CN114724707A - Hepatocellular carcinoma autoantibody marker combined diagnosis model - Google Patents
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Abstract
The invention belongs to the technical field of inspection medicine, and particularly relates to a combined diagnosis model of a hepatocellular carcinoma autoantibody marker. The invention screens more updated liver cancer related autoantibodies by utilizing a high-flux protein chip technology, provides a method for more accurately distinguishing liver cancer from healthy individuals, provides a group of autoantibody diagnosis markers of hepatocellular carcinoma for diagnosing the hepatocellular carcinoma, and simultaneously provides a kit for preparing the liver cancer detection by using the markers. The invention provides a combined diagnosis model of hepatocellular carcinoma autoantibody markers, which has a model formula of P (P = HCC,3TAabs) =1/{1+ exp [ - (-4.758+0.351APEX2+0.167RCSD1+0.123TP53) ] }, has good diagnosis capability for jointly diagnosing liver cancer, and has better distinguishing effect compared with single index detection.
Description
Technical Field
The invention belongs to the technical field of inspection medicine, and particularly relates to a combined diagnosis model of a hepatocellular carcinoma autoantibody marker.
Background
Primary liver cancer (plc) is one of the most common malignant tumors in the world, has the characteristics of high malignancy, strong infiltration and metastasis, poor prognosis and the like, and is the third position of the global cancer mortality. Clinical classification includes primary hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and hepatocellular carcinoma-mixed intrahepatic cholangiocarcinoma (HCC-ICC). Among them, hepatocellular carcinoma accounts for about 90% of primary liver cancer. China is the country with the highest incidence of liver cancer, and the death number of the Chinese is 18.8 percent of the death number of all malignant tumors. While surgical treatment may be effective at an early stage, the overall 5-year survival rate is only 50% -70%. The diagnosis of most liver cancer patients is already at the middle and late stages. At present, the molecular mechanisms of liver cancer occurrence, development, metastasis and recurrence are not completely clear. Research has shown that early diagnosis of liver cancer and effective treatment of patients significantly prolongs the survival of patients.
During tumorigenesis, abnormally expressed proteins, called tumor-specific antigens (TSAs) or tumor-associated antigens (TAAs), stimulate the immune system of the body to produce antibodies, and the corresponding antibodies called autoantibodies. About 30% of the sera of patients with liver cancer contain antibodies against cellular autoantigens, and more importantly, in some sera of patients gradually changing from chronic hepatitis and liver cirrhosis to liver cancer, autoantibodies often appear before clinical symptoms of liver cancer, which suggests that the autoantibodies are related to the densification of hepatocellular carcinoma. Compared with traditional serum markers, autoantibodies can persist in tumor serum and can be detected months or even years before clinical symptoms appear in tumor patients. In addition, since autoantibodies are present at much higher concentrations than their respective tumor associated antigens, autoantibodies may be more easily detected than their corresponding tumor associated antigens. The complexity of the tumor pathogenesis process causes that the diagnosis value of single TAAs or tumor-associated antigen autoantibodies as tumor diagnosis markers is limited, and researches show that the frequency of the single autoantibodies appearing in the serum of a tumor patient is low and is about 1% -15%, so that the defect of low positive rate of a single index is overcome by searching an optimal autoantibody combination. The sensitivity and specificity of diagnosis are improved, and the autoantibody becomes a feasible auxiliary means to provide service for tumor diagnosis.
In view of the fact that the existing autoantibody detection is still insufficient in clinical application of liver cancer diagnosis, continuous discovery and identification of new liver cancer-related TAAs is still an important task, in order to finally reduce the mortality rate of liver cancer and improve the survival rate, screening and identification of more sensitive and specific serological autoantibody markers are urgently needed in the field, a hepatocellular carcinoma autoantibody marker combination diagnosis model is established, and a kit for detecting the liver cancer autoantibody, which is simple in operation, low in cost and wide in application range, is developed.
Disclosure of Invention
To solve the above problems, the present invention detects tumor-associated antigen autoantibodies (TAabs) in the serum of 97 HCC patients and 60 healthy control people by using a custom chip containing 493 recombinant proteins. In a log2And (3) selecting 58 autoantibodies with potential diagnostic capacity by using FC | > 1 and P <0.05 as screening standards. Further comprehensively considering log2FC value and chip expression value, 9 kinds of autoantibodies are selected as candidate autoantibodies to distinguish the liver cell liver cancer from the healthy control population, wherein 8 kinds of TAabs are different between the liver cancer patient and the healthy control population, and P is less than 0.05. The variation range of AUC under the ROC curve is 0.601-0.716, the sensitivity range is 43.3% -87.6%, and the specificity range is 38.3% -81.7%. The area under the curve of the APEX2 autoantibody was 0.716 at the maximum, the sensitivity was 87.6%, the specificity was 48.3%, and the Yodane index was 0.36. Single-factor and multi-factor binary logistic regression analysis is carried out on 8 kinds of TAabs to find that RCSD1 and APEX2 are independent risk factors for liver cancer. The combination of TP53, APEX2 and RCSD1 showed that the AUC for the combined diagnosis of liver cancer was 0.751, and the combination of TP53, APEX2 and RCSD1 showed that the liver cancer has good diagnostic ability.
The invention aims to provide a group of liver cell and liver cancer autoantibody markers, which are used in combination for predicting and diagnosing hepatocellular carcinoma, judging disease progress and the like, mainly checking the autoantibody condition in blood of a hepatocellular carcinoma patient through a protein chip, and using the screened autoantibody markers in combination for diagnosing and monitoring hepatocellular carcinoma.
In order to achieve the purpose, the invention adopts the following technical scheme.
A first aspect.
A method for constructing a diagnosis system for hepatocellular carcinoma, comprising the steps of:
s1, screening a serum sample of a hepatocellular carcinoma patient and a serum sample of a healthy human;
s2, customizing a chip containing 493 recombinant proteins corresponding to the autoantibodies;
s3, detecting the serum sample of S1 by the custom chip of S2, and screening 58 liver cancer related autoantibodies with potential diagnosis capability by taking | log2FC | greater than 1 and P less than 0.05 as screening standards;
s4 comprehensive consideration log2FC value, chip expression value condition, and the ability of autoantibodies to distinguish liver cancer from liver benign lesions, and screening 9 kinds of autoantibodies, FKBP1A, EHD1, TGM5, TP53, VSIG1, APEX2, HHLA2, ZFYVE27, and RCSD 1;
s5, performing one-factor binary logistic regression analysis on the differential hepatocellular carcinoma-associated antigen autoantibodies obtained in S4, setting the screening condition to be p <0.05, screening 3 autoantibodies expressing differential tumor-associated antigens for hepatocellular carcinoma patients and healthy people, namely APEX2, TP53 and RCSD1, performing multi-factor binary logistic regression analysis, and constructing a model according to the multi-factor binary logistic analysis result to obtain a model for diagnosing hepatocellular carcinoma, wherein the model is a calculation formula as follows:
P(P=HCC,3TAAbs)=1/{1+exp[-(-4.758+0.351APEX2+0.167RCSD1+0.123TP53)]}。
a second aspect.
The invention provides the application of the construction method of the diagnosis system in a kit, a chip, a preparation, equipment, a diagnosis system and a calculation model.
In a third aspect.
The invention provides a combined diagnosis model of hepatocellular carcinoma autoantibody markers, wherein the autoantibody markers in the model comprise any one or a combination of TP53 antibody, APEX2 antibody and RCSD1 antibody.
Further, the diagnostic model formula constructed by combining TP53 with APEX2 and RCSD1 is as follows: p (P = HCC,3TAAbs) =1/{1+ exp [ - (-4.758+0.351APEX2+0.167RCSD1+0.123TP53) ] }.
Further, theTP53, APEX2 and RCSD1 in the model formula are respectively log of mean values obtained by standardization processing of corresponding signal values of TP53, APEX2 and RCSD1 autoantibodies during chip detection2Analysis of the value, i.e. Log2(normalized SNR)1（TP53）+ normalized SNR2（TP53））/2，Log2(normalized SNR)1（APEX2）+ normalized SNR2（APEX2））/2，Log2(normalized SNR)1（RCSD1）+ normalized SNR2（RCSD1））/2。
Further, the P value in the model formula is used as a threshold value, and if the threshold value is more than or equal to 0.645, the main body of the serum sample is the hepatocellular carcinoma patient.
Furthermore, the P value in the model formula is used as a threshold value to analyze the working characteristic curve of the subject, and the Johnson index, the area under the curve, the sensitivity and the specificity of the autoantibody combined diagnosis model are obtained according to the working characteristic curve of the subject.
A fourth aspect.
The invention provides an application of a combined diagnosis model according to hepatocellular carcinoma autoantibody markers in preparing products for jointly detecting and distinguishing hepatocellular carcinoma patients from healthy people.
Further, the products are kits, chips, preparations, devices, diagnostic systems and computational models.
Has the advantages that:
the invention screens more updated liver cancer related autoantibodies by utilizing a high-flux protein chip technology, provides a method for more accurately distinguishing liver cancer from healthy individuals, and provides a group of autoantibody diagnosis markers for diagnosing hepatocellular carcinoma.
The invention provides a liver cancer marker which is simple to operate, low in cost and high in accuracy and is applied to clinic and a kit for preparing the marker for detecting liver cancer.
The invention provides an autoantibody marker combined diagnosis model which has better distinguishing effect on the diagnosis efficiency of HCC than single index detection.
Drawings
FIG. 1: main component analysis result graphs of HCC group and HC group.
FIG. 2 is a drawing: the relative expression of the 9 potential autoantibodies in the HCC group and the HC group was compared.
FIG. 3: ROC profile of 8 TAAbs for diagnosis of hepatocellular carcinoma.
FIG. 4 is a drawing: TP53, APEX2, and RCSD1 combined diagnostic ability for HCC.
Detailed Description
In order that the invention may be more clearly understood, it will now be further described with reference to the following examples. The examples are for illustration only and do not limit the invention in any way. In the examples, each raw reagent material is commercially available, and the experimental method not specifying the specific conditions is a conventional method and a conventional condition well known in the art, or a condition recommended by an instrument manufacturer.
1. And (6) collecting a sample.
The samples were collected in the shengjing hospital affiliated to the university of medical science in china for diagnosis, 97 cases of primary hepatocellular carcinoma were diagnosed clearly by pathology or liver puncture, and 60 cases of patients with normal liver function, no serious basic disease, no tumor in other parts, and no primary or secondary liver disease among the physical examination groups were used as a control group.
2. The main reagent.
Cy3 AffiniPure Goat Anti-Human IgG (H+L) （Jackson Immuno Research）
BSA (Solebao)
Tween-20 (China fir gold bridge)
Ultrapure water (Dreches)
Sodium chloride (Beijing Yili fine chemicals Co., Ltd.)
Tris（amresco）
Hydrochloric acid (Beijing Yili fine chemicals Co., Ltd.)
Two chips and ELISA kit: a custom chip of 493 recombinant proteins was made by the american CDI laboratory.
3. Main instruments and consumables.
Centrifuge (KUBOTA 4000) (Japan KUBOTA Co., Ltd.)
50mL centrifuge tube (greiner)
Absorbent paper (kimtech)
Centrifuge (800 rpm) (eppendorf)
Shaking instrument (60rpm) (Taicang City laboratory type ZP-200)
Pipetting gun (eppendorf)
Chip scanner (Genepix type 4000B).
4. And (5) a specific operation process.
4.1 prophase high throughput protein chip (Huprot)TMHuman protein Microarray v 4.0) comprising 20240 recombinant Human proteins, 493 liver cancer-associated autoantibodies were selected and assigned to the us CDI laboratory to be fabricated into custom chips containing 493 recombinant proteins corresponding to the autoantibodies.
4.2 customizing the detection flow of the chip.
(1) And (3) sealing: 100uL of blocking solution (5% BSA (w/v), TBS-T) was added to each well of the plate. Incubate at room temperature for 2 hours, gently shake on a shaker (60 rpm).
(2) Sample preparation: diluting and mixing the serum sample with a confining liquid according to a ratio of 1:1000, and standing on ice for later use.
(3) And (3) hybridization: the blocking solution in the 4-well plate was aspirated off the corner, and diluted serum samples (100uL) were carefully added, keeping the chip right side up. The tip of the sample application tip must not touch the chip surface. Incubate on a shaker at room temperature for 1 hour (60 rpm).
(4) And (3) developing I: after incubation, the liquid in the 4-well plate was aspirated from the corner, 150uL TBS-T was added to each well, and the plate was rinsed 3 times quickly. Then 150uL TBS-T was added to each well and the wells were washed at room temperature for 10 minutes (60rpm) with shaking, and repeated 3 times. The chip cannot be dried off during the experiment.
(5) And (3) secondary antibody incubation: the fluorescent secondary antibody (goat anti-human IgG, Cy 3) was diluted 1:350 with blocking solution, 100uL was added to each well of a 4-well plate, and incubated at room temperature for 1.5hr with shaking (60 rpm).
(6) And (4) developing II: the solution was aspirated off, 150uL TBS-T (1 XTBS pH 7.5, 0.1% Tween-20) was added, and rinsed 3 times quickly. Then each hole add 150uL TBS-T, room temperature 5-10 minutes (60rpm), repeat 3 times, again 0.1 x TBS quick rinse 3 times.
(7) And (3) dry sheet: the chips were placed vertically in a 50mL centrifuge tube and centrifuged at 800rpm for 3 minutes.
(8) Scanning and saving: the chip is scanned according to the instructions of the chip scanner. And saves the scan results.
4.3 data acquisition and pretreatment.
(1) Data extraction: the raw data of the chip was collected into a table using genepix5.1, and to eliminate the bias caused by the difference in background values of different samples in the extracted data, the ratio of the foreground value to the background value (F mean/B mean) of each protein was calculated and defined as the signal-to-noise ratio (SNR), which is the mean of the foreground value/background value of two duplicate proteins.
(2) Data preprocessing: the differences of the control points of each Block in all chips are analyzed and observed, and are expressed by coefficient of variation CV values, the CV values of most positive control points are found to be within 25%, and the expression of the internal points of the blocks is considered to have no obvious deviation.
(3) Method for standardization of data: in order to eliminate the influence of background values and other factors among different samples, all points in the Block need to be corrected according to the positive control points, the Cy3/Cy5 is the positive control points, namely internal reference, the chip contains 14 blocks which are defined as blocks 1-14, and the mean value of Cy3/Cy5 of the blocks 1-14 on 9 chips is calculated to obtain the Cy3/Cy5 mean value corresponding to the blocks 1-14. And dividing the value of Cy3/Cy5 of each block by the mean value of the corresponding block to obtain a coefficient, and multiplying other points on each block by the coefficient to obtain a normalized value. The collected raw data are normalized by a Perl program, and finally the normalized SNR value is used for subsequent analysis.
5. And (5) carrying out statistical analysis.
Further analyzing the difference index between liver cancer and healthy control, firstly, taking log of chip expression value2And performing post-analysis, and performing normality test on the data, wherein the data are tested by using an independent sample T when being in accordance with normal distribution and are tested by using a nonparametric test when being not in accordance with the normal distribution. And performing ROC curve analysis, and calculating sensitivity, specificity and Jordan index. And performing a one-factor and multi-factor binary logistic regression analysis.
Example 1.
58 possible HCC-associated autoantibody markers were screened by chip detection.
Sera from 97 HCC and 60 HC were tested using a custom chip containing 493 autoantibodies against the recombinant protein. In a log2FC | > 1 and P <0.05 were used as screening criteria. The difference in HCC versus HC was indicated at 58. The data are subjected to principal component analysis and discrimination, and the liver cancer group is obviously distinguished from the health group (figure 1).
Example 2.
The expression levels of 11 potential markers of liver cancer are different in HCC and HC serum.
General consideration log2FC value, chip expression value condition, capacity of autoantibody to distinguish liver cancer from liver benign lesion, and finally screening out 9 potential TAabs of liver cancer (FKBP 1A, EHD1, TGM5, TP53, VSIG1, APEX2, HHLA2, ZFYVE27, RCSD 1), as shown in FIG. 2, wherein the expression levels of the other 8 TAabs in HCC patients and HCC population are different except TGM5, and P is P<0.05, ns means that the difference is not statistically significant,. indicates that P <0.05, the difference is statistically significant.
Example 3.
8 autoantibodies such as serum NHLH2 and the like are good diagnostic markers in hepatocellular carcinoma.
AUC-ROC analysis shows that 8 indexes of FKBP1A, EHD1, TP53, VSIG1, APEX2, HHLA2, ZFYVE27 and RCSD1 are used for diagnosing HCC difference HC and are better liver cell liver cancer screening and diagnosing markers. The area change range under the ROC curve is 0.601-0.716, the sensitivity range is 43.3-87.6%, and the specificity range is 38.3-81.7%. The area under the curve of the APEX2 autoantibody was 0.716 maximum, the sensitivity was 87.6%, the specificity was 48.3%, and the john index was 0.36 (fig. 3).
Example 4.
Diagnostic efficacy of autoantibody marker combination diagnostic models on HCC.
The individual analysis was performed by using 8 autoantibodies significantly different between the HCC group and the HC group as independent variables and the presence or absence of HCC as dependent variablesThe binary Logistic regression analysis of factors and multifactorial factors, wherein APEX2 and RCSD1 are independent influencing factors of liver cancer occurrence, and P is less than 0.05. Considering that TP53 is a common tumor marker at present, a diagnostic model formula constructed by combining TP53 with APEX2 and RCSD1 is that P (P = HCC,3TAabs) =1/{1+ exp [ - (-4.758+0.351APEX2+0.167RCSD1+0.123TP53)]In the formula, the value P is used as a threshold value, if the threshold value is more than or equal to 0.645, the main body of the serum sample is a hepatocellular carcinoma patient, otherwise, the blood sample is a healthy person; TP53, APEX2 and RCSD1 are respectively Log2(normalized SNR)1（TP53）+ normalized SNR2（TP53））/2，Log2(normalized SNR)1（APEX2）+ normalized SNR2（APEX2））/2，Log2(normalized SNR)1（RCSD1）+ normalized SNR2（RCSD1）)/2. The AUC for HCC diagnosis rose to 0.751, 95% CI:0.67-0.833 after the combination of the three (FIG. 4, Table 1).
Claims (9)
1. A method for constructing a diagnostic system for hepatocellular carcinoma, comprising the steps of:
s1, screening a serum sample of a hepatocellular carcinoma patient and a serum sample of a healthy human;
s2, customizing a chip containing 493 recombinant proteins corresponding to the autoantibodies;
s3, detecting the serum sample of S1 by the custom chip of S2, and screening 58 liver cancer related autoantibodies with potential diagnosis capability by taking | log2FC | greater than 1 and P less than 0.05 as screening standards;
s4 comprehensive consideration log2FC value, chip expression value condition, and the ability of autoantibodies to distinguish liver cancer from liver benign lesions, 9 autoantibodies were screened, FKBP1A, EHD1, TGM5, TP53, VSIG1, APEX2, HHLA2, ZFYVE27, RCSD 1;
s5, performing one-factor binary logistic regression analysis on the differential hepatocellular carcinoma-associated antigen autoantibodies obtained in S4, setting the screening condition to be p <0.05, screening 3 autoantibodies expressing differential tumor-associated antigens for hepatocellular carcinoma patients and healthy people, namely APEX2, TP53 and RCSD1, performing multi-factor binary logistic regression analysis, and constructing a model according to the multi-factor binary logistic analysis result to obtain a model for diagnosing hepatocellular carcinoma, wherein the model is a calculation formula as follows:
P(P=HCC,3TAAbs)=1/{1+exp[-(-4.758+0.351APEX2+0.167RCSD1+0.123TP53)]}。
2. the method of claim 1, applied to kits, chips, preparations, devices, diagnostic systems and computational models.
3. The combined diagnosis model of the hepatocellular carcinoma autoantibody marker is characterized in that the autoantibody marker comprises any one or a combination of TP53 antibody, APEX2 antibody and RCSD1 antibody.
4. The combined diagnosis model of hepatocellular carcinoma autoantibody marker in accordance with claim 2, wherein the TP53 is combined with APEX2 and RCSD1 to construct a diagnosis model having the formula of P (P = HCC,3TAabs) =1/{1+ exp [ - (-4.758+0.351APEX2+0.167RCSD1+0.123TP53) ] }.
5. The combined diagnostic model of hepatocellular carcinoma autoantibody marker in accordance with claim 4, wherein TP53, APEX2 and RCSD1 in the formula are values of log2 analysis using the mean values obtained by the normalization process of corresponding signal values of TP53, APEX2 and RCSD1 autoantibodies in chip detection, respectively.
6. The combined diagnostic model of hepatocellular carcinoma autoantibody marker in accordance with claim 4, wherein the P value is used as a threshold value, and if the threshold value is not less than 0.645, it indicates that the main body of the serum sample is the hepatocellular carcinoma patient.
7. The combined diagnosis model of hepatocellular carcinoma autoantibody marker as claimed in claim 4, wherein the P value is used as a threshold value to perform the analysis of the working characteristic curve of the subject, and the Johnson index, the area under the curve, the sensitivity and the specificity of the combined diagnosis model of the autoantibody are obtained according to the working characteristic curve of the subject.
8. The use of the combined diagnosis model of hepatocellular carcinoma autoantibody markers according to claim 4 for the preparation of a product for combined detection and differentiation of hepatocellular carcinoma patients from healthy humans.
9. The use of the combined diagnosis model of hepatocellular carcinoma autoantibody markers according to claim 4 for the preparation of a product for combined detection and differentiation of hepatocellular carcinoma patients from healthy humans, wherein the product is a kit, a chip, a preparation, a device, a diagnostic system and a computational model.
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