TWI700493B - Specific biomarker set for non-invasive diagnosis of liver cancer - Google Patents

Abstract

Cells within liver tumour mass comprise a unique set of proteins/tumour antigens when compared to the normal liver tissues epithelial cells juxtaposed to the tumour. The presence of tumour antigens couples the production of auto-antibodies against these tumour antigens. The present invention relates to the identification and elucidation of a protein set that can act as a novel marker set for liver cancer diagnosis and prognosis. Specifically, it relates to a kit that enables diagnostic and prognostic measurement of auto-antibodies in serum of liver cancer patients. The present invention provides a non-invasive, specific, sensitive, and cost effective detection and quantification method by evaluating a set of validated liver cancer proteins/tumour antigens, which includes Bmi-1, VCC1, SUMO-4, RhoA, TXN, ET-1, UBE2C, HDGF2, FGF21, LECT2, SOD1, STMN4, Midkine, IL-17A or IL26, to complement the conventional diagnostic methods.

Description

Specific biomarker panel for non-invasive diagnosis of liver cancer [Copyright Statement/License]

A part of this patent file reveals that the content contains copyrighted material. The copyright owner does not object to anyone's fax copying of the patent documents or patent disclosures in Patent and Trademark Office (Patent and Trademark Office) patent files or records, but all copyrights are reserved under any circumstances. The following introduction applies to the process, experiment and data described in the following and in the drawings attached to this article: Copyright © 2014, Vision Global Holdings Limited, all rights reserved.

[Cross reference of related applications]

This application claims priority to the U.S. Non-Provisional Patent Application No. 14/321,867 filed on July 2, 2014 and the U.S. Non-Provisional Patent Application No. 14/321,870 filed on July 2, 2014, and its disclosure The content is incorporated into this article by reference in its entirety.

The present invention describes a method for quantitatively detecting a series of specific and novel hepatocellular carcinoma (HCC) tumor biomarkers by measuring corresponding autoantibodies in the serum of patients with liver cancer. This group of biomarkers includes Bmi1, VCC1, SUMO-4, RhoA, TXN, ET-1, UBE2C, HDGF2, FGF21, LECT2, SOD1, STMN4, Midkine, IL-17A and IL26. In particular, the present invention further describes the design of a high-throughput, high-sensitivity testing equipment that can be used to collect peripheral serum samples from patients, Measure autoantibodies against at least one biomarker selected from the biomarker group to detect liver cancer in a non-invasive manner at an early stage. The present invention can further detect characteristic biomarkers for cancer identification and staging, and to detect recurrence in the monitoring stage after chemotherapy treatment. The present invention will support automatic data analysis.

Hepatocellular carcinoma (HCC) is the second most prevalent cancer in China, covering 5.7% of the total population [1]. Most HCC patients have rapid tumor progression, which leads to high mortality. To improve the overall survival rate, early diagnosis of the disease becomes necessary. Currently, the most common way to detect HCC is a blood test, which measures the content of HCC tumor markers such as alpha fetoprotein (AFP). AFP is a plasma protein produced by the yolk sac and liver during fetal development and is a form of serum albumin. Under normal conditions, the AFP content gradually decreases after birth and remains low in adults. Increased levels of tumor markers indicate the possibility of liver cancer. However, the main problem with the AFP test is too many false positives. Because HCC is not the only cause of increased AFP levels, alcoholic hepatitis, chronic hepatitis or cirrhosis are also related to increased AFP.

Although the AFP test is usually recommended for diagnosing liver cancer, its results are not conclusive. Suspected patients will need ultrasound imaging, CT scan or contrast MRI scan for further confirmation. A liver biopsy is performed to distinguish the tumor as benign or malignant. However, traditional detection of HCC has several limitations: (a) About 20% of liver cancers do not produce high levels of commonly used HCC tumor markers [2]. (b) Viral cirrhosis produces false positive results in blood tests [3]. (c) Ultrasound cannot detect small tumors [4]. (d) CT scan requires high radiation dose and is not sensitive to tumors smaller than 1 cm [5]. (e) MRI scans are expensive and the process is time-consuming. Due to these limitations, we need to develop novel biomarker screening methods with high sensitivity and specificity to diagnose HCC and/ Or determine the prognosis of HCC to supplement traditional methods.

The cells in the hepatic tumor mass contain a unique protein/tumor antigen group compared with the juxtaposed normal liver tissue epithelial cells. Evaluation of verified HCC tumor biomarkers can greatly facilitate the diagnosis of HCC. However, not all biomarkers themselves can be found in serum or urine for convenient diagnosis. As an alternative, autoantibodies against specific biomarkers provide an opportunity to evaluate such biomarkers. The fact that the existence of tumor biomarkers is associated with the production of autoantibodies against this tumor antigen has been demonstrated in many cancers [6-8]. The detection of autoantibodies in the patient's serum can more effectively detect the presence of biomarkers. Under ideal conditions, the examination of autoantibodies from peripheral blood will provide evidence for the early detection of liver cancer in a non-invasive manner. A common obstacle that hinders the clinical use of biomarkers is the failure of biomarkers to be verified after discovery. However, once verified, such testing will be a cost-effective and accurate method. Our prototype design also supports high throughput screening. This can reduce the cost of traditional liver cancer diagnosis.

The following is a list of references occasionally cited in this manual. The disclosures of the references are incorporated into this article by reference in their entirety.

[1] Chen JG, Zhang SW. Liver cancer epidemic in China: past, present and future. Semin Cancer Biol. 2011; 21(1):59-69

[2] Okuda K, Peters RL. Human alpha-1 fetoprotein. Hepatocellular Carcinoma. 1976:353-67

[3] Lok AS, Lai CL. Alpha-fetoprotein monitoring in Chinese patients with chronic hepatitis E virus infection: role in the early detection of hepatocellular carcinoma. Hepatolog.y 1989;9:110-115

[4] Colombo M, de Franchis R, Del Ninno E, Sangiovanni A, De Fazio C, Tommasini M, Donato MF, Piva A, Di Carlo V, Dioguardi N. Hepatocellular carcinoma in Italian patients with cirrhosis. N Engl J Med. 1991;325:675-80

[5] Sahani DV, Kalva SP. Imaging the Liver. The Oncologist. 2004; 9 (4): 385-397

[6] Masutomi K, Kaneko S, Yasukawa M, Arai K, Murakami S, Kobayashi K. Identification of serum anti-human telomerase reverse transcriptase (hTERT) auto-antibodies during progression to hepatocellular carcinoma. Oncogene. 2002 Aug 29;21( 38):5946-50.

[7] Karanikas V, Khalil S, Kerenidi T, Gourgoulianis KI, Germenis AE. Anti-survivin antibody responses in lung cancer. Cancer Lett. 2009 Sep 18;282(2):159-66.

[8] Wang YQ, Zhang HH, Liu CL, Xia Q, Wu H, Yu XH, Kong W. Correlation between auto-antibodies to survivin and MUC1 variable number tandem repeats in colorectal cancer. Asian Pac J Cancer Prey. 2012;13 (11):5557-62.

The present invention provides a method for detecting and quantifying cancer diagnosis and staging by measuring autoantibodies against a series of specific tumor biomarkers. Compared with normal liver epithelial cells, HCC tumor cells tend to produce unique protein sets. The evaluation of this unique proteome (biomarker) will complement traditional diagnostic methods and promote early detection of cancer.

Through a two-dimensional/mass spectrometry-based method, the present invention will identify a series of liver cancer biomarkers from patient paired biopsy (tumor biopsy and juxtaposed normal tissue), including Bmi1, VCC1, SUMO-4, RhoA, TXN, ET -1, UBE2C, HDGF2, FGF21, LECT2, SOD1, STMN4, midkine, IL-17A and IL26.

Subsequently, the specificity and accuracy of this set of liver cancer biomarkers will be verified for the diagnosis of liver cancer. In the present invention, the proteins of the listed biomarkers will be expressed by cDNA clones, purified, and coupled with fluorescent microsphere beads with different emission wavelengths. The autoantibodies against these proteins present in the patient's serum will immunologically bind to the protein-bead conjugate. These autoantibodies then interact with the secondary antibody conjugated to PE. The specific fluorescent signal of the microsphere beads will be used as the identifier of the conjugated biomarker. By measuring the fluorescence intensity of the secondary antibody conjugated to PE in the complex, the autoantibody can be detected and quantified. Since autoantibodies are produced in the patient's serum in proportion to the number of biomarkers in HCC tumor cells, the higher the fluorescence intensity caused by the higher the concentration of autoantibodies, the higher the performance of the corresponding biomarkers. The minimum detection limit of each biomarker for all serum autoantibodies is about 0.15 ng/mL.

Compared with serum from healthy individuals, the content of autoantibodies against target biomarkers is higher in cancer patients. In addition, comparing different sera from patients with different stages of liver cancer can generate characteristic patterns for staging. Therefore, the present invention can non-invasively evaluate target liver cancer biomarkers. This enables early detection of HCC, identification of biomarker signature patterns for staging, and detection of recurrence during the monitoring phase after chemotherapy treatment.

【definition】

The term "biomarker" refers to a protein that is uniquely expressed or up-regulated in tumors compared to normal epithelial cells.

The term "biomarker set" refers to a specific combination of biomarkers identified from a paired patient biopsy (tumor biopsy and juxtaposed normal tissue) and is the target of measurement in the present invention.

The term "auto-antibodies" refers to antibodies that are concomitantly produced by the patient's body and the performance of tumor biomarkers and are present in the circulation and can be collected in the surrounding serum.

Bmi1 (Polycomb Ring Finger) is a protein component of a polycomb group (PcG) polyprotein PRC1-like complex. It is responsible for maintaining the transcriptional repression of multiple genes (including Hox genes) during production. It is regulated by monoubiquitinated histone H2A "Lys-119", which modifies histones and reshapes chromatin for expression.

VCC1 or CXCL17 (chemokine (C-X-C motif) ligand 17) plays a major role in angiogenesis and possibly tumor production. It is also proposed to be a housekeeping chemokine that regulates the recruitment of non-activated blood monocytes and immature dendritic cells in tissues. It can also play a role in the innate defense against infection. The abnormality of VCC1 is related to duodenitis and cholera.

SUMO-4 (small ubiquitin-like modifier 4) belongs to the family of small ubiquitin-related modifiers and is located in the cytoplasm. It is covalently linked to the target protein IKBA to control the secondary cell location, Stability or activity. This ultimately leads to the negative regulation of NF-κ-B-dependent transcription of IL12B gene.

RhoA (Ras homologous family member A) regulates the signal transmission pathway that connects plasma membrane receptors with local focal adhesions and actin stress fibers. It is also involved in the microtubule-dependent signal transmission necessary during cell cycle cytokinesis and other signal transmission pathways involved in the stabilization of microtubules and cell migration and adhesion.

TXN (thioredoxin) forms a homodimer and participates in the redox reaction through the reversible oxidation of its active center dithiol to disulfide and catalyzes the dithiol-disulfide exchange reaction. It has been reported to be associated with mucinous carcinoma of the breast.

ET-1 (Endothelin 1) is an effective vasoconstrictor produced by vascular endothelial cells. It binds to endothelin receptors that are widely expressed in all tissues, including non-vascular structures such as epithelial cells, glial cells, and neurons. In addition to its main role in maintaining vascular tension, it is also proposed to have co-mitogenic activity and strengthen the role of other growth factors.

UBE2C (enzyme conjugated to ubiquitin E2C) belongs to the family of enzymes conjugated to E2 ubiquitin. It is one of the three enzymes involved in ubiquitination, which is an important cellular mechanism used to target abnormal proteins for degradation. More specifically, UBE2C is required for the targeted degradation of mitotic cyclins and cell cycle progression. Therefore, it is believed that this protein may also be involved in cancer progression.

HDGF2 is called growth factor 2 derived from liver tumors. It has been reported that this protein, which is highly expressed in a variety of tumors, plays a key role in the generation and progression of several tumors. Although the mechanism remains to be identified, it should be proposed that HDGF2 has mitogenic, angiogenic, neurotrophic and anti-apoptotic activities.

FGF21 (fibroblast growth factor 21) is a family member of the FGF family, It participates in different biological processes, including embryonic development, cell growth, morphogenesis, tissue repair, tumor growth and invasion. More specifically, FGF21 stimulates glucose turnover in differentiated adipocytes by inducing the glucose transporters SLC2A1/GLUT1. FGF21 has been found to be associated with fatty liver disease.

LECT2 (leukocyte-derived chemokine 1) is a secreted protein that acts as a chemotactic factor for neutrophils and stimulates the growth of chondrocytes and osteoblasts. This protein is associated with acute liver failure.

SOD1 (Superoxide Dismutase 1) is an antioxidant enzyme containing Cu/Zn, which is responsible for decomposing free superoxide radicals into molecular oxygen and hydrogen peroxide in the intermembrane space of cytosol, nucleus and mitochondria. It is important to maintain a low level of superoxide in the cytosol to protect cells from oxidative stress and subsequent cell death.

STMN4 (microtubule dissociation protein-like 4) is a small regulatory protein, which is believed to play a role in the integration of different intracellular signal transmission pathways of split transmission, thereby controlling cell proliferation, differentiation and function. It was also shown that this protein helps control microtubule dynamics by inhibiting microtubule polymerization and/or promoting its depolymerization.

Midkine or NEGF2 (neurite growth promoting factor 2) is a secreted growth factor that binds to heparin and reacts to retinoic acid. Midkine promotes cell growth, migration and angiogenesis, especially during tumor formation. It has been shown to be associated with breast cancer and soft tissue sarcoma.

IL-17A (Interleukin 17A) is a pro-inflammatory cytokine produced by activated T cells. It regulates the activity of NF-κB and mitogen-activated protein kinase, stimulates the performance of IL6 and cyclooxygenase-2, and promotes the production of nitric oxide. Several chronic inflammations and sclerosis are usually associated with elevated IL-17A.

IL-26 (Interleukin 26) belongs to the IL-10 cytokine family, which is produced by activated T cells and targets epithelial cells for signal transduction. It strongly binds to the glycosaminoglycans on the cell surface (such as heparin, acetylheparin sulfate and dermatan sulfate). These glycosaminoglycans act like co-receptors to act on the surface of production cells and target cells Enrich IL-26.

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Hereinafter, specific examples of the present invention will be described in more detail with reference to the following drawings, in which: Figure 1 shows the difference in protein expression patterns between tumor biopsy obtained by two-dimensional/mass spectrometry and juxtaposed normal tissues so that liver cancer can be identified 15 specific biomarkers up-regulated in the middle; the arrow indicates the position of the identified point on the 2-D gel of the mass spectrum.

Figure 2 shows 15 validated liver cancer biomarker panels and their corresponding molecular weights specified and measured in the present invention.

Figure 3 shows the workflow of expressing biomarkers from cDNA clones.

Figure 4 shows the workflow of biomarkers purified from E. coli expression.

Figure 5 shows the workflow of measuring autoantibodies by the BioPlex system.

Figure 6 shows the conjugation of biomarker proteins to BioPlex beads.

Figure 7 shows an illustration of a complex of a biomarker-BioPlex bead conjugate, a primary antibody immunoreactive with it, and a secondary antibody conjugated to PE.

Figure 8 shows gel electrophoresis of DNA inserts released from plastids cut by restriction enzymes HindIII and BamH1.

Figure 9 shows the SDS-PAGE stained with Coomassie Blue to test the biomarkers induced by IPTG (a) Bmi1, (b) SOD1, (c) IL-17A, (d) TXN and (e) midkine .

Figure 10 shows the elution curves of (a) Bmi1, (b) SOD-1 and (e) IL-17A in AKTA.

Figure 11 shows a Coomassie blue-stained SDS-PAGE to verify the purification of His-tagged (a) Bmi1, (b) SOD-1 and (d) IL-17A biomarkers; fraction A is without IPTG Induced bacteria; fraction B is the bacteria induced by IPTG; fraction C is the bacterial lysate.

Figure 12 shows a standard curve showing fluorescence intensity versus concentration of anti-Bmi1 antibody.

Figure 13 is a schematic diagram showing the design of the test: the patient serum containing autoantibodies is mixed into wells containing 15 types of beads corresponding to 15 biomarkers of the biomarker group, and then the secondary conjugated PE is added antibody.

In the following embodiments, specific examples of biomarkers, detection/verification/identification/quantification methods are described as preferred embodiments. It is obvious to those skilled in the art that modifications, including additions and/or substitutions, can be made without departing from the scope and spirit of the present invention. Specific details may be omitted so as not to make the present invention difficult to understand; however, the present invention has been written so that those skilled in the art can practice the teachings in this text without extensive experimentation.

In the present invention, firstly, two-dimensional/mass spectrometry is used to detect and quantify liver cancer by analyzing the difference in protein expression patterns (Figure 1) between paired patient biopsies (tumor biopsy and juxtaposed normal tissue). Tumor Biomarker Panel. These biomarkers were verified by immunohistochemical staining on HCC blocks of paraffin sections and Western blotting in the serum of HCC patients. This resulted in the final list of 15 biomarkers evaluated in the present invention for the purpose of liver cancer diagnosis (Figure 2).

Based on the amino acid sequence of the target biomarker, commercially synthesized cDNA clones were used to perform biomarker panel expression (Figure 3). The protein expressed from the cDNA clone was then subjected to a series of purification steps (Figure 4). The purified biomarkers are then conjugated to BioPlex beads via stable amide bonds (Figure 5, Figure 6). These beads are a type of fluorescent microsphere beads and can be used in groups, which individually produce unique fluorescent beads. Optical signal can be distinguished under multiple settings. The biomarkers on the beads are recognized by specific primary antibodies, which are then bound to the anti-human secondary antibody conjugated with PE (Figure 7). Therefore, the BioPlex machine measures the two signals from the complex at the same time. The fluorescence generated by the BioPlex beads is used as an identifier, and the signal from PE indicates the presence of a biomarker in the complex. This also helps distinguish between biomarker-bead conjugates that are bound to the antibody cascade and biomarker-bead conjugates that do not react with the antibody.

In the present invention, to prove the importance of biomarkers, cDNA clones were confirmed by restriction enzyme cleavage (Figure 8). IPTG induces transformed bacteria to express biomarker proteins. The protein expression by SDS-PAGE and Coomassie blue staining showed protein bands (Figure 9a-e). The His-tagged Bmi1, SOD1 and IL-17A proteins were purified by AKTA (Figure 10a-c), and then checked by SDS-PAGE and Coomassie blue staining (Figure 11a-c).

Measure the sensitivity of the test by adding serially diluted antibodies. The lowest concentration of the added antibody that can generate a signal indicates the sensitivity of the specific biomarker. At the same time, a standard curve was created, which showed the fluorescence intensity of PE relative to the serial dilution of the antibody (Figure 12). This standard curve will be used to estimate the concentration of biomarker-specific autoantibodies in the patient's serum by comparing the PE intensity.

In the present invention, multiple 15 different Bioplex beads that individually generate unique fluorescence are conjugated with the biomarker set and preloaded in the plate well (Figure 13). The patient serum containing autoantibodies is loaded into the wells and allowed to interact with the biomarker conjugate. Then the secondary antibody conjugated to PE is added and allowed to bind to the autoantibody. Excess secondary antibodies are washed away in the machine, and the complexes containing biomarker-bead conjugates and antibody cascades are measured individually. The unique fluorescent signal of Bioplex beads identifies the biomarker, while the PE signal from the same complex indicates the presence of autoantibodies as primary antibodies (Figure 7). Overall, this measurement will indicate the presence and relative concentration of autoantibodies in the patient's serum.

In a standard randomized trial design, a healthy group was compared with those diagnosed with liver cancer The average value of the relative content of autoantibodies between patients. The Student T test was used to analyze the significance of the change. This significant difference indicates that the biomarker is specific for liver cancer. After the verification test, the concentration range of biomarker-specific autoantibodies for liver cancer positive and negative patients will be obtained and used as a reference point for future diagnosis. At the same time, the expression patterns of autoantibodies among patients with liver cancer of different stages were also compared. The characteristic pattern of biomarker performance will indicate the HCC stage.

In general, by measuring the relative autoantibody content and the performance pattern of biomarkers, the present invention provides a different approach to supplement the conventional diagnosis of liver cancer. The present invention is further capable of non-invasively detecting autoantibodies against the verified target in the patient's serum of the present invention, thereby identifying the degree and characteristics of the disease. In addition to early detection of stage I liver cancer, the present invention can also generate characteristic patterns for staging and detect recurrence during mastectomy or post-chemotherapy treatment.

Example

Without intending to limit the scope of the present invention in any way, the following embodiments are provided by means of specific specific examples describing the present invention.

Example 1a Extract protein from patient biopsy

Collect 500 mg matched patient biopsy (tumor biopsy and juxtaposed normal tissue) and wash with PBS. The tissue is frozen by immersion in liquid nitrogen and immediately homogenized with a pestle and mortar. Add dissolving solution (8M urea, 4% CHAPS, 2% IPG buffer, 0.2mg/ml PMSF) to the homogenized sample, then vortex for at least 5 minutes until the tissue is completely dispersed. The lysate was then clarified by centrifugation at 14,000 rpm at 4°C for 10 minutes. The supernatant was further cleaned by 2D Clean Up kit (Amersham) to remove salt and impurities. Will gather The pellets were resuspended in the minimum amount of reconstituted solution (without DTT & IPG buffer). The protein concentration was then measured by Bio-Rad protein analysis and a 200 g aliquot per tube was stored at -70°C.

Example 1b Analysis of proteins by two-dimensional electrophoresis

Add 2.8mg DTT, 5μl pharmalyte or IPG buffer and 2μl bromophenol blue to 1ml reconstituted stock solution. Add 50-100μg of protein sample to 13cm Immobiline DryStrip (IPG strip) containing 250μl of reconstituted solution. After removing the protective cover, the IPG strip was placed in a strip holder with the gel side facing down and covered with a covering liquid to prevent dehydration during electrophoresis. Then the strip was placed on Ettan IPGphor (Amersham) for isoelectric focusing (first-dimensional electrophoresis).

After the first-dimensional electrophoresis, use a balance solution (6M urea, 2% SDS, 50mM Tris HCl (pH 6.8), 30% glycerol, 0.002% bromophenol blue, 100mg DTT per 10ml buffer and 250mg IAA per 10ml buffer) Equilibrate the IPG band, then wash 4-5 times with 1×SDS running buffer. Place the IPG strip on top of the second-dimensional gel and cover it with a sealing solution (0.5% low melting point agarose, 1×SDS electrophoresis buffer containing 0.002% bromophenol blue). This second-dimensional electrophoresis was then performed at 30 mA for the first 15 minutes, followed by 3-4 hours at 60 mA.

After completing the second-dimensional electrophoresis, the gel was removed from the box, fixed and stained with silver nitrate. Identify 15 points representing 15 up-regulated proteins (Figure 1). To identify these proteins (Figure 2), the silver-stained gel sections were de-stained and treated with trypsin to release the proteins from the gel for MALDI-TOF analysis.

Example 2a (SEQ ID NO. 1) Amino acid sequence of Bmi1

Example 2b (SEQ ID NO. 2) Amino acid sequence of VCC1

Example 2c (SEQ ID NO. 3) Amino acid sequence of SUMO-4

MANEKPTEEVKTENNNHINLKVAGQDGSVVQFKIKRQTPLSKLMKAYCEPRGLSVKQIRFRFGGQPISGTDKPAQLEMEDEDTIDVFQQPTGGVY

Example 2d (SEQ ID NO. 4) The amino acid sequence of RhoA

Example 2e (SEQ ID NO. 5) TXN amino acid sequence

MVKQIESKTAFQEALDAAGDKLVVVDFSATWCGPCKMIKPFFHSLSEKYSNVIFLEVDVDDCQDVASECEVKCMPTFQFFKKGQKVGEFSGANKEKLEATINELV

Example 2f (SEQ ID NO. 6) Amino acid sequence of ET-1

Example 2g (SEQ ID NO. 7) Amino acid sequence of UBE2C

Example 2h (SEQ ID NO. 8) The amino acid sequence of HDGF2

Example 2i (SEQ ID NO. 9) Amino acid sequence of FGF21

Example 2j (SEQ ID NO. 10) Amino acid sequence of LECT2

Example 2k (SEQ ID NO. 11) The amino acid sequence of SOD1

Example 21 (SEQ ID NO. 12) The amino acid sequence of STMN4

Example 2m (SEQ ID NO. 13) Midkine amino acid sequence

Example 2n (SEQ ID NO. 14) The amino acid sequence of IL-17A

Example 2o (SEQ ID NO. 15) The amino acid sequence of IL-26

Example 3a Performance of the biomarker panel

The His-tagged plastids containing the cDNA insert encoding the biomarker group were transformed into DH5 competent cells ( 301 , Figure 3). A single colony is selected and grown in a bacterial culture ( 302 ). Amplify the number of plastids and extract them from bacteria by miniprep. The plastids were further transformed into BL21DE3 or BL21DE3pLysS competent cells. The transformed bacteria were selected and grown in 2×100ml LB medium. When the bacterial culture reached an optical density of 0.06, 200 μM IPTG was added to 100 ml of bacterial culture ( 303 ). Another 100ml bacterial culture without IPTG was used as a negative control group. The bacterial culture was grown at 30°C with shaking. After the incubation and the next morning after the overnight incubation, 500 μl of bacterial culture were kept and stored at -20°C for 3 hours.

The bacterial cultures induced with or without IPTG were mixed together in a 500ml centrifuge bottle. The bacterial cells ( 304 ) were collected by centrifugation at 9000 rpm at 4°C for 20 minutes. Keep 500 μl of supernatant as another negative control group and discard the remaining supernatant. Bacterial cultures and negative control groups collected at different times were operated on SDS-PAGE to analyze proteins ( 305 ). Then stain the gel with Coomassie Blue overnight. After de-staining the gel, protein induction can be confirmed by checking the size and comparing with the negative control group.

Example 3b Biomarker group protein purification

Bacterial cell aggregates were resuspended in 10 ml of lysis buffer by vortexing at room temperature. The resuspended cells were kept in a 50ml centrifuge tube on ice, and the cells were completely lysed by sonication at 70% amplitude for 30 seconds at 30 second intervals for 10 rounds ( 401 , Figure 4). The lysed cells were centrifuged at 10,000 rpm at 4°C for 1 hour ( 402 ). The supernatant was transferred to a dialysis tube and immersed in 1 L of unfiltered starting buffer at 4°C under continuous stirring for 4-6 hours ( 403 ). Continue the dialysis overnight with another 1L starting buffer. The supernatant was further filtered with 0.22μm filter and syringe. Load the filtered sample ( 405 ) into an AKTA machine ( 404 ) equipped with a HiTrap chelating column loaded with 0.1M nickel sulfate. Set the program to automatically collect the eluent in the eluate on the AKTA machine ( 406 ). SDS-PAGE analysis was used to check the proteins purified from different elution fractions ( 407 ).

Example 4a Protein coupled with Bio-Plex beads

According to the manufacturer's manual, the purified protein of the biomarker group was coupled with Bio-Plex beads (Bio-Rad) ( 501 ). Briefly, the uncoupled beads were vortexed for 30 seconds and then sonicated for 15 seconds. 1,250,000 beads were collected in the reaction tube by centrifuging 100 μl of beads at maximum speed for 4 minutes. After washing with 100 μl of bead washing buffer by centrifugation, the beads were resuspended in 80 μl of bead activation buffer. Add 10 μl of 50 mg/ml freshly prepared S-NHS and 10 μl of 50 mg/ml freshly prepared EDAC to the beads, and then incubate for 20 minutes in the dark at room temperature (Figure 6). Then the beads were washed twice with 150 μl PBS.

Add 10 μg of protein to the washed beads and make the total volume up to 500 μl with PBS, and incubate for 2 hours in the dark with shaking. The supernatant was removed after centrifugation at maximum speed for 4 minutes. 250 μl of blocking buffer was added to the beads and shaken in the dark for 30 minutes, then centrifuged at maximum speed for 4 minutes and the supernatant was removed. Simply wash the beads and then resuspend them Store in storage buffer at 4°C. Use a hemocytometer to count the number of beads.

Example 4b Validation of protein-bead coupling

Add 50 μl of conjugated Bio-Plex beads (100 beads per μl) to the HTS 96-well plate to sequentially react with the primary antibody and the secondary antibody ( 502 ). Prepare serial dilutions of commercially available primary antibodies against the biomarker set as 8,000ng/ml, 4,000ng/ml, 1,000ng/ml, 250ng/ml, 62.5ng/ml, 15.625ng/ml, 3.906ng/ml, 0.977ng/ml, 0.244ng/ml and 0.061ng/ml. Add 50 μl of each dilution to each well. Two negative controls were obtained by excluding the primary antibody, and both the primary antibody and the secondary antibody were in the well. The culture plate was then sealed with foil and kept on a shaker at 350 rpm for 30 minutes to avoid exposure to light.

After incubation, the beads were washed three times with 150 μl PBS. Except for the negative control group, 50 μl of PE-conjugated secondary antibody (8,000 ng/ml) was added to each well. Seal the culture plate again and incubate in the dark with shaking for 30 minutes. Then wash off excess antibody with PBS. Calibrate the Bio-Plex machine with the calibration kit and verification kit. After loading the HTS disc in the Bio-Plex machine, the signal from the PE conjugated on the Bio-Plex beads and the secondary antibody was measured (a schematic diagram is shown in Figure 7) ( 503 ). Generate calibration curve by Logistic-5PL.

Example 4c: Collect serum samples and measure autoantibodies by BioPlex system

The whole blood sample was coagulated by standing at 37°C for 1 hour. After centrifugation at 1000 g for 10 minutes at room temperature, serum containing autoantibodies was collected in the supernatant. If necessary, dilute serum samples with PBS. Serum samples were loaded into HTS dishes preloaded with Bioplex beads conjugated to the biomarker set and incubated for 30 minutes under shaking (Figure 13). Similar to the procedure described in Example 4b, 50 μl of PE-conjugated secondary antibody (8000 ng/ml) was added to the beads washed with PBS, and then shaken for another 30 minutes. After three rounds of washing, the culture plate was loaded in the Bio-Plex machine and the fluorescence signal was measured ( 504 ). The concentration of autoantibody can then be calculated from the standard curve.

The foregoing description of the present invention is provided for the purpose of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and changes will be obvious to those familiar with this technology.

The specific examples are selected and described in order to best explain the principle of the present invention and its practical application, so that other skilled in the art can understand various specific examples of the present invention and make various modifications suitable for the specific intended use. The scope of the present invention is to be defined by the scope of the following patent applications and their equivalents.

Industrial availability

The method claimed in the present invention and the kit containing 15 identified biomarkers can not only be used to identify and quantify the presence of autoantibodies in the patient's serum to detect and/or stage liver cancer, but also be applicable to such markers as targets The development of drugs to treat liver cancer in particular.

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<120> Specific biomarker panel for non-invasive diagnosis of liver cancer

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<213> Homo sapiens

<400> 15

Claims (8)

A method for measuring the presence of HCC biomarkers in individuals suspected of having hepatocellular carcinoma (HCC), the method comprising: a. Measuring serum samples from individuals suspected of having HCC against the organism The presence of primary autoantibodies (primary biomarker autoantibodies) in the label group, where the biomarker group includes HDGF2, RhoA, TXN, ET-1, FGF21, SOD1, Midkine, IL-17A and IL26; b. Detect the presence of HCC biomarkers in the individual suspected of having HCC, the method comprises the following steps: i. Mix the serum sample with the biomarker conjugate group, so that the primary biomarker autoantibodies (if Exist in the serum sample) bind to the biomarker conjugate group, and wash away any unbound antibodies; wherein the biomarker conjugate group includes each biomarker in the biomarker group, which passes through the amide bond Conjugated to unique fluorescent microsphere beads, wherein each unique fluorescent microsphere bead linked to a specific specific biomarker in the biomarker set has a different emission wavelength for each biomarker; wherein the biomarkers share The conjugate can be bound by the specific primary biomarker autoantibodies present in the individual's serum sample, ii. adding an anti-human secondary antibody conjugated with phycoerythrin (PE) to the mixture formed in step i, It can bind to primary biomarker autoantibodies; and enables the anti-human secondary antibodies conjugated with PE to bind to the specific primary antibody bound to the biomarker conjugate to form fluorescent beads-biomarker-self Body antibody-a cascade of antibodies conjugated to PE, and Wash away unbound antibodies; and iii. measure the presence of the cascade of fluorescent beads-biomarkers-autoantibodies-antibodies conjugated to PE in the mixture formed in step ii to determine whether the individual’s serum Contains primary biomarker autoantibodies. Such as the method of the first item in the scope of patent application, wherein the unique fluorescent signal from the microsphere beads is used to identify which biomarker in the biomarker group exists, and the signal from PE represents the biomarker conjugate The presence. Such as the method of the second item in the scope of patent application, wherein the fluorescent beads produced by the secondary antibodies conjugated with PE are measured in the cascade of the fluorescent beads-biomarker-autoantibody-antibody conjugated with PE Light intensity, so that the primary biomarker autoantibodies can be detected and quantified. For example, the method of item 1 of the scope of the patent application further includes comparing different sera obtained from different HCC patients with different cancer stages to generate a characteristic pattern for the staging of HCC diagnosis. A method for measuring the presence of HCC biomarkers in a plurality of individuals with different stages of hepatocellular carcinoma (HCC), the method comprising: a. Serum from a plurality of individuals with different stages of HCC The sample is measured for the presence of primary autoantibodies (primary biomarker autoantibodies) against the biomarker group, where the biomarker group includes HDGF2, RhoA, TXN, ET-1, FGF21, SOD1, midkine, IL -17A and IL26; b. Detect the presence of HCC biomarkers in the plurality of individuals with different stages of HCC, the method includes the following steps: i. Mix the serum sample with the biomarker conjugate group, so that the Primary biomarkers Autoantibodies (if present in the serum sample) bind to the biomarker conjugate group and wash away any unbound antibodies; wherein the biomarker conjugate group includes each biomarker in the biomarker group, It is conjugated to unique fluorescent microsphere beads through an amide bond, wherein each unique fluorescent microsphere bead connected to a specific specific biomarker in the biomarker group has a different emission wavelength for each biomarker; wherein The biomarker conjugates can be bound by the specific primary biomarker autoantibodies present in the individual's serum sample, ii. To the mixture formed in step i, add anti-human two conjugated with phycoerythrin (PE) Secondary antibodies, which can bind to primary biomarker autoantibodies; and enable the anti-human secondary antibodies conjugated with PE to bind to the specific primary antibodies bound to the biomarker conjugate to form fluorescent beads-bio Label-autoantibody-a cascade of antibodies conjugated with PE, and wash away unbound antibodies; and iii. measure the fluorescent beads-biomarker-autoantibody-and PE in the mixture formed in step ii The cascade of conjugated antibodies exists to determine whether the sera of the plurality of individuals contain primary biomarker autoantibodies. Such as the method of item 1 or 5 in the scope of patent application, wherein the biomarker group includes Bmi-1, VCC1, SUMO-4, RhoA, TXN, ET-1, UBE2C, HDGF2, FGF21, LECT2, SOD1, STMN4, midkine (Midkine), IL-17A and IL26. A kit for the method according to item 1 or 5 of the scope of patent application, which comprises (i) a protein group, the protein of which is coupled with fluorescent microsphere beads with different emission wavelengths to form a protein-bead conjugate For immunological binding of autoantibodies, and (ii) anti-phycoerythrin (PE) conjugated Human secondary antibody; wherein the protein group includes HDGF2, RhoA, TXN, ET-1, FGF21, SOD1, midkine (Midkine), IL-17A and IL26; and wherein the antibody conjugated with phycoerythrin (PE) Human secondary antibodies can bind to primary biomarker autoantibodies. For example, the set of item 7 in the scope of patent application, in which the protein set includes Bmi-1, VCC1, SUMO-4, RhoA, TXN, ET-1, UBE2C, HDGF2, FGF21, LECT2, SOD1, STMN4, Midkine ), IL-17A and IL26.

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