CN116042839A

CN116042839A - Detection marker and application thereof

Info

Publication number: CN116042839A
Application number: CN202310248714.3A
Authority: CN
Inventors: 李兰娟; 赵俊生
Original assignee: First Affiliated Hospital of Zhejiang University School of Medicine
Current assignee: First Affiliated Hospital of Zhejiang University School of Medicine
Priority date: 2023-03-10
Filing date: 2023-03-10
Publication date: 2023-05-02

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 marker and application thereof

Technical Field

The invention relates to the field of biological detection, in particular to a detection marker and application thereof.

Background

For a long time, primary liver cancer seriously threatens the life and health of people. Data published by the world health organization (World Health Organization, WHO) international cancer research institute (International Agency forResearch on Cancer, IARC) in 2021 shows that primary liver cancer has a new incidence of over 90 ten thousand in 2020, and is a high-incidence malignancy with a third global incidence and mortality. And the number of primary liver cancers in China serving as the large country of hepatitis is about half of the number of worldwide morbidity, and the death rate is increased to the second position of tumor death.

Among all primary liver cancers, hepatocellular carcinoma (Hepatocellular carcinoma, HCC) accounts for about eight ingredients. HCC generally 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) infections or alcoholic/non-alcoholic fatty liver disease. Most liver cancer patients have reached a late stage when they are clearly diagnosed, as early symptoms are not apparent. Although survival time of patients with early and middle liver cancer is greatly improved in the past decade, prognosis of patients with late and middle liver cancer is still very poor. Median survival times are often only 1 to 2 years with limited therapeutic intervention, and 5 year survival rates are less than 16%. Improving early diagnostic rates and more effective therapeutic methods are key to improving survival of liver cancer. The average total survival time for early HCC (Stage a: single or three less than 3cm segments) was 80 months, but HCC from Stage C (portal vein invasion, lymphatic infiltration with extrahepatic metastasis) had an average survival of only 15 months, while HCC from end Stage (Stage D: severe liver injury, performance status 3-4) had an average survival of only about 4 months. Current guidelines recommend a regimen of ultrasound testing every 6 months for HCC high risk patients. However, due to the high sensitivity of ultrasonic detection and the high requirements on the technique and judgment capability of operators, the detection rate of the early liver cancer is limited. The sensitivity of ultrasound in early HCC diagnosis was 51% and the specificity 91%; whereas nuclear magnetism and CT have higher sensitivity to early HCC, 83.7% and 62.5% respectively, and specificity is 89.1% and 87.5% respectively. The popularity of use in HCC monitoring is affected by the high detection costs and the need to be irradiated or to take contrast agents. The sensitivity to HCC was 41% -65% and the specificity was 80% -90% when the concentration of AFP (alpha fetoprotein ) in serum was 20ng/mL as a critical value. However, the application value of alpha fetoprotein in clinical screening and monitoring of liver cancer is still controversial. The application of other markers in liver cancer monitoring is still to be clinically verified.

In recent years, the treatment methods for liver cancer are gradually increased, wherein the most common radical treatment means is surgical excision. In addition, methods such as ablation, intervention, targeting, immunotherapy and the like can be used for performing multidisciplinary comprehensive treatment on patients. However, the five-year survival rate of liver cancer is still very low, and is only 12.1% in China. Therefore, searching for more effective molecular targets and tumor markers is important for realizing early diagnosis and early treatment of liver cancer and accurate drug administration to improve survival prognosis of liver cancer.

DNA methylation is typically an epigenetic modification in which a methyl group is covalently bound to the carbon atom at position 5 of C in a CG (cytosine-guanine) double base. Abnormal DNA methylation usually occurs early in the tumor and also has a certain tissue specificity. Furthermore, DNA methylation often has consistent modification conditions in CpG islands. These several features make DNA methylation an ideal tumor monitoring and diagnostic marker.

Hypomethylation at the genomic level affects the stability of the cell genome, whereas hypomethylation at regulatory elements may activate expression of protooncogenes, such as CCAAT/enhancing-binding protein-beta (C/EBPb). There are also some studies that show that abnormal methylation of DNA affects tumor immunity and has a close relationship with tumor immunotherapy. Although many researches on liver cancer methylation are reported, how abnormal methylation is influenced by methylation-related regulatory enzymes and how liver cancer is promoted to develop by influencing gene transcription and the like are not completely clear at present. The application value of the DNA methylation as a biomarker in liver cancer diagnosis and treatment is still to be further mined.

Disclosure of Invention

In view of this, the present invention provides detection markers and uses thereof. The invention discovers that DNA methylation is used as an ideal biomarker type, methylation of a TSTYL 5 gene promoter region is a potential biomarker for monitoring HCC high risk groups, and diagnosis sensitivity can be improved by combining AFP.

In order to achieve the above object, the present invention provides the following technical solutions:

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.

In some embodiments of the invention, the above-described detection markers include the degree of methylation of TSPYL5 and GNA 14.

In some embodiments of the invention, the above-described detection marker comprises the degree of methylation of TSPYL 5.

The invention also provides application of the detection marker in preparation of products for detecting cancers.

In some embodiments of the invention, the product in the above application further comprises: HBV, AFP, AFU, CEA, ALT, AST, ALP or GGT.

In some embodiments of the invention, the test sample of the product in the above application comprises: plasma.

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

In some embodiments of the invention, the cancer in the above application comprises: liver cancer, bile duct cancer, thymus cancer, lung adenocarcinoma, glioma, lung squamous carcinoma, endometrial cancer, bladder urothelial cancer, esophageal cancer, pancreatic cancer, renal papillary cell carcinoma, cervical cancer, breast cancer, colon cancer, renal clear cell carcinoma, thyroid cancer, head and neck squamous cell carcinoma or rectal adenocarcinoma.

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

The invention also provides a primer, a probe or a combination thereof, and the detection marker in the application is an amplified target fragment.

In some embodiments of the invention, the primer, probe, or combination thereof, has:

(1) Nucleotide sequences as shown in any of SEQ ID NO. 1 to SEQ ID NO. 6; or (b)

(2) A nucleotide sequence which encodes the same protein as the nucleotide sequence set forth in (1) but which differs from the nucleotide sequence set forth in (1) by the degeneracy of the genetic code; or (b)

(3) A nucleotide sequence which is obtained by substituting, deleting or adding one or more nucleotide sequences to the nucleotide sequence shown in (1) or (2) and has the same or similar function as the nucleotide sequence shown in (1) or (2); or (b)

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

In some embodiments of the invention, in the above primer, probe, or combination thereof, the probe has:

(5) Nucleotide sequences as shown in any of SEQ ID NO. 7 to SEQ ID NO. 9; or (b)

(6) A nucleotide sequence which encodes the same protein as the nucleotide sequence shown in (5) but which differs from the nucleotide sequence shown in (5) due to the degeneracy of the genetic code; or (b)

(7) A nucleotide sequence which is obtained by substituting, deleting or adding one or more nucleotide sequences to the nucleotide sequence shown in (5) or (6) and has the same or similar function as the nucleotide sequence shown in (5) or (6); or (b)

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

The invention also provides a detection reagent, which takes the detection index in the application as a detection target object.

The invention also provides detection products comprising the above primers, probes or combinations thereof and/or the above detection reagents and acceptable adjuvants.

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

In some embodiments of the invention, the screening region in the above screening methods comprises one or more of a differential methylation site, a differential methylation region, or a differentially expressed gene.

The invention also provides a detection method, wherein a sample to be detected is taken to detect the detection marker or the detection index in the application.

It was found in the present invention that DNA methylation is the predominant apparent modification affecting gene expression. The HCC genome methylation level is overall low and the individual gene promoter regions are hypermethylated. Recent advances in genomic DNA methylation research techniques have allowed us to conduct comprehensive studies of genomic methylation levels in cell or tissue samples. In tumor research, WGBS has a more comprehensive understanding of the level of methylation of CpG sites and CpG islands at the genomic level than DNA methylation chips and other methylation detection technology platforms. In addition to Qian et al, which analyzed changes in HCC methylation levels using clinical samples with only 4 HCC samples, there are few reports of studies on HCC using WGBS technology. In this study, differential methylation sites (DML), differential Methylation Regions (DMR) and Differential Expressed Genes (DEG) between HCC and normal liver tissue were analyzed by WGBS and mRNA-Seq data of cancer and paracancerous tissues of 33 HCC patients. And then, analyzing the DML data by using the RF and ELNET regression algorithm, constructing a binary diagnosis model with excellent performance, and obtaining the diagnosis score (DScore) of each sample. Further, by ssGSEA analysis, DScore was found to be significantly inversely correlated with various immune cell infiltration levels, partially revealing that the mechanism of DScore and tumorigenesis may be related to immune deficiency. By integrally analyzing DMR and DEG, we found two potential liver cancer diagnostic plasma cfDNA methylation markers. The application potential of the marker is preliminarily verified by detecting the cfDNA of the blood plasma by a fluorescence quantitative PCR method and comparing the cfDNA with other serum markers. However, both the tissue diagnostic model and the plasma cfDNA markers present certain false negatives, i.e. missed diagnosis, and further analysis of the cause is required to improve diagnostic performance. The blood plasma cfDNA validation sample size was also small and did not adequately demonstrate the diagnostic performance of the markers. These data indicate that DNA methylation plays an important role in the development of liver cancer and has application value as a diagnostic marker for liver cancer.

Although both genetic and epigenetic changes are involved in the development and progression of cancer, DNA methylation changes often begin to occur early in the tumor. Although there are many studies on HCC gene mutation and reports on early HCC monitoring using gene mutation, mutation frequency of a specific gene is low due to high heterogeneity of HCC, thereby limiting performance in early HCC monitoring. DNA methylation is used as a biomarker, and has good application prospect in tumor diagnosis and early monitoring. At present, some tumor methylation related researches are successful in obtaining clinical transformation, such as colorectal cancer SEPT9 and SDC2, PTGER4/SHOX2 in lung cancer, GSTP1 in prostate cancer and other gene methylation detection kits, and clinical in vitro diagnostic reagent registration evidence is obtained. In particular, the methylation detection kit for colorectal cancer and lung cancer is suitable for noninvasive samples (faeces and peripheral blood), and brings hopes for early screening of cancers, monitoring of high-risk groups and the like. However, methylation changes in specific genes have not yet achieved consistent clinical performance in HCC diagnosis.

The most important result of the present invention, and the most important object, is to find a peripheral blood cfDNA methylation marker with the diagnosis of early HCC. An ideal diagnostic marker should be highly sensitive, recognize early disease, and be detectable by non-invasive, inexpensive techniques. The liver cancer screening marker which is widely applied clinically is serum AFP. However, the sensitivity and positive predictive value of serum AFP are low, limiting the clinical value of its detection. In this study, we analyzed that the regions of hypermethylation where Δβ is the greatest for both TSPYL5 and GNA14 gene promoters, and that the expression levels of both TSPYL5 and GNA14 genes are strongly inversely correlated with their methylation levels. Pan cancer analysis has found that TSPYL5 is hypermethylated in a variety of cancers, and TSPYL5 has also been reported to be hypermethylated and underexpressed in tumor types such as gastric cancer and glioma, indicating that the specificity of the tumor type for TSPYL5 methylation is not high. However, the application value of the marker in monitoring specific high-risk groups cannot be influenced no matter the specificity of the marker in the tumor types. In another aspect, TSPYL5 is also reported to be hypomethylated and highly expressed in lung cancer, thereby inhibiting p53, and is a potential therapeutic target for lung cancer, indicating that TSPYL5 also has a certain heterogeneity in different tumor types. GNA14 is also reported as an oncogene hypermethylated in HBV-related HCC and its low expression is associated with poor prognosis. The use of the methylation of the GNA14 gene promoter region in noninvasive diagnosis has not been reported.

The blood cell DNA of the promoter regions of the two genes has stable low methylation level, so that the methylation of the blood cell DNA does not interfere with the detection of the methylation of the two genes, and the methylation of the blood cell DNA is detected by using a peripheral blood cfDNA sample as an HCC diagnostic marker to clear the obstacle. Primers and probes were designed for PCR of the two gene promoter regions, and it was found that TSTYL 5 had higher sensitivity, and that diagnostic sensitivity could be further improved when AFP results were combined.

The invention discovers that DNA methylation is used as an ideal biomarker type, methylation of a TSTYL 5 gene promoter region is a potential biomarker for monitoring HCC high risk groups, and diagnosis sensitivity can be improved by combining AFP.

The invention provides a detection marker and application thereof in preparing products for detecting cancers, and simultaneously, the invention also provides a primer, a probe and a combination thereof, a detection product and a method for screening the marker.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.

FIG. 1 shows the path of DNA methylation liver cancer diagnostic marker study;

FIG. 2 shows the driving Hyper-pDBR methylation identification; wherein: a shows a pDBR volcanic plot; b shows a DEG volcanic map; c shows pDBR-DEG Pearson correlation; d shows a thermal map of CpG site methylation levels in 8 driving Hyper-pDBRs; wherein: pDBR, differential-methyl-region in gene promotor region; DEG, differential expression gene;

FIG. 3 shows a heat map of the common dataset verifying CpG site methylation levels in 8 driving hyper-pDRRs; wherein: a-H are TCGA, GSE37988, GSE54503, GSE56588, GSE57956, GSE89852, GSE99036, GSE113017 in that order;

FIG. 4 shows a schematic representation of methylation differential levels and genomic positions for 8 DMRs; wherein: the 8 DMRs corresponding to A-H are chr1_118983368_118990519 (A), chr1_585501_5855030 (B), chr7_128030940_128032590 (C), chr7_88306820_88307042 (D), ch10_9307481_93075498 (E), ch22_399994591_3995104 (F), ch8_97277329_97278175 (G) and ch9_77647531_77648367 (H), respectively; wherein: purple represents liver cancer tissue, yellow represents paracancerous liver tissue, and blue represents peripheral mononuclear cells; each vertical line in G and H represents a CpG site;

FIG. 5 shows the methylation level of TSTYL 5 in flood carcinoma; * P <0.05, < P <0.01, < P <0.001;

FIG. 6 shows differences in Discovery queue target region methylation levels between different sets of clinical features;

FIG. 7 shows the differences in methylation levels of target regions of TCGA-LIHC queues between different sets of clinical features;

FIG. 8 shows the results of a TSPHL 5 hypo-methylated GO (A) and KEGG (B) gene set enrichment analysis;

FIG. 9 shows the difference in survival of TSPHL 5 hypomethylation.

Detailed Description

The invention discloses a detection marker and 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 otherwise understood from the context and usage. The expression "and/or" in combination with three or more recited objects should be understood as having the same meaning unless otherwise understood from the context.

The use of the terms "comprising," "having," or "containing," including grammatical equivalents thereof, should generally be construed as open-ended and non-limiting, e.g., not to exclude other unrecited elements or steps, unless specifically stated otherwise or otherwise understood from the context.

It should be understood that the order of steps or order of performing certain actions is not important so long as the invention remains operable. Furthermore, two or more steps or actions may be performed simultaneously.

The use of any and all examples, or exemplary language, such as "e.g." or "comprising" 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.

Furthermore, the numerical ranges and parameters setting forth the present invention are approximations that may vary as precisely as possible in the exemplary embodiments. However, any numerical value inherently contains certain standard deviations found in their respective testing measurements. Accordingly, unless explicitly stated otherwise, it is to be understood that all ranges, amounts, 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 examples 1 to 11 of the present invention, samples were collected: paired cancer tissue and paracancerous tissue samples of 33 HCC patients used in this study were collected from the university of Zhejiang medical college affiliated with a hospital from 7 months 2015 to 6 months 2016. In the collecting process, the doctors, nurses and scientific researchers who are uniformly trained obtain relevant information of the specimens, including age, gender, height, weight and the like. The tissue sample is detected by a senior pathologist, and the tissue morphology of the sample is considered to be consistent with the characteristics of cancer tissue or paracancer tissue.

1. Clinical characteristics of the subject

The study collected liver cancer tissue and paired paracancerous tissue from 33 HCC patients. Of these, there were 27 men more than women, and only 6 women. The lowest positive rate among the 8 serum markers is alkaline phosphatase (alkaline phosphatase, ALP), which is higher than the normal range for only 2 patients, and the positive rate is only 6% (2/33); the highest positive rate of alpha fetoprotein (alpha fetal protein, AFP) was 60% (20/33), as shown in Table 1.

TABLE 1 33 clinical information for HCC patients

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

inclusion criteria: (1) outpatient or inpatient; (2) age 18-65 years; (3) the first diagnosis is liver cancer/hepatitis/liver cirrhosis; (4) understand study objectives and sign informed consent.

Exclusion criteria: this study will not be attended if any of the following are present: (1) tumor resection has been performed without recurrence; (2) transfusion or organ transplantation is performed within one month; (3) severe anemia;

the proportion of men and women in the plasma-validated group was similar to that in the tumor tissue group, with 43 men and 5 women. This is associated with a higher incidence in HCC men than in women.

Table 2 blood plasma sample subject clinical profile

Meanwhile, the data of the DNA methylation level of blood cells of non-cancer people used in the study is based on epidemiological study of Chinese people from the laboratory.

In addition, peripheral blood samples from 48 HCC patients (collected by the Hospital, denland) and 24 non-cancerous volunteers were also collected in this study using a streck blood collection tube (cat No. 218962). No statistical method is used to predetermine the sample size. By MagMAX ^TM The free DNA isolation kit (cat# A29319) extracts peripheral blood plasma free DNA (cfDNA).

In examples 1 to 7 and effect examples 1 to 10 of the present invention, all the raw materials and reagents used were commercially available.

The invention is further illustrated by the following examples:

EXAMPLE 1 liver cancer methylation sample public data collection

We downloaded 450K methylation chip data, 450K annotation files and corresponding clinical information from UCSC Xena website (https:// xenabowser. Net/datapages /) for 430 TCGA-LIHC queues. We downloaded HCC-related 450K methylation chip datasets 5 (GSE 54503, GSE56588, GSE89852, GSE99036 and GSE 113017) from the national center for biotechnology information (National Center for Biotechnology Information, NCBI) high throughput gene expression database (Gene Expression Omnibus, GEO).

EXAMPLE 2 investigation of liver cancer markers of plasma free DNA methylation

1. Identification of methylation-driven genes

To find methylation markers of HCC detected in potential blood cfDNA, we identified hypermethylation regions that affect gene expression. Specifically, we annotated the genomic position of DMR with genomics range package of R language, from which we find DMRs with hypermethylation (hyper-methyl) of the promoter region (i.e., the range of 1500bp upstream to 500bp downstream of the transcription start site) and differential methylation level (Δβ) of not less than 0.3, defined as hyper-DMR. Most studies suggest that high DNA methylation often inhibits gene expression, and that demethylation allows re-expression of the gene; abnormal DNA methylation affects related expression, resulting in the development of dysplasia, tumors, and the like. To increase the reliability of plasma methylation markers, we identified hyper-DMRs that significantly affected gene expression. Specifically, the expression level of DEG (transcription initiation site of gene is less than 2000bp from DMR) near the hyper-DMR promoter and the methylation level of DMRs are subjected to Spearman correlation test (Spearman), and the driving hyper-DMR is identified by taking the correlation less than-0.3 and the error discovery rate (BH-FDR) after multiple correction by the Benjamini & Hochberg (BH) method as a significance standard less than 0.05.

2. Methylation level of driving hyper-DMR in peripheral blood cell DNA

It is thought that only hyper-DMRs, whose blood cell DNA is at a low methylation level in non-cancerous people, can be used as a plasma marker for liver cancer. Therefore, we extracted the methylation level matrix of the driving hyper-DMR of DNA WGBS of peripheral blood cells of non-cancer population and compared with the data of our liver cancer tissue WGBS to exclude hypermethylated DMR in peripheral blood cells. We visualized the three sets of data with R package "Gviz" to intuitively display the level of methylation differences and genomic coordinates of potential plasma free DNA methylation markers.

3. Plasma free DNA methylation marker validation

We used fluorescent quantitative PCR method to verify the hype r-DMR that could potentially be used as liver cancer marker.

(1) First, we designed fluorescent quantitative PCR primer pairs (primers) and fluorescent probe (probe) sequences for genomic regions with the greatest difference in methylation levels within candidate hyper-DMR and small standard error (standard deviation, SD) using Beacon Designer 8 software and synthesized by Bio (Shanghai) Inc. The primer and probe sequences were as follows:

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

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

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

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

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

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

The reference gene is ACTB:

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

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

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

(2) The fluorescent quantitative PCR experiment steps are as follows:

the extracted cfDNA was subjected to bisulfite conversion using EZ DNA methylation kit (cat No. D5030).

1) Taking 10ng cfDNA, and supplementing water to 20 mu L;

2) Adding 130 mu L Lightning Conversion Reagent to DNA, shaking and centrifuging briefly;

3) The following procedure was run: 98 ℃ for 8min;54 ℃ for 60min; storage for up to 20h at 4 ℃;

4) Preparing a new Zymo-spinTM IC Column, adding 600 mu L M-Binding Buffer, and placing into a collection tube;

5) Transferring the sample in the step 3) into the tube in the step 4), and uniformly mixing the sample in the step 4) for a plurality of times in an upside-down way;

6) Centrifuging at full speed for 30s, and pouring out waste liquid;

7) Adding 100 mu L M-Washbuffer, centrifuging at full speed for 30s;

8) Adding 200 mu L of L-Desulphonation Buffer, standing at room temperature for 15-20 min, and centrifuging at full speed for 30s;

9) Adding 200 mu L M-Washbuffer, centrifuging at full speed for 30s, and repeating this step 1 time;

10 The column was placed in a new 1.5mL EP tube, 10. Mu. L M-ElutronBuffer was added, and the DNA was collected by centrifugation at full speed for 30 s.

11 Fluorescent quantitative PCR

We prepared the reaction solution using the purchased Qiagen EpiTect MethyLight PCRKit (product number: 59496) according to the following table;

each PCR reaction, a positive quality control, a negative quality control and a blank control are carried out while the sample is detected.

12 PCR procedure

We performed fluorescent quantitative PCR reactions using an ABI 7500 real-time fluorescent quantitative PCR instrument. The reaction procedure was as follows:

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

4. the ratio of the detection method to the target marker and the lower limit of DNA entry detection (Limit ofdetection, LOD)

Since cfDNA in plasma is derived from various tissues, organs and blood cells, the proportion of DNA derived from tumors, especially early tumor cells, is small. In order to ensure that the methylation change of the target methylation marker can be detected under the condition of low proportion, methylation and unmethylation standard substances are used for detection according to the methylation proportion of 5% and 1%, so that the detection sensitivity of the method is confirmed, and the potential of the method in application to cancer differential diagnosis and early screening is ensured.

Example 3 blood diagnostic marker contrast and changes in TSTYL 5 methylation levels in other cancer species

We have investigated various blood samples published in recent years for diagnosis of HCC and compared with the diagnostic properties of TSPYL5 methylation found in this study. At the same time, we analyzed the methylation level of TSPYL5 in 643 non-cancerous samples and 7843 tumor samples of 29 cancers of TCGA.

Example 4 comparison of TSPCL 5 methylation levels between different sets of clinical case characteristics

To confirm the independent diagnostic value of TSPYL5 methylation, we compared the differences in TSPYL5 promoter region methylation levels between different sexes, age groups, tumor diameters, hepatitis b conditions and serum AFP levels in the discovery and TCGA-LIHC queues.

Example 5 Gene enrichment analysis

To explore the molecular mechanisms and biological functions of altered methylation of TSPYL5, we performed a gene set enrichment analysis of the methylation level of the TSPYL5 promoter region (Gene set enrichment analysis, GSEA). Enrichment analysis used two gene sets of BroadMolecular Signatures Database (MSigDB v 7.1) set C5 and C2.Cp. Kegg. V7.4. Symbols. P <0.05 served as a criterion for significant enrichment of the gene set.

EXAMPLE 6 relationship of TSPYL5 methylation and prognosis

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

EXAMPLE 7 statistical analysis

All statistical analyses in this study were done in R (v.4.0.3). For WGBS and mRNA-seq data for 33 pairs of liver cancer and paracancerous samples, we used paired t-test to perform differential analysis on tumor tissue and paracancerous tissue. A Spearman (Spearman) correlation coefficient calculates the correlation between two sets of data, with an absolute value of the correlation coefficient (|cor|) greater than 3 being considered to be a correlation, cor >0 being a positive correlation and cor <0 being a negative correlation. P values less than 0.05 are a criterion of significance.

Effect example 1 identification of differential methylation sites (DML)

WGBS after QC was run down to an average coverage depth of 12.76×, containing 28,978,826 CpG sites. After smooths, about 34% (9,867,700) CpG sites have significant differences in methylation levels between cancerous and paracancerous tissues. Of these, 157,320 sites are hypermethylated and 9,710.380 sites are hypomethylated. The gene coordinate positions show that the hypermethylation sites are mainly located in the gene promoter region, while the hypomethylation sites are mainly located in the gene body region.

Effect example 2 identification results of Differential Methylation Region (DMR)

We further analyzed the identified DML to identify 608,279 Differential Methylation Regions (DMRs). Of these 6,924, hypermethylated DMR (hyper-DMR) and 601,355 hypomethylated DMR (hypo-DMR) were used in HCC.

Effect example 3 identification results of differentially expressed Gene (DGE)

We performed differential analysis of gene expression from cancer to paracancer on TPM data for mRNA-Seq. As a result, it was found that the difference in the expression of 11,672 genes reached a significant level. Among these, there were 6,912 high-expression genes and 4,760 low-expression genes. The expression levels of these low-expression genes (DEGs, differential expression genes) were used for subsequent Spearman correlation analysis with hypermethylated regions (DMRs, differential methylation regions).

Table 3 common 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

Effect example 4 driving DMR authentication

The experimental results are shown in fig. 2 and table 4, and the presence of small amounts of DNA in the plasma free DNA (cfDNA) of example 2, which is the cell shed (apoptosis) from the tumor, provides the possibility for clinical application of plasma cfDNA-based tumor with diagnosis or early screening. To search for methylation markers useful for early screening of liver cancer, we screened 30 liver cancer tissues for methylation regions (hyper-pDBR) with a stable hypermethylation and methylation level difference (Δβ) greater than 0.3 from all the differential methylation regions (DMRinpromotorregion, pDMR) located in the promoter region (as shown in FIG. 2A). In order to further improve the reliability of blood markers, we found 8 driving hyper-pDMR as target markers for further analysis, taking the screening condition that the gene expression corresponding to hyper-pDMR was significantly down-regulated and the methylation level correlation with DMR was less than-0.3.

TABLE 4 correlation of highly diverse methylation regions (hyper-pDBMR) with 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 in liver cancer and normal tissues for these 8 driving hyper-pDMR, we analyzed the methylation difference levels of CpG sites within these 8 DMRs in the common dataset of 8 liver cancer methylation chips. The common data set of the 8 liver cancer methylation chips is 6 450K methylation chip data sets, including TCGA-LIHC, GSE54503, GSE56588, GSE89852, GSE99036 and GSE113017;2 27K methylation chip datasets, including GSE37988 and GSE57956. The difference analysis results are shown in FIG. 3, where CpG sites within 8 driving hyper-pDRRs also show hypermethylation in the public dataset.

Effect example 5 methylation level of target DMR in blood cell DNA

Experimental results as shown in fig. 4, in example 2, to select liver cancer markers from 8 driving hyper-pDMRs that are likely to be used for plasma cfDNA diagnosis, and to select more specific genomic regions to be validated with plasma cfDNA, we further compared the blood cell DNA methylation levels in these hyper-DMRs and the density of CpG sites. We extracted methylation levels of Cp G sites within 8 target hyper-DMRs from non-cancer population blood cell WGBS data that have been published by this team. As a result, it was found that the methylation level of blood cell DNA in the non-cancerous population was relatively low in these several hyper-pDBR regions. In addition, the difference in methylation levels (Δβ) between two DMRs, chr8:97277329_97278175 (in the TSPHL 5 gene promoter region) and ch9:77647531_77648367 (in the GNA14 gene promoter region), was greatest, with average values of 0.416 and 0.400, respectively; these two DMRs have a methylation level at CpG sites that are more concentrated on the genome, with tumor group β values stabilized above 0.6 and normal group β values stabilized below 0.2.

Effect example 6 plasma cfDNA validation

We selected two regions of chr8:97278070-97277720 and chr9:77648350-77647950 as candidate markers based on the level of CpG site methylation difference (Δβ) within the DMR, named the gene in which they are located (as shown in Table 4), and further validated with plasma cfDNA. Methylation fluorescent quantitation PCR (methylight) primer and probe design was performed on the sequences of the two selected genomic regions using Beacon Designer 8, see example 2 for details. cfDNA was extracted from the plasma of collected liver cancer patients and healthy volunteers, followed by biosulfite conversion, and fluorescence quantitative PCR was performed. The sensitivity and specificity of the two markers were verified using the ct value (Cycle threshold) 38 as a threshold as shown in table 5. As a result of the TSTYL 5 gene methylation detection, 41 cases of 41 patients with CT values smaller than 38 could be detected out of 48 HCC patients, and the sensitivity was 85.4% (41/48); as a result of the GNA14 gene methylation detection, the 33 fluorescence quantitative PCR (polymerase chain reaction) of 48 HC C patients had a ct value of less than 38, i.e., 33 positives were detected, and the sensitivity was 68.8%. In 24 healthy volunteers, the TSPCL 5 gene methylation results were all negative with 100% specificity. Of particular concern, of 44 HCC patients undergoing AFP detection, only 31 were positive with a sensitivity of 70.5%. Compared to AFP, gene TSPYL5 methylation is more sensitive to HCC detection.

In addition, in 7 cases of HCC patients negative for methylation of the plasma TSPYL5 promoter, 1 case was removed without AFP detection, and in the other 6 cases, AFP results were positive. This result suggests that combining AFP with TSPYL5 methylation detection can improve diagnostic sensitivity.

We compared the results of several other blood samples for HCC diagnosis. Although the diagnostic performance of TSPYL5 methylation is not optimal, this 85.4% sensitivity and 100% specificity is achieved in a single gene methylation assay, which has the advantage of being simpler and highly generalizable relative to multiple gene assays and risk models.

Table 5 blood HCC marker diagnostic performance comparison

Effect example 7 levels of TSTYL 5 methylation in other cancer species

To understand the level of TSPYL5 methylation in cancer, we analyzed the pan cancer methylation data in the TCGA database. Of the Normal (Normal) control cancer species, only Cholangiocarcinoma (CHOL) and thymic carcinoma (THYM) had lower median levels of TSPYL5 methylation than Normal tissues, but the differences did not reach significant levels; methylation levels in lung adenocarcinoma (LUAD), glioma (GBM) lung squamous carcinoma (luc), endometrial carcinoma (UCEC), bladder urothelial carcinoma (BLCA), esophageal carcinoma (ESCA), pancreatic carcinoma (PAAD), renal papillary cell carcinoma (KIRP), cervical carcinoma (CESC), breast carcinoma (BRCA), colon Carcinoma (COAD), renal clear cell carcinoma (KIRC), thyroid carcinoma (THCA), head and neck squamous cell carcinoma (HNSC), and rectal adenocarcinoma (READ) were significantly higher than in normal tissues (as shown in fig. 5). These results suggest that TSPYL5 methylation may play a role in a variety of cancer occurrence processes; meanwhile, hypermethylation of this region is not cancer species specific.

Effect example 8 differences in TSTYL 5 methylation levels between different clinical feature sets

Methylation levels of the TSPYL5 gene promoter region (chr 8: 97278070-97277720) in tumor tissues of 33 HCC patients were not statistically different among patients of different age groups, gender, tumor diameter, hepatitis B status, and serum AFP status (as shown in FIG. 6).

Methylation levels in the chr8:97278070-97277720 region in the TCGA-LIHC cohort were significantly higher in the high age group (. Gtoreq.60) and male patients than in the low age group (. Gtoreq.60) and female patients; there were no statistical differences between different ethnicities, clinical grade, stage, child-Pugh grade (as shown in FIG. 7).

Effect example 9TSPYL5 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, the alternative splicing biological process that significantly enriches mRNA in the TSPYL5 hypermethylation phenotype; hypomethylated TSTYL 5 is significantly enriched in the biological processes of muscle hypertrophy negative regulation, potassium ion transmembrane transport negative regulation, deacetylase activity regulation, histone deacetylase regulation (as shown in FIG. 8A). The results of GO show that TSPYL5 promoter methylation may affect mRNA splicing and apparent modification of proteins. In the KEGG analysis, the 5 pathways most significantly enriched in the TSPYL5 hypomethylation phenotype were cell adhesion molecules, chemokine signaling pathways, cytokine-receptor interactions, focal adhesion and Hedgehog signaling pathways (as shown in fig. 8B). From the results of KEGG, it is possible that methylation of the TSPYL5 promoter reduces cell adhesion, thereby increasing the risk of distant metastasis of tumor cells.

Effect example 10TSPYL5 methylation and Total survival prognosis

Experimental results as shown in figure 9 we further analyzed the prognostic value of TSPYL5 promoter methylation levels, which showed that TSPYL5 methylation was worse overall survival, but the differences did not reach statistically significant levels.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. A detection marker comprising the degree of methylation of one or more of TSPYL5, GNA14, LRRC4, CYP26A1, TACSTD2, FAM83F, TBX15, or STEAP4, and combinations thereof.

2. Use of the detection marker of claim 1 in the manufacture of a product for detecting cancer.

3. The use of claim 2, wherein the product further comprises: HBV, AFP, AFU, CEA, ALT, AST, ALP or GGT.

4. Use according to claim 2 or 3, wherein the test sample of the product comprises: plasma.

5. The use of any one of claims 2 to 4, wherein the cancer comprises: liver cancer, bile duct cancer, thymus cancer, lung adenocarcinoma, glioma, lung squamous carcinoma, endometrial cancer, bladder urothelial cancer, esophageal cancer, pancreatic cancer, renal papillary cell carcinoma, cervical cancer, breast cancer, colon cancer, renal clear cell carcinoma, thyroid cancer, head and neck squamous cell carcinoma or rectal adenocarcinoma.

6. A primer, a probe or a combination thereof, characterized in that the detection marker in the use according to any one of claims 2 to 5 is an amplified fragment of interest.

7. A detection reagent, wherein the detection target is the detection index used in the method according to claim 3.

8. A test product comprising a primer, probe or combination thereof according to claim 6 and/or a test agent according to claim 7 and an acceptable adjuvant.

9. The screening method of the detection marker in the use according to claim 2 and/or the detection indicator in the use according to claim 3, characterized in that the screening method comprises whole genome sulfite resequencing or transcriptome sequencing.

10. The screening method of claim 9, wherein the screening region comprises one or more of a differential methylation site, a differential methylation region, or a differentially expressed gene.