Biomarker combination for assessing risk of occurrence of liver cancer microvessel invasion and application thereof

Abstract

The application discloses a biomarker combination for evaluating the occurrence risk of liver cancer microvessel invasion and application thereof, and the biomarker combination is composed of V2FC, H2FC, N, N-dimethylglycine, H1FC, lactic acid, creatine and pyruvic acid. The biomarker combination provided by the application can be applied to the preparation of a product for evaluating the occurrence risk of liver cancer microvessel invasion. The liver cancer-MVI prediction model constructed based on the biomarker combination provided by the application can quickly and accurately predict the occurrence risk of MVI, is favorable for the preoperative accurate evaluation of MVI, and is further favorable for realizing the personalized treatment of liver cancer patients and improving the prognosis of liver cancer patients.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology and relates to a combination of biomarkers for assessing the risk of microvascular invasion in liver cancer and their application. Background Technology

[0002] Primary liver cancer (HCC) is a malignant tumor with high incidence and mortality rates worldwide. Hepatocellular carcinoma (HCC) is the main pathological type of HCC. Surgical resection is the primary treatment option for patients with early-stage HCC, but the postoperative recurrence rate remains high, seriously affecting patient prognosis.

[0003] Microvascular invasion (MVI) is a key factor in postoperative recurrence and poor prognosis in HCC patients. Its presence indicates a more aggressive biological behavior in HCC and is a crucial reference factor for assessing the likelihood of recurrence and aiding in treatment strategy development. Studies show that for high-risk patients with MVI, surgical resection as first-line treatment provides better tumor control and a lower recurrence rate than radiofrequency ablation. P <0.05); MVI is closely related to poor prognosis after liver transplantation; TACE combined with sorafenib can improve the prognosis of HCC patients with MVI, while MVI-negative patients do not benefit from the above treatments (Xu Lingcong. Construction of a preoperative prediction model and prognostic analysis of microvascular invasion in hepatocellular carcinoma based on lipid metabolism markers [D]. Lanzhou University). Therefore, accurate preoperative prediction of the risk of MVI will help guide the selection of clinical treatment strategies and prognostic assessment for HCC. However, currently, MVI results can only be obtained through histopathological examination of the resected surgical specimen, which lacks practical guiding significance for treatment strategies. Although tumor volume, serum alpha-fetoprotein (AFP), and serum hepatitis B DNA load have been reported to be associated with the risk of MVI in HCC, their predictive efficacy for predicting the risk of MVI still needs to be improved. Summary of the Invention

[0004] The main objective of this invention is to provide a combination of biomarkers for assessing the risk of microvascular invasion in liver cancer and its application, in order to solve at least one of the above-mentioned technical problems.

[0005] According to a first aspect of the invention, a combination of biomarkers for assessing the risk of microvascular invasion in liver cancer is provided, comprising very low-density lipoprotein-free cholesterol-2 component (VLDL-2 free cholesterol, V2FC), high-density lipoprotein-free cholesterol-2 component (HDL-2 free cholesterol, H2FC), N,N-dimethylglycine, high-density lipoprotein-free cholesterol-1 component (HDL-1 free cholesterol, H1FC), lactic acid, creatine, and pyruvic acid.

[0006] The biomarker combination of seven metabolites or blood lipid indicators provided by this invention can be used to quickly and accurately predict the risk of MVI, which is beneficial for accurate preoperative assessment of MVI, and thus facilitates personalized treatment for liver cancer patients and improves their prognosis.

[0007] According to a second aspect of the invention, the use of the biomarker combination of the invention in the preparation of products for assessing the risk of microvascular invasion in liver cancer is provided.

[0008] Based on the biomarker combination provided by this invention, a liver cancer-MVI prediction model can be constructed using various algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB). Among these, the HCC-MVI prediction model constructed based on SVM achieved an AUC of 0.957 for predicting MVI in a complete cohort containing 86 HCC patients, outperforming the predictive efficacy of other clinical features. Furthermore, the model exhibits high robustness and clinical applicability.

[0009] In some implementations, the liver cancer-MVI prediction model can be deployed as an application, mini-program, APP, etc. Only the metabolic levels of the seven biomarkers in the biomarker combination provided by this invention in plasma need to be input to assess the risk of MVI in liver cancer patients.

[0010] According to a third aspect of the invention, the use of a product for detecting the metabolic levels of V2FC, H2FC, N,N-dimethylglycine, H1FC, lactate, creatine and pyruvate in a sample in the preparation of a product for assessing the risk of microvascular invasion in liver cancer is provided.

[0011] In some embodiments, the product used to detect the metabolic levels of V2FC, H2FC, N,N-dimethylglycine, H1FC, lactate, creatine, and pyruvate in a sample can be any reagent, kit, chip, and / or instrument known in the art that can quantitatively detect the metabolic levels of V2FC, H2FC, N,N-dimethylglycine, H1FC, lactate, creatine, and pyruvate in a sample.

[0012] In some implementations, products for detecting the metabolic levels of V2FC, H2FC, N,N-dimethylglycine, H1FC, lactate, creatine, and pyruvate in a sample can be reagents, kits, chips, and / or instruments suitable for detecting the metabolic levels of V2FC, H2FC, N,N-dimethylglycine, H1FC, lactate, creatine, and pyruvate in a sample by nuclear magnetic resonance spectroscopy (NMR).

[0013] In some embodiments, the sample can be plasma. Therefore, using the biomarker combination provided by this invention to assess the risk of MVI in liver cancer offers advantages such as convenient and non-invasive sample acquisition, making it easy to promote and apply clinically.

[0014] In some implementations, liver cancer is referred to as HCC. Attached Figure Description

[0015] Figure 1 A forest plot showing the AUC values ​​of MVI predictions for 10 HCC-MVI prediction models in the training set;

[0016] Figure 2 Forest plot of AUC values ​​for MVI predictions by 10 HCC-MVI prediction models on the validation set;

[0017] Figure 3 Line graphs showing the performance metrics of 10 HCC-MVI prediction models in the training set;

[0018] Figure 4 Line graphs showing the performance metrics of 10 HCC-MVI prediction models on the validation set;

[0019] Figure 5 A radar chart showing the performance metrics of the SVM model in the training set;

[0020] Figure 6 A radar chart showing the performance metrics of the SVM model on the validation set;

[0021] Figure 7 This is a confusion matrix diagram of the SVM model in the training set.

[0022] Figure 8The confusion matrix of the SVM model on the validation set;

[0023] Figure 9 The calibration curve of the SVM model in the training set;

[0024] Figure 10 The calibration curve of the SVM model on the validation set;

[0025] Figure 11 Decision curve analysis of the SVM model in a complete queue;

[0026] Figure 12 Predict the ROC curve of MVI in the complete cohort using SVM model and clinical features;

[0027] Figure 13 A SHAP summary plot of global feature importance sorted by average |SHAP| value, used to quantify the contribution of each variable to the prediction using the Shap method.

[0028] Figure 14 A bee colony diagram showing the direction and distribution of the characteristic influences;

[0029] Figure 15 This is the SHAP dependency graph of V2FC in the SVM model;

[0030] Figure 16 The SHAP dependency graph of creatine in the SVM model;

[0031] Figure 17 The SHAP dependency graph of H1FC in the SVM model;

[0032] Figure 18 The SHAP dependency graph of H2FC in the SVM model;

[0033] Figure 19 The SHAP dependency graph of lactate in the SVM model;

[0034] Figure 20 The SHAP dependency plot of pyruvate in the SVM model;

[0035] Figure 21 This is a SHAP dependency plot of N,N-dimethylglycine in the SVM model. Detailed Implementation

[0036] The present invention will be further described in detail below with reference to the embodiments. The embodiments are for illustrative purposes only and do not limit the invention in any way. Unless otherwise specified, the raw materials and reagents used in the embodiments are conventional products that can be obtained commercially; experimental methods that do not specify specific conditions in the embodiments are generally performed under conventional conditions in the art or according to the conditions recommended by the manufacturer.

[0037] Example 1: Validation of the diagnostic efficacy of a combination of biomarkers used to assess the risk of microvascular invasion in liver cancer.

[0038] 1. Participant Clinical Information

[0039] Preoperative blood samples were collected from 86 patients with pathologically confirmed hepatocellular carcinoma (HCC) who underwent radical resection. Clinical data were retrospectively collected, including age, sex, cirrhosis status, alpha-fetoprotein (AFP) level, abnormal prothrombin level, tumor size, tumor multiplication, vascular invasion status, and tumor stage. Among these patients, 37 were confirmed to have microvascular invading virus (MVI) by histopathological examination of the resected surgical specimens.

[0040] Eighty-six patients were randomly assigned to a training set and a validation set in a 6:4 ratio. Differences in continuous clinical characteristics between the groups were compared using t-tests or Mann-Whitney U tests, while differences in categorical clinical characteristics between the groups were compared using chi-square tests or Fisher's exact tests. The results are shown in Table 1. There were no significant differences in baseline clinical characteristics between the two groups.

[0041] Table 1. Baseline characteristics of training and validation set patients used to predict microvascular invasion in hepatocellular carcinoma patients.

[0042]

[0043] 2. Quantitative detection of the metabolic levels of biomarkers in plasma

[0044] (1) Sample processing: Blood samples were immediately incubated at 2-8℃ with a concentration of 1500-2000× g Centrifuge for 10 minutes, then aspirate the supernatant into cryovials and store the plasma samples at -80°C.

[0045] (2) Nuclear magnetic resonance metabolomics analysis: Nightingale proton nuclear magnetic resonance (NMR) was used. 1 Plasma samples were analyzed using the 1H-NMR platform (ProteinT, Tianjin, China). After thawing, 340 μL of plasma was mixed with an equal volume of NMR lipid buffer (Bruker Plasma Buffer), and 600 μL of the mixture was transferred to a 5 mm NMR tube for automated analysis.

[0046] (3) Metabolite identification and quantification: Spectroscopic data were processed using Bruker's built-in Amix software, and chemical shift correction was performed using Speaq software. Metabolite identification and quantification were performed based on Bruker's proprietary NMR database, and metabolic level data of V2FC, H2FC, N,N-dimethylglycine, H1FC, lactate, creatine, and pyruvate were obtained from 86 plasma samples.

[0047] 3. Construct and validate the HCC-MVI prediction model

[0048] Based on the metabolic levels of V2FC, H2FC, N,N-dimethylglycine, H1FC, lactate, creatine, and pyruvate in plasma, ten algorithms were used to construct ten HCC-MVI prediction models: Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), Generalized Linear Model with Elastic-Net (GLMNET), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Recursive Partitioning Tree (RPART). The Shapley Additive ExPlanations (Shap) were then used to interpret these models.

[0049] The model employed five-fold cross-validation and ten repeated runs. Model performance was evaluated based on sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. The optimal classification threshold was determined by maximizing the Youden index under the ROC curve. Calibration curve and decision curve analysis were used to assess predictive accuracy and net clinical benefit. The Shap method was used to quantify the contribution of each variable to the prediction.

[0050] The results are as follows Figures 1-21 As shown in Table 2.

[0051] Table 2 Performance metrics of 10 HCC-MVI prediction models on the training and validation sets.

[0052]

[0053] like Figures 1-2 As shown, in terms of MVI diagnosis, the AUC values ​​of the 10 HCC-MVI prediction models constructed ranged from 0.723 to 1.000 in the training set and from 0.613 to 0.921 in the validation set.

[0054] The multi-dimensional performance comparison results of 10 HCC-MVI prediction models are as follows: Figures 3-4 As shown in Table 2, compared with other models, the SVM model showed the best diagnostic performance on both the training and validation sets, with AUC values ​​of 0.981 and 0.921, respectively. The training set accuracy was 92.5%, sensitivity was 100%, and specificity was 86.7%; the corresponding validation set values ​​were 84.8%, 85.7%, and 84.2%. Figures 5-6 ).

[0055] The SVM model was selected as the best prediction model for further performance evaluation. The confusion matrix shows that the SVM model correctly identified all 23 MVI samples and 26 out of 30 non-MVI samples in the training set; in the validation set, the SVM model correctly classified 12 out of 14 MVI samples and 16 out of 19 non-MVI samples. Figures 7-8 The above results indicate that the SVM model exhibits good classification balance. The Brier scores obtained from the calibration curves on the training and validation sets were 0.080 (95% CI: 0.055–0.110) and 0.110 (95% CI: 0.066–0.169), respectively. Figures 9-10 No significant calibration differences were observed between the two queues, indicating consistent calibration performance. Figure 11 As shown, decision curve analysis reveals that the SVM model provides a higher net benefit than the "full treatment" and "no treatment" strategies at most threshold probabilities, indicating that the model has good clinical application value. The SVM model achieved an AUC of 0.957 for predicting MVI in the complete cohort, which is superior to other clinical predictive factors. Figure 12 ).

[0056] The Shap method is used to interpret the SVM model and quantify the contribution of each feature to the prediction results. Figures 13-14 The Shap dependency plot showed that lactate, creatine, and pyruvate were positively correlated with Shap values, indicating a positive contribution to MVI prediction. Conversely, V2FC, H1FC, H2FC, and N,N-dimethylglycine were negatively correlated with Shap values, suggesting a negative contribution to the prediction results. Figures 15-21 ).

[0057] The above descriptions are merely some embodiments of the present invention. Those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A combination of biomarkers for assessing the risk of microvascular invasion in hepatocellular carcinoma, characterized in that, The biomarker combination consists of very low-density lipoprotein-free cholesterol-component 2, high-density lipoprotein-free cholesterol-component 2, N,N-dimethylglycine, high-density lipoprotein-free cholesterol-component 1, lactic acid, creatine, and pyruvate.

2. The use of the biomarker combination according to claim 1 in the preparation of products for assessing the risk of microvascular invasion in hepatocellular carcinoma.

3. The use of a product that detects the metabolic levels of each biomarker in the biomarker combination according to claim 1 in a sample in the preparation of a product for assessing the risk of microvascular invasion in hepatocellular carcinoma.

4. The application according to claim 3, characterized in that, The sample was plasma.

5. The application according to claim 3 or 4, characterized in that, The product used to detect the metabolic levels of each biomarker in the biomarker combination according to claim 1 in the sample is a reagent, kit, chip, and / or instrument suitable for detecting the metabolic levels of each biomarker in the biomarker combination according to claim 1 in the sample by nuclear magnetic resonance spectroscopy.

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