CN111440866B - Application of DUSP3 gene methylation detection reagent in preparation of colorectal cancer prognosis diagnosis reagent - Google Patents

Abstract

The invention belongs to the field of gene diagnosis, and in particular relates to an application of a DUSP3 gene detection reagent in preparing a colorectal cancer prognosis diagnosis reagent and a colorectal cancer prognosis diagnosis reagent/kit. The present inventors have found that methylation of the DUSP3 gene as a representative marker can distinguish between hypermethylated colorectal cancer cases with a high risk of recurrence. Based on the optimal cutoff values determined in the independent training queues, all genes that are the two classification variables have the value of independently predicting disease-free survival in the training queues and the validation queues.

Description

Application of DUSP3 gene methylation detection reagent in preparation of colorectal cancer prognosis diagnosis reagent

Technical Field

The invention belongs to the field of gene diagnosis, and in particular relates to application of a DUSP3 gene methylation detection reagent in preparation of colorectal cancer prognosis diagnosis reagent and a colorectal cancer prognosis diagnosis reagent/kit.

Background

Colorectal cancer (Colorectal cancer, CRC) is common worldwide and remains the third leading cause of cancer-related death, with 39% of patients presenting with stage I-II disease. Surgery for radical cure is the standard method of treating patients with stage I-II colorectal cancer. However, the recurrence rate of post-operative fatal disease in these patients is 20-25%. In general, the clinical pathological factors currently used for the risk of early CRC stratification include T4 lesions, poor histological differentiation, ileus, perforated tumors, less than 12 lymphadenoctomy. However, these risk factors do not clearly distinguish between patients at high or low risk of disease recurrence. Thus, there is a need to increase prognostic and predictive value for current risk stratification systems, which can be achieved through the use of validated molecular markers.

In many human cancers, cpG island hypermethylation of tumor suppressor genes is used to develop biomarkers, such as WRN, MLH1 and CpG island methylation phenotypes (CpG island methylator phenotype, CIMP) with the accumulation of aberrant epigenetic changes in the progression of tumorigenesis. However, cpG islands in promoters represent only a small part of the methylation group, cpG open ses located widely in the genome also exhibit significantly wide variation in CRC patients, but have not been used for molecular markers.

Some studies have analyzed DNA methylation profiles in CRC, and their potential clinical relevance was detected using a Infinium HumanMethylation K (HM 450) chip. However, HM450 lacks coverage of the genome of the CpG open ses and reference genes and therefore the value of this method to screen for molecular markers is limited. Recently issued Infinium MethylationEPIC (EPIC) chips, new probes were designed specifically for these areas. Compared with the HM450 chip, the vast majority (78.2% of 413,745) of the newly added probes in the EPIC chip are located in CpG open ses. This provides a valuable tool for screening more clinically significant CpG sites.

Disclosure of Invention

The invention aims to provide an application of a methylation detection reagent of a colorectal cancer tumor marker in preparing a colorectal cancer prognosis diagnosis reagent.

It is another object of the present invention to provide a molecular marker for predicting colorectal cancer recurrence.

It is another object of the present invention to provide a prognostic diagnostic reagent for colorectal cancer.

It is yet another object of the present invention to provide a method for detecting genomic methylation of DUSP 3.

The above object of the present invention is achieved by the following technical means:

in one aspect, the invention provides the use of a gene detection reagent in the preparation of a colorectal cancer diagnostic reagent/kit, the gene being the DUSP3 gene.

As an alternative embodiment, the gene further comprises one or both of the TLE4 gene and the KAZN gene.

As a preferred embodiment, the gene is a combination of DUSP3 and TLE.

In a preferred embodiment, the diagnostic reagent/kit is a diagnostic reagent/kit for prognostic use of colorectal cancer.

The present invention also found that there was a positive correlation between hypermethylation of the DUSP3 gene and RNA expression. That is, as an alternative embodiment, it is likely that low expression of the gene may also be used for prognostic diagnosis of colorectal cancer.

In one embodiment, the DUSP3 gene detection reagent is a reagent for detecting the expression level of the DUSP3 gene, preferably, a reagent for detecting the expression level of the mRNA of the DUSP3 gene.

As another embodiment, the DUSP3 gene detection reagent is a DUSP3 gene methylation detection reagent.

The DUSP3 gene detection reagent is used for detecting the sequence of the DUSP3 gene modified by the conversion reagent.

As a preferred embodiment, the conversion reagent is selected from one or more of hydrazine salt, bisulfite and bisulfite;

as one embodiment, the conversion reagent is selected from bisulphite.

The present invention first discovered and systematically validated genomic methylation of DUSP3 as a representative marker, which can distinguish hypermethylated CRC cases with high risk of recurrence.

Therefore, the detection regions of the detection reagent for DUSP3 gene methylation are CpG open ses and genomic regions of the DUSP3 gene.

In a preferred embodiment, the sequence of the detection region of the detection reagent for methylation of the DUSP3 gene is SEQ ID NO:1, the specific sequence is as follows:

ATGAGTCCCCTCATTTGTAGGTGGTGGTTTCTGGATAACTCAGAGTGGCAGGGACACAGA[CG]AGCCTGTGGAAAGGTATACTGCTTTAAGATTGAGAAGAAAACCATTTGGCGCTCTAATTT。

wherein the detection site is CG in the sequence brackets.

The inventors have conducted intensive studies to obtain DNA methylation profiles at CpG open ses and the genome associated with early colorectal cancer recurrence. The study found that there were few recurrent specific differential methylation sites (differential methylation position, DMP) in CpG islands and promoters, but many in CpG open ses and genome. Tumor-specific DMP, on the contrary, has been widely reported to be located mainly in CpG islands and gene promoter regions. In the discovery cohort of the present invention, recurrence-specific DMP did not overlap with tumor-specific DMP. However, in previous studies, tumor-specific DMP is widely used to develop models for prognosis prediction.

The detection reagent contains a DNA chip.

As an alternative embodiment, in the detection of methylation of the DUSP3 gene by the detection reagent, the methylation degree is high, and the risk of colorectal cancer recurrence is high; when the degree of methylation is low, the risk of colorectal cancer recurrence is low.

As a preferable embodiment, the threshold value of the methylation degree of the DUSP3 gene is 69.21% -80.63%.

As a more preferable embodiment, the threshold value of the methylation degree of the DUSP3 gene is 72.17% -79.54%.

As a further preferable embodiment, the threshold value of the methylation degree of the DUSP3 gene is 75.93% -78.89%.

As a most preferred embodiment, the threshold value for the methylation level of the DUSP3 gene is 78.57%.

In the invention, the detection sample of the detection reagent is tissue.

In addition, these new methylation markers can also be studied in other clinical samples, including stool and blood samples, to investigate their broader clinical use in predicting early recurrence.

As a preferred embodiment, the detection sample of the detection reagent is a tissue.

As a more preferred embodiment, the test sample is intestinal mucosal tissue.

In another aspect, the invention provides a colorectal cancer prognostic diagnostic reagent/kit comprising a DUSP3 gene methylation detection reagent.

As a preferred embodiment, the kit further comprises a transforming reagent.

As a preferred embodiment, the reagent/kit contains reagents for detecting the sequence of the DUSP3 gene modified by the transforming reagent.

As a more preferred embodiment, the conversion reagent is selected from one or more of hydrazine salt, bisulfite and bisulfite.

As a most preferred embodiment, the conversion reagent is selected from the group consisting of bisulfites.

As an alternative embodiment, the reagent/kit further comprises a pair of oligonucleotide Taqman probes for detecting methylation of the DUSP3 gene.

As a more preferred embodiment, the probe is a probe comprising a probe specifically binding to CG and a probe specifically binding to TG.

As a further preferred embodiment, the probe is as set forth in SEQ ID NO: 2. SEQ ID NO: 3.

As a preferred embodiment, the reagent/kit further comprises primers for detecting methylation of the DUSP3 gene.

As a more preferred embodiment, the primer is selected from the group consisting of SEQ ID NO: 4. SEQ ID NO: 5.

As an alternative embodiment, the reagent/kit further comprises one or more of DNA polymerase, dNTPs, mg2+ ions and buffer.

As a preferred embodiment, the reagent/kit contains DNA polymerase, dNTPs, mg2+ ions and buffer.

In another aspect, the invention provides a colorectal cancer prognostic diagnostic kit comprising a reagent for detecting the expression level of the DUSP3 gene.

As a preferred embodiment, the reagent/kit contains a reagent for detecting the expression level of the DUSP3 gene mRNA. In another aspect, the invention provides a chip for prognosis of colorectal cancer, the chip comprising a solid support and probes for methylation of the DUSP3 gene immobilized on the solid support.

In yet another aspect, the invention provides a prognostic colorectal cancer diagnostic system comprising:

A detection member: the detection component is used for detecting the methylation degree of the DUSP3 gene of the diagnosis object;

and a result judgment means: the result judging component is used for outputting a methylation percentage parameter PMR or a disease risk result according to the methylation result of the DUSP3 gene detected by the detecting component.

As a preferred embodiment, the disease risk result is a probability of disease, or one of the disease types.

As a preferred embodiment, the percent methylation parameter PMR is methylation/(methylation+unmethylation). Times.100.

As a further preferred embodiment, the methylation percentage parameter pmr=methylation fluorescence value/(methylation fluorescence value+unmethylation fluorescence value) ×100.

As a still further preferred embodiment, the methylation percentage parameter pmr=100/(1+1/2) ^-ΔCT ) Δct = CT methylated fluorescence-CT unmethylated fluorescence.

As a preferred embodiment, the detection component is one or more of an ultra-micro spectrophotometer, a real-time fluorescence quantitative PCR instrument and an ultra-high sensitivity chemiluminescence imaging system.

As a preferred embodiment, the result judging means includes an input module, an analysis module, and an output module; the input module is used for inputting the methylation degree of the DUSP3 gene; the analysis module is used for analyzing the possibility or risk value of recurrence of colorectal cancer after cure or colorectal cancer of healthy people according to the methylation degree of the DUSP3 gene; the output module is used for outputting the analysis result of the analysis module.

As a preferred embodiment, the analysis module is used to analyze the likelihood or risk value of recurrence of colorectal cancer after healing.

As a preferred embodiment, the methylation level of the DUSP3 gene is the methylation ratio of the aforementioned CG sites in the genomic region of the DUSP3 gene in the sample.

As a preferred embodiment, the diagnostic sample of the diagnostic system is tissue, stool or blood.

As a more preferred embodiment, the diagnostic sample of the diagnostic system is tissue.

As a still further preferred embodiment, the test sample is intestinal mucosal tissue.

As a preferred embodiment, in the structural judgment means, when the methylation degree of the DUSP3 gene is high, it is judged that the risk of recurrence of colorectal cancer after cure or colorectal cancer in healthy subjects is high; when the methylation degree of the DUSP3 gene is low, the recurrence of colorectal cancer after cure or the low risk of colorectal cancer in healthy people is judged.

As a further preferred embodiment, in the structural judgment means, when the methylation degree of the DUSP3 gene is higher than the threshold value 69.21% -80.63%, it is judged that the risk of recurrence of colorectal cancer after cure or colorectal cancer of healthy subjects is high; when the methylation degree of the DUSP3 gene is lower than a threshold value 69.21% -80.63%, the recurrence of colorectal cancer after cure or the low risk of colorectal cancer of healthy people is judged.

As a further preferred embodiment, in the structural judgment means, when the methylation degree of the DUSP3 gene is higher than the threshold value of 72.17% -79.54%, it is judged that the colorectal cancer recurs after cure or the risk of colorectal cancer of healthy people is high; when the methylation degree of the DUSP3 gene is lower than the threshold value of 72.17-79.54%, the recurrence of colorectal cancer after cure or the low risk of colorectal cancer of healthy people is judged.

As a more preferred embodiment, in the structural judgment means, when the methylation degree of the DUSP3 gene is higher than the threshold value 75.93% -78.89%, it is judged that the colorectal cancer recurs after cure or the risk of the colorectal cancer of healthy people is high; when the methylation degree of the DUSP3 gene is lower than a threshold value 75.93% -78.89%, the recurrence of colorectal cancer after cure or the low risk of colorectal cancer of healthy people is judged.

As a most preferred embodiment, in the structural judgment means, when the methylation degree of the DUSP3 gene is higher than the threshold value of 78.57%, it is judged that the colorectal cancer recurs after cure or the colorectal cancer of healthy people is at high risk; when the degree of methylation of the DUSP3 gene is below the threshold value of 78.57%, it is judged that the colorectal cancer recurs after cure or the risk of suffering from colorectal cancer in healthy people is low.

In the present invention, the colorectal cancer described above is preferably stage I-II colorectal cancer.

The invention has the beneficial effects that:

1. most methylation prognosis-based markers used in the prior art target CpG islands. This may be one of the reasons that markers found in the past are highly heterogeneous in different queues. The present invention has found DNA methylation profiles at CpG open ses or the genome associated with early recurrence. Predictive models based on CpG open sea or genomic methylation can better predict early relapse in CRC patients.

2. The study of the invention discovers and systematically verifies that the genomic methylation of DUSP3 can be used as a marker for recurrence after early CRC radical surgery, the methylation of the DUSP3 gene can be used as a representative marker, and hypermethylated colorectal cancer cases with high recurrence risk can be distinguished. Based on the optimal cutoff values determined in the independent training queues, all genes that are the two classification variables have the value of independently predicting disease-free survival in the training queues and the validation queues.

Drawings

FIG. 1A is a schematic diagram of the sequence of the detection region of the DUSP3 gene, primers, probes and CpG sites to be detected after bisulfite treatment;

B. Schematic diagrams of FHIT gene detection region sequences, primers, probes and CpG sites to be detected after bisulfite treatment;

C. is a schematic diagram of SGIP1 gene detection region sequence, primer, probe and CpG sites to be detected after bisulphite treatment.

FIG. 2 comparison of DUSP3 gene with other genes or indicators in the prognostic diagnosis of colorectal cancer:

detection of DUSP3 gene methylation in predicting prognosis of a patient;

detection of CIMP phenotype in predicting prognosis of patient;

less than 12 total T4 lesions, tumor ileus or perforations or lymph node biopsies of stage C-e.ii colorectal cancer, in the detection of prognosis of early colorectal cancer;

f-h. value comparison of molecular typing KRAS mutations, BRAF mutations and high microsatellite instability in predicting early colorectal cancer prognosis.

FIG. 3 the value of the DUSP3 gene in prognosis diagnosis of colorectal cancer compared to the value of the FHIT and SGIP1 genes in predicting early colorectal cancer prognosis.

FIG. 4 chip analysis of DNA methylation signature and methylation in genomics and CpG OpenSeas associated with early relapse:

LASSO Cox model for discovery of representative probe selection in the set: FIG. 4A shows the LASSO coefficients for 3 CpG sites associated with DFS; the vertical lines in the right plot of FIG. 4A show λ (left) and λ (right), the numbers on the plot representing the number of non-zero coefficients;

B. Recurrence-specific differential methylation sites (DMP) heatmaps with most significant DNA methylation differences among CRC patients found to survive 21 and 24 relapse-free in the cohort. DNA methylation is represented by beta values, presented by using a color scale from dark blue (low DNA methylation) to yellow (high DNA methylation). We identified two subgroups by unsupervised cluster analysis based on RPMM model, as shown, (red) cluster a (n=13) and (blue) cluster B (n=32). KRAS mutations (no color = wild type), MSI (no color = MSS), CIMP positive (no color = CIMP negative), and clinical variables in the figures are represented by colored squares. Each column represents one patient and each row represents a probe targeting one DMP. Probes targeting CpG islands, open sea, promoters and genome are shown as horizontal color bars on the right side of the heatmap. The arrangement of probes is based on unsupervised cluster analysis using a correlation distance metric and an average linkage method.

C. Disease-free survival curves for two groups of patients obtained by cluster analysis using relapse-specific DMP.

D. Wien plots for different differential methylation sites in the queue were found: (left) EPIC chip probes targeting 2,420 tumor-specific, 1,406 recurrence-specific and 1,681 stage-specific DMP; (right) reference genes where probes targeting tumor-specific, recurrence-specific and stage-specific DMP are located.

E. Tumor specific DMP was subjected to cluster analysis to give disease-free survival curves for both groups of patients.

F. Cluster analysis of DNA methylation using recurrence-specific DMP located in CpG open ses (left panel) and CpG islands (right panel), respectively.

FIG. 5 is a flow chart for the discovery, training and validation of DNA methylation markers for early colorectal cancer recurrence.

FIG. 6 shows methylation status of 6 candidate CpG sites in the cohort in tumor tissue and normal tissue (A) and recurrent tumor and tumor-free survival tumor (B).

Fig. 7 left: a time-dependent ROC curve was found for 6 representative methylation markers in the cohort. AUC of 6 probes targeting KAZN, FAT3, DUSP3, TLE4, FHIT, SGIP1 genes reached 0.769-0.785, showing their high accuracy in predicting prognosis in the discovery queue.

Right figure: survival analysis plots of LASSO Cox model established using 6 candidate methylation markers.

Methylation of six candidate CpG sites in FIG. 8 predicts the DFS in the discovery cohort.

FIGS. 9 and 10 are schematic representations of EPIC chip and qMSP detection by sulfite pyrophosphate sequencing.

FIG. 11A there is a positive correlation between DNA methylation and mRNA expression of 6 candidate genes in colon cancer cells after treatment with the DNA methylation inhibitor 5-aza-2' -deoxyytidine; correlation analysis of methylation and expression profiles in tcga cohorts.

FIG. 12 finds, trains and validates six candidate gene methylation markers:

panel A (left panel) found time-dependent ROC curves for 6 representative methylation markers in the cohort. AUC of 6 probes targeting KAZN, FAT3, DUSP3, TLE4, FHIT, SGIP1 genes reached 0.769-0.785, showing their high accuracy in predicting prognosis in the discovery queue. (right panel) survival analysis graph of LASSO Cox model built using 6 candidate methylation markers;

graphs B-G: training the queue and validating the survival analysis in the queue. The training queue adopts a qMSP method to measure methylation data of KAZN, FAT3, DUSP3, TLE4, FHIT and SGIP1, and adopts a minimum-p method to divide patients into a hypermethylation group and a hypomethylation group; patients were classified into hypermethylated and hypomethylated groups in the validation cohort based on the cutoff values determined in the training cohort.

Wherein the data are expressed as a risk ratio (95% confidence interval). Each graph gives Log-rank test P values. The values in brackets are corrected P values in the multiplex assay. AUC = area under the curve, ROC = subject working characteristics, HR = risk ratio, DFS = disease free survival.

FIG. 13 training the DFS risk ratio of single and multiple gene models in the cohort

All models that were significantly correlated with DFS in the single factor cox regression analysis were corrected for the multiple cox regression analysis. The prognostic value of each gene or model was corrected by a multifactorial Cox analysis involving age, sex, stage and degree of tissue differentiation, and the risk ratio after correction was plotted as a forest map. Data are expressed as risk ratio (95% confidence interval). DFS = disease free survival. "Gene A/gene B" means that hypermethylation of either gene A or B is present, i.e., a high risk of recurrence is determined; "Gene A-gene B" means that both genes A and B are hypermethylated at the same time, and that recurrent high risk cases can be determined.

FIG. 14 demonstrates the DFS hazard ratio of single-gene and multiple-gene models in a cohort

All models that were significantly correlated with DFS in the single factor cox regression analysis were corrected for the multiple cox regression analysis. The prognostic value of each gene or model was corrected by a multifactorial Cox analysis involving age, sex, stage and degree of tissue differentiation, and the risk ratio after correction was plotted as a forest map. Data are expressed as risk ratio (95% confidence interval). DFS = disease free survival. "Gene A/gene B" means that hypermethylation of either gene A or B is present, i.e., a high risk of recurrence is determined; "Gene A-gene B" means that both genes A and B are hypermethylated at the same time, and that recurrent high risk cases can be determined.

Detailed Description

The technical solution of the present invention is further illustrated by the following specific examples, which do not represent limitations on the scope of the present invention. Some insubstantial modifications and adaptations of the invention based on the inventive concept by others remain within the scope of the invention.

The term "diagnostic reagent/kit" may be a diagnostic reagent or a diagnostic kit.

"prognosis" refers to predicting the likely course and outcome of a disease, predicting the likelihood of disease recurrence.

Genome: a gene is the entire nucleotide sequence required to produce a polypeptide chain or functional RNA, and the genome, i.e., the major portion of the gene, generally refers to the entire nucleotide sequence of a gene from which the promoter region (typically the 2000bp region upstream and downstream of the transcription initiation site) is removed.

CpG island: the distribution of CpG dinucleotides in the human genome is very heterogeneous, with CpG remaining at or above normal frequencies in certain sections of the genome. CpG islands are mainly located in promoter and exon regions of genes, are some regions rich in CpG dinucleotides, and have the length of 300-3000 bp. Typically defined as GC content exceeding 55% and the actual to expected number of CpG dinucleotides ratio greater than 65%, the expected number of CpG dinucleotides calculated as (number C x number G)/sequence length.

Colorectal cancer: colorectal cancer, CRC.

The degree of methylation may be determined in a manner commonly used in the art.

In one embodiment of the invention, the degree of methylation may be calculated or determined in accordance with the following manner. For example, the methylation degree is calculated in the present invention using the following formula: pmr=100/(1+1/2) ^-ΔCT ) Δct = CT methylated fluorescence-CT unmethylated fluorescence. The methylation ratio or percent methylation Parameter (PMR), i.e. the degree of methylation, occurs in the present invention.

Threshold of methylation degree: the invention uses the threshold value of methylation degree to define the value or the value range of the colorectal cancer recurrence risk, namely, the colorectal cancer recurrence risk is high when the value is higher than the preset threshold value; below a given threshold, colorectal cancer is at low risk of recurrence. The threshold value that appears in the present invention is determined corresponding to the methylation degree calculation method in the above-described one embodiment.

CIMP (CpG island methylator phenotype): refers to the CpG island methylation phenotype.

In the invention, KAZN is KIAA1026, which has the same name.

"Gene A/gene B" means that hypermethylation of either gene A or B is present, i.e., a high risk of recurrence is determined; "Gene A-gene B" means that both genes A and B are hypermethylated at the same time, and that recurrent high risk cases can be determined.

DMP (differential methylation position): refers to differential methylation sites, i.e., cpG sites where there are significant differences in methylation statistics (q-values) and biology (Δβ) in the two sets of samples.

Statistical analysis

The primary endpoint is disease-free survival (DFS), defined as the time from the day of surgery to the point of recurrent metastasis, cancer-related death, or follow-up cutoff. For each prognostic marker, training cohort patients were assigned the best cutoff value by using the minimum p-value method of R-package 'survivinMisc' into hypermethylated and hypomethylated groups, with highest χ ² The value (minimum p-value) is defined by Kaplan-Meier survival analysis and Log-rank test. Patients in the validation queue are divided into two groups based on the cutoff values defined in the training queue. Bonferroni correction was used for survival analysis of multiple candidate methylation markers. The prognostic value of candidate molecular markers is also corrected in a multifactorial Cox regression model that contains multiple markers and clinical pathology features. The predictive Cox model is built from estimated regression coefficients generated in a proportional hazards model. The invention also investigated the accuracy of marker prognosis or prediction by time-dependent ROC curve analysis using R-package "survivinvalroc". All statistical tests were done using R software 3.0.1. Statistical significance was set at 0.05.

Example 1 sample Source

Case sample patient characterization

Patients who were pathologically verified to be stage I-II CRC and received surgical resection may be included as a discovery, training or verification cohort of cases. Patients who had previously received any anti-cancer treatment, had a history of any tumor other than CRC, and had substantial degradation of the DNA sample were excluded.

First, 45 cases of fresh frozen tumor tissue and paracancerous normal tissue were collected in stage I-II CRC patients and analyzed on whole genome methylation chips. Patients with less than 12 lymph node resection assays were excluded from ileus or perforation, vascular or neurological aggression. Depending on age, sex, TNM stage, date of surgery (±5 years) and tumor location, the group of 45 patients contained 21 patients with recurrence in the follow-up and 24 patients who achieved survival without recurrence of tumor in the paired follow-up. These 45 patients consisted of a discovery cohort for finding molecular markers. Samples were obtained from the sixth hospital affiliated with the university of guangzhou mountain from 1 month 6 to 30 months 2011. For training set analysis, a retrospective study was performed on 174 formalin-fixed, paraffin-embedded (FFPE) phase I-II CRC samples collected at the university of middle mountain, guangzhou, from 1 st 2000 to 30 th 2011, attached to the first and sixth hospitals. These patients form a training cohort from which the best predictive model is determined and validated. To further independently verify the determined prognostic markers and models, retrospective analysis was performed using 267 histologically confirmed FFPE tissue DNA of stage I-II CRC patients collected at the university of guangzhou tumor center and the southern hospitals of southern medical science, guangzhou, at 1 month 6 to 30 month 2012.

In general, all patients were staged according to the TNM staging criteria and follow-up and treatment according to NCCN guidelines. Tumor marker prognostic study recommendation (Recommendations for Tumor Marker Prognostic Studies, REMARK) criteria were used to evaluate prognostic markers. The study was approved by the institute of university of chinese institutional review board and all patients had signed written informed consent.

Detailed clinical pathology features of the training and independent validation cohorts were found by sample analysis as shown in table 1. 486 patients received surgical resection and histological examination were negative resection margin. Median follow-up time was 77 months (quartile range IQR 54-102), with 98 out of 486 (20.1%) patients experiencing tumor recurrence during follow-up. In the discovery cohort, 21 relapsed and 24 paired non-relapsing patients were similar in clinical and demographic characteristics with a median follow-up time of 58 months (table 2).

Table 1 baseline characteristics of different cohorts of patients

Table 2 finds baseline characteristics of relapsed and non-relapsing CRC patients in cohorts

EXAMPLE 2 methylation detection of DUSP3 Gene

The level of methylation of gene CpG sites was detected using qMSP.

The gene detected: DUSP3;

comparison genes: FHIT, SGIP1.

1. Quantitative methylation-specific PCR

Genomic DNA was extracted and bisulphite modified using QIAamp DNA Mini Kit (Qiagen, 51306) and EZ DNA methylation kit (Zymo Research, D5002).

Quantitative methylation specific PCR (quantitative methylation-specific PCR, qMSP) was used to detect the CpG sites to be detected in the genome or CpG open sea in different queues to assess and verify their relationship to the prognosis of CRC patients.

In this assay, bisulfite-converted genomic DNA is amplified using primers and a pair of oligonucleotide probes covering the CpG sites to be tested, each of which is linked at its 5 'end to a fluorescent reporter dye 6FAM or VIC (specifically binding to the methylated and unmethylated sites, respectively) and at its 3' end to a quencher-MGB group (MGB-NFQ).

For three sites to be tested in the DUSP3, FHIT and SGIP1 genomes, the present invention designed three sets of primers and probes specific for the present invention, as shown in Table 3. The probe covers only a single CpG dinucleotide, so that the methylation level of a single CpG can be measured.

Fluorescent signals in the PCR reactions were detected by a Applied Biosystems QuantStudio Flex real-time PCR system. The methylation ratio (methylation percentage parameter PMR) of the CpG sites to be tested of each sample is equal to methylation signal/(methylation signal+unmethylation signal). Times.100, and when specifically calculated, the following formula is used: pmr=100/(1+1/2- Δct), Δct=ct methylated fluorescence-CT unmethylated fluorescence;

A20 uL reaction system was used, which included 500nM primer, 150nM probe, dATP, dCTP, dGTP and 200nM,2.25mM MgCl2,0.75U HotStar Taq enzyme dTTP, 1 XPCR buffer. The reaction conditions are as follows: first 15 minutes at 95℃and then 50 cycles of 30 seconds at 94℃and 1 minute at 56-60℃and 1 minute at 72 ℃.

2. Genetic locus information

(1)ID:cg16747321

UCSC_RefGene_Name:DUSP3

UCSC_RefGene_Accession:NM_004090

chr:chr17

pos:41844148

strand:+

Relation_to_Island:OpenSea

UCSC_RefGene_Group:3'UTR

Bisulfite pretreatment pre-sequence:

SEQ ID NO：1

ATGAGTCCCCTCATTTGTAGGTGGTGGTTTCTGGATAACTCAGAGTGGCAGGGACACAGA[CG]AGCCTGTGGAAAGGTATACTGCTTTAAGATTGAGAAGAAAACCATTTGGCGCTCTAATTT

as shown in FIG. 1A, the sequence of the detection region of the bisulphite treated DUSP3 gene, primers, probes and CpG sites to be tested are all labeled in the figure.

(2)ID:cg05704547

UCSC_RefGene_Name:FHIT

UCSC_RefGene_Accession:NM_002012

chr:chr3

pos:60067722

strand:+

Relation_to_Island:OpenSea

UCSC_RefGene_Group:Body

Bisulfite pretreatment pre-sequence:

SEQ ID NO：40

ATGAGTTCACTGCATTGTCTACTTATCTGTTTTTGTAATTTCAACTTTTATTTTTGATTT[CG]GGGTGCACATGTGGGTTTGTTCCATAGGTATATTGCATGATGCTCATGTTTGGGGTATGA

as shown in FIG. 1B, the sequence of the FHIT gene detection region after bisulphite treatment, the primer, the probe and the CpG sites to be detected are all marked in the figure.

(3)ID:cg05971061

UCSC_RefGene_Name:SGIP1

UCSC_RefGene_Accession:NM_032291

chr:chr1

pos:66998484

strand:+

Relation_to_Island:N_Shore

UCSC_RefGene_Group:TSS1500

Bisulfite pretreatment pre-sequence:

SEQ ID NO：41

TAGGCTGCCCTGCCCTTTTCTTCCTTCGCTGTCTGAGCTTTCTTGAAGGGAACCAAGGGT[CG]TAGATCCCCCAGGGCTGGGCCCTTCTGAAAGGCTCCATGGTCTCTGGAGAGCAGTCAGGT

as shown in FIG. 1C, the sequence of the SGIP1 gene detection region after bisulphite treatment, primers, probes and CpG sites to be detected are all marked in the figure.

TABLE 3 primer and probe sequences

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Example 3 detection of the degree of methylation of the DUSP3 Gene in tumor tissues of colorectal cancer patients

As shown in Table 4 below, in the tumor tissues of 8 colorectal cancer patients at stage I-II, the methylation degree of the DUSP3 gene was examined in the same manner as in example 2, and methylation ratio was higher than or equal to 78.57% and lower than 78.57% was judged to be hypermethylated. From the results in table 4, it can be seen that the recurrence risk for DUSP3 hypermethylated colorectal cancer patients is significantly higher than hypomethylated patients.

TABLE 4 detection results of methylation degree of DUSP3 Gene

EXAMPLE 4 comparison of the DUSP3 Gene with other genes or indicators in the prognosis of colorectal cancer

In the following experiments (1) and (2), the test samples were from 441 patients suffering from colorectal cancer at stage I-II in the same batch.

(1) Comparison with CpG Island Methylation Phenotype (CIMP)

CpG island methylation phenotype (CpG Island Methylator Phenotype, CIMP) is a type of colorectal cancer with different clinical and molecular characteristics, and CIMP is currently used as a molecular marker for prognosis and chemosensitivity of colorectal cancer, and is more widely used in Western countries. The invention adopts the international general technical flow, and uses the fluorescent quantitative methylation specific PCR technology to detect the methylation levels of CACNA1G, IGF, NEUROG1, RUNX3 and SOCS1 genes to determine the CIMP state of the sample (1]Shiovitz S,Bertagnolli MM,Renfro LA,et al.CpG island methylator phenotype is associated with response to adjuvant irinotecan-based therapy for stage III colon cancer. Pharmacology.2014.147 (3): 637-45.[2]Weisenberger DJ,Siegmund KD,Campan M,et al.CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer.Nat Genet.2006.38 (7): 787-93.). The primers and probes are shown in Table 3. The results are expressed by the methylation percentage parameter, greater than 4% defined as methylated, less than 4% defined as unmethylated, 3 to 5 genes methylation determined to be CIMP positive, and 0 to 2 genes methylation determined to be CIMP negative.

The present invention compares the value of the DUSP3 methylation with CIMP phenotype in predicting patient prognosis in 441 patients with stage I-II colorectal cancer. The proportion of colorectal cancer patients with CIMP phenotype (cimp+) in the chinese population (17/441,3.8%) was significantly lower than 10-15% of the western population reported in the literature (1]Jia M,Jansen L,Walter V,et al.No association of CpG island methylator phenotype and colorectal cancer survival:population-based student.br J cancer.2016.115 (11): 1359-1366: [2]Shiovitz S,Bertagnolli MM,Renfro LA,et al.CpG island methylator phenotype is associated with response to adjuvant irinotecan-based therapy for stage III colon cancer.gastroenterology.2014.147 (3): 637-45), but approximately comparable to BRAF mutation rates in the chinese population that are substantially consistent with their positive rates. The experimental results of the present invention are shown in fig. 2A, 2B, where the CIMP phenotype predicts significantly lower value for the risk of distant recurrence than DUSP3 methylation (HR 1.09vs.2.14, p=0.880 vs. < 0.001).

The results indicate that DUSP3 single gene methylation is used to predict the risk of recurrence in early colorectal cancer patients, and is superior to the CIMP phenotype consisting of five gene methylation.

(2) Comparison with clinical pathological risk factors and classical molecular typing

The prior literature reports that less than 12T 4 lesions, tumor ileus or perforations or lymph node biopsies of stage II colorectal cancer are high risk factors for tumor recurrence, metastasis and death. However, there is controversy that these clinical pathological factors have inconsistent results from one cohort to another (Zhang JX, song W, chen ZH, et al Prognosptic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis.Lancet Oncol.2013.14 (13): 1295-306.). Thus, the present invention further compares the value of the methylation of the DUSP3 gene with its value in predicting early colorectal cancer prognosis. In 441 patients with stage I-II colorectal cancer, as shown in FIGS. 2A, 2C, 2D, and 2E, less than 12 total tumor ileus or perforations or lymph node biopsies are predicted to be at significantly lower value than DUSP3 methylation.

KRAS mutations, BRAF mutations and high microsatellite instability (high-level microsatellite instability, MSI-H) are the most common molecular typing used in clinical diagnosis and treatment of colorectal cancer. Thus, the present invention compares the methylation of the DUSP3 gene with their prognostic value. As shown in FIGS. 2A, 2F, 2G, and 2H, the predicted value of these molecular species were significantly lower than DUSP3 methylation.

(3) Comparison with FHIT and SGIP1 genes

The invention compares the value of DUSP3 methylation with FHIT, SGIP1 methylation in predicting patient prognosis in 174 and 267 patients with stage I-II colorectal cancer.

In the training and validation cohorts, the methylation level of three candidate CpG sites was detected using qMSP. In the training cohort, all candidate genes were split into hypermethylated or hypomethylated groups based on the cutoff value determined by the least p method in Kaplan-Meier analysis. In the validation queue, patients are divided into two groups according to the cutoff values defined in the training queue.

As shown in fig. 3, methylation of all three genes was significantly correlated with patient disease-free survival in the first cohort; DUSP3 was still significantly associated with patient disease-free survival in the second independent cohort, while FHIT and SGIP1 were not statistically significant.

Thus, DUSP3 methylation as a molecular marker predicts better reproducibility of the risk of distant recurrence in early colorectal cancer patients.

Example 5 chip analysis of DNA methylation signatures and methylation in genomics and CpG Open sea associated with early relapse

DNA methylation status of 865,859 CpG sites was obtained by using an EPIC chip, which has been reported in the past to be technically proven stable. Methylation of each CpG was scored with beta values ranging from 0 (unmethylated) to 1 (fully methylated). The following equation is used: Δβ= (mean β of non-relapsed samples) - (mean β of relapsed samples). R-pack "glmnet" For performing the LASSO Cox regression mode, ¹⁹ and selecting the most useful methylation marker in the discovery queue.

Methylation status of CpG sites screened from the chip was verified using a pyrophosphate sequencing assay (FIG. 4A). The CRC subgroup is identified based on the EPIC chip data using a recursive partitioning mixture model (recursively partitioned mixture model, RPMM).

The DNA methylation status of 865,859 CpG sites was assessed by whole genome DNA methylation analysis using an EPIC chip on 45 colorectal tumor samples and 45 paracancestral normal tissue samples in the discovery cohort. Unreliable probes were filtered and chip data normalized.

The present invention first performed a DMP analysis between recurrent and non-recurrent tumors, and based on Δβ and q values, selected the first 5,000 probes with the greatest differences in DNA methylation, followed by an unsupervised RPMM-based cluster analysis. Two different tumor subgroups were identified by this method, defined as cluster a and cluster B, as shown in fig. 4B.

Cluster B with hypermethylation pattern showed significant high risk of recurrence in both single factor analysis (HR, 12.73 95%CI:1.71-94.75; P < 0.001) and multifactorial (HR, 7.29 95%CI:1.66-31.9; p=0.008) Cox regression analysis incorporating age, sex, stage and histological differentiation (log-rank test P < 0.001), as shown in fig. 4C.

Fig. 4B shows that KRAS mutant CRCs are mostly clustered in cluster B. These recurrence-specific DMPs are hypermethylated in paracancerous normal tissues, but show different levels of DNA methylation in tumors.

Notably, relapse-specific DMP is rare in CpG islands and promoters, but is abundant in CpG open ses and genomes. Tumor-specific DMP, on the contrary, has been widely reported to be located mainly in CpG islands and gene promoter regions. In the discovery cohort of the present invention, recurrence-specific DMP did not overlap with tumor-specific DMP (fig. 4D). However, in previous studies, tumor-specific DMP is widely used to develop models for prognosis prediction. In addition, the present invention uses tumor-specific DMP for DMP analysis between recurrent and non-recurrent tumors. As expected, cluster analysis failed to distinguish recurrent tumors (fig. 4A), and the two subgroups did not differ significantly in DFS results (fig. 4E).

The EPIC chip probes were further classified into CpG islands and open sea probes for hierarchical analysis. The RPMM based cluster analysis identified two CRC subgroups in each hierarchy. The recurrence differences between these two clusters were not apparent using probes targeting CpG islands, indicating that DNA methylation signature in CpG islands failed to distinguish high risk CRC cases (fig. 4F).

However, most of the methylation prognosis-based markers used previously target CpG islands. This may be one of the reasons that markers found in the past are highly heterogeneous in different queues.

Thus, predictive models based on CpG open sea or genomic methylation can better predict early recurrence in CRC patients.

Example 6 introduction to the exploration experiment

In addition to the DUSP3 gene, in other exploratory experiments of the present invention, 1,405 additional CpG sites were selected that best distinguished recurrent and non-recurrent patients based on methylation status (Δβ0.1, q < 0.05). Most of the selected sites (63.7%, 896/1406) were first added to the EPIC chip and located in open ses (81.5%, 1147/1406) and annotated in the genome (59.6%, 469/786 gene sites).

The present invention detects the intra-genic sites where early colorectal cancer recurrence can be predicted, detailed workflow and methods are in fig. 5.

In further experiments, 6 CpG sites located in 6 genes: FAT3, FHIT, SGIP1, KAZN, TLE4 and DUSP3 were selected (screened using LASSO Cox model) (fig. 4A and 6). All six gene loci are located in the CpG open sea and annotated in the genome. Hypermethylation of these genes can significantly distinguish early relapsing patients: AUC range was 0.769-0.785 with hrs greater than 71.5 (P < 0.001) (fig. 7 and 8). Of these sites, three are targeted by probes newly added to the EPIC chip, while FAT3, SGIP1 and TLE4 are also targeted by probes contained in the original HM450 chip. The beta value for each site was verified to be accurate and consistent with bisulphite pyrosequencing and qMSP assay results in 10 CRC tissue samples (fig. 9, 10).

The present invention also found that there was a positive correlation between DNA hypermethylation and RNA expression of 6 candidate genes following treatment with the DNA methylation inhibitor 5-aza-2' -deoxyytidine in colon cancer cells. Correlation analysis of methylation and expression profiles in TCGA cohorts supported these findings, suggesting that these genomic methylation might play a role in gene expression (fig. 11A, 11B).

In a further training and validation cohort, the present invention uses qMSP to detect methylation levels of six candidate gene CpG sites of FAT3, DUSP3, FHIT, KAZN, TLE4, SGIP 1. The primers and probes are shown in Table 3. In the training queue, the invention divides all candidate genes into hypermethylated or hypomethylated groups based on the cutoff value determined by the least p method in Kaplan-Meier analysis. All 6 bi-classified markers were significantly different in the single factor Cox regression analysis and Log-rank test, whereas only FAT3, DUSP3 and FHIT were significantly different in the multi-factor analysis (fig. 12). In the validation queue, the present invention groups patients into two groups according to the cutoff values defined in the training queue. The two classification variables FAT3, DUSP3, KAZN and TLE4 all varied significantly in the single and multi-factor analysis in the validation queue, whereas FHIT and SGIP1 did not (fig. 12) vary significantly in both analyses. Thus, the present invention utilizes FAT3, DUSP3, KAZN and TLE4 validated in both queues to build a combined model, further exploring the best recurrence prediction model.

Example 7 Multi-Gene model for predicting early relapse

CRC is epigenetically heterogeneous, so that a combination of multiple markers may perform better than a single marker, such as CIMP and mismatch repair status detection. Thus, the present invention explores all combinations of KAZN, FAT3, DUSP3 and TLE4 in both queues to test whether they can improve prognostic accuracy.

The invention first builds a synergistic model, and any patients whose incorporated genes are hypermethylated are classified as high risk groups. This is similar to the model used in mismatch repair status assays. In the multifactor Cox regression analysis of the training and validation cohorts, 7 out of 11 co-models had significant differences (correction P < 0.05) (fig. 13). A better synergy model included DUSP3/TLE4, which reached HRs 3.96 (95% CI 1.25-12.49) in the training queue and 2.37 (95% CI 1.31-4.27) in the validation queue (FIG. 14). The performance in both cohorts was significantly better than using a single gene, indicating that integration of other representative genes from the LASSO model into the polygenic model can accurately identify more patients at risk of relapse. Furthermore, the DUSP3/TLE4 model also has higher prognostic accuracy than any of the clinical pathology risk factors (fig. 13&14vs table S5). The present invention next builds a synthetic model in which all patients whose incorporated genes are hypermethylated are classified as high risk groups. The candidate genes rarely show common methylation in both cohorts, as expected, few patients are classified into high risk groups. Thus, while some synthetic models can identify a small proportion of patients at great risk for early recurrence, their value is limited. The results of the synergy model were combined, and the six candidate genes screened by the LASSO model in the discovery cohort were independent of each other and representative of the genomic methylation profile for high risk of recurrence.

TABLE 5 Single factor analysis of clinical pathological characteristics predictive disease-free survival

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Claims (15)

1. The application of a gene detection reagent in preparing a colorectal cancer prognosis diagnosis reagent, wherein the gene is DUSP3 gene, the DUSP3 gene detection reagent is a DUSP3 gene methylation detection reagent, and the sequence of the detection region of the DUSP3 gene methylation detection reagent is SEQ ID NO:1, the detection site of the DUSP3 gene methylation detection reagent is cg16747321, and the colorectal cancer is colorectal cancer of stage I-II.

2. The application of the gene detection reagent in preparing colorectal cancer prognosis diagnosis kit, wherein the gene is DUSP3 gene, the DUSP3 gene detection reagent is DUSP3 gene methylation detection reagent, and the sequence of the detection region of the DUSP3 gene methylation detection reagent is SEQ ID NO:1, the detection site of the DUSP3 gene methylation detection reagent is cg16747321, and the colorectal cancer is colorectal cancer of stage I-II.

3. The use according to claim 1 or 2, wherein the gene further comprises one or both of TLE4 gene, KAZN gene.

4. The use according to claim 1 or 2, wherein the gene further comprises the TLE4 gene.

5. The use according to claim 1 or 2, wherein the detection reagent for methylation of the DUSP3 gene is a reagent for detecting a sequence of the DUSP3 gene modified by a transforming reagent.

6. The use according to claim 5, wherein the conversion reagent is selected from one or more of hydrazine salt, bisulfite and bisulphite.

7. The use according to claim 5, wherein the conversion reagent is selected from bisulphites.

8. The use according to claim 1 or 2, wherein the DUSP3 gene detection reagent comprises a DNA chip.

9. The use according to claim 1 or 2, characterized in that in the detection of methylation of the DUSP3 gene, the methylation level is high, the risk of colorectal cancer recurrence is high; when the methylation degree is low, the recurrence risk of colorectal cancer is low, and the threshold value of the methylation degree is 78.57%.

10. The use according to claim 1 or 2, wherein the test sample is intestinal mucosal tissue.

11. A prognostic colorectal cancer diagnostic system, said diagnostic system comprising:

a detection member: the detection component is used for detecting the methylation degree of the DUSP3 gene of the diagnosis object;

and a result judgment means: the result judging component is used for outputting a methylation percentage parameter PMR or a disease risk result according to the result of the methylation degree of the DUSP3 gene detected by the detecting component; the detection component is one or more of an ultra-micro spectrophotometer, a real-time fluorescence quantitative PCR instrument and an ultra-high sensitivity chemiluminescence imaging system; the methylation degree of the DUSP3 gene is the methylation proportion of CG locus of a DUSP3 genome region in a sample, and the CG locus is CG16747321; the result judging component comprises an input module, an analysis module and an output module; the input module is used for inputting the methylation degree of the DUSP3 gene; the analysis module is used for analyzing the possibility or risk value of recrudescence of the colorectal cancer after healing according to the methylation degree of the DUSP3 gene; the output module is used for outputting the analysis result of the analysis module; in the result judging component, when the methylation degree of the DUSP3 gene is higher than the threshold value of 78.57%, judging that the risk of recurrence of the colorectal cancer after healing is high; when the methylation degree of the DUSP3 gene is lower than the threshold value of 78.57%, judging that the risk of colorectal cancer recurrence after healing is low; the colorectal cancer is colorectal cancer of stage I-II.

12. The diagnostic system of claim 11, wherein the percent methylation parameter PMR is methylation/(methylation+unmethylation) ×100.

13. The diagnostic system of claim 11, wherein the methylation percentage parameter PMR = methylation fluorescence value/(methylation fluorescence value + unmethylation fluorescence value) ×100.

14. The diagnostic system of claim 11, wherein the methylation percentage parameter PMR = 100/(1+1/2) ^-ΔCT ) Δct = CT methylated fluorescence-CT unmethylated fluorescence.

15. The diagnostic system of claim 11, wherein the sample is intestinal mucosal tissue.