein AFP) are also unsatisfactory in detecting microvascular invasion. Due to the lack of effective preoperative non-invasive detection methods, doctors are unable to accurately identify high-risk patients during preoperative evaluation, making it difficult to optimize surgical and treatment plans.

[0004] Therefore, it is necessary to find a preoperative non-invasive marker that can accurately predict microvascular invasion. Summary of the invention

[0005] The purpose of the first aspect of the present invention is to provide the use of ceramide CER (d18:1 / 16:0) as a marker in predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0006] The second aspect of the present invention aims to provide a reagent for detecting ceramide CER (d18:1 / 16:0) for use in preparing a product for predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0007] The third aspect of the present invention aims to provide an application of a combination of independent predictive factors in predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0008] The fourth aspect of the present invention aims to provide a method for constructing a model for predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0009] The fifth aspect of the present invention aims to provide a model operation module for predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0010] The sixth aspect of the present invention aims to provide a system for predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0011] The seventh aspect of the present invention aims to provide an electronic device.

[0012] The eighth aspect of the present invention aims to provide a storage medium.

[0013] In order to achieve the above-mentioned purpose of the present invention, the technical solution adopted by the present invention is:

[0014] In a first aspect of the present invention, a method for using ceramide CER (d18:1 / 16:0) as a marker for predicting the risk of microvascular invasion of hepatocellular carcinoma is provided; the structural formula of the ceramide CER (d18:1 / 16:0) is as follows: 34 H 67 NO3, CAS number: 24696-26-2.

[0015] In some embodiments of the present invention, the ceramide CER (d18:1 / 16:0) refers to the plasma level of a patient before undergoing hepatocellular carcinoma surgery.

[0016] In some embodiments of the present invention, the preoperative period refers to one week before the patient receives hepatocellular carcinoma surgery.

[0017] In some embodiments of the present invention, the hepatocellular carcinoma includes hepatitis B virus-related hepatocellular carcinoma.

[0018] The second aspect of the present invention provides the use of a reagent for detecting ceramide CER (d18:1 / 16:0) in the preparation of a product for predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0019] Preferably, the reagent comprises a reagent for detecting the content or abundance of ceramide CER (d18:1 / 16:0).

[0020] Preferably, the test sample of the product is selected from the blood of the subject to be tested; more preferably, it is the blood before undergoing hepatocellular carcinoma surgery.

[0021] Preferably, the blood includes at least one of serum, plasma and whole blood.

[0022] The third aspect of the present invention provides an application of a combination of independent predictive factors in predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0023] The combination of independent predictive factors includes ceramide CER (d18:1 / 16:0) and clinical predictive factors;

[0024] The clinical predictive factors include at least one of age, maximum tumor diameter, liver function, and tumor number;

[0025] Preferably, the liver function index comprises ICG-R15 level.

[0026] Preferably, the hepatocellular carcinoma comprises hepatitis B virus-related hepatocellular carcinoma.

[0027] Preferably, the inclusion criteria for the number of tumors as an independent predictive factor combination is: the number of tumors is greater than or equal to 3.

[0028] A fourth aspect of the present invention provides a method for constructing a model for predicting the risk of microvascular invasion of hepatocellular carcinoma, comprising the following steps:

[0029] The model is constructed using the combination of independent predictive factors described in the third aspect of the present invention.

[0030] Preferably, the algorithm for constructing the model includes at least one of receiver operating characteristic curve analysis, correlation analysis, survival analysis, regression analysis method, stratified analysis, continuous variable analysis, categorical variable analysis, and propensity score matching.

[0031] Specifically, the following steps are included:

[0032] 1. Patient Screening

[0033] Target population: Clinicians first identify patients who meet the diagnosis of hepatitis B virus-related hepatocellular carcinoma (HBV-HCC). These patients usually have confirmed the diagnosis of liver cancer through imaging (such as CT, MRI) and serology (such as AFP, PIVKA-II) and are planned to undergo surgical resection.

[0034] Screening criteria: Based on the patient's liver function status, tumor location, size and other factors, further confirm whether the patient is suitable for surgery, and exclude patients who cannot tolerate surgery or do not meet the surgical conditions.

[0035] 2. Plasma Sample Collection

[0036] Collection time: Doctors should collect plasma samples from patients within 7 days before surgery to ensure that the samples can reflect the preoperative physiological state and avoid preoperative treatment (such as chemotherapy or radiotherapy) interfering with the detection of ceramide levels.

[0037] 3. Laboratory testing

[0038] Sample processing: In the laboratory, take out the frozen plasma samples and use an optimized organic solvent mixture (ethyl acetate / isopropanol / water 60 / 30 / 10, v / v / v) to extract ceramide. The extraction process should be strictly controlled to ensure the stable concentration of ceramide in the extract.

[0039] Internal standard calibration: During the sample extraction process, an internal standard CER (d18:1 / 17:1) is added for calibration. This internal standard is used to correct the measurement differences between different samples to ensure the accuracy and repeatability of the data.

[0040] Detection equipment: Use a liquid chromatography-tandem mass spectrometry (LC-MS / MS) system, especially ThermoFisher's Prelude SPLC+TSQ Quantiva LC-MS / MS equipment is recommended for detection. This equipment can detect the content of CER (d18:1 / 16:0) and other ceramide subtypes with high precision, and the analysis results are accurate to the nanogram (ng / mL) level.

[0041] To ensure the stability of the results, the laboratory should set up quality control samples (such as standard plasma samples) and use the standard curve to quantify the ceramide concentration in each sample.

[0042] 4. Data input and processing:

[0043] After the data were sorted and cleaned, they were grouped according to whether there was microvascular invasion (MVI).

[0044] 1) Propensity score matching (PSM): To control the differences in patients' baseline characteristics (such as age, gender, tumor size, etc.), a caliper value of 0.05 was used for propensity score matching to ensure that the MVI group and the non-MVI group had balanced baseline characteristics. This step effectively reduced the impact of confounding variables and improved the reliability of the analysis.

[0045] 2) CER level stratification: When constructing the MVI risk prediction model, the optimal cutoff value of preoperative plasma CER (d18:1 / 16:0) level was determined by the maximum Youden Index, and patients were divided into high-level group and low-level group to optimize the sensitivity and specificity of the prediction model, thereby ensuring the significance of CER (d18:1 / 16:0) in MVI risk prediction.

[0046] 3) Recurrence-free survival (RFS) analysis: In the prognostic analysis, the Kaplan-Meier survival curve was used to visualize the patients' RFS in groups, and the Log-rank test was used to compare the differences in RFS between different groups, thereby evaluating the predictive value of preoperative plasma CER (d18:1 / 16:0) levels and the prediction model score based on preoperative plasma CER (d18:1 / 16:0) for RFS.

[0047] 5. Model Construction

[0048] Univariate logistic regression analysis was performed on all candidate variables (such as age, sex, tumor size, differentiation grade, CER level, etc.) to calculate the odds ratio (OR) of each variable and its significant association (P value) with MVI. Variables with P < 0.20 in the univariate logistic regression analysis were taken as potential predictors and entered the multivariate logistic regression analysis stage. In the multivariate logistic regression analysis, the candidate factors screened out by the univariate analysis were introduced by the stepwise forward method, and the significance of each variable and its contribution to the model fitting were gradually observed. The stepwise forward method helped to eliminate variables with less contribution to the prediction of MVI and only retained factors with independent predictive effects to optimize the model. The final independent predictors were determined by multivariate logistic regression analysis, including CER (d18:1 / 16:0) level, age, maximum tumor diameter, tumor number, and ICG R15 level. These variables were significant after adjusting other factors (P ≤ 0.05). The model displayed the weight of each variable in the form of a nomogram to facilitate clinical risk assessment.

[0049] A fifth aspect of the present invention provides a model operation module for predicting the risk of microvascular invasion of hepatocellular carcinoma;

[0050] The model operation module includes a computing component;

[0051] The calculation component calculates the data of the combination of independent predictive factors described in the third aspect of the present invention.

[0052] A sixth aspect of the present invention provides a system for predicting the risk of microvascular invasion of hepatocellular carcinoma:

[0053] The system includes at least one of 1) to 3):

[0054] 1) Data input module;

[0055] 2) Model operation module;

[0056] 3) Result output module;

[0057] The model operation module includes the model operation module for predicting the risk of microvascular invasion of hepatocellular carcinoma described in the fifth aspect of the present invention.

[0058] Preferably, the data input module is used to input data of the marking composition described in the third aspect of the present invention.

[0059] Preferably, the result output module is used to output the result of predicting the risk of microvascular invasion of hepatocellular carcinoma.

[0060] According to a seventh aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program stored in the memory and executable on the processor comprises the system according to the sixth aspect of the present invention.

[0061] An eighth aspect of the present invention provides a storage medium storing processor-executable instructions, wherein the processor-executable instructions, when executed by the processor, are used to execute the construction method as described in the fourth aspect of the present invention.

[0062] The beneficial effects of the present invention are:

[0063] 1. Non-invasive testing

[0064] The present invention provides the use of plasma ceramide CER (d18:1 / 16:0) as a marker for predicting the risk of microvascular invasion of hepatocellular carcinoma, avoiding the trauma of traditional histopathological testing. The risk of MVI can be effectively predicted by simply testing the patient's blood sample. This non-invasive test greatly reduces physical damage to the patient and improves the acceptability of the test and the convenience of clinical operation.

[0065] 2. High prediction accuracy

[0066] The inventors provide a model operation module for predicting the risk of microvascular invasion of hepatocellular carcinoma. By combining liquid chromatography-tandem mass spectrometry (LC-MS / MS) technology with a regression model, ceramide levels can be accurately quantified and the risk of recurrence of patients can be predicted with high accuracy. The prediction model based on plasma ceramide CER (d18:1 / 16:0) showed good discrimination ability in multicenter validation, and internal validation showed that its AUROC (area under the receiver operating characteristic curve) reached 0.806, showing a high degree of prediction accuracy.

[0067] 3. Improve personalized treatment

[0068] The present invention helps clinicians develop personalized treatment plans based on the patient's plasma ceramide CER (d18:1 / 16:0) level. For patients at high risk of MVI, doctors can take more active treatment measures such as expanding the scope of resection or preoperative neoadjuvant therapy based on the test results, thereby effectively reducing the risk of postoperative recurrence and improving patient survival. Compared with the traditional method that relies on postoperative pathological testing, the present invention can provide prognostic information in advance and optimize preoperative decision-making.

[0069] 4. Convenient and easy to operate:

[0070] The detection process is simple and easy, and the ceramide extraction and detection process has been standardized, which is suitable for large-scale clinical applications. Operators only need to have basic liquid chromatography and mass spectrometry techniques to efficiently complete the detection. Compared with traditional detection methods, this invention not only reduces trauma to patients, but also significantly shortens the detection time, and is suitable for daily clinical practice in various medical institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein:

[0072] Figure 1 The experimental flow chart of the present invention.

[0073] Figure 2 This is the nomogram of the MVI risk prediction model.

[0074] Figure 3 This is the ROC curve result of the MVI risk prediction model in Example 5 in the training set.

[0075] Figure 4 This is the ROC curve result of the MVI risk prediction model in Example 5 in the internal validation cohort.

[0076] Figure 5 This is the ROC curve result of the MVI risk prediction model in Example 5 in the external validation cohort.

[0077] Figure 6 This is the 5-year recurrence survival (RFS) analysis result of the prediction model.

[0078] Figure 7 This is the result of 5-year recurrence survival (RFS) analysis based on preoperative plasma CER (d18:1 / 16:0) levels. DETAILED DESCRIPTION

[0079] The following will be combined with the embodiments to clearly and completely describe the concept of the present invention and the technical effects produced, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0080] In the applicant's previous study, it was observed that the concentration of plasma ceramide before surgery was significantly correlated with the risk of microvascular invasion and prognosis of patients with hepatocellular carcinoma. Therefore, the present invention further explores its effectiveness in predicting microvascular invasion through a model based on actual experimental data.

[0081] The experimental process of the present invention is as follows Figure 1 shown.

[0082] Example 1 Patient inclusion and descriptive analysis

[0083] 1. Inclusion criteria

[0084] Clinicians first identify patients who meet the diagnosis of hepatitis B virus-related hepatocellular carcinoma (HBV-HCC). These patients usually have a confirmed diagnosis of liver cancer through imaging (such as CT, MRI) and serology (such as AFP, PIVKA-II) and are planned to undergo surgical resection.

[0085] The present invention collected a total of 155 patients from Nanfang Hospital of Southern Medical University, 52 patients from Shenzhen Second People's Hospital of Southern University of Science and Technology, and 103 patients from Foshan First People's Hospital.

[0086] 2. Exclusion criteria

[0087] Based on the patient's liver function status, tumor location, size and other factors, further confirm whether the surgery is suitable, and exclude patients who cannot tolerate the surgery or do not meet the surgical conditions. Specifically, they include: no hepatitis B infection, combined hepatitis C infection, history of anti-tumor treatment before surgery, and serious data.

[0088] 3. Data Diversity

[0089] After excluding 155 patients from Nanfang Hospital of Southern Medical University, a total of 101 patients were included as the experimental cohort.

[0090] After excluding 52 patients from the Southern University of Science and Technology Shenzhen Second People's Hospital and 103 patients from the Foshan First People's Hospital, a total of 100 patients were included as the external validation cohort.

[0091] Example 2 Data Collection and Grouping

[0092] 1. Clinical data collection:

[0093] The clinical data of the patients included in Example 1 were collected, including:

[0094] Basic clinical data: age, gender, BMI, Child-Pugh grade, BCLC grade, hypertension, diabetes, hyperlipidemia, hyperbilirubinemia, cirrhosis, ascites, portal hypertension, obstructive jaundice, splenomegaly, varicose veins, HBV viral DNA quantification, ABLI score, and APRI score.

[0095] Tumor-related data: degree of differentiation, maximum diameter, and number of tumors.

[0096] Surgery-related information: anatomical liver resection, subtotal liver resection, portal occlusion.

[0097] Laboratory indicators: alpha-fetoprotein (AFP), ICG-R15, white blood cells (WBC), lymphocytes (LY), neutrophils (NEU), monocytes (MONO), hemoglobin (HGB), platelets (PLT), hematocrit (HCT), prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (PT-INR), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), and creatinine (Cr).

[0098] 2. Collection of plasma samples

[0099] Plasma was collected from the patients included in Example 1.

[0100] Collection time: Doctors should collect plasma samples from patients within 7 days before surgery to ensure that the samples can reflect the preoperative physiological state and avoid preoperative treatment (such as chemotherapy or radiotherapy) interfering with the detection of ceramide levels.

[0101] Collection process: About 10 ml of blood is collected by routine venipuncture and collected using a blood vessel containing an anticoagulant. The plasma sample must be centrifuged within 1 hour after collection (3000rpm, 10 minutes), and the supernatant (plasma part) is separated and quickly transferred to a cryotube.

[0102] Sample storage: To avoid degradation of ceramide, plasma samples should be frozen immediately and stored in a -80°C freezer until testing.

[0103] 3. Biomarker detection

[0104] Preoperative plasma samples were collected from each patient, and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) was used to measure the levels of ten ceramides (CER), including CER (d18:1 / 16:0), CER (d18:1 / 18:0), CER (d18:1 / 20:0), CER (d18:1 / 22:0), CER (d18:1 / 24:0), CER (d18:1 / 18:1), CER (d18:1 / 20:1), CER (d18:1 / 22:1), CER (d18:1 / 24:1) and CER (d18:1 / 26:1).

[0105] The testing process is as follows:

[0106] 1) Sample processing: In the laboratory, take out the frozen plasma samples and use an optimized organic solvent mixture (ethyl acetate / isopropanol / water 60 / 30 / 10, v / v / v) to extract ceramide. The extraction process should be strictly controlled to ensure the stability of the ceramide concentration in the extract.

[0107] 2) Internal standard calibration: During the sample extraction process, the internal standard CER (d18:1 / 17:1) is added for calibration. This internal standard is used to correct the measurement differences between different samples to ensure the accuracy and repeatability of the data.

[0108] 3) Detection equipment: Use a liquid chromatography-tandem mass spectrometry (LC-MS / MS) system, especially ThermoFisher's Prelude SPLC+TSQ Quantiva LC-MS / MS equipment is recommended for detection. This equipment can detect the content of CER (d18:1 / 16:0) and other ceramide subtypes with high precision, and the analysis results are accurate to the nanogram (ng / mL) level.

[0109] 4) Detection standardization: In order to ensure the stability of the results, the laboratory sets up standard plasma samples) and uses the standard curve to quantify the ceramide concentration in each sample.

[0110] 4. Group division and stratification

[0111] 1) Preliminary grouping

[0112] Based on the results of pathological examination, patients were divided into the MVI-free group (M0) and the MVI-positive group (M1 or M2) to evaluate the association between CER and other clinical or laboratory variables (such as tumor size, HBV DNA level, liver function indexes, etc.) and MVI. This grouping laid the foundation for the preliminary analysis of the relationship between CER level and MVI risk and the influence of other related clinical characteristics.

[0113] 2) CER level stratification

[0114] Preliminary CER stratification: When constructing the MVI risk prediction model, the influence of preoperative plasma ceramide levels was determined by the maximum Youden Index. All ten CERs were converted into categorical variables based on the cutoff values ​​confirmed by the maximum Youden Index, and then logistic regression analysis was performed to confirm the variables with independent predictive ability for MVI among the 10 CERs.

[0115] In this example, the variable that was finally screened out to have independent predictive ability was CER (d18:1 / 16:0), and the other nine CERs had no independent predictive ability. The results are shown in Table 1.

[0116] The optimal cutoff value of CER (d18:1 / 16:0) level (47.9 ng / mL) divided patients into high-level group and low-level group to optimize the sensitivity and specificity of the prediction model, thereby ensuring the significance of CER (d18:1 / 16:0) in MVI risk prediction.

[0117] CER stratification in prognostic analysis: When further analyzing the prognosis, the X-tile software was used to redefine the optimal cutoff value of preoperative plasma CER (d18:1 / 16:0) levels based on relapse-free survival (RFS) data to optimize the effect of prognostic stratification. The software analyzed the relapse-free survival (RFS) data to find the optimal cutoff value (90.6ng / mL) and divided the patients into high-risk and low-risk groups, thereby more accurately evaluating the association between preoperative plasma CER (d18:1 / 16:0) levels and patient prognosis.

[0118] Example 3 Statistical Analysis Design

[0119] 1. Preliminary analysis of variables

[0120] Continuous variable analysis: All continuous data were expressed as mean ± standard deviation or median [Q1, Q3]. The differences in these continuous variables between the MVI group and the non-MVI group were compared by t test or Mann-Whitney U test.

[0121] Continuous variables included: age, BMI, maximum tumor diameter, alpha-fetoprotein (AFP), ICG-R15, white blood cells (WBC), lymphocytes (LY), neutrophils (NEU), monocytes (MONO), hemoglobin (HGB), platelets (PLT), hematocrit (HCT), prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (PT-INR), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), and creatinine (Cr).

[0122] Analysis of categorical variables: For categorical variables (such as sex, number of tumors, differentiation grade, etc.), chi-square test or Fisher's exact test was used to evaluate the association of these variables with the occurrence of MVI.

[0123] Categorical variables included: sex, Child-Pugh grade, BCLC grade, hypertension, diabetes, hyperlipidemia, hyperbilirubinemia, cirrhosis, ascites, portal hypertension, obstructive jaundice, splenomegaly, varicose veins, HBV viral DNA quantification, ABLI score, APRI score, tumor differentiation, tumor number, anatomical liver resection, subtotal liver resection, and portal occlusion.

[0124] Preliminary analysis results showed that certain clinical characteristics (such as BCLC grade, tumor size, CER (d18:1 / 16:0) level, etc.) were significantly different between the MVI group and the non-MVI group.

[0125] The results are shown in Table 1.

[0126] Table 1 Preliminary analysis results of clinical characteristics

[0127]

[0128]

[0129] 2. Propensity score matching (PSM): To control the differences in patients' baseline characteristics (such as age, gender, tumor size, etc.), a caliper value of 0.05 was used for propensity score matching to ensure that the MVI group and the non-MVI group had balanced baseline characteristics. This step effectively reduced the impact of confounding variables and improved the reliability of the analysis.

[0130] 3. Comparison of recurrence rates

[0131] The Kruskal-Wallis test was used to analyze the differences in preoperative plasma CER levels among patients with different MVI grades (M0, M1, and M2). The results are shown in Table 2.

[0132] Table 2 Differences in 10 preoperative plasma CER levels between groups with and without MVI and between groups with different levels of MVI

[0133]

[0134] Further analysis was performed to compare the recurrence rates between groups without MVI and between groups with different MVI grades. The results are shown in Tables 3 and 4.

[0135] Table 3 Comparison of recurrence rates with and without MVI

[0136]

[0137] Table 4 Comparison of recurrence rates among groups with different MVI grades

[0138]

[0139] The results showed that the preoperative plasma CER (d18:1 / 16:0) levels in the M1 and M2 groups were significantly higher than those in the M0 group, suggesting that it can effectively distinguish the M0 group from the M1 / M2 group, and in the subsequent recurrence rate analysis, the recurrence rate of the M1 / M2 group was higher than that of the M0 group. This result further supports the value of preoperative plasma CER (d18:1 / 16:0) levels in risk prediction and prognosis assessment.

[0140] Example 4 Correlation Analysis and Predictive Value of Ceramide Levels

[0141] 1. Spearman correlation analysis

[0142] The correlation between preoperative plasma ceramide levels and clinical characteristics (gender, age, BMI, SII, BCLC B&C grade, HBV viral DNA quantification, AFP, maximum tumor diameter, tumor number, tumor differentiation degree, MVI, MVI grade, AST, ALT, TBIL, DBIL, ALBI grade, APRI) of patients in each group of the experimental cohort was compared.

[0143] The results showed that the preoperative plasma CER (d18:1 / 16:0) level was significantly correlated with the following clinical variables (P<0.05):

[0144] It was positively correlated with the maximum diameter of the tumor (P<0.05), suggesting that it was associated with tumor growth;

[0145] It was positively correlated with HBV DNA level, indicating that it may be related to the activity of HBV;

[0146] It was significantly positively correlated with MVI and its grade, suggesting that it may be related to the degree of microvascular invasion of the tumor;

[0147] It is negatively correlated with the degree of tumor differentiation, indicating that it may be associated with the malignancy of the tumor.

[0148] The results of Spearman correlation analysis are shown in Tables 5-8.

[0149] Table 5 Spearman correlation analysis results (1)

[0150]

[0151] Table 6 Spearman correlation analysis results (2)

[0152]

[0153] Table 7 Spearman correlation analysis results (3)

[0154]

[0155] Table 8 Spearman correlation analysis results (4)

[0156]

[0157]

[0158] 2. Predictive value analysis

[0159] A variety of plasma ceramides and traditional clinical indicators were systematically screened using univariate and multivariate logistic regression analysis methods. In the experiment, CER (d18:1 / 16:0) was prominent as an independent risk factor for MVI. The association and statistical significance of each predictor with MVI were determined by calculating the odds ratio (OR), 95% confidence interval (CI) and P value. The results showed that CER (d18:1 / 16:0) had an OR of 1.023 (95% CI: 1.004-1.043, P = 0.017), which was significantly independent in predicting MVI. Other ceramides such as CER (d18:1 / 18:0) were only significant in univariate analysis (P = 0.040), but not statistically significant in multivariate (multivariate forward stepwise analysis of ten ceramides), and CER (d18:1 / 20:0) and CER (d18:1 / 22:0) were not significant. This highlights the independent predictive advantage of CER(d18:1 / 16:0).

[0160] The results are shown in Table 9.

[0161] Table 9 Results of univariate and multivariate logistic regression analysis methods

[0162]

[0163] The results showed that the odds ratio (OR) of preoperative plasma CER (d18:1 / 16:0) in predicting MVI was 1.023 (95% CI: 1.004-1.043, P=0.017), confirming it as a significant predictor of MVI.

[0164] 3. ROC curve analysis

[0165] To further verify the predictive ability of CER (d18:1 / 16:0), the experiment used ROC curve analysis to evaluate its accuracy in distinguishing MVI patients from non-MVI patients. The ROC curve measures the diagnostic accuracy by calculating the area under the curve (AUROC). The closer the AUROC value is to 1, the stronger the predictive ability. This experiment not only performed ROC analysis on CER (d18:1 / 16:0), but also comprehensively compared it with other traditional predictors, including the maximum tumor diameter, tumor number, alpha-fetoprotein (AFP), hepatitis B virus (HBV) DNA quantification, platelet count, aspartate aminotransferase to platelet ratio index (APRI), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), pre-nutritional index (PNI), aspartate aminotransferase to neutrophil ratio index (ANRI).

[0166] The results are shown in Table 10. The AUROC of CER (d18:1 / 16:0) is 0.647, which has moderate predictive ability. Although it is slightly lower than the AUROC value of the maximum tumor diameter of 0.729, CER (d18:1 / 16:0) performs better in terms of sensitivity (sensitivity), with a sensitivity of 0.843, which is significantly better than other indicators. This shows that CER (d18:1 / 16:0) has a significant advantage in early prediction of MVI, especially in high-sensitivity prediction.

[0167] In comparison with other traditional indicators, the performance of CER (d18:1 / 16:0) not only has a significant advantage in sensitivity, but its overall predictive ability is also higher than that of multiple traditional factors. For example, the AUROCs of AFP and HBV DNA quantification were 0.623 and 0.516, respectively, which were significantly lower than CER (d18:1 / 16:0). In addition, the balance of CER (d18:1 / 16:0) in predictive performance is better than some other indicators, such as platelet count and tumor number. Preoperative plasma CER (d18:1 / 16:0) performed better than other indicators in predicting the risk of MVI (AUROC value was 0.647), further supporting its value as an independent marker.

[0168] Table 10 Specificity and sensitivity results of different indicators as independent markers

[0169]

[0170] Example 5 Construction and verification of MVI risk prediction model

[0171] 1. Build an MVI prediction model

[0172] All candidate variables (age, sex, BMI, Child-Pugh grade, BCLC grade, diabetes, hypertension, hyperlipidemia, hyperbilirubinemia, cirrhosis, ascites, portal hypertension, obstructive jaundice, splenomegaly, varicose veins, HBV viral DNA quantification, ABLI score, APRI score, tumor differentiation, maximum tumor diameter, number of tumors, anatomical liver resection, subtotal liver resection, portal occlusion, alpha-fetoprotein (AFP), ICG-R15, white blood cells (WBC), lymphocytes (LY), neutrophils (NEU), Univariate logistic regression analysis was performed on monocytes (MONO), hemoglobin (HGB), platelets (PLT), hematocrit (HCT), prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (PT-INR), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), creatinine (Cr), etc.) to calculate the odds ratio (OR) of each variable and its significant association (P value) with MVI. Variables with P < 0.20 in univariate logistic regression analysis were taken as potential predictors and entered the multivariate logistic regression analysis stage. In the multivariate logistic regression analysis, the candidate factors screened out by univariate analysis were introduced by the stepwise forward method, and the significance of each variable and its contribution to the model fitting were gradually observed. The stepwise forward method helped to eliminate variables with less contribution to the prediction of MVI and retain only factors with independent predictive effects to optimize the model. Through multivariate logistic regression analysis, the final independent predictors were determined, including CER (d18:1 / 16:0) level, age, maximum tumor diameter, number of tumors, and ICG R15. These variables were significant after adjusting other factors (P ≤ 0.05). The model displays the weight of each variable in the form of a nomogram to facilitate clinical risk assessment. The results of variable regression analysis are shown in Table 11. The nomogram results are shown in Figure 2 shown.

[0173] Table 11 Regression analysis results of different variables

[0174]

[0175]

[0176] The effects of each variable are shown in Table 12.

[0177] Table 12

[0178]

[0179]

[0180] Among them, the regression coefficient of age is -0.055, indicating that age has a slight negative effect on the risk of MVI, that is, as age increases, the risk of MVI decreases slightly. The Exp(B) value is 0.947 (95% confidence interval: 0.903 to 0.993), indicating that the risk of MVI decreases by about 5.3% with each additional year of age, and the result is significant (P = 0.023).

[0181] The regression coefficient of the maximum tumor diameter was 0.305, indicating that an increase in tumor diameter would significantly increase the risk of MVI. The Exp(B) value was 1.356 (95% confidence interval: 1.117 to 1.646), indicating that for every unit increase in tumor diameter, the risk of MVI increased by 35.6%. This variable had significant predictive power in the model (P = 0.002).

[0182] When the number of tumors in a patient reached 3 or more, the risk of MVI increased significantly, with a regression coefficient of 2.438 and Exp(B) of 11.454 (95% confidence interval: 1.010 to 129.842), indicating that when the number of tumors was ≥ 3, the risk of MVI increased to approximately 11 times, which was statistically significant (P = 0.049).

[0183] The ICG-R15 variable is an indicator of liver function, with a regression coefficient of -0.123, showing a negative effect on the risk of MVI, and Exp(B) was 0.884 (95% confidence interval: 0.781 to 1.000), that is, for every unit increase in ICG-R15, the risk of MVI decreased by approximately 11.6% (P = 0.049), indicating that patients with low ICG-R15 values ​​may face a higher risk of MVI.

[0184] CER (d18:1 / 16:0) (≥47.9 ng / mL): This biomarker is a key factor in the prediction model, with a regression coefficient of 1.479 and Exp(B) of 4.390 (95% confidence interval: 1.422 to 13.554), indicating that when the plasma CER (d18:1 / 16:0) level reaches or exceeds 47.9 ng / mL, the risk of MVI increases significantly, reaching approximately 4.4 times (P=0.010), thus verifying the effectiveness of CER (d18:1 / 16:0) as an important predictive indicator.

[0185] 2. Model Performance Evaluation

[0186] The ROC curve is used to evaluate the prediction effect of the model. The results are as follows Figure 3 As shown in the figure, the AUROC value was 0.806 (95% CI: 0.722-0.890, P<0.001), and the prediction accuracy reached a high level.

[0187] 3. Internal and external verification

[0188] Internal validation (experimental cohort, n = 101): 1000 resamplings were performed using the bootstrap method. Figure 4 As shown, the mean AUROC value was 0.774 (95% CI: 0.683-0.864), indicating that the model had good reproducibility.

[0189] External validation (external validation cohort, n = 100): The model was validated in an external cohort, and the results were as follows Figure 5 As shown, the AUROC value was 0.765 (95% CI: 0.668-0.862, P<0.001), indicating the stability of the model in different patient groups.

[0190] 4. Comparison of MVI recurrence rates

[0191] Based on the stratification of preoperative plasma CER (d18:1 / 16:0) levels, the patients were divided into a high-level group and a low-level group, and the incidence of MVI between the two groups was compared by chi-square test.

[0192] Specifically: the cutoff value was calculated according to the nomogram score of the prediction model, and the patients were divided into high MVI risk group and low MVI risk group. The differences between the actual MVI incidence and the predicted incidence of these two groups were compared, as shown in Table 13.

[0193] Table 13

[0194]

[0195] Results: The predicted incidence of MVI in the "predicted MVI" group, which is the high-risk group, was 66.0% (66 out of 100), which was very close to the actual incidence of 71.0% (66 out of 93) (P = 0.484). The predicted incidence of MVI in the "predicted non-MVI" group, which is the low-risk group, was 32.5% (27 out of 83), which was consistent with the actual incidence of 30.0% (27 out of 90) (P = 0.640). The risk of postoperative recurrence in the "predicted MVI" group was significantly increased. This shows that the model has good application value.

[0196] Example 6 Decision Curve Analysis and Clinical Effectiveness Evaluation

[0197] Decision curve analysis (DCA): DCA results showed that the model was better at predicting MVI in the threshold probability range of 7.9%-91.2% than in the extreme case curves, indicating that using the prediction model in this range has a higher net benefit for clinical decision-making.

[0198] Clinical impact curve (CIC): CIC showed that when the threshold probability exceeded 30%, the predicted results for high-risk patients were more consistent with the actual situation, further verifying the clinical effectiveness of the model.

[0199] Example 7 Prognostic Analysis: Relapse-free Survival (RFS)

[0200] 1. Data Source

[0201] A total of 201 cases were included in the experimental cohort and the external validation cohort. After excluding 18 patients with missing available follow-up data, a total of 183 cases with follow-up data were obtained.

[0202] 2. Risk stratification and prognostic analysis

[0203] 1) CER stratification: X-tile software was used to determine the optimal cutoff value (e.g., 90.6 ng / mL) of preoperative plasma CER (d18:1 / 16:0) based on the maximum Youden Index, and patients were divided into a high-risk group (CER ≥ 90.6 ng / mL) and a low-risk group (CER < 90.6 ng / mL). The analysis showed that the recurrence rate in the high CER group was significantly higher than that in the low CER group:

[0204] Two-year recurrence risk: The recurrence risk ratio of the high CER level group at two years was 2.382 (HR=2.382, 95%CI: 1.247-4.547, P=0.009), indicating that the risk of recurrence within two years after surgery in patients with high CER levels was significantly higher than that in the low level group.

[0205] Five-year recurrence risk: During the five-year follow-up period, the recurrence risk ratio of the high CER level group reached 2.564 (HR=2.564, 95% CI: 1.407-4.674, P=0.002), further supporting CER (d18:1 / 16:0) as an effective indicator for predicting long-term recurrence risk.

[0206] 2) Clinical characteristics: Patients in the high CER level group often have larger tumor diameters, higher MVI grades, and poorer differentiation grades. These factors further enhance the value of preoperative plasma CER (d18:1 / 16:0) levels in prognostic evaluation.

[0207] 3. Recurrence survival (RFS) analysis: In the prognostic analysis, the Kaplan-Meier survival curve was used to visualize the patients' RFS in groups, and the Log-rank test was used to compare the differences in RFS between different groups, so as to evaluate the predictive value of preoperative plasma CER (d18:1 / 16:0) level and the prediction model score based on preoperative plasma CER (d18:1 / 16:0) for RFS.

[0208] Five-year RFS outcomes as scored by the prediction model Figure 6 As shown in Figure 2, the five-year RFS results of preoperative plasma CER (d18:1 / 16:0) levels were Figure 7 As shown in Figure 4, preoperative plasma CER (d18:1 / 16:0) has a good predictive effect.

[0209] In summary, the present invention reaches the following conclusions.

[0210] Preoperative plasma CER (d18:1 / 16:0) level as an independent marker: In this study, preoperative plasma CER (d18:1 / 16:0) was confirmed to be an independent significant factor in MVI risk prediction and was significantly correlated with the malignant characteristics of the tumor (such as larger tumor diameter and higher MVI grade), highlighting its value as a marker in identifying high-risk patients.

[0211] Model prediction performance: The prediction model based on preoperative plasma CER (d18:1 / 16:0) had an AUROC of 0.806 and was internally and externally validated, showing high accuracy and good repeatability, further supporting the clinical application of preoperative plasma CER (d18:1 / 16:0) as a reliable marker.

[0212] Prognostic risk stratification: The two-year and five-year recurrence rates of patients in the high preoperative plasma CER (d18:1 / 16:0) level group were significantly higher than those in the low level group, making it a core indicator for patient prognostic stratification and providing a strong basis for identifying patients at high risk of recurrence. This marker can provide a reliable means for clinical practice and help optimize follow-up strategies and intervention measures.

Claims

1. Application of ceramide CER (d18:1 / 16:0) as a marker in predicting the risk of microvascular invasion in hepatocellular carcinoma.

2. The use according to claim 1, characterized in that: The hepatocellular carcinoma includes hepatitis B virus-related hepatocellular carcinoma.

3. Application of reagents for detecting ceramide CER (d18:1 / 16:0) in the preparation of products for predicting the risk of microvascular invasion of hepatocellular carcinoma.

4. Application of a combination of independent predictive factors in predicting the risk of microvascular invasion in hepatocellular carcinoma; The combination of independent predictive factors includes ceramide CER (d18:1 / 16:0) and clinical predictive factors; The combination of clinical predictive factors includes at least one of age, maximum tumor diameter, liver function, and tumor number; Preferably, the hepatocellular carcinoma comprises hepatitis B virus-related hepatocellular carcinoma.

5. The use according to claim 4, characterized in that: The ceramide CER (d18:1 / 16:0) data were detected in the plasma of patients with hepatocellular carcinoma before surgery.

6. A method for constructing a model for predicting the risk of microvascular invasion of hepatocellular carcinoma, comprising the following steps: Using the combination of independent predictive factors described in any one of claims 4 or 5 to construct a model; Preferably, the algorithm for constructing the model includes at least one of receiver operating characteristic curve analysis, correlation analysis, survival analysis, regression analysis method, stratified analysis, continuous variable analysis, categorical variable analysis, and propensity score matching.

7. A model operation module for predicting the risk of microvascular invasion of hepatocellular carcinoma, characterized by: The model operation module includes a computing component; The calculation component calculates the data of the combination of independent predictive factors described in any one of claims 4 or 5.

8. A system for predicting the risk of microvascular invasion of hepatocellular carcinoma, characterized in that: The system includes at least one of 1) to 3): 1) Data input module; 2) Model operation module; 3) Result output module; The model operation module includes the model operation module for predicting the risk of microvascular invasion of hepatocellular carcinoma as described in claim 7.

9. The system according to claim 8, characterized in that: The data input module is used to input data of the marking composition according to claim 4; The result output module is used to output the result of predicting the risk of microvascular invasion of hepatocellular carcinoma.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The computer program stored on the memory and executable on the processor comprises the system of claim 8 or 9.

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