CN120446188A - Biomarker combination for prognosis evaluation of radiotherapy of liver cancer patient and application thereof - Google Patents
Biomarker combination for prognosis evaluation of radiotherapy of liver cancer patient and application thereofInfo
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- CN120446188A CN120446188A CN202510471206.0A CN202510471206A CN120446188A CN 120446188 A CN120446188 A CN 120446188A CN 202510471206 A CN202510471206 A CN 202510471206A CN 120446188 A CN120446188 A CN 120446188A
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- density lipoprotein
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Abstract
The invention discloses a biomarker combination for prognosis evaluation of radiotherapy of a liver cancer patient and application thereof, wherein the biomarker combination consists of IDPN, VLCH, V TG, VLFC, V CH, V1FC and V4 FC. The biomarker combination provided by the invention is used for evaluating the prognosis of the radiotherapy of a liver cancer patient, has the advantages of high accuracy, good sensitivity and specificity, small trauma to the patient, convenient sampling, low detection cost and the like, and is suitable for large-scale screening.
Description
Technical Field
The invention belongs to the technical field of biological medicines, and relates to a biomarker combination for prognosis evaluation of radiotherapy of a liver cancer patient and application thereof.
Background
Liver cancer, especially hepatocellular carcinoma (Hepatocellular Carcinoma, HCC), is one of the leading causes of cancer-related death worldwide, most of which are HCC patients. Despite significant advances in therapeutic approaches, including but not limited to surgical resection, liver transplantation, local ablation therapy, trans-arterial chemoembolization (TACE), and radiation therapy, the overall survival rate of patients remains low. For many patients, these treatments have limited effectiveness due to late stage tumor findings or the presence of non-surgical resection.
Radiation therapy, a non-invasive treatment, has shown potential in controlling tumor growth and alleviating symptoms. Particularly, with the development of precise radiotherapy technology, such as stereotactic radiotherapy (SBRT) and proton beam therapy, it is possible to treat liver cancer which is difficult to undergo surgical excision or recurrent. However, the efficacy of radiation therapy varies greatly from patient to patient. Some patients exhibit good response, with significantly reduced tumors and remission, while others may experience disease progression or obtain only short term remission. Such differences may be caused by a variety of factors including, but not limited to, the biological nature of the tumor, the overall health and immune status of the patient, and the like.
Currently, methods for assessing the effectiveness of radiotherapy in liver cancer patients rely mainly on imaging examinations (e.g., computed tomography CT, magnetic resonance imaging MRI), clinical indicators (e.g., alpha fetoprotein AFP levels), and other biomarkers. While these methods may reflect therapeutic effects to some extent, they often fail to predict early, accurate personalized therapeutic response. For example, imaging examinations generally take some time to observe post-treatment changes and have limited sensitivity to changes in microscopic lesions, while traditional blood markers such as AFP, which are not specific and sensitive enough to be a diagnostic criterion alone. In addition, abnormal prothrombin (Des-gamma-carboxy prothrombin, DCP) has been used as a biomarker associated with liver cancer to aid in diagnosis and monitoring disease progression. Research shows that DCP level is closely related to liver cancer occurrence and development, and can be used for distinguishing benign liver diseases from malignant tumors. However, the use of DCP in predicting the effectiveness of radiotherapy in liver cancer patients is still under investigation and its use alone may not be sufficient to fully reflect the diversity of therapeutic responses.
Therefore, it is important to develop a method for early and accurate evaluation of the response of liver cancer patients to radiotherapy.
Disclosure of Invention
The invention mainly aims to provide a biomarker combination for prognosis evaluation of radiotherapy of a liver cancer patient and application thereof, so as to solve at least one of the technical problems.
According to a first aspect of the present invention, there is provided a biomarker combination for prognosis evaluation of radiotherapy in a liver cancer patient, which consists of intermediate density lipoprotein (INTERMEDIATE DENSITY lipoprotein Particle Number, IDPN), very low density lipoprotein-Cholesterol (Very low density lipoprotein-Cholesterol, VLCH), very low density lipoprotein-Triglyceride-5 th fraction (Very low density lipoprotein-triglycolide-Component 5, v5 tg), very low density lipoprotein-Free Cholesterol (Very low density lipoprotein-Free Cholesterol, VLFC), very low density lipoprotein-Cholesterol-1 st fraction (Very low density lipoprotein-Cholesterol-Component 1, v1 ch), very low density lipoprotein-Free Cholesterol-1 st fraction (Very low density lipoprotein-Free Cholesterol-Component 1, v1 fc), very low density lipoprotein-Free Cholesterol-4 th fraction (Very low density lipoprotein-Free Cholesterol-Component 4, v4 fc).
The biomarker combination consisting of 7 lipoprotein parameter indexes provided by the invention can be applied to rapidly and accurately evaluating prognosis of a liver cancer patient after radiotherapy treatment, and is particularly suitable for early and accurately evaluating whether the liver cancer patient with abnormal prothrombin expression benefits after radiotherapy. Therefore, by detecting the metabolism level of each biomarker in the biomarker combination provided by the invention, different reaction types of a liver cancer patient after receiving radiotherapy can be effectively distinguished, so that more accurate treatment decisions are guided, more accurate treatment information can be provided for a clinician, personalized treatment decisions can be supported, and the life quality and the life expectancy of the patient are further improved.
In addition, when the biomarker combination provided by the invention is used for evaluating the prognosis of radiotherapy of a liver cancer patient, the difference among individuals of the patient can be overcome, and the sensitivity and the specificity are good.
According to a second aspect of the present invention there is provided the use of a biomarker combination according to the present invention in the manufacture of a product for prognosis evaluation of radiotherapy in a liver cancer patient.
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, V TG, VLFC, V CH, V1FC and V4FC in a sample in the manufacture of a product for prognosis evaluation of radiotherapy in a liver cancer patient.
In the present invention, the product for detecting the metabolic levels of IDPN, VLCH, V TG, VLFC, V CH, V1FC, and V4FC in a sample may be a reagent, a kit, a chip, and/or an instrument, which are known in the art to quantitatively detect metabolic levels of lipoprotein parameter indicators such as IDPN, VLCH, V5TG, VLFC, V1CH, V1FC, and V4FC in a sample.
In some embodiments, the product of detecting the metabolic levels of IDPN, VLCH, V TG, VLFC, V CH, V1FC, and V4FC in the sample may be a reagent, kit, chip, and/or instrument suitable for detecting the metabolic levels of IDPN, VLCH, V5TG, VLFC, V1CH, V1FC, and V4FC in the sample by nuclear magnetic resonance spectroscopy (Nuclear Magnetic Resonance, NMR) techniques.
In some embodiments, the sample may be plasma or serum. Therefore, the biomarker combination provided by the invention is used for evaluating the radiotherapy of a liver cancer patient, has the advantages of small trauma to the patient, convenient sampling, low detection cost and the like, and is suitable for large-scale screening.
According to a fourth aspect of the present invention, there is provided an evaluation model for evaluating prognosis of radiotherapy in a liver cancer patient by using the biomarker combination provided by the present invention, the evaluation model evaluating prognosis of radiotherapy in a liver cancer patient mainly by:
(1) Mutation index is calculated by the following equation (1):
mutation index＝W0+W1X1+W2X2+W3X3+W4X4+W5X5+W6X6+W7X7 (1)
Wherein, W 0 is-2.9373, X 1～X7 is the metabolism level of middle density lipoprotein, very low density lipoprotein-cholesterol, very low density lipoprotein-triglyceride-5 th component, very low density lipoprotein-free cholesterol, very low density lipoprotein-cholesterol-1 st component, very low density lipoprotein-free cholesterol-4 th component in sequence, W 1～W7 is 0.0023, 0.0429, 0.0480, 0.0556, 0.1512, 0.2698, 0.9336 in sequence;
(2) Carrying out normalization on mutation index by using a Sigmoid function to obtain a risk coefficient RS, namely RS=1/(1+e -x);
(3) And comparing the RS with a risk coefficient threshold, if the RS is more than or equal to the risk coefficient threshold, prompting the subject to possibly benefit in radiotherapy treatment and having good prognosis, and if the RS is less than the risk coefficient threshold, prompting the subject to possibly have poor curative effect in radiotherapy treatment, failing to benefit in radiotherapy treatment and having poor prognosis.
In some embodiments, the risk factor threshold may be 0.5.
According to a fifth aspect of the present invention there is provided a product for prognosis evaluation of radiotherapy in a patient with liver cancer, the composition of which comprises a product for detecting the metabolic levels of IDPN, VLCH, V TG, VLFC, V1CH, V1FC and V4FC in a sample and an evaluation model provided by the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments. The examples are for illustration only and are not intended to limit the invention in any way. Unless otherwise indicated, the starting materials and reagents used in the examples were conventional products commercially available, and the experimental procedures in which no particular conditions were noted in the examples were generally in accordance with the conventional conditions in the art or according to the manufacturer's recommendations.
Example 1 construction of evaluation model for evaluating prognosis of radiotherapy in liver cancer patient
1. Sample collection
And (3) collecting EDTA plasma within 1 week after the liver cancer patient with abnormal DCP expression is subjected to radiotherapy treatment, carrying out follow-up visit for half a year, obtaining the radiotherapy prognosis effect condition of the patient, judging that the patient meeting the following conditions benefits the radiotherapy, and judging that the patient not meeting the conditions does not benefit the radiotherapy:
(1) Symptomatic relief, relief of symptoms such as pain, obstruction or bleeding caused by a tumor. For example, patients may have symptoms of pain in the liver area prior to radiotherapy treatment, and may be able to feel a significant reduction in pain after radiotherapy treatment.
(2) Tumor control-tumor size growth is controlled, and in some cases even reduced, following radiation therapy. For example, in some cases, complete tumor disappearance can be observed using spiral tomotherapy techniques.
(3) Lifetime extension the radiotherapy helps patients to prolong lifetime.
(4) The quality of life is improved by alleviating symptoms and reducing complications in addition to prolonging life.
Together, 34 patient EDTA plasma samples benefiting from radiotherapy and 56 patient EDTA plasma samples not benefiting from radiotherapy are collected and stored at-20 ℃ for standby.
2. Materials and reagents
(1) Instrument nuclear magnetic resonance spectrometer (600 MHz) (Bruker Biospin AG Co.).
(2) The main reagents are Plasma buffer (Bruker Co.) and NMR Tubes/cap3 (Bruker Co.).
3. Biomarker screening
(1) Sample detection
S1, taking a Plasma sample out of a refrigerator, after the Plasma sample is completely thawed, mixing 340 mu L of Plasma with 340 mu L of NMR blood lipid buffer (Plasma buffer) according to a volume ratio of 1:1, and placing 600 mu L of the mixed solution in a 5mm nuclear magnetic tube after fully mixing.
S2, loading the nuclear magnetic tube into an automatic sampler for waiting detection, and detecting according to a plasma detection program by using Bruker 600M IVDr nuclear magnetic equipment.
S3, normalizing the lower spectrogram through a QuantRef management system built in Topspin software of Bruker, so that the intensity of the spectrogram is normalized to the proton concentration of mg/dL. Chemical shifts were corrected for using the alanine bimodal signal at TSP (3-trimethylsilyl propionic acid) and 1.48 ppm. And (3) carrying out qualitative analysis on the spectrogram of the nuclear magnetic detection based on Bruker nuclear magnetic self-built library. Metabolites were quantified using the integral of signal clusters at specific chemical shift positions.
S4, screening the obtained metabolite data, namely 114 blood lipid indexes and 39 micromolecular metabolite indexes according to the following method:
1) Filling the null value into the lowest detection limit LOD of the index;
2) Taking the median of each index as Mn_pos and Mn_neg in each group;
3) The median ratio of two groups of each index is set as a difference multiple Fn=Mn_pos/Mn_neg;
4) Performing t.test test operation on the numbers of each index in the two groups to obtain pn;
5) Screening the indexes of Fn more than or equal to 1.2 or Fn less than or equal to 0.8 and pn <0.05 as differentiation indexes.
As shown in Table 1, 7 differential indexes, i.e., intermediate density lipoprotein (IDPN), very low density lipoprotein-cholesterol (VLCH), very low density lipoprotein-triglyceride-5 th fraction (V5 TG), very low density lipoprotein-free cholesterol (VLFC), very low density lipoprotein-cholesterol-1 st fraction (V1 CH), very low density lipoprotein-free cholesterol-1 st fraction (V1 FC), very low density lipoprotein-free cholesterol-4 th fraction (V4 FC), were selected.
TABLE 1 differentiation index values
| FC | p | |
| IDPN | 0.37 | 4.10E-07 |
| VLCH | 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 |
4. Evaluation model construction
(1) And establishing a model based on the sample detection data of the 7 indexes by using a logistic regression method, picking out a preferred model, and taking a model with the maximum test set AUC (AUC=0.9941) in the preferred model as an optimal screening model, namely an evaluation model for evaluating the prognosis of radiotherapy of a liver cancer patient.
Wherein:
mutation index＝W0+W1X1+W2X2+W3X3+W4X4+W5X5+W6X6+W7X7
W is a weight coefficient corresponding to each biomarker, X is a quantitative detection value (i.e., metabolic level) corresponding to each biomarker, and W 0 is a constant.
Specifically, W 0 is-2.9373, X 1～X7 sequentially corresponds to quantitative detection values of biomarker intermediate density lipoprotein (IDPN), very low density lipoprotein-cholesterol (VLCH), very low density lipoprotein-triglyceride-5 th component (V5 TG), very low density lipoprotein-free cholesterol (VLFC), very low density lipoprotein-cholesterol-1 st component (V1 CH), very low density lipoprotein-free cholesterol-1 st component (V1 FC) and very low density lipoprotein-free cholesterol-4 th component (V4 FC), and W 1～W7 sequentially is 0.0023, 0.0429, 0.0480, 0.0556, 0.1512, 0.2698 and 0.9336.
(2) The mutation index of each sample was normalized using the Sigmoid function (1/(1+e -x)) to give a risk factor RS, rs=1/(1+e -x), and the threshold for RS was determined to be 0.5.
When the evaluation model is used for evaluating the prognosis of the radiotherapy of a liver cancer patient, the risk coefficient RS of the patient is compared with an RS threshold, if the RS is larger than or equal to the RS threshold, the subject is prompted to possibly benefit in the radiotherapy treatment, and the prognosis is good, if the RS is smaller than the RS threshold, the subject is prompted to possibly not benefit in the radiotherapy treatment, and the prognosis is bad.
Example 2 accuracy verification
Referring to the method of example 1"1, sample collection", EDTA plasma samples and their prognostic effect of radiotherapy within 1 week after radiotherapy of 27 liver cancer patients with abnormal DCP expression were additionally collected.
Referring to the quantitative detection values of IDPN, VLCH, V TG, VLFC, V CH, V1FC and V4FC measured by the method of steps S1-S3 in the sample detection of the embodiment 1 "(1)", then using an evaluation model to obtain a risk coefficient RS of a patient, finally evaluating whether the patient can benefit in radiotherapy treatment by judging whether the RS of the patient is not less than 0.5, evaluating the prognosis effect condition of the patient, comparing the prognosis effect condition with the actual prognosis effect condition, and calculating the accuracy of evaluating the radiotherapy prognosis of the liver cancer patient by using the biomarker combination provided by the invention.
The results are shown in Table 2.
TABLE 2 comparison of patient detection and actual radiotherapy prognosis
In Table 2, "-" indicates that the patient benefited in radiotherapy treatment, "+" indicates that the patient did not benefited in radiotherapy treatment, and "v" indicates that the evaluation result of evaluating the prognosis of radiotherapy in a liver cancer patient by using the biomarker combination provided by the present invention is consistent with the actual prognosis of radiotherapy in the patient, and "×" indicates that the evaluation result of evaluating the prognosis of radiotherapy in a liver cancer patient by using the biomarker combination provided by the present invention is inconsistent with the actual prognosis of radiotherapy in the patient.
From the statistical results in table 2, it is clear that, among the 27 collected patients, 10 patients with good prognosis and 17 patients with poor prognosis after radiotherapy treatment, and when the biomarker combination provided by the present invention is used for radiotherapy prognosis evaluation, the evaluation results of 26 patients are consistent with the actual prognosis conditions, and the overall accuracy of the verification queue is 96%.
What has been described above is merely some embodiments of the present invention. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit of the invention.
Claims (10)
1. The biomarker combination for prognosis evaluation of radiotherapy of a liver cancer patient is characterized by comprising middle density lipoprotein, very low density lipoprotein-cholesterol, very low density lipoprotein-triglyceride-5 th component, very low density lipoprotein-free cholesterol, very low density lipoprotein-cholesterol-1 st component, very low density lipoprotein-free cholesterol-1 st component and very low density lipoprotein-free cholesterol-4 th component.
2. Use of a biomarker combination according to claim 1, in the manufacture of a product for prognosis evaluation of radiotherapy in a liver cancer patient.
3. Use of a product for detecting the metabolic level of each biomarker in a biomarker combination according to claim 1 in a sample for the manufacture of a product for prognosis evaluation of radiotherapy in a liver cancer patient.
4. The use according to claim 3, wherein the sample is plasma or serum.
5. The use according to claim 3 or 4, wherein 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, wherein the liver cancer patient is a liver cancer patient with abnormal prothrombin expression.
7. The method for evaluating the prognosis of radiotherapy in a liver cancer patient by using the biomarker combination according to claim 1, comprising the steps of:
(1) Mutation index is calculated by the following equation (1):
mutation index＝W0+W1X1+W2X2+W3X3+W4X4+W5X5+W6X6+W7X7 (1)
Wherein, W 0 is-2.9373, X 1～X7 is the metabolism level of middle density lipoprotein, very low density lipoprotein-cholesterol, very low density lipoprotein-triglyceride-5 th component, very low density lipoprotein-free cholesterol, very low density lipoprotein-cholesterol-1 st component, very low density lipoprotein-free cholesterol-4 th component in sequence, W 1～W7 is 0.0023, 0.0429, 0.0480, 0.0556, 0.1512, 0.2698, 0.9336 in sequence;
(2) Carrying out normalization on mutation index by using a Sigmoid function to obtain a risk coefficient RS;
(3) Comparing RS with the risk coefficient threshold, if RS is more than or equal to the risk coefficient threshold, prompting good prognosis, and if RS is less than the risk coefficient threshold, prompting bad prognosis.
8. The assessment model of claim 7, wherein the risk factor threshold is 0.5.
9. The assessment model according to claim 7 or 8, wherein the liver cancer patient is a liver cancer patient with abnormal prothrombin expression.
10. A product for prognosis evaluation of radiotherapy in a liver cancer patient, characterized in that it comprises a product for detecting the metabolic level of each biomarker in a biomarker combination according to claim 1 in a sample and an evaluation model according to any of claims 7 to 8.
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