CN111748624B - Biomarker for predicting whether liver cancer is recurrent - Google Patents
Biomarker for predicting whether liver cancer is recurrent Download PDFInfo
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Abstract
The invention relates to the field of medical molecular diagnosis, in particular to a protein molecule used as a biomarker of liver cancer and a prognosis method thereof; the biomarker for predicting liver cancer recurrence comprises one or more protein molecules of LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein. The invention adopts LC-MS/MS mass spectrometry to carry out mass spectrometry on a large amount of clinical samples, and then 5 representative protein molecules are screened out in protein detection. Finally determining that 5 protein molecules have good detection benefit through the multiple difference (more than 2 or less than 0.5) of the corresponding molecular contents of the cancer tissues of the patients without liver cancer recurrence and the cancer tissues of the patients with liver cancer recurrence; the LAS1L protein, the CLTB protein, the JAGN1 protein, the ALYREF protein and the HNRNPA3 protein are used as biomarkers to carry out liver cancer prognosis diagnosis on a subject, and the method is simple and easy to implement, safe and effective in diagnosis process, easy to accept by patients, unified in diagnosis standard and less influenced by subjective factors.
Description
Technical Field
The invention relates to the field of medical molecular diagnosis, in particular to a biomarker for predicting whether liver cancer recurs.
Background
Liver cancer is one of the common malignant tumors of the digestive system, the morbidity rate is at the 6 th position in the world, the mortality rate is at the third position, and about 25 ten thousand people die from liver cancer every year. Liver cancer is one of malignant tumors seriously threatening human health and life quality due to occult disease, high malignancy and low cure rate, and the incidence rate of liver cancer is continuously increased every year. China is the country with the most new cases of liver cancer worldwide every year, accounts for half of the total incidence of the liver cancer worldwide, and the incidence of liver cancer in European and American countries is not high but is increasing year by year. Hepatocellular carcinoma (HCC) is the most prominent histological type of liver cancer, accounts for 70-85% of primary liver malignant tumors, and accounts for about 90% in China. The pathogenesis of HCC has remained unclear to date, but viral infection, aflatoxin B1 intake, alcohol abuse, and the like are considered to be major causative factors of HCC. Of these, 10% to 40% of chronic hepatitis B virus carriers will eventually develop hepatocellular carcinoma, with an estimated over 1 million patients dying from Hepatitis B Virus (HBV) and hepatitis C virus-associated liver cancer worldwide each year. In china and japan, 75% to 80% HCC incidence is closely related to chronic viral infection of the liver, with hepatitis B virus infection accounting for 50% to 55%, and 80% to 90% of HCC cases worldwide with cirrhosis. Thus, liver HBV infection and cirrhosis are the most major high risk factors and precancerous course of HCC.
The current treatment means of liver cancer mainly comprises interventional embolization, surgical excision, radio frequency ablation, biological targeting treatment and the like, but even then, the prognosis of liver cancer is still poor. Because of this, there is a considerable proportion of postoperative recurrence of liver cancer. Therefore, regular follow-up visit after liver cancer operation monitors the relevant indexes of the tumor of the patient, and early detection and diagnosis of liver cancer recurrence and metastasis are important subjects for the research of liver cancer prognosis. The clinically common tumor marker alpha-fetoprotein (AFP) has low sensitivity, and the tumor recurrence of partial postoperative patients of liver cancer cannot be detected in time. The pathological biopsy of liver puncture is most reliable for diagnosing liver cancer and recurrence, but has great damage to patients. Imaging is widely used in postoperative follow-up, and recurrent tumors can be better discovered according to the combination of the imaging and alpha-fetoprotein. Although the above methods have certain advantages, they all require the patients to review regularly, which is difficult to be realized by many patients with poor compliance.
The recurrence of liver cancer is caused by multicentric occurrence, that is, new tumor is recurred due to the continuous existence of the soil for liver cancer growth (liver cirrhosis) and other cancer promoting factors after radical resection of liver cancer; secondly, single-center generation, namely, the cancer cells are disseminated through the portal vein before and during the original operation of excising the focus, and the intrahepatic recurrence and extrahepatic metastasis are generated. At present, the research on factors influencing the postoperative prognosis of liver cancer mainly focuses on the analysis of clinical pathological factors such as tumor size, number, differentiation degree, vascular invasion, alpha-fetoprotein level and the like, and the recurrence of liver cancer after hepatectomy cannot be predicted more accurately. Therefore, aiming at the scientific difficult problem of liver cancer recurrence, how to effectively predict the tumor recurrence after resection, how to prevent the tumor recurrence after liver cancer surgery has great clinical significance for improving the curative effect of liver cancer, and exploring and establishing a simple, quick, high-sensitivity and strong-specificity molecular prediction index and a new treatment target point become urgent needs for improving the clinical curative effect.
Therefore, if the samples are analyzed in the resection operation and the risk of recurrence of the patients can be accurately predicted, the high-risk recurrence people can be paid attention to, and the survival period of the liver cancer is prolonged.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a biomarker for predicting whether liver cancer recurs, and the content of the biomarker is detected by adopting a mass spectrometry method to judge the risk of liver cancer recurrence; the method is simple and practical, and the sensitivity and specificity of the detection method can be better improved through the detection of various small molecules.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
the biomarker for predicting liver cancer recurrence comprises one or more protein molecules of LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein.
A prognostic method for biomarkers predictive of diagnosis of liver cancer recurrence comprising the steps of: and detecting the protein expression level intensity value of LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein or HNRNPA3 protein in the sample to be detected by adopting an LC-MS/MS mass spectrometry.
Preferably, the LAS1L protein in the sample has a protein expression intensity value greater than 52085687.5, and is determined to be relapsed, or else is determined to be non-relapsed after surgery, and the false positive rate is 22.9%.
Preferably, the CLTB protein in the sample has a protein expression level intensity value of more than 941197795, and is judged as a relapsed patient, otherwise, the CLTB protein is judged as a non-relapsed patient after surgery, and the false positive rate is 22.9%.
Preferably, when the protein expression intensity value of the JAGN1 protein in the sample is more than 467852297%, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient after operation, and the false positive rate is 8.6%.
Preferably, a sample with an intensity value of the protein expression level of the ALYREF protein of less than 149218152 is considered to be a relapsed patient, otherwise, the sample is judged to be a non-relapsed patient after surgery, and the false positive rate is 22.9%.
Preferably, the sample is judged to be a relapse patient when the protein expression intensity value of HNRNPA3 protein in the sample is less than 4386125605, otherwise, the sample is judged to be a non-relapse patient after operation, and the false positive rate is 20%.
A kit for diagnosing whether liver cancer recurs comprises one or more of a reagent for specifically detecting LAS1L protein, a reagent for specifically detecting CLTB protein, a reagent for specifically detecting JAGN1 protein, a reagent for specifically detecting ALYREF protein and a reagent for specifically detecting HNRNPA3 protein.
Preferably, the reagent for specifically detecting the LAS1L protein is a primer or probe that specifically recognizes the LAS1L protein nucleic acid; the reagent for specifically detecting the CLTB protein is a primer or a probe which specifically recognizes the CLTB protein nucleic acid; the reagent for specifically detecting the JAGN1 protein is a primer or a probe for specifically recognizing JAGN1 protein nucleic acid; the reagent for specifically detecting the ALYREF protein is a primer or a probe for specifically recognizing the ALYREF protein nucleic acid; the reagent for specifically detecting the HNRNPA3 protein is a primer or a probe for specifically identifying the nucleic acid of the HNRNPA3 protein; the reagents may be used to detect a tissue sample.
(III) advantageous effects
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention adopts LC-MS/MS mass spectrometry to detect the sample to be detected, and after mass spectrometry is carried out on a large amount of clinical samples, 5 protein molecules are determined to have good detection benefit by the difference multiple (more than 2 or less than 0.5) of the corresponding molecular contents of the cancer tissue of a patient who does not relapse after the liver cancer operation and the cancer tissue of a patient who relapses after the liver cancer operation. The 5 protein molecules (namely LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein) can be used as biomarkers for diagnosing liver cancer recurrence.
(2) According to the invention, LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein are used as biomarkers to diagnose whether the liver cancer recurs for a subject, and the method is simple and easy to implement, safe and effective in diagnosis process, easy to accept by patients, unified in diagnosis standard and less influenced by subjective factors.
(3) The method can provide a new treatment target and thought for the research and development of anti-liver cancer drugs in the future through the biomarkers detected by mass spectrometry.
Drawings
FIG. 1 is a ROC plot of the protein expression intensity values of LAS1L protein;
FIG. 2 is a graph showing the intensity of protein expression levels of LAS1L protein in a liver cancer recurrent tissue and a postoperative non-recurrent tissue;
FIG. 3 is a ROC graph showing the strength values of protein expression levels of CLTB protein;
FIG. 4 is a graph showing the intensity of protein expression levels of CLTB protein in a recurrent tissue of liver cancer and a non-recurrent tissue after surgery;
FIG. 5 is a ROC graph showing the protein expression level intensity values of JAGN1 protein;
FIG. 6 is a graph showing the intensity of protein expression levels of JAGN1 protein in a recurrent tissue of liver cancer and a non-recurrent tissue after surgery;
FIG. 7 is a ROC plot of protein expression intensity values for the ALYREF protein;
FIG. 8 is a graph showing the intensity of the protein expression level of the ALYREF protein in the recurrent tissue of liver cancer and the non-recurrent tissue after operation;
FIG. 9 is a ROC graph showing the protein expression intensity of HNRNPA3 protein;
FIG. 10 is a graph showing the intensity of protein expression levels of HNRNPA3 protein in a liver cancer recurrent tissue and a postoperative non-recurrent tissue;
FIG. 11 is a ROC plot of the protein expression intensity values for the combination of ALYREF protein and HNRNPA3 protein;
FIG. 12 is a graph showing the intensity of protein expression levels of a combination of ALYREF protein and HNRNPA3 protein in a liver cancer recurrent tissue and a postoperative non-recurrent tissue.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Screening of biomarkers associated with liver cancer diagnosis
1. Experimental procedure
(1) Protein sample information
Sample preparation: 5 samples of cancer tissues obtained from patients with recurrent liver cancer and 35 samples of liver cancer tissues obtained from patients without recurrent liver cancer after surgery were obtained.
(2) Sample pretreatment
Extracting protein from a sample by adopting an SDT (4% (w/v) sodium dodecyl sulfate, 100mM Tris/HCl pH7.6,0.1M dithiothreitol) cracking method, and then carrying out protein quantification by adopting a BCA method; taking a proper amount of protein from each sample, carrying out trypsin enzymolysis by using a Filter aid protein preparation (FAS) method, desalting peptide fragments by using C18 Cartridge, adding 40 mu L of 0.1% formic acid solution for redissolving after freeze-drying the peptide fragments, and quantifying the peptide fragments (OD 280).
The BCA method is used for protein quantification, and protein concentration can be calculated according to light absorption value, and Cu is converted by protein under alkaline condition 2+ Reduction to Cu + ，Cu + Form a purple colored complex with BCA reagent, two molecules of BCA chelate a Cu + . And comparing the absorption value of the water-soluble compound at 562nm with a standard curve to calculate the concentration of the protein to be detected.
(3) LC-MS/MS data acquisition
Each sample was separated using a nanoliter flow rate HPLC liquid phase system Easy nLC.
Wherein: the buffer solution A was 0.1% formic acid aqueous solution, and the solution B was 0.1% formic acid acetonitrile aqueous solution (acetonitrile: 84%).
The column was equilibrated with 95% solution A, and the sample was applied to a loading column (Thermo Scientific Acclaim PepMap100, 100. Mu.m. By 2cm, nanoViper C18) by an autosampler, separated by an analytical column (Thermo Scientific EASY column, 10cm, ID 75. Mu.m, 3. Mu.m, C18-A2) at a flow rate of 300nL/min.
After chromatographic separation, the sample is subjected to mass spectrometry by using a Q-exact mass spectrometer. The detection mode is positive ions, the scanning range of the parent ions is 300-1800m/z, the primary mass spectrum resolution is 70,000 at 200m/z, the AGC (Automatic gain control) target is 1e6, the Maximum IT is 50ms, and the Dynamic exclusion time (Dynamic exclusion) is 60.0s. The mass-to-charge ratio of the polypeptide and the polypeptide fragments was collected as follows: 20 fragment patterns (MS 2 scan) were collected after each full scan (full scan), with an MS2 Activation Type of HCD, an Isolation window of 2m/z, a secondary mass resolution of 17, 500 at 200m/z, a Normalized fusion Energy of 30eV, and an underfill of 0.1%.
(4) Protein identification and quantitative analysis
The RAW data of mass spectrometry is RAW file, and the software MaxQuant software (version number 1.5.3.17) is used for library checking identification and quantitative analysis.
iBAQ Intensity is the amount of protein expressed in sample X based on the iBAQ algorithm, and is approximately equal to the absolute concentration of protein in that sample. LFQ Intensity is the relative protein expression of sample X based on the LFQ algorithm, and is commonly used for group comparisons. One of them is generally selected by Labelfree as a quantitative result.
IBAQ (Intensity-based absolute quantification) and LFQ belong to two different protein quantification algorithms provided by Maxquant software.
iBAQ is generally used for absolute quantification of proteins in samples, the main algorithm being based on the ratio of the sum of the intensities of the peptides identified for the protein to the theoretical number of peptides.
LFQ is generally used for pairwise quantitative comparisons between groups, the main algorithm being pair-wise correction through peptide and protein multilayers. This patent uses LFQ for protein quantification.
(5) Statistical analysis
Carrying out ratio calculation and statistical analysis on data which conform to at least two non-null values in the same group of the three-time repeated data, wherein the data comprise LFQ or iBAQ strength value ratios and P-values of all comparison groups; and (4) preliminarily screening out the difference foreign matters among the groups.
Whether the differential protein substance has significance is further verified according to P-value. Selecting a protein which has multidimensional statistical analysis of Fold change >2 or <0.5 and is considered that the content of the protein has obvious Fold difference between the cancer tissue and the tissue beside the cancer, and screening out the protein with univariate statistical analysis P value <0.05 as the protein with significant difference; thereby obtaining the differential protein molecules. Then, SPSS software is used for making a ROC curve of the differential protein, and the area under the curve (AUC) is calculated, so that the diagnostic value of the differential protein is judged. Specifically, the area under the AUC line is greater than 0.7, P is less than 0.05, and the threshold value criterion (cut off value) at the time of maximum yotans index is used as the threshold value criterion for judging whether or not a tumor is present (the case where the fold is greater than 2 is considered as tumor detection positive, and the case where the fold is less than 0.5 is considered as liver cancer detection positive), thereby obtaining high sensitivity and specificity.
(6) Bioinformatics analysis
(1) GO functional notes
The GO Annotation of a target protein set by using Blast2GO can be roughly summarized into four steps of sequence alignment (Blast), GO entry extraction (Mapping), GO Annotation (Annotation), interProScan supplementary Annotation (Annotation), and the like.
(2) KEGG pathway annotation
The target protein set was annotated with the KEGG pathway using KAAS (KEGG automated Annotation Server) software.
(3) Enrichment analysis of GO annotations and KEGG annotations
And comparing the distribution conditions of each GO classification or KEGG channel in the target protein set and the total protein set by adopting Fisher's Exact Test, and performing GO annotation or KEGG channel annotation enrichment analysis on the target protein set.
(4) Protein clustering analysis
First, quantitative information of a target protein set was normalized (normalized to the (-1, 1) interval). Then, two dimensions of the expression amounts of the sample and the protein were classified simultaneously using a Complexheatmap R package (R Version 3.4) (distance algorithm: euclidean, ligation: average linkage) and a hierarchical clustering heat map was generated.
(5) Protein interaction network analysis
The interaction relationship between the target proteins was found based on the information in the STRING database, and the interaction network was generated and analyzed using the Cytoscape software (version number: 3.2.1).
(7) Differential expression protein screening
Differentially expressed proteins were screened for the number of differentially expressed proteins in each comparison group with a standard fold change of greater than 2.0 fold (greater than 2 fold up or less than 0.5 down) and a P value of less than 0.05.
(8) Basic principle of experiment
Unlabeled quantitative proteomics (Label-free) technology has become an important method of mass spectrometry in recent years. There are two main quantitative principles of the Label-free technology: firstly, the development of non-labeled quantitative methods of spectrum counts is earlier, and a plurality of quantitative algorithms are formed, but the core principle is that the identification result of MS2 is taken as the basis of quantification, and the difference of various methods lies in the correction of high-throughput data by a later algorithm; the principle of the second unlabeled quantification method is based on MS1 and the integration of each peptide fragment signal on the LCMS chromatogram is calculated. The Maxquant algorithm adopted by the invention is based on the second principle.
2. Results of the experiment
By mass spectrum data analysis and comparison of protein molecules of liver cancer tissues of patients with relapse and liver cancer tissues of patients without relapse (no relapse after operation), 5 protein molecules are obtained and can be used as biomarkers of liver cancer relapse.
In order to evaluate the diagnosis efficiency of the protein expression intensity value of the protein molecules on the recurrence of the liver cancer, the ROC curve analysis is adopted, and the AUC is the area under the ROC curve, is the most commonly used parameter for evaluating the characteristics of the ROC curve, and is also an important test accuracy index. If the AUC is below 0.7, the diagnosis accuracy is low; the AUC is more than 0.7, so that the requirement of clinical diagnosis can be met.
Specific results and analyses were as follows:
(1) The LC-MS/MS mass spectrometry is adopted to detect that the LAS1L protein has difference between the liver cancer recurrent tissue and the postoperative non-recurrent tissue.
The research shows that the LAS1L protein is significantly up-regulated by 11.68 times in the liver cancer recurrence sample, and the p value is less than 0.05.
As is clear from FIG. 1, the AUC of LAS1L protein was 0.806>0.7, which indicates that LAS1L protein has a good effect of determining whether or not liver cancer has recurred, and can be used as a biomarker for determining whether or not liver cancer has recurred.
When the cut off value of LAS1L protein is 52085687.5, the sensitivity is 80% and the specificity is 77.1%. When the individual detection was performed, the intensity value of the protein expression level of the LAS1L protein was found to be greater than 52085687.5, which was judged as a relapsed patient, whereas it was judged as a non-relapsed patient (false positive rate: 22.9%).
As can be seen from FIG. 2, the samples of the liver cancer recurrent tissue are mainly distributed above the detection threshold (solid line in FIG. 2), and the samples of the post-operative non-recurrent tissue are mainly distributed below the detection threshold, which indicates that the values of the protein expression levels of the liver cancer recurrent tissue and the post-operative non-recurrent tissue are greatly different, and the detection threshold has a good detection effect.
In conclusion, the LAS1L protein can be used as a biomarker for liver cancer recurrence.
(2) The difference of CLTB protein in the liver cancer recurrent tissue and the postoperative non-recurrent tissue is detected by adopting an LC-MS/MS mass spectrometry.
The research finds that the CLTB protein is remarkably up-regulated by 6.68 times in a liver cancer recurrence sample, and the p value is less than 0.05.
As can be seen from FIG. 3, the AUC of CLTB protein is 0.806> -0.7, which indicates that CLTB protein has a good effect of determining whether liver cancer has recurred or not and can be used as a biomarker for determining whether liver cancer has recurred or not.
When the protein expression intensity value of the CLTB protein was 941197795, the sensitivity was 80% and the specificity was 77.1%. When individual detection is carried out, the protein expression intensity value of the CLTB protein is greater than 941197795, and the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient (the false positive rate is 22.9%).
As can be seen from FIG. 4, the samples of the liver cancer recurrent tissue are mainly distributed above the detection threshold (solid line in FIG. 4), and the samples of the post-operative non-recurrent tissue are mainly distributed below the detection threshold, which indicates that the intensity values of the protein expression levels of the liver cancer recurrent tissue and the post-operative non-recurrent tissue are greatly different, and the detection threshold has a good detection effect.
In summary, the CLTB protein can be used as a biomarker for recurrence of liver cancer.
(3) The LC-MS/MS mass spectrometry is adopted to detect that the JAGN1 protein has difference between the liver cancer recurrent tissue and the postoperative non-recurrent tissue.
The research finds that the JAGN1 protein is remarkably up-regulated by 2.45 times on a liver cancer recurrence sample, and the p value is less than 0.05.
As is clear from FIG. 5, the AUC of JAGN1 protein was 0.840>0.7, which indicates that JAGN1 protein has a good effect of determining and can be used as a biomarker for determining whether liver cancer has recurred.
When the protein expression level intensity value of JAGN1 protein was 467852297%, the sensitivity was 80% and the specificity was 91.4%. When the individual detection is carried out on the JAGN1 protein, the protein expression intensity value is more than 467852297, the patient is judged as a relapse patient, otherwise, the patient is judged as a non-relapse patient (the false positive rate is 8.6%).
As can be seen from FIG. 6, the samples of the liver cancer recurrent tissue were mainly distributed above the detection threshold (solid line in FIG. 6), and the samples of the post-operative non-recurrent tissue were mainly distributed below the detection threshold, indicating that the values of the protein expression levels of the liver cancer recurrent tissue and the post-operative non-recurrent tissue are greatly different from each other, and that the detection threshold has a good detection effect.
In conclusion, the JAGN1 protein can be used as a biomarker for liver cancer recurrence.
(4) The differences of the ALYREF protein in the recurrent tissues and the non-recurrent tissues are detected by adopting an LC-MS/MS mass spectrometry.
The research finds that the ALYREF protein is down-regulated by 0.19 times in the significance of the liver cancer recurrence sample, and the p value is less than 0.05.
As can be seen from FIG. 7, the AUC of the ALYREF protein is 0.937> -0.7, which indicates that the ALYREF protein has a good judgment effect and can be used as a biomarker for determining whether liver cancer has relapsed.
When cut off value of the ALYREF protein is 149218152, the sensitivity is 100%, and the specificity is 77.1%. When the individual detection is carried out, the protein expression intensity value of the ALYREF protein is less than 149218152, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient after the operation (the false positive rate is 22.9%).
As can be seen from fig. 8, the liver cancer recurrent tissue samples were mainly distributed below the detection threshold (solid line in fig. 8), and the non-recurrent tissue samples were mainly distributed above the detection threshold, indicating that the intensity values of the protein expression levels of the liver cancer recurrent tissue and the non-recurrent tissue are greatly different, and the detection threshold has a good detection effect.
In summary, the ALYREF protein can be used as a biomarker for liver cancer recurrence.
(5) The difference between the HNRNPA3 protein in the recurrent tissue and the non-recurrent tissue is detected by LC-MS/MS mass spectrometry.
Research shows that the HNRNPA3 protein is significantly down-regulated by 0.49 times in a liver cancer recurrence sample, and the p value is less than 0.05.
As can be seen from FIG. 9, the AUC of HNRNPA3 protein was 0.920>0.7, which indicates that HNRNPA3 protein has a good effect of determining and can be used as a biomarker for determining whether liver cancer has recurred.
When the protein expression level of HNRNPA3 protein was 4386125605, the sensitivity was 100% and the specificity was 80%. When the individual detection is carried out, the protein expression intensity value of HNRNPA3 protein is less than 4386125605, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient after the operation (the false positive rate is 20%).
As can be seen from fig. 10, the liver cancer recurrent tissue samples were mainly distributed below the detection threshold (solid line in fig. 10), and the non-recurrent tissue samples were mainly distributed above the detection threshold, indicating that the intensity values of the protein expression levels of the liver cancer recurrent tissue and the non-recurrent tissue are greatly different, and the detection threshold has a good detection effect.
In conclusion, the HNRNPA3 protein can be used as a biomarker for liver cancer recurrence.
(6) The application of the combination of the ALYREF protein and the HNRNPA3 protein in the preparation of the early liver cancer diagnostic kit.
The invention also provides a diagnosis method of liver cancer, which comprises the following steps: the numerical value of P (recurrence probability after liver cancer operation) is calculated by adopting binary logistic regression analysis, and the formula obtained after binary logistic regression of SPSS software is as follows:
wherein a is the protein expression intensity value of ALYREF protein, and b is the protein expression intensity value of HNRNPA3 protein; if the detected P (recurrence probability after liver cancer operation) is more than 0.0694114, the patient is judged to be a patient with recurrence, otherwise, the patient is judged to be a patient without recurrence.
As shown in FIG. 11, the AUC of the combinatorial protein was 0.977> -0.7, which indicates that the combinatorial protein has a good effect of determining whether or not liver cancer has recurred as a biomarker.
When the protein expression intensity value of the combined protein is 0.0694114, the sensitivity is 100% and the specificity is 88.6%. When the individual detection is carried out, the protein expression amount intensity value of the combined protein is more than 0.0694114, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient after the operation (the false positive rate is 11.4%).
As can be seen from fig. 12, the liver cancer recurrent tissue samples were mainly distributed above the detection threshold (solid line in fig. 12), and the non-recurrent tissue samples were mainly distributed above the detection threshold, indicating that the difference in the protein expression level intensity values between the liver cancer recurrent tissue and the non-recurrent tissue is large, and the detection threshold detection effect is good.
In view of the above results, the combination protein composed of the ALYREF protein and the HNRNPA3 protein can be used as a biomarker for recurrence of liver cancer.
Example 1
A biomarker for predicting liver cancer recurrence comprising LAS1L protein.
A prognostic method for biomarkers for predicting liver cancer relapse diagnosis of liver cancer comprising the steps of: detecting the protein expression quantity intensity value of LAS1L protein in a sample to be detected by adopting an LC-MS/MS mass spectrometry; when the intensity value of the protein expression level of LAS1L protein in the sample is larger than 52085687.5, the patient is judged to be a relapsed patient, otherwise, the patient is judged to be a non-relapsed patient (the false positive rate is 22.9%).
A kit for diagnosing whether liver cancer recurs comprises a reagent for specifically detecting LAS1L protein, wherein the reagent for specifically detecting LAS1L protein is a probe for specifically recognizing LAS1L protein nucleic acid.
Example 2
A biomarker for predicting liver cancer recurrence comprising CLTB protein.
A prognostic method for biomarkers for predicting liver cancer relapse diagnosis of liver cancer comprising the steps of: detecting the protein expression quantity intensity value of the CLTB protein in the sample to be detected by adopting an LC-MS/MS mass spectrometry; when the protein expression intensity value of the CLTB protein in the sample is more than 941197795, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient (the false positive rate is 22.9%).
A kit for diagnosing whether liver cancer recurs comprises a reagent for specifically detecting CLTB protein, wherein the reagent for specifically detecting CLTB protein is a probe for specifically recognizing CLTB protein nucleic acid.
Example 3
A biomarker for predicting liver cancer recurrence, comprising JAGN1 protein.
A prognostic method for biomarkers for predicting liver cancer relapse diagnosis of liver cancer comprising the steps of: detecting a protein expression quantity intensity value of JAGN1 protein in a sample to be detected by adopting an LC-MS/MS mass spectrometry; when the protein expression level intensity value of JAGN1 protein in the sample is greater than 467852297%, the patient is judged as a relapse patient, otherwise, the patient is judged as a non-relapse patient (the false positive rate is 8.6%).
A kit for diagnosing whether liver cancer recurs comprises a reagent for specifically detecting JAGN1 protein, wherein the reagent for specifically detecting JAGN1 protein is a probe for specifically recognizing JAGN1 protein nucleic acid.
Example 4
Biomarkers for predicting liver cancer recurrence, including the ALYREF protein.
A prognostic method for biomarkers for predicting liver cancer relapse diagnosis of liver cancer comprising the steps of: detecting the protein expression intensity value of the ALYREF protein in the sample to be detected by adopting an LC-MS/MS mass spectrometry; when the protein expression intensity value of the ALYREF protein in the sample is less than 149218152, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient after operation (the false positive rate is 22.9%).
A kit for diagnosing whether liver cancer recurs comprises a reagent for specifically detecting ALYREF protein, wherein the reagent for specifically detecting the ALYREF protein is a probe for specifically recognizing ALYREF protein nucleic acid.
Example 5
Biomarkers for predicting liver cancer recurrence, including HNRNPA3 protein.
A prognostic method for biomarkers for predicting liver cancer relapse diagnosis of liver cancer comprising the steps of: detecting the protein expression intensity value of HNRNPA3 protein in the sample to be detected by adopting an LC-MS/MS mass spectrometry; if the protein expression intensity value of HNRNPA3 protein in the sample is less than 4386125605%, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient after the operation (the false positive rate is 20%).
A kit for diagnosing whether liver cancer recurs comprises a reagent for specifically detecting HNRNPA3 protein, wherein the reagent for specifically detecting HNRNPA3 protein is a probe for specifically recognizing HNRNPA3 protein nucleic acid.
Example 6
Biomarkers for liver cancer diagnosis, including ALYREF protein and HNRNPA3 protein.
A method for prognosis of biomarkers for liver cancer diagnosis, comprising the steps of: the numerical value of P (recurrence probability after liver cancer operation) is calculated by adopting binary logistic regression analysis, and the formula obtained after binary logistic regression of SPSS software is as follows:
wherein: a is the protein expression intensity value of ALYREF protein, and b is the protein expression intensity value of HNRNPA3 protein; if the detected P (recurrence probability after liver cancer operation) is more than 0.0694114, the patient is judged to be a patient with recurrence, otherwise, the patient is judged to be a patient without recurrence.
When the individual detection is carried out, the protein expression intensity value of the combined protein is more than 0.0694114, the patient is judged to be a relapse patient, otherwise, the patient is judged to be a non-relapse patient after the operation (the false positive rate is 11.4%).
The method takes at least one protein of LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein as a biomarker to diagnose liver cancer of a subject, is simple and easy to implement, has safe and effective diagnosis process, is easy to accept by patients, and has less influence of individual subjective factors on the diagnosis standard.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (1)
1. The application of a reagent for detecting one or more of LAS1L protein, CLTB protein, JAGN1 protein and HNRNPA3 protein in preparing a kit for predicting liver cancer recurrence diagnosis.
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