Application of HHAT gene and TTLL7 gene in liver cancer diagnosis and treatment

Abstract

The invention discloses an application of an HHAT gene and a TTLL7 gene in liver cancer diagnosis and treatment, relates to the technical field of biology, discloses new prospects of the HHAT gene and the TTLL7 gene in a human genome for the first time, reveals occurrence and development of liver cancer caused by change of a three-dimensional structure and change of chromatin opening degree, and reveals development and development of liver cancer caused by targeting of the gene. The gene can become a therapeutic target for diagnosing or treating liver cancer.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to the application of HHAT gene and TTLL7 gene in the diagnosis and treatment of liver cancer. Background Art

[0002] In recent years, with the continuous improvement of multi-omics sequencing capabilities, it has been possible to analyze the ubiquitous three-dimensional genome remodeling and chromatin remodeling phenomena that were difficult to detect in the past. At present, the treatment of liver cancer has evolved from single-drug targeted therapy (Sorafenib and Lenvatinib) to systemic treatment with checkpoint inhibitors plus targeted therapy (atezolizumab combined with bevacizumab treatment). Despite significant progress, only a small number of patients can obtain lasting clinical benefits, so substantial treatment challenges still exist.

[0003] Correct chromatin folding determines the appropriate expression of genes. Hi-C, a genome-wide chromosome conformation capture assay, shows that the human genome is three-dimensionally folded in the cell nucleus, and the entire genome is divided into two spatial compartments, called A compartment and B compartment, with distribution markers A and B compartments. Often, there are frequent interactions within the compartments, while there are fewer interactions between compartments. The A compartment belongs to open chromatin, is actively expressed, rich in genes, has a high GC content, contains histone marks for active transcription, and is usually located in the interior of the cell nucleus. The B compartment usually represents closed chromatin, which is inactive, lacks genes, has a compact structure, contains histone marks for gene silencing, and is located at the periphery of the nucleus. They are mainly composed of LaminaAssociating Domains (LADs) and contain late replication origins. In bioinformatics analysis, the two different compartments are distinguished by calculating the correlation of intrachromosomal interactions. Subsequent analysis showed that the compartments are divided into approximately 1Mb in size, called "topologically associated domains" (TADs). Generally, these regions are conserved in different cells of different mammals and are highly enriched with CTCF and adhesion proteins. Chromatin forms a ring structure in space, so chromatin regions that are far apart can also be brought together in three-dimensional space. Previous studies have shown that abnormal chromatin interactions contribute to tumorigenesis. The characterization of three-dimensional epigenomic features provides a more detailed epigenetic mechanism for the occurrence and development of liver cancer. Technologies such as ChIP-seq and CUT&Tag have been widely used to measure the binding profiles of various transcription factors and histone modifications. Among them, CUT&Tag is a new technology for studying protein-DNA interactions. It can detect DNA fragments that interact with histones, transcription factors, etc. on a genome-wide scale, and effectively analyze chromatin binding elements in cells. This technology helps to identify cell type-specific or common enhancers, and then use Hi-C ring data to find genes associated with these enhancers. Transposase-accessible chromatin high-throughput sequencing analysis (ATAC-seq) has become a sensitive and reliable method for open chromatin analysis, nucleosome site positioning, and TF occupancy analysis. ATAC-seq data can be jointly analyzed with histone modification CUT&Tag data, where transcriptional activation modifications (H3K4me3, H3K4me1, and H3K27ac, etc.) are positively correlated with chromatin openness, and transcriptional repression modifications (H3K27me3) are negatively correlated with chromatin openness. Combining known interaction data between enhancers and promoters can also help construct regulatory networks.

[0004] Currently, there is a lack of effective biomarkers for systemic treatment of liver cancer. Therefore, combined multi-omics analysis to explore the changes in chromatin three-dimensional structure during liver cancer differentiation and explore its potential mechanisms will help to better understand the development mechanism of liver cancer, and exploring potential therapeutic targets may provide new clues for the clinical treatment of liver cancer. Summary of the invention

[0005] In view of this, the present invention provides the application of HHAT gene and TTLL7 gene in the diagnosis and treatment of liver cancer. To achieve the above purpose, the present invention mainly provides the following technical solutions:

[0006] One aspect of the present invention relates to a kit for diagnosing liver cancer, the kit comprising primers for detecting the expression levels of HHAT gene and TTLL7 gene.

[0007] Another aspect of the present invention relates to a drug for treating liver cancer, which includes a kit for knocking out or knocking down the expression levels of HHAT gene and TTLL7 gene.

[0008] Another aspect of the present invention relates to the use of HHAT gene and TTLL7 gene in preparing a kit for diagnosing liver cancer; preferably, the kit is used to evaluate the progression of liver cancer.

[0009] Another aspect of the present invention relates to the use of HHAT gene and TTLL7 gene in the preparation of drugs for treating liver cancer; preferably, the drugs are used to knock out or knock down the expression levels of HHAT gene and TTLL7 gene.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] The present invention relates to the field of biotechnology, and specifically discloses new prospects for HHAT gene and TTLL7 gene in the human genome, revealing that they cause the occurrence and development of liver cancer through changes in three-dimensional structure and changes in the degree of chromatin openness. By targeting the gene, it can become a therapeutic target for diagnosing or treating liver cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Changes in the interaction strength and three-dimensional chromatin structure of L02 liver cell line and Hep3B liver cancer cell line. (A) Interaction strength of AB compartments in the three-dimensional genome of L02 normal liver cell line and Hep3B liver cancer cell line. (B) Examples of the conversion of compartment A to compartment B and compartment B to compartment A, marked with pink boxes. (C) The ratio of the A / B compartment state changes in the genome.

[0013] Figure 2Showing the splitting of contact domains around the HHAT locus and TTLL7 in L02 and Hep3B cells (between the dashed lines) and the switching of chromatin states (shown by Hi-C images and histone CUT&Tag tracks). (A). Contact domains with changes in chromatin states around the HHAT locus in L02 and Hep3B cell lines. (B) Changes in chromatin accessibility and H3K27ac histone modification of HHAT between L02 and Hep3B groups. (C) Contact domains with changes in chromatin states around the TTLL7 locus. (D) Changes in chromatin accessibility and H3K27ac histone modification of TTLL7 between L02 and Hep3B groups.

[0014] Figure 3 TTLL7 is highly expressed in liver cancer and disease progression, and high expression is not conducive to survival. (A) Box plot of TTLL7 expression changes in normal and liver cancer in three datasets. (B) Kaplan-Meier survival curve of TTLL7 expression in the TCGALIHC dataset for overall survival. (C) Box plot of TTLL7 expression changes in early and late BCLC. (D) Verification at the spatial level that TTLL7 expression in tumors is higher than that in normal tissues.

[0015] Figure 4 HHAT is highly expressed in tumors in chip datasets, and is gradually highly expressed in different HCC progressions, and is highly expressed in metastasis. (A) Box plot of HHAT expression changes in tumor and normal groups in three groups of chip data. (B) HHAT expression gradually increases in hepatitis, degenerative nodules, early liver cancer, and late liver cancer in the GSE114564 dataset. (C) HHAT is highly expressed in vascular invasion. (D) Box plot of HHAT expression changes in cirrhosis, early liver cancer, and late liver cancer in the GSE89377 and (E) GSE54238 datasets. (F) It is verified at the spatial level that HHAT is expressed higher in tumor tissues than in normal tissues. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, technical solutions, features and effects of the present invention application are described in detail below with preferred embodiments. The specific features, structures, or characteristics of the multiple embodiments in the following description can be combined in any suitable form.

[0017] Example 1

[0018] Experimental methods:

[0019] Hi-C data analysis: Raw Hi-C fastq files were filtered, mapped and raw contact count matrices were created using HiC-Pro. The matrix was normalized using iterative correction. AB compartment analysis was performed using the HOMER tool (http: / / homer.ucsd.edu / homer / index.html) and 100kb bins. Comparing the average observed / expected values ​​between active and inactive chromatin is a useful way to measure the strength of compartmentalization, which was then measured using the saddle_strength function, followed by cooltools to visualize the preference captured by the eigenvector. The correlation matrix was compared with the getHiCcorrDiff.pl script provided by HOMER to identify switches in the A and B compartments. The proportion of changes was statistically analyzed using the R language.

[0020] Cell culture:

[0021] L02 cells (normal human hepatocytes) and Hep3B human hepatoma cell lines were maintained in a cell culture incubator (ThermoFisher Scientific) at 37°C and 5% CO2. Cells were routinely cultured in DMEM medium (VivaCell, #c313-0500) or 1640 medium (VivaCell, #C3010-0500) supplemented with 10% fetal bovine serum (Lonsera, #S711-001S) and 1% penicillin-streptomycin antibiotic solution (Beyotime, #C0222). All cell lines were tested for mycoplasma contamination.

[0022] RNA isolation and real-time quantitative PCR (qRT-PCR):

[0023] Total RNA was isolated from whole cell lysates or tissues using a total RNA extraction kit (Beibei biotechnology, #082001) and TRIzol reagent (Sigma, #93289) according to the manufacturer's protocol. 1 μg of total RNA was reverse transcribed into cDNA using HiScriptIII RT SuperMix for qRT-PCR (+gDNAwiper) (Vazyme, #R323). qRT-PCR was performed on a CFX96TM Real-Time System (Bio-RAD, #CT032238). Each reaction was detected in a 20 μL reaction system containing AceQ UniversalSYBR qRT-PCR Master Mix system (Vazyme, #Q511), primers, ddH2O, and template. The following primers were used: HHAT-front primer: 5'-TCACCGGGATGTGGAGGTAT-3', HHAT-back primer: 5'-ATGCAAATGTCATCGCCGTG-3'; TTLL7-front primer: 5'-GATCAAGTCTCGGCCTCTGG-3'; TTLL7-back primer: 5'-ATCCCATGACCCATTGCACC-3'.

[0024] CUT&Tag and ATAC-seq experiments:

[0025] CUT&Tag was performed as described previously and modified using the Illumina Hyperactive Universal CUT&Tag Detection Kit (Vazyme Biotech, #TD903). Briefly, 1×10 5Cells were centrifuged at 600X g for 5 minutes at room temperature. Cell pellets were resuspended in 100μl wash buffer. 10μl Concanavalin A coated beads were washed twice with 100μl binding buffer and added to the tube of cells and incubated at room temperature for 10-15min. After removing the supernatant, the beads were resuspended in 50μl antibody buffer with 2μg rabbit anti-H3k27ac antibody. After incubation at room temperature for 2 hours or overnight at 4°C, the primary antibody was carefully discarded. Diluted with 50μl Dig-wash buffer. The cells were then rotated and incubated at room temperature for 1 hour. The cells were then gently washed 3 times with 200μl Dig-wash buffer, and 2μl pA / G-Tnp and 98μl Dig-300 buffer were added to the sample. After incubation at room temperature for 1 hour, the sample was gently washed 3 times with 200μl Dig-300 buffer. Then, 10 μl of 5X TTBL was added to each sample and mixed with 40 μl Dig-300 buffer, and the samples were incubated at 37°C for 1 hour. 5 μl of 20 mg / ml proteinase K, 100 μl of buffer L / B, and 20 μl of DNA extraction beads were added, and the samples were incubated at 55°C for 10 minutes to quench the interaction. The supernatant was discarded, the magnetic beads were washed once with 200 μl buffer WA, washed twice with 200 μl buffer WB, and resuspended with 22 μl nuclease-free water. For library amplification, 15 μl of purified DNA was mixed with 25 μl 2XCAM and 5 μl of unique barcode i5 and i7 primers from TruePrep Index Kit V2 for Illumina (Vazyme Biotech, #TD202). A total volume of 50 μl of sample was placed in a thermal cycler (BIO-RAD, #T100) using the following program: 72°C for 3 minutes; 98°C for 3 minutes; 98°C for 10 seconds, 60°C for 5 seconds, and 72°C for 1 minute for 12-16 cycles; and kept at 12°C. To purify the PCR product, 2X volume of VAHTSDNAClean Beads (Vazyme Biotech, #N411) was added and incubated at room temperature for 5 minutes. The beads were washed twice with 200 μl of fresh 80% ethanol and eluted in 22 μl ddH2O. All CUT&Tag libraries were sequenced by Novogene using the IlluminaNovaSeq X Plus platform in PE150 mode (Novogene, Beijing, China).

[0026] CUT&Tag and ATAC-seq data analysis: The standard CUTTag_tutorial analysis pipeline (https: / / yezhengstat.github.io / CUTTag_tutorial / ) was used. The raw data obtained by sequencing was quality controlled and filtered using trim_galore, and then the clean data was aligned to the human reference genome (hg19 version) using bowtie2. Picard removed repeated PCR repeat sequences, and samtools converted the sam file to a bam file. MACS2 searched for peaks, and macs2bdgdiff searched for differential peaks between groups, and finally IGV was used for visualization.

[0027] Public data: Publicly available RNA-seq data were downloaded from the NCBI Gene Expression Omnibus (GEO, https: / / www.ncbi.nlm.nih.gov / geo) dataset. The GSE84005 dataset collected 38 pairs of tumor tissues and adjacent non-tumor tissues from patients with liver cancer. GSE36376 contains 240 HCC tissues and 193 adjacent non-tumor liver tissues. GSE64041 contains 60 pairs of hepatocellular carcinoma biopsies (1 tumor biopsy and 1 non-tumor liver biopsy per patient) and 5 normal liver biopsies, with a total of 125 samples. The table matrix files of the above datasets were downloaded using the GEOquery package in R (v4.0.2). The probe IDs were mapped to gene names using the Soft format file to obtain the expression matrix of all genes in each sample. Then ggplot was used to analyze the expression changes of HHAT and TTLL7 genes between normal and tumor samples in each dataset. The Barcelona (BCLC) staging system is considered to be the best staging system and has been confirmed in a large number of clinical studies. The BCLC liver cancer clinical staging can be divided into five categories: the earliest stage, early stage, middle stage, late stage and terminal stage. The early stage can be divided into four subgroups. It has a strong ability to classify and predict prognosis. By monitoring high-risk groups, it can identify early liver cancer patients for diagnosis and treatment. The most important thing is that BCLC proposes different treatment methods for different patients, which is unmatched by other staging systems. We downloaded the clinical data of Barcelona in the early and late stages and analyzed the expression changes of HHAT and TTLL7 in the two periods. All box plots are generated by the functions ggplot and ggpubr in the ggplot2 package. The comparison function in the ggpubr package was used for significant statistical tests.

[0028] Spatial transcriptome analysis: We used the HCCDB liver cancer database (http: / / lifeome.net:809 / # / home) to quantitatively analyze the expression changes of HHAT and TTLL7 genes in tumors and normal cells at the spatial level. Hematoxylin-eosin staining (HE staining) is one of the most commonly used staining methods in paraffin section technology. Hematoxylin staining is alkaline, which mainly stains the chromatin in the cell nucleus and the nucleic acid in the cytoplasm purple-blue; eosin is an acidic dye that mainly stains the components in the cytoplasm and extracellular matrix red. The location of the cell nucleus and cytoplasm is checked by hematoxylin-eosin staining, and then the expression level of each location is analyzed by spatial transcriptome. Corresponding to the location of tumor and normal cells, the location of HHAT and TTLL7 in the tumor and the abundance of gene expression in that location can be quantitatively analyzed.

[0029] Survival analysis: GDC TCGA liver cancer cohort and clinical survival data were obtained from the UCSC Xena database (http: / / xena.ucsc.edu / ). The patients were divided into high expression group and low expression group according to the median TTLL7 expression level. Kaplan-Meier survival analysis and log-rank test were then performed. The risk table and p-value were calculated to evaluate the statistical significance of the survival difference. Boxplot was used to draw a boxplot to show the expression distribution of genes in different groups.

[0030] Experimental Results

[0031] The present invention uses high-throughput multi-omics sequencing data to find that the three-dimensional structures of HHAT and TTLL7 change in the human normal liver cell line L02 and the human liver cancer cell line Hep3B. Hi-C sequencing technology was used to conduct an in-depth analysis of the A and B compartments. Figure 1 As shown in A, the upper left corner represents the interaction strength of the B compartment in the genome, while the lower right corner shows the interaction strength of the A compartment. The analysis results show that the liver cancer cell line Hep3B has undergone significant compartmental rearrangement in compartmental interaction compared with the normal liver cell line L02. After counting the proportion of changes in the AB compartments, Figure 1B shows that during the occurrence of liver cancer, 18.7% of the proportion shifted from compartment A to compartment B, and 17.7% of the proportion shifted from compartment B to compartment A. This process revealed that some genes that were not actively expressed in the normal group showed high expression in the disease group Hep3B. Subsequently, in order to explore which genes were involved in chromatin transformation and caused the development of the disease during the disease process, ATAC-seq was performed for chromatin openness sequencing analysis. It was found that compared with L02, 26943 peaks in Hep3B had increased chromatin openness and 24422 peaks had decreased chromatin openness. H3K27ac refers to the acetylation modification on the 27th lysine of histone H3. This modification is usually associated with active gene transcription, especially in enhancer regions. H3K27ac marks the regulatory elements of actively expressed genes in cells. Therefore, the CUT&Tag technology was used to observe the changes in H3K27ac histone modification in the two groups of cell lines. The results showed that compared with L02, 4267 peaks in Hep3B were enhanced by H3K27ac modification, and 16362 peaks showed weakened H3K27ac modification. The above multi-omics were jointly analyzed to find genes with the following common characteristics: 1. There were significant changes in the A and B compartments in the Hi-C three-dimensional genome; 2. ATAC-seq showed that the chromatin openness of the gene had changed significantly; 3. The histone modification of H3K27ac had changed significantly. The threshold for significant changes was defined as the difference between the two groups was greater than 2, and the significant difference P value was less than 0.05. The three groups of sequencing results that met the above characteristics were intersected, and it was found that HHAT( Figure 2 A, B) and TTLL7( Figure 2 C, D) These two genes undergo compartmental changes, enhanced transcriptional activation modifications, and increased chromatin accessibility in liver cancer cell lines, i.e., altered chromatin states.

[0032] In order to verify whether the final transcription level of the gene has changed, we searched for three sets of chip sequencing data (GSE84005, GSE36376 and GSE64041) in the NCBI GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ) to detect the gene expression changes of HHAT and TTLL7. All three sets of data sets contain a large number of patient tumor tissues and normal tissues. The results showed that the TTLL7 gene is highly expressed in liver cancer ( Figure 3 A). High expression of this gene has a poor prognosis for the patient's survival ( Figure 3B), indicating that high expression of the TTLL7 gene promotes the development of liver cancer, exacerbates the disease, and is not conducive to patient survival. BCLC staging, the Barcelona Clinic Liver Cancer Staging System, is a staging system widely used to evaluate hepatocellular carcinoma (HCC). In the BCLC staging data, TTLL7 gene was also detected in the late stage ( Figure 3 C), further indicating that the TTLL7 gene is involved in the progression of liver cancer. In addition, based on the latest spatial transcriptome technology, we once again verified the expression comparison of TTLL7 in tumors and normal parts at the spatial level. The results showed that TTLL7 was more abundantly expressed in tumors ( Figure 3 D) The results confirmed that it promotes the growth of liver cancer tumors.

[0033] Similarly, in order to verify the expression changes of HHAT gene in liver cancer, the expression level of this gene in the above three groups of chip data was detected. The results showed that HHAT was highly expressed in liver cancer ( Figure 4 A), and compared with normal tissue, there is a gradual increase in hepatitis, cirrhosis, early liver cancer and late liver cancer ( Figure 4 B). Vascular invasion refers to the invasion of tumor cells into blood vessels, the formation of tumor thrombi or the growth of tumor cells in blood vessels, which may then spread to other parts of the body through blood circulation. It is one of the important indicators for judging the malignancy and prognosis of tumors in clinical practice. It is common in many solid tumors, such as liver cancer, lung cancer, gastric cancer, etc. We observed that HHAT was highly expressed in the vascular invasion group ( Figure 4 C), indicating that HHAT is involved in the progression of the disease. In addition, from more data (GSE89377 and GSE5238), it was found that the expression of HHAT in cirrhosis, early and late liver cancer was significantly higher than that in the normal group, further verifying that HHAT is involved in the development of liver cancer ( Figure 4 D, E). In addition, based on the latest spatial transcriptome technology, we once again verified the expression comparison of HHAT in tumors and normal parts at the spatial level. The results showed that HHAT was more abundantly expressed in tumors ( Figure 4 F) The results confirm that HHAT promotes the growth of liver cancer tumors.

[0034] TTLL7 (tubulin tyrosine ligase-like 7) is an enzyme related to the tubulin tyrosine ligase family, which plays a key role in various biological processes in cells. Current studies have revealed the potential role of TTLL7 in tumor development, but its exact mechanism is still unclear. The HHAT gene (Human Homogentisate 1,2-dioxygenase gene) encodes homocysteine ​​dioxygenase, an enzyme involved in the dopamine metabolic pathway. It plays an important role in humans and other mammals, especially in neurotransmitter synthesis and cellular antioxidant defense. The enzyme encoded by the HHAT gene catalyzes the conversion of 3,4-dihydroxyphenylacetic acid (DOPAC) to homovanillic acid (HPAA), which is a key step in the degradation of dopamine. Due to its role in the metabolic pathway, drug development targeting HHAT may help treat certain diseases, such as Parkinson's disease or other conditions involving dysregulation of the dopamine signaling pathway. This multi-omics joint analysis proved that HHAT and TTLL7 play an important role in the occurrence and development of liver cancer. Knocking down the gene can inhibit its role in liver cancer, thereby delaying the development of the disease, providing potential therapeutic targets for the treatment of liver cancer.

[0035] For matters not covered in the embodiments of the present invention, those skilled in the art can select from the prior art.

Claims

1. A kit for diagnosing liver cancer, comprising primers for detecting the expression levels of HHAT gene and TTLL7 gene.

2. A drug for treating liver cancer, comprising a kit for knocking out or knocking down the expression of HHAT gene and TTLL7 gene.

3. Application of HHAT gene and TTLL7 gene in the preparation of a diagnostic kit for liver cancer.

4. The use according to claim 3, wherein the kit is used to evaluate the progression of liver cancer.

5. Application of HHAT gene and TTLL7 gene in the preparation of drugs for the treatment of liver cancer.

6. The use according to claim 5, wherein the drug is used to knock out or knock down the expression of HHAT gene and TTLL7 gene.

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