Composition for predicting curative effect of gastric cancer chemotherapy based on methylation level of CpG site of MLH1 gene and application of composition

Abstract

The invention discloses a composition for predicting the curative effect of gastric cancer chemotherapy based on the methylation level of a CpG site of an MLH1 gene and application of the composition, the CpG site is derived from the MLH1 gene, and the CpG site is any one of chr3: 37033633, chr3: 37033626, chr3: 37033601 and chr3: 37033490 or a combination of more than two of the chr3: 37033633, the chr3: 37033626, the chr3: 37033601 and the chr3: 37033490. According to the CpG loci in the MLH1 gene, the composition of the CpG loci, the kit, the system, the computer equipment and the computer readable storage medium provided by the invention, the methylation level of the CpG loci of the MLH1 gene in a biological sample is measured, so that the chemotherapy curative effect of the gastric cancer can be accurately predicted; and a new thought and a new method are provided for precise medical treatment of patients with gastric cancer clinically.

Description

Technical Field

[0001] The present application belongs to the field of medical detection technology, and specifically relates to a composition for predicting the efficacy of chemotherapy for gastric cancer based on the methylation level of the CpG site of the MLH1 gene and its use. Background Art

[0002] Gastric cancer (GC) is a common human cancer with a high mortality rate, ranking fourth among the causes of cancer-related deaths. In the early stages of gastric cancer, due to the lack of obvious clinical symptoms and signs, early diagnosis and treatment of gastric cancer face great challenges. Most gastric cancer patients are already in the advanced stage when diagnosed, and the global 5-year survival rate of advanced gastric cancer is only 5% to 10%. In addition, during drug treatment, gastric cancer often fails due to the acquisition of drug resistance, and the occurrence of drug resistance (especially chemotherapy resistance) varies among different individuals and races. Therefore, there is an urgent need to conduct further research to explore the pathogenesis of gastric cancer, identify the development process of gastric cancer early, identify molecular markers for predicting the efficacy of gastric cancer, and improve the efficacy and prognosis of gastric cancer patients.

[0003] MutL homolog 1 (MLH1) is one of the human mismatch repair (MMR) genes, located on chromosome 3p21-23. The protein encoded by this gene can effectively repair mismatched bases, prevent the accumulation of DNA damage, and maintain genomic stability. Mutations in MMR genes can lead to defects in mismatch repair function, causing genetic instability and contributing to tumorigenesis. As a key gene in the MMR gene family, MLH1 also plays a crucial role in the development of cancer. Previous studies have shown that MLH1 methylation may play a crucial role in the development, efficacy, and prognosis of gastric cancer.

[0004] Gastric cancer has distinct locational and tissue characteristics, and cancer tissue is a commonly used specimen for gastric cancer research. However, there are difficulties and obstacles in sampling tissue specimens, which are not only costly but also have poor compliance among research subjects. Blood contains a large number of analytes, and research based on DNA methylation in peripheral blood (plasma, serum, and blood cells) provides a new approach for early identification, diagnosis, and treatment of cancer, becoming a research hotspot in recent years. The method of using abnormal peripheral blood DNA methylation as a cancer-related marker is convenient, rapid, and minimally invasive. It can effectively improve the compliance of research subjects, is suitable for population studies, and has good application prospects in cancer-related research.

[0005] Therefore, the present application is dedicated to clarifying a scheme for predicting the efficacy of chemotherapy for gastric cancer based on the methylation level of CpG sites of the MLH1 gene. Summary of the Invention

[0006] In view of this, the primary purpose of this application is to provide the CpG site methylation level of the MLH1 gene for predicting the efficacy of chemotherapy for gastric cancer. By detecting the CpG site methylation level of the MLH1 gene provided in this application, the efficacy of chemotherapy for gastric cancer can be accurately predicted, providing new ideas and methods for the clinical realization of precision medicine for gastric cancer patients.

[0007] In order to achieve the above objectives, this application adopts the following technical solutions:

[0008] One aspect of the present application discloses a CpG site for predicting the efficacy of chemotherapy for gastric cancer, wherein the CpG site is from the MLH1 gene, and the CpG site is any one of chr3:37033633, chr3:37033626, chr3:37033601, and chr3:37033490, or a combination of two or more thereof.

[0009] Another aspect of the present application discloses a composition for predicting the efficacy of chemotherapy for gastric cancer, wherein the composition comprises a reagent for detecting the methylation level of the aforementioned CpG site in a biological sample.

[0010] Another aspect of the present application discloses a kit for predicting the efficacy of chemotherapy for gastric cancer, comprising the composition described above.

[0011] Another aspect of the present application discloses a system for predicting the efficacy of chemotherapy for gastric cancer. The system includes the following modules:

[0012] Data acquisition module: obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from biological samples;

[0013] Data analysis module: Input the obtained methylation levels of MLH1 gene CpG sites into the pre-trained machine learning model;

[0014] Efficacy prediction module: Generates information for predicting the efficacy of chemotherapy for gastric cancer based on the output values ​​of the machine learning model.

[0015] Wherein, the CpG site is as described in this application.

[0016] Another aspect of the present application discloses a computer device or a computer-readable storage medium. The computer device includes a memory and a processor, the memory stores a program, and the computer-readable storage medium stores the program.

[0017] When a processor of a computer device executes the program, or when a program on a computer-readable storage medium is processed and executed, the following method is implemented:

[0018] obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from a biological sample;

[0019] The obtained methylation levels of the MLH1 gene CpG sites were input into the pre-trained machine learning model;

[0020] Generate information for predicting chemotherapy efficacy in gastric cancer based on the output of the machine learning model;

[0021] Wherein, the CpG site is as described in this application.

[0022] Beneficial effects of this application:

[0023] According to the CpG sites in the MLH1 gene provided in this application, as well as the compositions, kits, systems, computer devices and computer-readable storage media of these CpG sites, the methylation levels of the CpG sites of the MLH1 gene in biological samples can be measured, thereby accurately predicting the efficacy of chemotherapy for gastric cancer and providing new ideas and methods for precision medicine for gastric cancer patients in clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The nucleotide sequences of the four MLH1 gene CpG sites in this application, wherein the boxes are methylation sites.

[0025] Figure 2 This is the ROC curve for the combined prediction of the chemotherapy efficacy of gastric cancer using the three CpG sites chr3:37033626, chr3:37033601 and chr3:37033490 in Example 2.

[0026] Figure 3 The three CpG sites chr3:37033626, chr3:37033601 and chr3:37033490 in Example 2 were used to jointly predict the chemotherapy efficacy of gastric cancer based on time-related ROC analysis. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the embodiments of the present application. The technical solutions in the embodiments described below are exemplary and are only possible technical implementations of the present application, not all possible implementations. Those skilled in the art can fully combine the embodiments of the present application to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of the present application.

[0028] The first aspect of the present application discloses a CpG site for predicting the efficacy of chemotherapy for gastric cancer, wherein the CpG site is from the MLH1 gene, and the CpG site is any one of chr3:37033633, chr3:37033626, chr3:37033601, and chr3:37033490, or a combination of two or more thereof.

[0029] In some specific examples, the CpG site is chr3:37033633.

[0030] In other specific examples, the CpG site is chr3:37033626.

[0031] In other specific examples, the CpG site is chr3:37033601.

[0032] In other specific examples, the CpG site is chr3:37033490.

[0033] In other specific examples, the CpG sites are a combination of chr3:37033626, chr3:37033601, and chr3:37033490. The combined prediction of the chemotherapy efficacy of gastric cancer is more effective.

[0034] The second aspect of the present application discloses a composition for predicting the efficacy of chemotherapy for gastric cancer, wherein the composition comprises a reagent for detecting the methylation level of the CpG site described in the first aspect of the present application in a biological sample.

[0035] The third aspect of the present application discloses a kit for predicting the efficacy of chemotherapy for gastric cancer, comprising the composition described in the second aspect of the present application.

[0036] In this application, the chemotherapy mentioned herein refers to drug treatment using commonly used gastric cancer chemotherapy drugs in the art. Specifically, the chemotherapy drugs used include at least one of single-agent chemotherapy and combination chemotherapy. Wherein, the single-agent chemotherapy includes: (1) fluorouracils (such as fluorouracil, capecitabine, tegafur or raltitrexed); (2) taxanes (such as paclitaxel, liposome paclitaxel or docetaxel); (3) irinotecan. The combination chemotherapy includes: (1) platinum combined with fluorouracils; platinums can be oxaliplatin, cisplatin or lobaplatin, and fluorouracils can be fluorouracil, capecitabine, tegafur or raltitrexed; (2) taxanes combined with fluorouracils; taxanes can be paclitaxel, liposome paclitaxel or docetaxel, and fluorouracils can be fluorouracil, capecitabine, tegafur or raltitrexed; (3) taxanes combined with platinums; taxanes can be paclitaxel, liposome paclitaxel or docetaxel, and platinums can be cisplatin, oxaliplatin, lobaplatin or carboplatin; (4 ) Irinotecan combined with platinum; platinum can be cisplatin or lobaplatin; (5) Irinotecan combined with fluorouracil; fluorouracil can be fluorouracil, capecitabine, selegiline or raltitrexed; (6) Taxane combined with platinum combined with fluorouracil; taxane can be docetaxel, paclitaxel or liposome paclitaxel, platinum can be cisplatin or oxaliplatin, fluorouracil can be fluorouracil, capecitabine or selegiline; (7) Epirubicin combined with platinum combined with fluorouracil; platinum can be cisplatin or oxaliplatin, fluorouracil can be fluorouracil, capecitabine or selegiline. It is understood that chemotherapy drugs are not limited to the above. There is no special limitation on the specific medication. The medication is taken according to the doctor's requirements based on the medication or the condition, and will not be elaborated here.

[0037] In the present application, the prediction is achieved by detecting the methylation level of the CpG site.

[0038] As used herein, the term "CpG site" refers to genomic regions where CpG dinucleotides occur at high frequencies, where C represents cytosine, G represents guanine, and p may refer to the phosphodiester bond between cytosine and guanine. CpG sites are typically located in gene promoters or 5' exon regions.

[0039] The term "methylation" refers to the addition of a methyl group to a base, thereby changing the gene expression pattern, and can specifically occur on cytosine at a CpG site in a base sequence.

[0040] The term "CpG site methylation" refers to the epigenetic methylation modification of DNA that occurs on the cytosine group of the CpG site. Mammalian DNA contains a base called 5-methylcytosine (5-mC), whose methyl group is attached to the fifth carbon atom of the cytosine ring. Methylation of 5-methylcytosine only occurs on the cytosine group of CpG, and methylation of CpG sites can inhibit the expression of specific genes by interfering with the binding of transcription factors. In contrast, when unmethylated or hypomethylated occurs, the expression of specific genes increases. It also inhibits the expression of transposons and repetitive sequences in the genome.

[0041] The term "methylation level measurement" refers to measuring the degree of methylation of a nucleic acid sequence, specifically, measuring the methylation level occurring at a cytosine group in a CpG site.

[0042] In some examples, the reagents include a primer pair for amplifying the CpG site, or a probe capable of specifically binding to the CpG site.

[0043] In this application, the term "primer" refers to a nucleic acid sequence with a free 3' hydroxyl group that can form base pairs with a template complementary to a specific base sequence and serve as the starting point for replication of the template strand. The term "amplification" refers to increasing the copy number of a target sequence or its complementary sequence. In this application, it specifically refers to increasing the copy number of a sequence containing a methylated CpG site in the MLH1 gene.

[0044] Primers are capable of initiating DNA synthesis in the presence of a polymerization reagent (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates, in an appropriate buffer and temperature. Specific PCR conditions and the lengths of forward and reverse primers can be appropriately selected according to techniques known in the art without particular limitation.

[0045] Furthermore, the term "probe" refers to a nucleic acid fragment (such as DNA or RNA) that can specifically bind to a nucleic acid, particularly a CpG site of a target gene. Since the probe can be labeled, it is possible to confirm whether a specific nucleic acid sequence exists. The form of the specific probe is not particularly limited, and can be any one of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, an RNA probe, and the like. Specific probes and hybridization conditions can be appropriately selected by those skilled in the art based on known techniques, without particular limitation, and those skilled in the art possess such capabilities.

[0046] In the present application, primers or probes are designed based on measuring the methylation level of the CpG sites of the MLH1 gene. As a preferred example, the primers or probes are designed to be methylation-specific.

[0047] In the application, described primer or probe can be synthesized by phosphoramidite solid phase support or other known method chemical synthesis.In addition, these nucleotide sequences can be modified by the whole bag of tricks known in the art.The concrete example of this type of modification includes but not limited to methylation, capping, replacing with the homologue of one or more natural nucleotides, or internucleotide modification, for example, modifying with uncharged linker (for example methylphosphonate, phosphotriester, phosphoramidate, carbamate etc.) or charged linker (for example phosphorothioate, phosphorodithioate etc.).

[0048] Furthermore, the probe can be modified with a marker that can directly or indirectly provide a detectable signal. Specific examples of the marker include, but are not limited to, radioisotopes, fluorophores, or biotin. For example, the fluorophore can be FAM, TET, Texas Red, 6-JOE, HEX, Cy3, Cy5, rhodamine, or VIC, but is not limited thereto. Furthermore, the probe can also be labeled with a fluorescent substance at the 5' end and a quencher at the 3' end to facilitate visualization using the principle of fluorescence resonance energy transfer (FRET). Among them, specific quenchers can use 6-TAMRA (6-carboxytetramethylrhodamine), black hole quenchers such as BHQ-1, BHQ-2, BHQ-3, etc., but are not limited thereto.

[0049] The probe in the present application can determine and quantify the methylation level of the CpG region from the PCR amplification product by using the primer pair. The methylation level of the CpG site of the MLH1 gene can be detected by using the primer pair and the probe.

[0050] As an example, the reagent in the present application is a primer pair for amplifying the CpG site of the MLH1 gene. The primer pair for amplifying the chr3:37033633, chr3:37033626, chr3:37033601, and chr3:37033490 sites in the present application includes an upstream primer and a downstream primer. The nucleotide sequence of the upstream primer is shown in SEQ ID NO.5, and the nucleotide sequence of the downstream primer is shown in SEQ ID NO.6.

[0051] It is understandable that in the present application, the reagents may also include reagents required for DNA methylation analysis, such as DNA extraction reagents, PCR amplification reagents, conversion fluid, washing fluid, eluent, purification fluid, buffer solution, etc., which can be configured accordingly as needed, so they will not be described one by one here.

[0052] Furthermore, in the present application, the kit also contains instructions, which record the following steps:

[0053] (a) measuring the methylation level of CpG sites of the MLH1 gene in nucleic acid isolated from a biological sample of an individual;

[0054] and, (b) a step of predicting the efficacy of chemotherapy for gastric cancer based on the measured CpG site methylation level of the MLH1 gene.

[0055] In this application, the "biological sample" refers to a peripheral blood sample (which can be blood, plasma or serum) of a subject. The sampling of peripheral blood samples is convenient, rapid and minimally invasive, which can effectively improve the compliance of the research subjects and subsequent sampling and analysis. In this application, the "subject" refers to a patient diagnosed with gastric cancer who has been treated with the chemotherapy regimen described above.

[0056] Furthermore, nucleic acids are isolated from biological samples. Specifically, nucleic acids used to detect whether CpG is methylated include but are not limited to DNA. Samples containing DNA or RNA (including DNA and mRNA) can be used, wherein the DNA or RNA can be single-stranded or double-stranded, or samples containing DNA-RNA hybridization can be used. Nucleic acid mixtures can also be used. The nucleic acid sequence to be detected does not have to be a pure nucleic acid, and the nucleic acid can be a small part of a larger molecule, such as a part of the entire genomic DNA. As an example, the nucleic acid separated in this application is DNA. Based on peripheral blood samples isolated from subjects, the methylation level of the CpG site of the MLH1 gene is measured, thereby being able to predict the efficacy of chemotherapy for gastric cancer with excellent sensitivity, specificity and / or accuracy. Moreover, this method can achieve non-invasive prediction and shows excellent prediction accuracy when using peripheral blood samples.

[0057] As an example, the step (a) includes the following steps:

[0058] (1) Extract genomic DNA from peripheral blood of gastric cancer patients;

[0059] (2) The extracted DNA was subjected to bisulfite conversion and the target fragments were amplified by multiplex PCR;

[0060] (3) Adding specific tag sequences to the sample;

[0061] (4) quantification and sequencing analysis of the labeled products;

[0062] (5) Determine the methylation level of the CpG site of the MLH1 gene through bioinformatics analysis.

[0063] The specific peripheral blood collection adopts the operation method well known in the art, which will not be described here.

[0064] DNA methylation analysis typically utilizes bisulfite conversion, which can distinguish between cytosine (C) and 5-methylcytosine (5-mC). The principle is to convert cytosine (C) to uracil (U), while leaving 5-methylcytosine (5-mC) unchanged. Specifically, after bisulfite treatment, unmethylated cytosine is deaminated to uracil, while methylated cytosine remains unchanged. Subsequently, through PCR amplification, uracil (U) is converted to thymine (T), while 5-mC is restored to C, thereby enabling the identification of methylated and unmethylated sites.

[0065] During the experimental operation, each sample needs to be added with a unique tag sequence for identification. According to the examples of this application, PCR amplification is performed by introducing specific tag sequences into the upstream and downstream linker primers (SEQ ID NOs. 5 and 6); after labeling the sample, sequencing is performed using conventional methods in the field, and a high-throughput sequencing library is constructed by quantitative analysis.

[0066] Finally, bioinformatics analysis based on sequencing data can accurately calculate the methylation level of the target CpG site. The calculation formula is: methylation level = number of reads methylated at the site (i.e., number of reads detecting base C) / total number of reads at the site).

[0067] Further, the step of comparing methylation levels can be performed by algorithms such as random forest, logistic regression analysis, support vector machine, decision tree, neural network or deep learning. In some specific examples, algorithms such as multivariate Cox regression analysis and logistic regression analysis are used. Under the premise of statistical significance, the odds ratio OR or hazard ratio HR is defined as the risk of disease progression increases by (OR-1) or (HR-1) times for every increase in methylation level by one unit. Therefore, if the OR or HR corresponding to this site is <1, it shows that the risk of disease progression decreases for every increase in methylation level by one unit, and it is concluded that when this site is highly methylated, it is predicted that the chemotherapy effect of gastric cancer is good; if the OR or HR corresponding to this site is >1, it shows that the risk of disease progression increases for every increase in methylation level by one unit, and it is concluded that when this site is hypomethylated, it is predicted that the chemotherapy effect of gastric cancer is good. In the present application, the hypermethylation or hypomethylation of the site is judged using the median as the threshold value.

[0068] The present application further discloses a system for predicting the efficacy of chemotherapy for gastric cancer, the system comprising the following modules:

[0069] Data acquisition module: obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from biological samples;

[0070] Data analysis module: Input the obtained methylation levels of MLH1 gene CpG sites into the pre-trained machine learning model;

[0071] Efficacy prediction module: Generates information for predicting the efficacy of chemotherapy for gastric cancer based on the output values ​​of the machine learning model.

[0072] The present application further provides a computer device comprising a memory and a processor, wherein the memory stores a program, and wherein the processor implements the following method when executing the program:

[0073] obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from a biological sample;

[0074] The obtained methylation levels of the MLH1 gene CpG sites were input into the pre-trained machine learning model;

[0075] Generate information for predicting the efficacy of chemotherapy for gastric cancer based on the output values ​​of the machine learning model.

[0076] The present application also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the following method:

[0077] obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from a biological sample;

[0078] The obtained methylation levels of the MLH1 gene CpG sites were input into the pre-trained machine learning model;

[0079] Generate information for predicting the efficacy of chemotherapy for gastric cancer based on the output values ​​of the machine learning model.

[0080] In this application, the computer-readable storage medium may contain program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the storage medium may be program commands specifically designed and configured for the above-mentioned method, or may be program commands known and available to those skilled in the art of computer software.

[0081] By way of example, storage media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CDROMs (Compact Disc Read Only Media) and DVDs (Digital Video Discs), optical and magnetic media (e.g., floppy disks), and hardware devices specifically used to store and execute program instructions, such as ROM, RAM (Random Access Memory), and flash memory. Examples of program instructions include not only machine language code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter. These hardware devices can be configured as one or more software modules to perform the operations of the above-described methods, and vice versa.

[0082] In the above-mentioned system, computer device or readable storage medium, the above-mentioned terminology elements are the same as those mentioned above.

[0083] It is understood that in this application, the machine learning model can adopt any of a variety of types. Alternatively, multiple models can be used in combination. The machine learning model can be a variety of models such as random forest, logistic regression analysis, support vector machine, decision tree, association rule mining, neural network and deep learning, or a combination thereof.

[0084] The following are specific embodiments of the present application. It should be noted that the following specific embodiments are only for illustrative purposes and do not limit the scope of the present application in any way.

[0085] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0086] In addition, unless otherwise specified, methods without specific conditions or steps are conventional methods, and the reagents and materials used are all commercially available.

[0087] Example 1

[0088] This embodiment discloses a method for detecting specific methylation sites of the MLH1 gene in gastric cancer patients based on PCR, and the specific steps are as follows:

[0089] 2.1 Kit Composition

[0090] (1) Primers for detecting methylated CpG sites in the MLH1 gene (i.e., primers for amplifying methylated sites):

[0091]

[0092]

[0093] (2) DNA extraction reagents;

[0094] (3) PCR amplification reagents, conversion solution, buffer, washing solution, elution solution and purification solution.

[0095] 2.2 Sample collection and methylation detection

[0096] 2.2.1 Blood sample collection

[0097] Professional medical staff collected 5 mL of forearm venous blood from gastric cancer patients and placed it in a 0.5 M EDTA anticoagulant tube.

[0098] 2.2.2 DNA extraction

[0099] The FlexiGene DNA Kit (Mershack Biotech, Wuhan) was used for extraction. The specific steps are as follows:

[0100] (1) In a 1.5 mL centrifuge tube, take 300 μl of sample and mix thoroughly with 750 μl of Buffer FG1;

[0101] (2) Centrifuge at 10,000 g for 20 seconds; discard the supernatant and invert the centrifuge tube on clean absorbent paper for 2 minutes to retain the precipitate;

[0102] (3) Add 150 μl of Buffer FG2 / QIAGEN Protease to the centrifuge tube and vortex for 5 seconds to mix until the precipitate is completely dissolved;

[0103] (4) Centrifuge the sample quickly for 3-5 seconds and then place it in a water bath at 65°C for 5 minutes (the sample color changes from red to olive green, indicating protein digestion);

[0104] (5) Add 150 μl of 100% isopropanol to the centrifuge tube and mix thoroughly by inversion until the DNA precipitate becomes flocculent;

[0105] (6) Centrifuge at 10,000 g for 3 minutes; discard the supernatant and place the centrifuge tube upside down on clean absorbent paper for at least 5 minutes to absorb the water and retain the precipitate;

[0106] (7) Air-dry for at least 5 minutes until all the liquid evaporates;

[0107] (8) Add 200 μl of Buffer FG3 to the centrifuge tube, shake at low speed for 5 seconds, and incubate in a 65°C water bath for 1 hour to dissolve the DNA. Take 1 μl of DNA for electrophoresis quality inspection.

[0108] 2.2.3 DNA concentration and quality assessment

[0109] (1) Agarose gel electrophoresis to detect genomic DNA integrity: the electrophoresis bands are clearly visible, with no obvious degradation and no RNA contamination;

[0110] (2) Nanodrop 2000 was used to detect the quality of genomic DNA: concentration ≥ 20 ng / μL, total amount ≥ 1 μg, OD260 / 280 = 1.7-2.0, OD260 / 230 ≥ 1.8.

[0111] 2.2.4 Bisulfite treatment

[0112] Peripheral blood whole genomic DNA was treated with bisulfite to convert unmethylated C in the genomic DNA into U.

[0113] 2.2.5 PCR amplification

[0114] (1) Primers for amplifying methylation sites: upstream primers and downstream primers with nucleotide sequences as shown in SEQ ID NOs. 5 and 6.

[0115] (2) PCR reaction system: The reaction system (20 μL) contains 2 μL sample DNA, 1 liter of HotStarTaq buffer, 3.0 mM Mg 2+ , 0.2 mM dNTP, 1 U HotStarTaq polymerase (Qiagen Inc.) and 1 μL multiplex PCR primer.

[0116] (3) PCR reaction procedure:

[0117] Step 1 95℃, 2min Step 2 94°C, 20 s; 63°C, 40 s; 72°C, 1 min (11 cycles, -0.5°C / cycle) Step 3 94°C, 20 s; 65°C, 30 s; 72°C, 1 min (24 cycles) Step 4 72℃, 2min; 4℃, ∞

[0118] 2.2.6 Adding specific tag sequences to samples

[0119] A specific tag sequence is added to each sample to label the sample. A PCR reaction is performed by introducing a downstream adapter primer containing the specific tag sequence to add the specific tag sequence to the sample.

[0120] (1) PCR reaction system: The reaction system (20 μL) contains 1 μL of multiple amplification product, 1 μL of reaction buffer, 3.0 mM Mg 2+ , 0.2 mM dNTP, 1 U Q5™ DNA polymerase, and 0.3 μL upstream adapter primer, 0.3 μL downstream adapter primer containing the tag sequence; wherein, the upstream adapter primer and the downstream adapter primer are the same as the upstream and downstream primers.

[0121] (2) PCR amplification procedure:

[0122] Step 1 98℃，30s Step 2 98°C, 10 s; 65°C, 30 s; 72°C, 30 s (11 cycles) Step 3 72℃, 5min; 4℃, ∞

[0123] 2.2.7 Sequencing after quantification

[0124] Equal amounts of index PCR amplification products from all samples were mixed and recovered by gel tapping to generate the final high-throughput sequencing library. The fragment length distribution of the library was verified using an Agilent 2100 Bioanalyzer. After precise quantification of the library molarity, FastQ data were generated using an Illumina high-throughput sequencing platform using a 2×150bp paired-end sequencing mode.

[0125] 2.2.8 Bioinformatics Analysis of Sequencing Data

[0126] (1) Using the software FLASH (FLASH: Fast length adjustment of short reads to improve genome assemblies.), the filtered R1 and R2 reads were spliced ​​into longer reads to obtain FastQ files;

[0127] (2) Use FastX (http: / / hannonlab.cshl.edu / fastx_toolkit / index.html) to process the spliced ​​FastQ files and obtain the fa format sequence;

[0128] (3) Then, all reads in the fa were aligned with the reference sequence of the target region using blast+ (Camacho C, (2009) "BLAST+: architecture and applications"), and those that could cover 90% of the target sequence were selected as valid reads, and statistics were performed on them;

[0129] (4) Use blast+ (Camacho C, (2009) "BLAST+: architecture and applications") to align with the reference sequence of the target region again to obtain valid sequencing sequences and eliminate redundant data; calculate the efficiency of base C conversion to T after bisulfite treatment in the valid sequencing data of each sample;

[0130] (5) CpG site methylation analysis was performed to determine the methylation level of the CpG sites of the MLH1 gene in peripheral blood. (Methylation level = number of reads with methylation at the site (i.e., number of reads detecting base C) / total number of reads at the site).

[0131] Example 2

[0132] In this example, 109 gastric cancer patients treated with platinum-based combined with fluorouracil chemotherapy regimens were collected from the First Affiliated Hospital of Anhui Medical University. Peripheral blood samples were collected according to the method of Example 1, and the methylation levels of the CpG sites of the MLH1 gene were detected. The efficacy of the patients was evaluated every two chemotherapy cycles, and follow-up was continued.

[0133] 3.1 Patient enrollment and efficacy evaluation criteria

[0134] (1) Inclusion criteria for gastric cancer patients:

[0135] ① Patients with gastric cancer confirmed by gastroscopy or surgical histopathology, including patients with stage I-III gastric cancer who have undergone radical surgery, and patients with recurrent / metastatic advanced gastric cancer;

[0136] ② Gastric cancer patients with detailed clinical data including current medical history, personal history, family history, physical examination, laboratory tests, and imaging, and complete postoperative pathological data;

[0137] ③ Gastric cancer patients with an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 and an expected survival period of 3 months or more;

[0138] ④ Gastric cancer patients who have not received chemotherapy, radiotherapy, targeted therapy, immunotherapy, etc. before selection.

[0139] ⑤Voluntarily sign the informed consent form.

[0140] (2) Exclusion criteria for gastric cancer patients:

[0141] ① Patients with pathological diagnosis of esophagogastric junction adenocarcinoma, gastric stromal tumor, gastric lymphoma, and gastric melanoma;

[0142] ② No measurable lesions or lesions that cannot be assessed;

[0143] ③ Patients with poor compliance, difficult-to-control mental illness, or inability to stop taking psychotropic drugs.

[0144] ④ Those who are enrolled in clinical trials and treated with new drugs or new chemotherapy regimens;

[0145] ⑤ Those with missing relevant information;

[0146] ⑥ Patients with severe heart, lung, or kidney dysfunction who are unable to receive chemotherapy.

[0147] (3) The efficacy of chemotherapy for gastric cancer was determined using the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1). After every two cycles of chemotherapy, patients with gastric cancer underwent imaging (CT) to evaluate the efficacy of chemotherapy. If the target lesions and non-target lesions showed stable disease (SD), partial response (PR), or complete response (CR), the disease was considered to have not progressed, indicating a good chemotherapy efficacy. If the target lesions and non-target lesions showed progressive disease (PD), the chemotherapy efficacy was considered to be poor. (Dependent variable: 0 = good efficacy; 1 = poor efficacy).

[0148] 3.2 Predicting the efficacy of chemotherapy for gastric cancer

[0149] 48 gastric cancer patients were randomly selected from 109 cases. The methylation levels of the CpG sites of the MLH1 gene were detected according to the method of Example 1. After adjusting for gender, age, smoking, drinking, and differentiation level through logistic regression analysis, the relationship between the MLH1 methylation level in peripheral blood leukocytes and the efficacy of gastric cancer chemotherapy was statistically obtained as shown in Table 1:

[0150] Table 1 Relationship between MLH1 methylation level in peripheral blood leukocytes and chemotherapy efficacy in gastric cancer

[0151]

[0152] In Table 1, * adjusted for sex, age, smoking, alcohol consumption, and differentiation level. a: Methylation levels are expressed as percentages. Data are presented as medians (P25, P75). OR: odds ratio.

[0153] Through the above research, it was found that there were significant differences in the methylation levels of the four CpG sites MLH1-shore-1 (chr3:37033633), MLH1-shore-2 (chr3:37033626), MLH1-shore-3 (chr3:37033601), and MLH1-shore-5 (chr3:37033490).

[0154] Subsequently, the four screened CpG sites will be used as markers to construct and verify a model to predict the efficacy of chemotherapy for gastric cancer.

[0155] 3.3 Model construction and model effectiveness verification

[0156] A total of 48 gastric cancer patients under item 3.2 were used as the training set. Multivariate Cox regression analysis was performed after adjusting for factors such as age, gender, smoking, drinking, and degree of differentiation, and a nomogram model was constructed.

[0157] The remaining 61 gastric cancer patients were used as an independent validation set to verify the model performance. The results are shown in Tables 2-4 and Figure 1-Figure 2 .

[0158] It can be seen that the CpG sites MLH1-shore-1 (OR=1.074, 95%CI=1.012-1.140, P=0.019), MLH1-shore-2 (OR=1.074, 95%CI=1.016-1.135, P=0.012), MLH1-shore-3 (OR=1.072, 95%CI=1.012-1.137, P=0.019) and MLH1-shore-5 (OR=1.081, 95%CI=1.021-1.144, P=0.007) were associated with the efficacy of platinum combined with fluorouracil chemotherapy in patients with advanced gastric cancer (Table 2). Among them, MLH1-shore-2, MLH1-shore-3 and MLH1-shore-5 alone have a relatively good effect in predicting the efficacy of gastric cancer chemotherapy. Furthermore, the combined prediction effect of MLH1-shore-2, MLH1-shore-3 and MLH1-shore-5 is the best. (Table 3 and Figure 1 )

[0159] Furthermore, the methylation levels of MLH1 gene CpG sites (MLH1-shore-1: HR = 1.025, 95% CI = 1.006-1.044, P = 0.011; MLH1-shore-2: HR = 1.026, 95% CI = 1.008-1.045, P = 0.005; MLH1-shore-3: HR = 1.031, 95% CI = 1.011-1.051, P = 0.002; MLH1-shore-5: HR = 1.032, 95% CI = 1.013-1.052, P = 0.001) were significantly correlated with progression-free survival (PFS), and the higher the methylation level, the higher the risk of progression (Table 2). Based on time-related ROC analysis, the nomogram model constructed by combining MLH1-shore-2, MLH1-shore-3, and MLH1-shore-5 markers to predict the 1-, 2-, and 3-year non-progression rates of gastric cancer had an AUC greater than 0.950, and both the sensitivity and specificity were greater than 85.00% (Table 4, Figure 2 ), indicating high accuracy in predicting chemotherapy efficacy in gastric cancer patients.

[0160] Table 2 Correlation between the methylation level of CpG sites of MLH1 gene in peripheral blood and the efficacy of chemotherapy for gastric cancer

[0161]

[0162] Note: a: chr: chromosome; 37033633: genomic location; b: methylation levels are expressed as percentages, and data are described as medians (P 25， P 75); non-progression included stable disease, partial response, or complete response.

[0163] Table 3 Sensitivity, specificity, and area under the curve of the prediction model for gastric cancer chemotherapy efficacy based on the methylation level of CpG sites of the MLH1 gene in peripheral blood

[0164]

[0165] Table 4 ROC analysis of peripheral blood MLH1 gene CpG site methylation levels in predicting 1-year, 2-year, and 3-year PFS

[0166]

[0167] Based on the above results, under the premise of statistical significance, the odds ratio (OR) or hazard ratio (HR) is defined as the risk of disease progression increased by (OR-1) or (HR-1) times for each unit increase in methylation level. Therefore, if the OR or HR corresponding to this site is less than 1, it indicates that the risk of disease progression decreases with each unit increase in methylation level. Therefore, it is concluded that when this site is highly methylated, it is predicted that the chemotherapy response to gastric cancer is good. If the OR or HR corresponding to this site is greater than 1, it indicates that the risk of disease progression increases with each unit increase in methylation level. Therefore, it is concluded that when this site is low in methylation, it is predicted that the chemotherapy response to gastric cancer is good. Based on the statistical analysis results of this study, the chr3:37033633, chr3:37033626, chr3:37033601, and chr3:37033490 sites all showed OR or HR>1, indicating that for every one unit increase in methylation level, the risk of disease progression increased by (OR-1) or (HR-1) times. Taking the median as the threshold, the lower the methylation status of these sites, the better they are as molecular markers for predicting good chemotherapy efficacy in gastric cancer.

[0168] Example 3

[0169] This embodiment discloses a system for predicting the efficacy of chemotherapy for gastric cancer, which includes a data acquisition module, a data processing module, and an efficacy prediction module:

[0170] (1) Data acquisition module: used to obtain the methylation level of the selected MLH1 gene CpG site. The acquisition method can be to detect the methylation level of the specific CpG site of the MLH1 gene using the detection kit disclosed in Example 2.

[0171] (2) Data processing module: used to analyze the test results obtained by computer to obtain methylation levels, perform statistical analysis on the patient's methylation level and the results of chemotherapy efficacy (progression or non-progression), and use Logistic regression or Cox regression analysis to calculate the odds ratio (OR) or hazard ratio (HR). Under the premise of statistical significance, the odds ratio (OR) or hazard ratio (HR) is defined as the risk of disease progression increasing by (OR-1) or (HR-1) times for every unit increase in methylation level. It is understandable that the data processing module includes not only data acquisition modules such as computers, but also some necessary calculation or analysis software, which will not be elaborated here.

[0172] (3) Efficacy prediction module: used to predict the efficacy of chemotherapy for gastric cancer based on the methylation level of the MLH1 gene CpG site and output the predicted results of the chemotherapy efficacy of gastric cancer. Specifically, if the OR or HR corresponding to this site is less than 1, it indicates that the risk of disease progression decreases with each unit increase in the methylation level. Therefore, when this site is highly methylated, it is predicted that the chemotherapy efficacy of gastric cancer is good. If the OR or HR corresponding to this site is greater than 1, it indicates that the risk of disease progression increases with each unit increase in the methylation level. Therefore, when this site is low in methylation, it is predicted that the chemotherapy efficacy of gastric cancer is good.

[0173] It should be noted that the present application is not limited to the above-mentioned embodiments. The above-mentioned embodiments are merely examples, and any embodiments having substantially the same structure and effect as the technical concept within the scope of the present application are all included in the technical scope of the present application. In addition, without departing from the scope of the present application, any other embodiments that can be conceived by those skilled in the art and that combine some of the constituent elements in the embodiments are also included in the scope of the present application.

Claims

1. A CpG site for predicting the efficacy of chemotherapy for gastric cancer, characterized in that: The CpG site is from the MLH1 gene, and the CpG site is any one or a combination of two or more of chr3:37033633, chr3:37033626, chr3:37033601, and chr3:37033490; Preferably, the CpG site is a combination of chr3:37033626, chr3:37033601 and chr3:37033490.

2. The CpG site for predicting the efficacy of chemotherapy for gastric cancer according to claim 1, wherein: The prediction is achieved by detecting the methylation level of the CpG site in the biological sample.

3. A composition for predicting the efficacy of chemotherapy for gastric cancer, characterized in that: The composition comprises a reagent for detecting the methylation level of the CpG site according to any one of claims 1-2 in a biological sample; Preferably, the biological sample is a peripheral blood sample of a subject.

4. The composition according to claim 3, wherein The reagent is a primer pair for amplifying the CpG site, or a probe capable of specifically binding to the CpG site; Optionally, the primer pair for amplifying chr3:37033633, chr3:37033626, chr3:37033601, and chr3:37033490 sites includes an upstream primer and a downstream primer, the nucleotide sequence of the upstream primer is shown in SEQ ID NO.5, and the nucleotide sequence of the downstream primer is shown in SEQ ID NO.

6.

5. A kit for predicting the efficacy of chemotherapy for gastric cancer, characterized in that: Comprising the composition according to claim 3 or 4.

6. The kit according to claim 5, wherein The kit also contains instructions, which describe the following steps: (a) measuring the methylation level of CpG sites of the MLH1 gene in nucleic acid isolated from a biological sample of a subject; and, (b) a step of predicting the efficacy of chemotherapy for gastric cancer based on the measured methylation level of CpG sites of the MLH1 gene; Preferably, the prediction method of step (b) is: if the OR or HR corresponding to this site is less than 1, it indicates that the risk of disease progression decreases with each increase of one unit in the methylation level, and thus it is concluded that when this site is highly methylated, it is predicted that the chemotherapy efficacy of gastric cancer is good; if the OR or HR corresponding to this site is greater than 1, it indicates that the risk of disease progression increases with each increase of one unit in the methylation level, and thus it is concluded that when this site is low in methylation, it is predicted that the chemotherapy efficacy of gastric cancer is good.

7. The kit according to claim 6, wherein The step (a) comprises the following steps: (1) Extract genomic DNA from peripheral blood of gastric cancer patients; (2) The extracted DNA was subjected to bisulfite conversion and the target fragments were amplified by multiplex PCR; (3) Adding specific tag sequences to the sample; (4) quantification and sequencing analysis of the labeled products; (5) Determine the methylation level of the CpG site of the MLH1 gene through bioinformatics analysis.

8. A system for predicting the efficacy of chemotherapy for gastric cancer, characterized in that: The system includes the following modules: Data acquisition module: obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from biological samples; Data analysis module: Input the obtained methylation levels of MLH1 gene CpG sites into the pre-trained machine learning model; Efficacy prediction module: Generates information for predicting the efficacy of chemotherapy for gastric cancer based on the output values ​​of the machine learning model. Wherein, the CpG site is as described in any one of claims 1-2.

9. A computer device comprising a memory and a processor, wherein the memory stores a program, wherein: When the processor executes the program, the following method is implemented: obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from a biological sample; The obtained methylation levels of the MLH1 gene CpG sites were input into the pre-trained machine learning model; Generate information for predicting chemotherapy efficacy in gastric cancer based on the output of the machine learning model; Wherein, the CpG site is as described in any one of claims 1-2.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by the processor, the following method is implemented: obtaining data on the methylation level of CpG sites in the MLH1 gene from nucleic acids isolated from a biological sample; The obtained methylation levels of the MLH1 gene CpG sites were input into the pre-trained machine learning model; Generate information for predicting chemotherapy efficacy in gastric cancer based on the output of the machine learning model; Wherein, the CpG site is as described in any one of claims 1-2.

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