CN105823870A - Method for prejudging risk of liver cirrhosis patient suffering from liver cancer - Google Patents
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Abstract
The invention discloses a prejudging method for assessing the risk of liver cirrhosis patient suffering from liver cancer. The method comprises the following steps: collecting a blood sample from a liver cirrhosis patient; analyzing a set of clinical data which include fasting blood-glucose, insulin, high-density lipoprotein, thrombocyte, lymphocyte and neutrophil leucocyte; calculating a risk index according to the set of clinical data; and prejudging according to the risk index. According to the risk index, the risk of the liver cirrhosis patient suffering from liver cancer can be prejudged accurately and efficiently such that the patient can receive an appropriate treatment at real time.
Description
Technical Field
The present disclosure relates to the field of prognosing cancer. More particularly, the present disclosure relates to a method for predicting whether a patient with liver cirrhosis will suffer from liver cancer.
Background
Liver cancer highly occupies the fifth place of the global benign solid malignant tumor and is the third place of the cause of death related to cancer. About 70% -90% of liver cancer patients have a history of chronic liver disease and cirrhosis, wherein cirrhosis develops as a result of damaged liver cells being repaired by scar tissue. The major risk factors causing cirrhosis include chronic infection with hepatitis B virus or hepatitis C virus, alcoholic liver disease (alcoholic liver disease), and non-alcoholic fatty liver disease (NAFLD). The other carcinogenic factors causing liver cancer include genetic diseases and metabolic disorders such as intake of food contaminated with aflatoxin, diabetes, obesity, hemochromatosis (hemochromatosis), etc.
In view of the close correlation between cirrhosis and liver cancer, patients with cirrhosis should receive one or more tests such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or Ultrasound (US) every year for periodic follow-up. Liver cancer patients diagnosed early often have more treatment options. For example, patients with early stage liver cancer may receive more curative therapies such as liver transplantation, surgical resection and ablation; patients with advanced liver cancer lack effective treatment. The efficacy of treatment often reflects the survival rate of the patient. It is known that most patients with early stage liver cancer can survive more than 5 years, while the average survival rate of patients with late stage liver cancer is less than 1 year. However, conventional detection methods are limited to detecting only liver nodules (livernodules) greater than 2 centimeters. According to previous reports, the sensitivities of ultrasound, computed tomography and magnetic resonance imaging were only 21%, 40% and 47%, respectively, when liver nodules of less than 2 cm were detected. In addition, availability and high cost price of diagnostic instruments further limit the application of magnetic resonance imaging and computed tomography.
In view of the above, there is a need in the related art for a method for effectively predicting the risk of liver cancer; compared with the traditional magnetic resonance imaging and/or computed tomography, the pre-breaking method is more accurate and does not need to be matched with any expensive instrument and equipment; thereby, the patient can receive proper treatment in real time.
Disclosure of Invention
This summary is provided to provide a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and is intended to neither identify key/critical elements of the embodiments nor delineate the scope of the embodiments. This summary is provided to provide a simplified summary of the disclosure in order to provide a basic understanding to the reader.
The present disclosure relates to a method for predicting cancer, which uses a blood sample of a liver cirrhosis patient to predict the risk of liver cancer in the patient. The method comprises the following steps:
(1) measuring from the blood sample fasting blood glucose (mg/dL), insulin (μ IU/mL) and high density lipoprotein (mg/dL) concentrations, number of platelets (number/μ L) and percentage of lymphocytes and neutrophils;
(2) calculating a risk index using one of the following formulas:
wherein,
t＝-0.005PLT-0.029HDL-0.376log10(SIR)+0.854log10(LNR) - (0.015PLT × LNR) - (0.062SIR × LNR) + 4.253; e is the base of the natural logarithm function and
PLT is the number of platelets, HDL is the concentration of high density lipoprotein, SIR is the ratio of fasting plasma glucose concentration to insulin concentration, and LNR is the ratio of the percentage of lymphocytes to the percentage of neutrophils; and
(3) performing a prognosis from the risk index of step (2); wherein if the risk index is between 0 and 0.5, the risk of the patient suffering from liver cancer is low; if the risk index is between 0.5 and 0.65, the risk of the patient suffering from liver cancer is moderate; if the risk index is between 0.65 and 1, the risk of liver cancer in the patient is high.
According to embodiments of the present disclosure, the blood sample is a whole blood sample.
Generally, cirrhosis is caused by a chronic liver disease selected from the group consisting of alcoholic liver disease, non-alcoholic fatty liver, hepatitis B and hepatitis C.
The values of fasting plasma glucose, insulin, high density lipoprotein, platelets, lymphocytes and neutrophils may be obtained by any biochemical or blood detection method known to those skilled in the art. According to embodiments of the present disclosure, fasting glucose concentration is measured using an enzyme, an aromatic amine, or a continuous flow assay (continuous flow assay). Enzymes suitable for measuring fasting glucose concentration include, but are not limited to, glucose oxidase (glucose oxidase), glucose dehydrogenase (glucose dehydrogenase), and hexokinase (hexokinase). Aromatic amines suitable for measuring fasting blood glucose concentrations may be aniline (aniline), benzidine (benzidine), 2-aminobiphenyl (2-aminobiphenyl) or o-toluidine (o-tolidine).
According to embodiments of the present disclosure, the concentration of insulin is measured using an antibody that is specific for the A chain or B chain of insulin.
In some embodiments of the present disclosure, the concentration of high density lipoprotein may be measured using an enzyme, an electrophoretic analysis (electrophoresessay), a continuous flow analysis, or a nuclear magnetic resonance analysis (nuclear magnetic resonance analysis). Enzymes used to measure high density lipoproteins include, but are not limited to, cholesterol oxidase (cholestanoloxidase).
According to embodiments of the present disclosure, the number of platelets, the percentage of lymphocytes, and the percentage of neutrophils are measured with a hemocytometer, a hematology analyzer, and an antibody, respectively. To measure the amount of platelets, an antibody is used that is specific for a surface marker of platelets, wherein the surface marker is selected from the group consisting of CD41, CD42, CD49, CD61, CD62, and CD 109. In measuring the percentage of lymphocytes, the antibody used is specific for a surface marker of lymphocytes, wherein the surface marker is selected from the group consisting of CD3, CD4, CD25, CD40, CD62L, CD80, and CD 152. The antibody used to measure the percentage of neutrophils is specific for a marker for neutrophils selected from the group consisting of CD66, CD177, Ly6G, and neutrophil elastase (neutrophilelastase). Optionally, the antibodies used in the present disclosure are conjugated to a fluorescent dye to facilitate detection of cells bound to the antibodies.
The basic spirit and other objects of the present invention, as well as the technical means and embodiments adopted by the present invention, will be readily understood by those skilled in the art after considering the following embodiments.
Drawings
In order to make the aforementioned and other objects, features, advantages and embodiments of the invention more comprehensible, the following description is given:
FIG. 1 is a cumulative risk curve (cumulative risk) illustrating the risk of liver cancer in 1.5 years for 183 patients with liver cirrhosis divided into low, medium or high risk groups according to the risk index of the present disclosure; and
fig. 2 is a cumulative risk curve illustrating the risk of developing liver cancer in 1.5 years for 344 patients with liver cirrhosis divided into low, medium or high risk groups according to the risk index of the present disclosure.
In accordance with common practice, the various described features/elements are not intended to be exhaustive but rather are intended to depict the best mode contemplated for carrying out the present invention. Additionally, like numerals and designations in the various drawings are used to indicate like components/features.
Detailed Description
In order to make the description of the present disclosure more complete and complete, the following description is given for illustrative purposes with respect to the embodiments and specific examples of the present invention; it is not intended to be the only form in which the embodiments of the invention may be practiced or utilized. The embodiments are intended to cover the features of the various embodiments as well as the method steps and sequences for constructing and operating the embodiments. However, other embodiments may be utilized to achieve the same or equivalent functions and step sequences.
For convenience, the remainder of certain terms used in the specification, examples, and appended claims are collected here. Unless defined otherwise herein, the scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Furthermore, as used herein, the singular forms "a", "an", and "the" encompass plural referents unless the specification expressly states otherwise; the use of plural nouns also covers the singular form of such nouns. In particular, as used herein and in the appended claims, the singular forms "a," "an," and "the" encompass plural referents unless the context clearly dictates otherwise. Furthermore, the words "at least one (" along ")" and "one or more (" one or ")" as referred to herein and in the appended claims have the same meaning and include one, two, three or more.
Although the numerical ranges and parameter limits used to define the broader scope of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contains certain standard deviations found in their respective testing measurements. As used herein, "about" generally means that the actual value is within plus or minus 10%, 5%, 1%, or 0.5% of a particular value or range. Alternatively, the term "about" means that the actual value falls within the acceptable standard deviation of the mean, subject to consideration by those of ordinary skill in the art to which the invention pertains. Except in the experimental examples, or where otherwise expressly indicated, it is to be understood that all ranges, amounts, values and percentages herein used (e.g., to describe amounts of materials, length of time, temperature, operating conditions, quantitative ratios, and the like) are to be modified by the word "about". Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, these numerical parameters are to be understood as meaning the number of significant digits recited and the number resulting from applying ordinary carry notation.
The term "receiver operating characteristic curve" (roccurrent) as used herein refers to a curve drawn by using true positive rate (true positive) and false positive rate (false positive) to determine the cut-off point (cut-off) of a pre-cut test. The ROC curve generally defines (1-specificity) as the x-axis and sensitivity as the y-axis. A high sensitivity value indicates a low false positive rate. Similarly, a high specificity value also indicates a low false positive rate. The term "cut-off point" (cut-off) as used herein refers to a value obtained from the ROC curve to indicate that the sensitivity and specificity of the prognostic assay are balanced. A range of tangents can include several embodiments of tangents, wherein each tangents represents a balance between different sensitivities and specificities.
Herein, "area under the curve" (AUC) is a term used by those skilled in the art and is defined as the area under the ROC curve. An AUC value, usually between 0.5 and 1.0, is used to represent the accuracy of a prognostic test; wherein, the higher the AUC value, the better the prognosis effect of the prognosis test. The AUC value usually comes with a 95% Confidence Interval (CI), which is a statistical range with a certain probability that a set parameter can fall within the range.
The term "risk" as used herein refers to the possibility that an item may produce an adverse resultAnd, for example, the occurrence, progression or recurrence of liver cancer. Depending on the results of an analysis of an individual, sample thereof, or event associated therewith, the individual may be classified into a "high risk" (highhrisk), a "medium risk" (intermediaterisk), or a "low risk" (lowrisk) group. As used herein, the risk index refers to the risk index of a patient>75thPercent (i.e., a value between 0.65 and 1), the patient is a "high risk" group; that is, he/she has a higher likelihood of suffering liver cancer within five years than the remaining three-quarters of the patients in the analyzed cohort. When the risk index of a patient<50thPercent (i.e., a value between 0 and 0.5), the patient is a "low risk" group; that is, he/she has a lower likelihood of suffering liver cancer within five years than the remaining half of the patients in the analyzed cohort. Accordingly, if the percentage of risk index of a patient is between 50thAnd 75thIn between (i.e., a value between 0.5 and 0.65), then the patient is in the "intermediate risk" group.
In view of the need in the related art for a method for accurately and effectively predicting the incidence of liver cancer in a patient with liver cirrhosis, and thereby administering the appropriate treatment to the patient, the main objective of the present disclosure is to provide a set of clinical parameters related to the incidence of liver cancer. Once confirmed, the set of clinical parameters can be used to calculate a risk index, and then predict whether a liver cirrhosis patient is at risk for liver cancer according to the risk index, so that medical personnel can give appropriate treatment to the patient according to the prediction result.
Patients with cirrhosis who participate in the present invention will first receive a health check to assess the condition of their liver (e.g., cirrhosis or liver cancer) and personal health advice such as height, weight, age, race, medical history, and past surgical history (if any).
Suitable methods for diagnosing cirrhosis include, but are not limited to, liver tissue sections, endoscopy, blood tests, image tests, and combinations thereof. Generally, in patients with liver cirrhosis, due to liver function deficiency, liver enzymes such as albumin (albumin), alkaline phosphatase (alkaline phosphatase), bilirubin (bilirubin), and creatinine (creatinine) are abnormally expressed in vivo, and the abnormality can be accurately quantified and evaluated using a blood test, which may be a conventional method well known to those skilled in the art. If the patient has severe cirrhosis, the patient can be examined for signs such as irregular surface of the liver, varicose veins (gastrovarices), and splenomegaly (splenomegaly) by imaging examination including ultrasound, computed tomography, magnetic resonance imaging, and elastography. According to one embodiment, patients who participate in the present disclosure are diagnosed with cirrhosis using liver tissue slices, endoscopy, and imaging; wherein the image inspection is an ultrasound or elasticity image.
As for liver cancer, methods suitable for diagnosing liver cancer include, but are not limited to, liver tissue section, blood test and image test. In general, liver cancer patients express high amounts of growth-related oncogenic proteins such as alpha-fetoprotein (AFP), and the expression amounts of these oncogenic proteins can be accurately measured and presented using blood tests. In detecting tumor nodules, ultrasound, computed tomography, magnetic resonance imaging, or angiography (angiography) may be used. One embodiment of the present disclosure uses liver tissue slices, blood tests and image tests to assess whether a patient with cirrhosis has liver cancer, wherein the image tests are computed tomography or angiography.
According to the results of the health examination, patients with liver cirrhosis can be divided into two groups: (1) from a cirrhosis patient not suffering from liver cancer; and (2) patients with liver cirrhosis who had suffered from early stage liver cancer, but had been completely relieved after treatment. The second group of patients is used to simulate a group more susceptible to liver cancer than the first group of patients. Since the cancer cytokines associated with liver cancer should be considered diagnostic factors for liver cancer, rather than prognostic factors, complete remission of liver cancer in the second group of patients can prevent these cancer cytokines from interfering with the measurement and assessment results of the present invention.
Blood samples were taken from each patient to screen out clinical parameters with different expression levels in the first and second groups of patients with cirrhosis. Generally, a blood sample refers to a whole blood sample of the blood surrounding a patient. Clinical parameters analyzed by the present disclosure include: (1) aspartic acid transaminase (AST), alanine transaminase (ALT), bilirubin, fetal protein A, hemopexin (hemophilin), fasting plasma glucose, glycated hemoglobin (glycohemoglobin), thyrotropin (thyrotropin), free thyroxin (T4), total protein, albumin, alpha1-globulin (alpha1-globulin), alpha 2-globulin, beta-globulin, gamma-globulin, lipoprotein A1(apolipoprotein-A1, Apo-A1), uric acid (uric acid, UA), high density lipoprotein, low density lipoprotein (lowdensilypoprotein, LDL), very low density lipoprotein (hemoglobin, type), cholesterol (cholesterol), VLDL), trypsin (triglyceride, ferritin), total ferritin (ferritin ), ferritin (ferritin, total ferritin, thyroxine (T4), UIBC), complement component3 (complementary component3, C3), complement component4 (complementary component4, C4), Blood Urea Nitrogen (BUN), creatinine, and the concentration of insulin resistance (HOMA-IR) evaluated in a scale mode; (2) ratios of albumin/globulin, insulin/fasting plasma glucose, cholesterol/high density lipoprotein and low density lipoprotein/high density lipoprotein; (3) the number of white blood cells, hemoglobin, and platelets; (4) the percentage of neutrophils, lymphocytes and monocytes in leukocytes; and (5) Prothrombin Time (PT).
And evaluating the risk of the liver cancer of the liver cirrhosis patients according to the obtained clinical parameters. Specifically, clinical parameters with different expression levels between two groups of patients are screened, and then are combined by using an operation, wherein the operation can be a numerical operation, an algebraic operation, a logarithmic operation or a combination thereof. Different algorithms can be generated by combining different clinical parameters and are expressed by risk indexes, and the risk indexes can be further used as liver cancer incidence indexes; and evaluating the accuracy of the algorithm according to the AUC value of each algorithm. Different operations and adjustments are used to adjust the clinical parameters until an algorithm with the highest AUC is obtained. And calculating a risk index according to the finally obtained algorithm, and dividing the cirrhosis patients into families with low, moderate or high liver cancer risks.
Accordingly, the present disclosure provides an accurate prognosis method for predicting whether a patient with liver cirrhosis will suffer from liver cancer by using a blood sample of the patient. In general terms, the method comprises:
(1) measuring from the blood sample fasting blood glucose, insulin and high density lipoprotein concentrations, platelet counts and lymphocyte and neutrophil percentages;
(2) calculating a risk index using a formula:
wherein, t is-0.005 PLT-0.029HDL-0.376log10(SIR)+0.854log10(LNR)–(0.015PLT×LNR)–(0.062SIR×LNR)+4.253；
Wherein PLT is the number of platelets, HDL is the concentration of high density lipoprotein, SIR is the ratio of fasting plasma glucose concentration to insulin concentration, and LNR is the ratio of the percentage of lymphocytes to the percentage of neutrophils; and
(3) performing a prognosis from the risk index of step (2); wherein if the risk index is between 0 and 0.5, the risk of the patient suffering from liver cancer is low; if the risk index is between 0.5 and 0.65, the risk of the patient suffering from liver cancer is moderate; if the risk index is between 0.65 and 1, the risk of liver cancer in the patient is high.
According to embodiments of the present disclosure, the blood sample is a whole blood sample. The cirrhosis patient has a chronic liver disease, and the chronic liver disease can be alcoholic liver disease, non-alcoholic fatty liver, hepatitis B or hepatitis C. In a preferred embodiment, the patient with liver cirrhosis is suffering from hepatitis B.
In step (1), the concentration of fasting plasma glucose is expressed in mg/dL (milligrams per deciliter); the concentration of insulin is expressed in μ IU/mL (micro-international units per mL); the concentration of high density lipoprotein is expressed in mg/dL (milligrams per deciliter); the number of platelets is in number/μ L (number of platelets per microliter); the percentages of lymphocytes and neutrophils are values analyzed by using the total number of leukocytes as a reference value.
Methods suitable for analyzing fasting glucose concentrations according to embodiments of the present disclosure include, but are not limited to, enzymatic, aromatic amine, or continuous flow assays. The enzyme is selected from the group consisting of glucose oxidase, glucose dehydrogenase and hexokinase, according to techniques well known in the laboratory or clinical field; the aromatic amine may be aniline, benzidine, 2-aminobiphenyl or o-toluidine.
Non-limiting embodiments for analyzing insulin concentrations according to certain embodiments of the present disclosure include assays performed using antibodies that are specific for the A chain or B chain of insulin.
In embodiments of the present disclosure, the concentration of high density lipoprotein may be measured using an enzyme, electrophoretic analysis, continuous flow analysis, or nuclear magnetic resonance analysis. The enzyme used to measure the concentration of high density lipoprotein is cholesterol oxidase, according to assays well known to those skilled in the art.
Tests suitable for analyzing platelet count, lymphocyte percentage, and neutrophil percentage in accordance with embodiments of the present disclosure include, but are not limited to, hemoglobeters, hematology analyzers, and antibodies. In the present disclosure, the antibody used to analyze platelet number has specificity for the surface marker of platelets, wherein the surface marker is selected from the group consisting of CD41, CD42, CD49, CD61, CD62, and CD 109. The antibodies used to measure the percentage of lymphocytes are specific for surface markers of lymphocytes, including, but not limited to, CD3, CD4, CD25, CD40, CD62L, CD80, and CD 152. Similarly, the antibody used to measure the percentage of neutrophils is specific for a marker for neutrophils selected from the group consisting of CD66, CD177, Ly6G, and neutrophil elastase. In addition, the antibodies used in the present disclosure are conjugated to a fluorescent dye to facilitate detection of cells bound to the antibodies.
In step (2), the risk index is calculated using the formula of the present disclosure, and the number of platelets, the concentration of high density lipoprotein, the ratio of fasting plasma glucose to insulin, and the ratio of percentage of lymphocytes to percentage of neutrophils obtained in step (1) are used together.
In step (3), patients are classified into groups with low, moderate or high risk of liver cancer according to the risk index calculated in step (2). According to one embodiment of the present disclosure, patients in a high risk group are more susceptible to liver cancer than patients in a low or moderate risk group.
The following examples are set forth to illustrate some of the ways in which the present invention may be practiced by those of ordinary skill in the art. These examples should not therefore be considered as limiting the scope of the invention. Those skilled in the art, having the benefit of the teachings herein, will appreciate that the present invention may be practiced without these specific details. All documents mentioned herein are to be considered as having been fully incorporated into this specification.
Examples
Example 1 establishing a Risk index
193 patients with liver cirrhosis who participated in the test of the present invention were divided into two groups by medical staff according to the results of health examination. The first group comprised 161 patients who had hepatitis B and had never suffered from liver cancer (labeled "non-liver cancer group"). The second cohort consisted of 32 patients with hepatitis B, who had suffered from early liver cancer and were cured after receiving clinical treatment (labeled "hepatoma cohort"). However, within one year after the clustering, 5 patients of the primitive non-hepatoma group successively suffered from liver cancer. After regrouping the 5 patients, the liver cancer group included 37 patients, while the non-liver cancer group included 156 patients. According to the baseline demographics shown in table 1, there was no significant difference between the liver cancer group and the non-liver cancer group.
TABLE 1 Baseline demographics
HBsAg surface antigen of hepatitis B virus
Blood samples taken from patients with liver cancer and non-liver cancer (12 hours fasting before blood drawing) were analyzed by continuous flow analysis or blood analyzer for biochemical or blood analysis. Table 2 summarizes the 44 obtained clinical parameter values.
TABLE 2 clinical parameters from Biochemical or blood analysis
Note that:
(1) the bold labeled clinical parameters have significant differences between the liver cancer cohort and the non-liver cancer cohort.
(2) CI is confidence interval.
(3) The p-value is calculated by c-statistics.
As shown in table 2, of the 44 clinical parameters, 4 had significantly different expression levels between the liver cancer cohort and the non-liver cancer cohort: (1) the concentration of high density lipoprotein (AUC 0.405, p-value 0.074); (2) the ratio of fasting glucose concentration to insulin concentration (AUC 0.354, p-value 0.006); (3) the number of platelets (AUC 0.317 and p-value 0.001); and (4) the ratio of the percentage of lymphocytes to the percentage of neutrophils (AUC 0.373, p-value 0.017).
To optimize the evaluation accuracy, the 4 clinical parameters with high reliability and high discrimination are combined by operation. After several screenings and corrections, a final formula is obtained with the best cluster accuracy (AUC 0.78) and can be expressed as:
wherein,
t＝-0.005PLT-0.029HDL-0.376log10(SIR)+0.854log10(LNR)–(0.015PLT×LNR)–(0.062SIR×LNR)+4.253；
wherein,
PLT is the number of platelets (number of platelets per microliter);
HDL is the concentration of high density lipoprotein (mg per deciliter);
SIR is the ratio of fasting plasma glucose concentration to insulin concentration; and
LNR is the ratio of the percentage of lymphocytes to the percentage of neutrophils.
Based on the risk index, patients with cirrhosis can be divided into three groups: (1) low risk: patients with an index between 0 and 0.5; (2) moderate risk: patients with an index between 0.5 and 0.65; and (3) high risk: patients with an index between 0.65 and 1.
Accordingly, if a patient has 140 platelets per microliter, 45 mg high density lipoprotein per deciliter, 100 mg fasting glucose per deciliter, 8 micro-international units insulin per milliliter, 30% lymphocytes and 60% neutrophils of total leukocytes, the patient's risk index is 0.535 and the patient is classified into intermediate risk groups.
Example 2 validation of the Risk index of example 1
Calculating the risk indexes of 183 cirrhosis patients by using the formula established in the embodiment 1, and clustering according to the risk indexes; of these, 85 patients were in the low risk group, 38 patients were in the medium risk group, and 60 patients were in the high risk group. These patients were continuously followed for 1.5 years. During this observation, none of the low risk groups had liver cancer; while 3 and 7 patients in the moderate and high risk groups, respectively, were diagnosed with liver cancer. According to the cumulative risk curve shown in fig. 1, patients with high risk are more susceptible to liver cancer and have significant difference compared to those with low or medium risk (p 0.0213).
To further confirm the accuracy of the method of the present disclosure, risk indices of 344 patients (including 161 patients who established the formula of example 1 and 183 patients analyzed in fig. 1) were evaluated and grouped using the formula of example 1; of these, 167 patients were in the low risk group, 76 patients were in the medium risk group, and 101 patients were in the high risk group. No one in the low risk group had liver cancer during the 1.5 year observation period; while 3 and 12 patients were diagnosed with liver cancer in the moderate and high risk groups, respectively. Thus, patients in the higher risk group of patients with lower or moderate risk groups develop liver cancer more rapidly and with significant differences if measured on a predictive day (p 0.0001, fig. 2).
In summary, the present disclosure provides a prognosis method for predicting the risk of liver cancer in a patient with liver cirrhosis. The method includes a formula for calculating values of fasting plasma glucose, insulin, high density lipoprotein, platelets, lymphocytes and neutrophils. The risk index calculated according to the formula can classify the cirrhosis patients into the groups with low, moderate or high liver cancer risk. Compared with the traditional image inspection, the method for liver cancer prognosis is more accurate and effective; therefore, medical personnel can provide proper and timely treatment for patients.
It should be understood that the above-described embodiments and examples are illustrative only and that various modifications may be implemented by those skilled in the art. The above specification, examples and data are provided to complete the description and are intended to be exemplary of the practice of the invention. While the foregoing is directed to embodiments of the present disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (10)
1. A method for predicting whether a patient with liver cirrhosis is at risk for liver cancer using a blood sample of the patient with liver cirrhosis, comprising:
(1) measuring the concentration of fasting plasma glucose, insulin and high density lipoprotein, the number of platelets and the percentage of lymphocytes and neutrophils from the blood sample;
(2) calculating a risk index using a formula:
wherein,
t＝-0.005PLT–0.029HDL–0.376log10(SIR)+0.854log10(LNR) - (0.015PLT × LNR) - (0.062SIR × LNR) + 4.253; and
PLT is the number of platelets, HDL is the concentration of high density lipoprotein, SIR is the ratio of the concentration of fasting plasma glucose to the concentration of insulin, and LNR is the ratio of the percentage of lymphocytes to the percentage of neutrophils; and
(3) performing a prognosis from the risk index of step (2); if the risk index is between 0 and 0.5, the risk of the patient suffering from liver cancer is low; if the risk index is between 0.5 and 0.65, the risk of the patient suffering from liver cancer is moderate; if the risk index is between 0.65 and 1, the risk of liver cancer in the patient is high.
2. The method of claim 1, wherein the cirrhosis is caused by a chronic liver disease selected from the group consisting of alcoholic liver disease, non-alcoholic fatty liver disease, hepatitis B and hepatitis C.
3. The method of claim 2, wherein the cirrhosis is caused by hepatitis B.
4. The method of claim 1, wherein the concentration of fasting plasma glucose is measured using an enzyme, an aromatic amine, or a continuous flow assay.
5. The method of claim 4, wherein the enzyme is selected from the group consisting of glucose oxidase, glucose dehydrogenase and hexokinase.
6. The method of claim 4, wherein the aromatic amine is aniline, benzidine, 2-aminobiphenyl, or o-toluidine.
7. The method of claim 1, wherein the concentration of insulin is measured using an antibody specific for the a chain or the B chain of insulin.
8. The method of claim 1, wherein the concentration of high density lipoprotein is measured using an enzyme, an electrophoretic analysis, a continuous flow analysis, or a nuclear magnetic resonance analysis.
9. The method of claim 8, wherein the enzyme is cholesterol oxidase.
10. The method of claim 1, wherein the number of platelets, the percentage of lymphocytes, and the percentage of neutrophils are measured with a hemocytometer, a hematology analyzer, and an antibody, respectively.
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| CN116864127B (en) * | 2023-07-10 | 2023-12-01 | 首都医科大学附属北京世纪坛医院 | Construction method and system for predicting tumor cachexia prognosis index combining inflammation and insulin resistance |
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