WO2024046207A1

WO2024046207A1 - Tumor biomarker, and cancer risk information generation method and apparatus

Info

Publication number: WO2024046207A1
Application number: PCT/CN2023/114723
Authority: WO
Inventors: 许佳悦; 王晨阳; 羊星宇; 汉雨生; 李冰思
Original assignee: 广州燃石医学检验所有限公司
Priority date: 2022-09-01
Filing date: 2023-08-24
Publication date: 2024-03-07
Also published as: CN117594120A

Abstract

Provided in the present invention are a tumor biomarker, and a cancer risk information generation method and apparatus, and particularly a method and apparatus for determining, on the basis of a plurality of pieces of omics data, such as second-generation sequencing data and protein detection data, and by means of biological signal processing and model analysis of the data, whether a biological variation is present. The present invention particularly relates to a method for testing the richness, in serum, of several special protein markers for cancers, and a method for integrating test information. Compared with traditional test methods, the present method is more accurate, convenient and comprehensive, and causes almost no harm to a human body.

Description

A method and device for generating tumor biomarkers and cancer risk information

Technical field

The present invention relates to a method and device for generating tumor biomarkers and cancer risk information, and in particular to a method and device based on multiple omics data such as second-generation sequencing data and protein detection data, through biological signal processing and model analysis of the data. Methods and devices for determining whether biological variation exists. Among them, the present invention particularly relates to several special protein markers for cancer, methods for detecting their abundance in serum, and methods for integrating detection information.

Background technique

Previous studies have shown that the cure rate and five-year survival rate of early-stage cancer are much higher than those of late-stage cancer, so early detection and early treatment of tumors are very important for patients. As an important early cancer screening method, cancer liquid biopsy refers to the detection of genetic material (including DNA, RNA) or proteins contained in human body fluids (including blood, urine, saliva, cerebrospinal fluid, etc.) to Search for genetic variation signals related to tumors to diagnose human cancer. According to previous studies, DNA methylation variations, DNA gene mutations, and abundance changes are closely related to the occurrence of early cancer. However, the occurrence principles of different cancers are different, and they are also accompanied by the release of different tumor markers. Therefore, there is still no more effective way to organically combine multiple marker signals so that they can be applied to the detection of pan-cancer patients. method.

DNA methylation (methylation) is an epigenetic modification, which is catalyzed by DNA methyl-transferase (DNMT) to use S-adenosylmethionine (SAM) as a methyl donor. In the body, methyl groups are selectively added to the cytosine of the CG two nucleotides of DNA, mainly forming 5-methylcytosine (5-mC) (common in the 5′-CG-3′ sequence of genes) and A small amount of N6-methylpurine (N6-mA) and 7-methylguanine (7-mG) structural genes contain many CpG structures. The 5-position carbon atoms of the two cytosines in 2CpG and 2GPC are usually methylated. And the two methyl groups present a specific three-dimensional structure in the major groove of the DNA double strand.

All cancers are the result of acquired changes in the DNA of cancer cells. However, this does not mean that all systemic abnormalities in the oncogenome are involved in the development of cancer. In fact, some mutations are not involved. To embody this concept, the terms driver mutations and non-driver mutations were coined. Driver mutations are causally involved in cancer development, conferring a growth advantage on cancer cells, and are positively selected from the tissue microenvironment in which cancer arises. A driver mutation is not required for maintenance of the final stage of cancer, but it must be selected for at some point in the cancer-forming cell line.

Most solid tumors are derived from epithelial cells. When tumor cells rapidly differentiate and proliferate, some in normal tissues Cell types or components that are not expressed in the tumor appear in large numbers, such as keratin, which serves as a cell scaffold and become a tumor marker. Tumor markers whose chemical nature is protein include: ① enzymes; ② protein or peptide hormones; ③ other proteins that do not belong to the first two. Although precision tumor treatment technology has been continuously developed in recent years, given the complexity of cancer itself and the incomplete predictability of genomic information relative to proteomic information, it is still difficult for genomic information itself to fully provide information for many potential cancer patients. Clinical guidance.

Therefore, the industry lacks an effective method to correlate cancer-related proteome (tumor marker) information with the occurrence of cancer. It also lacks a way to organically combine proteome information with genomic information from second-generation sequencing to learn from each other's strengths and complement each other's weaknesses. Methods and systems to more effectively correlate with the occurrence of cancer.

Contents of the invention

The method of this application describes an integrated processing method based on multiple liquid biopsy data. Through the detection of cfDNA methylation, cfDNA mutation and peripheral blood protein, it is determined whether it contains biological variation, providing a method for early detection. Cancer discovery technology enables earlier intervention and treatment for cancer patients, improving the cure rate and survival period of cancer patients.

The overall steps are:

1) Methylation signal processing and model fitting;

2) Processing of mutation signals;

3)Protein signal processing and model regression verification;

4) Integration of multi-omics data analysis results

The method of this application uses the methylation and mutation detection signals of cell-free DNA (cfDNA) in peripheral blood, as well as the detection results of protein abundance, through signal processing and model integration to achieve prediction of human tumor occurrence. Compared with traditional detection methods, this method is more accurate, convenient, comprehensive, and will cause almost no harm to the human body. It can better detect early cancer, thereby enabling earlier intervention and treatment for cancer patients, and improving the cure rate and survival period of cancer patients.

The types of cancers involved in the present invention include: lung cancer, colorectal cancer, liver cancer, ovarian cancer, pancreatic cancer, gastric cancer, esophageal cancer, biliary tract cancer, and head and neck cancer.

In a first aspect, the present application provides a serum marker combination for assessing the risk of cancer, wherein the serum marker combination includes any one or more markers selected from the following: AFP, CA125, CA19- 9. CA72-4, CEA, CYFRA21-1, DCP, FER, HE4, MPO, PRL, ProGRP, SCC, TRF, CA15-3, T-PSA.

In some optional embodiments, the serum marker combination includes the following 16 markers: AFP, CA125, CA19-9, CA72-4, CEA, CYFRA21-1, DCP, FER, HE4, MPO, PRL, ProGRP, SCC, TRF, CA15-3 and T-PSA.

In a second aspect, the present application provides a kit for assessing the risk of cancer, wherein the kit includes a reagent for detecting the above serum marker combination.

In a third aspect, the present application provides the use of reagents for detecting the above serum marker combination in preparing a kit for assessing the risk of cancer.

In some optional embodiments, the cancer in any of the above aspects is selected from: lung cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, and gastric cancer.

In a fourth aspect, the present application provides a method for generating cancer risk warning information, wherein the method includes: obtaining detection data of a subject sample, wherein the detection data includes the corresponding cfDNA alpha of the subject. Basification level detection data, DNA mutation detection data and/or serum marker detection data, the serum markers include the serum marker combination according to claim 1 or 2; according to the detection data, based on the method used to characterize the subject The first cancer risk prediction model of the correlation between the test data and the occurrence of cancer is used to determine the first risk value of cancer occurrence of the subject; and based on the risk value of cancer occurrence, the prompt information of the risk of cancer occurrence of the subject is generated.

In some optional implementations, the first cancer risk prediction model is as follows:

Wherein, _xm is the detection concentration value of the serum marker, _βm is the weight parameter of each serum marker, y is the interpretation output, and m is the mth serum marker among the serum markers.

In some optional embodiments, the first cancer risk prediction model is trained through the following first training step: obtaining a first training sample set, the first training sample includes subject information, the subject or the corresponding serum marker detection data and the subject's cancer information; based on the first sample training set, a maximum likelihood function is used to determine the value of parameter β to obtain the first cancer risk Predictive model.

In some optional embodiments, the method further includes: based on the detection data, determining the subject's third cancer risk prediction model based on a second cancer risk prediction model used to characterize the correlation between the subject's detection data and cancer occurrence. 2. Cancer risk value.

In some optional embodiments, the second cancer risk prediction model is trained through the following preset training steps: obtaining a second training sample set, the second training sample includes subject information, the subject or the corresponding cfDNA methylation level detection data and/or the serum marker detection data and the subject's cancer information; based on the second training sample set, perform supervised training on the initial second cancer risk prediction model , to obtain the test data used to characterize the subject and The second cancer risk prediction model correlates with carcinogenesis.

In some optional embodiments, the cfDNA methylation level detection data includes methylation signal characteristic values; and, the methylation signal characteristic values are obtained in advance through the following steps: CpG in each specific capture region The average methylation level Beta of the site is used as the methylation signal characteristic value,

Among them, Beta is the average methylation level of CpG sites in the specific capture region, ΣM is the number of methylation sites in all reads in the specific capture region, and ΣU is all reads in the specific capture region. Number of non-methylated sites.

In some optional embodiments, the cancer is selected from: lung cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, and gastric cancer.

In a fifth aspect, the present application provides a device for generating cancer risk prompt information, wherein the device includes: an acquisition module configured to acquire detection data of a subject sample, wherein the detection data includes the subject sample. The subject's corresponding cfDNA methylation level detection data, DNA mutation detection data and/or serum marker detection data, the serum markers comprising the serum marker combination according to claim 1 or 2; the first determination module, configured to determine the subject's first cancer risk value based on the detection data and based on the first cancer risk prediction model used to characterize the correlation between the subject's detection data and cancer; the generation module is configured to determine the subject's first cancer risk value based on the detection data. The cancer risk value generates cancer risk reminder information for the subject.

In some optional embodiments, the device further includes: a second determination module configured to, according to the detection data, based on a second cancer risk prediction model for characterizing the correlation between the subject's detection data and cancer occurrence. , determine the risk value of the second cancer of the subject.

In some optional embodiments, the second cancer risk prediction model is trained through the following second training step: obtaining a second training sample set, the second training sample includes subject information, the subject or the corresponding cfDNA Methylation level detection data and/or the serum marker detection data and the subject's cancer information; based on the second training sample set, the initial second cancer risk prediction model is supervised and trained to obtain The second cancer risk prediction model characterizes the correlation between subject detection data and cancer occurrence.

In a sixth aspect, the present application provides an electronic device, including: one or more processors; a storage device on which one or more programs are stored. When the one or more programs are processed by the one or more When the processor executes, the one or more processors are caused to implement the method in any of the above aspects.

In a seventh aspect, the present application provides a computer-readable storage medium on which a computer program is stored, wherein the method of any of the above aspects is implemented when the computer program is executed by one or more processors.

Description of drawings

Figure 1A: Lung cancer-healthy person training set sample ROC curve, the abscissa is (1-specificity), and the ordinate is sensitivity.

Figure 1B: Lung cancer-healthy person validation set sample sensitivity distribution chart. The abscissa is healthy people and cancer patients at different stages, and the ordinate is the sensitivity value under specific specificity.

Figure 2A: Intestinal cancer-healthy person training set sample ROC curve, the abscissa is (1-specificity), and the ordinate is sensitivity.

Figure 2B: Sensitivity distribution diagram of bowel cancer-healthy human validation set samples. The abscissa is healthy people and cancer patients at different stages, and the ordinate is the sensitivity value under specific specificity.

Figure 3A: Liver cancer-healthy person training set sample ROC curve, the abscissa is (1-specificity), and the ordinate is sensitivity.

Figure 3B: Sensitivity distribution chart of liver cancer-healthy human validation set samples. The abscissa is healthy people and cancer patients at different stages, and the ordinate is the sensitivity value under specific specificity.

Figure 4A: Ovarian cancer-healthy human training set sample ROC curve, the abscissa is (1-specificity), and the ordinate is sensitivity.

Figure 4B: Ovarian cancer-healthy human validation set sample sensitivity distribution chart. The abscissa is healthy people and cancer patients at different stages, and the ordinate is the sensitivity value under specific specificity.

Figure 5A: Pancreatic cancer-healthy person training set sample ROC curve, the abscissa is (1-specificity), and the ordinate is sensitivity.

Figure 5B: Pancreatic cancer-healthy human validation set sample sensitivity distribution map. The abscissa is healthy people and cancer patients at different stages, and the ordinate is the sensitivity value under specific specificity.

Figure 6A: Gastric cancer-healthy person training set sample ROC curve, the abscissa is (1-specificity), and the ordinate is sensitivity.

Figure 6B: Gastric cancer-healthy person validation set sample sensitivity distribution chart. The abscissa is healthy people and cancer patients at different stages, and the ordinate is the sensitivity value under specific specificity.

Figure 7: Prediction accuracy icon of the multi-omics validation data of Example 7. The ordinate is the accuracy, and the abscissa is the method of different dimensions of different cancer types. Each cluster from left to right is: A Methyl, mutation, protein, methylation and mutation (M&M), methylation and protein (M&P), methylation, mutation and protein (M&M&P) .

Figure 8: Flow chart according to a preferred embodiment of the invention

Detailed ways

This application collects blood samples from cancer patients and non-cancer controls, uses the applicant's ELSA-seq technology (see Chinese invention patent application CN110892097A), and analyzes approximately 490,000 cfDNA (Cell-Free DNA, cell-free DNA). The methylation levels of individual CpGs were detected (1000X), and used by the applicant Burning Stone Langqing The kit detects 168 gene mutations (35,000X, matched white blood cells: 10,000X), and the obtained next-generation sequencing (NGS) result data is processed. Distributed according to age (for example, according to: 40-45; 46-50; 51-55; 56-60; 61-65; 66-70; 71-75 and other intervals, because as age increases, DNA methylation levels will also show an increasing trend. In practice, the influence of age needs to be removed to ensure the comparability of data) samples are randomly assigned to the cancer group and the control group (cancer-free group) for training and testing. At the same time, the data obtained through detection of a selected batch of protein tumor markers are integrated. As a result, a multi-cancer blood detection model was obtained, and the performance verification was completed through corresponding samples.

As an embodiment of the model construction in this application, it includes two parts: signal processing and model training for the above three tumor markers (DNA methylation, DNA gene mutation and protein tumor markers).

As the first tumor marker, DNA methylation signals were processed and model fitted.

Use the sequence comparison software Bismark to analyze the original output files of methylation sequencing to obtain the methylation detection output files of each sample. It includes the genomic position matched by each sequencing read, the detection results of each base included, and the methylation status of CpG sites.

For each specific capture region, the average Beta of the methylation level of CpG sites in the region is used as the signal characteristic value.

Among them: Beta (β) is the average methylation level of CpG sites in a specific capture region, ΣM is the number of methylated sites in all reads, and ΣU is the number of unmethylated sites in all reads.

Build a machine learning model that predicts the cancer status of a sample based on the methylation signal features of multiple regions, such as the svm model.

Utilize a set of training samples including cancer patients and non-cancer subjects to learn the model parameters and calculate the methylation score of a specific individual through the trained model For results greater than a certain threshold, cancer individuals are predicted.

As a second tumor marker, DNA mutation signals are processed.

The original output files of mutation sequencing were analyzed using the sequence comparison software BWA to obtain the mutation detection output files of each sample. It contains the genomic position matched by each sequencing read, including the detection results of each base and the matching score. Use mutation detection (calling) software (for example: Varscan/Vardict) to analyze and obtain each mutant SNV (single core). Genome location, mutation frequency/magnification, mutation type, and then use the detection data results of paired WBC samples to filter the mutation list. If the mutation list is in WBC ( The allele frequency (AF, Allele Frequency) of the same mutation in the paired white blood cell sample is greater than a specific threshold (for example: AF of WBC>0, or AF of WBC is greater than 1/10, or 1/9 of the AF of the mutation in the sample , or 1/8, or 1/7, or 1/6, or 1/5), the mutation is considered to be a false positive mutation and will be filtered, and the remaining filtered mutation list will be retained as the mutation result of the sample. The number of mutations Samples larger than a certain threshold (for example: 1) are predicted to be cancer individuals.

As a third tumor marker, protein (serological marker) signals are processed.

The protein data detected in each sample includes protein type, signal level and quality control results. The results that fail the quality control are filtered and the results that pass the quality control are retained. Construct model algorithms for six types of cancer using multiple protein detection data.

In this application, the 16 cancer-related serological markers screened out are shown in Table 1 below:

Table 1 16 cancer-related serological markers

Detection methods for 16 cancer-related serological markers include:

1.AFP, common name: alpha-fetoprotein detection kit (electrochemiluminescence method); English name: ElecsysAFP.

It is used for the in vitro quantitative detection of alpha-fetoprotein in human serum and plasma. It is mainly used for dynamic monitoring of patients with malignant tumors to assist in judging the disease process or treatment effect. It cannot be used as a basis for early diagnosis or confirmation of malignant tumors and is not used for the general population. Cancer screening.

It can be used clinically in the auxiliary diagnosis and treatment of non-seminomatous embryonal tumors.

The working principle of the cobase immunoanalyzer is electrochemiluminescence immunoassay "ECLIA".

α1-alpha-fetoprotein (AFP) is a glycosylated albumin with a molecular weight of 70 kDa derived from the embryonic yolk sac, undifferentiated liver cells and fetal gastrointestinal tract. 1,2 The main tumors that synthesize AFP are non-seminomatous tumors of the testis (NSGCT) and ovarian and hepatocellular carcinoma yolk sac tumors (HCC). In addition to this, in combination with hCG+β and other parameters, the AFP test helps evaluate the risk of trisomy 21 (Down syndrome) during the second trimester of pregnancy.

[Testing Principle]

Sandwich method, total detection time: 18 minutes.

●First incubation: 6 μL specimen, biotinylated specific AFP monoclonal antibody and ruthenium complex a) labeled specific AFP monoclonal antibody are incubated together to form an antigen-antibody sandwich complex.

●Second incubation: Add streptavidin-coated magnetic bead particles for incubation. The complex and the magnetic beads are combined through the action of biotin and streptavidin.

●Suck the reaction solution into the measurement cell and adsorb the magnetic beads on the electrode surface through electromagnetic action. Substances not bound to the magnetic beads are removed by ProCellllM. A certain voltage is applied to the electrode to cause the complex to chemiluminesce, and the luminescence intensity is measured by a photomultiplier.

●The test results are found on the standard curve. This curve is calibrated by the instrument through 2-point calibration and obtained from the standard curve obtained by cobaslink.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex(Ru(bpy){2+3}ruthenium terpyridyl

2.CA125, common name: Carbohydrate Antigen 125 Quantitative Assay Kit (Electrochemiluminescence Method); English name: CA125II.

For in vitro quantitative detection of reactive determinants of OC125 in human serum or plasma. These determinants are associated with a high-molecular-weight glycoprotein in the serum and plasma of women with primary invasive epithelial ovarian cancer (excluding less malignant tumors). This test can be used as an adjunct to detect residual or recurrent ovarian cancer in patients with ovarian cancer who have received first-line therapy and are considering reexploratory surgery. This test is also suitable for continuous monitoring of CA125, which helps in the treatment and management of cancer patients.

The ElecsysCA125II assay can also be used in conjunction with the ElecsysHE4 assay as the Risk of Ovarian Malignancy Algorithm (ROMA) to assess the risk of ovarian cancer in pre- and postmenopausal women with pelvic mass.

The Elecsys and cobase immunoanalyzers work on the electrochemiluminescence immunoassay "ECLIA".

CA125 is a tumor marker in the hybridoma tumor family. The detection uses monoclonal antibody (MAb) OC125.

CA125 is an antigenic determinant that exists on high molecular weight glycoproteins (200-1000KD) isolated from cell culture fluid or serum. The CA125 epitope has a protein structure and associated sugar side chains.

MAbOC125 was obtained from lymphocytes obtained from mice immunized with OVCA (ovarian cancer cell line) 433, an adenocarcinoma cell line derived from the ovary. In the Elecsys reagent, OC125 is used as the detection antibody. The second generation CA125 assay since 1992 uses MAbM11 as the capture antibody (solid phase antibody).

CA125 has a high detection rate in the serum of patients with non-mucinous ovarian tumors derived from epithelial cells. Normal ovarian (adult and fetal) epithelial cells do not express it. Ovarian cancer accounts for approximately 20% of gynecological tumors, with an incidence rate of 15/100,000. CA125 can be detected in amniotic fluid and fetal body cavity epithelial cells, both tissues of fetal origin. In tissues of adult origin, CA125 can be found in epithelial cells of the ovaries, fallopian tubes, endometrium, and cervix.

Certain benign gynecological diseases can cause elevated CA125 test results, such as ovarian cysts, ovarian metaplasia, endometriosis, uterine fibroids, and cervicitis. CA125 will be slightly elevated during early pregnancy and some benign diseases (such as acute and chronic pancreatitis, benign gastrointestinal diseases, renal failure, autoimmune diseases, etc.). Benign liver diseases (such as cirrhosis, hepatitis) CA125 Will be moderately elevated. CA125 in ascites caused by various diseases will rise sharply. Although the highest detected values of CA125 are seen in patients with ovarian cancer, significant elevations in CA125 are also seen in endometrium, breast, gastrointestinal tract, and other malignant diseases.

Although CA125 is a relatively non-specific marker, it is currently the most important marker in the treatment and progression monitoring of serous ovarian cancer. At the time of initial diagnosis, the sensitivity of CA125 depends on the FIGO (FIGO = Federation of Obstetrics and Gynecology) stage; high levels of CA125 are associated with more advanced tumor stages.

The diagnostic sensitivity and specificity of the ElecsysCA125II test were calculated by comparing patients with ovarian cancer (FIGO stages I to IV) at initial diagnosis and patients with benign gynecological diseases. When the Cutoff value is 65U/mL, the sensitivity is 79% (specificity is 82%). Increasing the Cutoff value can correspondingly increase the specificity. The best clinical decision value was 150U/mL (sensitivity 69%, specificity 93%). If we refer to the opinions of scholars such as vanDalen, the sensitivity is 63% when the specificity is 95% (cutoff is 190U/mL).

[Testing Principle]

Sandwich method principle, total detection time: 18 minutes.

●First incubation: 20 μL of sample, biotinylated CA125-specific monoclonal antibody and ruthenium complex a)-labeled CA125-specific monoclonal antibody are incubated together to form an antigen-antibody sandwich complex.

●Second incubation: After adding streptavidin-coated magnetic beads, the complex binds to the solid phase through the interaction between biotin and streptavidin.

●Suck the reaction solution into the measurement cell and adsorb the magnetic beads on the electrode surface through electromagnetic action. Substances not bound to the magnetic beads are removed by ProCell/ProCellM. A certain voltage is applied to the electrode to cause the complex to chemiluminesce, and the luminescence intensity is measured by a photomultiplier.

●The final detection result is obtained through the calibration curve of the detector. The calibration curve is generated through 2-point calibration and the first-level calibration curve obtained from the reagent barcode.

a)Tris(2,2'-bipyridyl)ruthenium(II)-complex (Ru(bpy))

3.CA19-9, common name: carbohydrate antigen 19-9; English name: CA19-9

For in vitro quantitative detection of CA19-9 in human serum or plasma. The Elecsys and cobase immunoanalyzers work on the electrochemiluminescence immunoassay "ECLIA".

The ElecsysCA19-9 test uses the 1116-NS-19-9 monoclonal antibody. The reaction site of 1116-NS-19-9 is located on the glycolipid molecule, with a molecular weight of approximately 10,000 Daltons. This mucin is similar to the hapten determinant of the Lewis blood group family and is a component of mucosal cells.

3-7% of people have the Lewisa-negative/b-negative blood group structure, which is unable to express this type of mucin like CA19-9. Therefore, care must be taken when interpreting the results.

Mucin is secreted by the fetal stomach, intestine, and pancreatic epithelial cells. Mucin is also found in low concentrations in the liver, lungs and pancreatic tissue of adults.

The detection value of CA19-9 can help in the differential diagnosis of pancreatic cancer and the monitoring of pancreatic cancer patients (sensitivity reaches 70-87%). There is no correlation between tumor size and CA19-9 test values, however, serum CA19-9 levels exceed 10,000

Almost all patients with U/mL or above have distant metastasis of tumors.

CA19-9 cannot be used as an early detection indicator for pancreatic cancer.

The sensitivity of CA19-9 for cholangiocarcinoma is approximately 50-75%. For gastric cancer, it is recommended to detect CA72-4 and CEA at the same time. For colon cancer, it is recommended to only test for CEA; in a very small number of CEA-negative cases, testing for CA19-9 is valuable.

Since mucin is secreted by the liver, mild cholestasis can lead to a significant increase in serum CA19-9 levels. Benign lesions or inflammation of the gastrointestinal tract and liver can also cause elevated CA19-9 levels, such as cystic fibrosis.

[Testing Principle]

Sandwich method principle, total detection time: 18 minutes.

●The first incubation: 10 μL specimen, biotinylated CA19-9 monoclonal specific antibody and ruthenium complex a-labeled CA19-9 specific monoclonal antibody were incubated together to form an antigen-antibody sandwich complex.

●The final detection result is obtained through the calibration curve of the detector. The calibration curve is generated through 2-point calibration and the master curve obtained from the reagent barcode.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex (Ru(bpy)32+)

4.CA72-4

For the in vitro quantitative detection of CA72-4 in human serum and plasma using immunological methods. Mainly used for monitoring the efficacy of gastric cancer and ovarian cancer.

The ElecsysCA72-4 test uses the following two monoclonal antibodies to detect serum mucinoid tumor-associated glycoprotein TAG72:

●B72.3 monoclonal antibody, extracted from metastatic breast cancer cell membrane and

●CC49 monoclonal antibody, specific for high-purity TAG72.

These antibodies react with the following tissues: breast, colon, non-small cell lung, epithelial ovarian, endometrial, pancreatic, gastric, and other cancers, and with fetal tissues such as the colon, stomach, and esophagus, but No reaction with normal adult tissues.

Benign diseases:

Elevated serum CA72-4 can be seen in the following benign diseases: pancreatitis, liver cirrhosis, lung disease, rheumatism, gynecological diseases, benign ovarian diseases, ovarian cysts, mastopathy, and benign disorders of the gastrointestinal tract. Compared with other markers, CA72-4 has higher diagnostic specificity for benign diseases.

Stomach cancer:

Diagnostic sensitivity is 28-80%, usually 40-46%. The diagnostic specificity for benign gastrointestinal diseases is >95%.

The degree of CA72-4 elevation is related to the stage of the disease. After surgery, CA72-4 levels can quickly drop to normal values, and if the tumor tissue is completely removed, CA72-4 can continue to maintain normal levels. In 70% of relapse cases, elevated CA72-4 concentrations precede or coincide with clinical diagnosis.

Some research results suggest that preoperative CA72-4 levels can be used as a prognostic criterion.

Ovarian cancer:

Its diagnostic sensitivity for ovarian cancer is reported to be 47-80%. The diagnostic sensitivity of CA72-4 for myxoid ovarian cancer is higher than that of CA125. The combination of the two can increase the diagnostic sensitivity of the initial diagnosis to 73% (CA125 alone is

60%); the diagnostic sensitivity of dynamic monitoring can be increased to 67% (CA125 alone is 60%).

Colorectal cancer:

The diagnostic sensitivity for colorectal cancer is 20-41%; and is related to Dukes clinical grade. CA72-4 has a diagnostic specificity of 98% for benign colon diseases. CA72-4 can be significantly decreased after complete tumor resection. Long-term follow-up found that CA72-4 continued to increase, which may indicate the presence of residual tumors. The combined detection of CA72-4 and CEA can increase the diagnostic sensitivity of postoperative tumor recurrence from 78% to 87%.

[Testing Principle]

Sandwich method principle, total detection time: 18 minutes.

●First incubation: 30 μL sample, biotinylated CA72-4-specific monoclonal antibody (CC49) and ruthenium complex a)-labeled CA72-4-specific monoclonal antibody (B72.3) react to form an antigen-antibody sandwich Complex.

●Second incubation: After adding streptavidin-coated magnetic beads, the complex binds to the solid phase through the interaction between biotin and streptavidin. The reaction solution is sucked into the measurement cell, and the magnetic beads are adsorbed to the electrode through electromagnetic action. surface. Substances not bound to the magnetic beads are removed by ProCell/ProCellM. A certain voltage is applied to the electrode to cause the complex to chemiluminesce, and the luminescence intensity is measured by a photomultiplier.

a)Tris(2,2'-bipyridyl)ruthenium(II)-complex (Ru(bpy)2+3)

5.CEA, common name: carcinoembryonic antigen assay kit (electrochemiluminescence method); English name: ElecsysCEA

For the in vitro quantitative determination of carcinoembryonic antigen content in human serum and plasma.

It is mainly used for dynamic monitoring of patients with malignant tumors to assist in judging the disease process or treatment effect. It cannot be used as a basis for early diagnosis or confirmation of malignant tumors and is not used for tumor screening in the general population.

Continuous monitoring of carcinoembryonic antigen can aid in the treatment of cancer patients.

Carcinoembryonic antigen (CEA) is a highly glycosylated molecule with a molecular weight of approximately 180kDa. CEA is similar to AFP and belongs to the carcinoembryonic antigen class produced during embryonic and fetal stages. CEA is thought to play a role in many biological processes, including cell adhesion, immunity, and apoptosis. After birth, CEA formation is inhibited and expression is lower in normal adult tissues.

Therefore, only very low levels of CEA are found in the blood of healthy adults. The CEA gene family includes 17 activated genes in 2 subtype groups. The first of these subtype groups contains CEA and non-specific cross-reactive antigens

(Non-specific Cross-reacting Antigens, NCA), the second isoform group contains pregnancy-specific glycoproteins

(pregnancy-specific glycoproteins, PSG). People with colon adenocarcinoma often have high CEA levels. Mild to moderate elevations in CEA levels may also be seen in nonmalignant intestinal, pancreatic, liver, and lung disorders (eg, cirrhosis, chronic hepatitis pancreatitis, ulcerative colitis, Crohn's disease). Smoking also causes elevated CEA levels and should be considered when interpreting CEA levels.

CEA measurement is not suitable for cancer screening in the general population, and CEA concentrations within the normal range do not rule out the possibility of malignant disease.

The main applications of CEA measurement are to monitor colorectal cancer treatment, confirm recurrence after treatment or surgical resection, and assist in staging and assessment of cancer metastasis.

It is best to measure CEA preoperatively, which provides independent prognostic information, facilitates surgical management, and provides a baseline for subsequent testing. For stage II or III patients, CEA levels should be measured every 2-3 months after diagnosis and for at least 3 years. When used to monitor treatment for advanced disease, CEA levels should also be tested every 2 to 3 months.

The antibodies in the ElecsysCEA test react with CEA and the meconium antigen NCA-2, specifically with NCA-2. Cross reaction can help early detection of colorectal cancer metastasis and recurrence.

The antigenic determinants of CEA have been defined, and the corresponding monoclonal antibodies can be divided into five categories. ElecsysCEA test

The monoclonal antibodies used in the kit react with the 2nd and 5th epitopes.

[Testing Principle]

Sandwich method, total testing time: 18 minutes

●First incubation: 6 μL specimen, biotinylated CEA monoclonal specific antibody and ruthenium (Ru)a-labeled CEA specific monoclonal antibody are incubated together to form an antigen-antibody sandwich complex.

●Second incubation: Add streptavidin-coated magnetic beads for incubation. The complex and the magnetic beads are combined through the action of biotin and streptavidin.

●Suck the reaction solution into the measurement cell and adsorb the magnetic beads on the electrode surface through electromagnetic action. Substances not bound to the magnetic beads are removed by ProCellIIM. A certain voltage is applied to the electrode to cause the complex to chemiluminesce, and the luminescence intensity is measured by a photomultiplier.

●The instrument automatically calculates the test results through the 2-point calibration curve and the master curve provided by cobaslink.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex(Ru(bpy){2+3}ruthenium terpyridyl

6. CYFRA21-1, common name: non-small cell lung cancer related antigen 21-1 detection reagent; English name: CYFRA21-1.

For the in vitro quantitative detection of cytokeratin 19 fragments in human serum or plasma by immunoassay.

Cytokeratins are structural proteins that form the intermediate fibers of epithelial cells. Twenty different cytokeratin polypeptide chains have been identified so far. Because of their specific segmentation patterns, they are particularly suitable for use as differentiation markers in tumor pathological diagnosis. The complete cytokeratin polypeptide chain is poorly soluble, but soluble protein fragments in serum can be detected.

With the help of two specific monoclonal antibodies (KS 19.1 and BM19.21), CYFRA 21-1 can be used to measure a fragment of cytokeratin 19 with a molecular weight of approximately 30,000 daltons.

The primary indication for CYFRA 21-1 is monitoring the progression of non-small cell lung cancer (NSCLC).

CYFRA 21-1 is also indicated for monitoring the progression of muscle-invasive bladder cancer. CYFRA 21-1 has good specificity relative to benign lung diseases (pneumonia, sarcoidosis, tuberculosis, chronic bronchitis, bronchial asthma, emphysema).

Mildly elevated test values (up to 10ng/mL) are rarely seen in severe benign liver disease and renal failure. There was no correlation between test results and gender, age or smoking status. These test values are also not affected by pregnancy.

The initial diagnosis of lung cancer should be based on clinical symptomatology, imaging or endoscopic findings, and intraoperative findings.

Fuzzy round lesions in the lungs combined with a CYFRA 21-1 test value >30ng/mL indicate a high likelihood of primary bronchial cancer.

High CYFRA 21-1 serum concentrations indicate advanced tumor stage and poor prognosis. Normal or only mildly elevated test values do not rule out the presence of tumors.

A rapid reduction in CYFRA 21-1 serum levels to the normal range indicates successful treatment. A fixed CYFRA 21-1 test value or a slight or only slow decrease in the CYFRA 21-1 test value indicates incomplete tumor resection or the presence of multiple tumors and the corresponding treatment and prognosis results. Disease progression often manifests as increased CYFRA 21-1 levels and often precedes clinical symptoms and imaging findings.

[Testing Principle]

Sandwich method principle, total detection time: 18 minutes.

●First incubation: 20 μL of specimen, biotinylated cytokeratin-19-specific monoclonal antibody and ruthenium complex a-labeled cytokeratin-19-specific monoclonal antibody are incubated together to form an antigen-antibody sandwich complex.

●Suck the reaction solution into the measurement cell and adsorb the magnetic beads on the electrode surface through electromagnetic action. Substances not bound to the magnetic beads are removed by ProCell/ProCell M. A certain voltage is applied to the electrode to cause the complex to chemiluminesce, and the luminescence intensity is measured by a photomultiplier.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex (Ru(bpy)2+3)

7. DCP, common name: abnormal prothrombin assay kit (magnetic particle chemiluminescence immunoassay).

This kit is used for the in vitro quantitative detection of abnormal prothrombin content in human serum samples. It is mainly used for condition monitoring and efficacy evaluation of patients with histologically confirmed liver cancer. It cannot be used as a basis for early diagnosis or confirmation of malignant tumors and is not used for tumor screening in the general population.

Prothrombin is a vitamin K-dependent serum coagulation factor synthesized in the liver. In the absence of vitamin K, liver cells cannot synthesize normal vitamin K-dependent coagulants (II, VH, IX, and X), but can only synthesize abnormal prothrombin without coagulation function. In hepatocellular carcinoma, the synthesis of prothrombin precursor is abnormal, and the carboxylation of prothrombin precursor is insufficient, thus generating a large amount of DCP. Hepatitis, cirrhosis, and alcoholic liver disease may also cause elevated DCP. Clinical diagnosis is mainly based on pathology, imaging and other diagnostic methods.

[Testing Principle]

The abnormal prothrombin determination kit (magnetic particle chemiluminescence immunoassay) uses a double-antibody sandwich method. During the measurement, magnetic particles coated with anti-DCP antibodies and alkaline phosphatase-labeled anti-DCP antibodies are mixed with the sample. The DCP in the sample combines with the anti-DCP antibody to form a magnetic particle immune complex of anti-DCP antibody-DCP-anti-DCP antibody enzyme label. After washing to remove free enzyme-labeled antibodies, chemiluminescent substrate is added to the immune complex. The luminescence signal generated by the enzyme reaction is detected by a fully automatic chemiluminescence immunoassay analyzer. The detected luminescence intensity is related to the concentration of DCP in the sample. The fully automatic chemiluminescence immunoassay analyzer can calculate the concentration value of DCP in the sample.

8.FER, common name: Ferritin detection kit (electrochemiluminescence method); English name: Elecsys Ferritin.

For the in vitro quantitative determination of ferritin content in human serum and plasma.

Ferritin is a known iron storage protein that is synthesized by many body cells. It is mainly found in the liver, spleen and bone marrow, and to a lesser extent in the blood. The amount of ferritin in serum is an indicator of iron storage and can indicate that there is too little iron available for use in the body (e.g.

Iron deficiency anemia) or excess (e.g. hemochromatosis).

This protein is involved in the cellular uptake, storage, and release of iron. Ferritin has a dual function: to store iron in a bioavailable form and to protect cells from the toxic effects of iron, due to its ability to produce reactive species that directly damage DNA and proteins.

Its iron-free protein, apoferritin, is composed of 24 subunits and has a molecular weight of approximately 450kDa. The iron core of ferritin contains approximately 4,500 iron atoms in the form of Fe 3+ ions.

Iron-loaded ferritin and hemosiderin (an insoluble ferritin complex) represent iron stores in each cell and throughout the body. There are many different ferritin subtypes in the body, which are composed of different subunits and have some

tissue specificity.

Under steady-state conditions, serum ferritin concentration is proportional to total body iron stores: 1 ng/mL of serum ferritin is equivalent to 10 mg of total iron stores. Therefore, in the literature, measurement of serum ferritin levels is considered to be the best and most convenient laboratory test for estimating iron stores and diagnosing iron deficiency or iron-related diseases. it has

It has replaced the invasive semi-quantitative histochemical detection method of bone marrow aspiration or biopsy as the gold standard for the diagnosis of iron deficiency anemia.

Serum ferritin is a good indicator of iron stores in the body; however, it does not provide information about the amount of iron actually available for erythropoiesis. A decrease in serum ferritin concentration to <15 μg/L indicates iron deficiency, which may be due to previous blood loss, altered iron intake, transferrin deficiency, or increased requirements (eg, pregnancy). Increased serum ferritin (>400μg/L) may be Many implications: Ferritin is an acute-phase reactant, and elevated serum ferritin levels may be seen in infection, acute or chronic inflammation, and malignancy, despite the presence of acute iron deficiency. Elevated serum ferritin levels independent of iron stores may also be seen in patients with alcoholic or viral hepatitis and chronic renal failure. The overall clinical picture of the individual patient should be considered when making the diagnosis.

[Testing Principle]

Principle of sandwich method, total testing time: 18 minutes.

●First incubation: 6 μL sample, biotinylated monoclonal ferritin-specific antibody and monoclonal ferritin-specific antibody labeled ruthenium complex react to form a sandwich complex.

●Second incubation: After adding streptavidin-coated particles, the complex combines into a solid phase through the interaction between biotin and streptavidin.

●The reaction mixture is sucked into the measuring cell, where the particles are magnetically adsorbed to the electrode surface. Unbound material is removed with Procell II M. Applying a voltage to the electrode produces chemiluminescence, which is measured by a photomultiplier tube.

●The results are measured using a calibration curve, which is generated by a specific instrument using 2-point calibration and the master curve obtained from cobas link.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex(Ru(bpy){2+3}ruthenium terpyridyl

9.HE4, common name: human epididymis protein 4 detection kit (electrochemiluminescence method); English name: Elecsys HE4.

For the in vitro quantitative determination of human epididymis protein 4 in human serum and plasma. It is mainly used for dynamic monitoring of patients with malignant tumors to assist in judging the disease process or treatment effect. It cannot be used as a basis for early diagnosis or confirmation of malignant tumors and is not used for tumor screening in the general population.

This assay is used as an adjunct to monitor disease recurrence or progression in patients with epithelial ovarian cancer. Serial testing of patient HE4 values should be used in conjunction with monitoring of other clinical outcomes of ovarian cancer.

Additionally, HE4 can be used in conjunction with the Elecsys CA 125 II assay to assist in assessing the risk of epithelial ovarian cancer in pre- and post-menopausal women with pelvic mass. Results must be interpreted in conjunction with other methods following standard clinical management guidelines.

The cobase immunoassay analyzer works on the electrochemiluminescent immunoassay "ECLIA"

Human epididymis protein 4 (HE4, also known as WFDC2) belongs to the whey acidic protein (WFDC) family of proteins with suspected trypsin inhibitor properties. When in the mature glycosylated form, this protein has a molecular weight of approximately 20-25 kD and contains two WFDC domains in a single peptide chain.

It was initially thought that HE4 expression was unique to the epididymis. Several recent findings indicate that HE4 is expressed in respiratory and reproductive tissues, including The expression is low in the epithelium of ovary) but is highly expressed in ovarian cancer tissues. High secretion levels may also occur in the serum of ovarian cancer patients.

Ovarian cancer ranks seventh among the causes of cancer-related deaths in women worldwide. Ovarian cancer is the deadliest form of gynecological cancer and is potentially curable if diagnosed early and treated by a doctor familiar with ovarian cancer treatment. However, the symptoms of ovarian cancer are often vague and unclear. Therefore, most ovarian cancers are detected at an advanced stage. The 5-year survival rate for stage I patients is 90%, and for stage IV patients, it drops to less than 20%.

As a single tumor marker, HE4 has the highest sensitivity for the detection of ovarian cancer, especially in stage I disease, which is an early asymptomatic stage. When CA 125 and HE4 are combined, the highest sensitivity of 76.4% and the highest specificity of 95% are achieved.

Combined with CA 125, HE4 can help determine whether a pelvic mass is benign or malignant in pre- and post-menopausal women.

The dual marker of CA 125 combined with HE4 can predict whether a tumor is malignant more accurately than a single marker.

Huhtinen et al. reported a sensitivity of 78.6% and a specificity of 95% for ovarian cancer compared with endometrioma. As reported by Moore et al., CA 125 and HE4 combined have an accuracy of 94% in distinguishing malignant from benign pelvic tumors using an algorithm called ROMA (Risk of Ovarian Malignancy Algorithm).

In addition, HE4 levels were associated with clinical response to treatment or recurrence status in women with ovarian cancer confirmed by CT imaging. Therefore, HE4 can serve as an important early indicator of disease recurrence.

[Testing Principle]

Sandwich method, total measurement time: 18 minutes.

●First incubation: 6 μL sample, biotinylated monoclonal HE4-specific antibody and ruthenium complex a-labeled monoclonal HE4-specific antibody form a sandwich compound.

●Second incubation: After adding streptavidin-coated magnetic beads, the complex binds to the particles through the reaction between biotin and streptavidin.

●Suck the reaction solution into the measurement cell and adsorb the magnetic beads on the electrode surface through electromagnetic action. Substances not bound to the magnetic beads are removed by ProCell II M. A certain voltage is applied to the electrode to cause the complex to chemiluminesce, and the luminescence intensity is measured by a photomultiplier.

●Results are determined using the analyzer's proprietary calibration curve generated by 2-point calibration and the master curve provided via cobas link.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex(Ru(bpy){2+3}ruthenium terpyridyl

10.MPO, common name: anti-myeloperoxidase antibody IgG detection kit (enzyme-linked immunosorbent assay); English name: Anti-Myeloperoxidase ELISA (lgG).

This product is used for the in vitro quantitative or quantitative detection of anti-MPO antibody immunoglobulin G (lgG) in human serum or plasma.

Serologic testing for antineutrophil plasma antibodies (ANCA) is helpful in some autoimmune diseases (eg, granulomatous vasculitis, acute progressive glomerulonephritis, polyarteritis, ulcerative colitis, primary sclerosis Cholangitis), there are many methods to detect ANCA. Among them, the indirect immunofluorescence method using ethanol-fixed neutrophils as the matrix is the standard method to detect ANCA. When using the indirect immunofluorescence method to detect, at least it can distinguish Two fluorescence models: granulocyte cytoplasmic granular fluorescence (cANCA: cytoplasmic type, seen in granulomatous vasculitis) and smooth or fine granular fluorescence surrounding the nucleus (pANCA, perinuclear type). The anti-proteinase 3 antibody produces a cANCA (cytoplasmic fluorescence) fluorescence pattern. Known pANCA (perinuclear fluorescence) target antigens are lactoferrin, myeloperoxidase, elastase, cathepsin G, lysosomes and β-glucuronidase. Anti-BPI antibodies can produce both cANCA and pANCA fluorescence models.

Indirect immunofluorescence is used as a preliminary screening experiment for anti-granulocyte antibodies, but it cannot distinguish the corresponding target antigen of pANCA. To distinguish the target antigen of pANCA, purified specific protein should be used as the detection matrix (European anti-granulocyte cytoplasmic antibody spectrum ELISA detection kit or single-specific ELISA detection kit). Occasionally, pANCA-positive serum by indirect immunofluorescence method does not react with any of the above target antigens, mainly because there are some other unknown antigens.

11.PRL, common name: Prolactin detection kit (electrochemiluminescence method); English name: Elecsys Prolactin II.

For the in vitro quantitative detection of prolactin (Prolactin) in human serum and plasma.

Prolactin is synthesized and secreted by the anterior pituitary gland. This hormone is composed of 198 amino acids and has a molecular weight of approximately 22-23kD. There are three forms of prolactin in serum, of which the monomeric ("small") form with biological and immunological activity is the most common, followed by the dimeric ("large") form with no biological activity and low biological activity. Tetrameric ("big-big") form. The target organ of prolactin is the mammary gland, which can promote the growth, development and differentiation of mammary gland tissue. High concentrations of prolactin inhibit ovarian steroid hormone synthesis and pituitary gonadotropin production and secretion.

During pregnancy, prolactin concentrations increase due to increased synthesis of estrogen and progesterone. The stimulating effect of prolactin on the mammary gland results in postpartum lactation. Prolactin in turn affects glucose and lipid metabolism and is involved in the development of insulin resistance. Hyperprolactinemia (in both men and women) is a major cause of reproductive disorders. Prolactin measurement is useful in the diagnosis of hyperprolactinemia and peritoneal endometriosis.

The Elecsys Prolactin II assay uses two human prolactin-specific monoclonal antibodies.

These two antibodies react weakly with most macroprolactins.

[Testing Principle]

Sandwich method, total measurement time: 18 minutes

●First incubation: 10 μL sample and 1 part of biotin-labeled prolactin-specific monoclonal antibody are incubated together to form a complex.

●Second incubation: After adding ruthenium complex a-labeled prolactin-specific monoclonal antibody and streptavidin-coated microparticles, the reaction generates a "sandwich" complex, which is passed through the interaction of biotin and streptavidin. Interactive binding to the solid phase.

●Suck the reaction mixture into the detection cell, and the particles in the detection cell are adsorbed on the electrode surface through electromagnetic action. Unbound material is removed by ProCell/ProCell M. Applying a certain voltage to the electrode causes the complex to chemiluminesce.

Use a photomultiplier to detect the intensity of the luminescence.

●The final detection result is obtained through the calibration curve of the detector. The calibration curve is generated through the 2-point calibration point and the master curve obtained from the reagent barcode or electronic barcode.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex(Ru(bpy){2+3}ruthenium terpyridyl

12.ProGRP, common name: gastrin radioimmunoassay kit.

【Measurement principle】

Gastrin and 1-gastrin in the sample (standard, blood sample, etc.) undergo a competitive immune reaction with a limited amount of gastrin antiserum. After the reaction reaches equilibrium, the antigen-antibody conjugate is separated using an immune separation agent and measured. The radioactivity in the conjugate is compared with the standard concentration of gastrin to obtain a competitive inhibition curve, and the gastrin content in the sample can be determined.

13.SCC, common name: squamous cell carcinoma antigen (SCC) detection kit-(magnetic particle chemiluminescence method).

This kit is used to quantitatively determine the content of squamous epithelial cell carcinoma antigens in human serum in vitro. It should be used for dynamic monitoring of patients with malignant tumors to assist in judging the disease process or treatment effect. It cannot be used as a basis for early diagnosis or confirmation of malignant tumors, and cannot be used for tumor screening in the general population.

Squamous cell carcinoma antigen (SCC) is a glycoprotein with a relative molecular mass of 48,000. It was first isolated from squamous cells of the cervix and is mainly affected by tumor infiltration and growth conditions. Squamous cell carcinoma antigen - Antigen is a highly specific tumor marker for squamous cell carcinoma, with the highest positive rates in lung squamous cell carcinoma and cervical squamous cell carcinoma.

[Testing Principle]

This product consists of Reagent 1 (biotin-Biotn-labeled anti-SCC alpaca monoclonal antibody), Reagent 2 (horseradish peroxidase HRP-labeled anti-SCC alpaca monoclonal antibody), SCC calibrator, quality control products and others The necessary auxiliary reagents are composed of The sandwich method principle is used to detect the SCC content in human serum.

Reagent 1 reacts with the magnetic particles-streptavidin working solution, and coats the anti-SCC camel monoclonal antibody on the surface of the magnetic particles; add the sample and reagent 2, and the SCC antigen in the sample forms a sandwich complex with reagent 1 and reagent 2. After the reaction, the free components are washed away through magnetic field separation. Add the chemiluminescence substrate solution and measure the luminescence value RLU of each reaction tube to detect the SCC content.

14.TRF, common name: Transferrin detection kit (immune turbidimetry); English name: Tina-quant Transferrinver.2 (TRSF2).

For the in vitro quantitative determination of transferrin concentration in human serum and plasma.

Transferrin is a glycoprotein with a molecular weight of 79,570 daltons. It consists of a polypeptide chain and two oligosaccharide chains linked by N-glycosidic bonds, and exists in multiple subtypes. The rate of transferrin synthesis in the liver varies with changes in body demand for iron and iron stores.

Transferrin is an iron transport protein in serum. When the body is deficient in iron, transferrin saturation is one of the most sensitive indicators indicating functional iron deficiency. When iron stores are insufficient, ferritin levels drop. For example, in inflammatory or less common diseases such as ascorbic acid deficiency, if the serum transferrin concentration is very low, iron deficiency or iron deficiency can be ruled out. Transferrin saturation is more suitable than ferritin for screening for homozygous genotypes of hereditary hemochromatosis. Erythropoietin is effective in treating anemia in patients with renal failure only if iron stores are adequate. It is best to monitor transferrin saturation during treatment.

Transferrin saturation combined with ferritin testing can be used as a clear criterion to rule out iron overload in patients with chronic liver disease.

There are several methods for detecting transferrin, including radioimmunodiffusion, turbidimetry, and transmission turbidimetry. The Roche transferrin assay is based on the principle of immunoagglutination.

[Detection principle]

Immunoturbidimetric assay.

Human transferrin forms a precipitate with specific antiserum, which can be measured by turbidimetric method.

15.CA15-3, common name: carbohydrate antigen 15-3 assay kit (electrochemiluminescence method); English name: Elecsys CA 15-3 II.

It is used for the in vitro quantitative detection of carbohydrate antigen 15-3 (CA 15-3) in human serum and plasma. It is mainly used for dynamic monitoring of patients with malignant tumors to assist in judging the disease process or treatment effect. It cannot be used for early diagnosis or treatment of malignant tumors. The basis for diagnosis is not used for tumor screening in the general population.

This product is suitable for auxiliary diagnosis and treatment of breast cancer patients. Combined with various other clinical diagnostic and treatment examinations, this analysis can continuously detect Applies to:

●Early diagnosis of tumor recurrence in stage II and III breast cancer.

●Efficacy monitoring of metastatic breast cancer.

CA 15-3 (cancer antigen 15-3) is derived from the glycoprotein Mucin-1 (MUC-1). The CA 15-3 test uses two monoclonal antibodies (MAbs), 115D8 and DF3, in a sandwich assay to detect two antigenic sites associated with breast cancer cells. MAb 115D8 recognizes human milk adipocyte membranes, while MAb DF recognizes human metastatic breast cancer cell membrane fragments.

This antigen is usually found in the luminal secretions of glandular cells and does not enter the blood circulation. This antigen can be detected in serum using the CA 15 3 test when cells become malignant and when their basement membrane becomes permeable. CA 15-3 overexpression plays an important role in epithelial to mesenchymal transition; this is an important and complex phenomenon that determines cancer progression. CA 15-3 concentration predicts disease-free and overall survival in Luminal B breast cancer.

In a review, Duffy et al. outline an instructive outlook for late-stage disease surveillance. Low-cost and minimally invasive methods for CA 15-3 monitoring are mentioned in the ASCO and EGTM guidelines, especially for those diseases that cannot be detected by conventional imaging methods. The ESMO Breast Cancer Guidelines consider CA 15-3 to have an auxiliary role in combination with other methods, especially in those undetectable diseases.

[Testing Principle]

Sandwich method, total testing time: 18 minutes

●First incubation: 12μL sample is automatically pre-diluted with universal diluent 1:20. Antigen (in 1220 μL of prediluted sample), biotinylated specific CA 15-3 mAb, and ruthenium complex a-labeled specific CA 15-3 mAb are incubated together to form an antigen-antibody sandwich complex.

●The instrument automatically calculates the test results through 2-point calibration and the calibration curve obtained from cobas link.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex(Ru(bpy){2+3}ruthenium terpyridyl

16.T-PSA, common name: total prostate-specific antigen (PSA) determination kit (electrochemiluminescence method); English name: Elecsys total PSA.

For the in vitro quantitative detection of total prostate-specific antigen in human serum or plasma, including free and bound forms.

Total prostate-specific antigen combined with digital rectal examination (DRE) is used as an auxiliary examination indicator for prostate cancer in men over 50 years old. Prostate biopsy is the diagnostic standard for prostate cancer. Regular testing of total prostate-specific antigen can help evaluate the efficacy of prostate cancer patients.

Prostate-specific antigen (PSA) is a glycoprotein with a molecular weight of 30,000-34,000d and a structure similar to glandular kallikrein. PSA acts as a serine protease.

In the blood, PSA irreversibly binds to α-1-antitrypsin (ACT) and α-2-macroglobulin, and its protease activity is inhibited. In addition to these complexes, approximately 10-30% of PSA in blood is in free form, which also does not have protease activity.

Autopsies show that prostate cancer is quite common. The incidence rate in men aged 70-79 years is 36-51%. Most of these are latent conditions, meaning they are asymptomatic and relatively benign. If the measurement shows an elevated PSA, subsequent decisions must take into account the possibility of underlying disease. Nonetheless, PSA screening can reduce prostate cancer-related mortality. Different models have been proposed to improve the predictive accuracy of PSA measurements.

Since paraurethral glands, anal glands, breast tissue or breast cancer tissue also secrete PSA, a small amount of PSA will be present in women's serum. Sometimes PSA can also be detected after laser removal of the prostate. The clinical application value of PSA is mainly reflected in the monitoring of the efficacy of prostate cancer and the evaluation of the efficacy of hormone therapy.

If PSA levels drop sharply or become undetectable after radiation therapy, hormone therapy, or laser surgery to remove the prostate, the treatment is effective.

If there is inflammation or trauma to the prostate (such as urinary retention, digital rectal examination, cystoscopy, colonoscopy, urethral biopsy, laser treatment, etc.), it may cause PSA to increase to varying degrees and for a sustained period.

When the free PSA/total PSA ratio (also known as the free PSA ratio) is 10-50%, the two monoclonal antibodies used by Elecsys can recognize equal amounts of PSA and PSA-ACT.

[Testing Principle]

Sandwich method, total testing time: 18 minutes

●First incubation: 12 μL specimen, biotinylated PSA-specific antibody and ruthenium (Ru) labeled a) PSA-specific monoclonal antibody are incubated together to form an antigen-antibody sandwich complex.

●Second incubation: Add streptavidin-coated magnetic beads for incubation. The complex and the magnetic beads are passed through biotin and Streptavidin binding.

●The test results are found on the standard curve. This curve is calibrated by the instrument through 2-point calibration and obtained from the standard curve obtained from cobas link.

a)Tris(2,2’-bipyridyl)ruthenium(II)-complex(Ru(bpy){2+3}ruthenium terpyridyl

As one of the preferred embodiments, in this application, a serological marker-based cancer detection (P-DOC) model is constructed using a logistic regression algorithm.

Based on the expression levels of serological markers related to 16 types of cancer in subjects, this application uses a logistic regression algorithm to construct 6 cancer detection models for six types of high-incidence cancers.

In the above formula, x _m is the actual detection concentration of the tumor marker after log-transformation, β _m is the weight parameter of each tumor marker in the binary classification model, and y is the interpretation output.

Using the training set data, we know the occurrence of event Y, and use the maximum likelihood function to estimate the most reasonable possibility of parameter βx, that is, calculate the best βx value that can predict event Y. When making predictions in the verification data, the optimal weight parameters of different swelling targets are substituted into the formula in the form of a combination to evaluate the negative and positive samples.

As another preferred embodiment, for multiple protein detection data, a machine learning model, such as a svm model, is constructed to predict the cancer status of the sample based on multiple protein signal features.

Utilize a set of training samples including cancer patients and non-cancer subjects to learn model parameters and calculate individual protein scores through the trained model For results greater than a certain threshold, cancer individuals are predicted.

Example

Example 1

Lung cancer detection model

Using the trained lung cancer detection model of 16 serological markers, the following sample set was trained and verified.

Table 2 Lung cancer detection model sample set

As shown in Figure 1A, using 5-fold (fold) 100-repeat (repeat) cross-validation, the AUC of the ROC curve of the above training set sample in the lung cancer detection model is 0.89. At the same time, in accordance with the Youden index optimal principle, we get The threshold of the lung cancer detection model is 0.12, that is, if the model prediction score is greater than 0.12, the sample is judged to be positive (lung cancer), and if the model prediction score is lower than or equal to 0.12, the sample is judged to be negative (healthy).

As shown in Figure 1B, using the lung cancer detection model constructed from the training set and the threshold setting rules, the specificity of the validation set samples was 0.898, the phase I sensitivity was 0.714, the phase II sensitivity was 0.875, and the phase III sensitivity was 0.909 , the stage IV sensitivity is 0.889.

Example 2

Intestinal cancer detection model

The following sample set was trained and validated using a trained 16 serological marker bowel cancer detection model.

Table 3 Intestinal cancer detection model sample set

As shown in Figure 2A, using 5-fold 100-repeat cross-validation, the AUC of the ROC curve of the above training set samples in the colorectal cancer detection model is 0.98. At the same time, according to the optimal principle of Youden index, the colorectal cancer detection model is obtained The threshold is 0.39, that is, if the model prediction score is greater than 0.39, the sample is judged to be positive (intestinal cancer), and if the model prediction score is lower than or equal to 0.39, the sample is judged to be negative (healthy).

As shown in Figure 2B, using the intestinal cancer detection model constructed from the training set and the threshold setting rules, the specificity of the validation set samples was 0.979, the phase I sensitivity was 0.833, the phase II sensitivity was 0.800, and the phase III sensitivity was 1.000, IV sensitivity period is 1.000.

Example 3

Liver cancer detection model

Using the trained 16 serological marker liver cancer detection models, the following sample set was trained and verified.

Table 4 Liver cancer detection model sample set

As shown in Figure 3A, using 5-fold 100-repeat cross-validation, the AUC of the ROC curve of the above training set samples in the liver cancer detection model is 0.98. At the same time, according to the optimal principle of Youden index, the threshold of the liver cancer detection model is obtained. is 0.63, that is, if the model prediction score is greater than 0.63, the sample is judged to be positive (liver cancer), and if the model prediction score is lower than or equal to 0.63, the sample is judged to be negative (healthy).

As shown in Figure 3B, using the liver cancer detection model constructed from the training set and the threshold setting rules, the specificity of the validation set samples was 0.979, the phase I sensitivity was 0.857, the phase II sensitivity was 0.857, and the phase III sensitivity was 1.000 .

Example 4

Ovarian cancer detection model

Using the trained ovarian cancer detection model of 16 serological markers, the following sample set was trained and validated.

Table 5 Ovarian cancer detection model sample set

As shown in Figure 4A, using 5-fold 100-repeat cross-validation, the AUC of the ROC curve of the above training set samples in the ovarian cancer detection model is 0.95. At the same time, according to the optimal principle of Youden index, the ovarian cancer detection model is obtained The threshold is 2.26, that is, if the model prediction score is greater than 2.26, the sample is judged to be positive (ovarian cancer), and if the model prediction score is lower than or equal to 2.26, the sample is judged to be negative (healthy).

As shown in Figure 4B, using the ovarian cancer detection model constructed from the training set and the threshold setting rules, the specificity of the validation set samples was 1.000, the phase I sensitivity was 0.667, the phase II sensitivity was 1.000, and the phase III sensitivity was 1.000, stage IV sensitivity is 1.000.

Example 5

Pancreatic cancer detection model

Using the trained pancreatic cancer detection model of 16 serological markers, the following sample set was trained and validated.

Table 6 Pancreatic cancer detection model sample set

As shown in Figure 5A, using 5-fold 100-repeat cross-validation, the AUC of the ROC curve of the above training set samples in the pancreatic cancer detection model is 0.95. At the same time, according to the optimal principle of Youden index, the pancreatic cancer detection model is obtained The threshold is 0.76, that is, if the model prediction score is greater than 0.76, the sample is judged to be positive (pancreatic cancer), and if the model prediction score is lower than or equal to 0.76, the sample is judged to be negative (healthy).

As shown in Figure 5B, using the pancreatic cancer detection model constructed from the training set and the threshold setting rules, the specificity of the validation set samples was 0.966, the phase I sensitivity was 0.750, the phase II sensitivity was 0.667, and the phase III sensitivity was 0.800, stage IV sensitivity is 1.000.

Example 6

Gastric cancer detection model

Using the trained gastric cancer detection model of 16 serological markers, the following sample set was trained and verified.

Table 7 Gastric cancer detection model sample set

As shown in Figure 6A, using 5-fold 100-repeat cross-validation, the AUC of the ROC curve of the above training set samples in the gastric cancer detection model is 0.90. At the same time, according to the optimal principle of Youden index, the threshold of the gastric cancer detection model is obtained. is 0.06, that is, if the model prediction score is greater than 0.06, the sample is judged to be positive (gastric cancer), and if the model prediction score is lower than or equal to 0.76, the sample is judged to be negative (healthy).

As shown in Figure 6B, using the gastric cancer detection model constructed from the training set and the threshold setting rules, the specificity of the validation set samples was 0.881, the phase I sensitivity was 0.333, the phase II sensitivity was 0.875, and the phase III sensitivity was 0.700. , stage IV sensitivity is 1.000.

Example 7

This application uses real samples of 6 cancer types, divided into training set and validation set, to evaluate the accuracy of the binary classifier (cancer vs. non-cancer).

Table 8 Evaluation sample set

This application uses multi-omics analysis results to integrate and obtain the results.

The methylation, mutation, and protein detection results {Me _i , Mu _i , Pr _i } of the i-th individual are integrated, and the patient's multi-omics determination result (methylation + mutation, methyl methylation+protein, mutation+protein, methylation+mutation+protein):

If S _i =1, it is judged that the patient has a high risk of developing cancer, otherwise the patient does not have a high risk of developing cancer.

This application collected a set of multi-omics data for verification including 257 cancer patients and 235 healthy subject samples. The types of cancer patients include lung cancer, colorectal cancer, liver cancer, ovarian cancer, pancreatic cancer, and esophageal cancer. Cancer, stomach cancer, bile duct cancer, head and neck cancer. Methylation, mutation and protein detection (logistic regression algorithm) of each subject's blood samples were collected separately data, and performed signal processing and model prediction based on the above multi-omics method. The results of each set of data and the accuracy of multi-omics data prediction are shown in Figure 7.

Table 9 Multi-omics data validation sensitivity and specificity

As shown in Figure 9, this application collects blood samples from cancer patients and non-cancer controls and divides them into a training group and a validation group. In the training group, samples distributed according to age are randomly assigned to the cancer group and the control group. (cancer-free group) for training. Subsequently, the data obtained through the detection of a selected batch of protein tumor markers, the detection data of the cfDNA methylation model and the detection data of the ctDNA mutation model are integrated and further processed (for example: SVM, 5-layer fold cross-validation), and then constructed The multi-omics model, through testing the validation set, obtained the performance verification results of samples distributed according to age, and obtained a practical and effective multi-cancer blood detection model.

The foregoing detailed description is provided by way of explanation and example, and is not intended to limit the scope of the appended claims. Various modifications to the embodiments described herein will be apparent to those of ordinary skill in the art and remain within the scope of the appended claims and their equivalents.

Claims

A serum marker combination for assessing the risk of cancer, wherein the serum marker combination includes any one or more markers selected from the following: AFP, CA125, CA19-9, CA72-4, CEA, CYFRA21-1, DCP, FER, HE4, MPO, PRL, ProGRP, SCC, TRF, CA15-3, T-PSA.
The serum marker combination according to claim 1, wherein the serum marker combination includes the following 16 markers: AFP, CA125, CA19-9, CA72-4, CEA, CYFRA21-1, DCP, FER, HE4 , MPO, PRL, ProGRP, SCC, TRF, CA15-3 and T-PSA.
A kit for assessing the risk of cancer, wherein the kit includes a reagent for detecting the serum marker combination as described in claim 1 or 2.
Use of a reagent for detecting the serum marker combination according to claim 1 or 2 in the preparation of a kit for assessing the risk of cancer.
The serum marker combination according to claim 1 or 2, the kit according to claim 3 or the use according to claim 4, wherein the cancer is selected from: lung cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer Cancer, stomach cancer.
A method for generating cancer risk warning information, wherein the method includes:

Obtain detection data of the subject's sample, wherein the detection data includes the subject's corresponding cfDNA methylation level detection data, DNA mutation detection data and/or serum marker detection data, and the serum marker includes claim 1 Or the serum marker combination described in 2;

According to the detection data, determine the first cancer risk value of the subject based on a first cancer risk prediction model used to characterize the correlation between the subject's detection data and cancer occurrence;

Generate cancer risk reminder information for the subject based on the cancer risk value.
The method of claim 6, wherein the first cancer risk prediction model is as follows:

Wherein, xm is the detection concentration value of the serum marker, βm is the weight parameter of each serum marker, y is the interpretation output, and m is the mth serum marker among the serum markers.
The method according to claim 7, wherein the first cancer risk prediction model is trained through the following first training step:

Obtain a first training sample set, where the first training sample includes subject information, the subject's corresponding serum marker detection data, and the subject's cancer information;

Based on the first sample training set, the maximum likelihood function is used to determine the value of parameter β, and the first cancer risk prediction model is obtained.
The method according to any one of claims 6-8, wherein the method further includes:

According to the detection data, the subject's second cancer risk value is determined based on a second cancer risk prediction model used to characterize the correlation between the subject's detection data and cancer occurrence.
The method of claim 9, wherein the second cancer risk prediction model is trained through the following second training step:

Obtain a second training sample set, the second training sample includes subject information, the subject's corresponding cfDNA methylation level detection data and/or the serum marker detection data and the subject's cancer risk information;

Based on the second training sample set, the initial second cancer risk prediction model is supervised and trained to obtain the second cancer risk prediction model used to characterize the correlation between subject detection data and cancer occurrence.
The method according to any one of claims 6-10, wherein the cfDNA methylation level detection data includes methylation signal characteristic values; and

The methylation signal characteristic value is obtained in advance through the following steps:

The average Beta of the methylation level of CpG sites in each specific capture region is used as the methylation signal characteristic value,

Where, Beta is the average methylation level of CpG sites in the specific capture region, and ΣM is the specific capture region The number of methylated sites in all reads within the domain, ΣU is the number of unmethylated sites in all reads within the specific capture region.
The method according to any one of claims 6-11, wherein the cancer is selected from the group consisting of: lung cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, and gastric cancer.
A device for generating cancer risk warning information, wherein the device includes:

The acquisition module is configured to obtain detection data of the subject sample, wherein the detection data includes the subject's corresponding cfDNA methylation level detection data, DNA mutation detection data and/or serum marker detection data, and the serum The marker includes the serum marker combination according to claim 1 or 2;

A first determination module configured to determine, according to the detection data, a first cancer risk value of the subject based on a first cancer risk prediction model used to characterize the correlation between the subject's detection data and cancer occurrence;

The generating module is configured to generate the cancer risk prompt information of the subject according to the cancer risk value.
The apparatus of claim 13, wherein the first cancer risk prediction model is as follows:

Wherein, x m is the detection concentration value of the serum marker, β m is the weight parameter of each serum marker, y is the interpretation output, and m is the mth serum marker among the serum markers.
The device according to claim 14, wherein the first cancer risk prediction model is trained through the following first training step:

Obtain a first training sample set, where the first training sample includes subject information, the subject's corresponding serum marker detection data, and the subject's cancer information;

Based on the first sample training set, the maximum likelihood function is used to determine the value of parameter β, and the first cancer risk prediction model is obtained.
The device according to any one of claims 13-15, wherein the device further includes:

The second determination module is configured to determine, according to the detection data, a second cancer risk value of the subject based on a second cancer risk prediction model used to characterize the correlation between the subject's detection data and cancer occurrence.
The device according to claim 16, wherein the second cancer risk prediction model is trained through the following second training step:

Obtain a second training sample set, the second training sample includes subject information, the subject's corresponding cfDNA methylation level detection data and/or the serum marker detection data and the subject's cancer risk information;

Based on the second training sample set, the initial second cancer risk prediction model is supervised and trained to obtain the second cancer risk prediction model used to characterize the correlation between subject detection data and cancer occurrence.
The device according to any one of claims 13-17, wherein the cfDNA methylation level detection data includes methylation signal characteristic values; and

The methylation signal characteristic value is obtained in advance through the following steps:

The average Beta of the methylation level of CpG sites in each specific capture region is used as the methylation signal characteristic value,

Among them, Beta is the average methylation level of CpG sites in the specific capture region, ΣM is the number of methylation sites in all reads in the specific capture region, and ΣU is all reads in the specific capture region. Number of non-methylated sites.
The device according to any one of claims 13 to 18, wherein the cancer is selected from the group consisting of lung cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, and gastric cancer.
An electronic device including:

one or more processors;

a storage device on which one or more programs are stored,

When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method according to any one of claims 6-12.
A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method according to any one of claims 6-12 when executed by one or more processors.