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

Abstract

The invention discloses a biomarker combination for radiotherapy prognosis evaluation of liver cancer patients and application of the biomarker combination. The biomarker combination is composed of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC. The biomarker combination provided by the invention is used for evaluating the radiotherapy prognosis of the liver cancer patient, not only is high in accuracy and good in sensitivity and specificity, but also has the advantages of small trauma to the patient, convenience in sampling, low detection cost and the like, and is suitable for large-scale screening.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology and relates to a biomarker combination for radiotherapy prognosis assessment of liver cancer patients and its application. Background Art

[0002] Liver cancer, especially hepatocellular carcinoma (HCC), is one of the leading causes of cancer-related deaths worldwide, with the majority of deaths occurring in HCC patients. Despite significant advances in treatment options, including but not limited to surgical resection, liver transplantation, local ablation therapy, transarterial chemoembolization (TACE), and radiotherapy, overall survival remains low. For many patients, these treatments are limited in effectiveness due to late-stage tumor detection or the presence of unresectable tumors.

[0003] As a non-invasive treatment method, radiotherapy shows potential in controlling tumor growth and relieving symptoms. In particular, with the development of precise radiotherapy technologies, such as stereotactic body radiation therapy (SBRT) and proton beam therapy, it has become possible to treat liver cancer that is difficult to surgically remove or recurrent. However, the efficacy of radiotherapy varies greatly between different patients. Some patients show a good response, with significant tumor shrinkage and symptom relief; while other patients may experience disease progression or only obtain short-term symptom relief. This difference may be caused by a variety of factors, including but not limited to the biological characteristics of the tumor, the patient's overall health status and immune status.

[0004] Currently, methods for assessing the efficacy of radiotherapy in patients with liver cancer primarily rely on imaging studies (such as computed tomography (CT) and magnetic resonance imaging (MRI), clinical indicators (such as alpha-fetoprotein (AFP) levels), and other biomarkers. While these methods can reflect treatment efficacy to a certain extent, they often fail to accurately and early predict individualized treatment responses. For example, imaging studies typically require time to detect changes after treatment and have limited sensitivity for changes in small lesions. Traditional blood markers, such as AFP, also lack specificity and sensitivity as standalone criteria. Furthermore, des-gamma-carboxy prothrombin (DCP), a biomarker associated with liver cancer, has been used to aid diagnosis and monitor disease progression. Studies have shown that DCP levels are closely associated with the development and progression of liver cancer and can be used to distinguish benign liver diseases from malignant tumors. However, the application of DCP in predicting the efficacy of radiotherapy in patients with liver cancer is still in the exploratory stage, and its use alone may not fully reflect the diversity of treatment responses.

[0005] Therefore, it is particularly important to develop a method that can early and accurately assess the response of liver cancer patients to radiotherapy. Summary of the Invention

[0006] The main purpose of the present invention is to provide a biomarker combination for radiotherapy prognosis assessment of liver cancer patients and its application, so as to solve at least one of the above technical problems.

[0007] According to a first aspect of the present invention, a biomarker combination for evaluating the prognosis of liver cancer patients undergoing radiotherapy is provided, which comprises intermediate density lipoprotein (IDPN), very low density lipoprotein-cholesterol (VLCH), very low density lipoprotein-triglyceride-Component 5 (V5TG), very low density lipoprotein-free cholesterol (VLFC), very low density lipoprotein-cholesterol-Component 1 (V1CH), very low density lipoprotein-free cholesterol-Component 1 (V1FC), very low density lipoprotein-free cholesterol-Component 4 (VLFC), very low density lipoprotein-cholesterol-Component 5 (VLCH), very low density lipoprotein-triglyceride-Component 5 (V5TG), very low density lipoprotein-free cholesterol (VLFC), very low density lipoprotein-cholesterol-Component 1 (V1CH), very low density lipoprotein-free cholesterol-Component 1 (V1FC), very low density lipoprotein-free cholesterol-Component 4 (VLFC), very low density lipoprotein-free cholesterol-Component 5 (VLFC), very low density lipoprotein-free cholesterol-Component 5 (VL 4, V4FC).

[0008] The biomarker combination of seven lipoprotein parameters provided by the present invention can be used to quickly and accurately assess the prognosis of liver cancer patients undergoing radiotherapy. It is particularly suitable for early and accurate assessment of whether liver cancer patients with abnormal prothrombin expression will benefit from radiotherapy. Therefore, by detecting the metabolic levels of each biomarker in the biomarker combination provided by the present invention, it is possible to effectively distinguish different response types of liver cancer patients after receiving radiotherapy, thereby guiding more accurate treatment decisions. This not only provides clinicians with more accurate treatment information, but also supports personalized treatment decisions, thereby improving patients' quality of life and life expectancy.

[0009] In addition, the biomarker combination provided by the present invention can overcome the differences between individual patients when used to evaluate the prognosis of liver cancer patients undergoing radiotherapy, and has good sensitivity and specificity.

[0010] According to a second aspect of the present invention, there is provided a use of the biomarker combination of the present invention in the preparation of a product for radiotherapy prognosis assessment of liver cancer patients.

[0011] According to a third aspect of the present invention, there is provided the use of a product for detecting the metabolic levels of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC in a sample in the preparation of a product for radiotherapy prognosis assessment in liver cancer patients.

[0012] In the present invention, the product for detecting the metabolic levels of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC in a sample can be any reagent, kit, chip and / or instrument known in the art that can quantitatively detect the metabolic levels of lipoprotein parameter indicators such as IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC in a sample.

[0013] In some embodiments, the product for detecting the metabolic levels of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC in a sample can be a reagent, kit, chip and / or instrument suitable for detecting the metabolic levels of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC in a sample by nuclear magnetic resonance spectroscopy (NMR) technology.

[0014] In some embodiments, the sample can be plasma or serum. Therefore, using the biomarker combination provided by the present invention to evaluate radiotherapy in patients with liver cancer has the advantages of minimal trauma to patients, convenient sampling, and low testing cost, and is suitable for large-scale screening.

[0015] According to a fourth aspect of the present invention, there is provided an evaluation model for evaluating the radiotherapy prognosis of liver cancer patients using the biomarker combination provided by the present invention. The evaluation model mainly evaluates the radiotherapy prognosis of liver cancer patients through the following steps:

[0016] (1) Calculate the mutation index using the following formula (1):

[0017] mutation index=W0+W1X1+W2X2+W3X3+W4X4+W5X5+W6X6+W7X7 (1)

[0018] Among them, W0 is -2.9373; X1 to X7 are the metabolic levels of intermediate density lipoprotein, very low density lipoprotein-cholesterol, very low density lipoprotein-triglyceride-fraction 5, very low density lipoprotein-free cholesterol, very low density lipoprotein-cholesterol-fraction 1, very low density lipoprotein-free cholesterol-fraction 1, and very low density lipoprotein-free cholesterol-fraction 4, respectively; W1 to W7 are 0.0023, 0.0429, 0.0480, 0.0556, 0.1512, 0.2698, and 0.9336, respectively;

[0019] (2) The mutation index is normalized using the Sigmoid function to obtain the risk coefficient RS, that is, RS = 1 / (1+e -x );

[0020] (3) Compare RS with the risk coefficient threshold. If RS ≥ the risk coefficient threshold, it indicates that the subject may benefit from radiotherapy and have a good prognosis; if RS < the risk coefficient threshold, it indicates that the subject may not benefit from radiotherapy and has a poor prognosis.

[0021] In some embodiments, the risk factor threshold may be 0.5.

[0022] According to the fifth aspect of the present invention, a product for evaluating the radiotherapy prognosis of liver cancer patients is provided, which comprises a product for detecting the metabolic levels of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC in a sample and the evaluation model provided by the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described in detail below with reference to the following embodiments. The examples are provided for illustrative purposes only and are not intended to limit the present invention in any way. Unless otherwise specified, the raw materials and reagents used in the examples are commercially available conventional products. Experimental procedures in the examples where specific conditions are not specified are generally performed in accordance with conventional conditions in the art or the conditions recommended by the manufacturer.

[0024] Example 1 Construction of an evaluation model for evaluating the radiotherapy prognosis of liver cancer patients

[0025] 1. Sample collection

[0026] EDTA plasma was collected from patients with liver cancer and abnormal DCP expression within one week after radiotherapy. The patients were followed up for six months to obtain the prognostic effect of radiotherapy. Patients who met the following conditions were judged to have benefited from radiotherapy, while those who did not meet the conditions were judged to have not benefited from radiotherapy:

[0027] (1) Symptom relief: Symptoms such as pain, obstruction, or bleeding caused by the tumor are alleviated. For example, a patient may have liver pain before radiotherapy, but may feel significantly less pain after radiotherapy.

[0028] (2) Tumor control: After radiotherapy, the growth of tumors is controlled and can even be reduced in some cases. For example, in some cases, complete disappearance of tumors can be observed using spiral tomotherapy.

[0029] (3) Prolonged survival: Radiotherapy helps patients prolong their survival.

[0030] (4) Improved quality of life: In addition to prolonging life, it improves the patient's quality of life by relieving symptoms and reducing complications.

[0031] A total of 34 EDTA plasma samples from patients who benefited from radiotherapy and 56 EDTA plasma samples from patients who did not benefit from radiotherapy were collected and stored at -20°C for future use.

[0032] 2. Materials and Reagents

[0033] (1) Instrument: Nuclear magnetic resonance spectrometer (600 MHz) (Bruker Biospin AG).

[0034] (2) Main reagents: Plasma buffer (Bruker), NMR Tubes / cap3 (Bruker).

[0035] 3. Biomarker screening

[0036] (1) Sample testing

[0037] S1. Remove the plasma sample from the refrigerator and wait for it to be completely thawed. Then, mix 340 μL of plasma with 340 μL of NMR lipid buffer in a 1:1 volume ratio. After thorough mixing, take 600 μL of the mixture and place it in a 5 mm NMR tube.

[0038] S2. Load the NMR tube into the autosampler and wait for testing. Use the Bruker 600M IVDr NMR equipment to perform testing according to the plasma testing procedure.

[0039] S3. Off-machine spectra were normalized using the QuantRef management system built into Bruker's Topspin software, normalizing the spectral intensity to proton concentration in mg / dL. Chemical shift correction was performed using TSP (3-trimethylsilylpropionic acid) and the doublet signal of alanine at 1.48 ppm. Qualitative analysis of NMR spectra was performed using a Bruker NMR library. Metabolites were quantified using the integration of signal clusters at specific chemical shift positions.

[0040] S4. The obtained metabolite data, i.e., 114 blood lipid indicators and 39 small molecule metabolite indicators, were screened for differentiation indicators using the following method:

[0041] 1) Fill the blank value with the minimum detection limit LOD of the indicator;

[0042] 2) Take the median of each indicator in each group as Mn_pos and Mn_neg;

[0043] 3) The ratio of the medians of the two groups for each indicator is taken as the difference multiple Fn = Mn_pos / Mn_neg;

[0044] 4) Perform t-test on the number of each indicator in the two groups to obtain pn;

[0045] 5) Screen the indicators with Fn ≥ 1.2 or Fn ≤ 0.8 and pn < 0.05 as differentiation indicators.

[0046] As shown in Table 1, a total of 7 differential indicators were screened: intermediate density lipoprotein (IDPN), very low density lipoprotein-cholesterol (VLCH), very low density lipoprotein-triglyceride-fraction 5 (V5TG), very low density lipoprotein-free cholesterol (VLFC), very low density lipoprotein-cholesterol-fraction 1 (V1CH), very low density lipoprotein-free cholesterol-fraction 1 (V1FC), and very low density lipoprotein-free cholesterol-fraction 4 (V4FC).

[0047] Table 1 Differentiation index values

[0048] FC p IDPN 0.37 4.10E-07 VLC 0.41 9.98E-09 V5TG 0.71 8.23E-04 VLFC 0.52 5.24E-09 V1CH 0.38 6.72E-06 V1FC 0.26 5.13E-07 V4FC 0.18 2.55E-12

[0049] 4. Evaluation model construction

[0050] (1) Based on the sample test data of the 7 indicators screened above, the model was established using the logistic regression method, and the optimal model was selected. The model with the largest test set AUC (AUC = 0.9941) among the optimal models was used as the optimal screening model, which is the evaluation model for evaluating the radiotherapy prognosis of liver cancer patients.

[0051] in:

[0052] mutation index = W0 + W1X1 + W2X2 + W3X3 + W4X4 + W5X5 + W6X6 + W7X7

[0053] W is the weight coefficient corresponding to each biomarker, X is the quantitative detection value (i.e., metabolic level) corresponding to each biomarker, and W0 is a constant.

[0054] Specifically, W0 is -2.9373; X1 to X7 respectively correspond to the quantitative detection values of the biomarkers intermediate density lipoprotein (IDPN), very low density lipoprotein-cholesterol (VLCH), very low density lipoprotein-triglyceride-component 5 (V5TG), very low density lipoprotein-free cholesterol (VLFC), very low density lipoprotein-cholesterol-component 1 (V1CH), very low density lipoprotein-free cholesterol-component 1 (V1FC), very low density lipoprotein-free cholesterol-component 4 (V4FC); W1 to W7 are 0.0023, 0.0429, 0.0480, 0.0556, 0.1512, 0.2698, 0.9336 in sequence.

[0055] (2) Normalize the mutation index of each sample using the Sigmoid function (1 / (1 + e -x )) to obtain the risk coefficient RS, RS = 1 / (1 + e -x ), and determine the threshold of RS to be 0.5.

[0056] When applying the above evaluation model to evaluate the radiotherapy prognosis of liver cancer patients, compare the risk coefficient RS of the patient with the RS threshold. If RS ≥ RS threshold, it indicates that the subject may benefit from radiotherapy treatment and has a good prognosis; if RS < RS threshold, it indicates that the efficacy of radiotherapy treatment for the subject may be poor, and the subject cannot benefit from radiotherapy treatment and has a poor prognosis.

[0057] Example 2 Accuracy Verification

[0058] Referring to the method in "1. Sample Collection" of Example 1, 27 EDTA plasma samples of liver cancer patients with abnormal DCP expression within 1 week after radiotherapy and their radiotherapy prognosis were additionally collected.

[0059] Referring to the method of steps S1 to S3 in "(1) Sample detection" of Example 1, the quantitative detection values of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC and V4FC are measured, and then the patient's risk coefficient RS is obtained using the evaluation model. Finally, by judging whether the patient's RS is not less than 0.5, it is evaluated whether the patient can benefit from radiotherapy treatment, and the prognosis effect is evaluated and compared with the actual prognosis effect. The accuracy of using the biomarker combination provided by the present invention to evaluate the prognosis of liver cancer patients undergoing radiotherapy is calculated.

[0060] The results are shown in Table 2.

[0061] Table 2 Comparison of patient test results and actual radiotherapy prognosis

[0062]

[0063]

[0064] In Table 2, “-” indicates that the patient benefited from radiotherapy and had a good prognosis; “+” indicates that the patient did not benefit from radiotherapy and had a poor prognosis; “√” indicates that the evaluation result of the radiotherapy prognosis of liver cancer patients evaluated using the biomarker combination provided by the present invention is consistent with the patient's actual radiotherapy prognosis; “×” indicates that the evaluation result of the radiotherapy prognosis of liver cancer patients evaluated using the biomarker combination provided by the present invention is inconsistent with the patient's actual radiotherapy prognosis.

[0065] The statistical results in Table 2 show that among the 27 validation patients collected, 10 patients had a good prognosis and 17 patients had a poor prognosis after radiotherapy. When the biomarker combination provided by the present invention was used to evaluate the prognosis of radiotherapy, the evaluation results of 26 patients were consistent with their actual prognosis, and the overall accuracy of the validation cohort was 96%.

[0066] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the inventive concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A biomarker combination for evaluating the prognosis of liver cancer patients undergoing radiotherapy, characterized in that: The biomarker combination consists of intermediate density lipoprotein, very low density lipoprotein-cholesterol, very low density lipoprotein-triglyceride-5th component, very low density lipoprotein-free cholesterol, very low density lipoprotein-cholesterol-1st component, very low density lipoprotein-free cholesterol-1st component and very low density lipoprotein-free cholesterol-4th component.

2. Use of the biomarker combination according to claim 1 in the preparation of a product for radiotherapy prognosis assessment in patients with liver cancer.

3. Use of a product for detecting the metabolic level of each biomarker in the biomarker combination according to claim 1 in a sample in the preparation of a product for radiotherapy prognosis assessment of liver cancer patients.

4. The use according to claim 3, characterized in that The sample is plasma or serum.

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

6. The use according to any one of claims 2 to 5, characterized in that: The liver cancer patient is a liver cancer patient with abnormal prothrombin expression.

7. An evaluation model for evaluating the radiotherapy prognosis of liver cancer patients using the biomarker combination according to claim 1, characterized in that: The method for evaluating the radiotherapy prognosis of liver cancer patients using the evaluation model comprises the following steps: (1) Calculate the mutation index using the following formula (1): mutation index=W0+W1X1+W2X2+W3X3+W4X4+W5X5+W6X6+W7X7 (1) Among them, W0 is -2.9373; X1 to X7 are the metabolic levels of intermediate density lipoprotein, very low density lipoprotein-cholesterol, very low density lipoprotein-triglyceride-fraction 5, very low density lipoprotein-free cholesterol, very low density lipoprotein-cholesterol-fraction 1, very low density lipoprotein-free cholesterol-fraction 1, and very low density lipoprotein-free cholesterol-fraction 4, respectively; W1 to W7 are 0.0023, 0.0429, 0.0480, 0.0556, 0.1512, 0.2698, and 0.9336, respectively; (2) Normalize the mutation index using the Sigmoid function to obtain the risk coefficient RS; (3) Compare RS with the risk coefficient threshold. If RS ≥ the risk coefficient threshold, it indicates a good prognosis; if RS < the risk coefficient threshold, it indicates a poor prognosis.

8. The evaluation model according to claim 7, characterized in that The risk factor threshold is 0.

5.

9. The evaluation model according to claim 7 or 8, characterized in that The liver cancer patient is a liver cancer patient with abnormal prothrombin expression.

10. A product for evaluating the prognosis of radiotherapy in patients with liver cancer, characterized in that: The composition comprises a product for detecting the metabolic level of each biomarker in the biomarker combination according to claim 1 in a sample and an evaluation model according to any one of claims 7 to 8.

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