CN117187390A - Novel marker combination for early detection of multi-target digestive tract tumor and application thereof - Google Patents
Novel marker combination for early detection of multi-target digestive tract tumor and application thereof Download PDFInfo
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Abstract
The invention provides a novel multi-target digestive tract tumor (colorectal cancer, liver cancer, esophageal cancer, gastric cancer and pancreatic cancer) early detection combined marker and application thereof, the invention is based on sequencing data of 5 digestive tract tumors, related digestive tract benign lesions and healthy people, and simultaneously combines literature investigation and the like, 15 high-performance digestive system tumor gene methylation markers are screened by utilizing an artificial intelligent analysis method, and the detection of the markers in peripheral blood can have good diagnosis performance for early digestive tract tumors. Meanwhile, 10 protein markers (PG I, PG II, SCCA, ferr, CEA, CA19-9, G-17, AFP-L3 and DCP) of clinical common digestive system tumors are introduced on the basis of methylation detection, and the multi-group chemical combination detection can further improve the diagnosis sensitivity and tracing accuracy and reduce the missed diagnosis rate.
Description
Technical Field
The invention relates to the field of cancer screening, in particular to a novel marker combination for early detection of multi-target digestive tract tumors and application thereof.
Background
Colorectal cancer, liver cancer, esophageal cancer, gastric cancer, pancreatic cancer and other digestive tract tumors, the number of cancer-related deaths caused by colorectal cancer, liver cancer, esophageal cancer, gastric cancer, pancreatic cancer and other digestive tract tumors exceeds lung cancer and breast cancer. Colorectal cancer (Colorectal cancer, CRC) is the most common digestive tract tumor, reaching 190 tens of thousands of new diagnostic cases worldwide in 2020, becoming the third most common cancer following lung and breast cancer. According to the data of the international cancer research institution, 110 cases of digestive tract tumors, 90 cases of liver cancer, 60 cases of esophageal cancer and 50 cases of pancreatic cancer are newly diagnosed worldwide in the same year (Cancers (Basel), 2023.15 (7)).
Although the prognosis of many digestive tract tumors has improved over the past several decades, diagnosis of advanced cancers remains a major cause of all digestive tract tumor-related deaths (Gastroenterology, 2020.159 (1): p.335-349.e15). The early diagnosis rate of the digestive tract tumor is improved, and a better prognosis can be brought to the digestive tract tumor patients. Currently, tissue biopsies under endoscopic or CT guidance in combination with serum tumor biomarkers are the main method for diagnosing digestive tract tumors (Ann Transl Med,2017.5 (8): p.187). Among them, tissue biopsy is considered as a gold standard, and can provide tumor tissue traceability information, determine the state of gene mutation, and provide prognostic information. However, the above detection methods also have limitations, such as the possibility of obtaining insufficient or inaccurate tissue samples, leading to false positive or false negative results. In addition, tissue biopsies can cause damage to the patient. To date, serum-based biomarkers, such as carcinoembryonic antigen (CEA), CA19-9, CA72-4, and Alpha Fetoprotein (AFP), have been identified for diagnosis, prognosis, and recurrence monitoring of digestive tract tumors (Hepato-gastrology, 2011.58 (112): p.2166-70). However, these biomarkers have limited sensitivity and specificity and are poorly effective in the detection of early gut tumors (Gastric Cancer,2014.17 (1): p.26-33). Thus, to facilitate population screening, reduce mortality associated with cancer, there is an urgent need for a non-invasive, simple and robust universal digestive tract tumor screening method.
In contrast to tissue biopsies, liquid biopsies are a minimally invasive method capable of monitoring and early identification of changes in cells or cell products transferred from malignant lesions into body fluids in real time. Meanwhile, due to the minimally invasive nature of the liquid biopsy, complications caused by the tissue biopsy can be prevented. Liquid biopsies include enrichment and isolation of Circulating Tumor Cells (CTCs), circulating tumor DNA (ctDNA) and other tumor genetic material, such as extracellular vesicles (Evs) (Mol Cancer,2023.22 (1): p.7). ctDNA has become a research hotspot for early cancer screening due to simple collection and abundant signal features. ctDNA contains a number of changes, including methylation, mutation, and copy number changes, and can be used for early cancer detection. However, methylation changes in ctDNA occur earlier than genomic changes such as mutations and copy number changes, exhibit abundant cancer and tissue specificity, and exhibit significant stability in body fluids (Nat Rev Clin Oncol,2018.15 (5): p.292-309). GRAIL performed a circulating cell-free genomic profile (CCGA) study with milestone significance. In their recently published paper (CCGA-3), the results of the test set (n=1969) showed that targeted methylation technology of GRAIL achieved high specificity (99.3%) in more than 50 cancer types. The sensitivity of stage I-III was 67.6% in 12 pre-specified cancers (approximately 63% of annual cancer deaths in the united states), 40.7% in all cancers, and cancer signals were detectable in more than 50 cancer types. The overall accuracy of tissue tracing (CSO) prediction of true positives was 88.7%. This study demonstrates the feasibility of multi-cancer early screening based on blood free DNA methylation.
Although single-panel detection techniques have been widely studied and applied, the occurrence of malignant tumors involves multiple pathological processes of different levels and dimensions, such as genes, epigenetic, transcriptome, microorganisms, proteins and metabolism, and if only single-panel features are analyzed, the screening of targets is greatly limited. On one hand, the multi-group characteristic integration analysis can mutually verify, and the persuasion of the detection result is enhanced; on the other hand, the method can capture early cancer signals in a multi-dimensional and omnibearing way, and improves the detection sensitivity.
In terms of multiple sets of single cancer detection, the U.S. FDA approved the first product cologard worldwide for colorectal cancer screening by fecal occult blood and gene multi-target combined detection (FIT-DNA) in 2014. The product is recommended by a plurality of authoritative organizations such as the American disease prevention working group and the like to be applied to early colorectal tumor screening of asymptomatic people of proper age. As a core product of Exact Science, cologard utilized three types of biomarkers: methylation of NDRG4 and BMP3 genes, point mutation of KRAS gene and hemoglobin in fecal occult blood. The final product exhibited a sensitivity of 92.3% for colorectal cancer and 42.4% for advanced adenomas, with a specificity of 87% (N Engl J Med,2014.371 (2): p.187). Meanwhile, the ESMO published liver cancer detection platform in 2022 years Liver is on line. The product is used for early screening of liver cancer by detecting methylation of free DNA and protein markers in peripheral blood. In 2022 Exact Sciences published +.5 in journal Clinical Gastroenterology and Hepatology>Recent work by Liver, chalasani et al (Clin Gastroenterol Hepatol,2022.20 (1): p.173-182.e7), incorporated blood samples from 540 patients (136 cases+404 controls), tested for inclusion of 3 DNA methylation markers (HOXA 1, TSTYL 5 and B3GALT 6) and 1 protein marker (AFP). Analysis results show thatThe sensitivity of Liver cancer detection by Liver is 88%, the specificity is 87%, and prospective verification tests are currently being developed. In the field of early detection of Multiple Cancers (MCED), the cancer seek of Thrive early detection uses 18 genes from plasma ctDNA and 8 plasma protein markers, creating a dual predictive model of cfDNA and protein. It covers 8 cancers, and has sensitivity up to 70% and specificity up to 99%. Average prediction accuracy was 83(Science, 2018.359 (6378): p.926-930). Exact Sciences published data for its MCED biomarker validation study at the institute of ESMO, 2022. The study severely evaluates the performance of four different biomarkers in blood, including aneuploidy, protein, DNA methylation, and gene mutation, for early cancer detection. The total sensitivity of stage I and stage II tumors was 38.7% (Annals of Oncology,2022.33: p.S575). A larger scale of case control studies are currently underway. The research shows that the multi-group chemical combined detection technology based on ctDNA methylation has good performance in screening of single cancer and flood cancer, and can be used as a favorable tool for screening of the flood cancer.
However, the product cologard for screening colorectal cancer is lack of a cost-effective screening tool aiming at screening of digestive tract tumors, chinese patent CN116356021A discloses a marker combination for early detection, tissue localization, diagnosis, prognosis detection and benign and malignant identification of digestive system cancers, but the number of the markers is up to 1656 chromosome regions, the determination method is that the average methylation level (AMF) and Methylation Haplotype Fraction (MHF) are calculated, the determination step is complicated and difficult to be practically applied, the specificity and sensitivity of the determination index are low, and the effect on screening of digestive tract tumors is general.
The general lack of cost-effective screening tools for digestive tract tumors has led to the diagnosis of most digestive tract tumors in advanced stages, leading to high mortality. This suggests that there is a need for improved screening tools to increase early diagnosis of digestive tract tumors, promote early detection and early treatment of digestive tract tumors, and meet the urgent clinical needs.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a novel multi-target combined marker and kit for early detection of digestive tract tumors and application thereof in early detection of digestive tract tumors. The invention finds out a series of novel methylation sites which can effectively distinguish digestive tract tumors from benign lesion patients from high-depth whole genome methylation (WGBS) data of 5 digestive tract tumors (esophageal cancer, gastric cancer, liver cancer, pancreatic cancer and intestinal cancer, and the digestive tract tumors refer to the 5 tumors and tissues beside the tumor without special description, and can be used for effectively detecting the digestive tract tumors; the novel gene methylation locus is combined with a clinically common protein marker, so that the sensitivity and specificity of screening the digestive tract tumor can be further improved, and the digestive tract tumor can be traced.
The data of the clinical blood plasma samples of the detected digestive tract tumor and normal people show that the 15 methylation sites are adopted to detect the digestive tract tumor, the AUC value of the clinical blood plasma samples of the test set can reach 0.948, the sensitivity reaches 83.5 percent, the specificity reaches 95.7 percent, and the clinical application prospect is achieved.
By detecting the methylation level of the series of novel methylation sites and combining the levels of PG I, PG II, SCCA, ferr, G17, AFP-L3, DCP, CA19-9 and CEA proteins in serum samples, the invention can more sensitively and specifically distinguish digestive tract tumor patients from non-cancer patients. The data of the clinical digestive tract tumor and non-cancer patient samples are detected, so that the combination provided by the invention can effectively distinguish the digestive tract tumor patient from the non-cancer patient, the maximum AUC value can reach 0.953, the sensitivity can reach 86.3%, the specificity can reach 98.6%, and the accuracy of tracing different digestive tract tumors can reach 73.8%.
Methylation refers to the process of catalytically transferring methyl groups from an active methyl compound to other compounds, which may form various methyl compounds, or which may chemically modify certain proteins or nucleic acids to form methylated products. In biological systems, methylation is enzymatically catalyzed, and involves heavy metal modification, regulation of gene expression, regulation of protein function, and ribonucleic acid processing.
CpG is an abbreviation for cytosine (C) -phosphate (p) -guanine (G), and in mammals CpG exists in two forms: one is dispersed in the DNA sequence; another type of CpG island, known as CpG island, is highly aggregated, in which 70% to 90% of the scattered CpG is methyl-modified in normal tissues, whereas CpG islands of about 100-1000bp and rich in CpG dinucleotides are often unmethylated. CpG islands often occur in regulatory regions of eukaryotic coding genes, where they are readily methylated to form 5' -methylcytosine due to the presence of C in CpG, deaminated to form uracil, and converted to thymine (T) by DNA replication or amplification, where T itself is not readily repaired due to its presence in DNA. CpG is therefore distributed in the genome in islands.
Methylation-modified C is not converted by bisulfite, unmethylated cytosine (C) is converted by bisulfite to uracil (U), U pairs with A and C pairs with G when the bases are complementary paired. In theory, all the sites of a tumor patient are methylated, so that methylation modification of the sites of a tumor patient is a common phenomenon, only partial modification is not generated, and CpG sites which are closer to each other on a genome are methylated or unmethylated at the same time, which means that only one cluster of CpG sites can be analyzed as a whole.
Methylation modification of cytosine number 5 is a DNA modification mode widely existing in eukaryotic cell organisms, and the methylation modification on DNA plays an important role in the growth and development of organisms and in the canceration process of cells. Because of the same base-pairing properties as cytosine, 5-methylcytosine cannot be directly measured by means of one-generation sequencing or high-throughput sequencing. The most common method for detecting 5-methylcytosine (C) is to convert the DNA to be detected by bisulfite, and after alkaline hydrolysis, unmethylated cytosine is converted to uracil (U), while 5-methylcytosine (C) is not converted. Uracil, when paired with an adenine, is distinguished from the complementary pairing of cytosine and guanine, and therefore, by detection of bisulfite treated DNA, it is possible to determine, by PCR techniques, which cytosines (C) have been methylated in the original DNA molecule without conversion.
Thus, a methylation site marker is a methylation of all the C's of the nearer CpG sites on the entire sequence genome, all the C's being methylation sites.
The invention also introduces protein markers to improve the detection accuracy and better tracing, wherein the protein markers consist of pepsinogen I (PG I), pepsinogen II (PG II), squamous Cell Carcinoma Antigen (SCCA), serum ferritin (Ferr), gastrin (G17), alpha Fetoprotein (AFP), alpha fetoprotein heteroplastid (AFP-L3), des-gamma-carboxyprothrombin (DCP), cancer antigen 19-9 (CA 19-9) and carcinoembryonic antigen (CEA). Because the protein markers are all common detection indexes in the clinic of digestive tract tumors, corresponding detection information can be provided:
1. epigenetic combined proteomics detection improves the accuracy of digestive tract tumor detection;
2. in the pathological change process of different digestive tract tumor patients, the level of a specific protein marker in the body can be increased, and the detection of different protein markers can be used for better realizing the traceability of digestive tract tumors. For example, elevated AFP is more likely to be liver cancer.
The invention finds out 15 methylation sites with obvious difference in methylation degree in patients with digestive tract tumor and benign lesions of digestive tract, and can realize high-efficiency detection of digestive tract tumor by using the group of methylation sites.
In one aspect, the present invention provides the use of a marker selected from the group consisting of the nucleotide sequences of Seq ID No.1 to Seq ID No.15 shown in Table 1 or the combination of the complete complementary sequences thereof for the preparation of a reagent for early detection of digestive tract tumors.
TABLE 1 digestive tract tumor detection sites
| Sequence number | Target sequence |
| 1 | Seq ID NO.1 |
| 2 | Seq ID NO.2 |
| 3 | Seq ID NO.3 |
| 4 | Seq ID NO.4 |
| 5 | Seq ID NO.5 |
| 6 | Seq ID NO.6 |
| 7 | Seq ID NO.7 |
| 8 | Seq ID NO.8 |
| 9 | Seq ID NO.9 |
| 10 | Seq ID NO.10 |
| 11 | Seq ID NO.11 |
| 12 | Seq ID NO.12 |
| 13 | Seq ID NO.13 |
| 14 | Seq ID NO.14 |
| 15 | Seq ID NO.15 |
The invention screens out methylation sites of patients with digestive tract tumor and benign lesions of digestive tract by methylation sequencing of digestive tract tumor tissues, paired cancer side tissues and plasma free DNA of patients with benign lesions of digestive tract, and then further verifies by a large number of plasma samples of patients with digestive tract tumor and benign lesions of digestive tract, and finally discovers and determines 15 target sequences with abnormal methylation in patients with digestive tract tumor. Clinical verification shows that the 15 novel target sequences provided by the invention have obvious methylation degree difference in patients with digestive tract tumor and digestive tract benign lesions.
The 15 target sequences are double-stranded DNA and have complementary sequences, and it is understood that the methylation site target sequences provided by the invention can be sense strands or antisense strands.
The data of the clinical blood plasma samples of the detected digestive tract tumor and normal people show that the 15 methylation sites are adopted to detect the digestive tract tumor, the AUC value of the clinical blood plasma samples of the test set can reach 0.948, the sensitivity reaches 83.5 percent, the specificity reaches 95.7 percent, and the clinical application prospect is achieved.
Further, the markers also include protein markers consisting of pepsinogen I (PG I), pepsinogen II (PG II), squamous Cell Carcinoma Antigen (SCCA), serum ferritin (Ferr), gastrin (G17), alpha Fetoprotein (AFP), alpha fetoprotein heteroplastid (AFP-L3), des-gamma-carboxyprothrombin (DCP), cancer antigen 19-9 (CA 19-9) and carcinoembryonic antigen (CEA).
Due to technical limitations, the ability to detect cancer solely through methylation sites has been a growing trend in a number of industries, such as genomics, epigenetics, proteomics, and the like. Several clinical studies have shown that the sensitivity and specificity of the detection of multiple sets of chemical markers is superior to that of a single set of chemical markers. Therefore, on the basis of the methylation site provided by the invention, the detection of the level of the binding protein can further improve the sensitivity and specificity of screening the digestive tract tumor and trace the digestive tract tumor.
The protein markers PG I, PG II, SCCA, ferr, G17, AFP-L3, DCP, CA19-9 and CEA provided by the invention have significant differences in the serum levels of the digestive tract tumor patients compared with the serum levels of the protein markers of digestive tract benign lesions patients.
The research proves that the methylation level of the series of novel methylation sites provided by the invention can be detected, and the protein levels of PG I, PG II, SCCA, ferr, G, AFP-L3, DCP, CA19-9 and CEA in serum samples are combined, so that digestive tract tumor patients and non-cancer patients can be distinguished more sensitively and specifically. The data of the clinical digestive tract tumor and non-cancer patient samples are detected, so that the combination provided by the invention can effectively distinguish the digestive tract tumor patient from the non-cancer patient, the maximum AUC value can reach 0.953, the sensitivity can reach 86.3%, the specificity can reach 98.6%, and the accuracy of tracing different digestive tract tumors can reach 73.8%.
In another aspect, the present invention provides a primer combination for detecting a tumor of the digestive tract, wherein the primer combination is selected from the group consisting of 15 primer, probe and target sequence combinations as shown in Table 2.
TABLE 2 primer probe combinations
| Group of | Target sequence | Forward primer | Reverse primer | Probe with a probe tip |
| 1 | Seq ID NO.1 | Seq ID NO.20 | Seq ID NO.22 | Seq ID NO.21 |
| 2 | Seq ID NO.2 | Seq ID NO.23 | Seq ID NO.25 | Seq ID NO.24 |
| 3 | Seq ID NO.3 | Seq ID NO.26 | Seq ID NO.28 | Seq ID NO.27 |
| 4 | Seq ID NO.4 | Seq ID NO.29 | Seq ID NO.31 | Seq ID NO.30 |
| 5 | Seq ID NO.5 | Seq ID NO.32 | Seq ID NO.34 | Seq ID NO.33 |
| 6 | Seq ID NO.6 | Seq ID NO.35 | Seq ID NO.37 | Seq ID NO.36 |
| 7 | Seq ID NO.7 | Seq ID NO.38 | Seq ID NO.40 | Seq ID NO.39 |
| 8 | Seq ID NO.8 | Seq ID NO.41 | Seq ID NO.43 | Seq ID NO.42 |
| 9 | Seq ID NO.9 | Seq ID NO.44 | Seq ID NO.46 | Seq ID NO.45 |
| 10 | Seq ID NO.10 | Seq ID NO.47 | Seq ID NO.49 | Seq ID NO.48 |
| 11 | Seq ID NO.11 | Seq ID NO.50 | Seq ID NO.52 | Seq ID NO.51 |
| 12 | Seq ID NO.12 | Seq ID NO.53 | Seq ID NO.55 | Seq ID NO.54 |
| 13 | Seq ID NO.13 | Seq ID NO.56 | Seq ID NO.58 | Seq ID NO.57 |
| 14 | Seq ID NO.14 | Seq ID NO.59 | Seq ID NO.61 | Seq ID NO.60 |
| 15 | Seq ID NO.15 | Seq ID NO.62 | Seq ID NO.64 | Seq ID NO.63 |
Further, the primer combination also comprises a primer and a probe of an internal reference gene, wherein the internal reference gene COL2A1 has a sequence shown as a sequence table Seq ID No.16, a forward primer has a sequence shown as a sequence table Seq ID No.17, a reverse primer has a sequence shown as a sequence table Seq ID No.19, and a probe has a sequence shown as a sequence table Seq ID No. 18.
In yet another aspect, the invention provides a kit for detecting a tumor of the digestive tract, the kit comprising a primer and probe combination as described above.
Further, the kit or chip also includes reagents or materials for detecting proteins including PG I, PG II, SCCA, ferr, G17, AFP-L3, DCP, CA19-9 and CEA.
In yet another aspect, the present invention provides a marker combination for detecting a tumor of the digestive tract, the marker combination comprising a methylation site and a protein marker, the methylation site being a combination selected from the group consisting of Seq ID No.1 to Seq ID No.15 of the sequence listing; the protein markers include PG I, PG II, SCCA, ferr, G17, AFP-L3, DCP, CA19-9 and CEA.
The method for in vitro detection of digestive tract tumors by the methylation sites provided by the invention comprises the following steps:
1) Separating genomic DNA or plasma free DNA in a biological sample to be detected;
2) Detecting the methylation state of the methylation site or combination of methylation sites;
3) And judging the state of the biological sample through the methylation site state of the target gene, and realizing in-vitro detection of digestive tract tumors.
The method for in vitro detection of digestive tract tumors by the methylation site and protein combination provided by the invention comprises the following steps:
1) Separating genomic DNA or plasma free DNA and serum in the biological sample to be detected;
2) Detecting the methylation status of the methylation site or combination of methylation sites and serum PG I, PG II, SCCA, ferr, G17, AFP-L3, DCP, CA19-9 and CEA protein levels;
3) And judging the state of the biological sample through the methylation site state of the target gene and the protein marker level, and realizing in-vitro noninvasive detection of digestive tract tumors.
In some aspects, the method further comprises the steps of:
1) Separating serum and plasma of the biological sample to be detected, and extracting plasma free DNA (cfDNA) of the biological sample to be detected;
2) Treating the DNA sample obtained in step 1) with a reagent to convert the 5-unmethylated cytosine base into uracil, the base after conversion to uracil being different from the 5-unmethylated cytosine in hybridization ability and being detectable;
3) Combining the DNA sample treated in step 2) with a polymerase chain reaction system comprising one or more of the following components: DNA polymerase, the primer or primer combination of the target sequence, the corresponding probe or probe combination, and a polymerase chain reaction buffer solution, and generating an amplification product after the polymerase chain reaction;
4) Detecting the amplified product with a fluorescent-labeled probe or a combination of probes, which can generate a fluorescent signal if combined with the amplified product; if the probe cannot bind to the amplification product, a fluorescent signal cannot be generated;
5) Determining the methylation status of at least one CpG of the target sequence of the target gene of interest based on whether a fluorescent signal is generated;
6) Measuring the concentrations of PG I, PG II, SCCA, ferr, G17, AFP-L3, DCP, CA19-9 and CEA in human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method;
in some embodiments, the polymerase chain reaction system wherein the DNA polymerase comprises a thermostable DNA polymerase, a hot-start DNA polymerase, or a polymerase lacking 5'-3' exonuclease activity.
The methylation status of at least one CpG in the target sequence of the target gene is determined by the difference between the cycle threshold Ct value of the PCR reaction or the Ct value of the target gene. Detection of the methylation state of one or more target sequences of a target gene of interest can be conveniently achieved by analyzing the methylation state of DNA in a biological sample using a PCR reaction.
In some embodiments, the reagent used to convert unmethylated cytosine at position 5 of the DNA to uracil is preferably bisulfite.
Methylation modification of cytosine number 5 is a DNA modification mode widely existing in eukaryotic cell organisms, and methylation modification on DNA plays an important role in the growth and development of organisms and in the process of cell proto-canceration. Because of the same base-pairing properties as cytosine, 5-methylcytosine cannot be directly measured by means of one-generation sequencing or high-throughput sequencing. The most commonly used method for detecting 5-methylcytosine is to convert the DNA to be detected by bisulphite, and after alkaline hydrolysis, unmethylated cytosine is converted into uracil, while 5-methylcytosine is not converted. Uracil will pair complementarily to adenine when base paired, unlike cytosine paired complementarily to guanine, so that by detecting bisulfite treated DNA, the remaining unconverted cytosine can be determined by sequencing techniques, polymerase chain reaction techniques or DNA molecule hybridization-related techniques to determine which cytosine is methylated in the original DNA molecule. Therefore, the invention preferably adopts bisulphite as a methylation conversion reagent, and after the DNA sample to be detected is treated, the methylation state of the CpG dinucleotide sequence in the target sequence of the target gene is determined through relevant technologies such as sequencing, polymerase chain reaction or DNA molecular hybridization.
In some embodiments, the methods of the invention are applicable to analyzing samples in a mixed state, such as low concentrations of tumor cells present in blood, stool, or tissue. Thus, when analyzing the methylation status of CpG dinucleotide sequences in such samples, a person skilled in the art can use quantitative determination methods to determine the methylation level, such as percentage, ratio, fraction or degree, of CpG dinucleotide sequences, and the like, rather than the methylation state of a single nucleotide molecule. Accordingly, the methylation state described herein should be considered to include methylation modified states of single nucleotide molecules, including methylation states that are reacted by quantifying the level of methylation.
In some modes, the present invention employs a real-time fluorescent quantitative PCR mode to determine methylation status, such as: real-time fluorescent quantitative PCR using Taqman probes, real-time fluorescent quantitative PCR using fluorescent dyes, methylation-specific PCR (MSP) and the like are used to determine the methylation status of at least one CpG dinucleotide of a target sequence of a target gene. Because of the different base complementary pairing abilities of the gene target sequences of different methylation states, quantitative testing of methylation states in genomic DNA samples can be performed by real-time fluorescent quantitative PCR, where sequence discrimination occurs at the level of probe hybridization.
As a control, the COL2A1 gene was used in the present invention, and the genomic DNA after the reagent treatment was subjected to the test by designing the primer probe so as not to cover any CpG dinucleotide position.
Real-time fluorescent quantitative PCR can be used with any suitable probe, such as Taqman probes, MGB probes, scorpion probes, and the like. The fluorescent probe conventionally comprises a luminescent group, a nucleic acid sequence, a quenching group, and if necessary, some chemical modifications or special nucleotides such as a thioate nucleotide, a locked nucleic acid, etc.
In general, in real-time fluorescent quantitative PCR detection, the probe will be designed to have a melting temperature exceeding 10℃for the forward and reverse primers, which allows complete binding of the probe to the PCR product during annealing and extension. Typically, for example, a Taqman probe will hydrolyze during extension by a DNA polymerase having 5'-3' exonuclease activity, so that the fluorescent groups and quenching groups in the probe are far away, and resonance energy transfer between the fluorescent groups and quenching groups is destroyed, so that fluorescence emitted by the fluorescent groups can be detected by an instrument, and at the same time, as the PCR product increases gradually, the fluorescence signal will exhibit an exponential level rise over a certain period of time, and finally an "S" -shaped amplification curve is presented on a fluorescent quantitative PCR instrument.
Reagents for real-time fluorescent quantitative PCR include, but are not limited to: forward and reverse primers for target sequence of target gene, taqman fluorescent probe, optimized PCR buffer, deoxynucleotide triphosphate, DNA polymerase with 5'-3' exonuclease activity, etc.
The invention adopts but not limited to a magnetic particle chemiluminescence immunoassay sandwich method for detecting the target protein level, and other common detection methods such as a flow fluorescence luminescence method, an enzyme-linked immunosorbent method and the like.
In some embodiments, the presence or absence of a positive biological sample is determined by combining the methylation of the above sites or combinations of sites with the status of protein marker levels.
The methylation site for screening digestive tract tumors provided by the invention has the following beneficial effects:
1. 15 brand new differential methylation sites are provided, and the methylation state of the methylation sites in the free DNA of the plasma of the digestive tract tumor is obviously different from that of the free DNA of the plasma of the patient with benign lesions of the digestive tract;
2. 15 methylation sites can be used for detecting digestive tract tumors with high sensitivity and high specificity;
3. 15 methylation sites Seq ID No. 1-Seq ID No.15 are combined with protein markers PG I, PG II, SCCA, ferr, G17, AFP-L3, DCP, CA19-9 and CEA, are used for noninvasive and rapid in-vitro detection of digestive tract tumors, can more effectively distinguish digestive tract tumors from other digestive tract benign lesions, can reach AUC value of 0.953 in a clinical sample of a test set, has sensitivity of 86.3%, has specificity of 98.6%, and can reach accuracy of 73.8% for tracing different digestive tract tumors;
4. The method is convenient and quick, and the detection result is highly consistent with the clinical gold standard detection result.
Drawings
FIGS. 1-5 are thermal graphs of WGBS sequencing data of 5 gastrointestinal tumors, paracancerous tissue, and normal human blood, respectively, wherein tumor is a gastrointestinal tumor tissue sample; adjacent is a paracancerous tissue sample, and normal is a normal cfDNA sample;
FIG. 6 is a three-dimensional scatter plot of regions tsne for differentiating paracancerous tissue (A) or neoplastic tissue (B);
FIG. 7 is a box plot of methylation level comparisons of 15 abnormal methylation sites in 3 different types of samples of normal human plasma from patients with digestive tract tumors, patients with benign lesions of the digestive tract;
FIG. 8 is a schematic diagram of the fluorescent quantitative PCR reaction for the sample of patient with benign lesions of digestive tract in example 2;
FIG. 9 is a schematic diagram showing the results of fluorescent quantitative PCR reaction for samples of patients with digestive tract tumor in example 2;
FIG. 10 is a ROC curve obtained by normalizing the detection of 15 methylation site binding proteins in a clinical sample of a test set;
FIG. 11 is a confusion matrix for tumor traceability accuracy of 15 methylation site binding proteins in a clinical sample of a test set.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, it being noted that the examples described below are intended to facilitate an understanding of the invention and are not intended to limit the invention in any way. The reagents used in this example are all known products and are obtained by purchasing commercially available products.
EXAMPLE 1 screening for early detection of methylation sites of genes in digestive tract tumor
This example, starting from the actual clinical application of the product, found and determined 15 target sequences that were abnormal in methylation in patients with digestive tract tumors. The specific screening procedure for gut tumor methylation qPCR candidate targets is as follows:
the screening process for methylated qPCR candidate targets includes two stages, the first stage: on the basis of five specific hypermethylated genomic regions of digestive tract tumors (esophageal cancer, gastric cancer, liver cancer, pancreatic cancer and intestinal cancer) obtained through literature investigation, screening DMR (Differential Methylation Region ) of cancer tissues and beside cancer tissues from 5 tumor tissues and beside cancer tissues WGBS data, and designing synthetic capture probes aiming at all the above genomic regions, wherein the genomic regions can be used for tracing and can be used for distinguishing different tumor tissues or beside cancer tissues; and a second stage: the cfDNA samples of the tumor and the corresponding high-risk group are captured and sequenced by using the probe, and a plurality of groups of CpG sites which are good in complementarity and continuous in front and back and can most distinguish the cfDNA samples of the tumor from the cfDNA samples of the non-tumor are screened out by analyzing sequencing data to serve as qPCR candidate targets.
The first stage: we performed first high depth (average depth exceeding 40X) whole genome sulfite sequencing (WGBS) on 55 pairs (19 pairs of liver cancer/beside cancer, 9 pairs of stomach cancer/beside cancer, 9 pairs of colorectal cancer/beside cancer, 10 pairs of pancreatic cancer/beside cancer and 8 pairs of esophageal cancer/beside cancer) of tumor tissue and corresponding tissue samples (fig. 1-5), WGBS can obtain more methylation information at CpG sites compared to traditional TCGA 450k chip data and sequencing data based on methylation sensitive restriction endonuclease treatment. Secondly, the tissue beside the cancer is particularly selected as a control, and the tumor tissue data is compared with the tissue data beside the cancer, so that the problem of insufficient specificity caused by directly comparing the tumor tissue data with the cfDNA data of a normal person can be effectively avoided. In addition, in screening for DMR, we cut the genome into a sliding window of 100bp (50 bp overlap), compare their average methylation in each pair of tumor tissue/paracancestral tissue for each region, and then select regions that show differential methylation in more than a certain number of pairs of tumor tissue/paracancel tissue (and the methylation changes are consistent in the direction in both samples) (fig. 6). This is FDMR (Frequently Differential Methylation Region), and our method can make full use of the information of paired samples, and avoid the influence of outliers of individual samples on the final result, as compared to directly dividing the different types of samples into two sets of direct comparisons. Finally, we conducted extensive and intensive studies on 5 tumor aberrant methylation-related documents, collecting 891 (the number after the pooled regions) regions or sites showing aberrant hypermethylation in 5 tumors in total, which a priori information enriches our final DMR list. For the tumor panel region of the digestive tract, I designed and synthesized 4 methylation capture probes, namely a probe with completely methylated genomic positive strand CpG sites and a probe with completely demethylated genomic negative strand CpG sites. The probe is designed aiming at the complete methylation and complete demethylation of CpG sites, accords with the biological characteristics of cooperative methylation of tumor DNA, and also simplifies the complexity of analysis data.
And a second stage: we collected 59 colorectal cancer patients, 17 intestinal benign patients, 53 pancreatic cancer patients, 19 pancreatic benign patients, 49 esophageal cancer patients, 14 esophagitis patients, 98 gastric cancer patients, 33 liver cancer patients, 18 hepatitis patients and 50 normal cfDNA samples, captured these samples with probes synthesized in the previous stage, and ultra-high depth sequencing of the captured library (average depth of probe target region exceeds 10000X). Because the peripheral blood of the early tumor patient only contains a trace amount of ctDNA, the ultra-high depth sequencing of the sample is beneficial to capturing a small early tumor methylation change signal and improving the sensitivity. In analyzing sequencing data, considering that CpG sites adjacent to each other often show similar methylation changes, we calculate the methylation rate (methylation rate) of 5CpG sites in each marker by taking any continuous 5CpG sites as one marker (5 CpG) in each DMR.
Process of screening methylation candidate targets from targeted sequencing data: comparing the tumor cfDNA sample with the sequencing data of the normal human cfDNA sample and the corresponding benign disease cfDNA sample, and selecting a region which shows methylation in more tumor cfDNA samples and shows demethylation in less normal human cfDNA samples and the corresponding benign disease cfDNA samples as a tumor-specific methylation candidate target. The method comprises the following specific steps:
(1) Counting the number n of samples with methylation rate of at least two (assuming that one is random) 5CpG haplotypes in each DMR being greater than 0.01, selecting the DMR with n > =3 in tumor cfDNA samples and n <2 in normal cfDNA samples;
(2) For each selected DMR, the number of samples covered by each 5CpG haplotype (methylation rate greater than 0.01) m was counted, and m > =3 in tumor cfDNA samples and m <2 5CpG haplotypes in normal human cfDNA samples were selected.
(3) Sorting the selected 5CpG haplotypes according to the number of the coverage samples in descending order according to the number of the coverage samples; duplicate 5CpG haplotypes were knocked out based on coverage of the samples.
(4) The cumulative number of coverage samples for the above 5CpG haplotypes was calculated and ranked according to the number of cumulative coverage samples.
(5) The 5CpG haplotypes with the top single coverage rank and the top cumulative rank are selected, de-duplicated, and expanded to the left and right by 2 consecutive 5CpG haplotypes.
(6) The heat maps of methylation rates of all 5CpG haplotypes obtained above on tumor cfDNA samples, normal cfDNA samples, and cfDNA samples corresponding to benign disease are plotted.
(7) And further manual screening is carried out by looking up the heat map drawn in the previous step.
After the above screening steps, 44 hypermethylated regions were finally selected, of which 6 stomach cancers, 6 liver cancers, 11 intestinal cancers, 10 pancreatic cancers and 11 esophageal cancers.
Based on the region settings, the sequences were downloaded from the NCBI database website and converted to methylation-modified sequences using Methyl Primer Express v 1.0.1.0 software. To ensure a well balanced GC content distribution in the design region sequence, the antisense strand is considered as a template for the sequence selection. According to the general primer design principle, corresponding detection primers and probes are designed for 6 hypermethylation regions (the methylation level is ranked at the front) of 5 cancer species. Designed primers were screened using the following concentrations of template:
1) Pure positive template MJ is selected, the template is obtained by purification after SSSI enzyme treatment, all CG sites have methylation modification, the template is positive substance template verified by the optimal methylation test, and medium concentration and low concentration levels of 100 pg/mu l and 10 pg/mu l are selected.
2) Selecting a pure negative template WBC, wherein the template is taken from peripheral blood leukocyte gDNA of healthy people, contains the whole set of gDNA background, and better simulates the state of human cfDNA after ultrasonic disruption. 1 ng/. Mu.l was chosen as a simulated true high concentration sample.
3) A low abundance mixed template MJ/WBC, which is used to simulate the mixed state in a real sample, typically 5% as the state simulating a low abundance sample.
According to the design combination of primer screening, methylation detection systems are respectively prepared for comparison, and 100 pg/mu l and 10 pg/mu l of methylation positive templates, 1 ng/mu l of methylation negative templates and 5% abundance methylation mixed templates are used for comprehensive screening, wherein screening standards are shown in the table 3 below. Finally, the primer combination is selected by taking the specificity, the detection rate and the Ct value of the amplification effect as judging indexes, and the screening result is shown in table 4.
TABLE 3 sequence combination screening criteria
| Numbering device | Template type | Concentration of | Performance requirements |
| 1 | WBC | 1ng/μl | Is not detected |
| 2 | MJ | 100pg/μl | CT value is 29-31 |
| 3 | MJ | 10pg/μl | Can be detected |
| 4 | 5％MJ/WBC | 1ng/μl | Can be detected |
TABLE 4 screening results
(UD means undetected)
Through screening, the regions 5, 26 and 28 have the problem of nonspecific amplification, the regions 9, 15 and 21 have the problem of insufficient amplification sensitivity, and the 6 regions are eliminated. The remaining 24 zones all reached a better level in detection performance (see table 5).
Wherein 5 liver cancer specific areas are provided, and then clinical performance verification of target spots is carried out in 20 liver cancers and 20 clinical healthy control samples; 5 stomach cancer specific areas, and then verifying the clinical performance of targets in 20 stomach cancers and 20 clinical healthy control samples; 5 intestinal cancer specific areas, and then verifying the clinical performance of targets in 20 intestinal cancers and 20 clinical healthy control samples; 5 pancreatic cancer-specific regions, followed by clinical performance validation of targets in 20 pancreatic cancer and 20 clinically healthy control samples; 4 esophageal cancer specific areas, and then carrying out clinical performance verification of targets in 20 esophageal cancers and 20 clinical healthy control samples; and (3) performing ROC analysis of a single region on the detection result, excluding the region with the AUC less than 0.8, and finally sorting according to the AUC size, and selecting the 3 regions with the highest sorting, thereby reducing the number of detection regions, being more suitable for qPCR detection clinical application and simultaneously maintaining better clinical performance.
TABLE 5 detection Performance of 24 regions screened
Through the above screening, the AUC of the regions 7, 12, 13 and 19 is less than 0.8, clinical performance is poor, and then the regions are excluded, and then the regions are sorted according to the AUC of each cancer species, and finally the regions of the liver cancer with high methylation specificity detection are confirmed to be region 1, region 2 and region 3, the region of the stomach cancer with high methylation specificity detection is confirmed to be region 8, region 10 and region 11, the region of the stomach cancer with high methylation specificity detection is confirmed to be region 14, region 16 and region 18, the region of the pancreatic cancer with high methylation specificity detection is confirmed to be region 20, region 22 and region 23, and the region of the esophageal cancer with high methylation specificity detection is confirmed to be region 27, region 29 and region 30. (having nucleotide sequences shown as SEQ ID No. 1-SEQ ID No.15, see Table 1 in the specification for details), the nucleotide sequences of the remaining 15 regions of all 30 regions are shown as SEQ ID No. 65-SEQ ID No.79, respectively. After the above screening, 15 target sequences hypermethylated in patients with digestive tract tumor were finally found and determined. The box line graphs of the methylation levels of the 15 abnormal methylation sites in the digestive tract tumor patients, the digestive tract benign lesion patients and the normal human plasma 3 different types of samples are shown in fig. 7 (all methylation values are added with 0.00001 on the original values, and then-log is taken), so that the methylation level difference of the 15 methylation sites obtained by screening according to the invention for the different types of samples is obvious, and the digestive tract tumor and the normal human can be distinguished efficiently.
Example 2 detection of digestive tract tumors Using 5 methylation site combinations (or binding protein markers)
In this example, 5 methylation sites (or binding protein markers) are used to detect digestive tract tumor, and the 5 methylation sites are target sequences shown by sequence numbers Seq ID No.3, seq ID No.6, seq ID No.8, seq ID No.11 and Seq ID No. 15; the protein markers are PG I, PG II, SCCA, ferr, CEA, CA-9, G-17, AFP-L3 and DCP. And the detection is carried out in two ways respectively: 1. detecting early stage digestive tract tumor by using 5 methylation sites; 2. early stage gut tumors were detected using a method of 5 methylation sites in combination with protein markers.
The specific method comprises the following steps:
step 1, separating serum and plasma of a blood sample, extracting plasma free DNA of a biological sample to be detected by using a magnetic bead method extraction reagent, wherein 374 cases are digestive tract tumor patients, and 108 cases are normal human controls.
Step 2, methylation conversion treatment is carried out on the plasma free DNA sample extracted in the step 1 by using a methylation conversion reagent with bisulphite as a main component, and 5ng of plasma free DNA is added to convert unmethylated cytosine into uracil.
And 3, putting the converted plasma free DNA into a reaction system containing real-time fluorescence quantitative PCR for detecting the gene target sequence. Wherein the fluorescent probes for detecting 15 target sequences are respectively marked by TXD, FAM and CY5 fluorescent dyes, and the fluorescent probes for detecting the internal reference gene COL2A1 are marked by VIC fluorescent dyes. Wherein, the upstream primer, the downstream primer and the probe refer to the upstream primer, the downstream primer and the probe corresponding to the 5 target sequences respectively as shown in Table 2.
The system for fluorescence quantitative PCR detection is a multiplex PCR system formed by mixing a plurality of target genes with primer probes of internal reference COL2A1, wherein the target genes are detected simultaneously with the internal reference COL2A 1. And (3) detecting at most 4 target genes and internal references simultaneously by a single tube, and detecting multiple tubes when the number of the target genes is more. In the reaction system, the forward and reverse primer input concentration of the target gene sequence is 0.167 mu M, the probe input concentration is 0.167 mu M, the real-time fluorescence quantitative PCR reaction system is 30 mu L, and the forward and reverse primer input concentration of the internal reference gene sequence is 0.083 mu M.
Step 4, setting a fluorescence quantitative PCR reaction detection program as follows:
and step 5, obtaining a fluorescent quantitative PCR reaction detection result.
Step 6: taking the serum sample separated in the step 1, and measuring the concentration of the protein marker in the human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method.
And referring to the instruction book of the protein detection kit corresponding to the Beijing thermal scenery organism, performing a magnetic particle chemiluminescence immunoassay sandwich method test on the protein markers in the sample, and determining the score as a P value.
The analysis and judgment method for the result of detecting digestive tract tumor by using the methylation combined protein marker comprises the following steps:
1) Recording the Ct value of each methylation site automatically output by software;
2) Respectively calculating the Ct value of each site in the sample and the internal reference COL2A1, and then carrying out normalization processing on the Ct: Δct (target sequence) = |ct (COL 2 A1) -Ct (target sequence) |;
3) 5 methylation sites, score for the ith methylation site being Mi. Mi is determined by a value of 0 or 1, respectively, based on the ΔCt (target sequence) value and the corresponding Youden's index. Let Mi=1 if ΔCt (target sequence) > Youden's index, and Mi=0 if ΔCt (target sequence) < Youden's index. Methylated M-score=sum_i++m (Mi) (i=1-5).
4) The values of PG I, PG II, SCCA, ferr, CEA, CA19-9, G-17, AFP-L3 and DCP were normalized for each sample: p1=log 10 P(PG I/PG II)，P2＝log 10 P(G17)，P3＝log 10 P(CEA)，P4＝log 10 P(SCCA)，P5＝log 10 P(Ferr)，P6＝log 10 P(DCP)，P7＝log 10 P(CA19-9)，P8＝log 10 P(AFP)，P9＝log 10 P(AFP-L3)，P10＝log 10 P(PGI)，P11＝log 10 P(PGII)，P12＝log 10 P (AFP-L3%); p-score=sum_j P (Pj) (j=1-12) of the protein;
5) The results obtained using the kit detection were corrected and calculated analytically according to logistic regression analysis. The threshold settings for M-score and P-score are set according to the ROC curve.
Detection performance is enhanced by integrating two complementary dimensions of methylation and protein markers. The integrated model was GI-score=m-score+p-score. In some embodiments, the result indicates a positive detection of a digestive tract tumor and/or an early digestive tract tumor in the patient when the GI-score value is equal to or greater than the set threshold. In some embodiments, when the GI-score value is less than the threshold value, the result indicates a negative detection of a digestive tract tumor and/or an early digestive tract tumor in the patient.
Sensitivity = number of patients diagnosed with a disease/total number of patients; specificity refers to the ability of a diagnostic test to exclude a disease when the real situation is not diseased, specificity = number of non-diseased/total number of non-diseased not diagnosed diseased; youden index = sensitivity + specificity-1; trace-source accuracy = number of correctly categorized tumor categories/total number of lesions;
detection result: auc=0.932 for 5 methylation sites; auc=0.942 for 5 methylation site binding protein markers, sensitivity 71.5%, specificity 97.5%, youden index 0.690, tracing accuracy 69.8%;
Example 3 detection of digestive tract tumors Using 10 methylation site combinations (or binding protein markers)
In this example, a combination of 10 methylation sites (or binding protein markers) was used to detect tumors of the digestive tract, 10 methylation sites being the target sequences shown as SEQ ID NO.1, SEQ ID NO.3, SEQ ID NO.4, SEQ ID NO.6, SEQ ID NO.7, SEQ ID NO.8, SEQ ID NO.10, SEQ ID NO.11, SEQ ID NO.13, and SEQ ID NO. 15; the protein markers are PG I, PG II, SCCA, ferr, CEA, CA-9, G-17, AFP-L3 and DCP. And the detection is carried out in two ways respectively: 1. detecting early stage digestive tract tumor by using 10 methylation sites; 2. early stage gut tumors were detected using a method of 10 methylation sites in combination with protein markers.
The specific method was the same as in example 2, except that 326 cases of the blood samples were digestive tract tumor patients and 101 cases were normal human controls.
Detection result: auc=0.938 for 10 methylation sites; auc=0.948, sensitivity 78.2%, specificity 98.1%, youden index 0.763, and traceability accuracy 72.5% for the 10 methylation site binding protein markers.
Example 4 detection of digestive tract tumors Using 15 methylation site combinations (or binding protein markers)
In the embodiment, 15 methylation site combinations (or binding protein markers) are adopted to detect digestive tract tumors, and 15 methylation sites are target sequences shown in sequence numbers Seq ID No. 1-15; the protein markers are PG I, PG II, SCCA, ferr, CEA, CA-9, G-17, AFP-L3 and DCP. And the detection is carried out in two ways respectively: 1. detecting early stage digestive tract tumor by using 15 methylation sites; 2. early stage gut tumors were detected using a method of 15 methylation sites in combination with protein markers.
The specific method was the same as in example 2, except that 351 cases of digestive tract tumor patients and 94 cases of normal human controls were included in the blood sample.
Detection result: auc=0.950 for 15 methylation sites; auc=0.960, sensitivity 87.7%, specificity 98.6%, youden index 0.863, tracing accuracy 75.7% for 15 methylation site binding protein markers.
Example 5 detection of digestive tract tumors Using 30 methylation site combinations (or binding protein markers)
In this example, 30 methylation site combinations (or binding protein markers) were used to detect digestive tract tumors, 30 methylation sites being the target sequences shown by SEQ ID NOS.1-15 and SEQ ID NOS.65-79; the protein markers are PG I, PG II, SCCA, ferr, CEA, CA-9, G-17, AFP-L3 and DCP. And the detection is carried out in two ways respectively: 1. detecting early stage digestive tract tumor by using 30 methylation sites; 2. early stage gut tumors were detected using a method of 30 methylation sites in combination with protein markers.
The specific method was the same as in example 2, except that 407 cases in the blood sample were digestive tract tumor patients and 146 cases were normal human controls.
Detection result: auc=0.952 for 30 methylation sites; auc=0.961, sensitivity 87.8%, specificity 98.5%, youden index 0.863, and tracing accuracy 75.9% for 30 methylation site binding protein markers.
Example 6 Performance comparative analysis Using combinations of different methylation sites
Mathematical modeling analysis of the 15 methylation sites (SEQ ID NOS.1-15) obtained in example 1 was performed on different combinations of sites to investigate the use of 15 methylation sites and proteins as biomarker combinations for detecting digestive tract tumors.
First, we evaluated the performance of the above model of a single site of the 15 methylation sites for diagnosing digestive tract tumorigenesis, and calculated AUC values according to the method described in example 1, respectively, and the results are shown in table 6.
TABLE 6 comparison of Performance of single methylation site diagnosis of digestive tract tumors
| SEQ ID NO | AUC |
| 1 | 0.905 |
| 2 | 0.890 |
| 3 | 0.916 |
| 4 | 0.831 |
| 5 | 0.801 |
| 6 | 0.832 |
| 7 | 0.881 |
| 8 | 0.902 |
| 9 | 0.870 |
| 10 | 0.873 |
| 11 | 0.903 |
| 12 | 0.826 |
| 13 | 0.857 |
| 14 | 0.834 |
| 15 | 0.924 |
As can be seen from Table 6, the 15 methylation sites provided in example 1 all have higher AUC values for diagnosing digestive tract tumors, and all have better diagnostic performance.
Second, the diagnostic efficacy of the different combinations of methylation sites and binding protein marker combinations were compared and the AUC values were from the test results in examples 2-5, as shown in table 7.
TABLE 7 comparison of the models of different combinations of methylation sites for diagnosis of digestive tract tumorigenesis
As can be seen from tables 6 and 7, the diagnostic performance of the multiple-group chemical model using a single methylation site as a diagnostic model is lower than that of the single-group chemical marker compared to the multiple-methylation site combination model. When 15 methylation site combined protein combinations are selected as a diagnosis model, the diagnosis performance is optimal; when more than 15 methylation sites are selected, the diagnostic AUC values do not increase significantly, but rather are more cumbersome to data acquisition and analysis, so 15 methylation sites are preferred.
This example further analyzed the diagnostic properties of the different methylation site combinations preferred therefrom, respectively, with the values of AUC, sensitivity, specificity, youden index, traceability accuracy from the test results in examples 2-5, as shown in table 8.
Table 8, comparison of preferred multiple methylation site models for diagnosis of digestive tract tumorigenesis and traceability
| Combination (SEQ ID NO) | AUC | Sensitivity of | Specificity of the sample | Youden index | Tracing accuracy |
| 3+6+8+11+15+ proteins | 0.942 | 71.5％ | 97.5％ | 0.690 | 69.8％ |
| 1+3+4+6+7+8+10+11+13+15+ proteins | 0.948 | 78.2％ | 98.1％ | 0.763 | 72.5％ |
| 1-15+ protein | 0.960 | 87.7％ | 98.6％ | 0.863 | 75.7％ |
| 1 to 15+65 to 79+ protein | 0.961 | 87.8％ | 98.5％ | 0.863 | 75.9％ |
As can be seen from Table 8, the preferred combinations of several groups of methylation sites and proteins can be used for high-efficiency detection of digestive tract tumors, wherein the combination of SEQ ID NO. 1-15+ proteins is selected, the highest AUC can reach 0.960, the sensitivity reaches 87.7%, the specificity reaches 98.6%, the tracing accuracy can reach 75.7%, and the noninvasive, global, higher sensitivity and specificity digestive tract tumor screening can be truly realized, so that the clinical requirements can be met; the methylation sites of SEQ ID NO. 65-79 are additionally added on the basis of the combination, so that the detection effect is not obviously different, but is more complicated in data acquisition and analysis, and therefore, the combination of SEQ ID NO. 1-15+ proteins is preferred.
The protein is introduced to improve the detection accuracy and better tracing, and because the protein markers are all common detection indexes in clinic of digestive tract tumors, corresponding detection information can be provided:
1. epigenetic combined proteomics detection improves the accuracy of digestive tract tumor detection;
2. in the pathological change process of different digestive tract tumor patients, the level of a specific protein marker in the body can be increased, and the detection of different protein markers can be used for better realizing the traceability of digestive tract tumors. For example, elevated AFP is more likely to be liver cancer.
Example 7 screening of digestive tract tumors Using 15 methylation sites (or binding protein markers) in a training set clinical sample
This example utilized the 15 methylation sites screened in example 1 and used for gut tumor detection in a training set of clinical samples using a method of methylation site and protein marker combination detection.
The specific method comprises the following steps:
step 1, separating serum and plasma of a blood sample, extracting plasma free DNA of a biological sample to be detected by using a magnetic bead method extraction reagent, wherein 285 cases are digestive tract tumor patients, and 69 cases are normal human controls.
Step 2, methylation conversion treatment is carried out on the plasma free DNA sample extracted in the step 1 by using a methylation conversion reagent with bisulphite as a main component, and 5ng of plasma free DNA is added to convert unmethylated cytosine into uracil.
And 3, putting the converted plasma free DNA into a reaction system containing real-time fluorescence quantitative PCR for detecting the gene target sequence. Wherein the fluorescent probes for detecting 15 target sequences are respectively marked by TXD, FAM and CY5 fluorescent dyes, and the fluorescent probes for detecting the internal reference gene COL2A1 are marked by VIC fluorescent dyes. The upstream primer, the downstream primer and the probe are those corresponding to the 15 target sequences as shown in Table 2.
The system for fluorescence quantitative PCR detection is a multiplex PCR system formed by mixing a plurality of target genes with primer probes of internal reference COL2A1, wherein the target genes are detected simultaneously with the internal reference COL2A 1. And (3) detecting at most 4 target genes and internal references simultaneously by a single tube, and detecting multiple tubes when the number of the target genes is more. In the reaction system, the forward and reverse primer input concentration of the target gene sequence is 0.167 mu M, the probe input concentration is 0.167 mu M, the real-time fluorescence quantitative PCR reaction system is 30 mu L, and the forward and reverse primer input concentration of the internal reference gene sequence is 0.083 mu M.
Step 4, setting a fluorescence quantitative PCR reaction detection program as follows:
and step 5, obtaining a fluorescent quantitative PCR reaction detection result.
Step 6: taking the serum sample separated in the step 1, and measuring the concentration of the protein marker in the human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method.
And referring to the instruction book of the protein detection kit corresponding to the Beijing thermal scenery organism, performing a magnetic particle chemiluminescence immunoassay sandwich method test on the protein markers in the sample, and determining the score as a P value.
The analysis and judgment method for the result of detecting digestive tract tumor by using the methylation combined protein marker comprises the following steps:
1) Recording the Ct value of each methylation site automatically output by software;
2) Respectively calculating the Ct value of each site in the sample and the internal reference COL2A1, and then carrying out normalization processing on the Ct: Δct (target sequence) = |ct (COL 2 A1) -Ct (target sequence) |;
3) 15 methylation sites, score for the ith methylation site being Mi. Mi is determined by a value of 0 or 1, respectively, based on the ΔCt (target sequence) value and the corresponding Youden's index. Let Mi=1 if ΔCt (target sequence) > Youden's index, and Mi=0 if ΔCt (target sequence) < Youden's index. Methylated M-score=sum_i++m (Mi) (i=1-15).
4) The values of PG I, PG II, SCCA, ferr, CEA, CA19-9, G-17, AFP-L3 and DCP were normalized for each sample: p1=log 10 P(PG I/PG II)，P2＝log 10 P(G17)，P3＝log 10 P(CEA)，P4＝log 10 P(SCCA)，P5＝log 10 P(Ferr)，P6＝log 10 P(DCP)，P7＝log 10 P(CA19-9)，P8＝log 10 P(AFP)，P9＝log 10 P(AFP-L3)，P10＝log 10 P(PGI)，P11＝log 10 P(PGII)，P12＝log 10 P (AFP-L3%); p-score=sum_j P (Pj) (j=1-12) of the protein;
5) The results obtained using the kit detection were corrected and calculated analytically according to logistic regression analysis. The threshold settings for M-score and P-score are set according to the ROC curve.
Detection performance is enhanced by integrating two complementary dimensions of methylation and protein markers. The integrated model was GI-score=m-score+p-score. In some embodiments, the result indicates a positive detection of a digestive tract tumor and/or an early digestive tract tumor in the patient when the GI-score value is equal to or greater than the set threshold. In some embodiments, when the GI-score value is less than the threshold value, the result indicates a negative detection of a digestive tract tumor and/or an early digestive tract tumor in the patient.
Aiming at the detection result of the clinical sample of the training set of the present time, 15 methylation sites and marker combinations combining PG I, PG II, SCCA, ferr, CEA, CA-9, G-17, AFP-L3 and DCP proteins are adopted for detecting the digestive tract tumor level, wherein the AUC=0.971, the sensitivity is 89.5%, the specificity is 98.6%, and the tracing accuracy is 78.0%; under the specificity of 98.6%, the sensitivity of intestinal cancer detection is 91.7%, the tracing accuracy is 87.3%, the sensitivity of esophageal cancer detection is 93.3%, the tracing accuracy is 82.1%, the sensitivity of liver cancer detection is 98.3%, and the tracing accuracy is 76.3%. The sensitivity for gastric cancer detection is 75.9%, the tracing accuracy is 65.9%, the sensitivity for pancreatic cancer detection is 87.2%, and the tracing accuracy is 75.6%.
Example 8 screening of digestive tract tumors Using 15 methylation sites (or binding protein markers) in test set clinical samples
This example uses the 15 methylation sites screened in example 1 and uses a method of methylation site and protein marker combined detection for gut tumor detection in a test set of clinical samples.
The specific method comprises the following steps:
Step 1, separating serum and plasma of a blood sample, extracting plasma free DNA of a biological sample to be detected by using a magnetic bead method extraction reagent, wherein 358 cases are digestive tract tumor patients, and 70 cases are normal human controls.
Step 2, methylation conversion treatment is carried out on the plasma free DNA sample extracted in the step 1 by using a methylation conversion reagent with bisulphite as a main component, and 5ng of plasma free DNA is added to convert unmethylated cytosine into uracil.
And 3, putting the converted plasma free DNA into a reaction system containing real-time fluorescence quantitative PCR for detecting the gene target sequence. Wherein the fluorescent probes for detecting 15 target sequences are respectively marked by TXD, FAM and CY5 fluorescent dyes, and the fluorescent probes for detecting the internal reference gene COL2A1 are marked by VIC fluorescent dyes. The upstream primer, the downstream primer and the probe are those corresponding to the 15 target sequences as shown in Table 2.
The system for fluorescence quantitative PCR detection is a multiplex PCR system formed by mixing a plurality of target genes with primer probes of internal reference COL2A1, wherein the target genes are detected simultaneously with the internal reference COL2A 1. And (3) detecting at most 4 target genes and internal references simultaneously by a single tube, and detecting multiple tubes when the number of the target genes is more. In the reaction system, the forward and reverse primer input concentration of the target gene sequence is 0.167 mu M, the probe input concentration is 0.167 mu M, the real-time fluorescence quantitative PCR reaction system is 30 mu L, and the forward and reverse primer input concentration of the internal reference gene sequence is 0.083 mu M.
Step 4, setting a fluorescence quantitative PCR reaction detection program as follows:
and step 5, obtaining a fluorescent quantitative PCR reaction detection result.
Step 6: taking the serum sample separated in the step 1, and measuring the concentration of the protein marker in the human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method.
And referring to the instruction book of the protein detection kit corresponding to the Beijing thermal scenery organism, performing a magnetic particle chemiluminescence immunoassay sandwich method test on the protein markers in the sample, and determining the score as a P value.
The analysis and judgment method for the result of detecting digestive tract tumor by using the methylation combined protein marker comprises the following steps:
1) Recording the Ct value of each methylation site automatically output by software;
2) Respectively calculating the Ct value of each site in the sample and the internal reference COL2A1, and then carrying out normalization processing on the Ct: Δct (target sequence) = |ct (COL 2 A1) -Ct (target sequence) |;
3) 15 methylation sites, score for the ith methylation site being Mi. Mi is determined by a value of 0 or 1, respectively, based on the ΔCt (target sequence) value and the corresponding Youden's index. Let Mi=1 if ΔCt (target sequence) > Youden's index, and Mi=0 if ΔCt (target sequence) < Youden's index. Methylated M-score=sum_i++m (Mi) (i=1-15).
4) The values of PG I, PG II, SCCA, ferr, CEA, CA19-9, G-17, AFP-L3 and DCP were normalized for each sample: p1=log 10 P(PG I/PG II)，P2＝log 10 P(G17)，P3＝log 10 P(CEA)，P4＝log 10 P(SCCA)，P5＝log 10 P(Ferr)，P6＝log 10 P(DCP)，P7＝log 10 P(CA19-9)，P8＝log 10 P(AFP)，P9＝log 10 P(AFP-L3)，P10＝log 10 P(PGI)，P11＝log 10 P(PGII)，P12＝log 10 P (AFP-L3%); p-score=sum_j P (Pj) (j=1-12) of the protein;
5) The results obtained using the kit detection were corrected and calculated analytically according to logistic regression analysis. The threshold settings for M-score and P-score are set according to the ROC curve.
Detection performance is enhanced by integrating two complementary dimensions of methylation and protein markers. The integrated model was GI-score=m-score+p-score. In some embodiments, the result indicates a positive detection of a digestive tract tumor and/or an early digestive tract tumor in the patient when the GI-score value is equal to or greater than the set threshold. In some embodiments, when the GI-score value is less than the threshold value, the result indicates a negative detection of a digestive tract tumor and/or an early digestive tract tumor in the patient.
For the detection results of the clinical samples of the present test set, 15 methylation sites and marker combinations combined with PG I, PG II, SCCA, ferr, CEA, CA-9, G-17, AFP-L3 and DCP proteins are adopted for detecting the digestive tract tumor level, wherein the AUC=0.953 (figure 10), the sensitivity is 86.3%, the specificity is 98.6%, and the tracing accuracy is 73.8% (figure 11); under the specificity of 98.6%, the sensitivity of intestinal cancer detection is 92.0%, the tracing accuracy is 76.8%, the sensitivity of esophageal cancer detection is 90.7%, the tracing accuracy is 72.1%, the sensitivity of liver cancer detection is 89.1%, and the tracing accuracy is 73.5%. The sensitivity for gastric cancer detection is 77.2%, the tracing accuracy is 70.5%, the sensitivity for pancreatic cancer detection is 83.8%, and the tracing accuracy is 75.8%.
The preferred combination of 15 methylation sites and 10 proteins can be used for high-efficiency detection of digestive tract tumors, wherein in a clinical sample of a test set, the AUC reaches 0.953, the sensitivity reaches 86.3%, the specificity reaches 98.6%, the tracing accuracy can reach 73.8%, and the non-invasive, global, higher-sensitivity, higher-specificity and tracing accuracy digestive tract tumor screening is truly realized, so that the clinical requirements can be met.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Sequence listing
Seq ID NO.1
Methylation site 1:
GTAGGTTTCGTAGGTTTTTTGTAGGGTTATTATCGCGGAATTTTGAAAGCGTAGATTTGTTTTGAAGTTTTGAGCGATTTTTCGTATTAGGCGTTGGAAAGGTAATTTGCGGATTAGTAGTTTAGTCGATTTTTGATAGCGGCGAATTTCGCGTAGAGTTACGGTGTCGGGACGGTAGCGATGAGGTTTTTTTACGTCGTCGGTGGTCGGCGCGTTTTTTCGAGTCGTTTTAGTGGTTAGTTG
Seq ID NO.2
methylation site 2:
GGGGCGTCGTTCGGAGTAGGGCGGATTCGGGTTTTGGCGCGGTTTTTGTTTCGGTTTTTGGCGCGGGAGGATTTTCGATTTCGATTTCGGTCGTTTATGGTGGCGGCGGAGGTAGTTTTAAAGATACGTTGTGATTTTGCGGTTTTTGACGTTAGTTTTCGGTCGGGATCGAGCGGGTTTTTTTACGGTAATCGTCGACGTTACGAACGTATAATTGTATCGTCGCGAGAGGACGTGATGCGT
Seq ID NO.3
methylation site 3:
TGGATATTGGAGTTATTAATTGGCGTGTTTTTGTTTATTGGAGTATTCGTATATTATTTTAAATTAAAAT
TATTAAGAGTTTTTTTCGCGTAGATTGTTGTTTTTTTAGTTGTTTTCGATTTTGTTTTACGTTTGTCGGTTAGAGTTTTTCGGCGTTTTTTTCGTTTTAGCGGAGTGCGTTGGGGCGCGTTAGGGTTAGGTTCGTCGGAGGAGCGCGTTTTTAGTTTTTCGCGTATAGAGTCG
Seq ID NO.4
methylation site 4:
GTTTAGTTTGATTTTAGGTCGTTTTTAGGTCGGTGTTTAGTTGAGGCGGGAACGTTGTAGTTTGGTTGAGCGTGATTTTTAGGTTTTGTGAGGAAAAGTCGAGCGCGTTATATCGAGGCGTTAGTCGTTTATTTTATTATAAGGTAAAAGATTTATGTTGTTTTAGTTATTTTAAAGTTGGGAGATATATTGTATTTTTTATTAGATTTCGAATGTTTTTAGTGT
Seq ID NO.5
methylation site 5:
GGTTTTTTTCGAGGTTTTCGGCGGTTTTACGAGTTCGTAGTAGTCGGTGGCGACGTCGTTTTCGTTTTATTTTTTTGCGTAAGTGCGAGGTTGTCGGTAGCGCGGCGTACGTTTCGGTCGTTTTCGGTTTTCGCGTAAAATTTTTATTTTGTTTACGTGAAGTTGTCGTTGTTTTAGAGAGGGGGAAAGAGTTGCGGGAAAAGTCGGGGAGTGACGATTGCGGCGGTTGGGCGCGTTTTTTT
Seq ID NO.6
methylation site 6:
ACGAGTTTAGGGGCGCGGAGGGGCGGGTAGCGCGCGGAGTGGTGAGATTGAGTCGCGATGGAACGCGTTGGGGAGATTTAGTTTGTTCGGTTTTAGGGTTCGGAGATATTTTGGGTTGAAGGCGGCGGCGAATCGAAGAAGTCGGTATATTTTGTTTTTCGCGAATTTTTTTGCGGTTTGGGTTTGTTGTATATAATTTAATAGTCGGAAGTTTTTATTTTAAAAAGTATTTGCGGAGGGCG
Seq ID NO.7
methylation site 7:
GGTAGTGGTATTCGGCGGGGAAGTAGTAGTTAAATTCGCGTATGATTTCGAGAGTTTTAGTAATATTTAGGGATTGGGTTTAGTTTCGGAGCGAGAGGGTCGTTCGTTGAGAAGTTGCGTCGGAGACGCGGGAAGTTGTTGTTATAAGGAGGGAGTTTTGGGAAGTCGGAGGATAGGAGGAGACGGGAGTTTAGGGGTAGACGAGTGGAGTTCGAGGAGGTAGGGTGGAGGGAGAGTTAAGG
Seq ID NO.8
methylation site 8:
CGCGGACGTGTTGGAGAGGATTTTGCGGGTGGGTTTGGCGCGGGACGGGGGTGCGTTGAGGGGAGACGGGAGTGCGTTGAGGGGAGACGGGATTTTTAATTTAGGCGTTTTTTCGTTGAGAGCGTCGCGCGTTTTCGGTTTCGTGTTCGCGTCGTTTACGTGGGGGATTTTGTTAGGGGTATTCGCGTAGATTTTGCGCGTTTTTATAGGATTTTGTGTTCGTTTTGCGTATTGTCGTTTGG
Seq ID NO.9
methylation site 9:
CGATTCGGGTTCGGGCGGTAGCGGTTATGGCGTTTGGAGTTTTTTTTTTTTTTCGGGTCGTTATATCGTTTTTTTCGTCGGTTAATTACGGAGGTTGTTCGTTTTTCGTTTTTCGGTCGCGTTTGTTTATTCGTTTCGCGGGGTTATTTGTTTAAGTTTTAAGTTAGTCGGGTTTAGGGTTATTTTTTTACGGAGTAAGAATTAGAACGTTAGTTGTTTTAGCGGCGTTCGTATTATTTATT
Seq ID NO.10
methylation site 10:
TTTTAAATAGAGTCGGTAGCGCGTTTCGTTCGGTATTTTTCGAAGAGTTAGATCGCGGTCGGCGTTAGCGTTATCGTTCGGTTTATTCGTTAGTTCGTATAGTCGCGTCGTCGTCGAGCGTTTCGTGAGCGGCGTTTCGAGGATTAGGAATGGGGTTTCGGGCGTTGGGCGCGTTTCGAATTCGGCGTACGTAAGAGTTTGGGAGCGTTCGAGTCGTTCGGTTGTTCGGAGTTTTATCGTTT
Seq ID NO.11
methylation site 11:
TCGGGGTAGGAAGGAGGGGCGGGATGGTTTTTCGCGTTCGAAATTTACGGCGTGGCGGACGCGGAGGAGTTTCGAGTTTTATAATTAAGGGGTGGGAAGGAAAAGGGATAGAGCGAGATAGAGCGTTTTCGAGAAATATAGGCGTATAAAAAAAGATAGAA
Seq ID NO.12
methylation site 12:
TTTGGATTGTGAAGGTTTTCGTTGTAAGAGCGGGTATTGGCGAATTTTAGTGTACGTCGCGGTCGAGAACGGATGTAGGGCGGGGGATGGTTGAGTCGTTTGATTTTTGTAGAGAATTTGTAGGGGTTTTCGGAGGGTATTTTTTGCGTTTAAGGGAGCGT
Seq ID NO.13
methylation site 13:
ATGAAGGTGAAGGTGGAGGTGGGATTTGATGGTTTCGTTTCGGATAGGGTTGAAGAAGTGGGAGAAAAGGAATTAAAATCGTGGATGGGATAAGATTGTGCGCGAATGTTTTTTGGGAGCGGATCGTGGTCGACGAGGGTTCGAAGATGTTGTTTTGTGATCGTAGGTGTGAGTGGAAGTTGTGTGAGTGTTAATGTGAGTTGTTTGTTTGGGAGATTAGTATTGTATTTTTGTATTAGGTA
Seq ID NO.14
methylation site 14:
TTTTTTTTAGGTATTGGTTTTTGTTTTTTGTAGGTGCGGAGTTAGGATTCGTAGGTCGTTTTGCGTACGGATTCGGAGAGTTTAGCGCGACGGAATATAGGGTTTTTTTTTCGTTTTTTTTATTCGTGCGTTTTTTGGTAAAGTTAATTTTCGGGTTAAACG
Seq ID NO.15
methylation site 15:
TTGAGTGGTTAGGGGGTTTCGTTTTTTTTTTCGATGTTTTTTGTTTTTTTTTGGGTTTTCGGAATTTAGTTTGTTTTAATCGTTTTCGTTGCGGGTAGCGTTGGTTACGCGGTTTTCGTCGTCGGCGGTTTTTCGTGGTTAAGTATTTTTGGTTTTGGAGTTTAGGGGTTGCGTTTTTTTTGGGGTCGGGGCGGGAGAGAGGATTTCGGTGGTATTCGTTCGTGCGTTGGGCG
Seq ID NO.16
COL2A1：
TTTTTTGTAAGGAGGGATGTGGAGGGATAGAGGAGTAGTAGGTAAGGTTAGTAGGAGGTGATATAGGTAGGGAGGATTAGGTTAAGGTTGGGAGGAGTTTATATTTGGTGTT
Seq ID NO.17
forward primer of COL2 A1:
ATGTGGAGGGATAGAGGAGTA
Seq ID NO.18
probes for COL2 A1:
CACCTCCTACTAACCTTACC
Seq ID NO.19
reverse primer of COL2 A1:
CCTCCCAACCTTAACCTAAT
Seq ID NO.20
forward primer for methylation site 1:
GTTTTGAGCGATTTTTCGTATTAG
Seq ID NO.21
Probe of methylation site 1:
GATAGCGGCGAATTTCG
Seq ID NO.22
reverse primer of methylation site 1:
TAAAAAAACCTCATCGCTACCGTC
Seq ID NO.23
forward primer for methylation site 2:
CGGGAGGATTTTCGATTT
Seq ID NO.24
probe of methylation site 2:
TCGGTCGTTTATGGTGGC
Seq ID NO.25
reverse primer of methylation site 2:
TATACGTTCGTAACGTCG
Seq ID NO.26
forward primer for methylation site 3:
GAGTTTTTTTCGCGTAGATTGTTGTTT
Seq ID NO.27
probe of methylation site 3:
GGTTAGAGTTTTTCGGCGTT
Seq ID NO.28
reverse primer for methylation site 3:
CGCGCCCCAACGCACT
Seq ID NO.29
forward primer for methylation site 4:
TGATTTTTAGGTTTTGTGAGGAAA
Seq ID NO.30
probe of methylation site 4:
CTCGATATAACGCGCTCGA
Seq ID NO.31
reverse primer of methylation site 4:
ACCTTATAATAAAATAAACGACTAACG
Seq ID NO.32
forward primer for methylation site 5:
GCGCGGCGTACGTTTC
Seq ID NO.33
probe of methylation site 5:
TCGTTTTCGGTTTTCGCG
Seq ID NO.34
reverse primer of methylation site 5:
CTAAAACAACGACAACTTCACGTAA
Seq ID NO.35
forward primer for methylation site 6:
GTTTTAGGGTTCGGAGATATTTTG
Seq ID NO.36
probe of methylation site 6:
GATTCGCCGCCGCCTTC
Seq ID NO.37
reverse primer of methylation site 6:
GCGAAAAACAAAATATACCGACTT
Seq ID NO.38
forward primer for methylation site 7:
GAGAGGGTCGTTCGTTGAGAA
Seq ID NO.39
probe of methylation site 7:
TTGCGTCGGAGACGCG
Seq ID NO.40
reverse primer for methylation site 7:
AACTCCCTCCTTATAACAACAACTTC
Seq ID NO.41
forward primer for methylation site 8:
GGATTTTTAATTTAGGCGTTTTTTCG
Seq ID NO.42
probe of methylation site 8:
ACCGAAAACGCGCGACGCTCT
Seq ID NO.43
reverse primer of methylation site 8:
ACGTAAACGACGCGAACACG
Seq ID NO.44
forward primer for methylation site 9:
GGTTAATTACGGAGGTTGTTCGTT
Seq ID NO.45
probe of methylation site 9:
TTCGTTTTTCGGTCGCGT
Seq ID NO.46
reverse primer of methylation site 9:
CCCCGCGAAACGAATAAAC
Seq ID NO.47
forward primer for methylation site 10:
GTTTATTCGTTAGTTCGTATAGTCGC
Seq ID NO.48
probe of methylation site 10:
TCGTCGTCGAGCGTTTCG
Seq ID NO.49
reverse primer of methylation site 10:
CCATTCCTAATCCTCGAAACG
Seq ID NO.50
forward primer for methylation site 11:
ATGGTTTTTCGCGTTCGAA
Seq ID NO.51
probe of methylation site 11:
TTTACGGCGTGGCGGAC
Seq ID NO.52
reverse primer of methylation site 11:
CCCCTTAATTATAAAACTCGAAACTC
Seq ID NO.53
Forward primer for methylation site 12:
CGGGTATTGGCGAATTTTAGT
Seq ID NO.54
probe of methylation site 12:
TACGTCGCGGTCGAGAACG
Seq ID NO.55
reverse primer of methylation site 12:
CATCCCCCGCCCTACAT
Seq ID NO.56
forward primer for methylation site 13:
TGTGCGCGAATGTTTTTTG
Seq ID NO.57
probe of methylation site 13:
CGTCGACCACGATCCGC
Seq ID NO.58
reverse primer of methylation site 13:
CACAAAACAACATCTTCGAACC
Seq ID NO.59
forward primer for methylation site 14:
GGTGCGGAGTTAGGATTCGTAG
Seq ID NO.60
probes for methylation site 14:
TCGTTTTGCGTACGGATTCG
Seq ID NO.61
reverse primer of methylation site 14:
CCTATATTCCGTCGCGCTAA
Seq ID NO.62
forward primer for methylation site 15:
CGGGTAGCGTTGGTTACGC
Seq ID NO.63
probe of methylation site 15:
TTTTCGTCGTCGGCGGT
Seq ID NO.64
reverse primer of methylation site 15:
AAAACCAAAAATACTTAACCACGAAA
Seq ID NO.65
methylation site 16:
GGGGTCGTATTTTTGGTTTCGTCGTCGATTATTATATCGCGTAGGGTTTGAGTGAGGATTAGTTCGTATCGCGTAAGACGTTTTATAGGTGAGGAGGTTTTCGGGACGGGTTTTTTAGAGGGTGGTTATTTGGAGGTAGGGCGGGGTGGGCGGGTCGTAGTA
Seq ID NO.66
methylation site 17:
TAGTGGTCGCGTCGTTAGCGGATTGTTGTAGCGGCGTGAGTAGCGGGGGTCGCGGTTGCGGGAAACGTTTCGGAGTTTAGGAATATAGTTTTCGTTGGTTAGCGGCGGTAGTAGTAGTAGCGGGGTTTTTGCGCGCGGCGTTTATCGTTTTTTTTTCGCGTCGGGTTCGCGGTGTTGTAGGCGGTAGTTACGTAGATTGTTTTTTTATTTTTTTGTTTTTTAGTTAGAACGTGAATGTATTG
Seq ID NO.67
methylation site 18:
GAGTCGTTCGTTATTTTTTGTTTTCGTTGTAGATTTTTTATTTATTTGGATCGGTTTTCGATCGTAATTATTCGGTGCGTTGGGTAGCGTTTTCGTTTTTAGTAGCGTTCGTATTTTTTTTATTCGATTTCGGGTCGCGGTCGTGGTTAGTTAGTTAGTCGAAGGTTTTATGTTGTTTTTCGTCGTCGGTTTTATGTTGTTTTTCGTCGTTCGTTGTTTGTTTTTTTTTTTTTCGTAGTCGT
Seq ID NO.68
methylation site 19:
GTTTTTCGTAGTTCGTAGGAGACGTAGCGTTTTTCGTTTAGGGGGAGTAGGAGGATAGTTGGTCGTTTTCGGGAGTTTTAGGCGCGAAGCGGTTTTTTTTCGTTTTAGGTGAGTTTTGTTCGACGTTTAAGGTTTAACGGTTAGAATCGTTTTTGTTCGTCGGATTTAGCGAAGTTA
Seq ID NO.69
methylation site 20:
AATAGGTTTTTTTTCGCGTATATTGATATATTTTTTATTTTTTATAATGAATTTAGTTATATGGTATTTTTTTTTATCGAAGGTTATCGGGAATGGTTTTAGGAAGTTGATTTTTAAGTTTTAAGCGGTAGTAGGTGTCGGTAGCGCGGGGATCGATCGA
Seq ID NO.70
methylation site 21:
GTTAGTCGATAATAAAAATGTTTGTATCGTTCGTAGGTGGAGGCGGTTTATTAGGTTAGGAATCGAAAGGAGGGAGGGATGTTGTTAGGTTAAATTTAACGTTTTGGTTAAGGGCGCGTTAAGGAAATCGTATCGTTTGGGTGGGGATTTTCGTGATATTTTATTTGTTATTAGTATTTTTAATATTATTTTTTTTTTTTGAAGGGATTTCGGTTATTATAGTCGAGGGAGGTTGAAAATTT
Seq ID NO.71
methylation site 22:
TGATTTCGGAGCGTACGGTAGGTGTATATTTTCGGGTAAGTTTTAATGGGATTTTTCGAGTTTAGCGTAAGCGTTGTTAGCGTAGCGGCGAGTAGTTTGCGAGGTAGGCGGATTGTATTTTAGAGTTGTAGTTCGGCGCGTTTTCGTTTCGATTTCGCGTTTCGTATTTAAGTAGTGTTATTTGTAGATTTAGATATTAGATGAGTGTTTTAAGATAATAATGTTGTTTTGTTTGTTTTTTT
Seq ID NO.72
methylation site 23:
ATTTTTTTTGTTTGAAGATTTTCGGACGGGGATTTTACGGGTTTGGTTTTTGGGTTGGGATGATAAGTGTTTTTGTGGGATATATAGTTTTAGGTTTTATCGGTTATTCGGTTTATTTTTGGTTTCGTCGGTTGGTTCGTGCGTTTTTGGTCGGGGTAGGAGTAGCGTTGCGCGCGGGGTACGGAGTTTCGGTCGTCGAGTTTTCGGTTTTTAAGTTTTTTTTTTAGGTTTTTTTTATTTTT
Seq ID NO.73
methylation site 24:
GTGTTAGTGGTTGTAAATTTGTGATTATTTTTAAGAAATTGAAAATTATGAGGTTTGTTTAATATTTGAGAAAATTTAGGTTAAAATTTTTTGTAGAAAGCGAGACGTGGGAAATTGGCGAAGTTGTTATTAGTCGTCGGCGTAAGGAGCGCGAGAGTTTTGGGTGCGCGTAGGGTATTTATTTTTTATTTTTTAGTAAGTATCGTCGTAAGTTTTTTAGGTGTAGAAATTGTTGGCGTTGT
Seq ID NO.74
methylation site 25:
TTTGTTTTAGGGTTGAGATCGGTAGGGTAGAGTTTTAGTTTTTTTTATATCGTATAAAATTCGTAGTTAAGTTTGGATGGTAGAGATAAGTCGGAAAAGTAGGAATTTTTTCGTGTTTCGTTTTTAGCGTTTTTTTTTTTGTTACGAGTAGGGTTTTTGTATTTTTTGCGGATTTTAGTTTTTTTGTATTTAATTGTTTTTAAGTTTTTTATTATATAGTATAAATTTTATTTTTATTTTTT
Seq ID NO.75
methylation site 26:
GAATAGGGGTGGTTTTTTGCGTTTAATTTGTTGTATTTTAGGTGTTTGTTTTTTTTTGGGATGTTGTGGTCGATTGTGTGTGAAAAGTGAACGGAGTAGTGTTGTTGCGCGAGGTTTTTTTTGCGTGTGTTTATTTTTAGATTAGGTGTGAGTCGTATTTGA
Seq ID NO.76
methylation site 27:
TGAGTTTTAGGAGGCGGTTGATGTTAGCGAATTTTGTGTCGCGTTGTTTTTTTTCGGAGTTAGGGAGTAGCGAGTTTTTTATTTAGATCGCGTTAGTGCGTTTCGGGTTTTATTAGTTTTTATTAGGCGTTGTTTTTTTCGTTTTTATTTAGTTTTTTTTT
Seq ID NO.77
methylation site 28:
TTTAAAAGATTGAGAGTGTCGGCGTAGGTATGATAGTGAGGGTATTTTATAGATTTTTTTTTAAAGTTTGGCGGGTTTTGGGGTTTTTCGGGGTTATTAGGTTCGGTGGAATTTTTGAAACGTTTTCGAAATATATAGTTTTTTTTGTGGAGTGAGTGTTTA
Seq ID NO.78
methylation site 29:
TGGAAAGGAAATAAAATTACGCGGATTGGCGATTGGTTTGCGGTTGGGAAGACGACGAAGAGGAGGAAAGAAAGAAAAAGGAGACGTGTGGGTATCGCGGAAAACGGTCGGCGTTGGTTTTTTTTCGGCGAATTCGAGTGAAAGTTTTTGGTTTCGGGGAATTAAATAATTTTGTTATTCGCGAGGGAGGGAGGAAGAAACGTGTTAAAAGGGTTGGTTTTGATTATTA
Seq ID NO.79
methylation site 30:
TGGTGGTAGTTTTTGATTTCGCGTGGGTCGTTGAATGTATGATTGGGATCGTTTAGCGGTGGATATATAATTGTGTGCGGTTGTGGAAGATTGTTTTATTCGTTACGTTTTTATTTTCGTGGAATAATAGTTTTATTAGAAGGTTTCGAAGGAATTTTAAA
Claims (8)
1. use of a marker combination for the preparation of a reagent for early detection of a digestive tract tumor, characterized in that the marker combination comprises a combination of any one or more of the nucleotide sequences shown in Seq ID No.1 to Seq ID No.15 or the complete complement thereof; the digestive tract tumor comprises colorectal cancer, liver cancer, esophageal cancer, gastric cancer and pancreatic cancer.
2. The use according to claim 1, wherein the marker combination is a combination of the nucleotide sequences shown in Seq ID No.1 to Seq ID No.15 or the complete complement thereof.
3. The use of claim 2, wherein the marker combination further comprises a protein marker comprising PG I, PG II, SCCA, ferr, CEA, CA19-9, G-17, AFP-L3, DCP.
4. A primer combination for detecting a marker combination for use according to claim 1, wherein the primer combination comprises 15 sets of primers, probes as shown in the following table:
5. A kit for detecting early stage gut tumor comprising the primer and probe combination of claim 4;
the digestive tract tumor comprises colorectal cancer, liver cancer, esophageal cancer, gastric cancer and pancreatic cancer.
6. A marker combination for detecting early stage digestive tract tumor, characterized by comprising a nucleotide sequence shown as any one or more of Seq ID No.1 to Seq ID No.15 of a sequence table; the digestive tract tumor comprises colorectal cancer, liver cancer, esophageal cancer, gastric cancer and pancreatic cancer.
7. The marker combination according to claim 6, wherein the marker combination is a nucleotide sequence shown as Seq ID No.1 to Seq ID No. 15.
8. The marker combination of claim 7, further comprising a protein marker comprising PGI, PGII, SCCA, ferr, CEA, CA19-9, G-17, AFP-L3, and DCP.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311144636.9A CN117187390A (en) | 2023-09-06 | 2023-09-06 | Novel marker combination for early detection of multi-target digestive tract tumor and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311144636.9A CN117187390A (en) | 2023-09-06 | 2023-09-06 | Novel marker combination for early detection of multi-target digestive tract tumor and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117187390A true CN117187390A (en) | 2023-12-08 |
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|---|---|---|---|
| CN202311144636.9A Pending CN117187390A (en) | 2023-09-06 | 2023-09-06 | Novel marker combination for early detection of multi-target digestive tract tumor and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117187390A (en) |
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2023
- 2023-09-06 CN CN202311144636.9A patent/CN117187390A/en active Pending
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