CN115725730A - Gastric cancer specific methylation marker and application thereof in differential diagnosis of gastric cancer and other digestive tract tumors - Google Patents

Gastric cancer specific methylation marker and application thereof in differential diagnosis of gastric cancer and other digestive tract tumors Download PDF

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CN115725730A
CN115725730A CN202111020616.1A CN202111020616A CN115725730A CN 115725730 A CN115725730 A CN 115725730A CN 202111020616 A CN202111020616 A CN 202111020616A CN 115725730 A CN115725730 A CN 115725730A
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gastric cancer
cancer
cpg
cgcgcgg
methylation
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付卫
文路
汤富酬
任杰
周鑫
陆平
廖雨涵
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Peking University
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Peking University
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Abstract

The invention relates to a method for researching the value of DNA methylation in gastric cancer detection by using MCTA-Seq technology, comprehensively analyzing the CpG island methylation pattern of cfDNA of gastric cancer patients, evaluating and mining the clinical value of a methylation CpG island marker for gastric cancer diagnosis, and identifying and diagnosing gastric cancer and other digestive tract tumors by the CpG island methylation pattern of cfDNA. In particular, the present invention relates to the use of a methylation marker or a combination of methylation markers for the preparation of a kit for the differential diagnosis of gastric cancer and other digestive tract tumors. The invention also relates to a kit for differential diagnosis of gastric cancer and other digestive tract tumors.

Description

Gastric cancer specific methylation marker and application thereof in differential diagnosis of gastric cancer and other digestive tract tumors
Technical Field
The invention relates to the field of biology, in particular to a gastric cancer specific methylation marker and application thereof in differential diagnosis of gastric cancer and other digestive tract tumors.
Background
Gastric cancer is one of the most common malignant tumors worldwide, the incidence rate of the gastric cancer is 5 th and the mortality rate is 4 th, and the gastric cancer seriously harms human health [13]. It is estimated that over 100 million new gastric cancer cases and 76.9 million death cases worldwide in 2020, which means that 1 of 13 deaths will die of gastric cancer. China is a high-incidence area of gastric cancer, and the new onset and death rate accounts for about 40 percent of gastric cancer cases all over the world. Reducing the morbidity and mortality of gastric cancer in China is a major public health problem to be solved urgently.
Early diagnosis is critical to improving the prognosis of gastric cancer patients. The survival rate of early gastric cancer after treatment for 5 years is more than 90% [14, 15], while the survival rate of advanced gastric cancer after operation for 5 years is only 30-40%. At present, the diagnosis rate of early gastric cancer in China is lower than 10%, which is far from the diagnosis rate of the early gastric cancer in Japan, korea and other countries. Endoscopic and endoscopic biopsies are the gold standard for diagnosis of gastric cancer. However, endoscopy depends on endoscope equipment and endoscope physician resources, and the cost of endoscopy is relatively high, so that the method belongs to invasive examination and is low in patient acceptance in China. Most patients are willing to undergo endoscopy when the symptoms of the digestive tract are markedly aggravated or continue to be unrelieved. Thus, endoscopes are not suitable for large-scale population screening.
The detection of peripheral blood tumor markers is an easily accepted screening method. Common markers for digestive tract tumors include carcinoembryonic antigen (CEA) and carbohydrate antigens (CA 19-9, CA125 and CA 724), but the sensitivity and specificity of the above markers in the diagnosis of gastric cancer are low. Taking the CEA marker which is most commonly increased for patients with gastric cancer as an example, the sensitivity for diagnosing gastric cancer is only about 20 percent; even if the above markers are combined, the sensitivity does not exceed 30%. In addition, the above all are broad-spectrum tumor markers, and the tumor is also increased to different degrees in other types of tumors such as colorectal cancer, hepatocellular carcinoma, breast cancer, ovarian cancer and the like. Therefore, the method finds the gastric cancer peripheral blood tumor marker with high sensitivity and high specificity, more accurately diagnoses the gastric cancer at early stage, and has important significance for improving the survival rate of gastric cancer patients.
Previous studies have demonstrated that abnormal DNA methylation occurs more frequently in gastric cancer than mutations, and therefore the method of detecting DNA methylation in a test sample is a more specific method of detecting gastric cancer. The MCTA-Seq technology is used for exploring the value of DNA methylation in gastric cancer detection, comprehensively analyzing the CpG island methylation pattern of plasma cfDNA of gastric cancer patients, evaluating and mining the clinical value of a methylation CpG island marker for gastric cancer diagnosis, and exploring a method for differential diagnosis of gastric cancer and other digestive tract tumors through the CpG island methylation pattern of cfDNA.
Disclosure of Invention
To find cancer-specific methylated CpG sites in human gastric cancer tissue, the present inventors first compared gastric cancer tissue (n = 28) with paracancerous gastric mucosal (n = 28) tissue using Principal Component Analysis (PCA). Compared with methylated CpG sites between the stomach cancer tissues and the tissues beside the cancer, 3734 CGCGG-CpG with differential hypermethylation in the stomach cancer tissues are identified. A total of 2332 differentially methylated cgcgcgcgg-CpG were identified from the gastric cancer patients plasma samples in the training group compared to the control sample. Comparing 2332 differentially methylated cgcgcgcgcgg-CpG in gastric cancer plasma with 3734 CGCGCGG-CpG in gastric cancer tissue, there were 1395 common differentially hypermethylated CGCGCGG-CpG. Based on the differential hypermethylated sites of gastric cancer tissues compared to paracancerous tissues, and considering the methylation levels of these sites in a separate set of normal control individuals (n =104, as a pre-selected group), 153 methylated CGCGCGG-CpG regions were finally identified. The classifier consisting of 153 methylated CGCGCGG-CpG regions detects stage I, II, III and IV gastric cancer with the sensitivity of 44%, 59%, 78% and 100% respectively and the specificity of 92%, and the sensitivity is superior to that of a carcinoembryonic antigen marker CEA detection method, so that the correlation between the methylation level of the specific CGCGCGCGG-CpG region and the gastric cancer is established. The inventor also finds that the MCTA-Seq method can effectively diagnose and distinguish gastric cancer, colorectal cancer and liver cancer.
Specifically, the inventors found that multiple methylation sites in circulating free DNA in human plasma are significantly associated with gastric cancer occurrence, and can be used for predicting gastric cancer occurrence and as a marker for early screening of gastric cancer.
In one aspect, the invention relates to a DNA methylation marker set for assessing the risk of or detecting gastric cancer in an individual, said marker set comprising one or more CGCGCGG-CpG regions as depicted in table 2, wherein a higher methylation level of the one or more CGCGCGG-CpG regions in the individual relative to a control is indicative for the individual having a risk of or having gastric cancer.
In one embodiment, the marker set comprises 153 cgcgcgcgg-CpG regions as set forth in table 2.
In another aspect, the invention relates to the use of an agent for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample for the manufacture of a kit for assessing the risk of having gastric cancer or detecting gastric cancer in an individual, the assessing comprising detecting one or more cgcgg-CpG regions in a biological sample from the individual and comparing the methylation level of the cgcgg-CpG regions to a control, wherein specific information for the cgcgg-CpG regions is as set forth in table 2, wherein a higher methylation level of one or more of the cgcgg-CpG regions relative to a control is indicative of the individual having a risk of having gastric cancer or having gastric cancer.
In one embodiment, the biological sample is a sample comprising circulating cell-free DNA (ccfDNA).
In yet another aspect, the present invention relates to a primer for detecting CGCGCGG-CpG regions, which amplifies one or more CGCGCGG-CpG regions among CGCGCGG-CpG regions as set forth in Table 2.
In another aspect, the invention relates to the use of an agent for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample in the manufacture of a kit for detecting one or more CGCGCGG-CpG regions in an individual, the detecting comprising:
1) Extracting a DNA sample from an individual;
2) Amplifying at least one CGCGCGG-CpG region selected from CGCGCGG-CpG regions as set forth in Table 2;
3) Determining the methylation level of the CGCGCGG-CpG region, wherein a higher methylation level of one or more CGCGCGG-CpG regions in the subject relative to a control indicates that the subject is at risk of having gastric cancer or has gastric cancer.
In a further aspect, the present invention relates to a kit for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample, said kit comprising the primers of claim 5 and one or more reagents required for amplifying DNA selected from the group consisting of amplification buffer, dNTPs and enzymes required for amplifying DNA; optionally, the kit is used for differential diagnosis of gastric cancer and other digestive tract tumors; optionally, the kit is used for differential diagnosis of gastric cancer and colorectal cancer; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer; optionally, the kit is used for diagnosing gastrointestinal tumors by MCTA-Seq method; optionally, the kit is used for diagnosing gastric cancer by MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and colorectal cancer by the MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer by MCTA-Seq method.
In another aspect, the invention relates to the use of a reagent for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample in the manufacture of a kit for assessing the risk of having gastric cancer or detecting gastric cancer in an individual, wherein the cgcgcgcgg-CpG region is selected from the group consisting of the CGCGCGG-CpG regions as described in table 2, wherein a higher methylation level of one or more cgcgg-CpG regions in the individual relative to a control is indicative of the individual having a risk of having gastric cancer or having gastric cancer.
In a further aspect, the present invention relates to the use of a reagent for detecting the methylation level of one or more DNA regions in a biological sample for the preparation of a kit for assessing the risk of having gastric cancer or detecting gastric cancer in an individual, wherein said assessing comprises detecting one or more DNA regions in a biological sample from said individual and comparing the methylation level of said DNA regions to a control, wherein said DNA regions are selected from the group consisting of one or more of the following CGIs:
(1)chr2:225906653-225907464，
(2)chr22:24551813-24552696，
(3)chr6:73330942-73333109，
(4)chr8:133492398-133493586，
(5)chr12:95941906-95942979，
wherein a higher methylation level of one or more of the DNA regions relative to a control is indicative of the individual being at risk of or suffering from gastric cancer.
In one embodiment, the DNA region further comprises one or more CGIs selected from the group consisting of:
(6)chr13:46960684-46961670，
(7)chr7:87229551-87229890，
(8)chr4:81951941-81952808，
(9)chr16:58497033-58498595，
(10)chr10:7449376-7455339。
in a further aspect of the invention, the invention relates to a set of DNA methylation markers for distinguishing between risk of having gastric cancer or colorectal cancer in an individual, the set of markers comprising one or more cgcgg-CpG regions as depicted in table 6 and table 7, wherein a methylation level of the CGCGCGG-CpG region in table 6 above the methylation level of the cgcgcgcgg-CpG region in table 7 is indicative for the individual having a higher risk of having gastric cancer than colorectal cancer; if in said individual the methylation level of the CGCGCGG-CpG region of Table 7 is higher than the methylation level of the CGCGG-CpG region of Table 6, then said individual is indicated to have a higher risk of having colorectal cancer than gastric cancer.
In another aspect of the invention, the invention relates to a DNA methylation marker set for distinguishing between individuals at risk of having gastric cancer or liver cancer, the marker set comprising one or more CGCGCGG-CpG regions as depicted in table 8 and table 9, wherein a methylation level of the CGCGCGG-CpG region in table 8 above the methylation level of the CGCGCGG-CpG region in table 9 is indicative for the individual at risk of having gastric cancer above liver cancer; if the methylation level of the CGCGCGG-CpG region of Table 9 is higher than the methylation level of the CGCGG-CpG region of Table 8 in said individual, then said individual is indicated as having a higher risk of having liver cancer than stomach cancer.
In a further aspect of the invention, the invention relates to a DNA methylation marker set for use in distinguishing between a subject having gastric cancer or colorectal cancer or liver cancer, the marker set comprising one or more CGCGCGG-CpG regions as depicted in table 6, table 7, table 8 and table 9, wherein a methylation level of the CGCGCGG-CpG region in table 6 is higher than the methylation level of the cgcgg-CpG region in table 7 and a methylation level of the cgcgg-CpG region in table 8 is higher than the methylation level of the cgcgcgcgg-CpG region in table 9, indicates that the subject has a higher risk of having gastric cancer than colorectal cancer and liver cancer; indicating that the individual is at higher risk of having colorectal or liver cancer than gastric cancer if the methylation level of the CGCGCGG-CpG region in Table 7 is higher than the methylation level of the CGCGG-CpG region in Table 6 or the methylation level of the CGCGCGG-CpG region in Table 9 is higher than the methylation level of the CGCGG-CpG region in Table 8 in the individual.
In a further aspect of the invention, the invention relates to the use of a methylation marker or a combination of methylation markers selected from the group consisting of the genes shown in table 2; optionally, the methylation marker is selected from DOCK10, CABIN1, KCNQ5, KCNQ3, and USP44; optionally, the methylation marker is selected from KIAA0226L, ABCB, BMP3, NDRG4, and SFMBT2; optionally, the methylation marker is selected from DOCK10 and CABIN1; optionally, the kit is for diagnosing gastrointestinal tumors; optionally, the kit is used for diagnosing gastric cancer; optionally, the kit is used for differential diagnosis of gastric cancer and other digestive tract tumors; optionally, the kit is used for differential diagnosis of gastric cancer and colorectal cancer; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer; optionally, the kit is used for diagnosing gastrointestinal tumors by MCTA-Seq method; optionally, the kit is used for diagnosing gastric cancer by MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and colorectal cancer by the MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer by MCTA-Seq method; optionally, the kit is for detecting cfDNA in a biological sample; optionally, the kit is used for detecting cfDNA in a biological sample by the MCTA-Seq method; further optionally, the biological sample is selected from blood, whole blood or plasma.
In another aspect, the invention relates to a kit comprising reagents for detecting a methylation marker or a combination of methylation markers selected from the group consisting of the genes set forth in table 2; optionally, the methylation marker is selected from DOCK10, CABIN1, KCNQ5, KCNQ3 and USP44; optionally, the methylation marker is selected from KIAA0226L, ABCB, BMP3, NDRG4, and SFMBT2; optionally, the methylation marker is selected from DOCK10 and CABIN1; optionally, the kit is for diagnosing gastrointestinal tumors; optionally, the kit is used for diagnosing gastric cancer; optionally, the kit is used for differential diagnosis of gastric cancer and other digestive tract tumors; optionally, the kit is for differential diagnosis of gastric cancer and colorectal cancer; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer; optionally, the kit is used for diagnosing gastrointestinal tumors by the MCTA-Seq method; optionally, the kit is used for diagnosing gastric cancer by MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and colorectal cancer by MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer by MCTA-Seq method; optionally, the kit is for detecting cfDNA in a biological sample; optionally, the kit is for detecting cfDNA in a biological sample by the MCTA-Seq method; further optionally, the biological sample is selected from blood, whole blood or plasma.
The invention discovers that a gastric cancer diagnosis classifier consisting of 153 pieces of methylated CGCGCGG-CpG is identified by an MCTA-Seq method, the gastric cancer diagnosis classifier has higher sensitivity and specificity, and the detection effect of the gastric cancer diagnosis classifier is obviously superior to that of CEA; the invention also identifies cfDNA methylation markers for distinguishing gastric cancer from colorectal cancer and gastric cancer from liver cancer, and shows that the MCTA-Seq method can effectively diagnose and distinguish gastric cancer, colorectal cancer and liver cancer, thereby proving the potential of the method for cancer noninvasive screening.
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FIG. 1 shows MCTA-Seq identifies gastric cancer specific methylation sites. (A) The main component analysis shows that the gastric cancer tissue (Tgc) and the paracancer gastric mucosa tissue (Tgcaj) have different methylation modes, and the NGM is a normal gastric mucosa tissue; (B) The volcano map of the differential sites of the gastric cancer and the para-carcinoma tissues discovers 3734 CGCGCGG-CpG abnormally hypermethylated in the gastric cancer tissues; (C) Differential site volcano plots of gastric cancer (Pgc) and control (Pn) plasma samples were found for a total of 2332 CGCGCGGs that were abnormally hypermethylated in gastric cancer plasma; two-tailed MWW assay, FDR (false discovery rate) <0.05, mean methylation differences more than 2-fold.
Figure 2 shows the tissue origin of differential methylation sites in gastric cancer plasma. (A) The wien diagram shows the coincidence of gastric cancer tissue specific hypermethylation sites (Tgc) and gastric cancer plasma hypermethylation sites (Pgc), wherein 937 sites are not coincident with the differential sites in gastric cancer tissues; (B) Boxplots show the methylation levels of gastric cancer plasma specific hypermethylated sites in 11 tissues (methylation levels are expressed as MePM, the statistics are the median of the methylation levels of 937 sites in each tissue, and normalized with the methylation value of leukocytes, with two replicates in each tissue).
FIG. 3 shows MCTA-Seq identifies markers for gastric cancer plasma methylation. (A) The heatmap shows the methylation condition of 153 CGCGCGG-CpG in gastric cancer and paracarcinoma tissues, and can effectively distinguish the gastric cancer from the paracarcinoma tissues. Tgc: gastric cancer tissue, tgcaj: normal gastric mucosa tissue adjacent to the cancer. These CGCGCGG-CpG are highly methylated in most colorectal cancer tissues, suggesting that cancer heterogeneity has less impact on the detection sensitivity of these markers. The upper bar graph shows the total methylation levels (MePM) of 153 CGCGCGG-CpG in gastric and paracancerous tissues; (B) Methylation levels of representative marker DOCK10 in gastric cancer plasma (Pgc), normal control plasma (Pn), gastric cancer tissue (Tgc), paracarcinoma tissue (Tgcaj), colorectal cancer tissue (Tcrc), paracarcinoma tissue (Tcrcaj); (C) The detection sensitivity of 153 CGCGCGG-CpG is evaluated, and all gastric cancer tissues are diluted into normal control plasma in a gradient manner, and the result shows that the detection rate of the gastric cancer is as low as 0.016%; the MWW test with two tails shows that the Tp is less than 0.001 and the Tp is less than 0.0001.
FIG. 4 shows two representative markers identified by MCTA-Seq. Methylation levels (MePM) of CABIN1 (a) and KCNQ5 (B) in gastric cancer plasma (Pgc), normal control plasma (Pn), gastric cancer tissue (Tgc), paracarcinoma tissue (Tgcaj), colorectal cancer tissue (Tcrc) and its paracarcinoma tissue (Tcrcaj). The MWW test shows that the Tguchi p is less than 0.01, the Tguchi p is less than 0.0001, ns, and there is no significant difference.
FIG. 5 shows the sensitive detection of gastric cancer methylation markers. The total methylated human genomic DNA was diluted into leukocytes according to a series of ratios, and the number of positive of 153 CGCGCGG-CpG in this series of samples was calculated.
FIG. 6 shows a flow chart of MCTA-Seq for identifying 153 gastric cancer diagnostic markers.
FIG. 7 shows the methylation levels of 153 CGCGG-CpG in different stages of gastric cancer and in normal control plasma. The left panel is the training set and the right panel is the test set. The methylation level was the total of 153 CGCGCGG-CpG methylated values expressed as uMePM. Ordinate through log 2 (uMePM) processing. Double tail MWW test, p<0.05,＊＊p<0.01，＊＊＊＊p<0.0001, ns, with no significant difference.
FIG. 8 shows the effect of 153 CGCGCGG-CpG in differentiating plasma from normal human plasma in patients with gastric cancer. And (A) and (B) displaying the results of the training set. A significant increase in positive counts of 153 markers in patients with early and late stage gastric cancer compared to the control group (a); receiver Operating Characteristic (ROC) curve analysis showed that the area under the curve (AUC) values for 153 CGCGCGG-CpG groups were 0.888, and for stage I, II and III/IV tumor patients 0.898, 0.821 and 0.907 (B), respectively. The positive rates of stage I, II, III and IV tumor patients are respectively 50%, 56%, 72% and 100% (E) by adopting the judgment value of 3 positive counts (more than or equal to 3), and the specificity is 93%; (C) and (D), displaying the results of the test group. AUC values were 0.655, 0.863, 0.958 and 0.860 for stages I, II, III/IV and all periods, respectively. For stages I, II, III, IV, the sensitivity was 36%, 63%, 83% and 100% (E), respectively, and the specificity was 91% (D). The AUC values for the combination training and test groups, stages I, II, III/IV and all stages were 0.778, 0.843, 0.930 and 0.874 (F), respectively, with diagnostic sensitivities of 44%, 59%, 78%, 100%, and specificity of 92%. The MWW test with two ends shows that the 'p' is less than 0.01, the 'p' is less than 0.001, the 'p' is less than 0.0001, ns, p >0.05.
FIG. 9 shows the methylation values, detection effects and heat maps comparing the detection results of 153 CGCGCGG-CpG in plasma cfDNA of gastric cancer patients and normal human plasma cfDNA. CEA (. Gtoreq.5.0 ng/mL) sensitivity to I, II and stage III/IV was 16%, 21% and 23%, respectively. Therefore, the methylation detection using 153 CGCGCGG-CpG is significantly better than the CEA detection.
FIG. 10 shows MCTA-Seq being used to differentiate gastric cancer, colorectal cancer and liver cancer. (A) Principal component analysis shows that the gastric cancer tissue (Tgc), the colorectal cancer tissue (Tcrc) and the liver cancer tissue (Thcc) can be distinguished; (B) The heatmap shows the methylation levels of 200 cancer type-specific methylated CGCGCGG-CpG in gastric and paracarcinoma tissues (Tgcaj), colorectal and paracarcinoma tissues (Tcrcaj), G vs C for gastric cancer specificity and C vs G for colorectal cancer specificity; (C) The heatmap shows the methylation levels of 213 cancer type-specific methylated CGCGCGG-CpG in gastric and paracarcinoma tissues, liver cancer and paracarcinoma tissues (Thccaj), G vs H for gastric cancer specificity and H vs G for liver cancer specificity.
Figure 11 shows the differentiation between gastric cancer and colorectal and liver cancer. (A) Total methylation levels (MePM) of 121 gastric cancer-specific methylated CGCGCGG-CpG (G vs C) and 79 colorectal cancer-specific methylated CGCGG-CpG (C vs G) in gastric and paracancerous tissues, colorectal cancer and its paracancerous tissues; (B) The total methylation levels (MePM) of 166 gastric cancer-specific methylated CGCGCGG-CpG (G vs H) and 47 liver cancer-specific methylated CGCGCGG-CpG (H vs G) in gastric cancer and paracarcinoma tissues, and liver cancer and paracarcinoma tissues; the median of the ratio in the tissues of the stomach cancer and the intestinal cancer is 4.57 and 0.31 respectively; (C) A ratio of the methylation levels of gastric cancer-specific (G vs C) and colorectal cancer-specific (C vs G) methylated CGCGCGG-CpG in gastric and colorectal cancer tissue; (D) (ii) the ratio of the methylation levels of gastric cancer-specific (G vs H) and liver cancer-specific (H vs G) methylated CGCGCGG-CpG in gastric and liver cancer tissues; the median of the ratios in gastric and liver cancer tissues were 73.05, 0.2, respectively; duoyi P is less than 0.0001, double-tail MWW test.
FIG. 12 shows MCTA-Seq distinguishes between gastric, colorectal and liver cancer plasma. (A) Principal component analysis showed that gastric cancer plasma (Pgc), colorectal cancer plasma (Pcrc), liver cancer plasma (Phcc), and normal control individuals could be distinguished; (B) Total methylation levels (uMePM) of 121 gastric cancer-specific methylated CGCGG-CpG (G vs C) and 79 colorectal cancer-specific methylated CGCGG-CpG (C vs G) in different stages of gastric cancer plasma (Pgc) and colorectal cancer plasma (Pcrc) samples; (C) Total methylation levels (uMePM) of 166 gastric cancer-specific CGCGCGG-CpG (G vs H) and 47 liver cancer-specific methylated CGCGG-CpG (H vs G) in different stages of gastric cancer plasma (Pgc) and liver cancer plasma (Phcc) samples.
FIG. 13 shows a flow chart of MCTA-Seq for differential diagnosis of gastric cancer, colorectal cancer and liver cancer.
FIG. 14 shows the construction and diagnostic effect of the MCTA-Seq differential diagnosis classifier. (A) Determining the judgment values of different cancer diagnoses according to the positive numbers of various cancer diagnosis markers in the normal control; (B) Different colors in the histogram indicate the sensitivity of the diagnosis as different results. For example, 48.4% of colorectal cancers at stage I, 3.2% and 9.6% of liver cancers and 38.7% of normal individuals are diagnosed as colorectal cancers. NA indicates no diagnostic result.
FIG. 15 shows the accuracy of MCTA-Seq for differential diagnosis of gastric cancer, colorectal cancer and liver cancer. The results of colorectal cancer, liver cancer and gastric cancer at different stages are differentially diagnosed according to the cancer specific methylation CGCGCGG-CpG, and the ordinate represents the correct diagnosis ratio.
Detailed Description
The present invention can be carried out by the following embodiments, but the present invention is not limited thereto.
The terms used herein have the meanings commonly understood by those of ordinary skill in the art to which the invention pertains. Terms such as "a," "an," and "the" are not intended to refer to only a singular entity, but include the general class of which is used to describe a particular embodiment. The terms used herein are used to describe specific embodiments of the invention, but their use does not limit the scope of the invention unless otherwise specifically indicated in the claims.
The "subject" of the invention may be any human or non-human mammal. Examples of non-human mammals include primates, livestock animals (e.g., horses, cows, sheep, pigs, donkeys), laboratory test animals (e.g., mice, rats, rabbits, guinea pigs), companion animals (e.g., dogs, cats) and captive wild animals (e.g., deer, fox). Preferably, the mammal is a human. According to the invention, the biological sample is selected from blood, whole blood or plasma samples, preferably plasma samples.
The series of DNA regions (markers) identified herein not only provide improved diagnostic results over prior art methods, but in addition enable the development of screening methods that can be designed to focus on providing a high level of diagnostic specificity or a high level of diagnostic sensitivity. As will be understood by those skilled in the art, in the context of diagnostics, "sensitivity" refers to the proportion of positive results that are correctly identified, i.e., the percentage of individuals correctly identified as having the disease in question; "specificity" refers to the proportion of negative results that are correctly identified, i.e., the percentage of individuals that are correctly identified as not having the disease in question.
A "DNA region" as referred to herein is understood to refer to a particular segment of genomic DNA. These DNA regions are designated by reference to a gene name or a set of chromosomal coordinates. Both these gene names and chromosome coordinates will be well known and understood by those skilled in the art. Herein, the chromosomal coordinates correspond to Hg19 version of the genome. In general, a gene can be determined routinely by reference to its name by which its sequence and chromosomal location can be obtained routinely, or by reference to its chromosomal coordinates by which its gene name and its sequence can also be obtained routinely.
DNA methylation
DNA methylation is ubiquitous in bacteria, plants and animals. DNA methylation is a chemical modification of DNA that is stable through multiple rounds of cell division, but which does not involve fundamental DNA sequence changes in organisms. Chromatin modification and DNA modification are two important features of epigenetics and play a role in cell differentiation, allowing cells to stably maintain different characteristics even though they contain the same genomic material. In eukaryotes, DNA methylation occurs only at the number 5 carbon of the pyrimidine ring of cytosine. In mammals, DNA methylation occurs mostly at the number 5 carbon of CpG dinucleotides. CpG dinucleotides account for approximately 1% of the human genome.
70-80% of all CpG's are methylated. CpG can be clustered, referred to as "CpG islands," which are present in the 5' regulatory region of many genes and are often unmethylated. In many disease processes, such as cancer, gene promoters and/or CpG islands acquire abnormal hypermethylation, which is associated with heritable transcriptional silencing. DNA methylation can affect transcription of a gene in two ways. First, methylation of DNA itself can physically block the binding of transcribed proteins to genes, thereby blocking transcription. Second, methylated DNA can be bound by a protein called the methyl-CpG-binding domain protein (MBD). Subsequently, MBD proteins recruit additional proteins to the locus, such as histone deacetylases and other chromatin remodeling proteins that can modify histones, thereby forming compact inactive chromatin (which is referred to as silenced chromatin). This link between DNA methylation and chromatin structure is very important. In particular, the loss of methyl-CpG-binding protein 2 (MeCP 2) has been shown to be significant in Rett syndrome and methyl-CpG binding domain protein 2 (MBD 2) mediates transcriptional silencing of hypermethylated genes in cancer.
"methylation state" is understood to mean the presence, absence and/or amount of methylation at a specific nucleotide or nucleotides within a region of DNA. The methylation state of a particular DNA sequence (e.g., a DNA region as described herein) can represent the methylation state of each base in this sequence, or can represent the methylation state of a base pair subunit in this sequence (e.g., the methylation state of cytosine or of one or more particular restriction enzyme recognition sequences), or can represent information about the methylation density of a region within this sequence, without providing precise information about where methylation occurs in the sequence. The methylation state can optionally be represented by a "methylation value". Methylation values can be generated, for example, by quantifying the amount of intact DNA present after restriction digestion with a methylation dependent restriction enzyme. In this example, if a particular sequence in the DNA is quantified using quantitative PCR, an amount of template DNA that is approximately equal to the mock-treated control indicates that the sequence is not highly methylated, while an amount of template that is significantly less than the amount of template present in the mock-treated sample indicates that methylated DNA is present in the sequence. Thus, for example, the values from the above examples (i.e., methylation values) represent methylation status and can therefore be used as quantitative indicators of methylation status. This is particularly useful when it is desired to compare the methylation state of a sequence in a sample to a threshold value.
The methods of the invention are based on comparing the methylation levels of specific DNA regions of a biological sample to control methylation levels of those DNA regions. A "control level" is a "normal level" which is the level of methylation of a region of DNA of a normal control cell or cell population or in another biological sample from which DNA can be isolated for analysis.
DNA methylation detection
Any method for detecting DNA methylation can be used in the methods of the invention. A number of methods are available for detecting differentially methylated DNA at specific loci in primary tissue samples or in patient samples such as blood, urine feces or saliva (for review see Kristensen and Hansen, clin chem.55:1471-83, 2009 Ammerpohl et al, biochim Biophys acta.1790: 847-62, 2009 Shames et al, cancer Lett.251:187-98, 2007 Clark et al, nat Protoc.1:2353-64, 2006. The method based on sodium bisulfite treatment is one of the commonly used methods for analyzing the proportion or degree of DNA methylation in a target gene. The DNA is normally treated with sodium bisulfite and the region of interest is amplified using primers and PCR conditions that will amplify the DNA independent of methylation status. Methylation of the entire amplicon or individual CpG sites can then be assessed by sequencing (including pyrosequencing), restriction enzyme digestion (COBRA), or by melting curve analysis. Alternatively, ligation-based methods for analyzing methylation at specific CpG sites can be used. The detection of abnormally methylated DNA, which is released from tumors and enters body fluids, is being developed as a means of cancer diagnosis. Here, in the case of over-methylated sequences, it is desirable to use sensitive methods that allow selective amplification of methylated DNA sequences in the background of unmethylated normal cellular DNA. Such methods are based on bisulfite treated DNA, for example; including Methylation Selective PCR (MSP), heavy methyl (Heavy methyl) PCR, head loop PCR and helper-dependent chain reaction (PCT/AU 2008/001475).
In a preferred embodiment, the present invention employs a method that can be used to study the methylation pattern of ccf DNA-Methylated CpG tandem Amplification and Sequencing technology (MCTA-Seq) (Wen L, li J, guo H, liu X, zheng S, zhang D, et al. Genome scale detection of Methylated CpG islands in circulating cell-free DNA of heterocyclic ligands. Cell Res.2015;25 1250-64.). The method comprises the steps of capturing a methylated CGI region (multiple in a gene expression regulation and control region) rich in CGCGCGG in a targeted mode, thereby realizing enrichment and constructing a library, obtaining high-throughput sequencing data based on an Illumina second-generation sequencing platform, and then obtaining a whole genome methylation profile through analysis.
In one embodiment, first the bisulfite converts unmethylated C to U, and methylated C remains as C, distinguishing C in different methylation states. Then adding a primer A to carry out complementary pairing with the transformed sequence, wherein the primer A consists of three parts: random Sequence (RS), unique Molecular Identifier (UMI), and anchor sequence. And then adding a primer B to capture a sequence containing the methylated CGCGCGG, wherein the primer B consists of a CpG short tandem sequence CGCGCGG and an anchoring sequence. Finally, primers C and D are added to carry out exponential amplification on the sequence matched with the primer B through an anchoring sequence pair, and a sequencing joint is added at the same time.
The present invention will be further described with reference to the following detailed description and accompanying drawings. Numerous changes and modifications may be made to the invention as shown in the specific embodiments by those skilled in the art without departing from the spirit and scope of the invention as broadly described.
Materials and methods
Design of research
Subjects were recruited from the general surgery department of the third hospital, beijing university, and were approved by the ethical Committee of the third hospital, beijing university (approval No. IRB 00006761-2016003). All subjects signed written informed consent for collection of samples and subsequent analysis prior to inclusion in the study. In the study, 237 clinical samples were obtained, including 31 gastric cancer and matched paracancer gastric mucosal tissues, 90 plasma samples of gastric cancer patients, and 85 plasma samples of healthy control individuals. Of these samples, 3 pairs of matched gastric cancer and paracancerous gastric mucosal tissues, 1 gastric cancer patient plasma sample, and 3 healthy control individual plasma samples were removed by quality control due to the unique alignment reading less than 10000. Plasma samples that passed quality control were randomly grouped into a training group consisting of 12 stage I, 9 stage II, 25 stage III, 1 stage IV gastric cancer plasma samples and 40 healthy control plasma samples and a test group consisting of 11 stage I, 8 stage II, 22 stage III, 1 stage IV gastric cancer plasma samples and 42 healthy control plasma samples (table 1). Analysis of MCTA-Seq data for gastric carcinoma is based on a Fully Methylated Molecules (FMM) algorithm that takes into account a single CpG site downstream of each CGCGCGG amplicon (CGCGCGG-CpG). MCTA-Seq data for plasma and tissue samples from HCC and CRC patients were from previous studies by the present inventors [4, 9].
TABLE 1 summary of gastric cancer and control plasma sample information
Figure BDA0003241262290000141
DNA Advance and MCTA-Seq library preparation
DNeasy Blood was used&The Tissue Kit (Qiagen) Kit extracts genomic DNA from gastric cancer and paracancerous tissues according to the manufacturer's instructions. 2-4mL of peripheral blood was drawn from gastric cancer patients and control subjects and treated within 6 hours. cfDNA was extracted using the QIAamp circular Nucleic Acid Kit (Qiagen) Kit and quantified using the Qubit HsDNA Kit. MCTA-Seq libraries were constructed as follows. Briefly, the DNA was synthesized by EZ-96DNA Methylation-Direct TM MagPrep (Zymo research) bisulfite treated cfDNA from 2mL plasma. All bisulfite converted cfDNA was subjected to MCTA-Seq library preparation. First, all bisulfite converted DNA was linearly amplified to obtain a hemi-amplicon in a 15. Mu.L reaction containing 1 XNEB buffer 2, 250. Mu.M each dNTP, 0.33. Mu.M MCTA-Seq primer A, and 2.5 units of Klenow fragment without 3 'to 5' exonuclease activity. The reaction was assembled without addition of Klenow fragment and incubated at 95 ℃ for 2 minutes, then maintained at 4 ℃. The Klenow fragment was then added. Then, the reaction conditions of the reaction system were as follows: 4 ℃ for 15 seconds, 10 ℃ for 1 minute, 20 ℃ for 4 minutes, 30 ℃ for 4 minutes, 37 ℃ for 4 minutes and 75 ℃ for 20 minutes (to inactivate the Klenow fragment). In the second step, 5. Mu.L of a mixture containing 1 XEx Taq buffer, 1.5 units of Hot Start E Taq and 1. Mu.M MCTA-Seq primer B was added to 15. Mu.L of the reaction to selectively amplify the enriched CpG region in a 20. Mu.L reaction. The reaction conditions were as follows: 95 ℃ for 3 minutes (to activate the hot start polymerase), followed by 50 ℃ for 2 minutes and 72 ℃ for 1 minute. Then, complete amplicons were amplified in a total of 50 μ L reaction system using index primers C and D by adding 30 μ L of a solution containing 1 × E × Taq bufferr, 250. Mu.M each dNTP, 2. Mu.M index primer C (5'-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACAC GACGCTCTTCCGATCT-3') and 2. Mu.M index primer D (5 ' -CAAGCAGAAGACGGCATACGAGAT)CTGATCGTGACTGGAGTT CAGACGTGTGCT-3', the underlined sequence in primer D corresponds to Illumina index primer), under 14 cycles of 95 ℃ for 30 seconds, 65 ℃ for 30 seconds, 72 ℃ for 1 minute and the last cycle of 72 ℃ for 5 minutes. After the third amplification step, each sample was mixed with six different Illumina index sequences to take 30 μ Ι _ reaction per sample, followed by DNA Clean& Concentrator TM -5Kit (Zymo research) Kit purification to concentrated volume. The resulting product was subjected to electrophoresis on a 3% Agarose gel (Takara, agarose LM SIEVE, D614) and a band of 180-250bp was excised, followed by purification. For plasma samples, two additional rounds of amplification (using primers QP1 (5'-AATGATACGGCGACCACCGA-3') and QP2 (5'-CAAGCAGAAGACGGCATACGA-3')) and gel purification are typically required to clear primer dimers and obtain enough material for sequencing. The final library was sequenced on the Illumina HiSeq2000 platform to generate a 150bp paired-end sequenced sequencing fragment.
Identification of cfDNA methylation markers
To explore cancer-specific methylation markers for gastric cancer diagnosis, the following candidate CGCGCGG-CpG loci were selected: (ii) (a) hypermethylated sites with significant differences in gastric cancer tissues compared to non-cancer tissues (P < 0.01), (b) within 60bp downstream of cgcgcgcgcgg, (c) moderate methylation levels in gastric cancer tissues above 10 MePM, (d) low background methylation frequencies ranging from 0% (0) to 5% (4) in a pre-selected cohort of 104 normal plasma controls from previous studies [34 ].
To identify markers capable of identifying gastric and colorectal cancer as well as gastric and liver cancer, selected from the following markers: (a) methylation that is significantly different in both types of cancer tissue (P <0.01, FC > 2), but not necessarily between cancer and paracancerous tissues, (b) hypermethylation in cancer tissue (median MePM >10 for gastric and colorectal cancer, and liver cancer > 5), hypomethylation in pre-selected normal plasma, at levels similar to diagnostic CGCGCGG-CpG, (c) overall methylation frequency similar to normal plasma.
Data processing
Quality control of paired-end sequencing data was first performed using the fastp tool. Next, the barcode and Unique Molecular Identifier (UMI) are pruned and added to the sequence ID, then further quality filtered using the following criteria: (i) a mass fraction per base in UMI greater than 53; (ii) a non-CG' number of less than 3; and (iii) the length of the trimmed sequenced fragment is greater than 20. Clean sequenced fragments were aligned to the hg19 reference genome using Bismark "- - - -pbat" (v0.20.0) 21493656. CpG tandem sequencing fragments were then extracted, which had to include 2 or 3CG within 3bp from the alignment start position of sequencing fragment 2. Duplicate sequenced fragments were then removed based on UMI information. Finally, methylation values of cgcgcgcgg downstream of the 1-10 CpG were calculated and a methylation matrix of 20,525CG in 9373 CpG islands was obtained. All analyses in this study were based on previously developed FMM algorithms that considered FMMs of methylated CGCGCGG amplicons linked to a single CpG site (CGCGCGG-CpG). In previous studies, it was found that the FMM algorithm outperformed other algorithms based on methylation status of all molecules and individual CpG sites, and that the signal-to-noise ratio of the FMM algorithm was higher, thereby improving the detection sensitivity. Therefore, the present study used the FMM algorithm to analyze MCTA-Seq data. MePM is defined as the number of methylated alleles per million located sequenced fragments in tissue sample analysis and uMePM is defined as MePM adjusted by a unique molecular identifier in plasma sample analysis [9].
Bioinformatics and statistical analysis
The two-sample Wilcoxon test was used to identify methylated gastric cancer sites between the two groups (FDR <0.05, fold change > 2). AUC was determined using the "roc" function of the pROC software package. Unsupervised clustering was performed using the "dist" function and the "Euclidean" method of R, as well as the "hclaust" function and the "ward.D2" method. PCA was performed using the factminer software package for R, discarding methylation sites that were too low (MePM =0 in all samples) or too high (MePM >50 in all samples). And drawing a bar chart, a box chart and a scatter chart by using a ggplot2 software package. Custom R scripts and R software packages are used to build heatmaps, bar charts and box charts and perform PCA and statistical analysis.
Examples
Example 1 identification of methylation markers for gastric cancer
To find cancer-specific methylated CpG sites in human gastric cancer tissue, the study first compared gastric cancer tissue (n = 28) with paracancerous gastric mucosal (n = 28) tissue using Principal Component Analysis (PCA). This analysis is a method used in bioinformatics to reduce variable dimensionality by transforming multiple variable values (20525 sites total, i.e., 20525 variables, in this study the first CpG site unbiased downstream of all CGCGCGG amplicons included in MCTA-Seq data) into a few integrated variable values (two integrated variables, principal component 1 and principal component 2 in this study). The results show that most gastric cancer tissues can be distinguished from paracancerous gastric mucosal tissues (fig. 1A), indicating that there is a large difference in methylation patterns between the two. Next, the present study compared the methylated CpG sites between gastric cancer tissue and paracarcinoma tissue, and identified 3734 CGCGCGG-CpG differentially hypermethylated in gastric cancer tissue (two-tailed MWW test, FDR <0.05, mean fold of methylation >2; FIG. 1B).
A total of 2332 differentially methylated CGCGCGG-CpG from gastric carcinoma was identified by comparing the plasma samples from gastric carcinoma patients in the training group with the control samples (two-tailed MWW test, FDR <0.05, mean fold difference in methylation >2; FIG. 1C). Comparing 2332 differentially methylated CGCGCGG-CpG in gastric cancer plasma with 3734 CGCGCGG-CpG in gastric cancer tissue, there were 1395 common differentially hypermethylated CGCGCGG-CpG (fig. 2A). It is likely that these common hypermethylated sites present in the plasma for gastric cancer result from gastric cancer tissue release. For the remaining characteristic differential methylation sites in gastric cancer plasma, the present study sought a tissue source for these sites. To eliminate background interference of leukocytes, methylation levels of these sites in various tissues were uniformly normalized by methylation levels of leukocytes, and finally 11 tissues from stomach, intestine, liver, lung, kidney, skin, pancreas, muscle, esophagus, heart and brain were compared, and data of these normal tissues were obtained from a previous published study on the source of normal human plasma cfDNA tissue. The results showed that these sites had the highest methylation levels in gastric tissue (fig. 2B), suggesting that these differential methylation sites characteristic of gastric cancer plasma are tissue specific.
Next, the present study further explored plasma cfDNA methylation markers that could be used for diagnosis of gastric cancer. Based on the differential hypermethylated sites of gastric cancer tissues compared to paracancerous tissues, and considering the methylation levels of these sites in a separate set of normal control individuals (n =104, as a pre-selected group), 153 methylated CGCGCGG-CpG were finally identified (table 2). These sites exhibited hypermethylation levels in gastric cancer tissue and a lower methylation background in the plasma of normal control individuals (fig. 3A).
Figure BDA0003241262290000191
Figure BDA0003241262290000201
Figure BDA0003241262290000211
Figure BDA0003241262290000221
Figure BDA0003241262290000231
Figure BDA0003241262290000241
Among the newly identified markers, DOCK10 and CABIN1 showed higher tissue specificity. The two markers were significantly different in methylation level between gastric and colorectal cancer tissues, they remained high in methylation level in gastric cancer, and low in colorectal cancer, indicating strong specificity in gastric cancer tissues (fig. 3B and fig. 4A), and their background values were also extremely low in the normal control group. In previous MCTA-Seq studies on colorectal cancer, KCNQ5 has been shown to be a well-behaved methylation marker for detecting colorectal cancer by blood. And other researchers detect the copy number of the methylated KCNQ5 by a micro-drop digital PCR method, and further reveal the effect of the methylated KCNQ5 on the aspect of blood detection of colorectal cancer. The MCTA-Seq results showed that KCNQ5 also exhibited higher methylation levels in gastric cancer tissues (fig. 4B). A total of 3 different sites of methylated CGCGCGG-CpG in the KCNQ5 genomic region or 3 different CpG sites under the same CGCGCGG were detected, and the methylation of the sites in the plasma of gastric cancer was observed, wherein in 89 cases of the plasma samples of gastric cancer, 37% (33/89) of the sites detected methylation signals in any one site, and the sites detected no methylation signals in the plasma of normal control individuals. These results all together suggest that KCNQ5 is a good marker for blood detection of gastric cancer. However, KCNQ5 was found to be less tissue specific than DOCK10 and CABIN1, showing similar methylation levels in both gastric and colorectal cancer tissues.
The total methylation level of 153 methylated CGCGCGG-CpG in gastric cancer tissue, quantified by MePM, ranged from 699-10319 with a median level of 4938 (FIG. 3A). Notably, these markers exhibited higher methylation levels in all 3 stage I gastric cancer samples, 5845, 6142, 10319, respectively, suggesting that these sites have been hypermethylated in early stage gastric cancer.
To evaluate the detection sensitivity of these 153 methylated CGCGCGG-CpG, the present study diluted the data of gastric cancer tissues into the data of normal plasma samples at a certain ratio to generate data of simulated plasma, and the results showed that the detection rate of gastric cancer was as low as 0.016% (fig. 3C). The present study further validated the absolute quantification of the lower limit of gastric cancer methylation marker detection from the actual data from previous studies (fully methylated genomic DNA was diluted at a certain ratio into leukocyte genomic DNA, and data generated at a series of different dilution ratios were obtained). The positive number of 153 methylated CGCGCGG-CpG is counted, and the result shows that the signal of the gastric cancer marker can be detected from methylated DNA as low as 7.5pg (figure 5).
Summarizing the above results, the present study identified a series of methylation markers that could be used for gastric cancer detection by MCTA-Seq method, showing the potential of MCTA-Seq method for noninvasive diagnosis of gastric cancer (see the flow chart in fig. 6).
Example 2-MCTA-Seq method for plasma testing the effects of gastric cancer
In this example, 153 methylated CGCGCGG-CpG were examined for the effect of detecting gastric cancer in plasma. The total methylation levels of 153 markers in the stomach cancer plasma and the control plasma in the training group and the test group, respectively, were compared. The results show that the methylation levels in the plasma of gastric cancer at different stages are almost significantly higher than those of normal control individuals (fig. 7). The number of positive markers in the stomach cancer plasma was counted (positive is defined as one positive reading as long as a methylation signal was detected at the site). The results showed that the positive numbers of 153 methylation markers in both early and late stage gastric cancer plasma samples were significantly increased compared to the normal control samples (number of positive markers in stage I, II, III/IV gastric cancer plasma samples was 3, 16, respectively, number of positive markers in control samples was 0, p < -0.01, two-tailed MWW test, fig. 8A). The receiver characteristic curve (ROC) analysis showed that the area under the curve (AUC) of 153 methylated CGCGCGG-CpG gastric cancer diagnostic classifiers were 0.90 (95% Confidence Interval (CI): 0.90-0.98), 0.82 (95% CI. According to the point at the upper left corner of the ROC curve, at least two positive (not less than 3) of 153 methylated CGCGCGG-CpG are defined as the judgment value for diagnosing gastric cancer. Based on this determination, the study first made a diagnosis of plasma samples in the training set. The results showed that the gastric cancer diagnostic classifier was able to detect gastric cancer with high sensitivity in the training group, with 50% (6/12), 56% (5/9), 73% (19/26) and 64% (30/47) of detection sensitivity for stages I, II, III and IV, respectively (FIG. 8E), and with 93% (37/40) of detection specificity for the normal control plasma samples. In the validation set, the ROC curves show that the AUC values of 153 methylated CGCGCGG-CpG gastric cancer diagnostic classifiers were 0.66 (95% CI. According to the diagnosis condition of stomach cancer diagnosis that at least two of 153 methylated CGCGCGG-CpG set in the training group are positive (not less than 3), the diagnosis result shows that in the verification group, the sensitivity of the stomach cancer diagnosis classifier for detecting stage I, II, III/IV and all stages of stomach cancer is respectively 36% (4/11), 63% (5/8), 83% (19/23) and 67% (28/42), and the detection specificity is 91% (38/42) for the normal control plasma sample (FIG. 8E). Combining the results of the training group and the validation group, the AUC values of stages I, II, III/IV and all stages were 0.778, 0.843, 0.930, 0.874, respectively. The sensitivity of 153 methylated CGCGCGG-CpG gastric cancer diagnosis classifiers for the detection of gastric cancer at stages I, II, III and IV is 44%, 59%, 78% and 100% respectively, and the specificity is 92%.
Clinically, the carcinoembryonic antigen marker CEA in serum is frequently used as a basis for judging tumors (more than or equal to 5 ng/mL), and the detection results of 153 methylated CGCGCGG-CpG markers in all the plasma including a training group and a test group are compared with the detection result of CEA in the study. The results show that the sensitivity of CEA for I, II and stage III/IV gastric cancer diagnosis is 16%, 21% and 23%, respectively; the heatmap also shows the effect of both detection methods, and it can be seen that MCTA-Seq technique is more effective than CEA in detecting gastric cancer (FIG. 9).
Summarizing the results, the research finds that the MCTA-Seq method identifies a gastric cancer diagnosis classifier consisting of 153 methylated CGCGCGG-CpG, the gastric cancer diagnosis classifier shows higher sensitivity and specificity, and the detection effect of the gastric cancer diagnosis classifier is obviously better than that of CEA.
Example 3 identification of cancer type-specific markers
The MCTA-Seq method has good application prospect in the detection of gastric cancer plasma cfDNA. Further, the present study will explore whether MCTA-Seq technology can distinguish gastric cancer from other digestive tract cancers including colorectal cancer and liver cancer, thereby establishing differential diagnosis classifiers for various cancers. MCTA-Seq data show the results of studies with colorectal cancer tissue (n = 33) and plasma (n = 142), and liver cancer tissue (n = 25) and plasma (n = 36). The main component analysis is carried out on all CGCGCGG measured on cancer tissues and tissues beside the cancer, and the result shows that all tissues of stomach cancer, intestinal cancer and liver cancer can be well distinguished from the corresponding tissues beside the cancer. From the analysis results, it can be seen that three branches are derived from three cancer species, and that the branches are derived from various cancer tissues based on the original non-cancer normal tissues (FIG. 10), which indicates that the cancer type-specific methylation patterns include tissue type-specific methylation patterns and the cancer tissue-specific methylation patterns compared to normal tissues.
The study firstly researches whether a gastric cancer diagnosis classifier consisting of 153 pieces of methylated CGCGCGG-CpG can be directly used for distinguishing gastric cancer, colorectal cancer and liver cancer. 153 markers were used for diagnosis of 142 colorectal cancer plasma samples and 36 liver cancer plasma samples, and the results showed that 93% (132/142) of colorectal cancer plasma samples and 92% (33/36) of liver cancer plasma samples showed positive results, suggesting that the cancer diagnosis classifier itself cannot be used to distinguish cancer types. This study then further identified differential diagnostic markers that could be used to differentiate different cancer types, which not only showed differential methylation between different cancer tissues, but also hardly methylated in normal human plasma of an independent pre-selected group. Comparing two common digestive system tumors, gastric cancer and colorectal cancer, 121 gastric cancer-specific (G vs C) and 79 intestinal cancer-specific (C vs G) methylated cgcgcgcgg-CpG were identified (fig. 10B). Comparison of the total methylation values of two cancer type-specific markers in tissues of gastric and intestinal cancers revealed that the two cancers were well differentiated (FIG. 11A). The ratio of the total methylation values (GAC/CRC) of the two markers in the two cancer types was compared and a significant difference was found between the two (median ratios of GAC/CRC in gastric and intestinal cancers 4.57 and 0.31, respectively, fig. 11C).
Differential diagnosis of gastric cancer and colorectal cancer revealed that 7 of the I-stage gastric cancers (n = 23) were accurately judged as gastric cancer, 4 were judged as colorectal cancer, and 12 were non-cancer. Among the stage II gastric cancers (n = 17), 9 cases were accurately judged as gastric cancers, 1 case was judged as colorectal cancers, and 7 cases were non-cancers. Among the stage III and IV gastric cancers (n = 49), 38 cases were accurately judged as gastric cancers, 0 cases were judged as colorectal cancers, and 11 cases were judged as non-cancers. The results show that the accuracy of correctly distinguishing the gastric cancer of stages I, II, III & IV by the markers is respectively as follows: 64%, 90% and 100%. Of colorectal cancers at stage I (n = 31), 20 cases were accurately judged as colorectal cancers, 5 cases as gastric cancers, and 6 cases as non-cancers. Of colorectal cancers at stage II (n = 70), 52 cases were accurately judged as colorectal cancers, 15 cases were judged as gastric cancers, and 3 cases were non-cancers. Of colorectal cancers at stage III and IV (n = 41), 33 cases were accurately judged as colorectal cancers, 6 cases as gastric cancers, and 2 cases as non-cancers. The results show that the accuracy of correctly distinguishing colorectal cancers at stages I, II, III & IV by the markers is respectively as follows: 80%, 78% and 85%. Of 82 normal control subjects, 74 were judged to be non-cancer, 7 were judged to be gastric cancer, and 1 was judged to be colorectal cancer, with a specificity of 90% (see table 3).
TABLE 3 specific representation of differential diagnosis of MCTA-Seq for gastric cancer and colorectal cancer
Figure DEST_PATH_IMAGE001
In comparison between gastric cancer and liver cancer, 166 gastric cancer-specific (G vs H) and 47 intestinal cancer-specific (H vs G) methylated CGCGCGG-CpG were identified (FIG. 10C). Similarly, the total methylation values of the two cancer type-specific markers were able to distinguish gastric cancer from liver cancer (fig. 11B). The ratio of the total methylation values of the two markers (GAC/HCC) in the two cancer types was also significantly different between gastric and liver cancer (median ratios of GAC/HCC in gastric and intestinal cancer of 7.05 and 0.20, respectively, fig. 11D).
Differential diagnosis of gastric cancer and liver cancer revealed that 10 of the I-stage gastric cancers (n = 23) were accurately determined as gastric cancer, 2 as liver cancer, and 11 as non-cancer. Of the stage II gastric cancers (n = 17), 10 cases were accurately judged as gastric cancers, 7 cases were judged as liver cancers, and 0 case was non-cancer. Among the stage III and IV gastric cancers (n = 49), 38 cases were accurately judged as gastric cancers, 0 case was judged as liver cancers, and 11 cases were judged as non-cancers. The accuracy of the markers for correctly distinguishing the gastric cancer of stages I, II and III & IV is respectively as follows: 83%, 100% and 100%. Of the stage I liver cancers (n = 12), 8 cases were accurately judged as liver cancers, 3 cases were judged as stomach cancers, and 1 case was non-cancer. Of the stage II liver cancers (n = 9), 7 cases were accurately judged as liver cancers, 2 cases were judged as stomach cancers, and 0 case was non-cancer. Of stage III and IV liver cancers (n = 15), 10 cases were accurately judged as liver cancer, 4 cases as stomach cancer, and 1 case as non-cancer. The results show that the accuracy of correctly distinguishing colorectal cancers at stages I, II, III & IV by the markers is respectively as follows: 73%, 78%, 71%. Among 82 normal control subjects, 71 were judged to be non-cancer, 6 were judged to be gastric cancer, and 5 were judged to be liver cancer, and the specificity reached 87% (see table 4).
TABLE 4 specific diagnosis of gastric cancer and liver cancer by MCTA-Seq identification
Figure DEST_PATH_IMAGE002
The principal component analysis of the gastric cancer, colorectal cancer, liver cancer and control plasma samples using these markers specific to different cancer types showed that the plasma samples of different cancers were well separated from the control plasma samples (fig. 12A). The gastric and colorectal cancer plasma samples were compared and the total methylation values of the two cancer type specific markers were counted (G vs C, C vs G), showing that the plasma of the two cancers could be well separated (fig. 12B). Similarly, the markers (G vs H, H vs G) specific to the types of gastric cancer and liver cancer plasma could also distinguish gastric cancer from liver cancer plasma (fig. 12C).
Summarizing the above results, the present study identified cfDNA methylation markers for differentiating gastric cancer from colorectal cancer, gastric cancer from liver cancer.
Example 4 gastric cancerKnotDifferential diagnosis between rectal cancer and liver cancer
Recent studies have shown that DNA methylation can be used to diagnose and differentiate between different cancer types, and thus can be better used for cancer screening. Next, MCTA-Seq technology was explored herein for the diagnosis of expressiveness in different types of cancer by plasma differential diagnosis, and thus for cancer screening.
Different types of cancer diagnostic markers, including 153 gastric cancer diagnostic markers, 80 colorectal cancer diagnostic markers, and 38 liver cancer diagnostic markers from previous studies, were used as the first round of screening. To ensure higher diagnostic specificity, the study was performed on normal control plasma samples from the prescreened group as described above, and the number of positive markers in each group in these normal control individuals was counted, and the criteria for preliminary screening were set based on this (fig. 15). On the basis of ensuring that the specific performance of each group of cancer markers is nearly close to 100%, setting the final judgment value standard as follows: at least 6 stomach cancer markers are positive and judged as stomach cancer (more than or equal to 6), at least 6 colorectal cancer markers are positive and judged as colorectal cancer (more than or equal to 6), and at least 5 liver cancer markers are positive and judged as liver cancer (more than or equal to 5). For the second round of differential diagnosis screening, the study distinguished colorectal and liver cancer by comparing the total methylation levels of two sets of cancer type-specific markers. The specific measures are that firstly, two of three cancers are compared in a plasma sample, three comparisons are carried out, judgment is carried out according to the total methylation value of each group of markers, the identification result of each comparison is obtained, and the cancer can be judged if two or more identification results of certain cancer appear in the three results. The final identification results include gastric cancer, colorectal cancer, liver cancer, normal persons and no judgment (refer to fig. 13 in the diagnosis process, and the diagnosis result is shown in fig. 14).
The markers and the process of the differential diagnosis are used for judging the plasma samples of the gastric cancer, the colorectal cancer, the liver cancer and the normal control individual so as to judge the expressive force of the differential diagnosis classifier. Since all diagnostic and differential diagnostic marker selections and settings of the diagnostic values are based on tissue samples and independent control plasma cohorts, the results of the determination should be unbiased. The results show that under the condition that the specificity reaches 100%, the accuracy rate of correctly distinguishing the gastric cancer at the I & II & III stage in the research is 94%, the accuracy rate of distinguishing the colorectal cancer at the I & II & III stage is 80%, and the accuracy rate of distinguishing the liver cancer at the I & II & III stage is 68%. The sensitivity for detecting I & II & III gastric, colorectal, liver cancers was 56%, 78%, 85%, respectively (see fig. 15 and table 5).
TABLE 5 specific diagnosis of MCTA-Seq for gastric cancer, colorectal cancer and liver cancer
Figure DEST_PATH_IMAGE003
The differential diagnosis marker (G vs C) of the invention for stomach cancer and colon cancer is shown in Table 6, the differential diagnosis marker (C vs G) for stomach cancer and colon cancer is shown in Table 7, the differential diagnosis marker (G vs H) for stomach cancer and liver cancer is shown in Table 8, and the differential diagnosis marker (H vs G) for stomach cancer and liver cancer is shown in Table 9.
Figure BDA0003241262290000321
Figure BDA0003241262290000331
Figure BDA0003241262290000341
Figure BDA0003241262290000351
Figure BDA0003241262290000361
Figure BDA0003241262290000371
Figure BDA0003241262290000381
Figure BDA0003241262290000391
Figure BDA0003241262290000401
Figure BDA0003241262290000411
Figure BDA0003241262290000421
Figure BDA0003241262290000431
Figure BDA0003241262290000441
Figure BDA0003241262290000451
Figure BDA0003241262290000461
Therefore, the results of the research show that the MCTA-Seq method can effectively diagnose and distinguish gastric cancer, colorectal cancer and liver cancer, thereby proving the potential of the MCTA-Seq method for noninvasive cancer screening.
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Although illustrative embodiments have been shown and described, it will be appreciated by those skilled in the art that the above embodiments should not be construed as limiting the present disclosure, and that changes, substitutions, and alterations may be made without departing from the spirit, principles, and scope of the present disclosure.

Claims (13)

1. A set of DNA methylation markers for use in assessing the risk of or detecting gastric cancer in an individual, the set of markers comprising one or more CGCGCGG-CpG regions as set forth in table 2, wherein a higher level of methylation of the one or more CGCGCGG-CpG regions in the individual relative to a control is indicative of the individual being at risk of or having gastric cancer.
2. The marker set of claim 1, wherein the marker set comprises 153 CGCGCGG-CpG regions as set forth in table 2.
3. Use of an agent for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample in the manufacture of a kit for assessing the risk of having gastric cancer or detecting gastric cancer in an individual, the assessing comprising detecting one or more cgcgg-CpG regions in a biological sample from the individual and comparing the methylation level of the cgcgg-CpG regions to a control, wherein the information for the cgcgg-CpG regions is as set forth in table 2, wherein a higher methylation level of one or more of the CGCGCGG-CpG regions relative to a control is indicative of the individual being at risk of having gastric cancer or having gastric cancer.
4. The use according to claim 3, wherein the biological sample is a sample comprising circulating cell-free DNA (ccfDNA).
5. A primer for detecting CGCGCGG-CpG regions, which amplifies one or more cgcgcgcgg-CpG regions among cgcgcgcgg-CpG regions as set forth in table 2.
6. Use of an agent for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample in the manufacture of a kit for detecting one or more CGCGCGG-CpG regions in an individual, the detecting comprising:
1) Extracting a DNA sample from an individual;
2) Amplifying at least one CGCGCGG-CpG region selected from CGCGCGG-CpG regions as set forth in Table 2;
3) Determining the methylation level of the CGCGCGG-CpG regions, wherein a higher methylation level of one or more CGCGCGG-CpG regions in the subject relative to a control indicates that the subject is at risk of having gastric cancer or has gastric cancer.
7. A kit for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample, the kit comprising the primers of claim 5 and one or more reagents required for amplification of DNA selected from the group consisting of amplification buffer, dntps and enzymes required for amplification of DNA; optionally, the kit is used for differential diagnosis of gastric cancer and other digestive tract tumors; optionally, the kit is used for differential diagnosis of gastric cancer and colorectal cancer; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer; optionally, the kit is used for diagnosing gastrointestinal tumors by MCTA-Seq method; optionally, the kit is used for diagnosing gastric cancer by MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and colorectal cancer by the MCTA-Seq method; optionally, the kit is used for differential diagnosis of gastric cancer and liver cancer by MCTA-Seq method.
8. Use of an agent for detecting the methylation level of one or more CGCGCGG-CpG regions in a biological sample in the manufacture of a kit for assessing the risk of having gastric cancer or detecting gastric cancer in an individual, wherein the CGCGCGG-CpG region is selected from the group consisting of the CGCGCGG-CpG regions as recited in table 2, wherein a higher methylation level of one or more cgcgg-CpG regions in the individual relative to a control is indicative of the individual having a risk of having gastric cancer or having gastric cancer.
9. Use of a reagent for detecting the methylation level of one or more DNA regions in a biological sample in the preparation of a kit for assessing the risk of having gastric cancer or detecting gastric cancer in an individual, wherein said assessing comprises detecting one or more DNA regions in a biological sample from said individual and comparing the methylation level of said DNA regions to a control, wherein said DNA regions are selected from one or more of the following CGIs:
(1)chr2:225906653-225907464，
(2)chr22:24551813-24552696，
(3)chr6:73330942-73333109，
(4)chr8:133492398-133493586，
(5)chr12:95941906-95942979，
wherein a higher methylation level of one or more of the DNA regions relative to a control is indicative of the individual being at risk of or suffering from gastric cancer.
10. The use according to claim 9, wherein the DNA region further comprises one or more CGIs selected from the group consisting of:
(6)chr13:46960684-46961670，
(7)chr7:87229551-87229890，
(8)chr4:81951941-81952808，
(9)chr16:58497033-58498595，
(10)chr10:7449376-7455339。
11. a set of DNA methylation markers for distinguishing between individuals at risk of having gastric cancer or colorectal cancer, the set of markers comprising one or more CGCGCGG-CpG regions as set forth in tables 6 and 7, wherein an individual is indicated as having a higher risk of having gastric cancer than colorectal cancer if the methylation level of the CGCGCGG-CpG region in table 6 is higher than the methylation level of the CGCGCGG-CpG region in table 7 in the individual; if in said individual the methylation level of the CGCGCGG-CpG region of Table 7 is higher than the methylation level of the CGCGG-CpG region of Table 6, then said individual is indicated to have a higher risk of having colorectal cancer than gastric cancer.
12. A set of DNA methylation markers for distinguishing between individuals at risk of having gastric cancer or liver cancer, the set of markers comprising one or more CGCGCGG-CpG regions as set forth in tables 8 and 9, wherein if in the individual the methylation level of the cgcgg-CpG region in table 8 is higher than the methylation level of the CGCGCGG-CpG region in table 9, then the individual is indicated as having a higher risk of having gastric cancer than liver cancer; if the methylation level of the CGCGCGG-CpG region of Table 9 is higher than the methylation level of the CGCGG-CpG region of Table 8 in said individual, then said individual is indicated as having a higher risk of having liver cancer than stomach cancer.
13. A set of DNA methylation markers for distinguishing in an individual a risk of having gastric cancer or colorectal cancer or liver cancer, the marker set comprising one or more CGCGCGG-CpG regions as set forth in table 6, table 7, table 8 and table 9, wherein if in the individual the methylation level of the CGCGCGG-CpG region in table 6 is higher than the methylation level of the cgcgg-CpG region in table 7 and the methylation level of the cgcgg-CpG region in table 8 is higher than the methylation level of the CGCGCGG-CpG region in table 9, then indicating that the individual has a higher risk of having gastric cancer than colorectal cancer and liver cancer; if in said individual the methylation level of the CGCGCGG-CpG region in Table 7 is higher than the methylation level of the CGCGCGG-CpG region in Table 6 or the methylation level of the CGCGCGG-CpG region in Table 9 is higher than the methylation level of the CGCGCGCGG-CpG region in Table 8, then said individual is indicated as having a higher risk of having colorectal or liver cancer than stomach cancer.
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