Application of hhats gene and ttll7 gene in diagnosis and treatment of liver cancer

Abstract

The application discloses application of HHAT gene and TTLL7 gene in diagnosis and treatment of liver cancer, and relates to the technical field of biotechnology, and discloses new prospects of HHAT gene and TTLL7 gene in human genome for the first time, reveals that the occurrence and development of liver cancer are caused by changes in three-dimensional structure and changes in chromatin openness, and through targeting the gene, the gene can become a therapeutic target for diagnosis or treatment of liver cancer.

Description

Technical Field

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

[0002] In recent years, with the continuous improvement of multi-omics sequencing capabilities, it has become possible to resolve the previously difficult-to-detect, pervasive three-dimensional genome remodeling and chromatin remodeling phenomena. Currently, the treatment of liver cancer has evolved from single-agent targeted therapy (Sorafenib and Lenvatinib) to systemic therapy combining checkpoint inhibitors and targeted therapy (atezolizumab combined with bevacizumab). Despite significant progress, only a small percentage of patients experience durable clinical benefits; therefore, substantial treatment challenges remain.

[0003] Proper chromatin folding determines appropriate gene expression. Hi-C, a genome-wide chromosome conformation capture assay, reveals the three-dimensional folding of the human genome within the cell nucleus, dividing the entire genome into two spatial compartments, called compartment A and compartment B, labeled A and B compartments. Interactions within compartments are often frequent, while interactions between compartments are less frequent. Compartment A, consisting of open chromatin, is actively expressed, gene-rich, has a high GC content, contains histone markers for active transcription, and is typically located in the inner nucleus. Compartment B, typically representing closed chromatin, is inactively expressed, gene-poor, compact, contains histone markers for gene silencing, and is located on the periphery of the nucleus. They are primarily composed of lamina-associating domains (LADs), containing late replication origins. In bioinformatics analysis, the correlation of intrachromosomal interactions is used to distinguish between the two compartments. Subsequent analysis showed that the compartments are divided into approximately 1 Mb units, termed "topologically associated domains" (TADs). These regions are generally conserved across different cells in different mammals and are highly enriched with CTCF and adhesion proteins. Chromatin forms loop structures in space, allowing geographically distant chromatin regions to aggregate in three dimensions. Previous studies have shown that aberrant chromatin interactions contribute to tumorigenesis. Characterization of three-dimensional epigenomic features provides a more detailed epigenetic mechanism for the development and progression 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. CUT&Tag, in particular, is a novel technique for studying protein-DNA interactions, capable of detecting DNA fragments interacting with histones, transcription factors, etc., across the entire genome, effectively analyzing chromatin-binding elements in cells. This technique helps identify cell type-specific or common enhancers, and then Hi-C loop data can be used to search for 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 locus localization, and TF occupancy analysis. ATAC-seq data can be jointly analyzed with histone modification CUT & Tag data. Transcriptional activating modifications (H3K4me3, H3K4me1, and H3K27ac, etc.) are positively correlated with chromatin openness, while transcriptional repressive modifications (H3K27me3) are negatively correlated with chromatin openness. Combining known enhancer and promoter interaction data can also help construct regulatory networks.

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

[0005] Therefore, this invention relates to the application of the HHAT gene and TTLL7 gene in the diagnosis and treatment of liver cancer. To achieve the above objectives, this invention mainly provides the following technical solutions:

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

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

[0008] Another aspect of the present invention relates to the application of the HHAT gene and the TTLL7 gene in the preparation of a kit for the diagnosis of liver cancer; preferably, the kit is used to assess the progression of liver cancer.

[0009] Another aspect of the present invention relates to the use of the HHAT gene and the 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 the HHAT gene and the TTLL7 gene.

[0010] Compared with the prior art, the beneficial effects of the present invention are:

[0011] This invention relates to the field of biotechnology, specifically disclosing new prospects for the HHAT and TTLL7 genes in the human genome, revealing that they cause the occurrence and development of liver cancer through changes in their three-dimensional structure and chromatin openness. Targeting these genes can serve as therapeutic targets for the diagnosis or treatment of liver cancer. Attached Figure Description

[0012] Figure 1 The interaction strength and three-dimensional chromatin structure of the L02 hepatocyte cell line and the Hep3B hepatocyte cancer cell line change. (A) The interaction strength between the L02 normal hepatocyte cell line and the Hep3B hepatocyte cancer cell line in the three-dimensional genome of the A and B compartments. (B) Examples of compartment A to compartment B and compartment B to compartment A are marked with pink boxes. (C) The ratio of changes in the A / B compartment state within the genome.

[0013] Figure 2The diagram shows the splitting (between dashed lines) of the contact domains around the HHAT locus and TTLL7 in L02 and Hep3B cells, and the switching of chromatin states (shown by Hi-C plots and histone cut & tag trajectories). (A) Contact domains showing changes in chromatin state around the HHAT locus in L02 and Hep3B cell lines. (B) Changes in chromatin openness and H3K27ac histone modification of HHAT between the L02 and Hep3B groups. (C) Contact domains showing changes in chromatin state around the TTLL7 locus. (D) Changes in chromatin openness and H3K27ac histone modification of TTLL7 between the L02 and Hep3B groups.

[0014] Figure 3 TTLL7 is highly expressed in hepatocellular carcinoma (HCC) and disease progression, and high expression is detrimental to survival. (A) Box plots of TTLL7 expression levels in normal tissue and HCC across three datasets. (B) Kaplan-Meier survival curves of overall survival for TTLL7 expression in the TCGALIHC dataset. (C) Box plots of TTLL7 expression levels in early and late-stage BCLC. (D) Spatial validation that TTLL7 expression is higher in tumors than in normal tissues.

[0015] Figure 4 HHAT is highly expressed in tumors in microarray datasets, showing progressively increasing expression across different HCC progressions, and is also highly expressed in metastases. (A) Box plot of HHAT expression changes in the tumor and normal groups of three microarray datasets. (B) Gradually increasing HHAT expression in hepatitis, degenerative nodules, early-stage HCC, and late-stage HCC in the GSE114564 dataset. (C) High expression of HHAT in vascular invasion. (D) Box plot of HHAT expression changes in cirrhosis, early-stage HCC, and late-stage HCC in the GSE89377 and (E) GSE54238 datasets. (F) Spatial validation that HHAT expression is higher in tumor tissues than in normal tissues. Detailed Implementation

[0016] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following preferred embodiments are provided to describe in detail the specific implementation methods, technical solutions, features, and effects according to the present invention. Specific features, structures, or characteristics in the various embodiments described below can be combined in any suitable form.

[0017] Example 1

[0018] Experimental methods:

[0019] Hi-C Data Analysis: The raw Hi-C fastq files were filtered, mapped, and the raw contact count matrix was created using HiC-Pro. Iterative correction was used to normalize the matrix. AB compartment analysis was performed using HOMER tools (http: / / homer.ucsd.edu / homer / index.html) and 100kb bins. Comparing the mean observed / expected values ​​between active and inactive chromatin is a useful way to measure compartmentalization strength; this was then measured using the `saddle_strength` function, followed by visualization of the eigenvector capture preferences using cooltools. The correlation matrix was compared with the `getHiCcorrDiff.pl` script provided by HOMER to identify A-B compartment switching. The percentage change was statistically analyzed using R.

[0020] Cell culture:

[0021] L02 cells (normal human hepatocytes) and Hep3B human hepatocellular carcinoma cells were preserved 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). Mycoplasma contamination was tested in all cell lines.

[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 (Sigama, #93289), according to the manufacturer's protocol. 1 μg of total RNA was reverse transcribed into cDNA using qRT-PCR (+gDNAwiper) with a HiScript III RT SuperMix (Vazyme, #R323). qRT-PCR was performed on a CFX96™ Real-Time System (Bio-RAD, #CT032238). Each reaction was detected in a 20 μL reaction mixture containing an 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] The CUT&Tag assay was performed according to previous descriptions, modified using Illumina's Hyperactive Universal CUT&Tag Detection Kit (Vazyme Biotech, #TD903). Simply put, wash 1×10⁻⁶ cells with 500 μl of washing buffer. 5Centrifuge cells at 600X g for 5 minutes at room temperature. Resuspend cell microspheres in 100 μl of wash buffer. Wash 10 μl of magnetic beads coated with concanavalin A twice with 100 μl of binding buffer, then add to cell tubes and incubate at room temperature for 10–15 min. After removing the supernatant, resuspend the microsphere-bound cells in 2 μg of rabbit anti-H3k27ac antibody and 50 μl of antibody buffer. After incubating at room temperature for 2 hours or overnight at 4°C, carefully discard the primary antibody. Dilute with 50 μl of Dig-wash buffer. Then incubate the cells by rotation at room temperature for 1 hour. Then gently wash the cells three times with 200 μl of Dig-wash buffer, and add 2 μl of pA / G-Tnp and 98 μl of Dig-300 buffer to the sample. After incubating at room temperature for 1 hour, gently wash the sample three times with 200 μl of Dig-300 buffer. Next, add 10 μl of 5X TTBL to each sample and mix with 40 μl of Dig-300 buffer, then incubate the sample at 37°C for 1 hour. Add 5 μl of 20 mg / ml proteinase K, 100 μl of buffer L / B, and 20 μl of DNA extraction beads, then incubate the sample at 55°C for 10 minutes to quench interactions. Discard the supernatant, wash the magnetic beads once with 200 μl of buffer WA, wash twice with 200 μl of buffer WB, and resuspend in 22 μl of nuclease-free water. For library amplification, mix 15 μl of purified DNA with 25 μl of 2XCAM and 5 μl of unique barcode i5 and i7 primers from the TruePrep Index Kit V2 for Illumina (Vazyme Biotech, #TD202). The following procedure was used to place 50 μl of sample in a thermal cycler (BIO-RAD, #T100): 72°C for 3 min; 98°C for 3 min; 98°C for 10 s, 60°C for 5 s, and 72°C for 1 min, for 12–16 cycles; and maintained at 12°C. To purify the PCR products, 2X volumes of VAHTSDNAClean Beads (Vazyme Biotech, #N411) were added and incubated at room temperature for 5 min. The beads were washed twice with 200 μl of fresh 80% ethanol and eluted in 22 μl of ddH2O. All CUT & Tag libraries were sequenced by Novogene using the Illumina NovaSeq 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 sequencing data was quality controlled and filtered using trim_galore, then bowtie2 was used to align the clean data to the human reference genome (hg19 version), picard was used to remove repetitive PCR sequences, and samtools was used to convert SAM files to BAM files. MACS2 was used for peak finding, macs2bdgdiff was used to find peaks of difference between groups, and finally IGV was used for visualization.

[0027] Publicly available data: 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 hepatocellular carcinoma (HCC) patients. GSE36376 contained 240 HCC tissues and 193 adjacent non-tumor liver tissues. GSE64041 contained 60 pairs of HCC biopsies (one tumor biopsy and one non-tumor liver biopsy per patient) and 5 normal liver biopsies, totaling 125 samples. The tabular 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 analysis was used to analyze the expression changes of HHAT and TTLL7 genes between normal and tumor samples in each dataset. The Barcelona Cognitive Susceptible Cell Lung Cancer (BCLC) staging system is considered the best staging system and has been validated in numerous clinical studies. The BCLC (Brain-Based Clinical Lung Cancer) staging system classifies liver cancer into five stages: very early, early, intermediate, advanced, and terminal. The early stage stage is further divided into four subgroups, providing strong classification and prognostic capabilities. Monitoring high-risk populations allows for the identification and treatment of early-stage liver cancer patients. Most importantly, BCLC proposes differentiated treatment approaches for different patients, a feature unmatched by other staging systems. We downloaded early and late-stage clinical data from the Barcelona Clinic and analyzed the expression changes of HHAT and TTLL7 in both stages. All boxplots were generated using the `ggplot` and `ggpubr` functions from the `ggplot2` package. Significance tests were performed using the comparison function from the `ggpubr` package.

[0028] Spatial Transcriptome Analysis: We used the HCCDB liver cancer database (http: / / lifeome.net:809 / # / home) to quantitatively analyze the spatial expression changes of HHAT and TTLL7 genes in tumors and normal cells. Hematoxylin-eosin staining (HE staining) is one of the most commonly used staining methods in paraffin sectioning. Hematoxylin is alkaline, primarily staining chromatin in the cell nucleus and nucleic acids in the cytoplasm a purple-blue color; eosin is an acidic dye, primarily staining components in the cytoplasm and extracellular matrix red. Hematoxylin-eosin staining was used to examine the location of the cell nucleus and cytoplasm, followed by spatial transcriptome analysis to determine the expression levels at each location. Corresponding to the locations of tumor and normal cells, we can quantitatively analyze the location of HHAT and TTLL7 in the tumor and the abundance of gene expression at that location.

[0029] Survival analysis: GDC TCGA hepatocellular carcinoma cohort and clinical survival data were obtained from the UCSC Xena database (http: / / xena.ucsc.edu / ). Patients were divided into high-expression and low-expression groups based on the median TTLL7 expression level. Kaplan-Meier survival analysis and log-rank tests were then performed. Hazard tables and p-values ​​were calculated to assess the statistical significance of survival differences. Boxplots were used to display the gene expression distribution in different groups.

[0030] Experimental results

[0031] This invention utilizes high-throughput multi-omics sequencing data to discover changes in the three-dimensional structures of HHAT and TTLL7 in the normal human hepatocyte cell line L02 and the human hepatocellular carcinoma cell line Hep3B. Hi-C sequencing technology was used for in-depth analysis of compartments A and B. Observation Figure 1 As shown in Figure A, the upper left corner represents the interaction strength of compartment B within the genome, while the lower right corner shows the interaction strength of compartment A. The analysis results indicate that the Hep3B liver cancer cell line, compared to the normal hepatocyte line L02, exhibits significant compartment rearrangement in compartment interactions. After statistically analyzing the proportion of changes in compartments A and B, from... Figure 1As shown in Figure B, during the development of liver cancer, 18.7% of cases involved a transition from compartment A to compartment B, while 17.7% involved a transition from compartment B to compartment A. This process reveals that some genes that are not actively expressed in the normal group are highly expressed in the disease group Hep3B. Subsequently, to investigate which genes are involved in chromatin transitions and contribute to disease progression, ATAC-seq was performed for chromatin openness sequencing analysis. This revealed that compared to L02, 26,943 peaks in Hep3B showed increased chromatin openness, while 24,422 peaks showed decreased chromatin openness. H3K27ac refers to the acetylation modification at lysine 27 of histone H3. This modification is typically associated with active gene transcription, particularly in enhancer regions. H3K27ac marks regulatory elements of actively expressed genes within the cell. Therefore, CUT&Tag technology was used to observe changes in H3K27ac histone modification in both cell lines. The results showed that compared to L02, 4267 peaks in Hep3B were enhanced by H3K27ac modification, while 16362 peaks showed weakened H3K27ac modification. A combined multi-omics analysis was performed to identify genes meeting the following common characteristics: 1. Significant changes in compartments A and B of the Hi-C three-dimensional genome; 2. ATAC-seq showed a significant change in the chromatin openness of this gene; 3. Significant changes in histone modification of H3K27ac. The threshold for significant change was defined as a fold change greater than 2 between two groups with a p-value less than 0.05. The intersection of the sequencing results of three groups meeting the above characteristics revealed that HHAT( Figure 2 A,B) and TTLL7( Figure 2 In hepatocellular carcinoma cell lines, the C and D genes undergo compartmental changes, transcriptional activating modifications are enhanced, and chromatin accessibility is increased, indicating a change in chromatin state.

[0032] To verify whether the final transcription level of this gene changed, we searched for three sets of microarray sequencing data (GSE84005, GSE36376, and GSE64041) in the NCBI GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ) to detect changes in HHAT and TTLL7 gene expression. All three datasets contained a large amount of patient tumor tissue and normal tissue. The results showed that the TTLL7 gene was highly expressed in liver cancer. Figure 3 A). High expression of this gene is associated with poor patient survival prognosis. Figure 3B) indicates that high expression of the TTLL7 gene promotes the development of liver cancer, exacerbates disease progression, and is detrimental to patient survival. The BCLC staging system, or Barcelona Clinical Hepatocellular Carcinoma Staging System, is a widely used staging system for assessing hepatocellular carcinoma (HCC). High expression of the TTLL7 gene was also detected in late-stage BCLC staging data. Figure 3 C), further demonstrating that the TTLL7 gene is involved in the progression of liver cancer. Additionally, based on the latest spatial transcriptomics technology, we again validated the spatial expression comparison of TTLL7 in tumor and normal sites, finding that TTLL7 is more abundant in tumors (C). Figure 3 D). The results confirmed that it promotes the growth of liver cancer tumors.

[0033] Similarly, to verify the changes in HHAT gene expression in liver cancer, the expression level of this gene was detected in the three sets of microarray data mentioned above. The results showed that HHAT was highly expressed in liver cancer. Figure 4 A), and compared to normal tissue, there is a gradual increasing process in hepatitis, cirrhosis, early-stage liver cancer, and late-stage liver cancer. Figure 4 B). Vascular invasion refers to the invasion of tumor cells into blood vessels, forming tumor emboli or the growth of tumor cells within blood vessels, which may then spread to other parts of the body via the bloodstream. Clinically, it is one of the important indicators for assessing the malignancy and prognosis of tumors. It is commonly seen in various solid tumors, such as liver cancer, lung cancer, and gastric cancer. We observed that HHAT was highly expressed in the vascular invasion group ( Figure 4 C) indicates that HHAT is involved in disease progression. Furthermore, additional data (GSE89377 and GSE5238) revealed significantly higher HHAT expression levels in cirrhosis, early-stage liver cancer, and late-stage liver cancer compared to the normal group, further validating HHAT's involvement in liver cancer development. Figure 4 D, E). Furthermore, based on the latest spatial transcriptomics technology, we again validated the spatial expression comparison of HHAT in tumor and normal sites, and the results showed that HHAT was expressed more abundantly in tumors (D, E). Figure 4 F). The results confirmed that HHAT promotes the growth of liver cancer tumors.

[0034] TTLL7 (tubulin tyrosine ligase-like 7) is an enzyme associated with the tubulin tyrosine ligase family and plays a crucial role in various cellular biological processes. Current research reveals a potential role for TTLL7 in tumor development, although the exact mechanism remains unclear. The HHAT gene (Human Homogentisate 1,2-dioxygenase gene) encodes homocysteine ​​dioxide enzyme, an enzyme involved in the dopamine metabolic pathway. It plays an important role in humans and other mammals, particularly 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), a key step in dopamine degradation. Due to its role in metabolic pathways, 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 analysis demonstrates that HHAT and TTLL7 play crucial roles in the development and progression of liver cancer. Knocking down these genes can inhibit their effects in liver cancer, thereby slowing disease progression and providing potential therapeutic targets for liver cancer treatment.

[0035] Any aspects not covered in the embodiments of this invention can be selected from the prior art by those skilled in the art.

Claims

1. Application of reagents for detecting HHAT and TTLL7 genes in the preparation of diagnostic kits for liver cancer.

2. The application according to claim 1, wherein the kit is used to assess the progression of liver cancer.

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