Application of ADRA1A as anti-liver cancer diagnosis and treatment target

Abstract

The invention discloses application of ADRA1A as an anti-liver cancer diagnosis and treatment target. The invention provides application of an ADRA1A gene or protein in preparation of a product for diagnosing and / or treating liver cancer. The diagnosis comprises diagnosis for distinguishing liver cancer patients from healthy people, liver cancer staging and grading diagnosis, liver cancer malignancy degree diagnosis, liver cancer lymph node metastasis diagnosis, liver cancer vascular invasion and liver cancer prognosis diagnosis. According to the application disclosed by the invention, the effect of the neurotransmitter receptors in liver cancer development is researched, large-sample liver cancer sequencing data analysis is taken as an entry point, the influence of various neurotransmitter receptors on liver cancer prognosis is comprehensively analyzed, and the receptor subtype gene ADRA1A which remarkably influences liver cancer prognosis and has the most clinical transformation potential is screened out from the analysis point. The invention provides a new thought and target for clinical diagnosis and treatment of liver cancer.

Description

Technical Field

[0001] The present invention relates to application of ADRA1A as an anti-liver cancer diagnosis and treatment target, and belongs to the technical field of biomedicine. Background Art

[0002] Hepatocellular carcinoma (HCC) is a malignant tumor that originates from liver cells and is the main histological subtype of liver cancer. Hepatocellular carcinoma is one of the malignant tumors with a high incidence worldwide, especially in Asia, Africa and southern Europe. It accounts for 90% of primary liver cancer and is the third most common cause of cancer-related mortality worldwide. The early symptoms of hepatocellular carcinoma are not obvious, and most patients are diagnosed in the middle and late stages.

[0003] High heterogeneity, lack of biomarkers for early diagnosis and prediction of prognosis, high tumor resistance, high inhibitory tumor immune microenvironment, and deviation in the effect of immunotherapy make the early diagnosis, treatment and postoperative management of HCC difficult and complicated. So far, the main treatments for HCC include surgical resection, liver transplantation and local ablation, chemotherapy, and immunotherapy. However, the recurrence and metastasis rates of HCC after treatment are very high, which greatly affects the prognosis of HCC patients. Studies have shown that the 5-year survival rate of non-metastatic local HCC is about 32%, while that of patients with distant metastasis drops sharply to 2%. In addition, studies have shown that most patients with liver cancer have missed the optimal treatment time window when liver cancer is diagnosed, and cannot be effectively surgically resected, resulting in high postoperative recurrence rate and high mortality rate. Therefore, high recurrence rate and low survival rate remain the main unmet medical needs in the clinical treatment of liver cancer.

[0004] The tumor microenvironment plays a crucial role in regulating the growth and metastasis of tumor cells. It mainly includes various infiltrating immune cells, fibroblasts, vascular endothelial cells, nerve fibers, and various cytokines and chemokines, which together regulate the growth and metastasis of tumor cells. As an important medium for transmitting neural signals, more and more studies have found that neurotransmitters also play a key role in the ecology of the tumor microenvironment. According to different chemical structures and properties, neurotransmitters can be divided into the following four main types: (1) acetylcholine (Ach); (2) amino acids, including glutamate, glycine and gamma-aminobutyric acid (GABA); (3) biogenic amines, including dopamine, norepinephrine (NE), epinephrine (E) and 5-hydroxytryptamine (5-HT); and (4) neuropeptides, including but not limited to neuropeptide Y (NPY), neurotensin, etc. Correspondingly, neurotransmitter receptors mainly include the following 8 categories according to their ligands: adrenergic receptors, glutamatergic receptors, glycinergic receptors, dopaminergic receptors, histaminergic receptors, 5-HT receptors, GABAergic receptors and acetylcholinergic receptors. Neurotransmitters are important neural signal messengers. Studies have shown that neurotransmitters and their receptors can regulate tumor proliferation, tumor angiogenesis and tumor metastasis through various mechanisms. Related studies have also shown that cancer cells can not only produce and secrete neurotransmitters, but also that neurotransmitter receptors are widely expressed in cancer cells. Therefore, clarifying the key role of neurotransmitter receptors expressed by tumor cells themselves in regulating tumor growth will help us better understand the regulation of tumors by the body's nervous system and provide ideas for finding new potential therapeutic targets.

[0005] Related studies have demonstrated that the role of neurotransmitter receptors in HCC is complex. Taking dopamine receptors as an example, Zhang QB et al. reported that dopamine produced under moderate-intensity swimming conditions can significantly inhibit the invasion of HCC cells both in vitro and in vivo. However, other studies have shown that patients with high expression of dopamine receptor 1 (DRD1) have a worse prognosis, and dopamine and its receptors can promote tumor progression through the activation of the PI3K / AKT signaling pathway. In addition, more and more studies are focused on exploring the interaction between adrenergic receptors and HCC progression. A study by Liu et al. showed that a comfortable environment can induce significant anti-tumor effects in the body and increase the sensitivity of HCC patients to immune checkpoint therapy by regulating the β-adrenergic receptor / CCL2 axis. However, some studies have also reported that activation of the adrenergic receptor pathway may significantly promote the development of liver cancer. For example, Wu et al. reported that β2-adrenergic receptors promote HCC progression and sorafenib resistance by inhibiting the autophagic degradation of hypoxia-inducible factor-1α (HIF-1α). Therefore, more comprehensive exploration and analysis are still needed on the role of the adrenergic receptor pathway in the occurrence and development of liver cancer.

[0006] In view of this, this application intends to conduct research on the role of neurotransmitter receptors in the development of liver cancer, and use large-sample liver cancer sequencing data analysis as a starting point to comprehensively analyze the effects of various neurotransmitter receptors on the prognosis of liver cancer, and screen out receptor subtype genes that significantly affect the prognosis of liver cancer and have the greatest clinical conversion potential, providing new ideas and targets for clinical liver cancer diagnosis and treatment. Summary of the invention

[0007] The purpose of the present invention is to address the deficiencies of existing liver cancer diagnosis and treatment methods and to provide application of ADRA1A as an anti-liver cancer diagnosis and treatment target.

[0008] In order to achieve the above-mentioned object, the present invention provides the use of ADRA1A gene or protein in the preparation of a product for diagnosing and / or preventing and / or treating liver cancer, characterized in that the diagnosis includes the diagnosis of distinguishing liver cancer patients from healthy people, the staging and grading diagnosis of liver cancer, the diagnosis of the malignancy of liver cancer, the diagnosis of lymph node metastasis of liver cancer, the diagnosis of vascular invasion of liver cancer and the prognosis of liver cancer.

[0009] Preferably, the product for diagnosing liver cancer comprises a diagnostic reagent and / or a kit, and the reagent and / or the kit at least comprises a reagent for detecting the expression level of the ADRA1A gene or protein.

[0010] Preferably, the reagents include reagents required for detecting the expression level of ADRA1A gene by RT-qPCR method.

[0011] Preferably, the reagents include reagents required for detecting the expression level of ADRA1A protein by Western blotting or immunohistochemical staining.

[0012] Preferably, the product for preventing or treating liver cancer comprises a pharmaceutical composition or preparation, wherein the pharmaceutical composition or preparation comprises an active ingredient and a pharmaceutically acceptable carrier, wherein the active ingredient is the ADRA1A gene, protein or a promoter thereof.

[0013] The promoter refers to a substance that can improve / promote the activity and / or content and / or expression level of the ADRA1A gene or its protein in vivo or in vitro; the substance can be an artificially synthesized or natural compound, protein, nucleotide, etc.

[0014] Preferably, the ADRA1A promoter is selected from the following group: a small molecule compound, a vector expressing ADRA1A, an ADRA1A protein agonist, or a combination thereof.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1) As an emerging research field, neuro-tumor research has potential clinical application prospects and research value. The present invention is based on the latest scientific frontiers, explores the role of neurotransmitter receptor-related genes in the occurrence and development of liver cancer, and verifies its therapeutic effect through in vivo animal experiments, which is innovative and pioneering.

[0017] 2) Immunotherapy is currently one of the important means of treating mid- and late-stage malignant tumors, and plays an important role in improving patient prognosis and prolonging patient survival time; however, the effectiveness of immunotherapy for liver cancer has always been low, and there is an urgent need to find targets and mechanisms to improve the immunotherapy effect of liver cancer; in view of this, the present invention discovered and verified the effect of ADRA1A on tumor immune cell infiltration and tumor immune microenvironment, and the present invention provides a potential immunotherapy target for the treatment of liver cancer, or helps to improve the immunotherapy effect of liver cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Analysis of pan-neurotransmitter receptor gene expression changes in liver cancer, including: A. Volcano plot of expression changes of 107 neurotransmitter receptors in liver cancer (the change fold is the ratio of the median expression of the gene in cancer and adjacent cells. If the median expression of the gene in adjacent cells or liver cancer is 0, the gene is considered to be extremely low in expression and the change fold cannot be calculated, so it will not be displayed in the volcano plot); B. Comparison of the expression levels of the adrenergic receptor subunit gene ADRA1A in liver cancer and adjacent cells; ADRA1A was significantly downregulated in liver cancer; ***P<0.001.

[0019] Figure 2The expression changes of adrenergic family genes in liver cancer, including: A. Volcano plot of the expression changes of 9 adrenergic receptors in liver cancer; B. Comparison of the expression of adrenergic receptor genes in liver cancer and adjacent tissues; *P<0.05, **P<0.01, ***P<0.001.

[0020] Figure 3 Analysis of ADRA1A expression changes in multiple liver cancer databases, including: A. Analysis of ADRA1A expression in paired liver cancer database (cancer and adjacent liver cancer cells were from the same patient); B. Analysis of ADRA1A expression in unpaired liver cancer database (cancer and adjacent liver cancer cells were from different patients); ***P<0.001.

[0021] Figure 4 Comparison of ADRA1A expression in different tumors and normal tissues, including: A. Comparison of ADRA1A expression in different types of tumors and adjacent tissues; B. Comparison of ADRA1A expression in different normal tissues of humans; ***P<0.001.

[0022] Figure 5 This is the analysis of mutation characteristics and sCNA (Somatic Copy Number Alterations) of ADRA1A in liver cancer and other tumors, including: A. Comparison of mutation characteristics of ADRA1A in different types of tumors; B. Comparison of mutation characteristics of 9 adrenergic receptor subunits in liver cancer; C. Comparison of gene mutation landscapes of each group under different ADRA1A expression levels. Among them, only the top 10 significantly mutated genes are shown, and the corresponding statistical P values ​​are listed after the gene name. Cases without gene mutations will not be shown in the figure; D. Comparison of sCNA of ADRA1A in different types of tumors.

[0023] Figure 6 This is a pan-neurotransmitter receptor gene survival analysis, including: A. Pan-neurotransmitter receptor gene expression changes and survival analysis volcano plot; B. Survival analysis of ADRA1A in the TCGA-LIHC liver cancer database and the ICGC-LIRI-JP liver cancer database; It shows that the overall survival rate of patients with high expression of ADRA1A is significantly higher.

[0024] Figure 7 The expression level of ADRA1A was significantly negatively correlated with the clinical progression indicators of liver cancer patients, including: A. Comparison of ADRA1A expression level in liver cancer with lymph node metastasis, tumor grade and stage in TCGA-LIHC database; B. Association between ADRA1A expression level in liver cancer with BCLC stage, tumor vascular invasion and tumor proliferation markers in GSE19977 and GSE9843 databases; ***P<0.001.

[0025] Figure 8 Detection of ADRA1A gene and protein expression levels in Renji cohort samples, including: A. Detection of ADRA1A gene expression level in Renji cohort liver cancer samples; B. Detection of ADRA1A protein expression level in Renji cohort liver cancer samples; C. IHC detection of ADRA1A protein in Renji cohort liver cancer samples; **P<0.01, ***P<0.001.

[0026] Fig. 9 The PVTT and TNM staging were compared between the two groups of patients in the Renji cohort sample. Compared with the patients in the high-expression ADRA1A protein group, the proportion of PVTT in the low-expression group was significantly higher, and the proportion of patients in stage III or IV in the TNM stage was also higher.

[0027] Fig.10 The postoperative RFS and OS of the Renji cohort patients were compared. Compared with the low expression group patients, the high expression group patients had significantly longer postoperative RFS, while there was no statistical difference in overall survival between the two groups.

[0028] Fig.11 To detect the expression of adrenergic receptor ADRA1A protein in human and mouse liver cancer cell lines, including: A. Comparison of ADRA1A protein expression levels in human normal hepatocytes and liver cancer cell lines. ADRA1A protein was lowest in 97-H and LM3 cell lines, while higher in 97-L and hep3b cell lines; B. Immunofluorescence staining experiment detected the expression distribution of ADRA1A protein in human and mouse liver cancer cell lines; ADRA1A protein was mainly expressed in the cytoplasm and cell membrane, with a small amount distributed in the nucleus.

[0029] Fig.12 Validation of Adra1a overexpression in liver cancer cell lines: A. RT-qPCR detection indicated that the expression level of Adra1a in Hep1-6OV cell line increased significantly; B. Western blotting detected the overexpression effect of ADRA1A protein at the protein level; ***P<0.001

[0030] Fig.13 The subcutaneous tumor model of Adra1a-overexpressing liver cancer cells in mice was established, including: A. Comparison of the actual pictures of subcutaneous tumors in the two groups of mice, among which the tumors in the Hep1-6 OV group were significantly smaller; B. Comparison of tumor growth curves between the two groups; C. Comparison of tumor weights between the two groups; **P<0.01.

[0031] Fig.14The apoptosis protein detection of mouse tumors, including: A. Detection of apoptosis-related proteins in tumor tissues of the two groups; B. Semi-quantitative statistical graph of anti-apoptotic protein Bcl-2, C. Semi-quantitative statistical graph of pro-apoptotic protein Bax; it was found that compared with the control group, the expression of pro-apoptotic molecule Bax in the overexpression group tumor was significantly increased, while the expression of anti-apoptotic molecule Bcl-2 was significantly downregulated. **P<0.01.

[0032] Fig.15 Knockdown of Adra1a promoted the growth of liver cancer in mice in vivo, including: A. Actual picture of subcutaneous tumor in mice. Compared with the control group, the tumor volume and weight in the knockdown group were significantly larger; B. Tumor growth curve and weight comparison; C and D. Detection of apoptosis-related proteins in tumor tissue (C) and semi-quantitative analysis (D).

[0033] Fig.16 This is the differential gene analysis of two groups of tumor sequencing samples, including: A. PCA analysis of two groups of tumor sequencing samples; B. Volcano plot of significantly differentially expressed genes; C. Heat map of Top500 significantly changed genes.

[0034] Fig.17 This is the GO-BP pathway enrichment and immune-related pathway enrichment analysis, including: A. GO-BP differential pathway enrichment; B. GO immune-related signaling pathway enrichment.

[0035] Fig.18 Figure 2 GSEA pathway enrichment analysis of two groups of tumors.

[0036] Fig.19 To analyze the correlation between the expression of T cell-related molecules and ADRA1A expression in liver cancer and adjacent tissues.

[0037] Fig. 20 These are the IHC detection results of T cell markers in tumors after Adra1a overexpression and knockdown.

[0038] Fig.21 Flow cytometry was used to detect the changes of immune cells in the tumors of the control group and the Adra1a overexpression group, including: A.CD4 + and CD8 + A. Comparison of the number of T cells; B. Comparison of the number of NK cells, macrophages and B cells; C. Comparison of the number of MDSC, granulocyte-derived MDSC and myeloid-derived MDSC.

[0039] Fig. 22 Flow cytometry was used to detect the changes of immune cells in the tumors of the control group and the Adra1a knockdown group, including: A.CD4 + and CD8 + A. Comparison of the number of T cells; B. Comparison of the number of NK cells, macrophages and B cells; C. Comparison of the number of MDSC, granulocyte-derived MDSC and myeloid-derived MDSC. DETAILED DESCRIPTION

[0040] In order to make the present invention more clearly understood, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0041] Example

[0042] 1. Experimental Methods

[0043] 1.1 Obtaining the list of neurotransmitter receptor genes

[0044] First, we searched for the signaling pathway of "neurotransmitter receptor" through the Gene Ontology (http: / / geneontology.org / ) website and extracted the list of related genes. Then, we searched them one by one using the "Gene" module in the Pubmed public database ( https: / / www.ncbi.nlm.nih.gov / gene / ? term=), confirming that the gene is related to neurotransmitter receptors. In addition, by consulting the literature reports

[29] , looking for a list of neurotransmitter receptor subunits. This study initially plans to analyze the following eight most common neurotransmitter receptor families: adrenergic receptors, glycinergic receptors, 5-HT receptors, GABAergic receptors, acetylcholine receptors, histidine receptors, dopamine receptors, and glutamate receptors. The detailed list of neurotransmitter receptor genes is shown in Table 1 below:

[0045] Table 1: List of 107 common neurotransmitter receptors

[0046]

[0047]

[0048] 1.2 Acquisition of TCGA-LIHC database data

[0049] In this example, the TCGA-LIHC (The Cancer Genome Atlas-LiverHepatocellular Carcinoma) database (https: / / tcga-data.nci.nih.gov / tcga / ) was downloaded and analyzed. The Cancer Genome Atlas (TCGA) is a landmark cancer genomics project. Currently, more than 20,000 primary cancers and matched normal samples have been sequenced and molecularly identified, covering 33 types of cancer. Among them, the sequencing samples for liver cancer include 371 liver cancer sequencing samples and 50 normal liver tissue sequencing samples (due to differences in version updates, the number of samples will vary in different versions), which is currently one of the more comprehensive sources of liver cancer sequencing data.

[0050] 1.3 Acquisition of ICGC-LIRI-JP database data

[0051] In this example, the ICGC-LIRI-JP database (https: / / dcc.icgc.org / projects / LIRI-JP) was downloaded and analyzed. The International Cancer Genome Consortium (ICGC) is a global initiative that aims to establish a comprehensive catalog of mutational abnormalities in major tumor types. The database currently contains data from 84 worldwide cancer projects, representing a total of approximately 77 million individual cell mutations and molecular data from more than 20,000 contributors, including 177 liver cancer paracancerous tissues and 212 liver cancer tissue sequencing data.

[0052] 1.4 Access to GEO database

[0053] The GEO database (https: / / www.ncbi.nlm.nih.gov / gds / ) was used to search for projects related to "human liver cancer" and "transcriptome sequencing", and databases that met the requirements were screened: 1. Sequencing samples were human; 2. Tumor type was liver cancer; 3. Para-cancer sequencing or normal liver tissue sequencing was included; 4. Sequencing type was transcriptome gene sequencing; 5. Data could be obtained and analyzed. After preliminary screening, the following databases were available for further analysis: GSE14520, GSE46444, GSE76427, GSE36376, GSE25097, GSE63898, and GSE10143.

[0054] 1.5 Access to the GTEx database

[0055] Through the GTEx (Genotype-Tissue Expression) database, we can compare and analyze the expression levels of specific genes in different tissues of the normal human body (https: / / www.gtexportal.org / home / index.html). The GTEx project collects and analyzes a variety of human postmortem tissues, including DNA data from 838 healthy human donors, covering 54 different tissue and organ sites, with up to 17,382 sequencing data.

[0056] 1.6 Analysis of changes in neurotransmitter receptor gene expression levels

[0057] Using R language (R project, version 3.6.0, https: / / www.r-project.org / ) and the online bioinformatics analysis website tool UALCAN (http: / / ualcan.path.uab.edu / index.html), we downloaded, cleaned and analyzed the data from each database, and then performed pan-gene analysis mainly in the TCGA-LIHC database, and performed secondary verification in the remaining databases. By analyzing the TCGA-LIHC database, we compared the changes of various subunits in cancer and para-cancer and performed statistical analysis. The UALCAN website is a comprehensive, user-friendly and interactive web resource processing tool that mainly includes sequencing data of all types of tumors in the TCGA database, which can help users quickly obtain the expression levels of specific genes in different tumors and corresponding para-cancers. In addition, this tool can also be used for survival analysis of specific genes in specific tumors.

[0058] 1.7 Survival analysis of neurotransmitter receptor genes

[0059] In order to screen neurotransmitter receptor genes that significantly affect the prognosis of liver cancer, we used the R language package "survminer" and the UALCAN online analysis website to perform relevant survival analysis. According to the level of gene expression, patients were divided into high expression group and low expression group, and the median of gene expression was selected as the grouping boundary to analyze the impact of gene expression level on the overall survival rate of patients.

[0060] 1.8 Collection of clinical liver cancer samples

[0061] In order to further prove the findings in bioinformatics analysis, this example intends to collect cancer and adjacent specimens of liver cancer patients in the tumor sample library and clinic to perform relevant receptor detection and prognosis analysis.

[0062] 1) Nature of study: Observational study.

[0063] 2) Research purpose: Through a multicenter current observational study, to explore the correlation between the expression of neurotransmitter receptors in hepatocellular carcinoma (ADRA1A, ADRA1B, ADRA2B, GABRE, GABRD, CHRM3, CHRNA5, GRIN2D, etc.) and the long-term prognosis of patients, and to prove that the expression level of neurotransmitter receptors in liver cancer cells can significantly affect the survival of patients.

[0064] 3) Sample size: It is planned to collect 100 pairs of liver cancer samples.

[0065] 4) Research subjects: Liver cancer patients who underwent liver cancer surgical resection due to primary liver cancer.

[0066] 5) Research methods: Take freshly cut liver cancer specimens, keep the cancer and adjacent tissues, and immediately store them in dry ice, and transfer them to liquid nitrogen as soon as possible for standby use; at the same time, retrieve the tumor tissue sample library information to find tumor samples that meet the inclusion criteria. After the samples are collected, RT-qPCR gene level detection, Western blotting protein level detection, IHC protein level detection are performed, and patient medical records are collected to analyze the relationship between the expression of neurotransmitter receptors and the malignancy and prognosis of the patient's tumor.

[0067] 6) Inclusion criteria: age ≥18 years; patients undergoing liver cancer resection due to primary hepatocellular carcinoma.

[0068] 7) Exclusion criteria: Intraoperatively, abdominal metastasis of the tumor was found and could not be surgically removed; follow-up data were incomplete; the patient also had other types of tumors.

[0069] 8) Main research indicators: Expression levels of neurotransmitter receptor proteins and mRNA in cancer and adjacent tissues (ADRA1A, ADRA1B, ADRA2B, GABRE, GABRD, CHRM3, CHRNA5, GRIN2D, etc.)

[0070] 9) Secondary research indicators: The relationship between the neurotransmitter receptor mRNA level or IHC score in the tumor and tumor size, tumor number, tumor TNM stage, tumor grade, whether the tumor has distant metastasis, whether the tumor has vascular invasion, whether the tumor has portal vein thrombosis, patient recurrence-free survival, and patient overall survival rate.

[0071] 10) Main clinical data collected: including but not limited to the following clinical data, such as age, gender, height, weight and other general information, tumor-related indicators, perioperative laboratory tests, surgery and anesthesia-related data, postoperative follow-up, etc.

[0072] 11) Storage and processing of biological samples: All biological samples are stored and registered by team members. Excess biological samples are destroyed in a timely manner after registration. For immunohistochemistry slides, protein samples and cDNA samples, they will be stored by a dedicated person for the convenience of re-examination and publication of papers, and will be destroyed three years after the successful publication of the paper.

[0073] 12) Informed consent process: When enrolling patients, inform the patients and their families of the detailed content, possible risks and benefits of the study in accordance with the content and requirements of informed consent, and enroll patients and their families after obtaining their consent and signing the informed consent form.

[0074] 13) Visit requirements: The acquisition of patients' perioperative clinical data and postoperative follow-up data is mainly obtained by reviewing case data, without direct contact with patients for collection. For patients with incomplete postoperative review data, we mainly collect and confirm them by telephone follow-up, and the frequency of follow-up is once every six months.

[0075] 14) Data security and monitoring plan: Original records refer to the original data such as various data, text, charts, pictures, photos, audio and video, which are directly recorded or statistically formed by means of experiments, observations, investigations or data analysis in the process of clinical and scientific research. Original records are direct records of the original data obtained in the process of scientific experiments, and can be used as important basic data for in-depth research on the subject at different times. The original records of the experiment should reflect the most real and original situation in the experiment. It must be timely, true, accurate and complete, and prevent omissions and arbitrary alterations. It is strictly forbidden to forge and fabricate data.

[0076] 15) Compliance with ethical principles and relevant regulations: This clinical study must comply with the Declaration of Helsinki and China's management norms and regulations on clinical research. This study must be submitted to the Ethics Committee of Renji Hospital for approval before it begins. If the study plan needs to be modified during the study, it must be submitted to the Ethics Committee of Renji Hospital for review and approval before it can be implemented.

[0077] 16) Statistical analysis plan: All statistical tests were performed using two-sided tests, and a P value less than or equal to 0.05 was considered to be statistically significant. (Unless otherwise specified). The description of quantitative indicators will calculate the mean, standard deviation, median, minimum, and maximum values. The description of categorical indicators will use the number of cases and percentages of each category. Parametric data were analyzed using independent sample t-tests, and non-parametric data were analyzed using Mann–Whitney U tests. Categorical variables were analyzed using chi-square tests or Fisher's exact tests. Repeatedly measured variables were analyzed using two-way analysis of variance and Bonferroni post hoc tests.

[0078] 2.9 RNA extraction from tumor tissue and detection of gene expression levels

[0079] 1) RNA extraction from tumor tissue: Weigh 0.1g-0.15g of solid tumor tissue (close to the tumor parenchyma area, excluding the necrotic area) with a precision balance and place it in a 1.5mL EP tube, add two small-size grinding beads and 0.5mL of Lysis buffer in the RNA extraction kit of EZB-bioscience, and then place it in a tissue homogenizer for tissue lysis. The parameters of the homogenizer are as follows: the working temperature is set to 4°C, the vibration frequency is 60Hz, and the vibration time is 60s. After grinding, observe whether the homogenization is complete. If there are large pieces of tissue, homogenize again for 30s with the same parameters as before. After homogenization, remove the magnetic beads, add 0.5mL of anhydrous ethanol and mix well, and then continue to complete RNA extraction according to the instructions of the RNA extraction kit. After extraction, use the Nanodrop instrument to measure the concentration of single-stranded RNA and check whether the 260nm / 280nm ratio meets the requirements (1.8-2.2). For samples whose purity does not meet the requirements or whose concentration is lower than 50ng / μL, re-extraction can be considered. Samples that failed to be extracted twice were considered as samples from which RNA could not be extracted. The RNA after extraction can be stored in a -80℃ refrigerator for future use.

[0080] 2) RNA reverse transcription: RNA reverse transcription was performed using the RNA reverse transcription kit from EZB-bioscience. The reverse transcription system is as follows: total reverse transcription RNA 500 ng, reverse transcription mixed reagent 2.5 μL, ddH2O volume (7.5 μL minus RNA volume). The total reverse transcription volume is 10 μL, and the number of cycles is 1. For detailed reverse transcription steps, see Table 2 below. After reverse transcription is completed, dilute to a certain volume with ddH2O according to the required cDNA amount, and the dilution multiple is generally not more than 5 times.

[0081] Table 2: Specific steps of RNA reverse transcription

[0082] step temperature time 1 42℃ 15min 2 95℃ 30s 3 4℃ Unlimited

[0083] 3) cDNA amplification: This study used real-time quantitative polymerase chain reaction (RTq-PCR) for amplification and quantitative analysis. Amplification used a 10μL system: 5μL SYBR Green fluorescent dye, 2.6μL ddH2O, 0.2μL front primer and 0.2μL rear primer, 2μL cDNA. The amplification program is detailed in Table 3. The primer sequences are detailed in Table 4.

[0084] Table 3: Specific steps for cDNA amplification

[0085] step temperature time cycle Preheat 95℃ 5min 1 Amplification 95℃ 30s 40 60℃ 30s 40 Melting curve 95℃ 15s 1 60℃ 60s 1 95℃ / 1 cool down 40℃ 30s 1

[0086] Table 4: Primer sequences

[0087]

[0088]

[0089] 4) Calculation of gene expression level: After obtaining the Ct value of the target gene, subtract the Ct value of the corresponding reference gene, and then use 2 -ΔΔCt The relative value of gene expression of each sample was calculated. Three replicate wells were set up for each sample, and the average value was taken as the final gene expression level of the sample.

[0090] 1.10 Tumor tissue protein extraction and detection

[0091] 1) RIPA lysis method was used for protein extraction of tumor tissue. Weigh 0.1g-0.15g of solid tumor tissue (close to the tumor solid area, excluding the necrotic area) with a precision balance and put it into a 1.5mL EP tube, add two small-particle grinding beads and 0.3mL of strong RIPA lysis solution, and 3μL of each triplicate tube (PMSF, phosphatase inhibitor, and protease inhibitor), and then put it into a tissue homogenizer for tissue lysis. The parameters of the homogenizer are as follows: the working temperature is set to 4°C, the vibration frequency is 60Hz, and the vibration time is 60s. After the grinding is completed, observe whether the homogenization is complete. If there are large pieces of tissue, homogenize again for 30s, and the parameters are the same as before. After the homogenization is completed, remove the magnetic beads, put the EP tube on ice for lysis for 15min, and then centrifuge the homogenate on the machine. The parameters are set to a speed of 12000g, a time of 15min, and a temperature of 4°C. After the centrifugation is completed, take the supernatant and discard the precipitate.

[0092] 2) Protein concentration quantification: This study used the BCA method to quantify protein concentration. First, refer to the Thermo manual to prepare standard samples: there are 9 standard samples, marked A, B, C, D, E, F, G, H, I, with concentrations of 2000μg / mL, 1500μg / mL, 1000μg / mL, 750μg / mL, 500μg / mL, 250μg / mL, 125μg / mL, 25μg / mL, 0μg / mL. Draw a fitting standard curve based on the standard samples. The BCA working solution ratio is reagent A and reagent B 50:1. Use a 96-well plate for detection, and add 200μL of working solution, 1μL of protein sample, and 9μL of PBS to each well. After all reagents are added, place them in a 37°C incubator and incubate for 30 minutes. After incubation is completed, use an enzyme reader to detect the absorbance (OD) of each well at a wavelength of 562nm to make a standard curve. Calculate the protein concentration of the sample according to the absorbance of the sample. If the protein concentration of the sample is lower than 1μg / μL, it is considered that the protein extraction has failed and the extraction is repeated. If both extractions fail, the sample is considered to have been degraded and cannot be extracted.

[0093] 3) Protein denaturation: Add the corresponding volume of 5× SDS-PAGE protein loading buffer to the protein sample to make the final buffer concentration 1×, then mix and heat in a 95℃ metal heater for 10 minutes. After the protein is fully denatured, it can be stored in a -80℃ refrigerator for future use.

[0094] 4) SDS-PAGE gel electrophoresis and membrane transfer

[0095] Electrophoresis: Take the gradient precast gel and carefully pull out the comb, add the protein samples in the required order, adjust the sample volume to 15-30μg according to the situation, and the pre-stained protein marker sample volume is 3-5μL. The electrophoresis parameters are constant voltage 120V electrophoresis and electrophoresis time 45-50min.

[0096] Transfer: Pre-cool the prepared 1× transfer solution, and then cut the appropriate PVDF membrane according to the number of gel blocks. The PVDF membrane needs to be activated by soaking in methanol for 30 seconds before use. After cutting off the excess glue strips, place them in the order of blackboard (negative electrode), sponge sheet, filter paper, gel, PVDF membrane, filter paper, sponge sheet, and white board (positive electrode), and expel the bubbles. Place the transfer clip in the transfer tank in the correct direction for transfer. The transfer parameters are a constant current of 300mA, and the time is determined by the molecular weight of the target protein. Generally, the transfer time is 1KD = 1min.

[0097] 5) Blocking and antibody incubation

[0098] After the transfer is completed, take out the PVDF membrane and put it into the incubation box. Add 10-15mL of WB special blocking solution to each membrane and shake it slowly on a shaker at room temperature for 30 minutes. After the blocking is completed, cut the membrane according to the experimental requirements and add different primary antibodies. The instructions for the use of the primary antibody refer to the instructions. The primary antibody incubation conditions are selected to be incubated overnight at 4°C (the total time should be greater than 12 hours). After the ADRA1A primary antibody incubation is completed, use 0.1% TBST to wash on a shaker 4 times, 5 minutes each time. After washing, add the corresponding secondary antibody according to the species source of the primary antibody (dilution ratio is 1:3000-5000) and incubate on a shaker at room temperature for 1 hour. After incubation, wash it 4 times with 0.1% TBST on a shaker, 5 minutes each time.

[0099] 6) Band development and exposure quantification

[0100] Select enhanced ECL luminescent developer, and prepare the developer according to the ratio of A solution: B solution = 1:1. Soak the PVDF membrane in the developer in advance, then take out the membrane and place it in the middle of the imaging plate of the developer. Select the chemiluminescence mode and automatic optimal exposure mode for exposure parameters, and the exposure time is automatic. The obtained development band data is analyzed by Image Lab image analysis software, and the semi-quantitative analysis mode is selected.

[0101] 1.11 Immunohistochemical staining (IHC) and hematoxylin-eosin staining (HE) of tumor tissue

[0102] The liver cancer tissue obtained clinically will be used to determine the clear range of cancer and adjacent tissues. IHC is used to detect the expression of neurotransmitter receptors. The HE and IHC staining steps refer to the instructions for use of the kit, and the antibody dilution ratio refers to the instructions. If the staining effect is less positive, the dilution ratio should be reduced as appropriate. In addition, the collected liver cancer and adjacent tissues will be aggregated to make liver cancer tissue chips to facilitate subsequent IHC testing.

[0103] 1.12 Acquisition and culture of liver cancer cell lines

[0104] The hepatocytes and hepatoma cell lines required for this example were purchased from Shanghai Fuheng Biotechnology Co., Ltd., mainly including the following: LO-2 (normal hepatocytes), HCCLM3 (LM3 for short), MHCC-97H (97H for short), MHCC-97L (97L for short), HepG2, Hep3B, PLC, Huh-7, Hep1-6, H22. All cells can be cultured using high-glucose DMEM complete medium, and the specific steps of recovery, amplification, passage and cryopreservation are as follows:

[0105] 1) Recovery: Take out the frozen cells from liquid nitrogen or -80℃ freezer, thaw them in a 37℃ constant temperature water bath, then add the cell solution to a 15mL centrifuge tube, add 2mL of the rewarmed complete medium, mix well and centrifuge at 800r / min for 5min. After centrifugation, remove the supernatant, remix with complete medium, and then inoculate in a 10cm culture dish. The total volume of the culture medium in each dish is 8-10mL.

[0106] 2) Expansion: After 24 hours, observe the growth of the revived cells and replace the culture medium in time. Then continue to culture for 1-2 days. When the cell density reaches 80%-90%, it can be prepared for passaging or freezing.

[0107] 3) Passaging: Take cells in the logarithmic growth phase, discard the old culture medium, and gently rinse with 3-5mL PBS 2-3 times. Add 1mL of trypsin (0.25% trypsin + 0.02% EDTA) to a 10cm culture dish, digest at room temperature, gently shake the culture dish and observe under an inverted microscope. When the cell protrusions retract, the cell gap increases, and the cells are almost shrunk into a circle, add 2-3mL of culture medium containing 10% fetal bovine serum to the culture dish to stop digestion, and use a pipette to blow the cells evenly and orderly. The blowing action should be gentle and not too violent to prevent bubbles from forming. Blow until the cells at the bottom of the culture dish are completely detached. Add the cell suspension after blowing to a 15mL centrifuge tube, centrifuge at 800rpm for 5min, discard the supernatant, resuspend with culture medium containing 10% fetal bovine serum, pass the culture at a ratio of 1:2-1:4, inoculate into a new culture dish, and place it in a 37℃, 5% CO2 cell culture incubator for static culture.

[0108] 4) Cryopreservation: Take cells with good growth status and cell density of 80%-90% or more for cryopreservation. First, digest the cells, add the cell suspension after blowing into a 15mL centrifuge tube, centrifuge at 800rpm for 5min, discard the supernatant, add 1mL of cell freezing solution to resuspend, and then put the cells into liquid nitrogen or -80℃ refrigerator.

[0109] 1.13 RNA extraction, gene expression level detection and protein extraction of liver cancer cell lines

[0110] RNA extraction from liver cancer cells is similar to that from tumor tissues, but no additional tumor tissue grinding is required. The remaining RNA extraction and gene expression level detection procedures refer to the kit instructions. The relevant primer sequences are shown in Table 6:

[0111] Table 6: Primer sequences

[0112]

[0113]

[0114]

[0115] The method of extracting cell protein is basically the same as that of extracting tumor tissue. The specific steps are as follows: Take out the culture dish from the incubator, wash twice with pre-cooled PBS solution, and dry. Add strong RIPA+ triple tube to lyse the cells, place on ice and grind evenly with a 1mL pipette tip, and let stand for 20 minutes. Then aspirate into a 1.5mL EP tube, centrifuge at 12000rpm for 15 minutes at 4℃, and aspirate the supernatant. The aspirated supernatant is quantified by protein BCA, and the remaining supernatant is added with the corresponding volume of 5×Loading buffer to make it finally become 1×Loading buffer, then mix well and put into a metal heater, and place in a 95℃ metal bath for 10 minutes. For long-term storage, it can be placed in a -80℃ refrigerator.

[0116] 1.14 Immunofluorescence staining of liver cancer cells

[0117] The specific steps of cell immunofluorescence staining are as follows:

[0118] 1) Cell preparation: Plant the cells to be tested in a 12-well plate in advance, place a cell slide in the well first, and the cell seeding density is 20,000-50,000 / well. The specific number of cells can also be determined according to the cell proliferation rate. The cell culture medium uses a conventional complete culture medium or a complete culture medium containing corresponding treatment drugs. When the cell growth density on the slide reaches 30-50%, immunofluorescence detection can be performed. Avoid too high a cell density, which will affect fluorescence observation.

[0119] 2) Fluorescent antibody staining: Take the cultured cells, discard the culture medium and wash twice with PBS, then fix the cells with 4% paraformaldehyde for 10 minutes. After fixation, remove the fixative and wash twice with PBS. After that, use the membrane permeabilization solution to permeate for 10 minutes, and then put it on a shaker and wash it with PBS, each time for 10 minutes, and the shaker parameter is 80 times / min. After washing, use immunofluorescence blocking solution to block at room temperature for 1 hour. After blocking, use the corresponding dilution ratio of the primary antibody (generally use a 1:400 dilution ratio. If the protein expression is low and the fluorescence is weak, the dilution ratio can be appropriately reduced) for overnight incubation at 4°C. After the primary antibody incubation, put it on a shaker and wash it with PBS, each time for 10 minutes, and the shaker parameter is 80 times / min. After washing, select the appropriate fluorescent secondary antibody according to the fluorescence color requirements (generally use a 1:800 dilution ratio. If the protein expression is low and the fluorescence is weak, the dilution ratio can be appropriately reduced), and incubate it at room temperature for 1 hour away from light. After incubation, continue to wash with PBS three times, with the same parameters as above. Finally, drop 20-30 μL of DAPI-containing mounting medium onto the slide, and place the cell slide upside down on the mounting medium.

[0120] 3) Fluorescence observation: Place the stained cell slide upside down on a confocal microscope for observation. The slides can be stored in a light-proof humidified box at -20°C.

[0121] 1.15 Experimental animal husbandry, grouping and model construction

[0122] The breeding of experimental animals refers to the article published by this research group (Cancer Communications. 2023, 43 (3): 365-386). Male C57BL / 6J mice, aged 6-8 weeks and weighing more than 20g, were selected and provided by Shanghai Jiesijie Experimental Animal Co., Ltd. and bred and experimented in the Anesthesia Laboratory of Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine and the Animal Experiment Center of Renji Hospital. All animals were kept in an SPF (SPF, Specified Pathogen Free) environment, with the breeding temperature maintained at around 22°C and a 12-hour light / 12-hour dark cycle. The mice were free to eat and the breeding environment was quiet. All animal experimental operations strictly abide by the Shanghai Laboratory Animal Operation Specifications, relevant ethical regulations, and animal welfare-related clauses, and the number of mice used during the experiment was minimized as much as possible to reduce the suffering of the mice.

[0123] The present invention mainly constructs a mouse liver cancer subcutaneous tumor model, and the specific steps are as follows: 1. Use a mouse anesthesia machine to anesthetize the mouse and maintain it, then use a razor to shave the mouse hair within 2-3 cm near the right armpit, and use iodine to disinfect the skin and clean up the broken hair in preparation for the injection of tumor cells. 2. The mouse tumor cells prepared in advance are divided into 1×10 8 Resuspend the concentration so that the number of tumor cells in each 50μL reaches 5 million, and then add an equal volume of matrix gel to mix. 3. Use a 1mL syringe for cell inoculation. Insert the needle obliquely at an angle of less than 30 degrees into the subcutaneous tissue. After the needle enters 3-5mm, lay the needle flat, turn the needle left and continue to move forward about 5mm. After there is no blood after aspiration and the movement of the needle under the skin can be seen with the naked eye, slowly inject the cell suspension. If there is a large resistance during the injection process, consider changing the needle angle and advancing and retreating 1-2mm appropriately. After the injection volume is reached, slowly withdraw the needle and observe the volume of liquid leaking from the injection point. If it is too much, consider sucking the leaked liquid back to make up for the injection. After the needle is withdrawn, gently put the mouse in the mouse cage. It is strictly forbidden to squeeze the injection point. If bleeding is found after the needle is removed, consider applying gauze pressure at the injection point, but this will affect the quality of the model. Consider removing and re-injecting one.

[0124] The ADRA1A-OV model of the present invention is mainly divided into: a control group, injected with Hep1-6 NC cell line; a Hep1-6 OV group, injected with Hep1-6 1A-OV cell line. Each group has n=6-8 mice. The ADRA1A-KD model is mainly divided into: a control group, injected with Hep1-6 NC cell line; a Hep1-6 KD-1 group, injected with Hep1-61A-sh1 cell line; a Hep1-6 KD-2 group, injected with Hep1-61A-sh2 cell line. Each group has n=6-8 mice.

[0125] 1.16 Preparation and analysis of mouse tissue samples

[0126] When the diameter of the mouse tumor grows to 1.5-2cm, the mouse will be killed by cervical dislocation after sevoflurane anesthesia, and tissue will be obtained immediately. After the tumor specimens are completely removed, they will be used for the following experiments: Western blotting protein detection, RT-qPCR gene expression detection, IHC protein detection, whole transcriptome sequencing analysis, and flow cytometry analysis. Flow cytometry analysis must be performed using freshly obtained tumor tissues, and other experiments can use tissue specimens frozen at -80°C. The mouse lungs and livers were removed and fixed with 4% paraformaldehyde for tumor metastasis detection. The mouse blood was drawn and placed in an EDTA-sodium citrate anticoagulant tube, and then placed in a high-speed centrifuge to obtain the supernatant. The centrifugation speed was 3000g and the time was 10 minutes. 2.17 Whole transcriptome RNA sequencing to detect differentially expressed genes and signaling pathway changes

[0127] The whole transcriptome RNA sequencing (RNA-seq) analysis of human and mouse tumor tissues in this invention was mainly completed by Biomarker Biotech Co., Ltd. The acquisition, preparation and preservation of the early tissue samples are the responsibility of the members of this research team. The sequencing company will not be able to obtain any grouping and intervention information about the samples to be tested, and the patient information will be strictly confidential. The whole transcriptome sequencing process is as follows:

[0128] The RNA-seq sequencing experiment process includes steps such as sample detection, library construction and quality inspection, and on-machine sequencing, mainly including total RNA detection, mRNA enrichment, mRNA shearing, end repair, fragment selection and PCR enrichment, library quality control, and on-machine sequencing.

[0129] Analysis process: After the sequencing data is downloaded from the machine, the bioinformatics analysis process provided by the Biomike Cloud Platform BMKCloud (www.biocloud.net) is used for data analysis. The downloaded data is filtered to obtain Clean Data, and the sequence is aligned with the specified reference genome. After obtaining Mapped Data, library quality assessment, structural level analysis, differential expression analysis, gene function annotation and functional enrichment, etc. are performed. In addition, through the Biomike Cloud Platform BMKCloud (www.biocloud.net), in-depth data mining and analysis can be performed to achieve a series of personalized interactive operations of the transcriptome, such as gene retrieval and mapping, unique and shared gene analysis, construction of protein interaction networks, gene set enrichment analysis (GSEA), differential gene co-expression analysis and gene co-expression network construction (WGCNA), etc.

[0130] 1.18 Construction of ADRA1A overexpression plasmid, overexpression virus and stable transfectant strain

[0131] The mouse Adra1a overexpression lentivirus used in the present invention was constructed and synthesized by Heyuan Biotechnology (Shanghai) Co., Ltd. The plasmid template used was GL119 pSLenti-CMV-MCS-3xFLAG-PGK-Puro-WPRE, and the constructed name was pSLenti-CMV-Adra1a-3xFLAG-PGK-Puro-WPRE. The constructed plasmid was transferred into the lentivirus for amplification and collection.

[0132] The mixture containing the virus stock solution, dilution medium and Polybrene is evenly dropped into the well plate, and then the well plate is placed in the incubator for culture. After 24 hours, replace with fresh complete medium and continue to culture. If the lentivirus has obvious toxic effects on the cells and affects the cell growth state, the culture can be continued after replacing the fresh culture medium for at least 12-24 hours after adding the virus. After the first change of medium, if the lentivirus has no obvious toxic effects on the cells, culture and change the medium according to normal culture conditions. After continuing to culture for 48-72 hours, collect cells as needed to detect the expression of the target protein. In particular, after the lentivirus infects the cells, it takes at least 48 to 72 hours to culture the expression of the target gene. The reason is that the genome of the lentiviral vector is RNA, which needs to be reverse transcribed into DNA and inserted into the cell chromosome before it can be expressed. Interference lentivirus needs to be reverse transcribed and inserted into the cell chromosome before it can act on the target gene, thereby playing an interference role. It is recommended to collect samples for at least 72 hours for RT-qPCR and WB detection.

[0133] After confirming that ADRA1A overexpression is successful, continue to screen the cells with puromycin. Replace the culture medium with complete culture medium containing 5 μg / mL puromycin for screening and culture. When the cells no longer die and the fluorescence under the microscope is full, it is a stable overexpression cell line. After that, add culture medium containing puromycin for screening every 3-5 generations to maintain the purity of the cell line.

[0134] 1.19 Construction of Adra1a knockdown virus and stable transfectant strain

[0135] The mouse-derived Adra1a knockout lentivirus used in the present invention was constructed and synthesized by Heyuan Biotechnology (Shanghai) Co., Ltd. All infection operations and lentivirus usage methods are consistent with overexpression lentiviruses. There are three different candidate sequences for Adra1a knockout lentivirus, and two sequences with the highest knockdown efficiency were screened out through preliminary experiments for subsequent experiments.

[0136] 1.20 Differential gene pathway enrichment and bioinformatics analysis of sequencing data

[0137] The pathway enrichment of differentially expressed genes in sequencing data mainly includes the following:

[0138] 1) KEGG pathway analysis (https: / / www.kegg.jp / ): KEGG is a database resource for understanding the high-level functions and utilities of biological systems such as cells, organisms, and ecosystems from information at the molecular level, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies.

[0139] 2) Gene ontology (GO) pathway analysis (http: / / geneontology.org / ): The Gene Ontology knowledgebase provides a computational representation of our current scientific knowledge about the functions of genes (or more appropriately, proteins and non-coding RNA molecules produced by genes) in many different organisms, from humans to bacteria. It is widely used to support scientific research and has been cited by tens of thousands of publications. The GO knowledgebase plays a vital role in supporting biomedical research and has been used in tens of thousands of scientific studies. The most common use of GO annotation is to interpret large-scale molecular biology experiments, sometimes called "all-purpose" experiments. Whether it is genomics, transcriptomics, proteomics or metabolomics, these experiments bring together biological molecules to gain insights into the structure, function and dynamics of organisms. This study mainly uses the annotation functions of four parts: GO-biological process (GO-BP), GO-cell component (GO-CC), GO-molecular function (GO-MF) and GO-immune pathways.

[0140] 3) GSEA analysis (Gene Set Enrichment Analysis, GSEA, http: / / www.gsea-msigdb.org / gsea / index.jsp): In order to further explore the pathways in which the changed genes are significantly enriched, we plan to use GSEA pathway enrichment.

[0141] All differential pathway enrichment analyses in the present invention are mainly completed with the assistance of the following analysis tools: the analysis tool provided by the Biomarker Cloud client; the Cytospace analysis tool, and the ClueGO application in the software; the SangerBox-biomedical data analysis box; the R language (version 3.4), etc.

[0142] 1.21 Analysis of tumor-infiltrating immune cells by flow cytometry

[0143] In order to detect the number and type of immune cells infiltrating in the tumor microenvironment, the present invention uses flow cytometry for detection. The specific operation steps are as follows:

[0144] 1) Obtain fresh tumor tissue and cut 0.1-0.2g of solid tumor, avoiding necrotic or liquefied areas of tumor. All operations should be performed on ice to ensure cell activity.

[0145] 2) Prepare the tumor tissue dissociation kit in advance and prepare various enzyme reagents. Then mix 100μL enzyme D + 50μL enzyme R + 12.5μL enzyme A + 2.35mL DMEM medium in each tube. Since we mainly perform intratumoral immune cell detection, the volume of enzyme R can be reduced to 10μL.

[0146] 3) After mixing, use ophthalmic scissors to cut the tumor tissue into small tissue pieces of about 2 mm, then transfer the suspension into a Miltenyi dedicated Type-C tube, place it on a Miltenyi tissue grinder for grinding, and select the program "m_impTumor_02".

[0147] 4) Place the Type-C tube in a 37°C constant temperature water bath, set the shaking speed to 100 times / min, and the water bath time to 40 minutes.

[0148] 5) After the enzymatic hydrolysis in water bath is completed, the tissue blocks are filtered out with a 40μm filter membrane, and the cell suspension is transferred to a 15mL centrifuge tube and centrifuged at 300g for 6 minutes. Then the supernatant is discarded, and 6-8mL of the previously prepared red blood cell lysis buffer is added to resuspend the cells for red blood cell lysis, and the cells are protected from light for 10-20 minutes.

[0149] 6) After the red lysis is completed, centrifuge again at 300g for 6 minutes. Then discard the supernatant, add an appropriate volume of PBS to resuspend, and divide into several portions of 100 μL each, ready to add antibody staining.

[0150] 7) Add antibodies to the cell suspension at a ratio of 1:100 for light-proof staining at room temperature for 1 hour. For intracellular antigens, staining after membrane permeabilization is required: add 2 mL of fixed membrane permeabilization solution to the cell suspension and keep it away from light for 30 minutes. Then centrifuge to remove the membrane permeabilization solution, resuspend and wash with PBS, centrifuge again to remove PBS, and add an appropriate amount of PBS to resuspend.

[0151] After staining, filter again with a 40μm filter membrane, add 1mL PBS and mix well, transfer to a flow tube, and then test on the machine. The specific flow cytometry staining schemes for various immune cells are as follows: ① B cells: CD45 + CD19 + ②NK cells: CD45 + NK1.1 + ; ③CD4 + T cells: CD4 + ④CD8 + T cells: CD8 + ⑤Macrophages: CD45 + CD11b + F4 / 80 + ; The M1 macrophage marker staining is CD45 + CD11b + F4 / 80 + CD86 + , M2 macrophage marker staining is CD45 + CD11b + F4 / 80 + CD206 + ⑥MDSC: CD45 + CD11b + Gr-1 / Ly-6G + , where the phenotype of M-MDSCs is CD11b + Ly6G-Ly6C high , while the phenotype of G-MDSCs (G-MDSCs) is CD11b + 6G + Ly6C low ⑦Treg cells: CD4 + CD25 + FOXP3 + After the tumor single cell suspension was stained accordingly, flow cytometry analysis was performed using a flow cytometer to compare the differences between different immune cells between the two groups.

[0152] 2.22 Statistical analysis

[0153] Statistical analysis was performed using IBM SPSS Statistics 26.0 software (SPSS Inc., Armonk, New York. USA) and GraphPad Prism 7 software. Categorical variables were expressed as frequency (n) or proportion (%). Continuous variables were expressed as mean ± standard deviation (SD) or median (25% interquartile range, 75% interquartile range). The differences between the two groups were analyzed using independent sample / paired Student's t-test. The differences between the four groups were analyzed using analysis of variance with repeated measures / block randomized univariate analysis (or bifactor analysis as needed), followed by post hoc analysis (Tukey's test) when appropriate. If the data did not meet the normal distribution criteria, nonparametric tests were used, followed by the Kruskal-Wallis test. Comparisons were made using nonparametric tests. Categorical variables were compared using the χ2 test with Yates correction. 2 Test or Fisher's exact test (when the total sample size <40 or the expected frequency <1). Survival curves were constructed using Kaplan-Meier survival analysis and log-rank t test. All statistical tests were two-sided, and P values ​​<0.05 were considered statistically significant.

[0154] 2. Experimental results

[0155] Through a series of bioinformatics analyses of liver cancer databases, collection and analysis of clinical liver cancer specimens, and in vitro neurotransmitter receptor detection of liver cancer cell lines, it was preliminarily found that ADRA1A was significantly downregulated in liver cancer, and low expression of ADRA1A was associated with poor prognosis in liver cancer patients, suggesting that ADRA1A may play an important role in inhibiting the development of liver cancer and is a potential therapeutic target. Furthermore, in a mouse liver cancer subcutaneous tumor model, it was verified that overexpression of Adra1a can significantly inhibit the in vivo growth of mouse liver cancer.

[0156] 2.1 Altered expression of pan-neurotransmitter receptor genes in liver cancer

[0157] According to the list of 107 neurotransmitter receptor genes in eight categories obtained in Table 1, and combined with the analysis of the transcriptome data from the TCGA-LIHC database, the present invention compared the changes of various subunits in cancer and adjacent tissues and performed statistical analysis. Figure 1As can be seen in A, many neurotransmitter receptors are significantly upregulated in liver cancer (upregulation times more than 4 times and statistical P value less than 0.05), such as GABRD, GABRE and GABBR2, which belong to GABA receptor subunits; GRM2 belongs to glutamate receptor subunits; HTR1D belongs to 5-HT receptor subunits. However, it is interesting that the only neurotransmitter receptor that is significantly downregulated in liver cancer (downregulation times more than 4 times and statistical P value less than 0.05) is the adrenergic receptor (AR) subunit ADRA1A, and the downregulation times are as high as more than 16 times ( Figure 1 AB). In addition, through the literature review, it was found that there is a lack of research on ADRA1A in tumors, and the innovation is relatively high. In view of this, the present invention intends to continue to analyze the changes in other genes of the adrenergic receptor family.

[0158] By analyzing the nine subunits of the adrenergic family separately, it was found that ( Figure 2 ), compared with adjacent adjacent cells, the subunits that were significantly downregulated in liver cancer were mainly ADRA1A, ADRA1B, ADRA2B, ADRB1, and ADRB2, while those that were significantly upregulated were ADRA1D, ADRA2A, ADRA2C, and ADRB3. The above results suggest that adrenergic receptor genes are commonly altered in liver cancer, and both upregulated and downregulated. However, the genes with higher expression abundance (TPM median greater than 1) were mainly ADRA1A, ADRA1B, ADRA2B, and ADRB2, and ADRA1A was the most significantly downregulated.

[0159] In order to prove whether the downregulation of ADRA1A in liver cancer is universal, the present invention also verified it in multiple different liver cancer databases. Through data analysis of multiple databases, it was found that ( Figure 3 ), the downregulation of ADRA1A in liver cancer is remarkably consistent and is not a phenomenon that exists only in a specific database.

[0160] Subsequently, the present invention analyzed the expression changes of ADRA1A in different tumors and normal tissues (the specific tumor name abbreviations are shown in Table 7. Through pan-cancer analysis, it can be found that ( Figure 4 A), ADRA1A is significantly downregulated in almost all tumors, such as liver cancer, bladder cancer, breast cancer, biliary tract cancer, brain glioma, pancreatic cancer, lung cancer, prostate cancer, etc. In addition, through the GTEx database, ADRA1A expression analysis in normal tissues found that ADRA1A is particularly highly expressed in the liver, lungs, spleen and prostate, suggesting that ADRA1A expression has a certain liver specificity and may play an important role in the liver ( Figure 4 B).

[0161] Table 7: Abbreviations of tumor names

[0162]

[0163]

[0164] 2.2 Analysis of genetic structural changes of ADRA1A in liver cancer and other tumors

[0165] In order to gain a deeper understanding of the characteristics of ADRA1A gene structure changes in different types of tumors, the present invention analyzed the ADRA1A gene mutation and copy number variation (significant Copy Number Alteration, sCNA). Figure 5 A found that ADRA1A has genetic changes in most tumors, and the proportion of genetic changes in endometrial cancer, prostate cancer, colon cancer, epithelial ovarian cancer, bladder cancer, non-small cell lung cancer and liver cancer is more than 6%. Analysis of its change types also found that the type of gene changes in ADRA1A in different tumors is mainly "deep deletion", especially in liver cancer, the type of gene changes is "deep deletion", indicating that there is a significant ADRA1A gene deletion in liver cancer. This finding is consistent with the above-mentioned significant downregulation of ADRA1A gene expression levels in liver cancer. Subsequently, by comparing with 8 other adrenaline receptor genes, it was found that ( Figure 5 B), ADRA1A has the highest proportion of gene changes in liver cancer, while the gene changes of other family genes except ADRB3 are very low, and are mainly of the "gene amplification" type. Furthermore, the present invention divides patients into high expression group and low expression group according to the expression level of ADRA1A, and compares the whole gene mutation landscape of the two groups. Generally speaking, the more gene mutations (also known as gene mutation load) that occur in a tumor, the more malignant the tumor is, and it is more likely to develop drug resistance. By comparison, it was found that ( Figure 5 C), compared with the patients in the low expression group, the overall gene mutation load of the patients in the high expression group was significantly lower, suggesting that the tumor may be less malignant.

[0166] In addition, the present invention also analyzes the sCNA status of ADRA1A in different tumors. Figure 5D shows that, except for the "diploid / normal" type sCNA, ADRA1A is mainly "Arm-level deletion" (chromatin arm length level deletion) and "deep deletion" in different tumors, suggesting that ADRA1A may have obvious functional loss in many tumors. Especially in liver cancer, the total proportion of "Arm-level deletion" and "deep deletion" reached nearly 75%, proving that ADRA1A function is significantly inhibited in liver cancer.

[0167] In summary, ADRA1A not only undergoes significant downregulation in many tumors, but also has typical changes such as gene deletion that inhibit gene function, which is particularly obvious in liver cancer. These changes may be closely related to the occurrence and deterioration of liver cancer.

[0168] 2.3 Low expression of ADRA1A is closely associated with poor prognosis of liver cancer

[0169] Combining the literature tips and the conventional mechanisms of tumor occurrence and development, the present invention found that genes significantly upregulated in tumors usually promote tumor development, while genes significantly downregulated inhibit tumor growth. Given that ADRA1A is significantly downregulated in liver cancer and its function is significantly inhibited, is the expression level of ADRA1A related to the prognosis of liver cancer patients? In response to this, the present invention conducted a relevant survival analysis. The present invention first systematically analyzed the relationship between the expression of 107 neurotransmitter receptor genes and the overall survival rate of liver cancer patients, and drew a volcano plot containing the differences in neurotransmitter receptor changes and the results of survival analysis ( Figure 6 A), found that a total of 10 neurotransmitter receptor genes were closely related to the prognosis of liver cancer, and belonged to six different neurotransmitter receptor families. These results suggest that neurotransmitter receptor genes may be closely related to the occurrence and development of liver cancer, and may be one of the important components of the body's nervous system regulating tumor growth. Among them, ADRA1A and ADRA2B are both adrenergic receptor family genes, and high expression of both is beneficial to the prognosis of liver cancer.

[0170] The bioinformatics analysis results of TCGA-LIHC and ICGC-LIRI-JP databases also suggest that ( Figure 6 B), the overall survival rate of HCC patients with low ADRA1A expression was significantly lower (P<0.001 and P=0.012, respectively).

[0171] At the same time, this example also analyzes the correlation between the ADRA1A expression level in liver cancer and the patient's tumor stage and grade, lymph node metastasis, vascular invasion, tumor proliferation markers and other indicators. Figure 7As shown in Figure 2, bioinformatics analysis of the TCGA-LIHC and GSE9843 databases suggests that lymph node metastasis, tumor grade, and stage of liver cancer patients are significantly negatively correlated with ADRA1A expression levels ( Figure 7 AB), in the high-expression ADRA1A group, the proportion of patients in TNM I / II or Barcelona stage 0 / A (BCLC-0 / A) was significantly higher than that in the low-expression ADRA1A group. In addition, the analysis of the GSE19977 and GSE9843 liver cancer databases also found that the proportion of tumor vascular invasion in the high-expression ADRA1A group was significantly lower, and the positive rate of the tumor proliferation marker pRPS6 was also significantly lower ( Figure 7 B). The above results indicate that the expression level of ADRA1A is significantly negatively correlated with the clinical progression of liver cancer patients, but significantly positively correlated with the overall survival rate of patients.

[0172] 2.4 Detection of ADRA1A in clinical liver cancer specimens and its impact on prognosis

[0173] This protocol was approved by the Ethics Committee of Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (approval number: KY-2020-185). A total of 71 pairs of clinical liver cancer and adjacent cancer tissues were collected for this study. Due to limitations in tissue preservation and RNA extraction conditions, a total of 31 pairs of tissues successfully obtained qualified RNA for subsequent experiments, and 34 pairs of protein samples for Western blotting experiments. In addition, ADRA1A protein expression was successfully detected in 71 pairs of tissues by immunohistochemistry (Immunohistochemistry, IHC). RT-qPCR experiments found that ADRA1A gene expression was significantly downregulated in liver cancer samples ( Figure 8 A), this finding is consistent with the results of previous clinical database analysis. We then detected ADRA1A protein expression at the protein level (Western blotting and IHC), and also found that ADRA1A protein was significantly downregulated in liver cancer. In summary, compared with adjacent adjacent cells, the adrenergic receptor subunit ADRA1A was significantly downregulated in liver cancer at both the gene and protein levels.

[0174] The basic clinical data of the 71 patients included in this study are shown in Table 5. According to the IHC score of ADRA1A protein in liver cancer, the present invention divided the patients into a low expression group (IHC score <4 points) and a high expression group (IHC score ≥4 points), and compared the basic conditions, intraoperative tumor and surgical indicators, anesthesia-related indicators, and postoperative complications between the two groups. It can be seen from Table 5 that, except for portal vein tumor thrombus (PVTT), TNM stage, and preoperative creatinine (Cr, creatinine) level, the basic conditions between the two groups were basically similar. Compared with patients in the high-expression ADRA1A protein group, the proportion of PVTT in the low-expression group was significantly higher (P=0.009), and the proportion of patients in stage III or IV in the TNM stage was also higher (P=0.018). These results suggest that liver cancer with low expression of ADRA1A protein may have stronger tumor malignancy (Tables 5 and Fig. 9 ). In addition, compared with the low expression group, the high expression group had a slight decrease in preoperative blood Cr level, but the difference between the two groups was not significant.

[0175] By analyzing the perioperative infusion, anesthetic drug use, and postoperative complications between the two groups, it was found that there was no significant difference between the two groups of patients (Table 6).

[0176] Table 6: Clinical data of patients enrolled in the Renji cohort

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[0181] Finally, the postoperative recurrence time and death time of the patients were collected through postoperative follow-up of the patients, and the corresponding survival analysis was performed. The follow-up endpoint of this study is February 13, 2023. The postoperative follow-up data are mainly obtained through the following methods: 1. Check the patient's postoperative review and re-examination through the clinical case data system. 2. Contact the patient's attending physician to understand the patient's condition. 3. Follow up with the patient and his family by phone or email. Through survival analysis, it was found that compared with patients in the low expression group, patients in the high expression group had a significantly longer postoperative tumor recurrence-free period (RFS, recurrence-free survival) (P=0.017), and due to insufficient follow-up time, there were fewer deaths (14 deaths occurred in the two groups), and there was no statistical difference in the overall survival rate between the two groups. In summary, through a prospective observational cohort study, we further verified the previous bioinformatics analysis findings, proving that ADRA1A is significantly downregulated in liver cancer, and the proportion of patients with PVTT in the low-expression group is significantly higher, the proportion of patients in stage III or IV in TNM staging is also higher, and the postoperative RFS of patients is significantly longer. ADRA1A may play an important anti-cancer role in the occurrence and development of liver cancer and has good clinical translation potential.

[0182] 2.5 Detection of ADRA1A expression in hepatoma cell lines in vitro

[0183] In order to further understand the expression of ADRA1A protein in human and mouse liver cancer cell lines, and its relationship with the malignancy of the cell lines, the present invention used Western blotting and immunofluorescence experiments to detect the expression of ADRA1A protein. Fig.11 A results show that ADRA1A protein is commonly expressed in human normal liver cell lines (LO-2) and liver cancer cell lines (97-L, 97-H, LM3, PLC, Huh-7, HepG2 and Hep3b). Literature and cell line background information suggest that 97-H and LM3 tumors are more malignant and have stronger metastatic ability, while 97-L and Hep3b tumors are less malignant. Combined with our experimental results, we found that ADRA1A protein is lowest in 97-H and LM3 cell lines, and higher in 97-L and hep3b cell lines. The above results suggest that the expression level of ADRA1A protein may be inversely proportional to the malignancy of liver cancer cells.

[0184] Immunofluorescence staining was then used to detect the expression distribution and expression level of ADRA1A protein. Fig.11 The results of B showed that ADRA1A protein was mainly expressed in the cytoplasm and cell membrane, with a small amount distributed in the nucleus, and was abundantly expressed in human liver cancer cell lines (LM3 and HepG2) and mouse liver cancer cell lines (hep1-6).

[0185] 2.6 Overexpression of Adra1a significantly inhibits the growth of mouse liver cancer in vivo

[0186] We demonstrated the effectiveness of the mouse Adra1a overexpression lentivirus by RT-qPCR and Western blotting, and it was used in subsequent experiments ( Fig.12 ).

[0187] By constructing a mouse liver cancer subcutaneous tumor model, it was found that compared with the control group, the tumor growth of the Hep1-6 OV group was significantly smaller ( Fig.13 ), suggesting that Adra1a overexpression in vivo would inhibit tumor growth.

[0188] We then extracted tumor proteins from the two groups and detected apoptosis-related proteins. We found that compared with the control group, the expression of the pro-apoptotic molecule Bax in the overexpression group was significantly increased, while the expression of the anti-apoptotic molecule Bcl-2 was significantly downregulated ( Fig.14 ), indicating that the level of apoptosis in the overexpression group was significantly increased.

[0189] In summary, the possible anti-liver cancer effect of ADRA1A suggested in previous database analysis and clinical sample analysis was further verified in the mouse tumor model, indicating that ADRA1A plays an important anti-tumor role in liver cancer.

[0190] 2.7 Knockdown of Adra1a promotes the growth of mouse liver cancer in vivo

[0191] At the same time, we constructed a corresponding subcutaneous tumor model by using the Adra1a knockdown cell line. The in vivo mouse model showed that compared with the tumors in the control group, the tumors in the knockdown group were significantly larger and grew faster ( Fig.15 AB), suggesting that Adra1a knockdown in vivo would inhibit tumor growth.

[0192] Subsequently, the tumor proteins of the two groups were extracted and the apoptosis-related proteins were detected. It was found that compared with the control group, the expression of the pro-apoptotic molecule Caspase2 in the knockdown group tumors was significantly reduced, while the expression of the anti-apoptotic molecule Bcl-2 was significantly upregulated ( Fig.15 CD), indicating that the apoptosis level of cells in the knockdown group was significantly inhibited.

[0193] 2.8 Immune-related signaling pathways in tumors of mice overexpressing Adra1a were significantly activated

[0194] In order to systematically analyze the pathway changes caused by overexpression of Adra1a in vivo, the present invention performed whole transcriptome gene sequencing analysis on the control group tumors and the overexpression group tumors. The control group n=4, the overexpression group n=6, and the samples tested on the machine all met the sequencing standards and were successfully built. Fig.16 A shows that principal component analysis (PCA) indicates that there are significant differences between the control group and the overexpression group, belonging to different dimensional groups. Genes with false discovery rates (FDR) < 0.05 and change fold > 2 times were screened and used as differentially expressed genes. By drawing a volcano map of differentially expressed genes, it can be seen that a total of 364 genes were significantly upregulated in the overexpression group, and 85 genes were significantly downregulated in the overexpression group ( Fig.16 B). We then plotted a gene heat map of the top 500 genes with the most significant changes ( Fig.16 C), indicating that gene expression changed significantly between the two groups.

[0195] We then enriched the relevant pathways based on the differentially expressed genes between the two groups. Through Gene ontology-biological process (GO-BP) enrichment analysis, we found that immune-related signaling pathways were significantly enriched, such as "immune response", "adaptive immune response", "lymphocyte mediated immunity", etc. ( Fig.17 A). Further enrichment of immune-related signaling pathways revealed that lymphocyte migration, chemotaxis signaling pathways, and T cell activation, differentiation, and regulation-related pathways were significantly enriched, suggesting that Adra1a overexpression is likely to exert a significant anti-tumor effect by affecting tumor immunity, and that lymphocytes, especially T lymphocytes, may play a crucial role in this process ( Fig.17 B).

[0196] In order to further comprehensively analyze the significantly changed pathways, we further completed GSEA pathway analysis ( Fig.18 ). GSEA analysis revealed that immune-related pathways were significantly activated, such as "Adaptive immune response", "Immune system process", and "Regulation immune system process". In addition, leukocyte activation-related pathways were also significantly enriched, indicating that leukocyte-related activity was significantly changed.

[0197] 2.9 Overexpression of Adra1a significantly promotes CD4 + and CD8 +T cell infiltration

[0198] According to the enrichment of GO-immune-related pathways in 2.5, we preliminarily found that T cell-related signaling pathways were significantly enriched, and accounted for the largest proportion of all enriched immune signaling pathways. Therefore, we speculate that overexpression of Adra1a may significantly promote the enrichment of T cells to the tumor immune microenvironment. First, the expression levels of T cell-related molecules were detected in liver cancer specimens collected from Renji Hospital. It was found that the expression levels of T cell marker molecules (CD3D, CD3G, CD4 and CD8A) in tumors were significantly positively correlated with the expression levels of tumor ADRA1A, while there was no similar correlation in adjacent cancer tissues ( Fig.19 ).

[0199] At the same time, the present invention performed IHC staining on subcutaneous tumor tissues overexpressing Adra1a and knocking down Adra1a to detect changes in T cell marker molecules (CD3, CD4 and CD8) in the tumors. It was found that compared with the control group, the positive signals of CD3, CD4 and CD8 in the tumors of the overexpression group were significantly increased, indicating that the infiltration of T cells may have increased, while the positive signal of CD4 in the tumors of the knocking down group was significantly reduced, indicating that CD4 + T cell infiltration may be reduced ( Fig. 20 ).

[0200] In order to further analyze the changes in immune cells infiltrating the tumor, the present invention detected the immune cells in the subcutaneous tumors of different groups of mice by flow cytometry. By detecting the immune cells in the tumors of the control group and the Adra1a overexpression group, it was found that the CD4 + and CD8 + The number of T cells increased significantly ( Fig.21 A), in addition, the present invention also found that compared with the control group, the NK cells in the tumor of the overexpression group decreased to a certain extent, while the macrophages increased to a certain extent ( Fig.21 B). However, the present invention found that the number of NK cells and macrophages infiltrating the tumor was relatively small, accounting for less than 1% of the total number of cells in the tumor. There was no significant difference in B cells and MDSC (including granulocyte-derived MDSC and myeloid-derived MDSC) between the two groups ( Fig.21 C). In addition, the present invention also detected Treg cells and M1 macrophages at the same time, but no positive signals were detected or only 1-2 / 10 4 cell.

[0201] In addition, the present invention also detected the changes of immune cells in the tumor model with Adra1a knockdown, and found that compared with the control group, the number of CD4 + The number of cells decreased significantly, while CD8 + There was no statistical difference between the two groups ( Fig. 22A). There was no statistical difference in NK cells, B cells, macrophages and MDSCs (including granulocyte-derived MDSCs and myeloid-derived MDSCs) between the two groups ( Fig. 22 BC). In summary, ADRA1A may mainly affect CD4 + and CD8 + The tumor infiltration process of T cells is used to activate tumor immunity, thereby exerting anti-tumor effects.

[0202] 3. Conclusion

[0203] Through a series of liver cancer databases, clinical data and experimental verification, we know that: 1) In liver cancer, the expression of the adrenergic receptor subunit gene ADRA1A is significantly downregulated, and survival analysis suggests that the expression level of ADRA1A is significantly positively correlated with the overall survival rate of liver cancer patients. 2) In human tissues, ADRA1A is particularly highly expressed in the liver, suggesting that ADRA1A expression has a certain liver specificity and may play an important role in the liver. 3) ADRA1A not only undergoes significant downregulation changes in many tumors, but also has typical changes such as gene deletion that inhibit the function of genes, and is particularly obvious in liver cancer. These changes may be closely related to the occurrence and deterioration of liver cancer. 4) Clinical data analysis found that the expression level of ADRA1A is significantly negatively correlated with the clinical progression of liver cancer patients, but significantly positively correlated with the overall survival rate of patients. 5) In a mouse liver cancer model, it was found that overexpression of Adra1a can significantly inhibit the growth of liver cancer, while knocking down Adra1a can significantly promote the growth of liver cancer. 6) Whole transcriptome sequencing analysis of mouse tumors revealed that compared with the control group, the immune-related signaling pathways in the tumors of the Adra1a overexpression group were significantly enriched and activated, suggesting that the anti-liver cancer effect of Adra1a overexpression may be closely related to the body's immune system. 7) Flow cytometry analysis revealed that by detecting immune cells in the tumors of the control group and the Adra1a overexpression group, CD4 + and CD8 + The number of T cells increased significantly; in the Adra1a knockdown tumor model, it was found that compared with the control group, the number of CD4 + The number of cells decreased significantly. In summary, ADRA1A may mainly affect CD4 + and CD8 + The tumor infiltration process of T cells is used to activate tumor immunity, thereby exerting anti-tumor effects.

[0204] The above description is only a preferred embodiment of the present invention and is not any formal or substantial limitation of the present invention. It should be pointed out that a person skilled in the art can make several improvements and supplements without departing from the present invention, and these improvements and supplements should also be regarded as the protection scope of the present invention.

Claims

1. Use of the ADRA1A gene or protein in the preparation of a product for diagnosing and / or preventing and / or treating liver cancer, characterized in that: The diagnosis includes distinguishing liver cancer patients from healthy people, liver cancer staging and grading diagnosis, liver cancer malignancy diagnosis, liver cancer lymph node metastasis diagnosis, liver cancer vascular invasion and liver cancer prognosis diagnosis.

2. The use according to claim 1, characterized in that The product for diagnosing liver cancer includes a diagnostic reagent and / or a kit, and the reagent and / or the kit at least includes a reagent for detecting the expression level of the ADRA1A gene or protein.

3. The use according to claim 2, characterized in that The reagents include reagents required for detecting the expression level of the ADRA1A gene by RT-qPCR method.

4. The use according to claim 2, characterized in that The reagents include reagents required for detecting the expression level of ADRA1A protein by Western blotting or immunohistochemical staining.

5. The use according to claim 1, characterized in that The product for preventing or treating liver cancer includes a pharmaceutical composition or preparation, wherein the pharmaceutical composition or preparation includes an active ingredient and a pharmaceutically acceptable carrier, wherein the active ingredient is the ADRA1A gene, protein or a promoter thereof.

6. The use according to claim 3, characterized in that The ADRA1A promoter is selected from the following group: a small molecule compound, a vector expressing ADRA1A, an ADRA1A protein agonist, or a combination thereof.

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