CN111748624A - Biomarkers for predicting recurrence of liver cancer - Google Patents

Abstract

The invention relates to the field of medical molecular diagnosis, in particular to a protein molecule as a biomarker for liver cancer and a prognosis method thereof; the biomarker for predicting the recurrence of liver cancer includes LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein of one or several protein molecules. The present invention adopts LC-MS/MS mass spectrometry analysis method, after mass spectrometry analysis of a large number of clinical samples, five representative protein molecules are screened out in protein detection. The final determination of five protein molecules by the difference multiples (greater than 2 or less than 0.5) of the corresponding molecular contents in the cancer tissues of patients with non-recurrence of liver cancer and those of patients with recurrence of liver cancer has a good test benefit; LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein Using HNRNPA3 protein and HNRNPA3 protein as biomarkers to diagnose the prognosis of liver cancer is simple and easy, the diagnosis process is safe and effective, easy to be accepted by patients, the diagnostic standard is unified, and it is less affected by subjective factors.

Description

Biomarkers for predicting recurrence of liver cancer

technical field

The invention relates to the field of medical molecular diagnosis, in particular to a biomarker for predicting the recurrence of liver cancer.

Background technique

Hepatocellular carcinoma (HCC) is one of the common malignant tumors in the digestive system. The incidence rate ranks sixth in the world and the mortality rate ranks third. About 250,000 people die of liver cancer every year. Liver cancer is one of the malignant tumors that seriously threaten human health and quality of life due to its insidious onset, high malignancy and low cure rate. The incidence of liver cancer continues to rise every year. China is the country with the largest number of new cases of liver cancer every year in the world, accounting for half of the world's total incidence. Although the incidence in Europe and the United States is not high, it is also increasing year by year. Hepatocellular carcinoma (HCC) is the most important histological type of liver cancer, accounting for 70% to 85% of primary liver malignancies, and about 90% in my country. So far, the pathogenesis of HCC is still unclear, but viral infection, aflatoxin B1 intake, and alcoholism are considered to be the main pathogenic factors of HCC. Among them, 10% to 40% of chronic hepatitis B virus carriers will eventually develop hepatocellular carcinoma, and it is estimated that more than 1 million patients worldwide die each year from hepatitis B virus (HBV) and hepatitis C virus-related diseases. Liver cancer. In China and Japan, 75% to 80% of HCC cases are closely related to chronic viral infection in the liver, of which hepatitis B virus infection accounts for 50% to 55%, while 80% to 90% of HCC cases worldwide are associated with liver cirrhosis. . Therefore, liver HBV infection and liver cirrhosis are the main risk factors and precancerous lesions of HCC.

The current treatment methods for liver cancer mainly include interventional embolization, surgical resection, radiofrequency ablation, biological targeted therapy, etc., but even so, the prognosis of liver cancer is still poor. The reason is that the recurrence of liver cancer after surgery occupies a considerable proportion. Therefore, regular follow-up and monitoring of tumor-related indicators in patients after liver cancer surgery, and early detection and diagnosis of liver cancer recurrence and metastasis are important topics in the study of liver cancer prognosis. Alpha-fetoprotein (AFP), a commonly used tumor marker in clinical practice, has low sensitivity, and tumor recurrence cannot be detected in time in some patients after liver cancer surgery. Liver biopsy is the most reliable method for the diagnosis of hepatocellular carcinoma and its recurrence, but it is more harmful to patients. Imaging methods are widely used in postoperative follow-up, and the combination with alpha-fetoprotein can better detect recurrent tumors. Although the above methods have certain advantages, they all require regular review by patients, which is difficult for many patients with poor compliance.

The first source of recurrence of liver cancer is multi-center occurrence, that is, after radical resection of liver cancer, due to the continued existence of the soil in which liver cancer grows (liver cirrhosis) and other cancer-promoting factors, new tumors develop; second, single-center occurrence, that is, the original resection Cancer cells spread through the portal vein before and during the operation, resulting in intrahepatic recurrence and extrahepatic metastasis. At present, the research on the prognostic factors after liver cancer surgery mainly focuses on the analysis of clinicopathological factors such as tumor size, number, degree of differentiation, vascular invasion, and alpha-fetoprotein level. more accurate predictions. Therefore, in view of the scientific problem of liver cancer recurrence, how to effectively predict tumor recurrence after resection and how to prevent tumor recurrence after liver cancer surgery are of great clinical significance for improving the curative effect of liver cancer. The exploration and establishment are simple, rapid and sensitive. Molecular predictors with high sex and specificity and new therapeutic targets have become an urgent need to improve clinical efficacy.

Therefore, if it is analyzed in surgically resected specimens, and the risk of recurrence of patients can be accurately predicted, then the high-risk recurrence population can be paid attention to and the survival time of liver cancer can be improved.

SUMMARY OF THE INVENTION

(1) Technical problems solved

In view of the deficiencies of the prior art, the present invention provides biomarkers for predicting the recurrence of liver cancer, and uses mass spectrometry to detect the content of the biomarkers to determine the risk of recurrence of liver cancer; the method is simple, practical, and can be achieved through various methods. The detection of small molecules can better improve the sensitivity and specificity of detection methods.

(2) Technical solutions

To achieve the above purpose, the present invention is achieved through the following technical solutions:

The biomarkers used to predict the recurrence of liver cancer include one or several protein molecules of LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein.

A prognostic method for biomarkers for predicting the diagnosis of liver cancer recurrence, comprising the following steps: detecting the protein expression levels of LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein or HNRNPA3 protein in a sample to be tested by LC-MS/MS mass spectrometry intensity value.

Preferably, when the protein expression intensity value of the LAS1L protein in the sample is greater than 52085687.5, it is judged as a relapsed patient, otherwise it is judged as a postoperative non-relapsed patient, and the false positive rate is 22.9%.

Preferably, when the protein expression intensity value of the CLTB protein in the sample is greater than 941197795, it is judged as a relapsed patient, otherwise it is judged as a postoperative non-relapsed patient, and the false positive rate is 22.9%.

Preferably, when the protein expression intensity value of the JAGN1 protein in the sample is greater than 467852297, it is judged as a relapsed patient, otherwise it is judged as a non-relapsed patient after surgery, and the false positive rate is 8.6%.

Preferably, when the protein expression intensity value of ALYREF protein in the sample is less than 149218152, it is judged as a relapsed patient, otherwise it is judged as a postoperative non-relapsed patient, and the false positive rate is 22.9%.

Preferably, when the protein expression intensity value of HNRNPA3 protein in the sample is less than 4386125605, it is judged as a relapsed patient, otherwise it is judged as a postoperative non-relapsed patient, and the false positive rate is 20%.

A kit for diagnosing the recurrence of liver cancer, comprising 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. one or more.

Preferably, the reagent that specifically detects LAS1L protein is a primer or probe that specifically recognizes LAS1L protein nucleic acid; the reagent that specifically detects CLTB protein is a primer or probe that specifically recognizes CLTB protein nucleic acid; the reagent that specifically detects JAGN1 protein It is a primer or probe that specifically recognizes JAGN1 protein nucleic acid; the reagent that specifically detects ALYREF protein is a primer or probe that specifically recognizes ALYREF protein nucleic acid; the reagent that specifically detects HNRNPA3 protein is a primer or probe that specifically recognizes HNRNPA3 protein nucleic acid. Probes; reagents can be used to detect tissue samples.

(3) Beneficial effects

Compared with the prior art, the present invention has the following beneficial effects:

(1) The present invention adopts the LC-MS/MS mass spectrometry method to detect the samples to be tested, and after mass spectrometry analysis of a large number of clinical samples, the corresponding molecular content of the cancer tissues of patients with non-recurrence after liver cancer surgery and the cancer tissues of patients with recurrence after liver cancer surgery is calculated. The fold difference (greater than 2 or less than 0.5) to identify 5 protein molecules has good test efficiency. The five protein molecules (ie LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein) can be used as biomarkers for diagnosing liver cancer recurrence.

(2) The present invention uses LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein as biomarkers to diagnose the recurrence of liver cancer for the subjects, which is simple and easy to implement, the diagnosis process is safe and effective, and is easily accepted and diagnosed by patients. The standard is uniform and less affected by subjective factors.

(3) The biomarkers detected by mass spectrometry analysis in the present invention can provide new therapeutic targets and ideas for the research and development of anti-cancer drugs in the future.

Description of drawings

Fig. 1 is the ROC curve diagram of the protein expression intensity value of LAS1L protein;

Figure 2 is a graph showing the protein expression intensity value of LAS1L protein in liver cancer recurrence tissue and postoperative non-recurrence tissue;

Fig. 3 is the ROC curve diagram of the protein expression intensity value of CLTB protein;

Figure 4 is a graph showing the protein expression intensity value of CLTB protein in liver cancer recurrence tissue and postoperative non-recurrence tissue;

Fig. 5 is the ROC curve diagram of the protein expression intensity value of JAGN1 protein;

Figure 6 is a graph showing the protein expression intensity value of JAGN1 protein in liver cancer recurrence tissue and postoperative non-recurrence tissue;

Fig. 7 is the ROC curve diagram of the protein expression intensity value of ALYREF protein;

Figure 8 is a graph showing the protein expression intensity value of ALYREF protein in liver cancer recurrence tissue and postoperative non-recurrence tissue;

Fig. 9 is the ROC curve diagram of the protein expression intensity value of HNRNPA3 protein;

Figure 10 is a graph of the protein expression intensity value of HNRNPA3 protein in liver cancer recurrence tissue and postoperative non-recurrence tissue;

Figure 11 is a ROC curve diagram of the protein expression intensity value of the combination of ALYREF protein and HNRNPA3 protein;

Fig. 12 is a graph showing the protein expression intensity value of the combination of ALYREF protein and HNRNPA3 protein in liver cancer recurrence tissue and postoperative non-recurrence tissue.

Detailed ways

In order to make the purposes, 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 in conjunction with the embodiments of the present invention. Obviously, the described embodiments are part of the present invention. examples, but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Screening of biomarkers related to the diagnosis of liver cancer

1. Experimental steps

(1) Protein sample information

Samples: 5 samples were taken from the cancer tissue of patients with recurrence of liver cancer and 35 samples of liver cancer tissue were taken from patients without postoperative recurrence.

(2) Sample preprocessing

The samples were lysed with SDT (4% (w/v) sodium dodecyl sulfonate, 100 mM Tris/HCl pH7.6, 0.1 M dithiothreitol) for protein extraction, and then BCA was used for protein quantification; each An appropriate amount of protein was taken from the sample for trypsin digestion by the Filteraided proteome preparation (FAS) method, and the peptide fragment was desalted by C18 Cartridge.

The BCA method is used for protein quantification, and the protein concentration can be calculated according to the absorbance value. Under alkaline conditions, the protein reduces Cu ²⁺ to Cu ⁺ , Cu ⁺ and BCA reagent form a purple complex, and two molecules of BCA chelate a Cu ⁺ . The concentration of the protein to be tested can be calculated by comparing the absorption value of the water-soluble complex at 562 nm with the standard curve.

(3) LC-MS/MS data acquisition

Each sample was separated using the HPLC liquid system Easy nLC at nanoliter flow rates.

Among them: buffer A solution is 0.1% formic acid aqueous solution, B solution is 0.1% formic acid acetonitrile aqueous solution (acetonitrile is 84%).

The chromatographic column was equilibrated with 95% liquid A, and the sample was loaded into the sample column (Thermo Scientific Acclaim PepMap100, 100μm*2cm, nanoViper C18) by the autosampler, and passed through the analytical column (Thermo scientific EASY column, 10cm, ID75μm, 3μm, C18 -A2) separation with a flow rate of 300nL/min.

The samples were chromatographically separated and analyzed by mass spectrometry using a Q-Exactive mass spectrometer. The detection method is positive ion, the precursor ion scanning range is 300–1800m/z, the primary mass spectrometry resolution is 70,000 at 200m/z, the AGC (Automatic gaincontrol) target is 1e6, the Maximum IT is 50ms, and the dynamic exclusion time (Dynamic exclusion) is 60.0s. The mass-to-charge ratios of peptides and peptide fragments were collected according to the following methods: 20 fragments were collected after each full scan (MS2 scan), MS2 Activation Type was HCD, Isolation window was 2m/z, MS2 resolution was 17, 500 at 200m/z, Normalized Collision Energy is 30eV, Underfill is 0.1%.

(4) Protein identification and quantitative analysis

The original data of mass spectrometry analysis were RAW files, and the software MaxQuant software (version number 1.5.3.17) was used for library identification and quantitative analysis.

iBAQ Intensity is the protein expression amount in sample X obtained based on the iBAQ algorithm, which is approximately equal to the absolute concentration of protein in the sample. LFQ Intensity is the relative protein expression of sample X obtained based on the LFQ algorithm, which is often used for comparison between groups. Generally Labelfree chooses one of them as the 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 protein quantification of samples. The main algorithm is based on the ratio of the sum of the intensities of the identified peptides to the theoretical number of peptides.

LFQ is generally used for pairwise quantitative comparison between multiple groups, and the main algorithm is pair-wise correction of peptide and protein layers. This patent uses LFQ for protein quantification.

(5) Statistical analysis

Ratio calculation and statistical analysis were performed on the data that conformed to three replicate data in the same group with at least two non-null values, including the ratio and P-value of the LFQ or iBAQ intensity values of each comparison group; the differences between the groups were initially screened out .

Further verify whether the difference protein is significant according to P-value. Selecting both with multi-dimensional statistical analysis Fold change>2 or <0.5, it is considered that there is a significant fold difference in the content of the protein between the cancer tissue and the adjacent tissue, and the protein with a univariate statistical analysis P value <0.05 is selected as the protein with a univariate statistical analysis P value <0.05. Significantly different proteins; thus obtaining differential protein molecules. Then use SPSS software to make ROC curve of differential protein, and calculate its area under the curve (AUC), and then judge its diagnostic value. The specific judgment method is that the area under the AUC line is greater than 0.7, and P < 0.05, and the threshold standard (cut off value) when the Youden index is the largest is used as the threshold standard for judging whether the tumor is or not (multiples greater than 2 are considered to be greater than the threshold as positive for tumor detection. , if the multiple is less than 0.5, it is considered as positive for liver cancer detection if it is less than the threshold), so as to obtain higher sensitivity and specificity.

(6) Bioinformatics analysis

① GO function comment

Using Blast2GO to perform GO annotation on the target protein collection, the process can be roughly summarized into four steps: sequence alignment (Blast), GO entry extraction (Mapping), GO annotation (Annotation) and InterProScan supplementary annotation (AnnotationAugmentation).

② KEGG pathway annotation

Using KAAS (KEGG Automatic Annotation Server) software, KEGG pathway annotation was performed on the target protein set.

③ Enrichment analysis of GO annotation and KEGG annotation

Fisher's Exact Test (Fisher's Exact Test) was used to compare the distribution of each GO classification or KEGG pathway in the target protein set and the overall protein set, and perform GO annotation or KEGG pathway annotation enrichment analysis on the target protein set.

④Protein cluster analysis

First, the quantitative information of the target protein set is normalized (normalized to the (-1,1) interval). Then, the Complexheatmap R package (R Version 3.4) was used to simultaneously classify the two dimensions of sample and protein expression (distance algorithm: Euclidean, connection method: Average linkage), and generate a hierarchical clustering heat map.

⑤Protein interaction network analysis

Based on the information in the STRING (http://string-db.org/) database, the interaction relationship between the target proteins was found, and the CytoScape software (version number: 3.2.1) was used to generate the interaction network and analyze the network.

(7) Screening of differentially expressed proteins

Differentially expressed proteins were screened by the criteria of fold change greater than 2.0 times (up-regulation greater than 2 times or down-regulation less than 0.5) and P value less than 0.05, and the number of differentially expressed proteins in each comparison group.

(8) The basic principle of the experiment

Label-free quantitative proteomics (Label-free) technology has become an important mass spectrometry quantitative method in recent years. There are two main quantitative principles of Label-free technology: First, the non-label quantitative methods of spectrum counts developed earlier, and a variety of quantitative algorithms have also been formed, but the core principles are based on the identification results of MS2 as the basis for quantification. The difference of the first method is the correction of the high-throughput data by the later algorithm; the principle of the second non-labeled quantitative method is based on MS1, and the integral of each peptide signal on the LCMS chromatogram is calculated. The Maxquant algorithm adopted in the present invention is based on the second principle.

2. Experimental results

After analyzing the mass spectrometry data and comparing the protein molecules of the liver cancer tissue of the recurrence patient with the liver cancer tissue of the non-recurrence patient (no recurrence after surgery), five protein molecules were obtained, which can be used as biomarkers for the recurrence of liver cancer.

In order to evaluate the diagnostic efficacy of the protein expression intensity value of the protein molecule on the recurrence of liver cancer, the present invention adopts the ROC curve analysis. AUC is the area under the ROC curve, which 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, it means that the accuracy of the diagnosis is low; if the AUC is above 0.7, it can meet the requirements of clinical diagnosis.

The specific results and analysis are as follows:

(1) Using LC-MS/MS mass spectrometry, it was detected that the LAS1L protein was different in the recurrence tissue of liver cancer and the non-recurrence tissue after surgery.

The study found that the LAS1L protein was significantly up-regulated by 11.68 times in the recurrence of liver cancer samples, p value < 0.05.

It can be seen from Figure 1 that the AUC of LAS1L protein is 0.806>0.7, indicating that LAS1L protein has a good judgment effect and can be used as a biomarker for the recurrence of liver cancer.

When the cut off value of LAS1L protein was 52085687.5, the sensitivity was 80% and the specificity was 77.1%. When the individual detection was performed, when the protein expression intensity value of LAS1L protein was greater than 52085687.5, it was judged as a relapsed patient, otherwise it was judged as a non-relapsed patient (false positive rate was 22.9%).

It can be seen from Figure 2 that the liver cancer recurrence tissue samples are mainly distributed above the detection threshold (solid line in Figure 2), and the postoperative non-recurrence tissue samples are mainly distributed below the detection threshold, indicating the intensity of protein expression in the liver cancer recurrence tissue and postoperative non-recurrence tissue. The value is very different, the detection threshold detection effect is good.

In conclusion, LAS1L protein can be used as a biomarker for liver cancer recurrence.

(2) Using LC-MS/MS mass spectrometry, it was detected that the CLTB protein was different in the recurrence tissue of liver cancer and the non-recurrence tissue after surgery.

The study found that CLTB protein was significantly up-regulated by 6.68 times in liver cancer recurrence samples, p value < 0.05.

It can be seen from Figure 3 that the AUC of CLTB protein is 0.806>0.7, indicating that CLTB protein has a good judgment effect and can be used as a biomarker for the recurrence of liver cancer.

When the protein expression intensity value of CLTB protein was 941197795, the sensitivity was 80% and the specificity was 77.1%. When the individual detection was performed, when the protein expression intensity value of CLTB protein was greater than 941197795, it was judged as a relapsed patient, otherwise it was judged as a non-relapsed patient (false positive rate was 22.9%).

It can be seen from Figure 4 that the liver cancer recurrence tissue samples are mainly distributed above the detection threshold (solid line in Figure 4), and the postoperative non-recurrence tissue samples are mainly distributed below the detection threshold, indicating the intensity of protein expression in the liver cancer recurrence tissue and postoperative non-recurrence tissue. The value is very different, the detection threshold detection effect is good.

In conclusion, CLTB protein can be used as a biomarker for liver cancer recurrence.

(3) Using LC-MS/MS mass spectrometry analysis, it was detected that the JAGN1 protein was different in the recurrence tissue of liver cancer and the non-recurrence tissue after surgery.

The study found that JAGN1 protein was significantly up-regulated by 2.45 times in the recurrence of liver cancer samples, p value < 0.05.

It can be seen from Figure 5 that the AUC of JAGN1 protein is 0.840>0.7, indicating that JAGN1 protein has a good judgment effect and can be used as a biomarker for the recurrence of liver cancer.

When the protein expression intensity value of JAGN1 protein was 467852297, the sensitivity was 80% and the specificity was 91.4%. When the JAGN1 protein was detected individually, when the protein expression intensity value was greater than 467852297, it was judged as a relapsed patient, otherwise it was judged as a non-relapsed patient (false positive rate was 8.6%).

It can be seen from Figure 6 that the liver cancer recurrence tissue samples are mainly distributed above the detection threshold (solid line in Figure 6), and the postoperative non-recurrence tissue samples are mainly distributed below the detection threshold, indicating the intensity of protein expression in the liver cancer recurrence tissue and postoperative non-recurrence tissue. The value is very different, the detection threshold detection effect is good.

In conclusion, JAGN1 protein can be used as a biomarker for liver cancer recurrence.

(4) Using LC-MS/MS mass spectrometry, it was detected that the ALYREF protein was different in the relapsed tissue and the non-relapsed tissue.

The study found that ALYREF protein was significantly down-regulated by 0.19 times in liver cancer recurrence samples, p value < 0.05.

It can be seen from Fig. 7 that the AUC of ALYREF protein is 0.937>0.7, indicating that ALYREF protein has a good judgment effect and can be used as a biomarker for the recurrence of liver cancer.

When the cut off value of ALYREF protein was 149218152, the sensitivity was 100% and the specificity was 77.1%. When the individual detection was performed, when the protein expression intensity value of ALYREF protein was less than 149218152, it was judged as a recurrence patient, otherwise it was judged as a non-relapsed patient after surgery (false positive rate was 22.9%).

It can be seen from Figure 8 that the samples of recurrent liver cancer tissue are mainly distributed below the detection threshold (solid line in Figure 8), and the non-recurrence tissue samples are mainly distributed above the detection threshold, indicating that the protein expression intensity values of recurrent liver cancer tissue and non-recurrence tissue are very different. The detection threshold detection effect is good.

In conclusion, ALYREF protein can be used as a biomarker for liver cancer recurrence.

(5) Using LC-MS/MS mass spectrometry, it was detected that the HNRNPA3 protein was different in the relapsed and non-recurrent tissues.

The study found that HNRNPA3 protein was significantly down-regulated by 0.49 times in liver cancer recurrence samples, p value <0.05.

It can be seen from Figure 9 that the AUC of HNRNPA3 protein is 0.920>0.7, indicating that HNRNPA3 protein has a good judgment effect and can be used as a biomarker for liver cancer recurrence.

When the protein expression intensity value of HNRNPA3 protein was 4386125605, the sensitivity was 100% and the specificity was 80%. When the individual detection was performed, when the protein expression intensity value of HNRNPA3 protein was less than 4386125605, it was judged as a relapsed patient, otherwise it was judged as a postoperative non-relapsed patient (false positive rate was 20%).

It can be seen from Figure 10 that the samples of liver cancer recurrence tissue are mainly distributed below the detection threshold (solid line in Figure 10), and the non-recurrence tissue samples are mainly distributed above the detection threshold, indicating that the protein expression intensity values of liver cancer recurrence tissue and non-recurrence tissue are very different. The detection threshold detection effect is good.

In conclusion, HNRNPA3 protein can be used as a biomarker for liver cancer recurrence.

(6) Application of the combination of ALYREF protein and HNRNPA3 protein in the preparation of early-stage liver cancer diagnostic kit.

The present invention also provides a method for diagnosing liver cancer. The steps are: adopting binary logistic regression analysis to calculate the value of P (probability of recurrence of liver cancer after operation), and the formula obtained after the binary logistic regression of SPSS software is:

Among them, 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 (probability of recurrence after liver cancer surgery) is greater than 0.0694114, it is judged as a patient with recurrence, otherwise it is judged as a patient without recurrence .

As shown in Figure 11, the AUC of the combined protein was 0.977>0.7, indicating that it has a good judgment effect, that is, the combined protein can be used as a biomarker for the recurrence of liver cancer.

When the protein expression intensity value of the combined protein was 0.0694114, the sensitivity was 100% and the specificity was 88.6%. When the individual detection was performed, when the protein expression intensity value of the combined protein was greater than 0.0694114, it was judged as a patient with recurrence, otherwise it was judged as a patient without recurrence after surgery (false positive rate was 11.4%).

It can be seen from Figure 12 that the samples of liver cancer recurrence tissue are mainly distributed above the detection threshold (solid line in Figure 12), and the non-recurrence tissue samples are mainly distributed above the detection threshold, indicating that the protein expression intensity values of liver cancer recurrence tissue and non-recurrence tissue are very different. The detection threshold detection effect is good.

In view of the above results, the combined protein composed of ALYREF protein and HNRNPA3 protein can be used as a biomarker of liver cancer recurrence.

Example 1

Biomarkers for predicting liver cancer recurrence, including LAS1L protein.

A prognostic method for biomarkers for predicting the recurrence of liver cancer and liver cancer diagnosis, comprising the following steps: detecting the protein expression intensity value of LAS1L protein in a sample to be tested by LC-MS/MS mass spectrometry; the protein expression level of LAS1L protein in the sample When the intensity value is greater than 52085687.5, it is judged as a relapsed patient, otherwise it is judged as a non-relapsed patient (false positive rate 22.9%).

A kit for diagnosing the recurrence of liver cancer comprises a reagent for specifically detecting LAS1L protein, and the reagent for specifically detecting LAS1L protein is a probe for specifically recognizing nucleic acid of LAS1L protein.

Example 2

Biomarkers for predicting liver cancer recurrence, including CLTB protein.

A prognostic method for biomarkers for predicting liver cancer recurrence and liver cancer diagnosis, comprising the following steps: detecting the protein expression intensity value of CLTB protein in a sample to be tested by LC-MS/MS mass spectrometry; the protein expression level of CLTB protein in the sample When the intensity value is greater than 941197795, it is judged as a relapsed patient, otherwise it is judged as a non-relapsed patient (false positive rate 22.9%).

A kit for diagnosing the recurrence of liver cancer includes a reagent for specifically detecting CLTB protein, and the reagent for specifically detecting CLTB protein is a probe for specifically recognizing nucleic acid of CLTB protein.

Example 3

Biomarkers for predicting liver cancer recurrence, including JAGN1 protein.

A prognostic method for biomarkers for predicting the recurrence of liver cancer and liver cancer diagnosis, comprising the following steps: detecting the protein expression intensity value of JAGN1 protein in a sample to be tested by LC-MS/MS mass spectrometry; the protein expression level of JAGN1 protein in the sample When the intensity value is greater than 467852297, it is judged as a relapsed patient, otherwise it is judged as a non-relapsed patient (the false positive rate is 8.6%).

A kit for diagnosing the recurrence of liver cancer includes a reagent for specifically detecting JAGN1 protein, and the reagent for specifically detecting JAGN1 protein is a probe for specifically recognizing nucleic acid of JAGN1 protein.

Example 4

Biomarkers for predicting liver cancer recurrence, including ALYREF protein.

A prognostic method for biomarkers used to predict the recurrence of liver cancer and liver cancer diagnosis, comprising the following steps: detecting the protein expression intensity value of ALYREF protein in a sample to be tested by LC-MS/MS mass spectrometry; the protein expression level of ALYREF protein in the sample When the intensity value is less than 149218152, it is judged as a recurrence patient, otherwise it is judged as a postoperative non-recurrence patient (false positive rate is 22.9%).

A kit for diagnosing the recurrence of liver cancer comprises a reagent for specifically detecting ALYREF protein, and the reagent for specifically detecting ALYREF protein is a probe for specifically recognizing nucleic acid of ALYREF protein.

Example 5

Biomarkers for predicting liver cancer recurrence, including HNRNPA3 protein.

A prognostic method for biomarkers for predicting liver cancer recurrence and liver cancer diagnosis, comprising the following steps: detecting the protein expression intensity value of HNRNPA3 protein in a sample to be tested by LC-MS/MS mass spectrometry; the protein expression level of HNRNPA3 protein in the sample When the intensity value is less than 4386125605, it is judged as a recurrence patient, otherwise it is judged as a postoperative non-recurrence patient (false positive rate is 20%).

A kit for diagnosing the recurrence of liver cancer includes a reagent for specifically detecting HNRNPA3 protein, and the reagent for specifically detecting HNRNPA3 protein is a probe for specifically recognizing nucleic acid of HNRNPA3 protein.

Example 6

Biomarkers for liver cancer diagnosis, including ALYREF protein and HNRNPA3 protein.

The prognostic method of biomarkers for liver cancer diagnosis includes the following steps: using binary logistic regression analysis to calculate the value of P (probability of recurrence after liver cancer operation), the formula obtained after binary logistic regression in SPSS software is:

Among them: a is the protein expression intensity value of ALYREF protein, and b is the protein expression intensity value of HNRNPA3 protein; if the detection P (probability of recurrence after liver cancer surgery) is greater than 0.0694114, it is judged as a patient with recurrence, otherwise it is judged as a patient without recurrence.

When the individual detection was performed, when the protein expression intensity value of the combined protein was greater than 0.0694114, it was judged as a patient with recurrence, otherwise it was judged as a patient without recurrence after surgery (false positive rate was 11.4%).

The present invention uses at least one protein among LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein as biomarkers to diagnose liver cancer for subjects, which is simple and easy to implement, safe and effective in the diagnosis process, and easy to be accepted and diagnosed by patients. Standard unification is less affected by individual subjective factors.

The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. Biomarkers for predicting the recurrence of liver cancer, including one or several protein molecules of LAS1L protein, CLTB protein, JAGN1 protein, ALYREF protein and HNRNPA3 protein. 2. The prognostic method for predicting the biomarker of liver cancer recurrence diagnosis according to claim 1, characterized in that, comprising the steps of: using LC-MS/MS mass spectrometry to detect LAS1L protein, CLTB protein in the sample to be tested , the protein expression intensity value of JAGN1 protein, ALYREF protein or HNRNPA3 protein. 3. The prognostic method of biomarkers for liver cancer diagnosis according to claim 2, characterized in that, when the protein expression intensity value of LAS1L protein in the sample is greater than 52085687.5, it is judged as a recurrence patient, otherwise it is judged as a patient with no postoperative recurrence. Relapsed patients, and the false positive rate was 22.9%. 4. The prognostic method of protein molecule as a biomarker for diagnosing liver cancer according to claim 2, characterized in that, when the protein expression intensity value of CLTB protein in the sample is greater than 941197795, it is judged as a recurrence patient, otherwise it is judged as a recurrence patient. There was no recurrence after surgery, and the false positive rate was 22.9%. 5. The prognostic method of protein molecule as a biomarker for diagnosing liver cancer according to claim 2, characterized in that, when the protein expression intensity value of JAGN1 protein in the sample is greater than 467852297, it is judged as a recurrence patient, otherwise it is judged as a recurrence patient. There was no recurrence after surgery, and the false positive rate was 8.6%. 6. The prognostic method of protein molecule as a biomarker for diagnosing liver cancer according to claim 2, characterized in that, when the protein expression intensity value of ALYREF protein in the sample is less than 149218152, it is judged as a recurrence patient, otherwise it is judged as a recurrence patient. There was no recurrence after surgery, and the false positive rate was 22.9%. 7. The prognostic method of protein molecule as a biomarker for diagnosing liver cancer according to claim 2, characterized in that, when the protein expression intensity value of HNRNPA3 protein in the sample is less than 4386125605, it is judged as a recurrence patient, otherwise it is judged as a recurrence patient. There was no recurrence after surgery, and the false positive rate was 20%. 8. A kit for diagnosing whether liver cancer recurs, characterized in that it comprises a reagent for specific detection of LAS1L protein, a reagent for specific detection of CLTB protein, a reagent for specific detection of JAGN1 protein, a specific detection of ALYREF protein and a specific detection One or more of the reagents for HNRNPA3 protein. 9. The test kit for diagnosing whether liver cancer recurs according to claim 8, wherein the reagent for specifically detecting LAS1L protein is a primer or probe for specifically identifying LAS1L protein nucleic acid; the specific detection CLTB protein The reagent is a primer or probe that specifically recognizes CLTB protein nucleic acid; the reagent that specifically detects JAGN1 protein is a primer or probe that specifically recognizes JAGN1 protein nucleic acid; the reagent that specifically detects ALYREF protein is a specific recognition The primer or probe for ALYREF protein nucleic acid; the reagent for specifically detecting HNRNPA3 protein is a primer or probe for specifically recognizing HNRNPA3 protein nucleic acid; the reagent can be used to detect tissue samples.

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