Lung cancer marker and application thereof

Abstract

The invention relates to the field of biological medicine, and discloses a lung cancer marker and application thereof. The invention aims to solve the problem that more new biomarkers are needed due to the fact that different lung adenocarcinoma patients have different responses to treatment due to the specificity of the patients at present, and particularly provides a lung cancer marker and application thereof.

Description

Technical Field

[0001] The present invention relates to the field of medical drugs, and in particular to a lung cancer marker and application thereof. Background Art

[0002] Lung cancer is a common malignancy among all cancers. Lung adenocarcinoma (LUAD) is a common subtype of lung cancer, accounting for over 70% of lung cancer cases. Lung cancer incidence ranks first among males and second among females in China, and lung cancer mortality is the highest among both men and women. Early-stage lung cancer presents insidious symptoms and a poor prognosis, with the five-year survival rate for patients with advanced lung cancer below 20%. According to the American Cancer Society, improvements in diagnosis and treatment have led to rapid improvements in the survival rate of non-small cell lung cancer (NSCLC). However, the incidence of LUAD has been increasing in recent years. Targeted therapies and immunotherapies combined with conventional treatments have shown significant benefits in improving the prognosis of patients with LUAD. However, due to patient-specific characteristics, responses to treatment vary, necessitating the continued research of new biomarkers to aid in the diagnosis and treatment of LUAD.

[0003] Purine metabolism is a key component of intracellular nucleotide synthesis and energy metabolism. Tumor cells require large quantities of purine nucleotides to support their rapid cell division and proliferation. Disturbances in purine metabolism are significantly involved in tumor growth, invasion, and metastasis and are believed to have a significant impact on the development of LUAD. Purine metabolism is associated with cellular interactions, immune cell infiltration, and prognosis in LUAD patients. Comprehensive analysis of purine metabolism can aid in predicting prognosis and therapeutic decisions for patients with lung adenocarcinoma. However, the regulatory mechanisms of purine metabolism in lung adenocarcinoma cells remain unclear. Summary of the Invention

[0004] The purpose of the present invention is to solve the current problem that due to patient specificity, different lung adenocarcinoma patients have different responses to treatment and need more new biomarkers, and to provide a lung cancer marker and its application.

[0005] The present invention provides a technical solution: application of GAD1 glutamate decarboxylase in the preparation of lung adenocarcinoma medicine.

[0006] The GAD1 (glutamatedecarboxylase 1) gene, located on chromosome 2q31.1, encodes an isoform of glutamate decarboxylase, GAD1. GAD1 catalyzes the conversion of L-glutamate to γ-aminobutyric acid (GABA). As an important inhibitory neurotransmitter, GABA plays a crucial role in normal life, and GABA signaling is implicated in various cancers. GAD1 synthesis of GABA promotes the malignant progression of oral squamous cell carcinoma and restricts macrophage anti-tumor immunity. GAD1 expression, mutation, and methylation are significantly correlated with clinicopathological features and the tumor immune microenvironment. GAD1 is a pivotal gene for the development and progression of drug resistance in prostate cancer. miR-4284 acts as a tumor suppressor in renal cell carcinoma cells by targeting glutamate decarboxylase. However, current research on GAD1 is limited, and much of it focuses on its role in amino acid metabolism and tumor immunity, with a lack of studies examining its effects on purine metabolism in lung adenocarcinoma.

[0007] The present invention has found that GAD1 expression is significantly increased in the cancer tissues of LUAD lung adenocarcinoma patients, GAD1 is correlated with the prognosis of LUAD lung adenocarcinoma patients, and LUAD lung adenocarcinoma patients with low expression of the GAD1 gene have a better prognosis.

[0008] Therefore, the application of GAD1 in the preparation of lung adenocarcinoma drugs and the reduction of GAD1 levels in lung adenocarcinoma patients can effectively control the progression of lung adenocarcinoma in patients.

[0009] A technical solution of the present invention is: use of GAD1 glutamate decarboxylase in the preparation of MAPK pathway regulating drugs for lung adenocarcinoma.

[0010] The present invention discovered through gene set enrichment analysis (GSEA) of transcriptomics results that activation of GAD1 in lung cancer cells can significantly regulate the expression of the MAPK signaling pathway, thereby being applied to the preparation of drugs that regulate the MAPK signaling pathway of lung adenocarcinoma, thereby achieving the purpose of treating and assisting in the treatment of lung adenocarcinoma.

[0011] A technical solution of the present invention is: use of GAD1 glutamate decarboxylase in the preparation of xanthine, adenine, guanine or inosine regulating drugs for lung adenocarcinoma.

[0012] This study analyzed data from a GAD1 glutamate decarboxylase overexpression model and found that GAD1 overexpression significantly altered the levels of four purine metabolites in LUAD lung adenocarcinoma cells. Xanthine significantly increased; Adenine significantly decreased; Guanine significantly decreased; and Inosine significantly decreased. Activating GAD1 can regulate the reprogramming of purine metabolism, increasing the high levels of cellular building blocks required for cancer cell proliferation. In practical applications, GAD1 glutamate decarboxylase can be used to prepare drugs that regulate xanthine, adenine, guanine, or inosine for the treatment and adjuvant treatment of lung adenocarcinoma.

[0013] A technical solution of the present invention is: use of GAD1 glutamate decarboxylase in the preparation of ADA2 and GDA gene regulatory drugs for lung adenocarcinoma.

[0014] The experimental results of the present invention indicate that activating GAD1 expression may affect the purine metabolism of lung cancer cells by downregulating the ADA2 gene level and upregulating the GDA gene level. In terms of application, GAD1 can also be used to prepare ADA2 and GDA gene regulatory drugs for lung adenocarcinoma, thereby achieving the purpose of treating and assisting in the treatment of lung adenocarcinoma.

[0015] A technical solution of the present invention is: a lung cancer marker includes GAD1 glutamate decarboxylase.

[0016] Furthermore, a lung cancer marker includes GAD1 glutamate decarboxylase according to any one of the above applications.

[0017] Furthermore, the lung adenocarcinoma marker is used in the preparation of a drug or device for diagnosing lung adenocarcinoma.

[0018] Furthermore, the lung adenocarcinoma marker is used in the preparation of a drug or device for treating lung adenocarcinoma.

[0019] Furthermore, the lung adenocarcinoma marker is used in the preparation of drugs or devices for the companion diagnosis of lung adenocarcinoma.

[0020] Furthermore, the application of lung adenocarcinoma markers in the preparation of lung adenocarcinoma targeted drugs.

[0021] The specific research results of the present invention are: GAD1, AGXT2 mRNA and protein levels are significantly increased in clinical tissue samples of lung adenocarcinoma, and increased expression of GAD1 is associated with worsening prognosis. Therefore, high expression of the GAD1 gene can be used as a biomarker for the diagnosis and prognosis of LUAD lung adenocarcinoma. The present invention found through gene set enrichment analysis (GSEA) of transcriptomics results that activation of GAD1 in lung cancer cells can significantly regulate the expression of genes related to cancer pathways and MAPK signaling pathways. These results all indicate that the significant expression of GAD1 in LUAD lung adenocarcinoma is significantly correlated with the occurrence and progression of cancer.

[0022] To further investigate the metabolic reprogramming mechanism of GAD1 in lung adenocarcinoma, metabolomics analysis revealed that GAD1 activation significantly increases xanthine levels in lung cancer cells, decreases adenine, guanine, and inosine levels, and significantly regulates purine metabolism. Xanthine dehydrogenase reshapes the metabolism and survival of nutrient-deprived lung adenocarcinoma cells by promoting the unfolded protein response and autophagic degradation. A high caffeine-to-paraxanthine ratio is a risk factor for non-small cell lung cancer. An imbalance in the adenosine-to-inosine ratio frequently occurs during carcinogenesis. Adenosine synthesized from adenine is believed to be a key factor in regulating tumor cell immune evasion. Furthermore, GAD1 was found to significantly increase the proportion of the late DNA synthesis phase (G2) of the cell cycle. The G2 phase (second gap) is the late DNA synthesis phase and the preparatory phase for mitosis. During this phase, DNA synthesis ceases and RNA and protein synthesis proceed in high quantities. These results suggest that GAD1 activation in lung cancer cells can regulate purine metabolism reprogramming, increasing the levels of cellular building blocks required for cancer cell proliferation.

[0023] Multi-omics analysis revealed that activated GAD1 expression may affect purine metabolism in lung cancer cells by downregulating ADA2 and upregulating GDA. The ADA2 gene encodes a member of the adenosine deaminase family of proteins involved in purine metabolism. Adenosine deaminase 2 (ADA2) converts adenosine to inosine and is secreted by differentiated monocytic cells, regulating cell proliferation and differentiation. Low ADA2 expression is associated with a poor prognosis in LUAD lung adenocarcinoma. Enzymatic removal of adenosine using engineered ADA2 has potential for cancer immunotherapy. Furthermore, the GDA gene encodes an enzyme responsible for the hydrolytic deamination of guanine. Guanine is converted to xanthine by this ubiquitous guanine deaminase, GDA. Xanthine oxidase further metabolizes xanthine to uric acid, generating reactive oxygen species. Therefore, GAD1 activation in lung cancer cells may regulate purine metabolism reprogramming through ADA2 and GDA.

[0024] The present invention found that GAD1 can be used as a diagnostic marker for lung adenocarcinoma, confirming the regulatory role of GAD1 in purine metabolism. Using multi-omics sequencing and in vitro experimental results, it was found that GAD1 can regulate purine metabolism.

[0025] GAD1 has potential clinical significance as a diagnostic marker for lung adenocarcinoma. GAD1 increases xanthine levels in lung cancer cells, modulating cancer pathways and purine metabolism pathways, and inducing an increase in the G2 / M phase ratio of lung cancer cells. Activating GAD1 expression may influence purine metabolism reprogramming in lung cancer cells by downregulating ADA2 and upregulating GDA.

[0026] The lung cancer markers of the present invention can be used in the preparation of drugs or diagnostic devices for diagnosing lung adenocarcinoma, can be used in the preparation of drugs or diagnostic devices for treating lung adenocarcinoma, can be used in the preparation of drugs or devices for medical companion diagnosis of lung adenocarcinoma, and of course, can also be used in the preparation of targeted drugs for lung adenocarcinoma. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is the expression spectrum of GAD1 at the mRNA level in Example 1.

[0028] Figure 2 This is a graph showing the expression of GAD1 in LUAD lung adenocarcinoma patients and its effect on the cell cycle in Example 2.

[0029] Figure 3 This is the analysis diagram of differential metabolites and metabolic pathways detected by LC-MS in the GAD1 overexpression cell model in Example 3.

[0030] Figure 4 This is an analysis diagram of differentially expressed genes and gene pathways in the GAD1 overexpression cell model detected by RNA-seq in Example 4.

[0031] Figure 5 This is a joint analysis diagram of the LC-MS and RNA-seq detection results in Example 5.

[0032] Figure 6 This is an analysis diagram of the effect of GAD1 on the cell cycle of lung adenocarcinoma cells in Example 6. DETAILED DESCRIPTION

[0033] Glossary: XENA database: https: / / xenabrowser.net / .

[0034] GTEx database: https: / / xenabrowser.net / datapages / .

[0035] GTEx: The (Gene Expression in Tissue) database is a comprehensive resource containing gene expression data and associated genetic variation information for human tissue samples; TCGA: TCGA (The Cancer Genome Atlas) is an international research project jointly launched by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI) in 2006.

[0036] TCGA database: https: / / portal.gdc.cancer.gov / .

[0037] LUAD: is the abbreviation of lung adenocarcinoma.

[0038] FPKM format: FPKM (Fragments Per Kilobase of exon model per Million mapped reads) is a unit used to estimate gene expression, mainly used in RNA-seq data analysis.

[0039] BioGPS database: http: / / biogps.org / .

[0040] GAD1: is a specific type of glutamate decarboxylase that is primarily responsible for converting glutamate into gamma-aminobutyric acid (GABA) in the human body.

[0041] GEPIA2: Developed by Professor Zhang Zemin's team at Peking University, GEPIA2 is a gene expression data analysis tool primarily used for gene expression analysis of tumor and normal samples from the TCGA and GTEx projects. GEPIA2 offers a variety of functions, including differential expression analysis, survival analysis, and correlation analysis, to aid researchers in cancer research. http: / / gepia2.cancer-pku.cn /

[0042] GEO database: http: / / www.ncbi.nlm.nih.gov / geo / .

[0043] GSE32863: is a gene expression profile chip dataset stored in the NCBI-GEO database, containing gene expression data of 58 normal lung tissues and 58 cancer tissue samples from lung adenocarcinoma patients.

[0044] GSE46539, GSE46539: is a dataset number in NCBI's GEO database. Each GSE number corresponds to a specific gene expression dataset, which contains gene expression profile data, chromatin status, genomic variation and other information in various biomedical research.

[0045] BLUEFBIO Cell Bank: A series of standardized, high-quality cell line resource libraries established by BLUEFBIO Biotechnology Co., Ltd. These cell lines are widely used in life science research, drug development, disease model construction and other fields.

[0046] Ad: adenovirus; Ad-GFP is a recombinant adenovirus carrying the green fluorescent protein (GFP) gene.

[0047] Ad-GAD1: Adenovirus carrying GAD1 glutamate decarboxylase.

[0048] TRIzol (Takara): A total RNA extraction reagent used primarily for extracting total RNA from cells or tissues. TRIzol's main ingredients include phenol and guanidine isothiocyanate, which rapidly disrupt cells and inhibit nucleases released by cells, thereby maintaining RNA integrity.

[0049] Illumina HiSeq platform: It is a series of high-throughput sequencing systems launched by Illumina, mainly including HiSeq 2000, HiSeq 4000 and HiSeq X Ten models.

[0050] NGS technology: The full name is Next Generation Sequencing technology, also known as high-throughput sequencing technology or second-generation sequencing technology.

[0051] DESeq: is a statistical method for analyzing sequence count data, mainly used for differential expression analysis.

[0052] GSEA (Gene Set Enrichment Analysis) is a method used to identify gene sets associated with specific biological processes or signaling pathways.

[0053] Gene Ontology (GO) is an ontology widely used in bioinformatics, which aims to uniformly describe the characteristics of genes and gene products in all species.

[0054] BlasTaqTM 2X qPCR MasterMix: is a kit for quantitative real-time analysis of DNA samples, providing a convenient, reliable and efficient formula.

[0055] GAPDH is the abbreviation of glyceraldehyde-3-phosphate dehydrogenase.

[0056] Invitrogen: is a multinational corporation founded in 1987 and headquartered in Carlsbad, California, USA.

[0057] DMEM: (Dulbecco's Modified Eagle Medium) is a widely used cell culture medium, mainly used to support the growth of a variety of mammalian cells.

[0058] Mann-Whitney U test: It is a statistical test method proposed by HB Mann and DR Whitney in 1947.

[0059] GraphPad Prism: A data analysis and visualization software that integrates biostatistics, curve fitting, and scientific graphing, designed specifically for scientific research.

[0060] 1-Methylhistidine: 1-Methylhistidine.

[0061] L-erythro-4-Hydroxyglutamate: L-erythro-4-hydroxyglutamate.

[0062] Dimethylglycine: Dimethylglycine.

[0063] Xanthine: xanthine.

[0064] Maltol: maltol.

[0065] 3-Ketosphingosine: 3-Ketosphingosine.

[0066] L-2-Hydroxyglutaric acid: L-2-Hydroxyglutaric acid.

[0067] Guanine: Guanine.

[0068] Adenine: Adenine.

[0069] Leucine: Leucine.

[0070] WB experiment: Western Blot is mainly used to detect proteins and evaluate their expression levels.

[0071] The following is further described in detail through specific implementation methods: Materials and methods for the following examples: 1. Data and Sample Collection We downloaded and organized RNAseq data from 33 tumor types from the XENA database, extracted data in TPM format, and processed the data using the Toil pipeline. These data included GTEx normal tissue samples (n=7568), TCGA adjacent tissue samples (n=727), and TCGA tumor tissue samples (n=9807).

[0072] RNA-seq data and corresponding clinical information of LUAD lung adenocarcinoma tissue samples (n = 515) and adjacent tissue samples (n = 59) were downloaded from the TCGA database, and data in FPKM format were extracted. Data of normal lung tissue samples (n = 288) were downloaded from the GTEx database.

[0073] The BioGPS database was used to analyze the expression profile of the GAD1 gene in different organs and tissues of the normal human body. The present invention used GEPIA2 to analyze the effect of GAD1 on the clinical prognosis of patients with LUAD lung adenocarcinoma.

[0074] The RNAseq data of the GSE32863 and GSE46539 datasets of lung adenocarcinoma patients were downloaded from the GEO database to analyze the differential expression of the GAD1 gene.

[0075] This study collected cancerous and adjacent tissues from 25 patients with LUAD lung adenocarcinoma who underwent surgery at the Department of Thoracic Surgery at a hospital in Chongqing, China, and analyzed differential expression of the GAD1 gene. All patients provided written informed consent, and the research was approved by the research ethics committee of the hospital in Chongqing.

[0076] 2. Lentivirus Production The GAD1 gene (Gene ID: 2571; Pathology Number: NM_000817.3) was cloned from the plasmid pHBLV-GAD1 (Cat). The vector is named plenti-GIII-CMV-CBH-GFP-2A-Puro. The CDS region of the target gene was synthesized and inserted downstream of the CMV promoter into the plenti-GIII-CMV-CBH-GFP-2A-Puro vector to generate the target vector plasmid. The blank control lentiviral vector is named pLenti-CMV-CBH-GFP-2A-Puro-Blank Vector. Lentiviral service company: Applied Biological Materials Inc. 3. Western Blot Analysis Proteins in the cells were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis. After transfer to a membrane, the membrane was incubated with primary antibodies against GAD1 (1:1000; Cat No #10408-1-AP) and β-actin (1:6000; Cat #CPA9066). β-actin was used as an internal control.

[0077] IV. Cell Culture The present invention used human epithelial A549 cells from the BLUEFBIO cell bank (Shanghai, BFN60800665). The cell line was cultured in DMEM (Thermo Fisher Scientific, USA) supplemented with 10% fetal bovine serum and penicillin / streptomycin in a humidified incubator at 37°C and 5% carbon dioxide.

[0078] 5. Transcriptomic Sequencing Analysis (RNA-seq) For RNA sequencing, A549 cells were infected with Ad-GFP or Ad-GAD1 at a 1 / 2 volume ratio for 48 hours. Total RNA was extracted using TRIzol (Takara). RNA-seq experiments were performed by Panomix (Suzhou). Total and effective concentrations of the libraries were determined using an Agilent 2100 Bioanalyzer. Paired-end sequencing was performed using NGS technology on an Illumina HiSeq platform.

[0079] VI. Differential Gene Expression Analysis The present invention conducted differential gene expression analysis between the Ad-GAD1 overexpression group and the Ad-GFP control group. DESeq was used to analyze gene expression differences, and differentially expressed genes were selected under the following conditions: expression difference |log2FC| > 1, and significance p-value < 0.05. Volcano plots, Venn diagrams, and annular heatmaps were generated using the ggplot2 software package. Functional enrichment analysis was performed using gene set enrichment analysis (GSEA), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and progeny enrichment analysis.

[0080] VII. Reverse Transcription and Real-Time PCR Transfected cells were lysed with TRIzol (Takara). RNA was reverse transcribed using the G592 All-In-One 5XRT MasterMix kit from abm. The expression levels of specific genes were measured using the BlasTaq™ 2X qPCR MasterMix (abm, Cat No. G891). Relative expression was determined by comparing transcript levels with GAPDH expression using a real-time PCR system (qTower 2.2, 313BO305, Jena, Germany). Primers were synthesized by Invitrogen and are listed in Table 1.

[0081] Table 1: Primers used for qPCR analysis 8. Metabolomics Sequencing Analysis (UPLC-MS / MS) The specimens were sonicated with 0.3 ml of ethanol at 25°C for 30 minutes and centrifuged at 12,000 rpm for 10 minutes. The supernatant was filtered through a 0.22 μm membrane and analyzed using UPLC-MS / MS from BioNovoGene. A quality control sample was prepared by combining 30 μl of filtrate from each supernatant. The quality assurance sample was used to track the variability of the analytical results of the combined sample mixture and to compare this variability with the inherent error of the analyzer. The remaining samples were analyzed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS).

[0082] IX. SiRNA Transfection siRNA (GeneBiogist, Shanghai, China) was diluted to a concentration of 40 nM in RNase-free water and stored at 4°C. Thermo Fisher Scientific's lip3000 was diluted 1:50 in DMEM. The siRNA and transfection reagent were combined in solution. The siRNA sequences are shown in Table 1.

[0083] 10. Plasmid Transfection The GAD1 overexpression plasmid and the corresponding control vector, pcDNA3.1(+), were purchased from the Hubei Miaoling Plasmid Platform. The plasmids were transfected into lung cancer A549 cells using Lipofectamine 3000 (Thermo Fisher Scientific) according to the manufacturer's instructions. Serum-free medium was used during transfection, and serum-supplemented medium was used for further culture 6 hours after transfection.

[0084] 11. Quantitative determination of DNA content (cell cycle) After transfecting A549 cells with GAD1 plasmid and siRNA, the cells were harvested. Using the Solarbio DNA Content Assay Kit (Cell Cycle), Catalog No. CA1510, cells were washed once with PBS and centrifuged at 1500 rpm for 5 minutes. The cell concentration was adjusted to 1 × 10⁶ / mL. 1 mL of single-cell suspension was collected and centrifuged. The supernatant was removed and fixed with 500 μL of 70% pre-cooled ethanol for 2 hours to overnight. The cells were then stored at 4°C. The fixation solution was washed with PBS and stained. Cells were precipitated with 100 μL of RNase A solution, then suspended and immersed at 37°C for 30 minutes. 400 μL of PI staining solution was added and mixed thoroughly. The cells were incubated in the dark at 4°C for 30 minutes. Red fluorescence was detected using a fluorescence microscope at an excitation wavelength of 488 nm.

[0085] 12. Quantification and Statistical Analysis RNA-seq data downloaded from public databases were analyzed using the Mann-Whitney U test (Wilcoxon ranksum test). GAD1 gene differential analysis and visualization were performed using the R language ggplot2, stats, and car packages. GraphPad Prism was used for statistical analysis, using t-tests and one-way analysis of variance. Statistical significance was considered to be less than 0.05.

[0086] Example 1: GAD1 mRNA expression profile analysis based on TCGA, BioGPS, GTEx, and GEO databases Based on the TCGA and GTEx datasets, the present invention analyzed RNA data of 33 tumors and found that GAD1 expression was increased in many tumors (such as Figure 1 -A). Based on the analysis of the BioGPS database, it was found that GAD1 expression was significantly increased in chronic myeloid leukemia K-562, colorectal adenocarcinoma, and CD105+ endothelial cell tissues (e.g. Figure 1 -B). Figure 1 As shown in Figure 2C, based on the TCGA and GTEx datasets, a comparison of LUAD lung adenocarcinoma patient cancer tissue samples (n=515) and normal lung tissue samples (n=347) revealed that GAD1 mRNA was significantly upregulated in LUAD lung adenocarcinoma patients (e.g. Figure 1 Based on GEO database analysis, in the GSE32863 and GSE46539 datasets, the expression of the GAD1 gene was significantly increased in the cancer tissues of LUAD lung adenocarcinoma patients (e.g. Figure 1 -D, E). Based on the GEPIA2 database analysis, the GAD1 gene is associated with the prognosis of LUAD lung adenocarcinoma patients, and LUAD lung adenocarcinoma patients with low GAD1 gene expression have a better prognosis (as shown in Figure 2). Figure 1 -F) (P=0.042<0.05).

[0087] in Figure 1 -A. The expression status of the GAD1 gene in different cancers or specific cancer subtypes was analyzed in the TCGA and GTEx datasets. Figure 1 -B. GAD1 expression in various organs and tissues according to the BioGPS database. Figure 1 -C. In the TCGA and GTEx datasets, GAD1 mRNA was significantly overexpressed in LUAD lung adenocarcinoma tissues compared with normal liver tissues. Figure 1 -D. In GSE23863, the expression of GAD1 in LUAD lung adenocarcinoma tissues was significantly higher than that in normal liver tissues. Figure 1E. In GSE46539, the expression of GAD1 in LUAD lung adenocarcinoma tissues was significantly higher than that in normal liver tissues. Figure 1 -F. GAD1 was associated with the prognosis of patients with LUAD lung adenocarcinoma in the GEPIA2.0 dataset. * P ≤ 0.05; *** P ≤ 0.001.

[0088] Example 2: GAD1 expression is upregulated in LUAD lung adenocarcinoma patient tissues Western Blot analysis showed that GAD1 protein expression was significantly upregulated in the cancer tissues (T) of 8 LUAD lung adenocarcinoma patients compared with the adjacent lung tissues (N). Figure 2 -A), and statistical analysis was performed using ImageJ and Graphpad software, with statistically significant differences (as shown in Figure 2 -B). RT-qPCR experiments showed that GAD1 mRNA expression was significantly upregulated in cancer tissues (T) of 25 patients with LUAD lung adenocarcinoma (as shown in Figure 2 -C).

[0089] in, Figure 2 -A. In 8 patients with LUAD lung adenocarcinoma, GAD1 protein expression was increased in the cancer tissues of LUAD lung adenocarcinoma patients. Figure 2 -B. Grayscale statistical analysis was performed using ImageJ and Graphpad software. Figure 2 -C. GAD1 mRNA expression was elevated in LUAD lung adenocarcinoma tissues in 25 patients. * P ≤ 0.05; *** P ≤ 0.001.

[0090] Example 3: LC-MS metabolomics data analysis of the metabolic regulation of GAD1 in LUAD lung adenocarcinoma cells The present invention first established a GAD1 overexpression cell model by transfecting Ad-GAD1 and Ad-GFP lentiviruses into A549 cells. Metabolomics identified differentially expressed metabolites (DETs). A total of 227 metabolites were detected in the comparison between Ad-GAD1 and Ad-GFP. A cluster heat map (e.g., Figure 3 -A). The total number of differential metabolites was 21, and a cluster heat map was drawn based on the differential metabolites (as shown in Figure 2). Figure 3 Different metabolites have synergistic or mutually exclusive relationships. If a certain type of metabolites has the same change trend, it means that the relative content changes of this type of metabolites are positively correlated; if the trends are opposite, it is a negative correlation.

[0091] In order to check the consistency of the change trends among metabolites, the correlation between each metabolite was analyzed by calculating the Pearson correlation coefficient between the two differential metabolites (e.g. Figure 3 Among the 21 differential metabolites, 10 were decreased and 11 were increased (e.g. Figure 3 -D). Among them, the top five metabolites with increased expression are: 1-Methylhistidine, L-erythro-4-Hydroxyglutamate, Dimethylglycine, Xanthine, and Maltol; the top five metabolites with decreased expression are: 3-Ketosphingosine, L-2-Hydroxyglutaric acid, Guanine, Adenine, and Leucine (such as Figure 3 -E). We drew a volcano plot based on the differential metabolites (as shown in Figure 3 -F). MetaboAnalyst (www.metaboanalyst.ca) was used to perform KEGG pathway enrichment analysis on the differential metabolite list. The top two metabolic pathways enriched by the present invention were: Purine metabolism pathway, P value = 0.0026, -Log10 (P value) = 2.59; Basal cell carcinoma pathway, P value = 0.0063, -Log10 (P value) = 2.20 (as shown in Figure 2). Figure 3 -G). Among them, in the accompanying drawings Figure 3 -A. Cluster heat map of overall metabolites, red indicates high expression and blue indicates low expression. Figure 3 -B. Cluster heat map of differential metabolites, red indicates high expression and blue indicates low expression. Figure 3 -C. Correlation analysis of differential metabolites. When the linear relationship between two metabolites strengthens, it approaches 1 for positive correlation and -1 for negative correlation. Statistical analysis of the significance of metabolite correlations was performed, with a P value < 0.05 considered significant. Red indicates positive correlation, and blue indicates negative correlation. Figure 3 -D. Histogram of the number of differential metabolites. Figure 3 -E. Top five differentially up-regulated or down-regulated metabolites. Figure 3 -F. Volcano plot of differential metabolites. Figure 3-G. Scatter plot of metabolic pathway impact factors. The horizontal axis represents the impact value of enrichment in different metabolic pathways, and the vertical axis represents the -log10 (P value). Colors are correlated with P values: darker colors indicate smaller P values, and lighter colors indicate larger P values. The closer a pathway is to the upper right corner, the more significantly enriched the differential metabolite is in that pathway, and the greater its impact on that pathway.

[0092] Example 4: RNA-Seq transcriptomics data analysis of the gene regulatory role of GAD1 in LUAD lung adenocarcinoma cells The present invention first established a GAD1 overexpression model in A549 cells infected with Ad-GAD1 and Ad-GFP. Total RNA was extracted, analyzed by RNA-Seq, and then differentially expressed genes (DEGs) were screened. The percentage of bases with a base recognition accuracy of more than 99.9% (the proportion of Q30 bases) was greater than 94.55%, and the alignment rate of Clean Reads with the specified reference genome was 98.64%-98.76%. A total of 17,883 genes were detected, of which 1,082 were upregulated and 901 were downregulated in cells overexpressing GAD1. Cluster diagrams were drawn based on the differentially expressed genes (e.g., Figure 4 -A) and volcano plots (as Figure 4 -B). In the GAD1 overexpression model, we screened the top 20 differentially expressed genes, among which the top 10 significantly upregulated genes were MAGEA4, AF1, FGF5, MAGEA10, IFIT1, TRIM58, OAS2, RSAD2, IFI27, and TDRD12, and the top 10 significantly downregulated genes were GNG7, DDX4, ADAMTS4, NCAM2, GRID2, VIT, HSF5, TTR, ABCB5, and DHRS2 (as shown in Figure 2). Figure 4 -C). At the same time, GO enrichment analysis was performed. During the analysis, the gene list and number of genes for each term were calculated using the GO-annotated differential genes (the standard for significant enrichment was P value < 0.05), and the GO terms that were significantly enriched in the differential genes compared with the whole genome background were found, thereby determining the main biological functions of the differential genes. The GO enrichment analysis results of the differentially expressed genes were classified according to molecular function MF, biological process BP and cellular component CC. The top 10 GO term entries with the smallest p-value, i.e., the most significant enrichment, were selected for display in each GO classification. The results are shown in (see Figure 4-D). Based on the KEGG enrichment results, the degree of enrichment was measured by Rich factor, FDR value, and the number of genes enriched in this pathway. The top 20 KEGG pathways with the smallest FDR value, i.e., the most significantly enriched ones, were selected to draw a bubble chart. The results showed that GAD1 significantly regulated multiple signaling pathways, including the MAPK signaling pathway (P=0.005) (e.g. Figure 4 -E). Among them, in the accompanying drawings Figure 4 -A. Cluster diagram of differentially expressed genes, red indicates highly expressed genes, and green indicates lowly expressed genes. Figure 4 -B. Volcano plot of differentially expressed genes, red indicates highly expressed genes and green indicates lowly expressed genes. Figure 4 -C. Top 10 significantly up-regulated or down-regulated genes. Figure 4 -D. GO enrichment analysis histogram. The horizontal axis represents the Go level 2 terms, and the vertical axis represents the -log10 (p-value) of each term enrichment. Figure 4 -E. KEGG Pathway enrichment results bubble chart. The Rich Factor refers to the ratio of the number of differentially enriched genes in the pathway to the number of annotated differentially enriched genes. A larger Rich Factor indicates a greater degree of enrichment. The FDR generally ranges from 0 to 1, with values ​​closer to zero indicating more significant enrichment.

[0093] Example 5: Combined LC-MS and RNA-seq analysis reveals that GAD1 increases xanthine levels and regulates the Purinemetabolism pathway A549 cells were transfected with either an empty GFP virus or a GAD1-overexpressing lentivirus to construct a GAD1-overexpressing cell model. Combined analysis of LC-MS and RNA-seq data from the GAD1-overexpressing model revealed significant changes in the levels of four purine metabolites in LUAD lung adenocarcinoma cells after GAD1 overexpression, with a significant increase in xanthine (log2FC=1.22, P=0.0057). Figure 5 -A); Adenine was significantly reduced, log2FC=-1.23, P=0.0057 (as shown in Figure 5 -B); Guanine was significantly reduced, log2FC=-2.51, P=0.0045 (as shown in Figure 5 -C); Inosine was significantly decreased, log2FC=-0.6, P=0.028 (as shown in Figure 5-D). Total RNA was extracted from A549 cells transfected with empty GFP virus or GAD1 overexpressing lentivirus, and RNA sequencing was performed (n=3). GSEA enrichment plots of differentially expressed genes in the basal cell carcinoma signaling pathway gene set were drawn (as shown in Figure 2). Figure 5 -E). It was found that GAD1 overexpression affected the expression of 11 differentially expressed genes in the basal cell carcinoma signaling pathway in lung adenocarcinoma cells, including SHH, PTCH1, GADD45A, GLI1, GLI3, WNT16, FZD1, LEF1, AXIN2, and WNT5A, and a heat map of the pathway differentially expressed genes was drawn (as shown in Figure 2). Figure 5 -F). Draw the GSEA enrichment map of the differentially expressed genes in the Purinemetabolism signaling pathway gene set (as shown in Figure 5 -G). GAD1 overexpression affected the expression of 12 differentially expressed genes in the purine metabolism signaling pathway in lung adenocarcinoma cells, including GMPR, GUCY1B1, GDA, RRM2, and a heat map of the pathway differentially expressed genes was drawn (as shown in Figure 2). Figure 5 -H). By combining metabolomics and transcriptomics, we mapped the mechanism by which GAD1 overexpression affects purine metabolism in lung cancer cells (e.g. Figure 5 -I). Among them, Figure 5 middle, Figure 5 -A. LC-MS detection of xanthine expression differences. Figure 5 -B. LC-MS detection of differential expression of adenine. Figure 5 -C. LC-MS detection of differential expression of guanine. Figure 5 -D. LC-MS detection of differential expression of inosine. Figure 5 -E. Total RNA was extracted from A549 cells transfected with either empty GFP virus or GAD1-overexpressing lentivirus, and RNA sequencing was performed (n = 3). GSEA enrichment plots of differentially expressed genes in the basal cell carcinoma signaling pathway gene set were generated. Figure 5 -F. Draw a heat map of differentially expressed genes in the basal cell carcinoma signaling pathway gene set. Figure 5 -G. Draw the GSEA enrichment map of the differentially expressed genes in the Purine metabolism signaling pathway gene set. Figure 5 -H. Draw a heat map of differentially expressed genes in the Purine metabolism signaling pathway gene set. Figure 5 -I. Diagram of the mechanism by which GAD1 overexpression affects purine metabolism in lung cancer cells. * P ≤ 0.05; ** P ≤ 0.01.

[0094] Example 6: GAD1 affects the cell cycle of lung adenocarcinoma cells A GAD1 overexpression cell model was constructed and transfected with ad-GAD1 lentivirus and ad-GFP lentivirus control groups. After 48 h of transfection, cell proteins were extracted and the increase in GAD1 protein expression was detected by WB experiments and fluorescence photography (e.g. Figure 6 -A, such as Figure 6 -AB). This indicates that the GAD1 overexpression cell model was successfully constructed. Cell cycle experiments showed that after GAD1 overexpression, the proportion of the late DNA synthesis (G2 phase) in lung cancer cells increased significantly (as shown in Figure 2). Figure 6 -C, such as Figure 6 -D). Among them, Figure 6 middle, Figure 6 -A. Western blot analysis validated the adGAD1 cell model. Figure 6 -B. Fluorescence images of adGAD1 cell model. Figure 6 -C. Cell cycle alterations after GAD1 knockdown and overexpression in lung cancer cells. Figure 6 D. Graphpad statistical analysis of cell cycle experiments. ***P ≤ 0.001.

[0095] Thus, GAD1 of the present invention can increase xanthine levels in lung cancer cells, regulate cancer pathways and purine metabolism pathways, and induce an increase in the G2 / M phase ratio of lung cancer cell cycles. Activating GAD1 expression may affect purine metabolism reprogramming in lung cancer cells by downregulating ADA2 gene levels and upregulating GDA gene levels.

[0096] The above is only an embodiment of the present invention, and common knowledge such as the specific structure and characteristics of the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the structure of the present invention. These should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. Application of GAD1 glutamate decarboxylase in the preparation of lung adenocarcinoma drugs.

2. Application of GAD1 glutamate decarboxylase in the preparation of MAPK pathway regulatory drugs for lung adenocarcinoma.

3. Application of GAD1 glutamate decarboxylase in the preparation of xanthine, adenine, guanine or inosine regulating drugs for lung adenocarcinoma.

4. Application of GAD1 glutamate decarboxylase in the preparation of ADA2 and GDA gene regulatory drugs for lung adenocarcinoma.

5. A lung cancer marker, characterized by: Including GAD1 glutamate decarboxylase.

6. A lung cancer marker, characterized by: The invention comprises the GAD1 glutamate decarboxylase according to any one of claims 1 to 4.

7. Use of the lung adenocarcinoma marker according to claim 5 or 6 in the preparation of a drug or device for diagnosing lung adenocarcinoma.

8. Use of the lung adenocarcinoma marker according to claim 5 or 6 in the preparation of a drug or device for treating lung adenocarcinoma.

9. Use of the lung adenocarcinoma marker according to claim 5 or 6 in the preparation of a drug or device for companion diagnosis of lung adenocarcinoma.

10. Use of the lung adenocarcinoma marker according to claim 5 or 6 in the preparation of a lung adenocarcinoma targeted drug.

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