CN118006768A

CN118006768A - Gastric cancer related molecular marker and application thereof

Info

Publication number: CN118006768A
Application number: CN202211398364.0A
Authority: CN
Inventors: 万康康; 董兰兰; 张良禄; 张燕
Original assignee: Wuhan Aimisen Life Technology Co ltd
Current assignee: Wuhan Aimisen Life Technology Co ltd
Priority date: 2022-11-09
Filing date: 2022-11-09
Publication date: 2024-05-10

Abstract

The application relates to a gastric cancer related molecular marker and application thereof. The methylation level of the molecular marker is detected, so that the kit can be used for noninvasive diagnosis or auxiliary diagnosis of gastric cancer, has higher sensitivity and specificity, and has important significance for early diagnosis and treatment of gastric cancer.

Description

Gastric cancer related molecular marker and application thereof

Technical Field

The application relates to the biomedical field, in particular to a gastric cancer related molecular marker and application thereof.

Background

Currently, common gastric cancer screening means include gastroscopy, barium meal imaging, serological detection and the like. Gastroscopy and tissue biopsy are gold standards for diagnosing gastric cancer, but their high price, high requirements for equipment and physicians, low patient compliance, and the like limit the large-scale application of gastroscopy screening. Imaging screening is also unsuitable for large-scale screening due to the disadvantages of radioactivity, low sensitivity (e.g., easy missing of flat, non-recessed lesions), etc. By serological detection of pepsinogen I (PG) or the ratio of pepsinogen I to pepsinogen II (PGR) levels, chronic gastritis, gastric mucosal atrophy, intraepithelial neoplasia, canceration and other diseases can be predicted, but the existing data show that the sensitivity and specificity of diagnosing stomach diseases are not more than 80%, and the detection limits of PG I and PGR for screening common people are not defined in China. Therefore, there is an urgent need to screen reliable biomarkers for predicting the onset of gastric cancer for early detection of gastric cancer.

The occurrence and development of gastric cancer are the result of the combined action of genetic factors and environmental factors, and involve genetic variation and epigenetic changes. One of the environmental factors affects the organism is the methylation regulation of key genes in the genome. Methylation of CpG islands in the gene promoter region plays an important role in regulating gene expression. Typically hypermethylation of the promoter region of an oncogene results in silencing of gene expression and thus in the occurrence of cancer. Thus, the risk of a subject for gastric cancer can be predicted by analyzing whether the methylation level thereof is altered by DNA methylation detection. However, there is no DNA methylation serum marker with high sensitivity and high specificity for diagnosing gastric cancer.

Disclosure of Invention

Based on this, it is necessary to provide one or several highly sensitive, highly specific molecular markers for the preparation of diagnostic gastric cancer products.

In addition, a kit for diagnosing gastric cancer, a risk assessment system for predicting gastric cancer and a device for diagnosing gastric cancer are provided.

The specific technical scheme is as follows:

In a first aspect of the application, there is provided the use of a reagent for detecting the methylation level of a molecular marker comprising one or more of the following regions 1 to 14 or a part of regions 1 to 14 in the preparation of a diagnostic gastric cancer product:

GRCh37 is used as a reference genome, region 1 is Ch1: 53067949-53068398, region 2 is Ch1: 55013461-55013910, region 3 is Ch1: 108507220-108507669, region 4 is Ch4: 2061675-2062124, region 5 is Ch5: 38258470-38258919, region 6 is Ch6: 70576293-70576742, region 7 is Ch6: 152129200-152129649, region 8 is Ch7: 93519983-93520432, region 9 is Ch10: 47083035-47083484, region 10 is Ch11: 44330703-44331152, region 11 is Ch17: 46655139-46655588, region 12 is Ch19: 58238728-58239177, region 13 is Ch19: 58238874-58239323, and region 14 is Ch22: 42827925-42828374.

In one embodiment, the molecular marker comprises a combination of regions 1-14 or a combination of partial regions 1-14.

In one embodiment, the reagent comprises a reagent used in one or more of the following methods, the method comprising: methylation specific fluorescent quantitative PCR, methylation specific PCR, pyrosequencing, bisulfite sequencing, whole genome bisulfite sequencing, digital polymerase chain reaction, methylation specific high resolution dissolution profile, methylation sensitive restriction enzymes, and CpG island microarrays.

In one embodiment, the reagent comprises a primer pair that detects one or more of the methylation levels in regions 1 through 14.

Optionally, the nucleotide sequence of the primer pair in the region 1 is shown as SEQ ID NO. 1-2; the nucleotide sequence of the primer pair in the region 2 is shown as SEQ ID NO. 4-5; the nucleotide sequence of the primer pair in the region 3 is shown in SEQ ID NO. 7-8; the nucleotide sequence of the primer pair of the region 4 is shown as SEQ ID NO. 10-11; the nucleotide sequence of the primer pair in the region 5 is shown as SEQ ID NO. 13-14; the nucleotide sequence of the primer pair of the region 6 is shown as SEQ ID NO. 16-17; the nucleotide sequence of the primer pair of the region 7 is shown as SEQ ID NO. 19-20; the nucleotide sequence of the primer pair of the region 8 is shown as SEQ ID NO. 22-23; the nucleotide sequence of the primer pair of the region 9 is shown as SEQ ID NO. 25-26; the nucleotide sequence of the primer pair of the region 10 is shown as SEQ ID NO. 28-29; the nucleotide sequence of the primer pair of the region 11 is shown as SEQ ID NO. 31-32; the nucleotide sequence of the primer pair of the region 12 is shown as SEQ ID NO. 34-35; the nucleotide sequence of the primer pair of the region 13 is shown in SEQ ID NO. 37-38; the nucleotide sequences of the primer pairs of the region 14 are shown in SEQ ID NO. 40-41.

In one embodiment, the reagent further comprises detection probes corresponding to one or more of regions 1-14.

Optionally, the nucleotide sequence of the detection probe in the region 1 is shown as SEQ ID NO.3, the nucleotide sequence of the detection probe in the region 2 is shown as SEQ ID NO.6, the nucleotide sequence of the detection probe in the region 3 is shown as SEQ ID NO.9, the nucleotide sequence of the detection probe in the region 4 is shown as SEQ ID NO.12, the nucleotide sequence of the detection probe in the region 5 is shown as SEQ ID NO.15, the nucleotide sequence of the detection probe in the region 6 is shown as SEQ ID NO.18, the nucleotide sequence of the detection probe in the region 7 is shown as SEQ ID NO.21, the nucleotide sequence of the detection probe in the region 8 is shown as SEQ ID NO.24, the nucleotide sequence of the detection probe in the region 9 is shown as SEQ ID NO.27, the nucleotide sequence of the detection probe in the region 10 is shown as SEQ ID NO.30, the nucleotide sequence of the detection probe in the region 11 is shown as SEQ ID NO.33, the nucleotide sequence of the detection probe in the region 12 is shown as SEQ ID NO.36, and the nucleotide sequence of the detection probe in the region 13 is shown as SEQ ID NO. 42.

In a second aspect of the application, there is provided a kit for diagnosing gastric cancer comprising a reagent as defined in any one of the preceding claims.

In one embodiment, the test sample of the kit comprises blood, saliva, stool or tissue.

In one embodiment, the kit further comprises one or more of a sequencing reagent, an amplification reagent, a reagent that converts unmethylated cytosine bases to uracil, and a DNA extraction reagent, wherein the amplification reagent further comprises a buffer, dntps, mg ²⁺, a DNA polymerase, and the like.

In a third aspect of the present application, there is provided a risk assessment system for predicting gastric cancer, comprising:

methylation information acquisition module: the method comprises the steps of obtaining the methylation level of a molecular marker in a sample to be detected; and

Sample type judging module: and the method is used for judging whether the type of the sample to be tested is positive for gastric cancer according to the methylation level.

Wherein the molecular marker comprises at least one of region 1 to region 14 or a partial region of region 1 to region 14 defined above.

In one embodiment, the determining method includes constructing a risk assessment model for predicting gastric cancer according to the methylation level to obtain a probability value, and determining a positive sample according to the probability value.

In one embodiment, the risk assessment model is constructed by the following algorithm: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, neural network model and random forest.

In a fourth aspect of the present application, there is provided an apparatus for diagnosing gastric cancer, the apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to effect the steps of:

obtaining the methylation level of a molecular marker in a sample to be tested

Judging whether the type of the sample to be tested is positive for gastric cancer according to the methylation level;

wherein the molecular marker comprises at least one of region 1 to region 14 or a partial region of region 1 to region 14 defined in the above.

In one embodiment, the determining method includes modeling the methylation levels of the regions 1-14 to obtain probability values.

Optionally, the calculation formula of the probability value includes formula I and formula II,

Probability value (P) =e ^f(x)/(1+e^f(x)) formula I

f(x)＝(-0.0387)*ΔCt_{( Region(s) 6)}+(-0.0439)*ΔCt_{( Region(s) 4)}+(-0.0447)*ΔCt_{( Region(s) 2)}+(-0.1087)*ΔCt_{( Region(s) 8)}+(-

0.0428)*ΔCt_{( Region(s) 1)}+(0.0284)*ΔCt_{( Region(s) 14)}+(-0.0458)*ΔCt_{( Region(s) 12)}+(-0.072)*ΔCt_{( Region(s) 7)}+(-0.1004)*ΔCt_{( Region(s) 9)}+(-0.0532)*ΔCt_{( Region(s) 11)}+(-0.1585)*ΔCt_{( Region(s) 13)}+(-0.0964)*ΔCt_{( Region(s) 3)}+(-0.0212)*ΔCt_{( Region(s) 10)}+(-0.1205)*ΔCt_{( Region(s) 5)}+42.6726 Formula II.

Alternatively, when the probability value is 0.5826 or more, it is determined that gastric cancer is positive.

Compared with the prior art, the application has the following beneficial effects:

The application detects methylation levels of 14 selected target areas, and can be used for diagnosing or assisting diagnosis of gastric cancer. The methylation level of the 14 target areas is combined for diagnosing the sensitivity of a blood sample of a gastric cancer patient to 93.8%, the specificity of the blood sample is 90.8%, the blood sample has high sensitivity and specificity, a new thought is provided for noninvasive diagnosis of gastric cancer, and good news is brought to gastric cancer patients.

Drawings

FIG. 1 is a probe with significant methylation level differences;

FIG. 2 is a graph showing methylation levels of a target region in a GSE25869 dataset;

FIG. 3 is a graph showing methylation levels of target regions in GSE 30401 data sets;

FIG. 4 shows methylation levels of target regions in GSE31788 datasets;

FIG. 5 is a ROC curve of training set samples;

fig. 6 is a ROC curve for a validation set sample.

Detailed Description

In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the application, whereby the application is not limited to the specific embodiments disclosed below.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

Interpretation of the terms

In the present application, the term "and/or" is intended to include any and all combinations of one or more of the associated listed items.

The present application relates to "plural", "plural" and the like, and, unless otherwise specified, refers to a number of 2 or more. For example, "one or more" means one kind or two or more kinds. "above" includes the present number, for example "two or more" includes two, three or more.

In the present application, "at least one" and "at least one" mean any one of the listed items, or a combination of any two or more thereof.

As used in this disclosure, "a combination thereof," "any combination thereof," and the like include all suitable combinations of any two or more of the listed items.

In the present application, "preferred", "better", "preferred" are merely embodiments or examples which are better described, and it should be understood that they do not limit the scope of the present application.

In the present application, "further", "still further", "particularly" and the like are used for descriptive purposes to indicate differences in content but should not be construed as limiting the scope of the application.

In the present application, the terms "first", "second", "third", "fourth", etc. are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or quantity, nor as implying an importance or quantity of a technical feature being indicated. Moreover, the terms "first," "second," "third," "fourth," and the like are used for non-exhaustive list description purposes only, and are not to be construed as limiting the number of closed forms.

In the application, the technical characteristics described in an open mode comprise a closed technical scheme composed of the listed characteristics and also comprise an open technical scheme comprising the listed characteristics.

The term "diagnosis" includes auxiliary diagnosis, recurrence risk assessment, assessment of risk and extent of cancerous lesions, prognosis, and the like.

The term "oligonucleotide" or "polynucleotide" or "nucleotide" or "nucleic acid" refers to a molecule having two or more deoxyribonucleotides or ribonucleotides, preferably more than three, and typically more than ten. The exact size will depend on many factors, which in turn depend on the ultimate function or use of the oligonucleotide. The oligonucleotides may be produced in any manner, including chemical synthesis, DNA replication, reverse transcription, or a combination thereof. Typical deoxyribonucleotides of DNA are thymine, adenine, cytosine and guanine. Typical ribonucleotides of RNA are uracil, adenine, cytosine and guanine.

The term "methylation" is a form of chemical modification of DNA that can alter genetic manifestations without altering the DNA sequence. DNA methylation refers to covalent binding of a methyl group at the 5 th carbon position of cytosine of a genomic CpG dinucleotide under the action of a DNA methyltransferase. DNA methylation can cause alterations in chromatin structure, DNA conformation, DNA stability, and the manner in which DNA interacts with proteins, thereby controlling gene expression.

The term "methylation level" refers to whether or not cytosine in one or more CpG dinucleotides in a DNA sequence is methylated, or the frequency/proportion/percentage of methylation, representing both qualitative and quantitative concepts. In practical application, different detection indexes can be adopted to compare the DNA methylation level according to practical conditions. As in some cases, the comparison may be made based on Ct values detected by the sample; in some cases, the ratio of gene methylation in the sample, i.e., number of methylated molecules/(number of methylated molecules+number of unmethylated molecules). Times.100, can be calculated and then compared; in some cases, statistical analysis and integration of each index is also required to obtain a final decision index.

The term "primer" refers to an oligonucleotide that can be used in an amplification method (e.g., polymerase chain reaction, PCR) to amplify a sequence of interest based on a polynucleotide sequence corresponding to a gene of interest or a portion thereof. Typically, at least one of the PCR primers used to amplify a polynucleotide sequence is sequence specific for that polynucleotide sequence. The exact length of the primer will depend on many factors, including temperature, source of primer, and method used. For example, for diagnostic and prognostic applications, the oligonucleotide primers will typically contain at least 10, 15, 20, 25 or more nucleotides, but may also contain fewer nucleotides, depending on the complexity of the target sequence. In the present disclosure, the term "primer" refers to a pair of primers that hybridize to the double strand of a target DNA molecule or to regions of the target DNA molecule that flank the nucleotide sequence to be amplified.

The term "methylation-specific PCR" is one of the most sensitive experimental techniques currently studied for methylation, and a minimum of about 50pg of DNA methylation can be found. After the single-stranded DNA is subjected to bisulfite conversion, all unmethylated cytosines are deaminated to uracil, and methylated cytosines in CpG sites are kept unchanged, so that two pairs of primers aiming at methylated and unmethylated sequences are respectively designed, and the methylated and unmethylated DNA sequences can be distinguished through PCR amplification.

The term "methylation specific fluorescent quantitative PCR (q-MSP)" is an experimental technique combining fluorescent quantitative PCR technology and methylation specific PCR technology. In the technology, proper primer pairs are designed based on sequence differences of DNA in different methylation states after bisulfite conversion, so that methylated sequences and unmethylated sequences are distinguished, but the final detection index of the q-MSP is a fluorescent signal, so that a fluorescent probe or a fluorescent dye is required to be added in addition to a methylation detection primer in a q-MSP reaction system. Compared with the traditional methylation specific PCR technology, the q-MSP detection DNA methylation level has higher sensitivity and specificity, is more suitable for detecting trace amounts of DNA fragments with abnormal methylation mixed in the DNA of patients in early cancer, does not need gel electrophoresis detection, and is simpler and more convenient to operate.

The term "TaqMan probe" refers to a stretch of oligonucleotide sequences comprising a 5 'fluorescent group and a 3' fluorescence quenching group. When the probe binds to the corresponding site on the DNA, the probe does not fluoresce because of the presence of a quenching group near the fluorescent group. During amplification, if the probe binds to the amplified strand, the 5'-3' exonuclease activity of the DNA polymerase (e.g., taq enzyme) digests the probe and the fluorescent group is far from the quenching group, its energy is not absorbed, i.e., a fluorescent signal is generated. The fluorescence signal is also identical to the target fragment with a synchronous exponential increase per PCR cycle.

The term "AUC" is an abbreviation for "area under the curve". Specifically, it refers to the area under the Receiver Operating Characteristic (ROC) curve. ROC curves are graphs of true positive versus false positive rates for different possible cut points of a diagnostic test. Depending on the trade-off between sensitivity and specificity of the selected cut point (any increase in sensitivity will be accompanied by a decrease in specificity). The area under the ROC curve (AUC) is a measure of the accuracy of the diagnostic test (the larger the area the better; the best value is 1; the random test will have the ROC curve lying on the diagonal with an area of 0.5).

The term LASSO is a data dimension reduction method, which comprises the steps of firstly constructing a penalty function, carrying out variable selection on sample data based on the penalty function, compressing original regression coefficients, directly compressing originally small coefficients to 0, thereby treating variables corresponding to the coefficients as non-significant variables, directly discarding the non-significant variables, and reducing certain variables to obtain a more refined model. The method retains the advantage of subset contraction, which is a biased estimate of processing data with complex co-linearity.

An embodiment of the present application provides the use of a reagent for detecting the methylation level of a molecular marker comprising one or more of the following regions 1 to 14 or a partial region of regions 1 to 14 in the preparation of a gastric cancer diagnostic product:

Region 1 is Chr1:53067949-53068398, region 2 is Chr1:55013461-55013910, region 3 is Chr1:108507220-108507669, region 4 is Chr4:2061675-2062124, region 5 is Chr5: 47083035-47083484 8470-47083035-47083484 8919, region 6 is Chr6:70576293-70576742, region 7 is Chr6:152129200-152129649, region 8 is Chr7:93519983-93520432, region 9 is Chr10:47083035-47083484, region 10 is Chr11:44330703-44331152, region 11 is Chr17:46655139-46655588, region 12 is Chr19:58238728-58239177, region 13 is Chr19:58238874-58239323, and region 14 is Chr22:42827925-42828374. The molecular markers provided by the application have higher sensitivity and specificity in the aspect of diagnosing or assisting in diagnosing gastric cancer.

The region positions mentioned in the present application all use GRCh37 as reference genome. In the present application, if the DNA of the region is not specified as a positive strand or a negative strand, it means that the DNA of the region may be a positive strand, a negative strand, or both the positive and negative strands of the DNA of the region. Alternatively, in the present embodiment, the regions 1 to 7, 9 and 11 are positive strands, and the other regions are negative strands.

In a specific example, the partial area of area 1 comprises chr1:53068151-53068285 and/or chr1:53068149-53068198, the partial area of area 2 comprises chr1:55013673-55013767 and/or chr1:55013661-55013710, the partial area of area 3 comprises chr1:108507450-108507597 and/or chr1:108507420-108507469, the partial area of area 4 comprises chr4:2061904-2062015 and/or chr4:2061875-2061924, the partial area of area 5 comprises chr5:38258761-38258872 and/or chr5:38258670-38258719, the partial area of area 6 comprises chr6:70576483-70576607 and/or chr6:70576493-70576542, the partial area of area 7 comprises chr6:152129426-152129539 and/or chr6:152129400-152129449, the partial area of area 8 comprises chr7:93520078-93520189 and/or chr7:2, the partial area of area 9 comprises chr10: 93520183-93520232 and/or chr10: 93520183-93520232, the partial area of area 10 comprises chr11: 93520183-93520232 and/or chr11:2, the partial area of area 11 comprises chr17: 93520183-93520232 and/or chr19: 93520183-93520232 and/or chr19:39319:3: 93520183-93520232, the partial area of area 11 comprises chr7:93520183-93520232 and/or chr19: 93520183-93520232.

It will be appreciated that the partial areas of the areas 1 to 14 may be other partial areas located in the areas not listed above.

In one specific example, the molecular markers include a combination of regions 1-14 or a combination of partial regions of regions 1-14. In this example, by using the combination of regions 1 to 14 as a marker for diagnosing gastric cancer, gastric cancer patients and healthy persons can be effectively distinguished, and the sensitivity and specificity of diagnosis are high.

In one specific example, the reagent comprises a reagent used in one or more of the following methods, the method comprising: methylation specific fluorescent quantitative PCR, methylation specific PCR, pyrosequencing, bisulfite sequencing, whole genome bisulfite sequencing, digital polymerase chain reaction, methylation specific high resolution dissolution profile, methylation sensitive restriction enzymes, and CpG island microarrays.

In one specific example, the reagent includes one or more sets of methylated primer pairs in detection region 1 through region 14.

In one specific example, the nucleotide sequences of the primer pairs of region 1 to region 14 are shown in sequence SEQ ID NO.1～2、SEQ ID NO.4～5、SEQ ID NO.7～8、SEQ ID NO.10～11、SEQ ID NO.13～14、SEQ ID NO.16～17、SEQ ID NO.19～20、SEQ ID NO.22～23、SEQ ID NO.25～26、SEQ ID NO.28～29、SEQ ID NO.31～32、SEQ ID NO.34～35、SEQ ID NO.37～38、SEQ ID NO.40～41.

In one specific example, the reagent further includes detection probes corresponding to one or more of regions 1-14. The probe may be a TaqMan probe, and a fluorescent reporter group and a fluorescent quenching group are labeled.

In a specific example, the probe is labeled with a fluorescence reporter group FAM or ROX or VIC at the 5 'end and a fluorescence quencher group MGB or BHQ-1 at the 3' end. It is understood that the fluorescent groups attached to the probes are not limited thereto, but may be other fluorescent groups.

In a specific example, the nucleotide sequences of the detection probes of region 1 to region 14 are shown in sequence at SEQ ID NO.3、SEQ ID NO.6、SEQ ID NO.9、SEQ ID NO.12、SEQ ID NO.15、SEQ ID NO.18、SEQ ID NO.21、SEQ ID NO.24、SEQ ID NO.27、SEQ ID NO.30、SEQ ID NO.33、SEQ ID NO.36、SEQ ID NO.39、SEQ ID NO.42.

An embodiment of the application also provides a kit for diagnosing gastric cancer, comprising a reagent as defined in any one of the above.

In a specific example, the test sample of the kit may be from blood (whole blood, plasma, serum), saliva, stool, or tissue, or the like.

In a specific example, the kit further comprises nucleic acid extraction reagents, purification reagents, bisulfite conversion reagents, and/or PCR reagents, among others. Wherein the PCR reagents may include buffers, dNTPs, mg ²⁺, DNA polymerase, and the like.

An embodiment of the present application provides a risk assessment system for predicting gastric cancer, including:

In a specific example, the determining method includes constructing a gastric cancer prediction model according to the methylation level to obtain a probability value, and determining the positive sample according to the probability value.

In some specific examples, the molecular marker is a single region, and when the Ct value of the single region is equal to or less than the cut-off value, the sample is considered to be a positive sample, namely gastric cancer positive. The cut-off value, i.e. the positive judgment value, is usually a reference value range of a single side of a healthy human sample within a 95% confidence interval, and can be calculated by using a normal distribution method, a percentile method, or the like.

In some specific examples, the molecular markers are selected from any combination of the regions, a gastric cancer prediction model is required to be constructed according to the methylation level of the molecular markers, whether the sample to be tested is positive for gastric cancer is judged according to the probability value obtained by the prediction model, and when the probability value is greater than or equal to a threshold value, the sample to be tested is positive for gastric cancer.

In one specific example, the gastric cancer prediction model is constructed by the following algorithm: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, neural network model and random forest.

Alternatively, the gastric cancer predictive model is constructed by Logistic regression analysis.

Specifically, a Logistic regression model is constructed according to the methylation level of each region in the sample to be tested. Preferably, the sample to be measured is a blood sample.

Further, an embodiment of the present application also provides an apparatus for diagnosing gastric cancer, the apparatus including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:

obtaining the methylation level of a molecular marker in a sample to be tested

And judging whether the type of the sample to be tested is positive for gastric cancer according to the methylation level.

In one embodiment, the determination method includes constructing a gastric cancer prediction model according to the methylation levels of the regions 1 to 14 to obtain probability values.

Probability value (P) =e ^f(x)/(1+e^f(x)) formula I

In addition, an embodiment of the present application provides a method for diagnosing gastric cancer by detecting the methylation level of the above molecular marker, or diagnosing gastric cancer by using the above reagent, kit, system or device. The method can diagnose or assist in diagnosing gastric cancer, is noninvasive or minimally invasive, has high sensitivity and high specificity, and has important significance for early diagnosis and treatment of gastric cancer.

DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION

Embodiments of the present application will be described in detail below with reference to examples. It is to be understood that these examples are illustrative of the present application and are not intended to limit the scope of the present application. The experimental methods in the following examples, in which specific conditions are not noted, are preferably referred to the guidelines given in the present application, and may be according to the experimental manual or conventional conditions in the art, the conditions suggested by the manufacturer, or the experimental methods known in the art.

In the specific examples described below, the measurement parameters relating to the raw material components, unless otherwise specified, may have fine deviations within the accuracy of weighing. Temperature and time parameters are involved, allowing acceptable deviations from instrument testing accuracy or operational accuracy.

Example 1 screening of target markers

And logging in National Center for Biotechnology Information websites, respectively obtaining data sets numbered GSE25869, GSE30601 and GSE31788 from a GEO database, wherein the 3 data sets are all output by a Illumina HumanMethylation27BeadChip sequencing platform. Wherein the GSE25869 dataset comprises genome-wide methylation data for 32 pairs of gastric cancer tissue and corresponding paracancerous tissue samples; the GSE30601 dataset included genome-wide methylation data for 203 gastric cancer tissues and matched 94 paracancerous tissue samples; the GSE31788 dataset included genome-wide methylation data for 51 gastric cancer tissues and 2 gastric normal tissue samples. Introducing methylation data of the 3 datasets into R software (version 4.1.0), comparing whether each probe has a difference in methylation level on a gastric cancer tissue sample and a corresponding paracancerous sample for GSE25869 dataset and GSE30601 dataset using a two-tailed t-test of paired samples; for the GSE31788 dataset, a two-tailed t-test of unpaired samples was used to compare whether each probe had a difference in methylation level across gastric cancer tissue samples and gastric normal samples. When the P value is less than 0.05, it is considered that the difference in methylation level of the probe in stomach cancer tissue and stomach normal tissue is statistically significant. By analytical comparison, 286, 137 and 1492 probes with significant methylation level differences were obtained from the 3 data sets, respectively, and these probes were crossed to give 67 probes with significant methylation level differences in total, as shown in fig. 1.

67 Differential probes with significant methylation levels in the intersection were further screened to obtain the fewest, most preferred differential methylation probe sets. In this embodiment, the model feature selection is performed by using the LASSO (Least mean solution SHRINKAGE AND selection operator) regression method, and it is desirable to determine a smaller variable model to perform better. The process of further screening probes using LASSO algorithm to reduce the characteristics of the characteristic variables is as follows:

1) Taking a gastric cancer tissue sample and a paracancerous tissue sample of a GSE 30401 data set as data for constructing a regression model, and extracting methylation beta values corresponding to 67 probes;

2) The methylation beta values of 67 probes and the sample type (cancer/paracancerous) were imported into the LASSO algorithm (using the glmnet method in R software);

3) Executing glmnet method, outputting probes with coefficients different from 0, wherein the probes are the probes after screening again;

4) The second and third steps were repeated 1000 times, counting the number of occurrences of 67 probes in 1000 times, and finally retaining the probes that all occurred 1000 times.

The screening was performed to obtain 14 probes satisfying the above conditions. The frequency of occurrence of probes with coefficients other than 0 in the 1000 LASSO regression algorithm is shown in Table 1.

Table 1 frequency of occurrence of probes in 1000 LASSO regression algorithm

The methylation levels of the 14 probes, each of which appeared 1000 times in the LASSO regression algorithm, in the 3 data sets are shown in fig. 2, 3 and 4, respectively, and it can be seen that the 14 probes all showed high methylation levels in gastric cancer tissues, but very low methylation levels in paracancerous normal tissues in the 3 data sets. Based on this, the inventors considered that the combination of the 14 probes could be used as a methylation molecular marker for potential diagnosis of gastric cancer, and thus selected the DNA sequences recognized by the 14 probes numbered 35 to 48 in table 1 as target regions, and verified the performance of diagnosing gastric cancer with the target regions in a sample of gastric cancer patients.

Example 2 method for detecting methylation level of target region

1. Methylation primer pair and detection probe for amplifying target region

The DNA sequences and chromosome positions identified by 14 probes were searched using GRCh37 as a reference genome, and 200bp DNA sequences were used as reference sequences (i.e., target regions) at the upstream and downstream positions identified by the probes, as shown in Table 2. And (3) taking a target area corresponding to each probe in the table 2 as a template, selecting a proper area from the template, designing a methylation detection primer pair and a detection probe, and detecting the methylation level of the target area in the sample to be detected by using a TaqMan real-time fluorescent quantitative PCR system. After the target area is determined, the sequence which is completely methylated and converted by the bisulphite is artificially synthesized on a carrier and used as a template for PCR detection, then primer screening is carried out, a plurality of pairs of methylation detection primers are simultaneously designed for one target area, a SYBR Green real-time fluorescent quantitative PCR system is used for amplification, the specificity of primer amplification is analyzed according to a melting curve, a standard curve is prepared, the amplification efficiency of the primers is calculated, non-specific amplification is reserved, and the methylation primer pair with the amplification efficiency of 90% -110% is obtained. Secondly, in order to ensure that the diagnostic system is suitable for detecting clinical plasma samples, considering that the fragment length of free DNA in plasma is generally about 170bp, the methylation primer pair is designed for a target region, and the length of an amplicon is required to be not more than 170bp. The sequences of the finally determined methylation primer pairs and detection probes for amplifying the target region are shown in Table 3, and the DNA sequences and methylated cytosine sites of the target region detectable by each methylation primer pair and detection probe are shown in tables 4 and 5, respectively.

TABLE 2 target zone positioning

TABLE 3 nucleotide sequences of methylation detection primer pairs and probes

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TABLE 4 methylation detection primer pairs and probe amplified DNA sequences

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TABLE 5 methylation detection primer pairs and probe-recognizable methylated cytosine sites

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2. Extraction of DNA from a sample to be tested

When the sample to be tested is an anticoagulation sample, a plasma layer of the anticoagulation sample is collected through centrifugation, and then a magnetic bead method serum/plasma free DNA (cfDNA) extraction kit (DP 709) of Tiangen biochemical technology (Beijing) limited is used for extracting the plasma cfDNA, and the specific operation is carried out according to the instruction of the kit.

3. Bisulphite conversion

The extracted sample DNA is subjected to bisulphite conversion and DNA purification recovery, and the used nucleic acid conversion kit is a nucleic acid conversion reagent (Ehan mechanical equipment 20200843) of the life technology limited company of Wuhan Ai Misen, and specific experimental operation is described in the specification of the kit.

4. Real-time fluorescent quantitative PCR reaction

And (3) carrying out methylation fluorescent quantitative PCR reaction on the template DNA subjected to bisulfite conversion to detect the methylation state of each target region in the sample to be detected. Since it is difficult to detect the methylation state of 14 target regions simultaneously in the same PCR reaction, a total of 7 PCR reactions are required for a certain sample, and two different target regions and internal genes are detected in each PCR reaction. In the PCR reaction, necessary components such as reaction buffer, dNTP, DNA polymerase, templates and the like, methylation detection primer pairs and detection probes corresponding to two target areas, and detection primer pairs and detection probes of an internal reference gene ACTB are added into one PCR tube. The detection probe used here is a TaqMan probe, the reporter group at the 5 'end of the detection probe of the target region is FAM or ROX, the 3' end quenching groups are MGB, the reporter group at the 5 'end of the detection probe of the ACTB gene is VIC, and the 3' end quenching group is BHQ1. In addition, the upstream detection primers of the ACTB gene are: 5'-AAGGTGGTTGGGTGGTTGTTTTG-3' SEQ ID NO.71, downstream detection primer: 5'-AATAACACCCCCACCCTGC-3' SEQ ID NO.72, the detection probe is: 5'-GGAGTGGTTTTTGGGTTTG-3' SEQ ID NO.73.

PCR amplification was performed using Invitrogen Platinum II Taq hot-start DNA polymerase (Invitrogen, cat: 14966005), and the PCR reaction solution was prepared as shown in Table 6. When detecting each target area in a biological sample, a quality control experiment needs to be set simultaneously, namely a positive control PCR tube and a negative control PCR tube need to be set. For a certain target area, the configuration system of the positive control PCR tube and the negative control PCR tube is the same as that of an experimental test PCR tube, but the template of the positive control PCR tube is formed by mixing 10 ³ copies/microliter of plasmid containing the target area after transformation and 10 ³ copies/microliter of plasmid containing the ACTB gene fragment after transformation in equal volume, and the template of the negative control PCR tube is TE buffer solution. After the system configuration of the positive control PCR tube, the experimental test PCR tube and the negative control PCR tube was completed, the reaction was performed on a fluorescent quantitative PCR instrument according to the procedure provided in table 7.

Table 6 real-time fluorescent quantitative PCR reaction system

TABLE 7 real-time fluorescent quantitative PCR reaction procedure

After qPCR reaction is finished, a baseline is adjusted, a threshold value is set, the threshold value is required to be located in an exponential amplification period, a straight line crossing the threshold value and parallel to the X axis is called a threshold line, and the cycle number corresponding to the intersection point of the threshold line and the amplification curve is called a Ct value. Analyzing the result of qPCR reaction, requiring ① negative control PCR tubes without amplification (i.e., without line-up); ② The positive control PCR tube has obvious index increasing period, and the Ct value of the target gene of the positive control PCR tube is between 26 and 30; ③ The Ct value of the reference gene of the sample to be detected is less than or equal to 33. If the positive control, the negative control and the reference gene all meet the requirements, the detection result of the sample to be detected can be analyzed and the result can be interpreted, otherwise, the experiment is considered invalid, and the detection is required to be carried out again.

Example 3 construction of gastric cancer prediction model Using 14 target regions

1. Sample collection

A total of 225 blood samples of patients diagnosed with progressive gastric cancer by pathological tissue biopsy and 246 blood samples of healthy persons enrolled in the hospital were collected. The volume of each blood sample collected was greater than 10mL. All samples were approved by the ethics committee, all volunteers signed informed consent, and all samples were anonymized.

Blood samples of 135 gastric cancer patients and 148 healthy persons were randomly selected as training sets.

2. Extraction, bisulfite conversion, purification of free DNA from all blood samples in the training set were the same as in example 1.

3. The procedure of example 1 was followed, and the obtained converted training set sample DNA was used as a template, and a real-time fluorescent quantitative PCR was performed, with the same quality control as in example 1.

4. Construction of gastric cancer prediction model

QPCR results of 283 total blood samples in the training set were analyzed, delta Ct values of different target areas in each sample were calculated, delta ct=ct (target area) -Ct (ACTB), and then a Logistic regression model was constructed using IBMSPSS 22.0.0 software. The method comprises the steps of setting a state variable of a gastric cancer plasma sample to be 1, setting a state variable of a healthy human plasma sample to be 0, importing a delta Ct value and a corresponding state variable value of each target area, clicking analysis, regression, binary logic, inputting the state variable value into a dependent variable data frame, inputting the delta Ct value of each target area into a covariant data frame, selecting forward-LR by an analysis method, storing a predicted probability value to obtain a final analysis result, and writing an equation for predicting a risk probability value of gastric cancer of the sample according to a Logistic regression model, wherein the equation is as follows:

Probability(P)＝e^f(x)÷(1+e^f(x))

f(x)＝(-0.0387)*ΔCt_{( Region(s) 6)}+(-0.0439)*ΔCt_{( Region(s) 4)}+(-0.0447)*ΔCt_{( Region(s) 2)}+(-0.1087)*ΔCt_{( Region(s) 8)}+(-0.0428)*ΔCt_{( Zone(s)}

_{Domain 1)}+(0.0284)*ΔCt_{( Region(s) 14)}+(-0.0458)*ΔCt_{( Region(s) 12)}+(-0.072)*ΔCt_{( Region(s) 7)}+(-0.1004)*ΔCt_{( Region(s) 9)}+(-0.0532)*ΔCt_{( Region(s) 11)}+(-0.1585)*ΔCt_{( Region(s) 13)}+(-0.0964)*ΔCt_{( Region(s) 3)}+(-0.0212)*ΔCt_{( Region(s) 10)}+(-0.1205)*ΔCt_{( Region(s) 5)}+42.6726.

The probability value obtained by binary Logistic analysis is used as a test variable, the pathological state of a sample is used as a state variable, the value of the state variable is also set to be 1, a larger test result is selected to represent more definite test, a ROC analysis result is obtained, the probability value when the about log index (sensitivity+specificity-1) is maximum is selected as a cut-off value, and an AUC value, sensitivity and specificity value are obtained, as shown in figure 5, the area under the curve of the prediction model is 0.94, the probability value (cut-off value) is 0.5826 when the about log index is maximum, and the sensitivity of the prediction model is 95.3% and the specificity is 88.0%. The 14 target areas are combined to diagnose the gastric cancer, so that the gastric cancer diagnosis method has good diagnosis effect and can effectively distinguish gastric cancer patients from healthy people.

5. Interpretation of results

And (3) taking the delta Ct value of each target area in the sample to be tested into the formula to obtain a probability value P, wherein if the P value is more than or equal to 0.5826, the sample is positive for gastric cancer, and if the P value is less than 0.5826, the sample is negative for gastric cancer.

Example 4 diagnostic Performance of 14 target regions in validation set

Blood samples of the remaining 90 gastric cancer patients and 98 healthy persons were selected as the validation set.

The methylation detection primer pair and the detection probe provided in example 1 were used to test and verify the delta Ct values of blood samples of 90 gastric cancer patients and 98 healthy persons in the set at the 14 target areas, respectively, by the real-time fluorescence quantitative PCR detection method provided in example 1. The obtained delta Ct values of the target areas in the samples are brought into the formula provided in the embodiment 2, the final probability value P is calculated, and the samples to be tested are evaluated as gastric cancer positive samples or gastric cancer negative samples according to the standard of the result interpretation in the embodiment 2. Evaluation of diagnostic properties of the test sample with the predictive model in example 2 is shown in fig. 6.

As can be seen from FIG. 6, the prediction model constructed by detecting the methylation levels of 14 target regions has excellent effect of diagnosing the sample of the validation set, the area under the curve is 0.96, the sensitivity of diagnosis can reach 93.8%, and the specificity of diagnosis can reach 90.6%. The method provides a noninvasive or minimally invasive, high-sensitivity and high-specificity diagnosis or auxiliary diagnosis method for gastric cancer, and has important significance for early diagnosis and treatment of gastric cancer.

The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. The scope of the application is therefore intended to be covered by the appended claims, and the description and drawings may be interpreted in accordance with the contents of the claims.

Claims

1. Use of a reagent for detecting the methylation level of a molecular marker in the preparation of a diagnostic gastric cancer product, characterized in that the molecular marker comprises one or more of the following regions 1-14 or a partial region of regions 1-14:

2. The use according to claim 1, wherein the molecular markers comprise a combination of regions 1 to 14 or a combination of partial regions of regions 1 to 14.

3. The use according to claim 1 or 2, wherein the agent comprises an agent for use in one or more of the following methods, the method comprising: methylation specific fluorescent quantitative PCR, methylation specific PCR, pyrosequencing, bisulfite sequencing, whole genome bisulfite sequencing, digital polymerase chain reaction, methylation specific high resolution dissolution profile, methylation sensitive restriction enzymes, and CpG island microarrays.

4. The use according to claim 1 or 2, wherein the reagent comprises a primer pair detecting one or more methylation levels in regions 1 to 14;

5. The use of claim 4, wherein the reagent further comprises detection probes corresponding to one or more of regions 1-14;

6. A kit for diagnosing gastric cancer, characterized by comprising a reagent as defined in any one of claims 1 to 5.

7. The kit of claim 6, further comprising one or more of sequencing reagents, amplification reagents, reagents for converting unmethylated cytosine bases to uracil, and DNA extraction reagents.

8. A risk assessment system for predicting gastric cancer, comprising:

Sample type judging module: the method is used for judging whether the type of the sample to be tested is positive for gastric cancer according to the methylation level;

Wherein the molecular marker comprises at least one of region 1 to region 14 or a partial region of region 1 to region 14 as defined in claim 1.

9. The risk assessment system according to claim 8, wherein the determination method comprises constructing a gastric cancer prediction model according to the methylation level to obtain a probability value, and determining a positive sample according to the probability value.

10. The risk assessment system of claim 9, wherein the gastric cancer prediction model is constructed by the following algorithm: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, neural network model and random forest.

11. An apparatus for diagnosing gastric cancer, the apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the program:

obtaining the methylation level of a molecular marker in a sample to be tested

12. The apparatus according to claim 11, wherein the determination method comprises constructing a gastric cancer prediction model according to methylation levels of the regions 1 to 14 to obtain probability values;

Probability value (P) =e ^f(x)/(1+e^f(x)) formula I

f(x)＝(-0.0387)*ΔCt_{( Region(s) 6)}+(-0.0439)*ΔCt_{( Region(s) 4)}+(-0.0447)*ΔCt_{( Region(s) 2)}+(-0.1087)*ΔCt_{( Region(s) 8)}+(-0.0428)*ΔCt_{( Region(s) 1)}+(0.0284)*ΔCt_{( Region(s) 14)}+(-0.0458)*ΔCt_{( Region(s) 12)}+(-0.072)*ΔCt_{( Region(s) 7)}+(-0.1004)*ΔCt_{( Region(s) 9)}+(-0.0532)*ΔCt_{( Region(s) 11)}+(-0.1585)*ΔCt_{( Region(s) 13)}+(-0.0964)*ΔCt_{( Region(s) 3)}+(-0.0212)*ΔCt_{( Region(s) 10)}+(-0.1205)*ΔCt_{( Region(s) 5)}+42.6726 Formula II;