CN116042839A - Detection markers and their applications - Google Patents

Abstract

The invention relates to the field of biological detection, in particular to a detection marker and application thereof. The present invention provides for detecting the methylation level of a marker, including one or more of TSPYL5, GNA14, LRRC4, CYP26A1, TACSTD2, FAM83F, TBX15, or STEAP4, and combinations thereof. The invention can detect the peripheral blood plasma sample as a target, thereby realizing noninvasive diagnosis. Experiments showed that methylated TSPHL 5 had a sensitivity of 85.4% to HCC and a specificity of 100% for plasma samples. The effect is superior to that of other detection primers or probes. The invention also finds that the detection sensitivity can be further improved by combining AFP detection.

Description

Detection markers and their applications

technical field

The invention relates to the field of biological detection, in particular to detection markers and applications thereof.

Background technique

For a long time, primary liver cancer has seriously threatened people's life and health. According to data released by the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) in 2021, the number of new cases of primary liver cancer in 2020 will exceed 900,000, which is the second highest incidence rate in the world. Sixth, the third highest incidence of malignant tumors in terms of mortality. As a big hepatitis country, my country's primary liver cancer accounts for about half of the global incidence, and the mortality rate has risen to the second place among tumor deaths.

Hepatocellular carcinoma (HCC) accounts for about 80% of all primary liver cancers. HCC usually occurs in patients with cirrhosis caused by long-term liver inflammation and repair. Most hepatitis is caused by chronic viral (mainly hepatitis B and C virus) infection or alcoholic/nonalcoholic fatty liver. Because the early symptoms are not obvious, most liver cancer patients have already reached the advanced stage when they are definitely diagnosed. Although the survival time of patients with middle and early stages of liver cancer has been greatly improved in the past ten years, the prognosis of patients with middle and advanced stages of liver cancer is still very poor. Under the intervention of limited treatment methods, the median survival time is often only 1 to 2 years, and the 5-year survival rate is less than 16%. Improving the early diagnosis rate and more effective treatment is the key to improving the survival rate of liver cancer. The average overall survival time of early HCC (Stage A: a single nodule or three nodules less than 3cm) is 80 months, but the average survival time of HCC of Stage C (portal vein invasion, lymphatic invasion and extrahepatic metastasis) is only 15 months , while the average survival period of HCC in the terminal stage (Stage D: severe liver injury, performance status 3-4) is only about 4 months. Current guidelines recommend ultrasonography every 6 months for patients at high risk for HCC. However, due to the sensitivity of ultrasonic detection and the high requirements for the operator's skills and judgment ability, the detection rate of early liver cancer is limited. Ultrasound has a sensitivity of 51% and a specificity of 91% in the diagnosis of early HCC; while MRI and CT have higher sensitivities, 83.7% and 62.5%, and specificities of 89.1% and 87.5%, respectively. . Due to the high cost of detection and the need to be exposed to radiation or to take contrast agents, the application of HCC monitoring has been affected. When the concentration of AFP (alpha fetoprotein) in serum is 20ng/mL as the critical value, the sensitivity to HCC is 41%-65%, and the specificity is 80%-90%. However, the application value of alpha-fetoprotein in clinical screening and monitoring of liver cancer is still controversial. The application of other markers in the monitoring of liver cancer still needs to be clinically verified.

In recent years, the treatment methods for liver cancer have gradually increased, and the most commonly used radical treatment method is surgical resection. In addition, there are methods such as ablation, intervention, targeting, and immunotherapy that can provide multidisciplinary comprehensive treatment for patients. But the five-year survival rate of liver cancer is still very low, only 12.1% in our country. Therefore, finding more effective molecular targets and tumor markers is crucial to achieve early diagnosis and treatment of liver cancer and precision medicine to improve the survival and prognosis of liver cancer.

DNA methylation is a typical epigenetic modification in which a methyl group is covalently bonded to the fifth carbon atom of C in the CG (cytosine-guanine) dibase. Abnormal DNA methylation usually occurs in the early stage of tumors and has certain tissue specificity. Moreover, DNA methylation often has a consistent modification status at CpG islands. These characteristics make DNA methylation an ideal marker for tumor monitoring and diagnosis.

Hypomethylation at the genome level affects the stability of the cell genome, and hypomethylation on regulatory elements may activate the expression of proto-oncogenes, such as CCAAT/enhancer-binding protein-beta (C/EBPb). Some studies have also found that abnormal DNA methylation affects tumor immunity and is closely related to the response to tumor immunotherapy. Although there have been many research reports on the methylation of liver cancer, how abnormal methylation is affected by methylation-related regulatory enzymes and how to promote the development of liver cancer by affecting gene transcription is still unclear. clear. The application value of DNA methylation as a biomarker in the diagnosis and treatment of liver cancer has yet to be further explored.

Contents of the invention

In view of this, the present invention provides detection markers and applications thereof. The present invention finds that DNA methylation is an ideal biomarker type, and the methylation of the TSPYL5 gene promoter region is a potential biomarker for monitoring HCC high-risk groups, and the combination with AFP can improve diagnostic sensitivity.

In order to achieve the above-mentioned purpose of the invention, the present invention provides the following technical solutions:

The present invention provides detection markers, including the degree of methylation of one or more of TSPYL5, GNA14, LRRC4, CYP26A1, TACSTD2, FAM83F, TBX15 or STEAP4 and their combination.

In some embodiments of the present invention, the above detection markers include the degree of methylation of TSPYL5 and GNA14.

In some embodiments of the present invention, the above detection markers include the degree of methylation of TSPYL5.

The present invention also provides the application of the above detection marker in the preparation of products for detecting cancer.

In some embodiments of the present invention, the product described in the above application further includes: one or more detection indicators of HBV, AFP, AFU, CEA, ALT, AST, ALP or GGT.

In some embodiments of the present invention, the test samples of the products in the above applications include: blood plasma.

In some embodiments of the present invention, the detection sample of the product in the above application is plasma cfDNA.

In some embodiments of the present invention, the cancers described in the above application include: liver cancer, cholangiocarcinoma, thymic carcinoma, lung adenocarcinoma, glioma, lung squamous cell carcinoma, endometrial carcinoma, bladder urothelial carcinoma, esophageal carcinoma One or more of , pancreatic cancer, renal papillary cell carcinoma, cervical cancer, breast cancer, colon cancer, clear cell renal cell carcinoma, thyroid cancer, squamous cell carcinoma of the head and neck, or rectal adenocarcinoma.

In some embodiments of the present invention, the cancer in the above uses is liver cancer.

The present invention also provides primers, probes or combinations thereof, using the detection marker in the above application as the target fragment for amplification.

In some embodiments of the present invention, in the above-mentioned primers, probes or combinations thereof, the primers have:

(1), a nucleotide sequence as shown in any of SEQ ID NO: 1 to SEQ ID NO: 6; or

(2), a nucleotide sequence encoding the same protein as the nucleotide sequence shown in (1), but different from the nucleotide sequence shown in (1) due to the degeneracy of the genetic code; or

(3), the nucleotide sequence obtained by substituting, deleting or adding one or more nucleotide sequences with the nucleotide sequence shown in (1) or (2), and the nucleotide sequence shown in (1) or (2) A nucleotide sequence that is functionally identical or similar to the indicated nucleotide sequence; or

(4) A nucleotide sequence having at least 90% sequence homology with the nucleotide sequence described in (1), (2) or (3).

In some embodiments of the present invention, in the above-mentioned primers, probes or combinations thereof, the probes have:

(5), a nucleotide sequence as shown in any of SEQ ID NO:7 to SEQ ID NO:9; or

(6), a nucleotide sequence encoding the same protein as the nucleotide sequence shown in (5), but different from the nucleotide sequence shown in (5) due to the degeneracy of the genetic code; or

(7), the nucleotide sequence obtained by substituting, deleting or adding one or more nucleotide sequences with the nucleotide sequence shown in (5) or (6), and the nucleotide sequence shown in (5) or (6) A nucleotide sequence that is functionally identical or similar to the indicated nucleotide sequence; or

(8) A nucleotide sequence having at least 90% sequence homology with the nucleotide sequence described in (5), (6) or (7).

The present invention also provides a detection reagent, which uses the detection index in the above application as the detection object.

The present invention also provides detection products, including the above-mentioned primers, probes or combinations thereof and/or the above-mentioned detection reagents and acceptable auxiliaries.

The present invention also provides a screening method for the detection marker in the above application and/or the detection index in the above application, including whole genome sulfite resequencing or transcriptome sequencing.

In some embodiments of the present invention, the screening region in the above screening method includes one or more of differentially methylated sites, differentially methylated regions, or differentially expressed genes.

The present invention also provides a detection method. The sample to be tested is taken to detect the above-mentioned detection marker or the detection index in the above-mentioned application.

The present invention finds that DNA methylation is the main form of epigenetic modification affecting gene expression. The methylation level of the HCC genome is generally low, and the promoter regions of individual genes are hypermethylated. In recent years, the advancement of DNA methylation research technology at the genome level has enabled us to conduct a comprehensive study of the genome methylation level of cells or tissue samples. In tumor research, WGBS has a more comprehensive understanding of the methylation level of CpG sites and CpG islands at the genome level than DNA methylation chips and other methylation detection technology platforms. Except for Qian et al. who analyzed changes in HCC methylation levels using only 4 clinical samples of HCC samples, there are few reports on the study of HCC using WGBS technology. In this study, the differentially methylated loci (DMLs) and differentially methylated regions (DMRs) between HCC and normal liver tissues were analyzed by WGBS and mRNA-Seq data of 33 HCC patients and adjacent tissues. and differentially expressed genes (DEGs). Afterwards, the DML data were analyzed using RF and ELNET regression algorithms, and a binary classification diagnostic model with superior performance was constructed to obtain a diagnostic score (DScore) for each sample. Further through ssGSEA analysis, it was found that DScore was significantly negatively correlated with the infiltration level of various immune cells, which partially revealed that the mechanism of DScore and tumorigenesis may be related to immune deficiency. Through the integrated analysis of DMR and DEG, we found two potential markers of plasma cfDNA methylation for HCC diagnosis. The detection of plasma cfDNA by fluorescent quantitative PCR and the comparison with other serum markers preliminarily verified the application potential of the markers. However, both tissue diagnostic models and plasma cfDNA markers have some false negatives, that is, missed diagnoses, and further analysis of the reasons is needed to improve diagnostic performance. The sample size of plasma cfDNA validation is also small, which cannot fully prove the diagnostic performance of the markers. These data indicate that DNA methylation plays an important role in the occurrence of liver cancer and its application value as a diagnostic marker for liver cancer.

Although both genetic and epigenetic changes are involved in the development of cancer, changes in DNA methylation usually begin early in tumors. Although there are many studies on HCC gene mutations, and there are also reports on the use of gene mutations for early monitoring of HCC, the high heterogeneity of HCC leads to a low mutation frequency of specific genes, which limits its performance in the early monitoring of HCC. performance. As a biomarker, DNA methylation has a good application prospect in tumor diagnosis and early monitoring. At present, some tumor methylation-related researches have successfully achieved clinical transformation, such as colorectal cancer SEPT9 and SDC2, PTGER4/SHOX2 in lung cancer, GSTP1 in prostate cancer and other gene methylation detection kits have obtained clinical in vitro diagnostic reagent registration certificate. In particular, methylation detection kits for colorectal cancer and lung cancer have been applied to non-invasive samples (feces, peripheral blood), bringing hope to the application of early cancer screening and monitoring of high-risk groups. However, in the diagnosis of HCC, the methylation changes of specific genes have not yet achieved consistent and good clinical manifestations.

The most important result and the main purpose of the present invention is to find a cfDNA methylation marker in peripheral blood for diagnosis of early HCC. An ideal diagnostic marker should have high sensitivity, identify early disease, and be detectable by noninvasive and inexpensive techniques. Serum AFP is a widely used clinical marker for liver cancer screening. However, the sensitivity and positive predictive value of serum AFP are low, which limits the clinical value of its detection. In this study, we found that the promoter regions of TSPYL5 and GNA14 had the largest Δβ hypermethylation regions, and the expression levels of TSPYL5 and GNA14 were strongly negatively correlated with their methylation levels. Pan-cancer analysis found that TSPYL5 is highly methylated in a variety of cancers, and TSPYL5 has also been reported to be hypermethylated and lowly expressed in tumor types such as gastric cancer and glioma, indicating that the tumor type specificity of TSPYL5 methylation is not high. However, no matter how specific the marker is in tumor type, it will not affect its application value in the monitoring of specific high-risk groups. On the other hand, TSPYL5 has also been reported to be hypomethylated and highly expressed in lung cancer, thereby inhibiting p53. It is a potential therapeutic target for lung cancer, indicating that TSPYL5 also has certain heterogeneity in different tumor types. GNA14 has also been reported as a hypermethylated tumor suppressor gene in HBV-associated HCC, and its low expression was associated with poor prognosis. The application of methylation in the promoter region of GNA14 gene in noninvasive diagnosis has not been reported.

Blood cell DNA in the promoter regions of the above two genes has a stable low methylation level, so blood cell DNA methylation will not interfere with the detection of methylation of the two genes, in order to detect its methylation in peripheral blood cfDNA samples as HCC Diagnostic markers clear the way. We designed primers and probes for the promoter regions of the two genes, and found that TSPYL5 has higher sensitivity, and the diagnostic sensitivity can be further improved when combined with AFP results.

The present invention finds that DNA methylation is an ideal biomarker type, and the methylation of the TSPYL5 gene promoter region is a potential biomarker for monitoring HCC high-risk groups, and the combination with AFP can improve diagnostic sensitivity.

The invention provides a detection marker and its application in preparing a product for detecting cancer. At the same time, the invention also provides a primer, a probe and a combination thereof, a detection product and a method for screening the above marker.

The present invention finds that DNA methylation is an ideal biomarker type, and the methylation of the TSPYL5 gene promoter region is a potential biomarker for monitoring HCC high-risk groups, and the combination with AFP can improve diagnostic sensitivity.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings that are required in the description of the embodiments or the prior art.

Figure 1 shows the research path of DNA methylation liver cancer diagnostic markers;

Figure 2 shows the methylation identification of driving Hyper-pDMR; among them: A shows the pDMR volcano map; B shows the DEG volcano map; C shows the pDMR-DEG Pearson correlation; D shows the CpG sites in the 8 driving Hyper-pDMRs Methylation level heat map; where: pDMR, differential-methylation-region in gene promoter region; DEG, differential expression gene;

Figure 3 shows the heat map of the methylation level of CpG sites in 8 driving hyper-pDMRs verified by the public data set; where: A-H are TCGA, GSE37988, GSE54503, GSE56588, GSE57956, GSE89852, GSE99036, GSE113017;

Figure 4 shows the schematic diagram of methylation difference level and genomic location of 8 DMRs; among them, the 8 DMRs corresponding to A-H are chr1_118983368_118990519 (A), chr1_58575901_58577030 (B), chr7_128030940_128032690 (C), chr7_88306620 _88307042(D), chr10_93074811_93075498(E ), chr22_39994591_39995104(F), chr8_97277329_97278175(G) and chr9_77647531_77648367(H); among them: purple represents liver cancer tissue, yellow represents liver tissue adjacent to cancer, and blue represents peripheral mononuclear cells; each vertical line in G and H represents a CpG site;

Figure 5 shows the methylation level of TSPYL5 in pan-cancer; *P<0.05, **P<0.01, ***P<0.001;

Figure 6 shows the difference in the methylation level of the target region of the Discovery cohort among different clinical feature groups;

Figure 7 shows the difference in the methylation level of the target region of the TCGA-LIHC cohort among different clinical feature groups;

Figure 8 shows the enrichment analysis results of GO (A) and KEGG (B) gene sets of high and low methylation of TSPYL5;

Figure 9 shows the difference in survival between high and low methylation of TSPYL5.

Detailed ways

The invention discloses a detection marker and an application thereof.

It should be understood that the expression "one or more of" individually includes each of the objects recited after the expression and various combinations of two or more of the recited objects, unless from The context and usage understand otherwise. The expression "and/or" combined with three or more stated items should be understood as having the same meaning unless otherwise understood from the context.

The terms "comprising", "having" or "containing", including the use of grammatical synonyms thereof, should generally be read as open-ended and non-limiting, such as not excluding other unrecited elements or steps, unless specifically stated otherwise. stated or otherwise understood from the context.

It should be understood that the order of steps or order for performing certain actions is immaterial so long as the invention remains operable. Furthermore, two or more steps or actions can be performed simultaneously.

The use of any and all examples, or exemplary language, such as "such as" or "including," herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

In addition, the numerical ranges and parameters used to define the present invention are approximate numerical values, and the relevant numerical values in the specific embodiments have been presented here as precisely as possible. Any numerical value, however, inherently inherently contain standard deviations resulting from their individual testing methodology. Therefore, unless expressly stated otherwise, it should be understood that all ranges, numbers, values and percentages used in this disclosure are modified by "about". As used herein, "about" generally means that the actual value is within plus or minus 10%, 5%, 1%, or 0.5% of a particular value or range.

In Examples 1 to 7 and Effect Example 1 to Effect Example 11 of the present invention, the collection of samples: The paired cancer tissue and paracancerous tissue samples of 33 HCC patients used in this study were from July 2015 to 2016 It was collected in a hospital affiliated to Zhejiang University School of Medicine in June. During the collection process, doctors, nurses, and scientific researchers who have undergone uniform training will obtain information about the specimens, including age, sex, height, and weight. Tissue samples are tested by senior pathologists, and it is determined that the samples are consistent with the characteristics of cancer tissue or paracancerous tissue in terms of histomorphology.

1. Clinical characteristics of the research subjects

In this study, liver cancer tissues and matched paracancerous tissues were collected from 33 HCC patients. Among them, there were more men than women, 27, while only 6 were women. Among the 8 serum markers, the lowest positive rate was alkaline phosphatase (alkaline phosphatase, ALP), which was higher than the normal range in only 2 patients, and the positive rate was only 6% (2/33); alpha-fetoprotein (alpha Fetal protein, AFP) had the highest positive rate of 60% (20/33), as shown in Table 1.

Table 1 Clinical information of 33 HCC patients

This study also included plasma cell-free DNA (cfDNA) samples from 48 HCC patients and 24 non-cancer volunteers. Our inclusion and exclusion criteria for HCC patients were:

Inclusion criteria: ①Outpatient or hospitalized patients; ②Aged 18-65 years old; ③Newly diagnosed as liver cancer/hepatitis/cirrhosis; ④Understand the purpose of the study and sign the informed consent.

Exclusion criteria: If you have any of the following conditions, you will not be able to participate in this study: ① Tumor resection has been performed without recurrence; ② Blood transfusion or organ transplantation has been performed within one month; ③ Severe anemia;

The male to female ratio of HCC patients in the plasma validation group was similar to that in the tumor tissue group, with 43 more males than females, compared to only 5 female patients. This is related to the higher incidence of HCC in men than in women.

Table 2 Plasma sample subject clinical characteristics information

At the same time, the data of DNA methylation level in blood cells of non-cancer population used in this study comes from an epidemiological study based on Chinese in our laboratory.

In addition, this study also collected peripheral blood samples from 48 HCC patients (collected through the Department of Infectious Diseases, Shulan Hospital) and 24 non-cancer volunteers using streck blood collection tubes (Catalog No.: 218962). No statistical methods were used to predetermine sample sizes. Free DNA (cell free DNA, cfDNA) from peripheral blood plasma was extracted with MagMAX ^TM Cell Free DNA Isolation Kit (Product No.: A29319).

In Embodiment 1 to Embodiment 7 and Effect Example 1 to Effect Example 10 of the present invention, the raw materials and reagents used can be purchased from the market.

Below in conjunction with embodiment, further set forth the present invention:

Example 1 Public data collection of liver cancer methylation samples

We downloaded 450K methylation microarray data, 450K annotation files and corresponding clinical information of 430 TCGA-LIHC cohorts from the UCSC Xena website (https://xenabrowser.net/datapages/). We downloaded 5 HCC-related 450K methylation chip datasets (GSE54503, GSE56588, GSE56588, GSE89852, GSE99036 and GSE113017).

Example 2 Exploration of Liver Cancer Markers of Plasma Free DNA Methylation

1. Identification of methylation-driven genes

To find methylation markers underlying HCC detected in blood cfDNA, we identified hypermethylated regions affecting gene expression. Specifically, we use the GenomicRanges package of the R language to annotate the genomic position of the DMR, and find out the hypermethylated (hyper-methylated) and DMRs with a differential methylation level (Δβ) not less than 0.3 were defined as hyper-DMRs. Most studies believe that high DNA methylation usually inhibits gene expression, and demethylation can cause gene re-expression; abnormal DNA methylation will affect related expression, leading to abnormal development, tumors and other diseases. To improve the reliability of plasma methylation markers, we identified hyper-DMRs that significantly affect gene expression. Specifically, the Spearman correlation test (Spearman) was performed on the expression level of the DEG located near the hyper-DMR promoter (the transcription start site of the gene is less than 2000bp away from the DMR) and the methylation level of the DMRs. Less than -0.3 and false discovery rate (ie BH-FDR) less than 0.05 after Benjamini&Hochberg (BH) multiple correction method were the significance criteria, and the driving hyper-DMR was identified.

2. The methylation level of driving hyper-DMR in DNA of peripheral blood cells

We believe that only hyper-DMRs with low methylation levels in blood cell DNA of non-cancerous people can be used as plasma markers of liver cancer. Therefore, we extracted the methylation level matrix of driving hyper-DMR in peripheral blood cell DNA WGBS of non-cancerous population, and compared it with the data of our liver cancer tissue WGBS to exclude hypermethylated DMR in peripheral blood cells. We used the R package "Gviz" to visualize the three sets of data to visually display the methylation differential levels and the genomic coordinates of potential plasma cell-free DNA methylation markers.

3. Verification of plasma free DNA methylation markers

We used the collected peripheral blood cfDNA samples to verify the potential hyper-DMR that can be used as a liver cancer marker by fluorescent quantitative PCR.

(1) First, we used Beacon Designer 8 software to design fluorescent quantitative PCR primer pairs (primers) and fluorescent probes ( probe) sequence, which was synthesized by Sangon Biotech (Shanghai) Co., Ltd. The primer and probe sequences are as follows:

TSPYL5 Forward primer: 5'-GCGTTAATTATCGGGCGTATTACG-3' (as shown in SEQ ID NO: 1)

TSPYL5 Reverse primer: 5'-GCTATAACCCTACGACTCCTAACG-3' (as shown in SEQ ID NO: 2)

TSPYL5 Probe: 5'-6-FAM-ATCGAAACCGAACGAATCTCTCCACGACA-BHQ1-3' (as shown in SEQ ID NO: 7)

GNA14 Forward primer: 5'-CGTTTTAATTCGTTTGCGTTTTCG-3' (as shown in SEQ ID NO: 3)

GNA14 Reverse primer: 5'-AACGAAATAAATACCGAACGCTAAA-3' (as shown in SEQ ID NO:4)

GNA14 Probe: 5'-CY5-TACACCCCGAATCCGAACTCAACCCG-BHQ2-3' (as shown in SEQ ID NO:8)

The internal reference gene is ACTB:

ACTB Forward primer: 5'-TGGTGATGGAGGAGGTTTAGTAAGT-3' (as shown in SEQ ID NO:5)

ACTB Reverse primer: 5'-AACCAATAAAACCTACTCCCTCCCTAA-3' (as shown in SEQ ID NO: 6)

ACTB Probe: 5'-TET-ACCACCACCCAACACACAATAACAAACACA-3' (as shown in SEQ ID NO: 9).

(2) Fluorescence quantitative PCR experiment steps are as follows:

The extracted cfDNA was subjected to bisulfite conversion using the EZ DNA Methylation Kit (Cat. No.: D5030).

1) Take 10ng of cfDNA and add water to 20μL;

2) Add 130μL Lightning Conversion Reagent to the DNA, shake and centrifuge briefly;

3) Run the following program: 98°C for 8min; 54°C for 60min; 4°C storage forup to 20h;

4) Prepare a new Zymo-SpinTM IC Column, add 600μL M-Binding Buffer, and put it into a collection tube;

5) Transfer the sample from step 3) to the tube in step 4), and mix it upside down several times;

6) Centrifuge at full speed for 30s, and discard the waste liquid;

7) Add 100μL M-WashBuffer, centrifuge at full speed for 30s;

8) Add 200μL of L-Desulphonation Buffer, place at room temperature for 15-20min, and centrifuge at full speed for 30s;

9) Add 200μL M-WashBuffer, centrifuge at full speed for 30s, repeat this step once;

10) Put the spin column into a new 1.5mL EP tube, add 10μL M-ElutionBuffer, and centrifuge at full speed for 30s to collect DNA.

11) Fluorescence quantitative PCR

We used Qiagen's EpiTect MethyLight PCRKit (Cat. No.: 59496) purchased to prepare the reaction solution according to the following table;

For each PCR reaction, when the sample is tested, a positive quality control product, a negative quality control product and a blank control are carried.

12) PCR program

We used ABI 7500 real-time fluorescent quantitative PCR instrument for fluorescent quantitative PCR reaction. The reaction procedure is as follows:

Stage1 Reps: 1 95°C 5min Stage2 Reps: 40 95°C 15sec 60℃ 60sec

4. The ratio of the detection method to the target marker and the lower limit of detection (LOD) of DNA ingress

Because cfDNA in plasma comes from a variety of tissues, organs and blood cells, but the proportion of DNA from tumors, especially early tumor cells, is very small. In order to ensure that methylation changes of the target methylated markers can be detected even when the ratio is very low, we use methylated and unmethylated standards according to the methylation of 5% and 1%. The ratio was tested to confirm the detection sensitivity of the method and ensure the potential of the method for differential diagnosis and early screening of cancer.

Example 3 Comparison of blood diagnostic markers and changes of TSPYL5 methylation level in other cancer types

We surveyed various blood samples for the diagnosis of HCC published in recent years and compared the diagnostic performance of TSPYL5 methylation found in this study. Meanwhile, we analyzed the methylation level of TSPYL5 in 643 non-cancer samples and 7843 tumor samples of 29 cancers in TCGA.

Example 4 Comparison of TSPYL5 methylation levels between different clinical case characteristics groups

To confirm the independent diagnostic value of TSPYL5 methylation, we compared the methylation level of TSPYL5 promoter region in the discovery cohort and the TCGA-LIHC cohort in different sexes, age groups, tumor diameters, hepatitis B status and serum AFP levels difference.

Example 5 Gene enrichment analysis

In order to explore the molecular mechanism and biological function of TSPYL5 methylation changes, we performed gene set enrichment analysis (Gene set enrichment analysis, GSEA) on the methylation level of the TSPYL5 promoter region. Enrichment analysis used BroadMolecular Signatures Database (MSigDB v7.1) set C5 and c2.cp.kegg.v7.4.symbols two gene sets. P<0.05 was used as the criterion for significant enrichment of gene sets.

Example 6 Relationship between TSPYL5 methylation and prognosis

To explore the prognostic value of TSPYL5 methylation level, we performed K-M analysis of TSPYL5 methylation level and overall survival in the TCGA-LIHC cohort.

Embodiment 7 statistical analysis

All statistical analyzes in this study were done in R (v.4.0.3). For the WGBS and mRNA-seq data of 33 pairs of HCC and paracancerous samples, we used the paired t test to analyze the difference between tumor tissues and paracancerous tissues. The Spearman correlation coefficient calculates the correlation between two sets of data. The absolute value of the correlation coefficient (|cor|) greater than 3 is considered to be correlated, cor>0 is positive correlation, and cor<0 is negative correlation. . P value less than 0.05 is the standard of significance.

Effect Example 1 Identification Results of Differentially Methylated Sites (DML)

After QC, the WGBS off-machine data has an average coverage depth of 12.76×, including 28,978,826 CpG sites. After smooth treatment, about 34% (9,867,700) of the CpG sites had significant differences in methylation levels between cancer and paracancerous tissues. Among them, 157,320 sites were hypermethylated, and 9,710.380 sites were hypomethylated. Gene coordinate positions show that hypermethylation sites are mainly located in the gene promoter region, while hypomethylation sites are mainly located in the gene body.

Effect Example 2 Identification Results of Differentially Methylated Regions (DMRs)

Our further analysis of the identified DML identified 608,279 differentially methylated regions (DMRs). Of these, 6,924 were hypermethylated DMRs (hyper-DMRs) and 601,355 were hypomethylated DMRs (hypo-DMRs) in HCC.

Effect Example 3 Identification Results of Differentially Expressed Genes (DGE)

We performed differential analysis of gene expression between cancer and paracancerous TPM data from mRNA-Seq. It was found that there were 11,672 genes whose expression differences reached a significant level. Among them, there were 6,912 highly expressed genes and 4,760 low expressed genes. The expression levels of these low-expression genes (DEGs, differential expression genes) were used for subsequent Spearman correlation analysis with highly methylated regions (DMRs, differential methylation regions).

Table 3 Public dataset sample information

Tissue Control(N) HCC(N) Platform GSE54503 66 66 450K GSE56588 10+9 224 450k GSE89852 37 37 450k GSE113017 27 27 450k TCGA-HCC 50 380 450k Total 199 734 the

Effect example 4 identification of driving DMR

The experimental results are shown in Figure 2 and Table 4. In Example 2, there is a small amount of DNA from tumor shedding (apoptotic) cells in the plasma free DNA (cfDNA), which is the clinical application of plasma cfDNA-based tumor companion diagnosis or early screening offers possibilities. In order to explore methylation markers that can be used for early screening of liver cancer, we screened 30 liver cancer tissues from all differentially methylated regions (DMRinpromotorregion, pDMR) located in the promoter region, and the methylation level Methylated regions with a difference (Δβ) greater than 0.3 (hyper-pDMR) (shown in Figure 2A). In order to further improve the reliability of blood markers, we used the gene expression corresponding to hyper-pDMR to be significantly down-regulated and the correlation with the methylation level of DMR to be less than -0.3 as the screening condition. As a result, 8 driving hyper-pDMRs were found, as Markers of interest for further analysis.

Table 4 Correlation between highly differentially methylated regions (hyper-pDMR) and corresponding gene expression

DMR.ID gene_name Cor chr8_97277329_97278175 TSPYL5 -0.62 chr9_77647531_77648367 GNA14 -0.47 chr7_128030940_128032690 LRRC4 -0.53 chr10_93074811_93075498 CYP26A1 -0.43 chr1_58575901_58577030 TACSTD2 -0.39 chr22_39994591_39995104 FAM83F -0.52 chr1_118983368_118990519 TBX15 -0.40 chr7_88306620_88307042 STEAP4 -0.43

To further confirm the stability of methylation differences between these 8 driver hyper-pDMRs in HCC and normal tissues, we analyzed the methylation of CpG sites within these 8 DMRs in 8 HCC methylation microarray public datasets. level of differentiation. The 8 liver cancer methylation chip public datasets are 6 450K methylation chip datasets, including TCGA-LIHC, GSE54503, GSE56588, GSE89852, GSE99036 and GSE113017; 2 27K methylation chip datasets, including GSE37988 and GSE57956 . The differential analysis results are shown in Figure 3, and the CpG sites within the 8 driving hyper-pDMRs also showed hypermethylation in the public dataset.

Effect Example 5 The methylation level of target DMR in blood cell DNA

The experimental results are shown in Figure 4. In Example 2, in order to select liver cancer markers that may be used for the diagnosis of plasma cfDNA from the 8 driving hyper-pDMRs, and to select more specific genomic regions to be verified by plasma cfDNA, We further compared blood cell DNA methylation levels and the density of CpG sites in these hyper-DMRs. We extracted the methylation levels of Cp G sites in 8 target hyper-DMRs from the WGBS data of blood cells in non-cancer populations published by our team. It was found that the DNA methylation levels in these hyper-pDMR regions in non-cancerous populations were relatively low. In addition, chr8:97277329_97278175 (located in the promoter region of TSPYL5 gene) and chr9:77647531_77648367 (located in the promoter region of GNA14 gene) had the largest difference in methylation level (Δβ), with the mean values being 0.416 and 0.400, respectively; The methylation level of CpG sites in each DMRs is relatively concentrated on the genome, and the β value of the tumor group is stable above 0.6, while the β value of the normal group is stable below 0.2.

Effect Example 6 Plasma cfDNA Verification

We selected chr8: 97278070-97277720 and chr9: 77648350-77647950 as candidate markers according to the level of methylation difference (Δβ) of the CpG sites in the DMR, and named them after the genes they were located in (as shown in Table 4 ), which were further verified with plasma cfDNA. Beacon Designer 8 was used to design methylation fluorescent quantitative PCR (methyllight) primers and probes for the sequences of the two selected genomic regions, see Example 2 for details. cfDNA was extracted from the collected plasma of patients with liver cancer and healthy volunteers, and then transformed into bisulfite, followed by fluorescent quantitative PCR. As shown in Table 5, the sensitivity and specificity of the two markers were verified with a ct value (Cycle threshold) of 38 as the critical value. The results of TSPYL5 gene methylation detection showed that among 48 HCC patients, 41 patients had a ct value less than 38, that is, 41 cases could be detected positive, and the sensitivity was 85.4% (41/48); for GNA14 gene methylation The results of chemical testing were that among 48 HCC patients, 33 of them had a ct value of less than 38 by fluorescent quantitative PCR, that is, 33 of them were detected positive, and the sensitivity was 68.8%. Among 24 healthy volunteers, the methylation results of TSPYL5 gene were all negative and the specificity was 100%. It is particularly noteworthy that among the 44 HCC patients who underwent AFP detection, only 31 were positive, with a sensitivity of 70.5%. Compared with AFP, gene TSPYL5 methylation has better sensitivity for HCC detection.

In addition, among the 7 HCC patients with negative plasma TSPYL5 promoter methylation, except for 1 case who did not undergo AFP detection, the AFP results of the other 6 cases were all positive. This result suggested that the combination of AFP and TSPYL5 methylation detection can improve the sensitivity of diagnosis.

We compared the results of several other studies of blood samples for the diagnosis of HCC. Although, the diagnostic performance of TSPYL5 methylation is not the best, however, this 85.4% sensitivity and 100% specificity were obtained in single gene methylation detection, compared with multigene detection and risk model, single gene Methylation detection has the advantage of being simpler and highly generalizable.

Table 5 Comparison of diagnostic performance of blood HCC markers

Effect Example 7 The level of TSPYL5 methylation in other cancer types

To understand the level of TSPYL5 methylation in cancer, we analyzed the pan-cancer methylation data in the TCGA database. Among the cancer types with normal controls, only cholangiocarcinoma (CHOL) and thymic carcinoma (THYM) had a lower median TSPYL5 methylation level than normal tissues, but the difference did not reach a significant level; in lung adenocarcinoma (LUAD) ), glioma (GBM), lung squamous cell carcinoma (LUSC), endometrial carcinoma (UCEC), bladder urothelial carcinoma (BLCA), esophageal carcinoma (ESCA), pancreatic carcinoma (PAAD), renal papillary cell carcinoma ( KIRP), cervical cancer (CESC), breast cancer (BRCA), colon cancer (COAD), renal clear cell carcinoma (KIRC), thyroid cancer (THCA), head and neck squamous cell carcinoma (HNSC), and rectal adenocarcinoma (READ) The methylation level in was significantly higher than that in normal tissues (as shown in Figure 5). These results suggest that methylation of TSPYL5 may play a role in the occurrence of various cancers; at the same time, hypermethylation in this region is not cancer-specific.

Effect Example 8 Differences in TSPYL5 methylation levels among different clinical feature groups

The methylation level of the TSPYL5 gene promoter region (chr8:97278070-97277720) in the tumor tissues of 33 HCC patients had no statistical difference among different age groups, gender, tumor diameter, hepatitis B status and serum AFP status (As shown in Figure 6).

In the TCGA-LIHC cohort, the methylation level located in chr8:97278070-97277720 region was significantly higher in the high age group (≥60) and male patients than in the low age group (<60) and female patients; in different races, clinical grades, There was no statistical difference between stages and Child-Pugh classification (as shown in Figure 7).

Effect example 9 TSPYL5 methylation gene enrichment analysis

To further understand the biological function of TSPYL5 promoter region methylation, we performed GO and KEGG gene set enrichment analysis. In GO analysis, in the TSPYL5 hypermethylation phenotype, the biological process of alternative splicing of mRNA was significantly enriched; in the hypomethylation TSPYL5, it was significantly enriched in the negative regulation of muscle hypertrophy, negative regulation of potassium ion transmembrane transport, and deacetylase activity regulation, biological processes regulated by histone deacetylases (as shown in FIG. 8A ). GO results showed that TSPYL5 promoter methylation may affect mRNA splicing and protein appearance modification. In the KEGG analysis, among the TSPYL5 hypomethylation phenotypes, the five most significantly enriched pathways were cell adhesion molecules, chemokine signaling pathways, cytokine-receptor interaction, focal adhesions, and Hedgehog signaling pathways (such as Figure 8B). From the results of KEGG, it is possible that the methylation of the TSPYL5 promoter will reduce the adhesion of cells, thereby increasing the risk of distant metastasis of tumor cells.

Effect Example 10 TSPYL5 Methylation and Overall Survival Prognosis

The experimental results are shown in Figure 9. We further analyzed the prognostic value of the methylation level of the TSPYL5 promoter, and the results showed that overall survival was worse with hypermethylated TSPYL5 methylation, but the difference did not reach a statistically significant level.

The above is only a preferred embodiment of the present invention, it should be pointed out that, for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications can also be made. It should be regarded as the protection scope of the present invention.

Claims (10)

1. Detect markers, characterized in that the methylation degree of one or more of TSPYL5, GNA14, LRRC4, CYP26A1, TACSTD2, FAM83F, TBX15 or STEAP4 and their combination is included. 2. The application of the detection marker as claimed in claim 1 in the preparation of products for detecting cancer. 3. The application according to claim 2, wherein the product further comprises: one or more detection indicators of HBV, AFP, AFU, CEA, ALT, AST, ALP or GGT. 4. The application according to claim 2 or 3, characterized in that the test sample of the product comprises: blood plasma. 5. The application according to any one of claims 2 to 4, wherein the cancers include: liver cancer, bile duct cancer, thymus cancer, lung adenocarcinoma, glioma, lung squamous cell carcinoma, endometrial cancer, One or more of urothelial carcinoma of the bladder, esophagus, pancreas, papillary cell carcinoma of the kidney, cervix, breast, colon, clear cell renal cell, thyroid, squamous cell carcinoma of the head and neck, or adenocarcinoma of the rectum kind. 6. A primer, a probe or a combination thereof, characterized in that the detection marker in the application according to any one of claims 2 to 5 is used as the target fragment for amplification. 7. The detection reagent, characterized in that the detection target in the application as claimed in claim 3 is used as the detection target. 8. The detection product, characterized in that it comprises the primer, probe or combination thereof as claimed in claim 6 and/or the detection reagent as claimed in claim 7 and acceptable auxiliaries. 9. The screening method of the detection marker in the application as claimed in claim 2 and/or the detection index in the application as claimed in claim 3, is characterized in that, the screening method comprises whole genome subgroups Kraft resequencing or transcriptome sequencing. 10. The screening method according to claim 9, wherein the screening region includes one or more of differentially methylated sites, differentially methylated regions, or differentially expressed genes.

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