Application of ceramide in predicting risk of microvascular invasion in hepatocellular carcinoma

Abstract

The application belongs to the technical field of biomedicine, and particularly relates to application of ceramide as a marker in predicting risk of microvessel invasion of hepatocellular carcinoma. The application first discovers that preoperative plasma ceramide CER(d18:1 / 16:0) level can be used for predicting MVI of HBV-HCC patients and evaluating postoperative prognosis of the patients. The application provides a preoperative non-invasive detection method based on plasma ceramide CER(d18:1 / 16:0), which avoids the trauma of traditional histopathological detection. The method can effectively predict the occurrence risk of MVI through simple detection of a blood sample of a patient. The method evaluates the MVI risk of the patient through a multi-factor prediction model, and provides a basis for personalized treatment. The method is not only suitable for preoperative MVI risk evaluation, but also can assist in formulating a postoperative follow-up and treatment plan, and help reduce the postoperative recurrence rate and improve the survival rate of the patients.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to the application of ceramides as biomarkers in predicting the risk of microvascular invasion in hepatocellular carcinoma. Background Technology

[0002] Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, especially in the context of hepatitis B virus (HBV) infection, where the condition is often complex. Postoperative recurrence rates are high in HCC patients, with microvascular invasion (MVI) being a significant pathological factor contributing to recurrence and poor prognosis. Detection of MVI typically relies on histopathological analysis of the resected specimen, currently the only reliable diagnostic method. However, this method is invasive and can only be performed postoperatively, providing no relevant information preoperatively. This limitation presents numerous challenges for HCC patients in preoperative risk assessment, treatment decisions, and postoperative follow-up.

[0003] While existing imaging modalities (such as CT and MRI) can provide basic information about tumors, their sensitivity and specificity in predicting microvascular invasion (MVI) are relatively low. Furthermore, conventional serum biomarkers (such as alpha-fetoprotein (AFP) are not entirely effective in detecting microvascular invasion. Due to the lack of effective preoperative non-invasive diagnostic methods, physicians cannot accurately identify high-risk patients during preoperative assessments, making it difficult to optimize surgical and treatment plans.

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

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

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

[0007] A 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 in hepatocellular carcinoma.

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

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

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

[0011] The seventh aspect of this invention is to provide an electronic device.

[0012] An eighth aspect of the present invention is to provide a storage medium.

[0013] To achieve the above-mentioned objectives of this invention, the technical solution adopted by this invention is as follows:

[0014] A first aspect of the present invention provides the application of ceramide CER(d18:1 / 16:0) as a biomarker in predicting the risk of microvascular invasion in hepatocellular carcinoma; the structural formula of said ceramide CER(d18:1 / 16:0) is as follows: C 34 H 67 NO3, CAS No.: 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 surgery for hepatocellular carcinoma.

[0016] In some embodiments of the present invention, the term "preoperative" refers to the period within one week prior to the patient undergoing surgery for hepatocellular carcinoma.

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

[0018] A second aspect of the invention provides the use of a reagent for detecting ceramide CER (d18:1 / 16:0) in the preparation of products for predicting the risk of microvascular invasion in 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 for 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] A third aspect of the present invention provides the application of a combination of independent predictive factors in predicting the risk of microvascular invasion in hepatocellular carcinoma.

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

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

[0025] Preferably, the liver function indicators include ICG-R15 levels.

[0026] Preferably, the hepatocellular carcinoma includes hepatitis B virus-associated hepatocellular carcinoma.

[0027] Preferably, the inclusion criterion for the number of tumors as an independent predictor of the combination of factors 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 in hepatocellular carcinoma, comprising the following steps:

[0029] Model construction is performed using the combination of independent predictive factors described in the third aspect of this invention.

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

[0031] Specifically, it includes the following steps:

[0032] 1. Patient screening

[0033] Target population: Clinicians first identify patients who meet the diagnostic criteria for hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC). These patients have typically already been diagnosed with liver cancer through imaging (such as CT, MRI) and serological methods (such as AFP, PIVKA-II) and are scheduled for surgical resection.

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

[0035] 2. Plasma sample collection

[0036] Collection time: Doctors should collect plasma samples within 7 days before the patient's surgery to ensure that the sample can reflect the preoperative physiological state and avoid interference with the detection of ceramide levels by preoperative treatments (such as chemotherapy or radiotherapy).

[0037] 3. Laboratory testing

[0038] Sample preparation: In the laboratory, frozen plasma samples were retrieved and ceramides were extracted using an optimized organic solvent mixture (ethyl acetate / isopropanol / water 60 / 30 / 10, v / v / v). The extraction process was strictly controlled to ensure a stable concentration of ceramides in the extract.

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

[0040] Detection equipment: A liquid chromatography-tandem mass spectrometry (LC-MS / MS) system is used, with ThermoFisher's Prelude SPLC+TSQ Quantiva LC-MS / MS system being particularly recommended. This instrument can accurately detect the content of CER (d18:1 / 16:0) and other ceramide subtypes, with analytical results accurate to the nanogram (ng / mL) level.

[0041] Standardization of testing: To ensure the stability of results, the laboratory should establish quality control samples (such as standard plasma samples) and use a standard curve to quantitatively analyze 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 for differences in patient baseline characteristics (such as age, sex, tumor size, etc.), a caliper score 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 influence 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, Kaplan-Meier survival curves were used to visualize the RFS of patients in groups, and the differences in RFS between different groups were compared by the Log-rank test, thereby assessing the predictive value of preoperative plasma CER (d18:1 / 16:0) level and the predictive value of the predictive model score based on preoperative plasma CER (d18:1 / 16:0) for RFS.

[0047] 5. Model Building

[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) for each variable and its significant association with MVI (P-value). Variables with P < 0.20 in the univariate logistic regression analysis were considered potential predictors and proceeded to the multivariate logistic regression analysis stage. In the multivariate logistic regression analysis, a stepwise forward approach was used to introduce the candidate factors screened by the univariate analysis, progressively observing the significance of each variable and its contribution to the model fit. The stepwise forward approach helped eliminate variables with small contributions to MVI prediction, retaining 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 level. These variables all showed significance (P ≤ 0.05) after adjusting for other factors. The model displays the weight of each variable in the form of a nomogram for easy clinical risk assessment.

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

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

[0051] The computing component performs calculations on the data of the combination of independent predictive factors as 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 in hepatocellular carcinoma:

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

[0054] 1) Data input module;

[0055] 2) Model execution module;

[0056] 3) Result output module;

[0057] The model running module includes the model running module for predicting the risk of microvascular invasion in hepatocellular carcinoma as described in the fifth aspect of this 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 results of predicting the risk of microvascular invasion in hepatocellular carcinoma.

[0060] A seventh aspect of the present invention provides an electronic device including a storage device, a processor, and a computer program stored in the storage device and executable on the processor, wherein the computer program stored in the storage device and executable on the processor includes the system described in the sixth aspect of the present invention.

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

[0062] The beneficial effects of this invention are:

[0063] 1. Non-invasive detection

[0064] This invention provides the application of plasma ceramide CER (d18:1 / 16:0) as a biomarker for predicting the risk of microvascular invasion in hepatocellular carcinoma (HCC), avoiding the invasiveness of traditional histopathological testing. The risk of MVI can be effectively assessed through a simple test of a patient's blood sample. This non-invasive testing significantly reduces physical harm to patients, improving the acceptability of the test and the convenience of clinical operation.

[0065] 2. High prediction accuracy

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

[0067] 3. Improve personalized treatment

[0068] This invention helps clinicians develop personalized treatment plans based on patients' plasma ceramide CER (d18:1 / 16:0) levels. For high-risk MVI patients, doctors can take more aggressive treatment measures, such as expanding the resection area or preoperative neoadjuvant therapy, based on the test results, thereby effectively reducing the risk of postoperative recurrence and improving patient survival rates. Compared to traditional methods that rely on postoperative pathological examination, this 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 to perform, and the extraction and detection procedures for ceramides have been standardized, making it suitable for large-scale clinical applications. Operators only need basic knowledge of liquid chromatography and mass spectrometry 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, making it suitable for routine clinical practice in various medical institutions. Attached Figure Description

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

[0072] Figure 1 This is a flowchart of the experimental process of the present invention.

[0073] Figure 2 This is a nodal plot of the MVI risk prediction model.

[0074] Figure 3 The ROC curve results of the MVI risk prediction model in Example 5 on the training set are shown.

[0075] Figure 4 The ROC curve results of the MVI risk prediction model in Example 5 in the internal validation queue.

[0076] Figure 5 The ROC curve results of the MVI risk prediction model in Example 5 in the external validation queue are shown.

[0077] Figure 6 The results of the 5-year relapse survival (RFS) analysis for the predictive model are shown.

[0078] Figure 7 The results are based on the 5-year relapse survival (RFS) analysis of preoperative plasma CER (d18:1 / 16:0) levels. Detailed Implementation

[0079] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0080] In the applicant's previous research, experiments showed a significant correlation between preoperative plasma ceramide concentration and the risk and prognosis of microvascular invasion in hepatocellular carcinoma patients. Therefore, this invention further explores its effectiveness in predicting microvascular invasion using a model based on actual experimental data.

[0081] The experimental procedure of this invention is as follows: Figure 1 As shown.

[0082] Example 1: Patient Inclusion and Descriptive Analysis

[0083] 1. Inclusion criteria

[0084] Clinicians first identify patients who meet the diagnostic criteria for hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC). These patients have typically already had their liver cancer diagnosis confirmed by imaging (such as CT, MRI) and serological (such as AFP, PIVKA-II) methods and are scheduled to undergo surgical resection.

[0085] This invention collected data from 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 factors such as the patient's liver function status, tumor location, and size, further confirmation is needed to determine suitability for surgery, excluding patients who cannot tolerate surgery or do not meet the surgical criteria. Specifically, this includes: no hepatitis B infection, concurrent hepatitis C infection, a history of preoperative anti-tumor treatment, and severely deficient data.

[0088] 3. Data set

[0089] After excluding 155 patients from Nanfang Hospital of Southern Medical University, a total of 101 patients were included in 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 Foshan First People's Hospital, a total of 100 patients were included as an external validation cohort.

[0091] Example 2: Data Acquisition and Grouping

[0092] 1. Clinical data collection:

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

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

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

[0096] Surgical information: Anatomical liver resection, partial liver resection, and porta hepatis 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), creatinine (Cr).

[0098] 2. Plasma sample collection

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

[0100] Collection time: Doctors should collect plasma samples within 7 days before the patient's surgery to ensure that the sample can reflect the preoperative physiological state and avoid interference with the detection of ceramide levels by preoperative treatments (such as chemotherapy or radiotherapy).

[0101] Collection Procedure: Approximately 10 ml of blood is collected via routine venipuncture using a blood vessel containing an anticoagulant. After collection, the plasma sample must be centrifuged (3000 rpm, 10 minutes) within 1 hour. The supernatant (plasma fraction) is then quickly transferred to a cryovial.

[0102] Sample preservation: To avoid degradation of ceramides, plasma samples must 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 the levels of ten ceramides (CERs) were determined using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS), 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 preparation: In the laboratory, frozen plasma samples were retrieved and ceramides were extracted using an optimized organic solvent mixture (ethyl acetate / isopropanol / water 60 / 30 / 10, v / v / v). The extraction process should be strictly controlled to ensure a stable concentration of ceramides in the extract.

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

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

[0109] 4) Standardized testing: To ensure the stability of the results, the laboratory sets up standard plasma samples and uses a standard curve to quantitatively analyze the ceramide concentration in each sample.

[0110] 4. Grouping and Stratification

[0111] 1) Preliminary grouping

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

[0113] 2) CER horizontal stratification

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

[0115] In this embodiment, the variable with independent predictive power was finally selected as CER(d18:1 / 16:0), while the other nine CERs did not have independent predictive power. The results are shown in Table 1.

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

[0117] CER stratification in prognostic analysis: When conducting further prognostic analysis, X-tile software was used to redetermine the optimal cutoff value for preoperative plasma CER (d18:1 / 16:0) levels based on relapse-free survival (RFS) data to optimize prognostic stratification. This software analyzed RFS data to find the optimal cutoff value (90.6 ng / mL), dividing patients into high-risk and low-risk groups, thereby more accurately assessing 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 are expressed as mean ± standard deviation or median [Q1, Q3]. Differences in these continuous variables were compared between the MVI group and the non-MVI group using t-tests or Mann-Whitney U tests.

[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] Categorical variable analysis: For categorical variables (such as gender, number of tumors, differentiation grade, etc.), the chi-square test or Fisher's exact test is used to assess the association between these variables and the occurrence of MVI.

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

[0124] Preliminary analysis results showed that certain clinical characteristics (such as BCLC grade, tumor size, CER (d18:1 / 16:0) level, etc.) differed significantly 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 for differences in patient baseline characteristics (such as age, sex, tumor size, etc.), a caliper score 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 influence 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, M2). The results are shown in Table 2.

[0132] Table 2. Differences in preoperative plasma CER levels of 10 types among groups with and without MVI, and among different grades of MVI.

[0133]

[0134] Further analysis compared the recurrence rates among the groups without MVI and with different grades of MVI. 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 different grades of MVI in different groups

[0138]

[0139] The results showed that preoperative plasma CER (d18:1 / 16:0) levels were significantly higher in patients in the M1 and M2 groups than in the M0 group, suggesting that it can effectively distinguish patients in the M0 group from the M1 / M2 group. Furthermore, in subsequent recurrence rate analysis, the recurrence rate in the M1 / M2 group was higher than that in the M0 group. This result further supports the value of preoperative plasma CER (d18:1 / 16:0) levels in risk prediction and prognostic 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, number of tumors, tumor differentiation degree, MVI, MVI grade, AST, ALT, TBIL, DBIL, ALBI grade, APRI) was compared among patients in each experimental cohort.

[0143] The results showed that preoperative plasma CER (d18:1 / 16:0) levels were 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 is related to tumor growth;

[0145] It showed a positive correlation with HBV DNA levels, suggesting that it may be related to hepatitis B virus activity.

[0146] It showed a significant positive correlation 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, suggesting that it may be related to the malignancy of the tumor.

[0148] The results of the Spearman correlation analysis are shown in Table 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] Univariate and multivariate logistic regression analyses were used to systematically screen various plasma ceramides and traditional clinical indicators. In the experiment, CER(d18:1 / 16:0) showed outstanding independent risk factor for MVI. The association and statistical significance with MVI were determined by calculating the odds ratio (OR), 95% confidence interval (CI), and p-value for each predictor. The results showed that the OR for CER(d18:1 / 16:0) was 1.023 (95% CI: 1.004–1.043, P = 0.017), demonstrating significant independence in predicting MVI. Other ceramides, such as CER(d18:1 / 18:0), were only significant in univariate analysis (P = 0.040), and showed no statistical significance in multivariate analysis (multivariate stepwise analysis of ten ceramides). CER(d18:1 / 20:0) and CER(d18:1 / 22:0) were also 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

[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 validate the predictive ability of CER (d18:1 / 16:0), the experiment employed ROC curve analysis to assess its accuracy in distinguishing MVI patients from non-MVI patients. The ROC curve measures diagnostic accuracy by calculating the area under the curve (AUROC); a higher AUROC value (closer to 1) indicates stronger predictive ability. This experiment not only performed ROC analysis on CER (d18:1 / 16:0) but also comprehensively compared it with other traditional predictive factors, including maximum tumor diameter, tumor number, alpha-fetoprotein (AFP), 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), prenutrition index (PNI), and aspartate aminotransferase to neutrophil ratio index (ANRI).

[0166] The results are shown in Table 10. The AUROC of CER(d18:1 / 16:0) was 0.647, indicating moderate predictive ability. Although slightly lower than the AUROC value of 0.729 for the maximum tumor diameter, CER(d18:1 / 16:0) performed better in terms of sensitivity, reaching 0.843, significantly superior to other indicators. This suggests 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, CER(d18:1 / 16:0) not only demonstrated a significant advantage in sensitivity but also exhibited higher overall predictive ability than several traditional factors. For example, the AUROC values ​​for AFP and HBV DNA quantification were 0.623 and 0.516, respectively, significantly lower than those for CER(d18:1 / 16:0). Furthermore, CER(d18:1 / 16:0) showed better balance in predictive performance than some other indicators, such as platelet count and tumor number. Preoperative plasma CER(d18:1 / 16:0) outperformed other indicators in predicting MVI risk (AUROC value of 0.647), further supporting its value as an independent biomarker.

[0168] Table 10 Results of the specificity and sensitivity of different indicators as independent biomarkers

[0169]

[0170] Example 5: Construction and Validation of the MVI Risk Prediction Model

[0171] 1. Construct an MVI prediction model

[0172] For all candidate variables (age, sex, BMI, Child-Pugh classification, BCLC classification, diabetes, hypertension, hyperlipidemia, hyperbilirubinemia, cirrhosis, ascites, portal hypertension, obstructive jaundice, splenomegaly, varicose veins, HBV viral DNA quantification, ABLI score, APRI score, tumor differentiation grade, maximum tumor diameter, number of tumors, anatomical liver resection, partial hepatectomy, port occlusion, alpha-fetoprotein (AFP), ICG-R15, white blood cell count (WBC), lymphocyte count (LY), neutrophil count (NEU))... Univariate logistic regression analysis was performed on monocyte count (MONO), hemoglobin (HGB), platelet count (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) to calculate the odds ratio (OR) for each variable and its significant association with MVI (p-value). Variables with a p-value < 0.20 in the univariate logistic regression analysis were used as potential predictors and then proceeded to the multivariate logistic regression analysis. In the multivariate logistic regression analysis, a stepwise forward approach was used to introduce candidate factors selected from the univariate analysis, progressively observing the significance of each variable and its contribution to the model fit. The stepwise forward approach helped eliminate variables with small contributions to MVI prediction, retaining only factors with independent predictive power to optimize the model. Multivariate logistic regression analysis was used to identify the final independent predictors, including CER (d18:1 / 16:0) level, age, maximum tumor diameter, number of tumors, and ICG R15. These variables were all significant after adjusting for other factors (P≤0.05). The model uses a nomogram to display the weight of each variable for clinical risk assessment. The results of the variable regression analysis are shown in Table 11, and the nomogram results are shown below. Figure 2 As shown.

[0173] Table 11 Regression analysis results for different variables

[0174]

[0175]

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

[0177] Table 12

[0178]

[0179]

[0180] The regression coefficient for age was -0.055, indicating that age has a slight negative impact on the risk of MVI, meaning that the risk of MVI decreases slightly with increasing age. The Exp(B) value was 0.947 (95% confidence interval: 0.903 to 0.993), indicating that the risk of MVI decreases by approximately 5.3% for each additional year of age, and this result was statistically significant (P = 0.023).

[0181] The regression coefficient for the maximum tumor diameter was 0.305, indicating that an increase in tumor diameter significantly increases 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 increases by 35.6%, and this variable has significant predictive power in the model (P = 0.002).

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

[0183] The ICG-R15 variable is an indicator of liver function. Its regression coefficient is -0.123, indicating a negative effect on the risk of MVI. The Exp(B) is 0.884 (95% confidence interval: 0.781 to 1.000), which means that for every unit increase in ICG-R15, the risk of MVI decreases by about 11.6% (P = 0.049). This suggests 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 predictive model, with a regression coefficient of 1.479 and an Exp(B) of 4.390 (95% confidence interval: 1.422 to 13.554). This indicates 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 validating the effectiveness of CER(d18:1 / 16:0) as an important predictive indicator.

[0185] 2. Model Performance Evaluation

[0186] The predictive performance of the model was evaluated using ROC curves, and the results are as follows: Figure 3 As shown, the AUROC value was 0.806 (95% CI: 0.722-0.890, P<0.001), indicating a high level of prediction accuracy.

[0187] 3. Internal and external validation

[0188] Internal validation (experimental cohort, n=101): 1000 resampling operations were performed using the bootstrap method, and the results are as follows... Figure 4 As shown, the mean AUROC value is 0.774 (95% CI: 0.683-0.864), indicating that the model has good reproducibility.

[0189] External validation (external validation queue, n=100): The model is validated in the external queue, and the results are as follows. Figure 5 As shown, the AUROC value was 0.765 (95% CI: 0.668–0.862, P<0.001), demonstrating the stability of the model in different patient populations.

[0190] 4. Comparison of MVI recurrence rates

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

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

[0193] Table 13

[0194]

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

[0196] Example 6: Decision Curve Analysis and Clinical Efficacy Assessment

[0197] Decision curve analysis (DCA): DCA results show that the model outperforms the extreme case curve in predicting MVI within a threshold probability range of 7.9%-91.2%, indicating that using the predictive model within this range has a high net benefit for clinical decision-making.

[0198] Clinical Impact Curve (CIC): The CIC shows that when the threshold probability exceeds 30%, the predicted results for high-risk patients are in good agreement with the actual situation, further validating the clinical effectiveness of the model.

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

[0200] 1. Data Source

[0201] The experimental and external validation cohorts totaled 201 cases. After excluding 18 patients with unavailable follow-up data, a total of 183 follow-up data were obtained.

[0202] 2. Risk stratification and prognostic analysis

[0203] 1) CER Stratification: Using X-tile software, the optimal cutoff value (e.g., 90.6 ng / mL) for preoperative plasma CER (d18:1 / 16:0) was determined based on the maximum Youden Index. Patients were then divided into a high-risk group (CER ≥ 90.6 ng / mL) and a low-risk group (CER < 90.6 ng / mL). Analysis showed that the recurrence rate was significantly higher in the high CER group than in the low CER group.

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

[0205] Five-year relapse risk: During the five-year follow-up period, the relapse risk ratio in 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 relapse risk.

[0206] 2) Clinical characteristics: Patients with high CER levels 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 assessment.

[0207] 3. Relapsed Survival (RFS) Analysis: In the prognostic analysis, Kaplan-Meier survival curves were used to visualize the RFS of patients in different groups, and the differences in RFS between different groups were compared by the Log-rank test, thereby assessing the predictive value of preoperative plasma CER (d18:1 / 16:0) level and the predictive value of the predictive model score based on preoperative plasma CER (d18:1 / 16:0) for RFS.

[0208] Five-year RFS results of the prediction model score are as follows Figure 6 As shown, the five-year RFS results of preoperative plasma CER (d18:1 / 16:0) levels are as follows: Figure 7 As shown, preoperative plasma CER (d18:1 / 16:0) has a good predictive effect.

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

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

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

[0212] Prognostic risk stratification: Patients with high preoperative plasma CER (d18:1 / 16:0) levels had significantly higher recurrence rates at both two and five years compared to those with low levels, making it a core indicator for patient prognostic stratification and providing strong evidence for identifying patients at high risk of recurrence. This biomarker can provide a reliable tool for clinicians to optimize follow-up strategies and interventions.

Claims

1. Use of a reagent for detecting ceramide CER (d18:1 / 16:0) in the manufacture of a product for predicting the risk of microvascular invasion of hepatocellular carcinoma. The hepatocellular carcinoma is hepatitis B virus-related hepatocellular carcinoma.

2. The use of claim 1, wherein: The data of ceramide CER (d18:1 / 16:0) is detected in the plasma of the patient before the operation of hepatocellular carcinoma.

3. A method for constructing a model for predicting the risk of microvascular invasion of hepatocellular carcinoma, comprising the following steps: Using an independent predictor combination to construct the model; The independent predictor combination comprises ceramide CER (d18:1 / 16:0) and clinical predictors; The clinical predictors include at least one of age, maximum tumor diameter, liver function, and tumor number; The hepatocellular carcinoma is hepatitis B virus-related hepatocellular carcinoma; The data of ceramide CER (d18:1 / 16:0) is detected in the plasma of the patient before the operation of hepatocellular carcinoma.

4. The method of claim 3, wherein: The algorithm for constructing the model includes at least one of receiver operating characteristic curve analysis, correlation analysis, survival analysis, regression analysis method, stratification analysis, continuous variable analysis, categorical variable analysis, and propensity score matching.

5. A model running module for predicting the risk of microvascular invasion of hepatocellular carcinoma, comprising: The model running module comprises a computing component; The computing component calculates the data of an independent predictor combination; The independent predictor combination comprises ceramide CER (d18:1 / 16:0) and clinical predictors; The clinical predictors include at least one of age, maximum tumor diameter, liver function, and tumor number; The hepatocellular carcinoma is hepatitis B virus-related hepatocellular carcinoma; The data of ceramide CER (d18:1 / 16:0) is detected in the plasma of the patient before the operation of hepatocellular carcinoma.

6. A system for predicting the risk of microvascular invasion of hepatocellular carcinoma, comprising at least one of 1) to 3): 1) a data input module; 2) a model running module; 3) a result output module; The model running module comprises the model running module for predicting the risk of microvascular invasion of hepatocellular carcinoma according to claim 5.

7. An electronic device comprising a storage, a processor, and a computer program stored on the storage and executable on the processor, wherein: The computer program stored on the storage and executable on the processor comprises the system according to claim 6. ​

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