Biomarker combination for prognosis evaluation of liver cancer and application of biomarker combination

Abstract

The invention discloses a biomarker combination for prognosis evaluation of liver cancer and application of the biomarker combination. The biomarker combination is prepared from H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutaric acid, lysine, L2TG and glycine. The biomarker combination provided by the invention can be applied to preparation of a product for liver cancer prognosis evaluation, and a liver cancer recurrence prediction model for evaluating liver cancer prognosis, which is constructed based on the biomarker combination provided by the invention, is high in prediction efficiency and can quickly and accurately evaluate the liver cancer recurrence risk.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology and relates to a combination of biomarkers for prognostic assessment of 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. Although surgical resection, liver transplantation, and local ablation are recommended for the treatment of early-stage HCC (Barcelona Clinical Stages of Liver Cancer, BCLC Stage 0-A), the postoperative recurrence rate remains high, seriously affecting patient prognosis. Accurate prognostic prediction of HCC can help clinicians develop more scientific, precise, and personalized treatment plans, allocate medical resources more rationally, and improve patients' quality of life. Developing precise prognostic prediction tools is crucial for identifying high-risk patients and guiding individualized treatment. Summary of the Invention

[0003] The main objective of this invention is to provide a combination of biomarkers for prognostic assessment of liver cancer and its application, in order to solve at least one of the above-mentioned technical problems.

[0004] According to a first aspect of the invention, a combination of biomarkers for prognostic assessment of liver cancer is provided, comprising: high-density lipoprotein-apolipoprotein A2-fraction 4 (HDL-4 Apo-A2, H4A2), high-density lipoprotein-cholesterol-fraction 4 (HDL-4 Cholesterol, H4CH), high-density lipoprotein-apolipoprotein A1-fraction 4 (HDL-4 Apo-A1, H4A1), very low-density lipoprotein-triglycerides-fraction 1 (VLDL-1 Triglycerides, V1TG), high-density lipoprotein-phospholipids-fraction 4 (HDL-4 Phospholipids, H4PL), high-density lipoprotein-free cholesterol-fraction 4 (HDL-4 Free Cholesterol, H4FC), tyrosine, 2-oxoglutaric acid, lysine, and low-density lipoprotein-triglycerides-fraction 2 (LDL-2... It is composed of triglycerides (L2TG) and glycine.

[0005] The biomarker combination of 11 metabolites or blood lipid indicators provided by this invention can be used to quickly and accurately assess the prognosis of liver cancer, and is especially suitable for effectively predicting the recurrence risk of HCC.

[0006] According to a second aspect of the invention, the use of the biomarker combination of the invention in the preparation of products for the prognostic assessment of liver cancer is provided.

[0007] Based on the biomarker combination provided by this invention, a high-predictive-efficacy hepatocellular carcinoma (HCC) recurrence prediction model that can be used to assess the prognosis of HCC can be constructed through various algorithms. Among them, the HCC recurrence prediction model constructed based on stepwise Cox regression variable selection-forward stepwise regression (StepCox[Forward]) and random survival forest (RSF) achieves a C-index of 0.76 in the validation set. In a complete cohort containing 88 HCC patients, its AUC for predicting 2-year recurrence of HCC is 0.811, which is superior to the AUC for predicting 2-year recurrence of HCC by traditional staging systems such as the Chinese Cancer Staging System (CNLC) and the Barcelona Clinical Cancer Staging System (BCLC).

[0008] In some implementations, the liver cancer recurrence prediction model can be deployed as an application, mini-program, APP, etc. By simply inputting the metabolic levels of the 11 biomarkers in the biomarker combination provided by this invention in plasma, the recurrence risk of liver cancer patients can be determined.

[0009] According to a third aspect of the invention, a product for detecting the metabolic levels of H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in a sample is provided for use in the preparation of a product for prognostic assessment of liver cancer.

[0010] In some embodiments, the product for detecting the metabolic levels of H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in a sample can be any reagent, kit, chip, and / or instrument known in the art capable of quantitatively detecting the metabolic levels of H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in a sample.

[0011] In some implementations, products for detecting the metabolic levels of H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in a sample can be reagents, kits, chips, and / or instruments suitable for detecting the metabolic levels of H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in a sample by nuclear magnetic resonance spectroscopy (NMR).

[0012] In some embodiments, the sample can be plasma. Therefore, using the biomarker combination provided by this invention to assess the prognosis of liver cancer offers advantages such as convenient and non-invasive sample acquisition, making it easy to promote and apply clinically.

[0013] In some implementations, liver cancer is referred to as HCC. Attached Figure Description

[0014] Figure 1 Among 117 prognostic models, the C-index of the validation set is ranked from largest to smallest, and the C-index of the top 20 prognostic models is compared between the training set and the validation set. Figure 2 ROC curves for predicting 1-year recurrence for HCC recurrence prediction models on the training and validation sets; Figure 3 ROC curves for predicting 2-year recurrence for HCC recurrence prediction models on the training and validation sets; Figure 4 Survival analysis of high-risk and low-risk groups in RS stratification determined by HCC recurrence prediction model; Figure 5 Comparison of ROC curves for HCC recurrence prediction models, CNLC staging, and BCLC staging in predicting 2-year recurrence. Detailed Implementation

[0015] 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.

[0016] Experimental Example 1: Validation of the diagnostic efficacy of a combination of biomarkers for prognostic assessment of liver cancer 1. Participant Clinical Information Preoperative blood samples were collected from 88 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 (PIVKA-II) level, tumor size, tumor multifocality, vascular invasion status, tumor stage, and recurrence-free survival (RFS). The median RFS for the 88 patients was 457 days.

[0017] Eighty-eight 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, indicating that the clinical characteristics of the two groups were balanced.

[0018] Table 1. Comparison of baseline characteristics of patients in the training and validation sets used to predict HCC recurrence.

[0019] 2. Quantitative detection of the metabolic levels of biomarkers in plasma (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.

[0020] (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.

[0021] (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, obtaining metabolic level data for H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in 88 plasma samples.

[0022] 3. Construction and validation of HCC recurrence prediction model (1) Model building Based on the metabolic levels of H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in plasma, a prognostic assessment model was constructed using the Mime1 R package. This R package can execute 117 ensemble machine learning algorithms, resulting in 117 prognostic models. The C-index was used to evaluate the model performance.

[0023] The results are as follows Figure 1As shown, the prognostic assessment model (hereinafter referred to as the "HCC recurrence prediction model") built based on the StepCox[forward]+RSF (forward stepwise regression + random survival forest) algorithm achieved the highest C-index in both the training set and the validation set, which were 0.9 and 0.76, respectively.

[0024] (2) Validation of the HCC recurrence prediction model on the predictive performance of HCC recurrence The predictive performance of the HCC recurrence prediction model for predicting 1-year and 2-year HCC recurrence was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC).

[0025] The results are as follows Figures 2-3 As shown, the HCC recurrence prediction model can effectively predict the risk of HCC recurrence. In the training and validation sets, the AUC values ​​for predicting 1-year recurrence are 0.955 and 0.843, respectively, and the AUC values ​​for predicting 2-year recurrence are 0.959 and 0.698, respectively.

[0026] (3) Assessment of independent prognostic factors We assessed whether the relative risk (RS) derived from the HCC recurrence prediction model was an independent prognostic factor for HCC recurrence-free survival (RFS) using univariate and multivariate Cox regression analysis.

[0027] The results are shown in Table 2.

[0028] Table 2 Results of univariate and multivariate Cox regression analysis

[0029] Univariate and multivariate Cox regression analysis showed that RS derived from the HCC recurrence prediction model is an independent prognostic factor for HCC recurrence-free survival (RFS), indicating that the RS scoring system constructed by the HCC recurrence prediction model can independently predict the recurrence-free survival of HCC patients and has good risk stratification ability and clinical applicability.

[0030] The optimal cutoff value for RS was determined using Xtile software. Based on the optimal cutoff value, 88 patients were divided into high-risk and low-risk groups, and Kaplan-Meier curves and log-rank tests were used to analyze survival differences.

[0031] The results are as follows Figure 4 As shown.

[0032] Based on the Xtile analysis results, a cutoff value of 14.90 was set, and patients were divided into a high-risk group (n = 17) and a low-risk group (n = 71). Survival analysis showed a significant difference in recurrence-free survival (RFS) between the two groups. P <0.001).

[0033] (4) Comparison of 2-year relapse prediction efficacy The predictive performance of the HCC recurrence prediction model, the Chinese Cancer Staging System (CNLC), and the Barcelona Clinical Cancer Staging System (BCLC) for predicting HCC recurrence at 2 years was evaluated and compared using ROC curves. The results are as follows: Figure 5 As shown.

[0034] Comparative analysis of 2-year recurrence prediction results showed that, in the complete cohort, the AUC value of the HCC recurrence prediction model provided by this invention was significantly higher, reaching 0.811, which was significantly better than the CNLC staging system (AUC=0.607) and the BCLC staging system (AUC=0.588).

[0035] To facilitate clinical application, the HCC recurrence prediction model provided by this invention can be deployed as a web application. Thus, by simply inputting the metabolic levels of H4A2, H4CH, H4A1, V1TG, H4PL, H4FC, tyrosine, 2-ketoglutarate, lysine, L2TG, and glycine in a plasma sample, the RS value can be obtained, thereby predicting the risk of HCC recurrence.

[0036] 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 prognostic assessment of liver cancer, characterized in that, The biomarker combination consists of high-density lipoprotein-apolipoprotein A2-component 4, high-density lipoprotein-cholesterol-component 4, high-density lipoprotein-apolipoprotein A1-component 4, very low-density lipoprotein-triglycerides-component 1, high-density lipoprotein-phospholipids-component 4, high-density lipoprotein-free cholesterol-component 4, tyrosine, 2-ketoglutarate, lysine, low-density lipoprotein-triglycerides-component 2, and glycine.

2. The use of the biomarker combination according to claim 1 in the preparation of products for prognostic assessment of liver cancer.

3. The application of a product for detecting the metabolic levels of each biomarker in the biomarker combination according to claim 1 in a sample in the preparation of a product for prognostic assessment of liver cancer.

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 liver cancer mentioned is hepatocellular carcinoma.

6. The application according to claim 5, 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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