KR102047186B1 - A high-throughput disease diagnostic system by fingerprinting of blood protein and metabolome based on MALDI-TOF mass spectrometry - Google Patents

Abstract

The present invention relates to an ultrafast disease diagnosis system using blood protein and metabolite fingerprinting based on MALDI-TOF mass spectrometry, and according to the present invention, blood protein and metabolite fingerprinting based on MALDI-TOF mass spectrometry. The analysis enables a significant time reduction and mass analysis of the disease diagnosis, and the diagnosis method through the blood analysis is less costly and timely, and the patient's objection to the diagnosis is eased and the disease diagnosis is easy to access. In addition, by comparing and analyzing the diagnosis result by extracting the metabolite in the blood and building the database and the diagnosis result using the protein-based database in the blood, the diagnosis with high accuracy and reliability is possible by double checking. In addition, the present invention is a database-based diagnostic method that can be applied to a variety of diseases, the scalability of the technology is good, and socially effective people can be secured by barcoded human health status by establishing a mass analysis barcode database of blood analysis It can contribute to health care. In addition, as a diagnostic method that can be performed through a simple education rather than a conventional diagnostic method requiring a skilled expert, the entry barrier of the technology is low, there is a strong impact of the technology.

Description

A high-throughput disease diagnostic system by fingerprinting of blood protein and metabolome based on MALDI-TOF mass spectrometry

The present invention relates to an ultrafast disease diagnosis system using blood protein and metabolite fingerprinting based on matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) mass spectrometry.

Human serum is one of the most important body fluids used clinically for cancer biomarker discovery and diagnosis. The sample may include serum specific proteins as well as proteins synthesized or secreted from human cancer tissues and cells. Thus, serum protein profiles can reflect an individual's health and disease status, and cancer biomarker proteins present in serum are the most common targets for diagnosis and treatment because serum samples are relatively easy. Can be used. For example, α-fetoprotein (AFP), carcinoembryonic antigen (CEA), prostate-specific antigen (PSA), immunoglobulins and chorionic gonadotropin; Several cancer biomarkers present in serum, such as hCG), have generally been used as clinical monitoring parameters. However, specific cancer biomarker based screening has the disadvantages of false positives and low specificity due to other immunological events that can raise the level of biomarker protein. Therefore, the development of innovative and reliable methods for diagnosing serum-based cancer is urgent.

Most cancer diagnostic techniques have relied on immunoassays or mass spectrometry. In conventional enzyme-linked immunosorbent assays (ELISA), specific antibodies are used to identify and quantify cancer biomarkers from a mixture of serum proteins, which have the advantage of being accurate, specific and sensitive (pg mL ⁻ Detection limit of ¹ level). In addition, ELISA has been used in many hospitals and diagnostic institutions for the target evaluation of biomarkers because it can quickly and quickly test a large number of samples in parallel. However, ELISA has some disadvantages such as quantitative measurement of multiple biomarkers, high initial cost for assay development, and the like. In particular, the number of serum protein biomarkers and their specific antibodies available for ELISA based cancer screening is still limited.

Mass spectrometry, on the other hand, has been used as a cancer diagnostic method that simultaneously performs new serum protein biomarker screening and multiple biomarker identification. For this purpose, liquid chromatography tandem-mass spectrometry (LC-MS / MS) can be applied to both untargeted and targeted serum protein biomarker screening. Recently, multiple biomarker identification and monitoring has been achieved by target proteomics (multi reaction monitoring (MRM) or parallel reaction monitoring (PRM)) using high resolution LC-MS / MS. LC-MS / MS provides quantitative reproducibility through high selectivity, high sensitivity and robust target peptide identification, but is not suitable for robust and high throughput assays for processing large numbers of patient serum samples.

Therefore, we intend to develop a simple and powerful cancer diagnosis method through full profiling of matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) based serum proteins. MALDI-TOF MS can provide fast processing time and high speed processing analysis platform. The present invention is to provide a new technology for diagnosing cancer without pretreatment of serum samples or MALDI plates using a serum protein fingerprinting method based on MALDI-MS.

Republic of Korea Patent No. 10-1214317 (Registered Dec. 13, 2012)

An object of the present invention is to provide an ultrafast disease diagnosis system using blood protein and metabolite fingerprinting based on MALDI-TOF mass spectrometry.

In order to achieve the above object, the present invention performs a mass-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) mass spectrometry with serum samples isolated from patient and control groups to obtain serum protein spectral data. Data acquisition means; MSP library conversion means for converting the spectral data into a main spectrum profile (MSP) library; A real-time matching score converting means for comparing the MSP library of the serum sample separated from the suspected patient with the MSP library of the patient group or the control group and converting the MSP library into a matching score; And diagnosing means for diagnosing a disease of an object suspected of a patient using the matching score.

According to the present invention, blood protein and metabolite fingerprinting analysis based on MALDI-TOF mass spectrometry enables a significant time reduction and mass analysis of disease diagnosis, and a low cost and time burden as a diagnostic method through blood analysis. It is easy to access disease diagnosis by relieving the patient's rejection of diagnosis. In addition, by comparing and analyzing the diagnosis result by extracting the metabolite in the blood and building the database and the diagnosis result using the protein-based database in the blood, the diagnosis with high accuracy and reliability is possible by double checking. In addition, the present invention is a database-based diagnostic method that can be applied to a variety of diseases, the scalability of the technology is good, and socially effective people can be secured by barcoded human health status by building a mass analysis barcode database of blood analysis It can contribute to health care. In addition, as a diagnostic method that can be performed through a simple education rather than a conventional diagnostic method requiring a skilled expert, the entry barrier of the technology is low, there is a strong impact of the technology.

1 is a diagram illustrating a conventional method for diagnosing a disease.
Figure 2 shows an ultrafast disease diagnosis system using blood protein and metabolite fingerprinting based on MALDI-TOF mass spectrometry.
Figure 3 is performed by MALDI-TOF mass spectrometry with the serum of the experimental group (liver cancer patients) and control group (normal person) to collect the protein profile, and then converted to the main spectrum profile (main spectrum profile (MSP)) to the horizontal baseline and each It is expressed as the relative strength of the group.
4 is a serum sample was applied to the experimental group MSP and control MSP to confirm whether the disease can be diagnosed by an automated matching system.
5 is a serum sample applied to the experimental group MSP and control MSP to confirm whether the disease can be diagnosed by the partial least squares discrimination analysis.

Currently, there are various diagnostic methods such as a blood test, an ultrasound test, a biopsy, etc., but a conventional method of diagnosing a disease is divided into three types, a blood test, an imaging test, and a biopsy. Firstly, as a blood test for liver testing, a diagnostic method using a relative quantitative difference of α-fetoprotein (AFP) in blood of normal and patient was widely used. However, the blood test is inaccurate, and in addition to disease, pregnancy, inflammation and other cases, there is a problem that there is a probability of misdiagnose accuracy is lowered. Second, imaging methods include liver ultrasonography, computerized tomography (CT), magnetic resonance imaging (MRI), and hepatic angiography, which are simple, economical, and convenient. Highly accurate and widely used, imaging methods such as CT and MRI have high cost of diagnosis, long diagnostic time, complicated diagnosis process, and require the supply of high-quality personnel such as skilled professionals. Finally, if the results are not obtained by the above two methods, there was a method of diagnosing by histological analysis by performing an acupuncture biopsy to collect a tissue by inserting a thin needle under ultrasound or computed tomography. However, as described above, the conventional method for diagnosing a disease has a complicated procedure and is not suitable for analyzing a large amount of samples in a short time, so the development of a new disease diagnosis system is urgently needed.

Thus, the inventors of the present invention, as shown in Figure 2, to provide a high-speed disease diagnosis system using blood proteins and fingerprinting based on MALDI-TOF mass spectrometry.

The inventors of the present invention diluted the serum of the experimental group (liver cancer patients) and the control group (normal person) with distilled water, and then collected MALDI-MS protein profile through Flexcontrol, without further purification or separation process, each using a MALDI Biotyper Converted to main spectrum profile (MSP). By applying the MSP of the experimental group and the control group as a platform, it is possible to diagnose a disease with high accuracy and reliability from a small amount of blood samples, and to diagnose and classify the disease by a high-speed processing through an automated matching system.

Accordingly, the present invention provides a data acquisition means for acquiring serum protein spectral data by performing a matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) mass spectrometry with serum samples isolated from patient and control groups, respectively; MSP library conversion means for converting the spectral data into a main spectrum profile (MSP) library; A real-time matching score converting means for comparing the MSP library of the serum sample separated from the suspected patient with the MSP library of the patient group or the control group and converting the MSP library into a matching score; And diagnosing means for diagnosing a disease of an object suspected of a patient using the matching score.

The serum sample may be diluted to 2 to 8% by volume with distilled water, but is not limited thereto.

The serum protein spectral data can be obtained by loading and drying the serum samples of the patient group and the control group on the MALDI plate, loading the MALDI matrix solution, and then performing MALDI-TOF mass spectrometry.

The MALDI matrix solution may include acetonitrile, trifluoroacetic acid (TFA), and α-cyano-4-hydroxycinnamic acid (HCCA). .

As shown in FIG. 3, the MSP library converting means for converting mass spectra obtained from MALDI flexcontrol software may be Biotyper 3 software, but is not limited thereto.

In addition, the real-time matching score conversion means for confirming homology between the main spectrum profile (MSP) and the clinical sample of the experimental group and the control group converted through the Biotyper 3 software may be Biotyper RTC software, but Specifies that it is not limited.

The matching score is displayed in the range of 0 to 3, and the diagnostic means may determine that the object suspected to be a patient has high homology with a group having a matching score close to 3 of the matching scores of each of the patient group or the control group. .

The patient may be a cancer patient, the cancer may be liver cancer, lung cancer, skin cancer, non-small cell lung cancer, colon cancer, bone cancer, pancreatic cancer, head or neck cancer, uterine cancer, ovarian cancer, rectal cancer, gastric cancer, anal muscle cancer, colon cancer, breast cancer , Fallopian tube carcinoma, endometrial carcinoma, cervical carcinoma, vaginal carcinoma, vulvar carcinoma, Hogkin's disease, esophageal cancer, small intestine cancer, endocrine adenocarcinoma, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penis cancer, Prostate cancer, chronic or acute leukemia, lymphocytic lymphoma, bladder cancer, kidney or ureter cancer, renal cell carcinoma, renal pelvic carcinoma, central nervous system (CNS) tumor, primary central nervous system lymphoma, spinal cord tumor, brain stem glioma and It may be selected from the group consisting of pituitary adenoma, but is not limited thereto.

Hereinafter, the present invention will be described in more detail with reference to Examples. These examples are only for illustrating the present invention in more detail, it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples in accordance with the gist of the present invention. .

Example 1: Chemicals and Reagents

Acetonitrile was purchased from Junsei Chemical Co. (Tokyo, Japan), and distilled water and trifluoroacetic acid (TFA) were purchased from Sigma-Aldrich (St. Louis, MO, USA). α-cyano-4-hydroxycinnamic acid (HCCA), peptide standard and bacterial tests were purchased from Bruker (Bremen, Germany).

Example 2: Serum Collection of Experimental Group (Liver Cancer Patients) and Control Group (Normal Persons)

The extracted blood was left in the tube for at least 20 minutes for reaction with the coagulant. All serum samples were collected according to the standardized protocol of Kyungpook National University Hospital after approval by the Institutional Review Board (IRB). Blood was collected into a 10 cc tube (serum separator vacutainer tube, BD Vacutainer SST II Advance REF 367953), and serum was collected by centrifugation at 2,500 rpm for 5 minutes. Serum samples were dispensed by 300 μl into cryovials and stored at −80 ° C. or in a liquid nitrogen tank until the experiment.

Serum samples were obtained from 54 liver cancer patients, and the mean age of liver cancer patients was 57.2 years (32.6-90.3 years), and the experimental group (liver cancer patients) included liver cancer of common subtypes of all stages of pathologically proven. The control group (normal) was 99 healthy subjects without cancer disease and the mean age was 50.0 years (25.9 ~ 76.7 years).

Example 3: MALDI-TOF Mass Spectrometry and MALDI Biotyper

Serum from the experimental group (n = 40) and the control group (n = 80) was diluted to 4% (v / v) using distilled water. 1 μl of diluted serum was immediately loaded onto MSP 96 target polished steel MALDI plates and dried at room temperature. Thereafter, 1 μl of a solution in which 2 mg of α-cyano-4-hydroxycinnamic acid matrix was dissolved in 250 μl of 47.5 vol% distilled water-50 vol% acetonitrile-2.5 vol% trifluoroacetic acid was titrated.

After drying, a mixture of peptide standards and bacterial test standards (Bruker Daltonics, Bremen, Germany) was used to calibrate the Bruker Daltonics Microflex LRF MALDI-TOF MS (Bruker, Bremen, Germany) as follows: : Cation and linear mode, detector value = 7.0, laser frequency = 60.0 Hz, laser power = 44%. A mixture of standards was applied to cover the low mass range (m / z 1,000-2,000). Subsequently, each spectrum of the experimental and control groups was obtained by collecting spots per 240 shots using AutoeXecute: mass range (m / z 1,000 to 20,000), peak resolution> 400 ppm , Laser power = 52-56%. For identically detected peaks, including different peaks in each serum, the acquired spectra were visually evaluated for suitability for MSP by threshold according to the protocol provided by the manufacturer. In other words, Flexanalysis 3.3 software (Bruker, Bremen, Germany) was used for MSP for spectra with peaks with errors of the two most different spectra less than 500 ppm, and samples were reanalyzed if they exceeded 500 ppm.

Then, using the Biotyper 3 software (Bruker, Bremen, Germany) as shown in FIG. 3, the spectrum obtained from the serum of the experimental group and the control group was converted to each line spectrum (mono spectrum) form of a single MSP. In other words, each MSP contains precipitates of all serum from each experimental and control group. In addition, the software and matching score were used to confirm MSP heterogeneity between the experimental and control groups.

Finally, integrated MSPs were validated by matching between the integrated MSPs and the blinded test set of the experimental (n = 13) and control (n = 18) using Biotyper RTC software (Bruker, Bremen, Germany). As described above, the test set samples were automatically matched to the integrated MSP converted using the Biotyper 3 software method using the Biotyper RTC software. In this case, the coincidence of the spectral peaks and intensities of the integrated MSP in the form of a line spectrum and the blind test set is expressed as a matching score ranging from 0 to 3, and the closer the matching score is to 3, the closer the integrated MSP and the new spectrum are. This means that the probability of homology is high (see FIG. 4).

Example 4: multivariate analysis

The acquired data set was processed with SIMCA-P + 12.0 software (Umetrics, Sweden) for multivariate statistical analysis. The data was adjusted on a Pareto-scale to reduce the effects of baseline deviation and noise. Principal component analysis (PCA) was performed to identify outliers, and partial least squares analysis (PLS-DA) was performed to distinguish between control and experimental sample sets. Significant peaks associated with group separation were explained by variable importance in projection (VIP> 2) and fold change values greater than 2 or less than 0.5 between groups.

Experimental Example 1: MALDI mass spectrometry pattern recognition system for liver cancer diagnosis

To verify the cancer diagnostic platform, a three step process of assay validation, clinical validation and clinical application was performed. First, analytical verification was performed to confirm whether the sample preparation process was appropriate. In addition, it was tested whether the mass spectral pattern is different between the experimental group and the control group. The collected serum was diluted with distilled water and then used for MALDI-MS profiling. The procedure was completed within 10 seconds for each serum sample without time consuming additional purification steps. Compared to previous studies based on serum protein profiles using surface-enhanced laser desorption / ionization (SELDI), there has been a significant time shortening. In particular, SELDI has the disadvantage of requiring a process of immobilizing the antibody as a plate preparation. After sample preparation, mass spectra collection for the serum of the experimental and control groups was performed automatically. As a result, the spectral quality was confirmed to be more than 2,000 horizontal baselines and intensities per 40 shots, confirming the suitability of the sample preparation process. It was also confirmed that the experimental and control groups had different mass patterns.

In the next step, clinical validation, serum protein profiles were converted to integrated MSP to detect control (n = 80) and experimental (n = 40), respectively. The heterogeneity of the integrated MSPs was then confirmed by comparison scores using Biotyper 3 software and confirmed the possibility of liver cancer classification by Biotyper RTC. In the last step, a biotyper RTC was used to perform blinding with clinical application to confirm that the serum-based sorting platform for liver cancer provided a definite result.

Experimental Example 2: Analysis and Clinical Validation

One of the key requirements for cancer screening platforms is high-speed processing, and the process and types of samples (such as serum or tissue) also make up a significant portion of the platform's availability. Various mass spectrometry methods have been used to diagnose cancer based on protein profiles of samples, but there are some disadvantages. According to a recent study (Geoffrey Barioude et al.), Tissue-based MALDI-TOF mass spectrometry has been reported to be satisfactory for lung cancer diagnosis without any surface treatment on the chip. However, this method has the disadvantage that the preparation process is simple but the tissue as analytical specimen is not suitable for high-speed screening.

Therefore, in the present invention, the serum was used as a sample for high speed treatment, and prepared by diluting with water. The prepared samples (liver cancer patients (n = 40), controls (n = 80)) were analyzed by MALDI-TOF mass spectrometry and the overall profile of serum proteins was automatically collected by AutoeXecute: resolution> 300 ppm, range: 1,000 to 8,000. The quality of the acquired spectrum was confirmed with more than 3,000 horizontal baselines and intensities per 40 shots, and the spectrum is shown in FIG. 3. As a result, before conversion to MSP, it was confirmed that the spectra of the experimental group and the control group had different mass profiles. In particular, the peaks of the mass spectra obtained in the experimental and control sera differed from each other in the low mass range (<10 kDa). In addition, it was confirmed that the relative intensity of each mass peak in the experimental group serum was higher than that of the whole control serum.

Even within the experimental group, the mass spectra of each sample were slightly different, but the main peaks detected were not different, and the peaks with low detection frequency in most mass spectra were not converted to MSP through the minimum required peak frequency parameter. Therefore, the serum spectra of each experimental and control group were converted to integrated MSP by Biotyper 3 with the minimum required peak frequency parameter set to 25% in the MSP production method. This can eliminate unneeded peaks in the classification through integrated MSP deployment. The MSPs integrated through the parameter setting contain the main peaks that are frequently detected, and then the integrated MSPs were applied to the clinic.

Experimental Example 3: Clinical application

MALDI-MS analysis confirmed whether the test sample (liver cancer serum) can be correctly classified into experimental or control group. Biotyper-RTC assays, which are commonly used to classify bacteria through automated MALDI analysis, are scored. In this experiment, a comparison score was applied to compare the spectrum of the integrated MSP with the sample. Scores range from 0 to 3, with scores closer to 3 indicating a higher probability of homology between the integrated MSP and the analyzed spectrum from the database. Validation tests of integrated MSPs were performed to confirm whether the classification method for diagnosing liver cancer can provide reliable results.

A control group (n = 18) was used to verify the integrated MSP for diagnosing liver cancer. The control test set matched the control serum integration MSP with a high mean matching score of 2.558 and a standard deviation of 0.130 (see Table 1 and Table 2 below). However, this was consistent with hepatocellular carcinoma serum integration MSP with an average score of 1.632 (Table 1). As a result, the specificity of the integrated MSP was confirmed to be 100%, and automatic classification was performed using the liver cancer serum (n = 13) test to confirm the sensitivity. The liver cancer test set was consistent with liver cancer serum integration MSP with a mean matching score of 2.271 and a standard deviation of 0.123 (see Table 1 and Table 3 below). The difference between the classification scores of the control and experimental groups was 0.614 to 1.206. However, this was consistent with the serum integration MSP of the control group with an average matching score of 1.381. From the above results, the sensitivity of the integrated MSP to liver cancer was confirmed. In FIG. 4, only the results of liver cancer sample 1 (Liver Cacner 1) and control serum 8 (Healthy Control 8) are representatively shown.

On the other hand, the mean difference between the matched scores of the experimental group and the control group MSP was 0.891 in the experimental group test set and 0.926 in the control group test set. The difference in scores was considered sufficient to classify liver cancer according to Bruker's guideline, and provided a degree of identification based on a 0.3 score difference for microbial identification: scores 2.3 to 3.0: very likely species identification, 2.0 to 2.299 : Certain genus identification or species identification, 1.7 to 1.99: Genus identification, 0.0 to 1.69: Unreliable identification. In addition, the reproducibility of MALDI based classification must be performed because the mass spectral pattern through MALDI analysis, i.e. intensity and m / z, depends on the raster spot. Therefore, the classification was repeated three times, and it was confirmed that the reproducibility was 100%. The analysis method was able to provide a consistent result for diagnosing liver cancer, and it was confirmed that the method was optimized in the RTC sample preparation, MSP conversion, and classification method. Obvious differences in peak patterns in the serum mass spectra of the experimental and control groups appear to make stable reproducibility, so PLS-DA was performed to confirm how different each MSP was.

Experimental Example 4: partial least squares discriminant analysis (PLS-DA)

Partial least squares discriminant analysis was used to determine whether there was a statistically significant difference in MALDI-MS profiles between experimental and control serum samples. Referring to FIG. 5A, except for the six healthy control serums, the experimental group and the control group were classified as the first component to obtain the most spectral information of each serum sample in the score chart, and the experimental group was found to be scattered more than the control group. All experimental samples showed negative scores (approximately -20 to -48), but control samples showed almost positive scores (approximately 2 to 10), indicating that the experimental and control groups clearly differentiated from each other.

5B shows the PLS-DA plot of the test set and the integrated MSP. The results show a pattern similar to that of FIG. 5A: Control-MSP and Control-T (Control Test Set) were dense, whereas Experiment-MSP and Experiment-T (Experiment Test Set) were widely scattered. The blinding test confirmed that each database was in close proximity, and statistical results also completely separated the experimental integrated MSP and the control integrated MSP, and supported the results of MALDI-MS profile-based liver cancer classification.

In the present invention, by using a modified Biotyper method to build a reliable integrated MSP, a new method for diagnosing liver cancer based on serum was developed. To validate the integrated MSP, blinded tests were completed within 30 minutes using serum samples diluted with distilled water, and the results were scored and confirmed that the integrated MSP provided consistent results. In addition, the PLS-DA analysis confirmed that the result is due to the difference in the peak pattern. The construction of an integrated MSP that reflects the peak pattern differences in serum protein mass spectra and can be applied to liver cancer diagnosis enables the diagnosis of liver cancer using serum. This diagnostic method is easier to sample than conventional diagnostic methods, is made of a simple preparation process, and is suitable for diagnosing liver cancer in that it establishes a reliable MSP.

As described above in detail certain parts of the present invention, it is apparent to those skilled in the art that these specific descriptions are merely preferred embodiments, and the scope of the present invention is not limited thereto. Thus, the substantial scope of the present invention will be defined by the appended claims and equivalents thereof.

The scope of the present invention is represented by the following claims, and it should be construed that all changes or modifications derived from the meaning and scope of the claims and their equivalents are included in the scope of the present invention.

Claims (7)

Without pretreatment of serum samples or matrixassisted laser desorption / ionization (MALDI) plates, serum isolated from patient and control groups was diluted to 2 to 8% by volume with distilled water, and then MALDI-TOF (matrix-assisted), respectively. data acquisition means for performing serum desorption / ionization time-of-flight mass spectrometry) to obtain serum protein spectral data;
MSP library conversion means for converting the spectral data into a main spectrum profile (MSP) library;
Real-time matching score converting means for comparing the MSP library of the serum sample separated from the suspected patient to the MSP library of the patient group or the control group and converting the MSP library into a matching score; And
Diagnostic means for diagnosing a disease of an object suspected of a patient using the matching score;
Disease diagnosis system comprising a. delete The method of claim 1, wherein the serum protein spectral data is obtained by loading and drying the serum samples of the patient group and the control group on the MALDI plate, and after loading the MALDI matrix solution, MALDI-TOF mass spectrometry, respectively. Disease diagnosis system. According to claim 3, wherein the MALDI matrix solution is acetonitrile (acetonitrile), trifluoroacetic acid (TFA) and α-cyano-4-hydroxycinnamic acid (α-cyano-4-hydroxycinnamic acid; HCCA A disease diagnosis system comprising a). The method of claim 1, wherein the matching score is displayed in the range of 0 to 3,
The diagnosis means is a disease diagnosis system, characterized in that the object suspected of a patient having a high homology with a group having a matching score close to three of each of the patient group or control group matching score. The disease diagnosis system of claim 1, wherein the patient is a cancer patient. According to claim 6, wherein the cancer is liver cancer, lung cancer, skin cancer, non-small cell lung cancer, colon cancer, bone cancer, pancreatic cancer, head or neck cancer, uterine cancer, ovarian cancer, rectal cancer, gastric cancer, anal muscle cancer, colon cancer, breast cancer, fallopian tube carcinoma , Endometrial carcinoma, cervical carcinoma, vaginal carcinoma, vulvar carcinoma, Hogkin's disease, esophageal cancer, small intestine cancer, endocrine gland cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penis cancer, prostate cancer, Chronic or acute leukemia, lymphocytic lymphoma, bladder cancer, kidney or ureter cancer, renal cell carcinoma, renal pelvic carcinoma, central nervous system (CNS) tumor, primary central nervous system lymphoma, spinal cord tumor, brain stem glioma and pituitary adenoma Disease diagnosis system, characterized in that selected from the group consisting of.

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