Application of a set of glycosylation-related gene markers in detection of liver cancer

Abstract

The application discloses a set of applications of glycosylation related gene markers in liver cancer detection and belongs to the technical field of biotechnology.The glycosylation related gene markers are composed of five types of 10 genes, namely, neuraminidase 1 (NEU1), N-acetylneuraminic acid synthase (NANS), cytidine monophosphate N-acetylneuraminic acid hydroxylase (CMAH), solute carrier family 35 member A1 (SLC35A1) and glycosyltransferase (B3GALT4, B3GALNT2, ALG2, MGAT2, FUT8 and ST3GAL2). The application first uses the markers as liver cancer diagnostic markers for liver cancer detection, and the average AUC of liver cancer can reach 82.14% through combined diagnosis of the multiple indexes of the set of genes.Compared with AFP which is widely used at present, the sensitivity and specificity of diagnosis can be greatly improved, and the technology still takes blood samples as the main detection target, and does not cause secondary damage to patients, the technology provides a new technical platform and means for clinical detection of liver cancer, and has a strong practical application value.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically relating to a set of glycosylation-related gene markers for liver cancer detection and their applications, particularly suitable for the detection of early-stage liver cancer and / or AFP-negative liver cancer. Background Technology

[0002] Liver cancer is one of the most serious malignant tumors affecting the health of the Chinese population. According to the latest data released by the National Cancer Registry Center in 2020, its incidence rate ranks fifth among all malignant tumors, while its mortality rate ranks second among all cancer deaths. Epidemiological statistics show that the peak age of liver cancer incidence in China has shifted from 40-60 years old to 30-60 years old. The trend of liver cancer onset at a younger age seriously damages the national population's quality, social stability, and regional economic development. The incidence of liver cancer is at least 2 to 3 times higher in men than in women. Early symptoms are often subtle and atypical, and the long time between tumor growth and the first appearance of signs is a major reason for the high mortality rate. Liver cancer often presents at an advanced stage, leading to high recurrence and metastasis rates after resection. Therefore, early diagnosis of liver cancer is the best measure to improve surgical resection rates, reduce mortality, prolong survival time, and improve quality of life.

[0003] Internationally, liver cancer screening primarily utilizes diagnostic techniques combining alpha-fetoprotein (AFP), ultrasound, and other imaging modalities. However, ultrasound examinations rely heavily on operator experience and equipment; early-stage liver cancer (<1cm) is often undetectable by imaging. Furthermore, AFP has low sensitivity, leading to missed or misdiagnosed cases. Delayed early diagnosis and treatment result in over two-thirds of liver cancer patients receiving treatment only at an advanced stage. AFP-negative liver cancer accounts for approximately 30% of all liver cancer cases, with small hepatocellular carcinomas (<3cm) being particularly common, often poorly differentiated with poor prognosis. Early diagnosis and surgical resection can improve clinical cure rates. Patients with AFP-negative liver cancer often have normal serum AFP levels (<20μg / L), mild and nonspecific clinical symptoms, and current clinical diagnosis relies heavily on imaging and pathological examinations. Especially for AFP-negative liver cancer with tumors <3cm, valuable and practical diagnostic markers are lacking, making it easily misdiagnosed as benign liver disease, thus delaying treatment. Therefore, how to diagnose AFP-negative liver cancer is a crucial issue. The combined detection of other tumor markers is a more preferable method for diagnosing liver cancer, especially AFP-negative liver cancer.

[0004] Currently, tumor biomarkers are typically obtained from tumor tissue specimens or peripheral blood. Blood, broadly speaking, is a circulating connective tissue that comes into contact with all organs of the body, containing indicators reflecting changes in the body and participating in disease processes under pathological conditions. Compared to obtaining tumor tissue specimens, obtaining biomarkers from circulating blood is non-invasive, offering advantages such as repeatable sampling and dynamic monitoring, making it an ideal sample for non-invasive molecular testing and an alternative to biopsies at pathological sites. Research on novel tumor biomarkers, including those at the transcriptome level (mRNA and miRNA) and the nucleic acid mutation level (including circulating tumor DNA (ctDNA), is gaining increasing attention. Compared to traditional serum protein molecular biomarkers, nucleic acid molecular biomarkers have significant advantages in stability, detection sensitivity, and throughput, better meeting the requirements of combined multi-marker detection to improve diagnostic rates, and represent the development trend in tumor biomarker research and detection.

[0005] Studies have shown that peripheral blood transcriptomics can accurately reveal disease-specific gene expression characteristics, which can be applied to the early diagnosis and prognosis of diseases. The United States has already developed and launched a peripheral blood gene diagnostic product based on mRNA levels for colorectal cancer risk prediction. Summary of the Invention

[0006] This study found that the mRNA levels of genes related to N-glycosylation on Neu5Gc-modified IgG glycoprotein and genes related to Neu5Gc synthesis metabolism were abnormally high in the peripheral blood of patients with liver cancer. The expression of multiple genes can be combined for the diagnosis of liver cancer.

[0007] The first technical problem to be solved by the present invention is to provide a set of effective peripheral blood gene expression profiles based on glucose markers for the detection of liver cancer; the other technical problem to be solved by the present invention is to provide the application of the above-mentioned gene markers.

[0008] The present invention adopts the following technical solution:

[0009] The first aspect of this invention provides a set of glycosylation-related gene markers for liver cancer detection, including neuraminidase 1 (NEU1); N-acetylneuraminate synthase (NANS); cytidine monophosphate-N-acetylneuraminic acid hydroxylase (CMAH); solute carrier family 35 member A1 (SLC35A1); beta-1,3-galactosyltransferase 4 (B3GALT4); and beta-1,3-N-acetylgalactosaminyltransferase 2. The five types of genes include: α-1,3 / 1,6-mannosyltransferase (ALG2); α-1,6-mannosyl-glycoprotein 2-β-N-acetylglucosaminyltransferase (MGAT2 / GNT-II); fucosyltransferase 8 (FUT8); and CMP-N-acetylneuraminate-beta-galactosamide-alpha-2,3-sialyltransferase 2 (ST3GAL2). This involves one or more combinations of these genes. Using TP53I3 (NCBI Gene ID 8540) and GPC3 (NCBI Gene ID 2719) as positive control genes, this invention is the first to discover that these genes are differentially expressed in the peripheral blood of liver cancer patients (especially AFP-negative liver cancer patients).

[0010] Based on the above technical solutions, furthermore, the gene accession number for neuraminidase 1 (NEU1) is NCBI Gene ID 4758, the gene accession number for sialic acid synthase (NANS) is NCBI Gene ID 54187, the gene accession number for cytidine monophosphate-N-acetyl-neuraminic acid hydroxylase (CMAH) is NCBI Gene ID 8418, the gene accession number for solute carrier family 35member A1 (SLC35A1) is NCBI Gene ID 10559, and the gene accession number for glycosyltransferase β-1,3-galactosyltransferase 4 (B3GALT4) is NCBI Gene ID 4758. The gene accession number for glycosyltransferase β-1,3-N-acetylgalactosaminyltransferase 2 (B3GALNT2) is NCBI Gene ID 148789; the gene accession number for glycosyltransferase α1,3 / 1,6-mannosyltransferase (ALG2) is NCBI Gene ID 85365; the gene accession number for glycosyltransferase α-1,6-mannosyl-glycoprotein 2-β-N-acetylglucosaminyltransferase (MGAT2 / GNT-II) is NCBI Gene ID 4247; and the gene accession number for glycosyltransferase fucosyltransferase 8 (Fucosyltransferase 8) is NCBI Gene ID 148789; the gene accession number for glycosyltransferase α1,3 / 1,6-mannosyltransferase (ALG2) is NCBI Gene ID 85365; the gene accession number for glycosyltransferase α-mannosyl-glycoprotein 2-β-N-acetylglucosaminyltransferase (MGAT2 / GNT-II) is NCBI Gene ID 4247; and the gene accession number for glycosyltransferase fucosyltransferase 8 (Fucosyltransferase 8) is NCBI Gene ID 4247. The gene accession number for FUT8 is NCBI Gene ID 2530, and the gene accession number for glycosyltransferase CMP-N-acetylneuraminate-beta-galactosamide-alpha-2,3-sialyltransferase 2 (ST3GAL2) is NCBI Gene ID 6483.

[0011] This invention is based on quantitative analysis of human whole-genome expression profiles. In addition to quantitatively analyzing the gene expression status of total RNA in peripheral blood and comparing the differences in gene expression in peripheral blood samples from liver cancer patients, cirrhosis patients, chronic hepatitis B patients, and normal individuals, it can also use gene expression profiling chips or RNA sequencing technology to quantitatively detect the relative expression levels of 12 gene markers in the peripheral blood of the subject, thereby determining the probability of the subject having liver cancer.

[0012] A second aspect of the invention provides the use of a reagent for detecting the expression levels of the above-mentioned glycosylation-related gene markers for liver cancer detection in peripheral blood samples in the preparation of products for detecting liver cancer.

[0013] Based on the above technical solutions, further, liver cancer includes early-stage liver cancer and / or AFP-negative liver cancer.

[0014] Based on the above technical solutions, the products for detecting liver cancer include, but are not limited to, real-time quantitative PCR kits, gene probes, gene chips, and microfluidic chips.

[0015] Based on the above technical solution, the reagent further includes primers for detecting the glycosylation-related gene markers used for liver cancer detection.

[0016] Based on the above technical solution, the nucleotide sequence of the primer is shown in SEQ ID NO.1-20.

[0017] Based on the above technical solution, the real-time fluorescence quantitative PCR kit further includes RNA extraction reagent, cDNA reverse transcription reagent, and TB Green reagent. Attached Figure Description

[0018] To more clearly illustrate the embodiments of the present invention, the accompanying drawings involved in the embodiments will be briefly described below.

[0019] Figure 1 The changed sugar form was confirmed by N-glycan analysis;

[0020] Figure 2 A heatmap of differential expression of genes related to Neu5Gc synthesis and N-glycan production detected by Q-PCR (relative expression level based on β-actin (NCBI Gene ID 345651)). HN represents the normal group, CHB represents the hepatitis B group, LC represents the cirrhosis group, and HCC represents the liver cancer group.

[0021] Figure 3 Q-PCR analysis showed that the liver cancer group had significantly higher gene expression compared to the liver cirrhosis group;

[0022] Figure 4Q-PCR analysis showed that the cirrhosis group had significantly higher gene expression compared to the liver cancer group;

[0023] Figure 5 This is a bar chart showing the genes related to the 95% confidence interval (AUC>70%) of the ROC curve screened in this invention. Detailed Implementation

[0024] The present invention will be described in detail below with reference to the embodiments. However, the implementation of the present invention is not limited thereto. Obviously, the embodiments described below are only some embodiments of the present invention. For those skilled in the art, other similar embodiments can be obtained without creative effort and all fall within the protection scope of the present invention.

[0025] Example 1

[0026] The specific steps for isolating and purifying natural IgG glycoproteins from human serum are as follows:

[0027] (1) Take 15 normal people and 15 liver cancer patients respectively. Dilute 100 μL of serum from each sample in 0.9% physiological saline at a ratio of 1:10, filter through a 0.22 μm filter membrane, and incubate with Protein G agarose gel column at 4℃ for 6 h by rotation mixing.

[0028] (2) Wash the purification equipment with approximately 30 mL of 20% ethanol aqueous solution;

[0029] (3) Use Binding Buffer (0.1M NaCl, 20mM NaHPO4, pH=7.0-7.4) to balance the purification equipment. After the reading of the purification equipment stabilizes, adjust the reading to "0".

[0030] (4) Sample loading: Load the sample obtained in step (1) at a rate of 1 mL / min. After the serum premix in the column is loaded (the effluent can be received when the reading of the purification equipment rises rapidly, which is called "flow-through"), use Binding Buffer to balance the purification equipment.

[0031] (5) Equilibration: Add Binding Buffer to the column at a rate of 2 mL / min. After the reading drops rapidly to a stable level, elution can be performed (equilibration can take a longer time to elute non-specifically bound proteins).

[0032] (6) Elution: Elution buffer (0.1M citric acid, pH=2.5-3.0) was used to elute the target protein, approximately 500 μL / tube;

[0033] (7) During the elution process, stop collecting the sample once the instrument reading is basically stable. The collected sample is in an acidic liquid environment. Add a small amount of Tris-HCl to neutralize the pH value to about 7.0 to obtain an IgG glycoprotein solution.

[0034] (8) The obtained IgG glycoprotein was detected by electrophoresis and dialyzed in 1×PBS.

[0035] (9) Collect IgG glycoprotein after dialysis for concentration determination. It can be stored at -80℃ for long-term use.

[0036] Example 2

[0037] This embodiment involves the glycan mass spectrometry analysis of serum IgG glycoprotein, and the specific steps are as follows:

[0038] In this embodiment, the GPSeeker1,2 search engine for intact N-glycopeptides databases was used to search and match N-glycosylation in the serum-derived IgG samples obtained in Example 1. Static modifications were alkylation and light-heavy diethylation, while dynamic modifications were N-glycosylation. The results of the targeted positive library search, namely intact N-glycopeptide spectrum matches (GPSMs) and the number of intact N-glycopeptide identification IDs (IDs), are listed in a single TRExcel Sheet1.

[0039] Using the GPSeekerQuan tool, precursor ions were identified in the primary mass spectrum corresponding to each ID. The sum of the three peak intensities in the isotopic profile of each precursor ion was used for relative quantification, and the relative ratio of each complete N-glycopeptide experimental / control group (Exp / Con) was calculated.

[0040] The three most significantly differentially expressed complete N-glycopeptides were selected and viewed in GPSeeker View, from which their visualized structural diagrams, matched precursor ion fingerprint profiles, and secondary mass spectra with matched fragment ion annotations were exported. The analytical workflow of this project mainly includes the experimental section, GPSeeker database establishment and searching, GPSeekerQuan quantitative analysis, and result statistics.

[0041] 1. Experimental Section

[0042] 1.1 Protein reduction and alkylation

[0043] IgG glycoprotein placed in a 10K ultrafiltration tube was reduced in Tris(2-carboxyethyl)phosphine (TCEP, 75259, Sigma), cooled, reacted in IAA, and finally quenched with TCEP to alkylate the reaction.

[0044] 1.2 Protein digestion

[0045] Remove the IgG glycoprotein solution from the centrifuge tube, add trypsin to the protein solution, and incubate overnight on a shaker for enzyme digestion.

[0046] 1.3 Desalting of peptides

[0047] Using C18 (Phenomenex, 15μm), The enzymatically hydrolyzed peptides are desalted and subjected to gradient elution, and then concentrated to dryness under vacuum.

[0048] 1.4 Intact N-glycopeptide Enrichment

[0049] The desalted peptides were reconstituted using ZIC-HILIC (Merck Millipore, 5μm). Intact N-glycopeptides were enriched and eluted using a gradient, then concentrated to dryness under vacuum.

[0050] 1.5 Isotopic labelling

[0051] The intact N-glycopeptide was redissolved in 100 μL of TFE. The control group (HN) was the light standard, and the experimental group (HCC) was the heavy standard, with a ratio of 0.25 μL / μg intact N-glycopeptide. 20% CH3CHO (or...) was added. 13 CH3 13 The CHO solution and an equal volume of 0.6M NaBH3CN solution were shaken and reacted in a shaker at 37°C for 1 h. Then, an equal volume of 4% NH4OH solution was added to quench the labeling reaction. The two samples, one lightly labeled and one heavily labeled, were mixed and concentrated to dryness under vacuum, and then redissolved in 50 μL of H2O.

[0052] 1.6 Desalting of isotopic labelled intact N-glycopeptides

[0053] Using a ratio of intact N-glycopeptide to filler material of 1:50 w / w, C18 (Phenomenex, 15μm) was employed. For the enriched or labeled complete N-glycopeptides, a final desalting step was performed before entering the liquid chromatography-mass spectrometry (LC-MS) system. The final elution was carried out with a gradient of 250 μL 50% ACN and 250 μL 80% ACN. After vacuum concentration to dryness, 30 μL of ultrapure water was added for redissolution, and the mixture was divided into three equal portions for three technical replicates (TRs).

[0054] 1.7 Liquid chromatography-mass spectrometry (LC-MS) 18 -RPLC-MS / MS(HCD) Analysis)

[0055] 1.7.1 Liquid Chromatography Separation

[0056] Analytical column: 360μod × 75μid, 75cm long; Packing material: Phenomenex Jupiter C18, 5μm. Trapping column: 360μod × 200μid, 5cm long; Packing material: Phenomenex Jupiter C18, 5μm. Mobile phase: Buffer A was a mixture of 99.9% H₂O and 0.1% FA, and Buffer B was a mixture of 99.9% ACN and 0.1% FA. Flow rate: Loading pump mobile phase flow rate (sample loading): 5 μL / min; Nano pump mobile phase flow rate (separation): 300 nL / min; Gradient: Buffer B was 2% for sample loading for the first 12 min; then, the proportion of Buffer B increased linearly from 2% to 40% over 188 min for elution; then, the proportion of Buffer B was increased to 95% over 10 min and maintained at 95% for 5 min for impurity removal; finally, the proportion of Buffer B was reduced to 2% for equilibration over the last 25 min.

[0057] 1.7.2 Primary mass spectrometry and tandem mass spectrometry

[0058] Nano source electrospray ionization, the temperature of the ion transport tube is set to 300℃, and the spray voltage is 1.9kV.

[0059] 2. GPSeeker Database Establishment and Search

[0060] Based on the IgG glycoproteome database and the N-glycosylation modification database, trypsin was selected as the protease, with a limit of two allowed cleavage sites. Static modifications included alkylation, light labeling, or heavy labeling diethylation. The MS m / z range was 700–2000. An N-glycosylation analysis database was established using GPSeeker, and complete N-glycopeptide matching and identification were performed on triple-repeat mass spectrometry data.

[0061] Based on the search results, with FDR ≤ 1%, duplicates were removed according to peptide sequence (p-Seq.), post-translational modifications (p-PTMs), and monosaccharide linkage (g-Linkage) to obtain the final IDs identification list. This list includes qualitative results such as the number of complete N-glycopeptide identifications (IDs), peptide sequence (p-Seq.), N-glycosylation site (N-glycoSite), N-glycan composition (composition) and linkage (g-Linkage), corresponding complete N-glycoprotein (Accession Number), and structure-diagnostic ions of the glycan structure.

[0062] 3. Quantitative analysis of GPSeekerQuan

[0063] Based on the matching results from GPSeeker, the precursor ions were identified in the primary mass spectrum corresponding to each ID using the GPSeekerQuan tool. The sum of the intensities of two or more peaks in the isotopic profile of each precursor ion was used for relative quantification (TOP2 and TOP3 were used for relative quantification). The relative ratio of HCC / HN (Exp / Con) was calculated from this. The quantification results were further filtered to ensure that at least two out of three technical replicates were observed, the fold change (Ratio) was ≥1.5-fold and the p-value was <0.05, thus obtaining a list of differentially expressed complete N-glycopeptides. The differentially expressed complete N-glycopeptides that appeared more than twice in three technical replicates were selected and the relevant data were viewed in GPSeekerView and exported.

[0064] 4. Results Statistics

[0065] In the IDs identification list, duplicate data are removed based on peptide sequences to obtain the number of characteristic peptides; duplicate data are removed based on N-glycosylation sites and protein sequence numbers to obtain the number of N-glycosylation sites; the number of complete N-glycoproteins is obtained, and finally, differential analysis of monosaccharides in N-glycan chains is performed.

[0066] Example 3

[0067] This invention provides a Q-PCR kit for detecting differential expression of genes related to Neu5Gc synthesis and N-glycan formation in peripheral blood of patients with liver cancer, comprising RNA extraction reagent, cDNA reverse osmosis reagent, and TB Green reagent, etc., and the specific implementation steps are as follows:

[0068] 1. Sample pretreatment: 250 μL of blood (normal group, hepatitis B group, cirrhosis group and liver cancer group) was transferred to 1.5 mL RNase-free centrifuge tube;

[0069] 2. Add 750 μL of lysis buffer (RNAiso), repeatedly pipette and vigorously vortex to mix, and let stand at room temperature for 5 min;

[0070] 3. Add 200 μL of chloroform (self-prepared), shake vigorously for 15 seconds to mix, and let stand at room temperature for 2 minutes;

[0071] 4. Centrifuge at 12,000 rpm for 10 min at 4℃ to separate the sample into layers. Transfer the upper aqueous phase to a 1.5 mL RNase-free centrifuge tube, add 0.5 times the volume of anhydrous ethanol, and mix by inverting.

[0072] 5. RNA extraction:

[0073] 5.1 Insert the RNA adsorption column into a 2mL collection tube for later use;

[0074] 5.2 Add the above pretreatment mixture to the RNA adsorption column, centrifuge at 12,000 rpm for 1 min, and discard the waste liquid;

[0075] 5.3 Add 500 μL of protein removal solution (isopropanol), centrifuge at 12,000 rpm for 30 seconds, and discard the waste liquid;

[0076] 5.4 Place the RNA adsorption column back into the collection tube, add 500 μL of washing buffer (75% ethanol), centrifuge at 12,000 rpm at room temperature for 30 seconds, and discard the waste liquid;

[0077] 5.5 Repeat step 5.4;

[0078] 5.6 Place the RNA adsorption column back into the collection tube and centrifuge the empty column at 12,000 rpm at room temperature for 2 min to remove residual wash buffer.

[0079] 5.7 Place the RNA adsorption column into a new 1.5 mL RNase-free centrifuge tube, add 30-50 μL of RNase-free H2O to the center of the RNA adsorption column, and incubate at room temperature for 2 min; then centrifuge at 12,000 rpm for 1 min. Collect the filtrate, which is the RNA solution;

[0080] 5.8 The RNA solution can be stored at -80℃ for a long time or directly reverse transcribed to prepare cDNA for subsequent experimental steps.

[0081] 6. Analyze the concentration of the extracted RNA and calculate the reverse transcription system;

[0082] The A260 / A280 ratio is used to determine the quality of the RNA (which also needs to be confirmed by agarose gel electrophoresis). The volume required for reversal is calculated based on the concentration in ng / ul.

[0083] 7. After removing genomic gDNA contamination, cDNA reverse DNA conversion is performed;

[0084] 7.1 Preparation of gDNA Removal System:

[0085]

[0086] 42℃ for 2 minutes;

[0087] Hold at 4℃.

[0088] 7.2 Preparation of the reverse transcription system:

[0089]

[0090]

[0091] 37℃ for 15 minutes;

[0092] 85℃ for 5 seconds;

[0093] Hold at 4℃.

[0094] 8. The expression of the target gene was detected by real-time quantitative PCR.

[0095] Preparation of the reaction system:

[0096]

[0097] Two-step PCR reaction conditions:

[0098] Stage 1: Pre-variation;

[0099] Reps: 1;

[0100] 95℃ for 30 seconds;

[0101] Stage 2: PCR reaction

[0102] Reps: 40;

[0103] 95℃ for 5 seconds;

[0104] 60℃ for 34 seconds;

[0105] Dissociation Stage.

[0106] Table 1. Information on glucose-related genes for diagnosing liver cancer

[0107]

[0108]

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The use of reagents for detecting the expression levels of glycosylation-related gene markers in peripheral blood samples in the preparation of products for detecting liver cancer; The glycosylation-related gene marker mentioned is solute vector family 35 member A1 SLC35A1; The gene accession number for solute vector family 35 member A1 SLC35A1 is NCBI Gene ID 10559.

2. The use according to claim 1, characterized in that, The products for detecting liver cancer include real-time quantitative PCR kits, gene probes, gene chips, and microfluidic chips.

3. The use according to claim 1, characterized in that, The reagents include primers for detecting the glycosylation-related gene markers.

4. The use according to claim 3, characterized in that, The nucleotide sequences of the primers are shown in SEQ ID NO.7-8.

5. The use according to claim 2, characterized in that, The real-time quantitative PCR kit includes RNA extraction reagent, cDNA reverse transcription reagent, and TB Green reagent.

Images