Method for evaluating treatment effect of sorafenib prepared from mitochondrial autophagy protein on liver cancer

Abstract

The invention discloses a method for evaluating the treatment effect of sorafenib on liver cancer by applying mitochondrial autophagy protein, and relates to the technical field, the evaluation method comprises the following steps: selecting a target sample, obtaining liver cancer tissue from the target sample, detecting the protein expression quantity of NIX and PINK1 in the liver cancer tissue by using an immunohistochemical method, and determining the treatment effect of sorafenib on liver cancer. Synchronously storing the basic data of the target sample in real time; the technical key points are as follows: a function of dynamically adjusting the sample collection period according to the physical condition of a target sample is realized by establishing a rule engine for adjusting the collection period based on a physical state index, so that the collection period can be properly prolonged when the physical condition of the sample is relatively good, and unnecessary sample collection is reduced; the model not only considers the influence of the change of NIX and PINK1 protein expression quantities on the treatment effect, but also combines the physical state index and the treatment feedback value, so that the treatment effect can be evaluated more comprehensively and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitochondrial autophagy protein. Background Art

[0002] Hepatocellular carcinoma is a common malignant tumor. Most liver cancer patients are already in the middle or advanced stage when they are diagnosed. They have lost the opportunity for surgical treatment and can only choose non-surgical treatment. In drug treatment, sorafenib is currently the first molecular targeted drug in the world that has been proven to be effective for liver cancer. However, global multicenter, randomized, double-blind Phase III clinical trial evidence shows that although sorafenib can indeed benefit patients with advanced liver cancer and prolong their survival, the extension time is extremely limited, with an average of only 2.8 months. Therefore, how to improve its anti-cancer efficacy is a prominent problem faced by clinicians and pharmacologists. Studies have shown that liver cancer cells are generally less sensitive to the in vitro effects of sorafenib, with a killing rate of only 20% to 30%. This may be related to drug-induced autophagy of liver cancer cells, and similar cell autophagy phenomena have also been found in other traditional anti-tumor drugs, and even restrict the development of a variety of new anti-liver cancer drugs.

[0003] Continuous research has shown that under the stimulation of sorafenib, the number of autophagosomes with mitochondria as substrate in Hep3B cells with liver cancer increases. In immunoblotting, sorafenib can induce a significant increase in the expression of NIX, a marker of mitochondrial autophagy, in a concentration-dependent manner. The latter is negatively correlated with the shear changes of Caspase3, a drug-induced apoptosis signal, suggesting that high expression of NIX in liver cancer may be unfavorable to drug treatment. Different from the high expression of NIX, the basal level of Pink1 in Hep3B cells with liver cancer is low, and sorafenib alone has no effect on the expression of Pink1 and Parkin in cells. The latter used melatonin to stimulate the mitochondrial autophagy activity of the Pink1 / Parkin pathway, and found that it could improve the efficacy of sorafenib in treating liver cancer, providing new evidence for the anti-cancer effect of mitochondrial autophagy of the Pink1 / Parkin pathway; through the new perspective of mitochondrial autophagy, the authors attempted to investigate the mechanism of action of NIX and PINK1-dependent mitochondrial autophagy in the treatment of liver cancer with sorafenib, in order to verify their effects on the drug treatment regimen for liver cancer based on sorafenib; thus, they sought new ideas and new targets for the clinical treatment and efficacy evaluation of sorafenib for liver cancer;

[0004] How to accurately evaluate the therapeutic effect of sorafenib prepared by mitochondrial autophagy protein on liver cancer is an urgent problem that needs to be solved. The traditional evaluation method is not targeted and has the problem of one-sided evaluation of a single indicator. At the same time, sample collection is required during the treatment process to monitor changes in the disease, but the collection cycle is usually set manually and cannot be flexibly and effectively adjusted, which may affect the efficiency and accuracy of the research to a certain extent. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitochondrial autophagy protein, which achieves the effect of improving the accuracy and objectivity of the evaluation and solves the problems raised in the background technology.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A method for evaluating the therapeutic effect of sorafenib on liver cancer is prepared by using mitochondrial autophagy protein. The steps of the evaluation method are as follows:

[0010] Select the target sample, obtain liver cancer tissue from the target sample, use immunohistochemistry to detect the protein expression of NIX and PINK1 in the liver cancer tissue, and simultaneously save the basic data of the target sample in real time;

[0011] Formulate a sorafenib medication regimen for the target sample, and treat it according to the established medication regimen, and detect the liver cancer tumor volume and physical condition index of the target sample in real time;

[0012] Build a rule engine to adjust the collection cycle according to the physical condition index of the target sample, generate a correction cycle to regularly collect liver cancer tissues of the target sample, and perform a secondary test on the protein expression of NIX and PINK1 in liver cancer tissues;

[0013] The changing trends of NIX and PINK1 protein expression levels before and after treatment were analyzed. Under the condition of selecting the target samples of group S, the statistical analysis method was used to calculate the correlation coefficients between the protein expression levels of NIX and PINK1 and the tumor volume of liver cancer, respectively, which were recorded as: r NIX and r PINK1 ;

[0014] According to the physical condition index and correlation coefficient of the current target sample, combined with the treatment feedback value, a comprehensive effect evaluation calculation model is built to generate the treatment effect evaluation value of the corresponding target sample. When the treatment effect evaluation value exceeds 0, the treatment effect evaluation value is positively correlated with the treatment efficacy.

[0015] Furthermore, the methods of obtaining liver cancer tissue include either primary lesion puncture or surgical resection;

[0016] The basic data of the target sample is saved in real time before and after treatment;

[0017] Basic data include: weight, BMI and liver function indicators.

[0018] Furthermore, when detecting the liver cancer tumor volume of the target sample in real time, the liver cancer tumor volume is obtained by combining medical imaging technology with three-dimensional modeling software. The medical imaging technology includes computer tomography and magnetic resonance imaging.

[0019] Furthermore, the process of real-time detection of the body state index of the target sample is as follows:

[0020] S2.1. Extract the current weight, BMI and liver function index of the target sample, and obtain the normal range of each data, respectively using [W min , W max ]、[B min , B max ] and [L min , L max ]express;

[0021] S2.2. Standardize body weight, BMI and liver function indicators;

[0022] S2.3. Take the average of the standardized weight, BMI and liver function index to obtain the body condition index:

[0023] Is=(W+B+L) / 3;

[0024] Where Is represents the physical condition index of the target sample.

[0025] Furthermore, in S2.2, the weight standardization process is as follows:

[0026]

[0027] Where, W represents body weight;

[0028] The standardization of BMI and liver function indexes is the same as that of body weight:

[0029]

[0030] Wherein, B and L represent BMI and liver function indexes, respectively.

[0031] Furthermore, a rule engine is built to adjust the collection cycle based on the physical condition index. The formula established in the engine is as follows:

[0032] T xz = int(T*(1+(α-1)*Is));

[0033] Where, T xz represents the correction period, int represents the rounding function, T represents the given initial period, and α is a correction coefficient greater than 0.

[0034] Furthermore, the value of S is at least 5 groups;

[0035] The statistical analysis method includes either Pearson's correlation coefficient or Spearman's rank correlation coefficient.

[0036] Furthermore, the formula for calculating the correlation coefficient is: r = (n∑(xy)-∑x∑y) / √((n∑x 2 -(∑x) 2 )(n∑y 2 -(∑y) 2 ));

[0037] Among them, r represents the correlation coefficient, and x and y represent the values ​​of two variables respectively;

[0038] When x represents the protein expression of NIX and y represents the tumor volume of liver cancer, r NIX ;

[0039] When x represents the protein expression of PINK1 and y represents the tumor volume of liver cancer, r PINK1 .

[0040] Furthermore, the process of obtaining the treatment feedback value is as follows:

[0041] The therapeutic response value is measured by the reduction ratio of liver cancer tumor volume, namely:

[0042]

[0043] Among them, V before and V after are the liver cancer tumor volumes before and after treatment, respectively, and K represents the treatment feedback value.

[0044] Furthermore, the formula for building a comprehensive effect evaluation calculation model is as follows:

[0045] Ek=q1*r NIX *ΔNIX+q2*r PINK1 *ΔPINK1+Q3*Is*K;

[0046] Wherein, Ek represents the evaluation value of the treatment effect, ΔNIX and ΔPINK1 represent the changes in NIX and PINK1 protein expression before and after treatment, respectively, q1, q2, and q3 are weight coefficients, and q3>q2>q1>0.

[0047] (III) Beneficial effects

[0048] The present invention provides a method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitochondrial autophagy protein, which has the following beneficial effects:

[0049] (1) This protocol uses immunohistochemistry to detect the protein expression of NIX and PINK1 in liver cancer tissues, providing a direct basis for evaluating the role of mitochondrial autophagy in liver cancer treatment. Then, using medical imaging technology combined with three-dimensional modeling software, the precise measurement of liver cancer tumor volume is achieved, providing an objective basis for evaluating the treatment effect.

[0050] By standardizing weight, BMI, and liver function indicators and calculating the body state index, real-time monitoring of the target sample's physical state is achieved. This method not only takes into account the unit and dimension differences of different indicators, but also ensures that when the indicator value exceeds the normal range, the standardized value can still remain within a reasonable range, thereby more accurately reflecting the overall physical condition of the target sample;

[0051] (2) This solution achieves the function of dynamically adjusting the sample collection cycle according to the physical condition of the target sample by building a rule engine for adjusting the collection cycle based on the physical condition index. This can not only ensure that the collection cycle is appropriately extended when the sample is in good physical condition and reduce unnecessary sample collection, but also collect samples in time when the physical condition is poor, so as to more accurately monitor the changes in the condition. This intelligent cycle management helps to improve the efficiency and accuracy of the research;

[0052] The physical status index can not only be used to adjust the collection cycle, but also provide a basis for the subsequent calculation of the treatment effect evaluation value;

[0053] By using statistical analysis to calculate the correlation coefficient between NIX and PINK1 protein expression and liver cancer tumor volume, a quantitative evaluation of the relationship between protein expression changes and treatment effects was achieved. This quantitative evaluation helps to more accurately understand the impact of protein expression changes on liver cancer tumor volume, thereby providing strong support for the formulation of more personalized treatment plans; at the same time, correlation analysis can also help researchers identify potential variable relationships and guide subsequent experimental design or data analysis;

[0054] (3) This program builds a comprehensive effect evaluation calculation model, which not only considers the impact of changes in NIX and PINK1 protein expression on the treatment effect, but also combines the physical condition index and treatment feedback value, so as to more comprehensively and accurately evaluate the treatment effect. This comprehensive evaluation method helps doctors to more accurately understand the patient's condition changes and treatment effects, and provide strong support for the formulation of subsequent treatment plans. This evaluation method also avoids the one-sidedness of single indicator evaluation and improves the accuracy and reliability of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the expression of mitochondrial proteins NIX and PINK1 detected by immunohistochemistry in the present invention;

[0056] Figure 2 This is a schematic diagram of mitochondrial NIX protein and mRNA expression under the action of different concentrations of sorafenib in the present invention;

[0057] Figure 3 This is a schematic diagram of mitochondrial PINKI protein and mRNA expression under the action of different concentrations of sorafenib in the present invention;

[0058] Figure 4 Schematic diagram of mitochondrial NIX protein and mRNA expression at different times of sorafenib action in the present invention;

[0059] Figure 5 This is a schematic diagram of the expression of mitochondrial PINKI protein and mRNA at different times of sorafenib action in the present invention;

[0060] Figure 6 Flow chart of the method for evaluating the therapeutic effect of sorafenib on liver cancer using mitochondrial autophagy proteins. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] See also Figures 1 to 6 This embodiment provides a method for evaluating the therapeutic effect of sorafenib on liver cancer using mitochondrial autophagy protein, the method comprising the following steps:

[0063] S1. Select the target sample, obtain liver cancer tissue from the target sample, use immunohistochemistry to detect the protein expression of NIX and PINK1 in the liver cancer tissue, and simultaneously save the basic data of the target sample in real time;

[0064] Among them, NIX and PINK1 improve the efficacy of sorafenib in treating liver cancer by regulating mitochondrial autophagy;

[0065] The target samples can be mice or volunteers. After mice are selected, the subsequent target samples all use mice of the same type as experimental subjects. In this embodiment, volunteers are used as an example.

[0066] Methods for obtaining liver cancer tissues include primary lesion puncture or surgical resection, which can be selected according to actual needs; when using immunohistochemistry to detect the protein expression of NIX and PINK1 in liver cancer tissues, adjacent cancer tissues and normal liver tissues can also be collected as controls; immunohistochemistry can also be replaced by Western blotting;

[0067] like Figure 1 The following table shows the expression of mitochondrial autophagy proteins NIX and PINK1 in hepatocellular carcinoma; A1-A3 show the expression of NIX in liver cancer tissues, paracancerous tissues and normal control group; B1-B3 show the expression of PINK1 in liver cancer tissues, paracancerous tissues and normal control tissues;

[0068] Thirty-nine cases of hepatocellular carcinoma tissues and their adjacent tissues were collected, and 36 cases of normal liver tissues adjacent to hepatic hemangioma were taken as controls during the same period. Immunohistochemistry was used to detect the expression levels of mitochondrial autophagy proteins NIX and PINK1. The results showed that there were significant differences in the expression levels of mitochondrial proteins NIX and PINK1 between cancer tissues, adjacent tissues and normal control group (P < 0.05). Among them, the expression level of NIX in liver cancer tissues was significantly higher than that in adjacent tissues, and the expression level of NIX in adjacent tissues was significantly higher than that in normal control group; the expression level of PINK1 in adjacent tissues was significantly higher than that in liver cancer tissues, and the expression level of PINK1 in liver cancer tissues was significantly higher than that in normal control group.

[0069] The basic data of the target sample is saved in real time before and after treatment;

[0070] Basic data include: weight, BMI and liver function indicators; weight can be collected in real time by using a weight scale, and BMI is obtained by collecting weight and height and dividing weight by the square of height; the collection of liver function indicators usually requires blood testing;

[0071] S2. Formulate a sorafenib medication regimen for the target sample, perform treatment according to the established medication regimen, and detect the liver cancer tumor volume and physical condition index of the target sample in real time;

[0072] When formulating a sorafenib medication regimen for the target sample, follow the doctor's instructions, for example: regularly collect liver cancer tissue from the target sample to accurately assess the status of the sample, and take the medication three times a day;

[0073] When detecting the liver cancer tumor volume of the target sample in real time, the accurate liver cancer tumor volume can be obtained by combining medical imaging technology with 3D modeling software. Medical imaging technology includes computed tomography (CT) and magnetic resonance imaging (MRI);

[0074] Take CT as an example:

[0075] CT is an imaging technology that uses X-rays to scan the human body. It can provide detailed anatomical information of the liver and tumors. X-rays are emitted by an X-ray tube and received by detectors after passing through the human body. Computers process the received signals and reconstruct tomographic images of the liver and tumors. Based on CT images, specialized software can be used to reconstruct tumors in three dimensions and calculate their volume.

[0076] The process of real-time detection of the body state index of the target sample is as follows:

[0077] S2.1. Extract the current basic data of the target sample, namely weight, BMI and liver function index, and obtain the normal range (ideal range) of each data, which are [W min , W max ]、[B min , B max ] and [L min , L max ] indicates that the normal range is collected and obtained based on actual conditions;

[0078] For example, if the target sample is an adult male, according to the conventional estimation method, the standard weight (kg) of a male can be obtained by subtracting 105 from the height (cm). For example, the standard weight of a male with a height of 175cm is 175-105=70kg. On this basis, it is generally believed that weight within the range of ±10% of the standard weight is normal. Therefore, for a male with a height of 175cm, the normal range of his weight is approximately between 63kg and 77kg. Therefore, W min =63,W max =77;

[0079] S2.2. Standardize weight, BMI and liver function indicators:

[0080] Weight standardization:

[0081]

[0082] Where W represents body weight. This formula ensures that when W is less than W min When W is greater than W max When W is within the normal range, W is proportionally mapped to between 0 and 1;

[0083] The standardization of BMI and liver function indexes is the same as that of body weight:

[0084]

[0085] In the formula, B and L represent BMI and liver function index, respectively;

[0086] S2.3. Take the average of the standardized weight, BMI and liver function index to obtain the body condition index:

[0087] Is=(W+B+L) / 3;

[0088] In the formula, Is represents the physical condition index of the target sample;

[0089] It should be noted that since the standardized value of each data is between 0 and 1, the value of the body condition index Is also naturally falls between 0 and 1; since the units and dimensions of weight, BMI and liver function indicators are different, direct addition or averaging is meaningless; therefore, each indicator needs to be standardized first and converted to the same scale (between 0 and 1); the standardization method used here ensures that even if the indicator value exceeds the normal range, the standardized value will not exceed the range of 0 to 1.

[0090] The solution achieved the following technical effects:

[0091] Comprehensive monitoring of protein expression: Immunohistochemistry (or Western blotting) was used to detect the protein expression of NIX and PINK1 in liver cancer tissues, providing a direct basis for evaluating the role of mitochondrial autophagy in liver cancer treatment; at the same time, adjacent cancer tissues and normal liver tissues were collected as controls to enhance the reliability and accuracy of the data;

[0092] Accurately measure tumor volume: Using medical imaging technology (such as CT and MRI) combined with 3D modeling software, the volume of liver cancer tumors can be accurately measured, providing an objective basis for evaluating treatment effects;

[0093] Real-time monitoring of physical condition: By standardizing weight, BMI, and liver function indicators and calculating the physical condition index, real-time monitoring of the physical condition of the target sample is achieved. This method not only takes into account the unit and dimension differences of different indicators, but also ensures that when the indicator value exceeds the normal range, the standardized value can still remain within a reasonable range, thereby more accurately reflecting the overall physical condition of the target sample;

[0094] Comprehensive evaluation of treatment effect: The comprehensive analysis of protein expression, tumor volume and physical condition index can more comprehensively and accurately evaluate the efficacy of sorafenib in treating liver cancer. This method not only considers the direct effect of drug treatment on liver cancer tissue, but also considers the effect of drug treatment on the overall physical condition of the target sample, thus providing strong support for the formulation of more personalized treatment plans;

[0095] In summary, this technical solution provides a more accurate and reliable basis for evaluating the efficacy of sorafenib in treating liver cancer by comprehensively and in real time monitoring multiple key indicators of target samples, and is expected to provide new ideas and methods for the clinical treatment of liver cancer.

[0096] S3. Build a rule engine to adjust the collection cycle according to the physical condition index of the target sample, generate a correction cycle to regularly collect liver cancer tissues of the target sample, and use immunohistochemistry to detect the protein expression of NIX and PINK1 in liver cancer tissues for a second time;

[0097] Build a rule engine that adjusts the collection cycle based on the physical condition index. The formula established in the engine is as follows:

[0098] T xz = int(T*(1+(α-1)*Is));

[0099] Where, T xz represents the correction period, int represents the rounding function, T represents the predetermined initial period, α is a correction coefficient greater than 0, which is used to control the influence of the body state index on the period, and the value of α in this embodiment is 2;

[0100] Logic description:

[0101] When Is=0, it means the body is in the worst condition. xz =T, that is, the correction period is equal to the given initial period; when Is increases from 0 to 1, the value of (1+(α-1)*Is) also gradually increases. Since α>1, (α-1)*Is is a positive number, which increases with the increase of Is, resulting in T xz The value of α determines the degree of influence of Is on the cycle adjustment. The larger α is, the more significant the influence of Is on the cycle is. The closer α is to 1, the smaller the influence of Is on the cycle is. When Is = 1, it means that the body is in the best condition. At this time, T xz =T*α, that is, the correction period is α times the initial period;

[0102] Example:

[0103] Assuming the initial period T = 10 days and the adjustment coefficient α = 2, then:

[0104] When Is=0, the correction period=10×(1+(2-1)×0)=10 days;

[0105] When Is = 0.5, the correction period = 10 × (1 + (2-1) × 0.5) = 15 days;

[0106] When Is=1, the correction period=10×(1+(2-1)×1)=20 days;

[0107] Through this formula, we can dynamically adjust the collection cycle according to the body condition index to achieve intelligent and personalized cycle management.

[0108] S4. Analyze the changing trends of NIX and PINK1 protein expression levels before and after treatment. Under the condition of selecting the target samples of group S, use statistical analysis method to calculate the correlation coefficient between the protein expression levels of NIX and PINK1 and the tumor volume of liver cancer, which is recorded as: r NIX and r PINK1 , to evaluate the correlation between changes in protein expression and treatment effects;

[0109] The value of S is at least 5 groups;

[0110] The statistical analysis method includes the Pearson correlation coefficient and the Spearman rank correlation coefficient. The Pearson correlation coefficient is mainly used in this embodiment. The function of the correlation coefficient is explained as follows:

[0111] Quantifying the strength and direction of a relationship: The correlation coefficient can quantify the strength of the linear relationship between two variables. The closer the absolute value of the coefficient is to 1, the stronger the linear relationship between the two variables is; the closer it is to 0, the weaker the linear relationship is. The positive or negative sign of the correlation coefficient indicates the direction of the relationship. A positive number indicates a positive correlation, that is, when one variable increases, the other variable tends to increase as well; a negative number indicates a negative correlation, that is, when one variable increases, the other variable tends to decrease.

[0112] Auxiliary hypothesis testing: The correlation coefficient is usually used together with the p-value to determine whether the observed correlation is statistically significant; that is, whether the observed correlation is likely to be caused by chance alone; in hypothesis testing, a significance level (such as 0.05) is usually set. If the p-value is less than this level, the observed correlation is considered to be significant, that is, it is unlikely to be caused by chance.

[0113] Guiding follow-up research: The correlation coefficient can help researchers identify which variables may have potential relationships, thereby guiding subsequent experimental design or data analysis; for example, if a significant correlation is found between NIX protein expression and liver cancer tumor volume, the mechanism behind this relationship can be further explored, or the potential of NIX as a liver cancer treatment target can be evaluated;

[0114] The formula for calculating the correlation coefficient is: r = (n∑(xy)-∑x∑y) / √((n∑x 2 -(∑x) 2 )(n∑y 2 -(∑y) 2 )), where r represents the correlation coefficient, and x and y represent the values ​​of two variables respectively;

[0115] When x represents the protein expression of NIX and y represents the tumor volume of liver cancer, r NIX ;

[0116] When x represents the protein expression of PINK1 and y represents the tumor volume of liver cancer, r PINK1 ;

[0117] For example:

[0118] Assume that there are the following data on NIX protein expression and liver cancer tumor volume (the units are arbitrary):

[0119] Sample No. NIX protein expression Hepatocellular carcinoma tumor volume 1 5 8 2 7 6 3 9 4 4 12 3 5 15 2

[0120] Substituting into the above formula, assuming that the calculated Pearson correlation coefficient is -0.95, this indicates that there is a strong negative correlation between the protein expression of NIX and the tumor volume of liver cancer; that is, as the protein expression of NIX increases, the tumor volume of liver cancer tends to decrease;

[0121] It should be noted that the auxiliary proof and expression process of the protein expression of NIX and PINK1 can be understood by referring to the following exploration process, specifically including Reference Example 1 and Reference Example 2;

[0122] Reference Example 1: Investigate the drug concentration characteristics of the expression of mitochondrial proteins NIX and Pink1 in liver cancer cells.

[0123] The liver cancer cell line HepG2 preserved in our laboratory was taken, and resuscitated, passaged, and cultured according to the conventional methods of our laboratory. The liver cancer cells in the logarithmic growth phase were divided into 5 groups and inoculated in 6-well culture plates. After the cells were completely attached to the wall and grew, gradient final concentrations of sorafenib (2.5, 5, 10, 20 μM) or an equal volume of solvent DMSO were added, and the cells were incubated in a 5% CO2, 37°C incubator for 24 h, and the cells were collected respectively; total cell protein was extracted, and the expression of mitochondrial proteins NIX and Pink1 was detected by western blotting; total cell RNA was extracted, and the expression of NIX and Pink1 mRNA was detected by qRT-PCR;

[0124] Research results: The results of HepG2 cells under the action of different concentrations of sorafenib are as follows:

[0125] As the concentration of sorafenib increased, the expression of NIX protein in HepG2 cells showed a downward trend. Figure 2 (A, B), the mRNA expression of NIX also showed a downward trend, e.g. Figure 2 (C); PINK1 protein expression in HepG2 cells showed an increasing trend. Figure 3 (A, B), the mRNA expression of PINK1 also showed an increasing trend, e.g. Figure 3 (C);

[0126] Reference Example 2: Investigate the temporal characteristics of the expression of mitochondrial proteins NIX and Pink1 in liver cancer cells.

[0127] The liver cancer cell line HepG2 preserved in this laboratory was taken and revived, passaged and cultured according to the routine methods of this laboratory. The liver cancer cells in the logarithmic growth phase were divided into 5 groups and inoculated into 6-well culture plates. When the cells were completely attached to the wall and grew, the final concentration of sorafenib (10μM) or an equal volume of solvent DMSO was added, and the cells were incubated in a 5% CO2, 37°C incubator for 6, 12, 24, and 48 hours, and the cells were collected respectively. The total cell protein was extracted, and the expression of mitochondrial proteins NIX and Pink1 was detected by western blotting; the total cell RNA was extracted, and the expression of NIX and Pink1 mRNA was detected by qRT-PCR;

[0128] Research results: The results of HepG2 cells under the same concentration of sorafenib for different time periods are as follows:

[0129] As time goes by, the expression of NIX protein in HepG2 cells shows a downward trend. Figure 4 (A, B), the mRNA expression of NIX also showed a downward trend, e.g. Figure 4 (C); PINK1 protein expression in HepG2 cells showed an increasing trend. Figure 5 (First and second from left), the mRNA expression of PINK1 also showed an increasing trend, e.g. Figure 5 (Third from left).

[0130] By adopting the above technical solution, the main technical problems solved are how to intelligently adjust the sample collection cycle during liver cancer treatment and how to quantitatively evaluate the correlation between NIX and PINK1 protein expression and liver cancer tumor volume. This solution achieves the following technical effects:

[0131] Intelligent adjustment of collection cycle: By building a rule engine for adjusting the collection cycle based on the physical condition index, the function of dynamically adjusting the sample collection cycle according to the physical condition of the target sample is realized. This can not only ensure that the collection cycle is appropriately extended when the sample is in good physical condition and reduce unnecessary sample collection, but also collect samples in time when the physical condition is poor, so as to more accurately monitor the changes in the condition. This intelligent cycle management helps to improve the efficiency and accuracy of research;

[0132] The physical status index can not only be used to adjust the collection cycle, but also provide a basis for the subsequent calculation of the treatment effect evaluation value;

[0133] Quantitative evaluation of correlation: By using statistical analysis methods (such as Pearson correlation coefficient) to calculate the correlation coefficient between NIX and PINK1 protein expression and liver cancer tumor volume, a quantitative evaluation of the relationship between protein expression changes and treatment effects was achieved. This quantitative evaluation helps to more accurately understand the impact of protein expression changes on liver cancer tumor volume, thereby providing strong support for the formulation of more personalized treatment plans; at the same time, correlation analysis can also help researchers identify potential variable relationships and guide subsequent experimental design or data analysis;

[0134] In summary, this technical solution provides a more accurate and efficient method for the research of liver cancer treatment by intelligently adjusting the sample collection cycle and quantitatively evaluating the correlation between protein expression and liver cancer tumor volume. This not only helps to improve the scientificity and reliability of the research, but also can provide more detailed guidance for the subsequent evaluation of treatment effects.

[0135] S5. Based on the physical condition index and correlation coefficient of the current target sample, combined with the treatment feedback value, a comprehensive effect evaluation calculation model is built to generate a treatment effect evaluation value of the corresponding target sample. When the treatment effect evaluation value exceeds 0, the treatment effect evaluation value is positively correlated with the treatment efficacy;

[0136] The process of obtaining the treatment feedback value is as follows:

[0137] The treatment feedback value is measured by the reduction ratio of liver cancer tumor volume (preliminary reflection of treatment effect), namely:

[0138]

[0139] Among them, V before and V after These are the liver cancer tumor volumes before and after treatment;

[0140] The formula for building a comprehensive effect evaluation calculation model is as follows:

[0141] Ek=q1*r NIX *ΔNIX+q2*r PINK1 *ΔPINK1+Q3*IS*K;

[0142] In the formula, Ek represents the evaluation value of treatment effect, ΔNIX and ΔPINK1 represent the changes in NIX and PINK1 protein expression before and after treatment, respectively (after treatment minus before treatment), q1, q2, and q3 are weight coefficients, indicating the contribution of each factor to the effect evaluation value, which needs to be set according to the actual situation, and q3>q2>q1>0;

[0143] Logic description:

[0144] The first and second terms in the formula (i.e., the two parts corresponding to q1 and q2) take into account the impact of changes in NIX and PINK1 protein expression on the treatment effect, which is reflected by the correlation coefficient and the change in protein expression; the third term (i.e., the part corresponding to q3) takes into account the impact of the physical condition index on the treatment effect. The better the physical condition, the more likely it is to withstand the side effects of treatment, thereby achieving better treatment effects; the setting of weight coefficients q1, q2, and q3 should be based on expert knowledge, clinical experience, or data analysis results to ensure the rationality and accuracy of the formula;

[0145] The weight coefficient in this embodiment can be determined by the coefficient of variation method, which is a method of weighting each indicator according to the degree of variation between the current value of each evaluation indicator and the target value; if the numerical difference of a certain indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich discrimination information, and thus the indicator should be given a larger weight; conversely, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluation objects is weak, and thus the indicator should be given a smaller weight; this method directly uses the information contained in each indicator to obtain the weight of the indicator by calculation, and therefore is objective.

[0146] By adopting the above technical solution, the technical problem of how to comprehensively evaluate the treatment effect during the treatment of liver cancer is mainly solved; this solution achieves the following technical effects:

[0147] Comprehensive evaluation of treatment effect: By building a comprehensive effect evaluation calculation model, the model not only considers the impact of changes in NIX and PINK1 protein expression on the treatment effect, but also combines the physical condition index and treatment feedback value, so as to more comprehensively and accurately evaluate the treatment effect. This comprehensive evaluation method helps doctors to more accurately understand the patient's condition changes and treatment effects, and provide strong support for the formulation of subsequent treatment plans;

[0148] Improve the accuracy and objectivity of the evaluation: In the comprehensive effect evaluation calculation model, the accuracy and objectivity of the evaluation are ensured by introducing the correlation coefficient and weight coefficient. The correlation coefficient can quantify the relationship between the expression of NIX and PINK1 proteins and the volume of liver cancer tumors, while the weight coefficient is set based on expert knowledge, clinical experience or data analysis results, ensuring that the contribution of each factor to the effect evaluation value is reasonably reflected. This evaluation method avoids the one-sidedness of single indicator evaluation and improves the accuracy and reliability of the evaluation;

[0149] Guiding personalized treatment: Through the treatment effect evaluation value generated by the comprehensive effect evaluation calculation model, doctors can formulate personalized treatment plans according to the specific conditions of patients. For patients with higher treatment effect evaluation values, they can continue to use the current treatment plan or appropriately increase the treatment intensity; for patients with lower treatment effect evaluation values, they need to adjust the treatment plan in time to improve the treatment effect. This personalized treatment method helps to better meet the treatment needs of patients and improve the success rate of treatment;

[0150] In summary, this technical solution solves the technical problem of how to comprehensively evaluate the treatment effect during the treatment of liver cancer by building a comprehensive effect evaluation calculation model, achieving the technical effect of improving the accuracy and objectivity of the evaluation and guiding personalized treatment.

[0151] In the application, the several formulas involved are all calculated by taking their numerical values ​​after removing the dimensions, and the formulas are established by collecting a large amount of data and performing software simulation to obtain a formula for the most recent actual situation. Some coefficients or weights in the formulas are set by technical personnel in this field according to actual conditions, so they will not be elaborated here.

[0152] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for evaluating the therapeutic effect of sorafenib on liver cancer using mitochondrial autophagy protein, characterized in that: The steps of this assessment method are as follows: Select the target sample, obtain liver cancer tissue from the target sample, use immunohistochemistry to detect the protein expression of NIX and PINK1 in the liver cancer tissue, and simultaneously save the basic data of the target sample in real time; Formulate a sorafenib medication regimen for the target sample, and treat it according to the established medication regimen, and detect the liver cancer tumor volume and physical condition index of the target sample in real time; Build a rule engine to adjust the collection cycle according to the physical condition index of the target sample, generate a correction cycle to regularly collect liver cancer tissues of the target sample, and perform a secondary test on the protein expression of NIX and PINK1 in liver cancer tissues; The changing trends of NIX and PINK1 protein expression levels before and after treatment were analyzed. Under the condition of selecting the target samples of group S, the statistical analysis method was used to calculate the correlation coefficients between the protein expression levels of NIX and PINK1 and the tumor volume of liver cancer, respectively, which were recorded as: r NIX and r PINK1 ; According to the physical condition index and correlation coefficient of the current target sample, combined with the treatment feedback value, a comprehensive effect evaluation calculation model is built to generate the treatment effect evaluation value of the corresponding target sample. When the treatment effect evaluation value exceeds 0, the treatment effect evaluation value is positively correlated with the treatment efficacy.

2. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 1, characterized in that: The methods of obtaining liver cancer tissue include primary lesion puncture and surgical resection; The basic data of the target sample is saved in real time before and after treatment; Basic data include: weight, BMI and liver function indicators.

3. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 1, characterized in that: When detecting the liver cancer tumor volume of the target sample in real time, the liver cancer tumor volume is obtained by combining medical imaging technology with three-dimensional modeling software. The medical imaging technology includes computer tomography and magnetic resonance imaging.

4. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 2, characterized in that: The process of real-time detection of the body state index of the target sample is as follows: S2.

1. Extract the current weight, BMI and liver function index of the target sample, obtain the normal range of each data, and use [W min , W max ]、[B min , B max ] and [L min , L max ]express; S2.

2. Standardize body weight, BMI and liver function indicators; S2.

3. Take the average of the standardized weight, BMI and liver function index to obtain the body condition index: I s =(W+B+L) / 3; Where Is represents the physical condition index of the target sample.

5. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 4, characterized in that: In S2.2, the weight is normalized as follows: Where, W represents body weight; The standardization of BMI and liver function indexes is the same as that of body weight: Wherein, B and L represent BMI and liver function indexes, respectively.

6. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 1, characterized in that: Build a rule engine that adjusts the collection cycle based on the physical condition index. The formula established in the engine is as follows: T xz =int(T*(1+(α-1)*Is)); Where, T xz represents the correction period, int represents the rounding function, T represents the given initial period, and α is a correction coefficient greater than 0.

7. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 1, characterized in that: The value of S is at least 5 groups; The statistical analysis method includes either Pearson's correlation coefficient or Spearman's rank correlation coefficient.

8. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 7, characterized in that: The formula for calculating the correlation coefficient is: r = (n∑(xy)-∑x∑y) / √((n∑x 2 -(∑x) 2 )(n∑y 2 -(∑y) 2 )); Among them, r represents the correlation coefficient, and x and y represent the values ​​of two variables respectively; When x represents the protein expression of NIX and y represents the tumor volume of liver cancer, r NIX ; When x represents the protein expression of PINK1 and y represents the tumor volume of liver cancer, r PINK1 .

9. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitophagy protein according to claim 1, characterized in that: The process of obtaining treatment feedback value is as follows: The therapeutic response value is measured by the reduction ratio of liver cancer tumor volume, namely: Among them, V before and V after are the liver cancer tumor volumes before and after treatment, respectively, and K represents the treatment feedback value.

10. The method for evaluating the therapeutic effect of sorafenib on liver cancer prepared by using mitochondrial autophagy protein according to claim 9, characterized in that: The formula for building a comprehensive effect evaluation calculation model is as follows: <h2 style=";text-align:left;direction:ltr">Ek=q1*r<h2 style=";text-align:left;direction:ltr"> NIX <h2 style=";text-align:left;direction:ltr"> *ΔNIX+q2*r<h2 style=";text-align:left;direction:ltr"> PINK1 <h2 style=";text-align:left;direction:ltr"> *ΔPINK1+q3*I<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> *K; Wherein, Ek represents the evaluation value of the treatment effect, ΔNIX and ΔPINK1 represent the changes in NIX and PINK1 protein expression before and after treatment, respectively, q1, q2, and q3 are all weight coefficients, and q3>q2>q1>0.

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