A marker group for liver cancer screening and diagnosis and use thereof

Abstract

The application discloses a marker group for liver cancer screening and diagnosis and application thereof, and belongs to the field of molecular biological technology.The marker group comprises at least four polypeptides shown in sequences SEQ ID NO.1-35.The marker group constructed by the application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of liver cancer, is superior to a GALAD model recommended by a current liver cancer clinical guideline and a detection kit based on seven miRNAs, and is also superior to AFP-L3% and PIVKA-II recommended by a current liver cancer early screening expert consensus, and is expected to be applied to the diagnosis and treatment of liver cancer.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of molecular biology technology, and particularly relates to a marker group for liver cancer screening and diagnosis and use thereof. BACKGROUND

[0002] The main causes of liver cancer include hepatitis B, hepatitis C, alcohol and non-alcoholic steatohepatitis (NASH). With the widespread vaccination of hepatitis B vaccine and the widespread use of antiviral therapy, the burden of hepatitis B virus-related liver cancer has decreased, but in recent years, the burden of alcohol-related liver cancer and NASH-related liver cancer has increased. At present, although anti-HBV and anti-HCV treatment can significantly reduce the risk of liver cancer, it cannot completely avoid the occurrence of liver cancer.

[0003] The current diagnosis of liver cancer is mainly through imaging, biopsy and biomarkers. Conventional gray-scale ultrasound imaging can early and sensitively detect intrahepatic occupying lesions. Dynamic contrast-enhanced CT and multi-parameter MRI scanning are the preferred imaging examination methods for liver cancer diagnosis. It should be noted that although these imaging methods have high accuracy in the diagnosis of liver cancer, misdiagnosis may still occur. For example, some benign liver nodular lesions (such as liver adenoma and hemangioma) may be similar to liver cancer in imaging, so it is necessary to combine other examination methods (such as hematology examination and pathology examination) for comprehensive judgment.

[0004] Serum AFP is an important indicator for the diagnosis of liver cancer and efficacy monitoring. In addition to the screening combined with ultrasound, it has also been recommended as a diagnostic standard for HCC. However, the sensitivity of this biomarker is low, and the specificity is also defective. More than 40% of HCCs have normal AFP levels, and AFP levels can be observed in other cancers such as intrahepatic cholangiocarcinoma, gastric cancer and germ cell tumors. Due to the lack of accuracy, AFP has been recommended by many literatures and expert consensus for the diagnosis of HCC. For serum AFP negative population, PIVKA II, miRNA detection kit, AFP-L3 and GALAD-like model can be used for early diagnosis. However, the accuracy of these reagents is still not high enough. Although multiple marker diagnosis strategies are used, the early screening / diagnosis of hepatocellular carcinoma is still far from satisfactory. SUMMARY

[0005] In view of the above deficiencies in the prior art, the present application provides a marker group for liver cancer screening and diagnosis and use thereof. In the auxiliary diagnosis and early screening of liver cancer, it has excellent sensitivity and specificity, and is expected to be applied to the diagnosis and treatment of liver cancer.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present application to solve its technical problems is:

[0007] A marker group for liver cancer screening and diagnosis, comprising at least four of the polypeptides shown in sequences SEQ ID NO. 1~35, and the specific sequences are shown in Table 1.

[0008] Table 1 Polypeptide sequences

[0009]

[0010] Among them, the second amino acid K in polypeptide 16 has Acetyl modification; the fourth amino acid G in polypeptide 18 has Phospho modification; the first amino acid Q in polypeptide 19 has Gln->pyro-Glu modification, and the sixth amino acid N has Dehydrated modification; the fourth amino acid G in polypeptide 24 has Dehydrated modification; the third amino acid G in polypeptide 30 has Phospho modification.

[0011] Further, the marker group comprises polypeptides shown in sequences 1 and 2, and the following polypeptide combinations:

[0012] The polypeptide combination is one of sequences 3 and 4; sequences 5 and 6; sequences 11 and 12; sequences 30 and 35; sequences 3, 4 and 5; sequences 5, 6 and 7; sequences 11, 12 and 20; sequences 9, 30 and 35.

[0013] Further, the marker group comprises polypeptides shown in sequences 7 and 8, and the following polypeptide combinations:

[0014] The polypeptide combination is one of sequences 3 and 4; sequences 9 and 10; sequences 3, 4 and 9; sequences 9, 10 and 11.

[0015] Further, the marker group comprises polypeptides shown in sequences 1, 10 and 30, and sequences 35; sequences 20; sequences 35 and 9 or sequences 20 and 22.

[0016] Further, the marker group comprises polypeptides shown in sequences 5 and 15, and the following polypeptide combinations:

[0017] The polypeptide combination is one of sequences 25 and 35; sequences 2 and 16; sequences 9, 25 and 35; sequences 2, 16 and 20.

[0018] Further, the marker group comprises polypeptides shown in sequences 4, 8, 19, 20 and / or 22.

[0019] Further, the marker group comprises polypeptides as shown in Sequence 7, Sequence 10, Sequence 17, Sequence 18 and / or Sequence 22.

[0020] Further, the marker group comprises polypeptides as shown in Sequence 1, Sequence 6, Sequence 7, Sequence 9 and Sequence 21.

[0021] Further, the marker group comprises polypeptides as shown in Sequence 1~35.

[0022] The use of the above marker group in the preparation of a preparation for the screening and diagnosis of liver cancer.

[0023] The use of the above marker group or in medical basic research for non-diagnostic / therapeutic purposes.

[0024] Further, the medical basic research is Western Blot, immunohistochemistry or flow cytometry analysis, etc.

[0025] Advantages of the present application:

[0026] The marker combination constructed by the present application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of liver cancer, and is expected to be applied to the diagnosis and treatment of liver cancer. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 ROC curve diagram of marker combination 1;

[0028] Figure 2 ROC curve diagram of marker combination 2;

[0029] Figure 3 ROC curve diagram of marker combination 3;

[0030] Figure 4 ROC curve diagram of marker combination 4;

[0031] Figure 5 ROC curve diagram of marker combination 5;

[0032] Figure 6 ROC curve diagram of marker combination 6;

[0033] Figure 7 ROC curve diagram of marker combination 7;

[0034] Figure 8 ROC curve diagram of marker combination 8;

[0035] Figure 9 ROC curve diagram of marker combination 9;

[0036] Figure 10 ROC curve plot for marker combination 10;

[0037] Figure 11 ROC curve plot for marker combination 11;

[0038] Figure 12 ROC curve plot for marker combination 12;

[0039] Figure 13 ROC curve plot for marker combination 13;

[0040] Figure 14 ROC curve plot for marker combination 14;

[0041] Figure 15 ROC curve plot for marker combination 15;

[0042] Figure 16 ROC curve plot for marker combination 16;

[0043] Figure 17 ROC curve plot for marker combination 17;

[0044] Figure 18 ROC curve plot for marker combination 18;

[0045] Figure 19 ROC curve plot for marker combination 19;

[0046] Figure 20 ROC curve plot for marker combination 20;

[0047] Figure 21 ROC curve plot for marker combination 21;

[0048] Figure 22 ROC curve plot for marker combination 22;

[0049] Figure 23 ROC curve plot for marker combination 23;

[0050] Figure 24 ROC curve plot for marker combination 24;

[0051] Figure 25 ROC curve plot for marker combination 25;

[0052] Figure 26 ROC curve plot for marker combination 26. DETAILED DESCRIPTION

[0053] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0054] The patient samples used in the present application are from Zhongshan Hospital Affiliated to Fudan University, and have passed the ethical review.

[0055] The experimental methods used in the present application are as follows:

[0056] I. Serum sample collection

[0057] 1) Sample type: serum.

[0058] 2) Collection requirements: fasting collection, 5 mL of venous blood is drawn using a coagulation tube, and after standing for 30 min, 3000 rpm centrifugation for 15 min, about 1 mL of serum is taken out and placed in a cryopreservation tube.

[0059] 3) Sample storage:

[0060] Used on the same day, the storage condition is 2-8℃;

[0061] If it cannot be used on the same day, it is stored at -20℃, and can be stored for 30 days;

[0062] If stored for a long time (more than one month), it needs to be stored at -80℃.

[0063] Repeated freezing and thawing should not exceed 3 times.

[0064] II. Extraction of the analyte in serum

[0065] 1) After calibrating the mass spectrometer, open the solid-phase biosystem full-automatic sample analysis system instrument SPS1000 / SPS4000, put in consumables, matched reagent kit and the sample to be tested;

[0066] Select the program method "solid-phase sample loading";

[0067] Run the program:

[0068] a. Open the hole;

[0069] b. Take not less than 10µL of serum sample and activated reagent, mix them in a ratio of 1:1, and then place them in the G row reserved hole for use;

[0070] c. Wash the customized suction head in cleaning reagent 1 and cleaning reagent 2 in order. When washing, take not less than 10µL of liquid each time, and repeat the suction and beating not less than 3 times;

[0071] d. Use the cleaned custom tip to process the serum mixture in the G row well. Each time the solution is not less than 10 μL, and the repeated suction is not less than 3 times;

[0072] e. Use cleaning reagent 3 to clean the custom tip after adsorbing the serum mixture. Each time the solution is not less than 10 μL, and the repeated suction is not less than 3 times;

[0073] f. Transfer not less than 10 μL of buffer reagent to the H row reserved well, and place the custom tip after using cleaning reagent 3 into the liquid to suck not less than 10 μL of liquid, and the repeated suction is not less than 3 times;

[0074] g. Transfer not less than 10 μL of sample matrix solution to the H row reserved well to complete the sample processing;

[0075] h. Suck the solution in the H well to sample 2.0 μL to the hydrophobic coated biochip (Wuxi Pimcore Technology Co., Ltd.);

[0076] i. Vacuum dry for 240 s.

[0077] The main components of each reagent are shown in Table 2.

[0078] Table 2 Reagent composition

[0079]

[0080] III. Mass spectrometry data acquisition and uploading

[0081] Place the vacuum-dried hydrophobic coated biochip (Wuxi Pimcore Technology Co., Ltd.) into the mass spectrometer;

[0082] Use the set SP1 voltage (target high voltage), SP2 voltage (pulse high voltage), focusing voltage (lens high voltage), detector voltage (MCP voltage), pulse delay time, acquisition card range, target point diameter, laser frequency, calibration method and laser intensity to collect data.

[0083] IV. Quality control

[0084] 1) After data collection, upload the data to the mass spectrometry data analysis software;

[0085] 2) The software reads the sample information and signal spectrum, and judges whether the sample and sample pretreatment are qualified according to the quality control model. The unqualified quality control may include various possibilities, including non-liver lesion samples, signal spectrum intensity not meeting the standard, etc.

[0086] 3) If the quality control is unqualified, according to the quality control result, correct the corresponding parameters and reprocess the serum sample extraction process;

[0087] 4) If the quality control is qualified, enter the next process.

[0088] V. Establishment of positive judgment value and result analysis

[0089] The study of positive judgment value uses liver cancer samples with clear diagnostic information and normal samples, covering non-liver cancer populations such as hepatitis B, hepatitis C, and fatty liver. Then, based on the known liver cancer sample atlas, supervised learning is performed through a series of processes such as smoothing, noise reduction, and baseline removal to filter characteristic peaks, build a classification model, and calculate the similarity between the hormone signal atlas and the known hormone signal atlas stored in the software (Cannataro M, Guzzi P H, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data [J]. 2005.). Finally, the maximum Youden index method is used to determine the similarity score of the kit as the positive judgment value. When the similarity score is < the positive judgment value, the sample test result is negative; when the similarity score is ≥ the positive judgment value, the sample test result is positive. Taking the maximum similarity score as the positive judgment value, the sensitivity = true positive number / (true positive number + false negative number) x 100%, and the specificity = true negative number / (true negative number + false positive number) x 100% when assisting in the diagnosis of liver cancer or early screening of liver cancer.

[0090] Example 1. Screening and identification of markers

[0091] The application is found that 35 polypeptide substances in blood have liver cancer diagnostic ability through time-of-flight mass spectrometry on 300 normal human samples (158 males (52.7%), 142 females (47.3%), age distribution range: 25 to 68 years old, average age: 46.2 ± 11.8 years old. Specific age distribution: 63 cases of 25-34 years old, 87 cases of 35-44 years old, 98 cases of 45-54 years old, and 52 cases of 55-68 years old), 300 liver cancer samples (231 males (77.0%), 69 females (23.0%), age distribution range: 38 to 75 years old, average age: 58.6 ± 9.7 years old. Specific age distribution: 48 cases of 38-47 years old, 127 cases of 48-57 years old, 98 cases of 58-67 years old, and 27 cases of 68-75 years old) are tested, the relative abundance difference of the characteristic peak data in normal people and liver cancer patients is considered, the statistical difference (p<0.05, t test) of the data, the influence factor sorting of the machine learning algorithm (random forest) and the matching degree of the data in the database, and 35 polypeptide substances in blood are found to have liver cancer diagnostic ability. The mass-to-charge ratio (m / z) of these polypeptides, relative abundance and influence factor are shown in Table 1. The relative abundance is normalized based on the normal sample, that is, ln (average signal intensity of cancer patient sample / average signal intensity of normal sample). The screening of characteristic peaks in machine learning is based on the feature importance evaluation of ensemble learning. By constructing multiple decision trees, the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction is quantified. The feature importance score, that is, the influence factor, is obtained by calculating the average reduction of impurity brought by the feature in all tree node splitting (Biau, G., Scornet, E. A random forest guided tour. TEST 25, 197-227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0092] The specific primary mass spectrometry parameters are as follows:

[0093] Ionization method: matrix-assisted laser desorption ionization (MALDI), and the matrix is alpha-cyano-4-hydroxycinnamic acid (CHCA).

[0094] Mass range: 100-4000 Da.

[0095] Resolution: 20000 (full mass range).

[0096] Laser energy: 30-40%.

[0097] Acquisition mode: positive ion mode.

[0098] Calibration: External mass calibration was performed using peptide standards (Bruker Peptide Calibration Standard).

[0099] Subsequently, the sequences of these 35 substances in clinical serum were confirmed by secondary mass spectrometry (secondary mass spectrometry (MS / MS or TOF / TOF) is recommended by the China Food and Drug Administration and the U.S. Food and Drug Administration (FDA) guidelines for polypeptide identification). The secondary mass spectrometry data analysis process is as follows:

[0100] Data analysis method: Database search was performed using Mascot software (version 2.8), and the database was the UniProt human proteome database (published in 2023). Search parameters: enzyme setting "no enzyme digestion", parent ion mass error allowed ± 0.5 Da, fragment ion mass error allowed ± 0.3 Da, fixed modification cysteine urea methylation, variable modification methionine oxidation.

[0101] Sequence confirmation standard: The confirmation of polypeptide sequence is based on the matching of fragment ion spectrum (b- and y-ions) with theoretical spectrum, and Mascot score higher than 30 (p<0.05) is considered significant.

[0102] False positive exclusion: Specificity is verified by reverse database search, and the false positive rate is controlled below 1%.

[0103] Secondary mass spectrometry parameters:

[0104] Collision-induced dissociation (CID).

[0105] Collision energy: 30 eV.

[0106] Fragment ion mass range: 100-3500 Da.

[0107] Data acquisition: At least 1000 laser scans were collected for each sample to improve the signal-to-noise ratio.

[0108] The sequences and specificities of the 35 markers were confirmed by secondary mass spectrometry, and the false positive problem was excluded. The specific sequences are shown in Table 1.

[0109] Example 2 Verification of marker combination

[0110] According to the 35 polypeptide markers identified and confirmed in Example 1 (see Table 1 for sequences and mass-to-charge ratios), we used machine learning algorithm (Random Forest) to perform feature screening, forming different marker combinations shown in Table 3, and verified the sensitivity and specificity of the marker combinations in the auxiliary diagnosis and early screening of liver cancer. The analysis process is as follows: for all validation cohort samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain mass spectrometric peak intensity data for all markers in Table 1. The entire detection process was carried out in a blind manner, i.e., the experimental operator was unaware of the grouping information of the samples. The raw mass spectrometric data were processed by baseline correction, smoothing and normalization (using the internal standard peak intensity as the reference), and then the peak area or intensity value of each marker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed marker intensity data were input into the Logistic Regression model. For auxiliary diagnosis verification, ten-fold cross-validation was used for all samples in the cohort (Sun T, Liu J, Yuan H, Li X, Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front Oncol. 2024 Aug 9;14:1403392. doi: 10.3389 / fonc.2024.1403392. PMID: 39184040; PMCID: PMC11341396.). For early screening verification, due to the uneven sample size, the SMOTE technique was used to process the data before model training and testing (van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J Am Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093. PMID: 35686364; PMCID: PMC9382395.).The data analysis procedure referred to general guidelines for clinical prediction model construction (Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993 Apr;39(4):561-77. Erratum in: Clin Chem 1993 Aug;39(8):1589. PMID: 8472349.). Sensitivity, Specificity and Area under the Receiver Operating Characteristic Curve (AUC) were calculated for each marker combination. The detailed calculation results of performance indicators are shown in Table 3, and the corresponding ROC curves are shown in Figures 1-26 .

[0111] 1) 500 cases of hepatocellular carcinoma patients (male 382 cases (76.4%), female 118 cases (23.6%), age range 40 to 76 years old, average age 57.8±9.3 years old. The specific distribution is: 63 cases of 40-48 years old, 128 cases of 49-57 years old, 187 cases of 58-66 years old, 122 cases of 67-76 years old), 500 cases of healthy people (male 258 cases (51.6%), female 242 cases (48.4%), age range 28 to 72 years old, average age 46.7±11.5 years old. The specific distribution is: 78 cases of 28-36 years old, 126 cases of 37-45 years old, 138 cases of 46-54 years old, 108 cases of 55-63 years old, 50 cases of 64-72 years old) were used to verify the auxiliary diagnosis of hepatocellular carcinoma by each marker combination.

[0112] 2) 400 cases of hepatocellular carcinoma patients (male 306 cases (76.5%), female 94 cases (23.5%), age range 42 to 74 years old, average age 56.3±9.6 years old. The specific distribution is: 58 cases of 42-50 years old, 127 cases of 51-59 years old, 143 cases of 60-68 years old, 72 cases of 69-74 years old), 2000 cases of healthy people (male 1023 cases (51.2%), female 977 cases (48.8%), age range 30 to 75 years old, average age 47.9±12.3 years old. The specific distribution is: 348 cases of 30-39 years old, 523 cases of 40-49 years old, 612 cases of 50-59 years old, 598 cases of 60-69 years old, 319 cases of 70-75 years old) were used to verify the early screening of hepatocellular carcinoma by each marker combination.

[0113] Table 3 Sensitivity and specificity of marker combination for auxiliary diagnosis and early screening

[0114]

[0115] Table 1

[0116]

[0117] Table 1

[0118]

[0119] According to the detection results of Table 3 and Figures 1-26 The selected marker combination of the present application is superior to the GALAD model recommended by the current clinical guidelines for liver cancer (sensitivity and specificity are 85.6% and 93.3%, respectively) and the detection kit based on 7 miRNAs (sensitivity and specificity are 86.1% and 76.8%, respectively, see the article Zhou, J.; Yu, L.; Gao, X.; Hu, J.; Wang, J.; Dai, Z.; Wang, J. F.; Zhang, Z.; Lu, S.; Huang, X.; Wang, Z.; Qiu, S.; Wang, X.; Yang, G.; Sun, H.; Tang, Z.; Wu, Y.; Zhu, H.; Fan, J., Plasma microRNA panel to diagnose hepatitis B virus-related hepatocellular carcinoma. J Clin Oncol 2011, 29 (36), 4781-8).

[0120] It is also superior to AFP-L3% (AFP-L3 accounts for a percentage of AFP, sensitivity and specificity are 48% and 81%, respectively) and PIVKA-II (sensitivity and specificity are 64% and 89%, respectively) recommended by the current expert consensus on early screening of liver cancer (see the article National Multicenter Prospective Early-Stage Liver Cancer Early Warning Screening Project Expert Group, China Liver Cancer Early Screening Strategy Expert Consensus. Liver 2021, 26 (8), 825-831). It is shown that the marker combination constructed by the present application has more excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of liver cancer, and is expected to be applied to the diagnosis and treatment of liver cancer.

[0121] Finally, it should be noted that the above detailed description is merely illustrative of the technical solutions of the present application and is not limiting, and although the present application has been described in detail with reference to the examples, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A biomarker set for liver cancer screening and diagnosis, characterized in that, The biomarker group is selected from one of the following combinations of peptides: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequences 1, 2, 5, and 6; C) Sequence 1, Sequence 2, Sequence 11 and Sequence 12; D) Sequences 1, 2, 30, and 35; E) Sequence 1, Sequence 2, Sequence 3, Sequence 4 and Sequence 5; F) Sequence 1, Sequence 2, Sequence 5, Sequence 6 and Sequence 7; G) Sequence 1, Sequence 2, Sequence 11, Sequence 12 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 9, Sequence 30 and Sequence 35; I) Sequences 1, 10, 30, and 35; J) Sequences 1, 10, 30, and 20; K) Sequence 1, Sequence 10, Sequence 30, Sequence 35 and Sequence 9; L) Sequence 1, Sequence 10, Sequence 30, Sequence 20 and Sequence 22; M) Sequence 1, Sequence 6, Sequence 7, Sequence 9 and Sequence 21; N) Sequence 1~Sequence 35; The amino acid sequences of sequences 1 to 35 are shown in SEQ ID NO. 1 to 35; In sequence 16, the second amino acid K has an Acetyl modification; in sequence 18, the fourth amino acid G has a Phospho modification; in sequence 19, the first amino acid Q has a Gln->pyro-Glu modification, and the sixth amino acid N has a Dehydrated modification; in sequence 24, the fourth amino acid G has a Dehydrated modification; and in sequence 30, the third amino acid G has a Phospho modification.

2. Use of the reagent for detecting the biomarker group of claim 1 in the preparation of preparations for liver cancer screening and diagnosis.

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