Method for regulating lipid metabolism and influencing mechanism of action caused by polycyclic aromatic hydrocarbon on lung cancer by piRNA and application

Abstract

The invention relates to the field of biomedicine, in particular to a method for regulating lipid metabolism and influencing a polycyclic aromatic hydrocarbon induced lung cancer action mechanism through piRNA and application. A new target spot and a new strategy are provided for preventing and treating the lung cancer caused by the PAHs. The invention adopts the following technical scheme: coke oven tail gas escape with polycyclic aromatic hydrocarbon as a main component is adopted to induce HBE cell aversion to lung squamous cell carcinoma cloning, and RNA-seq, small molecule RNA-seq and lipid metabonomics analysis and bioinformatics analysis are systematically carried out; through cytology, zoology and lung cancer sample research, the action mechanism that piRNA regulates lipid metabolism genes and further affects the occurrence of the lung cancer caused by the polycyclic aromatic hydrocarbon is clarified.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a research method and application for exploring the mechanism of piRNA regulating lipid metabolism and affecting polycyclic aromatic hydrocarbons in causing lung cancer. Background Art

[0002] Polycyclic aromatic hydrocarbons (PAHs) are a class of organic pollutants widely present in the environment, and their content is high in coke oven exhaust emissions. Long-term exposure to PAHs is closely related to the occurrence of lung cancer. However, the specific molecular mechanism of PAHs-induced lung cancer is not yet fully understood. In recent years, the role of non-coding RNA in the occurrence and development of tumors has received widespread attention. Among them, piRNA, as a new type of non-coding small RNA, has gradually been revealed to play a role in gene regulation and various diseases. However, the role and mechanism of piRNA in the process of PAHs-induced lung cancer still need to be further studied. . Summary of the invention

[0003] The technical problem to be solved by this technical solution is: The present invention aims to provide a comprehensive and systematic research method to clarify the mechanism of action of piRNA in affecting the occurrence of lung cancer caused by PAHs by regulating lipid metabolism genes. This method will integrate a variety of advanced technical means, including cell culture, molecular biology experiments, omics analysis, animal model construction and clinical sample research, etc., to provide new theoretical basis and potential therapeutic targets for the prevention and treatment of PAHs-induced lung cancer.

[0004] To achieve the above objectives, the present invention creates a technical solution: a method for piRNA to regulate lipid metabolism and affect the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer, comprising the following steps:

[0005] a) Construction of cell model: Human bronchial epithelial cells (such as HBE cells) are selected as the research basis. By exposing them to coke oven exhaust emissions containing polycyclic aromatic hydrocarbons, the cells are induced to clone and metastasize into lung squamous cell carcinoma, thereby constructing a cell model that can simulate the development process of lung cancer. The construction of the cell model also includes the identification of the lung cancer cell model, specifically: through morphological observation, cell proliferation experiments, cell cycle analysis and marker detection methods, the constructed lung cancer cell model is identified to determine whether the constructed cell model meets the characteristics of lung cancer cells.

[0006] b) Multi-omics analysis: RNA-seq and small molecule RNA-seq sequencing were performed on the constructed lung cancer cell model to accurately identify the 2.5Abnormal expression of piRNA (especially piR-27222) during the development of lung cancer induced by induced mitochondria; lipid metabolomics analysis, using mass spectrometry technology to deeply analyze the changes in lipid metabolites in cell culture fluid and cell lysate to reveal the specific role of lipid metabolism in the development of lung cancer; RNA-seq and small molecule RNA-seq sequencing include the following steps:

[0007] b1) RNA extraction: Use an appropriate RNA extraction kit to extract total RNA from the constructed lung cancer cell model and control cells. Avoid RNA degradation and contamination during the extraction process to ensure the quality and integrity of RNA;

[0008] b2) Library construction: The extracted RNA is reverse transcribed to synthesize cDNA, and then the cDNA is fragmented using appropriate kits and methods, and connected to adapters to construct a sequencing library;

[0009] b3) Sequencing: Use a high-throughput sequencing platform to sequence the constructed library, and the sequencing depth and coverage are selected according to experimental requirements;

[0010] b4) Data analysis: Analyze the data obtained from sequencing, including data quality assessment, sequence alignment, gene expression quantification and differential expression analysis; identify 2.5 The abnormal expression of piRNA, especially piR-27222, during the induction of lung cancer was investigated. Specifically, by comparing with the control group, piRNAs whose expression was significantly upregulated or downregulated in the lung cancer cell model were screened out, and further functional studies were performed on them.

[0011] Lipid metabolomics analysis includes the following steps:

[0012] b5) Sample preparation: Collect cell culture fluid and lysate, and use appropriate methods to extract lipid metabolites, avoiding oxidation and degradation of lipid metabolites to ensure the quality and stability of the samples;

[0013] b6) Mass spectrometry analysis: Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) technology is used to analyze the extracted lipid metabolites, and the molecular weight, structural information and relative content of lipid metabolites are obtained through mass spectrometry analysis;

[0014] b7) Data analysis: The data obtained from the mass spectrometry analysis are processed and analyzed, including data preprocessing, peak identification, quantitative analysis and differential metabolite screening. These analyses are used to understand the changes in lipid metabolism during the development of lung cancer, as well as the regulatory effects of piRNA on lipid metabolism. Specifically, by comparing with the control group, lipid metabolites with significantly changed levels in the lung cancer cell model are screened out, and their correlation with piRNA expression is analyzed.

[0015] c) Bioinformatics analysis: Integrate RNA-seq, small molecule RNA-seq and lipid metabolomics data resources to screen out differentially expressed genes and piRNAs closely related to lipid metabolism, and then construct a gene regulatory network to deeply analyze the interaction between piR-27222 and lipid metabolism genes; For specific bioinformatics analysis, please refer to the following detailed steps:

[0016] c1) Data Integration:

[0017] c11) Data collection: Integrate RNA-seq (transcriptome sequencing), small RNA-seq (small RNA sequencing, mainly used to detect small RNA molecules such as miRNA and piRNA) and lipid metabolomics data resources; RNA-seq data can provide gene expression level information, small RNA-seq data can be used to identify and analyze the expression of piRNA, and lipid metabolomics data can reflect the types and contents of lipid metabolites in cells;

[0018] c12) Data preprocessing: Quality control and preprocessing of the collected raw data, including removal of low-quality sequencing reads, correction of sequencing errors, and standardization and normalization of the data to ensure the accuracy and reliability of the data;

[0019] c2) Differential expression analysis:

[0020] c21) Gene differential expression analysis: Use appropriate statistical methods and bioinformatics tools to perform differential expression analysis on RNA-seq data and screen out genes with significant differential expression between different experimental conditions or sample groups;

[0021] c22) piRNA differential expression analysis: Similarly, small molecule RNA-seq data were analyzed to screen out differentially expressed piRNAs. Considering the important role of piRNAs in gene regulation, special attention was paid to piRNAs related to lipid metabolism;

[0022] c3) Functional annotation and enrichment analysis:

[0023] c31) Gene function annotation: Functionally annotate differentially expressed genes to understand their biological functions, cellular localization, and signal pathways involved; use databases such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional annotation and enrichment analysis;

[0024] c32) piRNA target prediction: Use bioinformatics algorithms to predict potential targets of differentially expressed piRNAs. By binding to the targets, piRNAs can regulate gene expression.

[0025] c4) Construction of gene regulatory network:

[0026] c41) Integrative analysis: Integrate the information of differentially expressed genes and piRNAs to construct a gene regulatory network. In this network, genes and piRNAs are nodes, and their interactions, such as transcriptional regulation and RNA interference, are edges.

[0027] c42) Network analysis: Analyze the constructed gene regulatory network to identify key nodes and modules; these key nodes and modules can play a role in the regulation of lipid metabolism. By deeply analyzing the topological structure and functional modules of the network, the regulatory mechanism of lipid metabolism and the interaction between piR-27222 and lipid metabolism genes can be revealed.

[0028] d) Functional experimental verification: Through cell proliferation, apoptosis, migration and invasion experiments, the specific effects of piR-27222 on the malignant phenotype of cells were fully verified. The expression levels of piR-27222 and its target genes were precisely manipulated by gene knockout and overexpression techniques to further observe the actual effects of these changes on cell biological behavior and lipid metabolism. Functional experimental verification specifically includes the following:

[0029] d1) piRNA overexpression and knockdown experiments: piRNA overexpression and knockdown vectors were constructed and transfected into lung cancer cell models to achieve piRNA overexpression and knockdown. Then, real-time fluorescence quantitative PCR (qPCR) and Northern blot methods were used to verify whether the expression level of piRNA was successfully changed.

[0030] d2) Detection of lipid metabolism-related indicators: In lung cancer cell models with piRNA overexpression and knockdown, detect lipid metabolism-related indicators, such as changes in the content of triglycerides (TG), cholesterol (CHOL), free fatty acids (FFA), etc.; at the same time, detect the activity of lipid metabolism-related enzymes, such as fatty acid synthase (FASN), acetyl-CoA carboxylase (ACC), etc., to understand the regulatory effect of piRNA on lipid metabolism;

[0031] d3) Cell function experiments: Through cell proliferation experiments, cell apoptosis experiments, cell migration and invasion experiments, the effects of piRNA on lung cancer cell proliferation, apoptosis, migration and invasion were detected; at the same time, the sensitivity of lung cancer cells to PAHs was detected to understand the role of piRNA in the process of PAH-induced lung cancer;

[0032] d4) Animal experiments: Construct an animal model of lung cancer, inoculate lung cancer cells with piRNA overexpression or knockdown into the animal, and observe the growth and metastasis of the tumor; at the same time, detect changes in the content of lipid metabolites in the animal to further verify the regulatory effect of piRNA on lipid metabolism and the occurrence and development of lung cancer.

[0033] e) Mechanism study: In-depth study of the relationship between piR-27222 and m 6 The complex regulatory relationship between piR-27222 and related proteins (such as WTAP and eIF4B) was studied to clarify the specific mechanism of action of piR-27222 in regulating lipid metabolism genes and affecting the occurrence of lung cancer. The mechanism research specifically includes the following:

[0034] e1) Target gene prediction: Use bioinformatics methods to predict the target genes of piRNAs, and screen out possible target genes by analyzing the complementarity between piRNAs and target genes, as well as the functions and biological pathways of target genes;

[0035] e2) Verify target genes: Use luciferase reporter gene experiments and RNA immunoprecipitation (RIP) experiments to verify the interaction between piRNA and predicted target genes; at the same time, use qPCR and Western blot methods to detect whether the expression level of the target gene is regulated by piRNA;

[0036] e3) Signaling pathway research: By detecting the expression and activity changes of related signaling pathway molecules, such as PI3K / Akt and MAPK, the signaling pathway mechanism of piRNA regulating lipid metabolism and affecting PAH-induced lung cancer is studied.

[0037] Furthermore, the above sequencing analysis and experiments showed that piR-27222 and piR-7327 are important response factors in the process of PAH-induced lung cancer. piR-27222 can promote the proliferation, invasion and migration of lung cancer cells and inhibit the death of lung cancer cells.

[0038] Furthermore, the above-mentioned mechanism studies revealed the mechanism of action of piR-27222: 1) piR-27222 increases the expression of WTAP through eIF4B, thereby increasing the mRNA of Casp8 6 A modification level, reducing the stability of Casp8 mRNA and inhibiting cell apoptosis; 2) piR-27222 directly binds to eIF4B, thereby inhibiting the ubiquitination of eIF4B by the ubiquitin ligase PARK2 and increasing the stability of eIF4B protein.

[0039] Furthermore, the above studies show that piR-7327 is well conserved among species. piR-7327 binds to NCL but does not affect its expression. Instead, it affects the binding of NCL to ITGB1, thereby upregulating the expression of the oncogene ITGB1, thereby affecting the occurrence and development of lung cancer.

[0040] The beneficial effects of this technical solution are:

[0041] (1) PM 2.5 Based on a cell model of malignant transformation induced by organic phase extracts, the changes in piRNA expression profiles during PAH-induced lung cancer were studied for the first time, and piR-27222 and piR-7327 were screened out as molecular markers for early warning and early diagnosis of PAH exposure and high-risk groups for lung cancer.

[0042] (2) The functions and mechanisms of piR-27222 and piR-7327 in tumors were systematically studied. The study found that piR-27222 and piR-7327 are both tumor-promoting factors that can promote the proliferation, migration and invasion of lung cancer cells and inhibit the death of lung cancer cells. Mechanism studies have shown that the promotion of the malignant phenotype of lung cancer cells by piR-27222 depends on the regulation of WTAP protein. In addition, this study found that piR-27222 can increase the expression of WTAP protein in cells by increasing the m 6 A modification level. The results of MeRIP-seq and mRNA-seq showed that piR-27222 could increase the mRNA level of Casp8 transcripts. 6 A modification level, reducing the stability of Casp8 transcripts, thereby inhibiting the pan-apoptotic process of cells. This study systematically confirmed that piR-27222 6 The mechanism by which pathway A exerts its biological function provides a sufficient theoretical basis for the important role of the "epi-epi" regulatory model in the process of PAHs inducing lung cancer.

[0043] (3) This study confirmed that piR-27222 promotes the expression of WTAP by promoting the expression of eIF4B, providing evidence for the regulation of m 6A provides a new model for the mechanism of action. In addition, this study also found that piR-27222 can inhibit the ubiquitination of eIF4B protein and increase its stability by directly binding to eIF4B protein. This study confirmed for the first time that piRNA can affect the stability of protein by directly binding to protein, providing a new idea for the study of the mechanism of piRNA regulating gene expression. On the other hand, piR-7327 affects the binding of NCL to ITGB1 by binding to NCL (but does not affect its expression), thereby upregulating the expression of ITGB1 and promoting the occurrence and development of lung cancer. This is a different mechanism of action from piR-27222.

[0044] In conclusion, this study revealed the mechanism of piRNA's involvement in the process of PAH-induced lung cancer, and was the first to explain that piRNA is a key regulatory molecule in the process of PAH-induced lung cancer, and confirmed the regulatory mode of piRNA increasing protein stability by binding to proteins. These results not only confirm that epigenetic factors play an important regulatory role in the process of PAH-induced lung cancer, but also provide new ideas for the way piRNA regulates gene expression. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a diagram of an experiment to verify the biological mechanism by which piR-27222 of the present invention promotes the progression of lung cancer.

[0046] Figure 2 This is a diagram of the direct binding experiment between piR-2722 and eIF4B protein of the present invention.

[0047] Figure 3 This is an experimental diagram showing that piR-7327 of the present invention promotes the expression of ITGB1.

[0048] Figure 4 This is an experimental diagram showing that piR-7327 of the present invention promotes the expression of ITGB1 by affecting the binding between NCL and ITGB1. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings. The description of this module is only exemplary and explanatory and should not have any limiting effect on the protection scope of the present invention.

[0050] One of the technical solutions is to deeply study the mechanism of piRNA regulating lipid metabolism and affecting the action of polycyclic aromatic hydrocarbons in lung cancer, and explore its potential application in the diagnosis and treatment of lung cancer. It includes the following aspects: a) Construction of a cell model, selecting human bronchial epithelial cells (such as HBE cells) as the research basis, and inducing the cells to transform into lung squamous cell carcinoma clones by exposing them to coke oven exhaust emissions containing polycyclic aromatic hydrocarbons, thereby constructing a cell model that can simulate the process of lung cancer occurrence; after the construction of the cell model, it also includes the identification of the lung cancer cell model, specifically: through morphological observation, cell proliferation experiments, cell cycle analysis and marker detection methods, the constructed lung cancer cell model is identified to determine whether the constructed cell model meets the characteristics of lung cancer cells; b) Multi-omics analysis includes RNA extraction and sequencing analysis and lipid metabolomics analysis, and RNA-seq and small molecule RNA-seq sequencing are performed on the constructed lung cancer cell model to accurately identify the PM 2.5 The abnormal expression of piRNA (especially piR-27222) during the occurrence of lung cancer induced by PD-1; lipid metabolomics analysis, using mass spectrometry technology to deeply analyze the changes in lipid metabolites in cell culture fluid and cell lysate to reveal the specific role of lipid metabolism in the occurrence of lung cancer; c) bioinformatics analysis, integrating RNA-seq, small molecule RNA-seq and lipid metabolomics data resources, screening out differentially expressed genes and piRNAs closely related to lipid metabolism, and then constructing a gene regulatory network to deeply analyze the interaction between piR-27222 and lipid metabolism genes; d) functional experimental verification, including animal experimental verification, through Cell proliferation, apoptosis, migration and invasion experiments were used to comprehensively verify the specific effects of piR-27222 on the malignant phenotype of cells. The expression levels of piR-27222 and its target genes were precisely manipulated using gene knockout and overexpression techniques to further observe the actual effects of these changes on cell biological behavior and lipid metabolism. Animal models of lung cancer were constructed, and lung cancer cells with piRNA overexpression or knockdown were inoculated into animals to observe tumor growth and metastasis. At the same time, changes in the content of lipid metabolites in animals were detected to further verify the regulatory effects of piRNA on lipid metabolism and the occurrence and development of lung cancer. e) Mechanism studies were conducted to further explore the relationship between piR-27222 and m 6 The complex regulatory relationship between A modification and related proteins (such as WTAP, eIF4B) is used to clarify the specific mechanism of action of piR-27222 in regulating lipid metabolism genes and affecting the occurrence of lung cancer. The specific contents are as follows:

[0051] The construction of the cell model includes the following steps:

[0052] a1) Human normal bronchial epithelial cells (BEAS-2B) were selected as the research object and routinely cultured in RPMI 1640 medium containing 10% fetal bovine serum and 1% double antibiotics (penicillin and streptomycin) in an incubator at 37°C and 5% CO2;

[0053] a2) To simulate the exposure to PAHs, BEAS-2B cells were treated with a mixture containing PAHs at a specific concentration. The treatment time and concentration were optimized based on previous experimental results and relevant literature to induce malignant transformation of cells.

[0054] RNA-seq and small RNA-seq sequencing involves the following steps:

[0055] b1) RNA extraction: Use an appropriate RNA extraction kit to extract total RNA from the constructed lung cancer cell model and control cells. Avoid RNA degradation and contamination during the extraction process to ensure the quality and integrity of RNA;

[0056] b2) Library construction: The extracted RNA is reverse transcribed to synthesize cDNA, and then the cDNA is fragmented using appropriate kits and methods, and connected to adapters to construct a sequencing library;

[0057] b3) Sequencing: Use a high-throughput sequencing platform to sequence the constructed library, and the sequencing depth and coverage are selected according to experimental requirements;

[0058] b4) Data analysis: Analyze the data obtained from sequencing, including data quality assessment, sequence alignment, gene expression quantification and differential expression analysis; identify 2.5 The abnormal expression of piRNA, especially piR-27222, during the induction of lung cancer was investigated. Specifically, by comparing with the control group, piRNAs whose expression was significantly upregulated or downregulated in the lung cancer cell model were screened out, and further functional studies were performed on them.

[0059] Lipid metabolomics analysis includes the following steps:

[0060] b5) Sample preparation: Collect cell culture fluid and lysate, and use appropriate methods to extract lipid metabolites, avoiding oxidation and degradation of lipid metabolites to ensure the quality and stability of the samples;

[0061] b6) Mass spectrometry analysis: Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) technology is used to analyze the extracted lipid metabolites, and the molecular weight, structural information and relative content of lipid metabolites are obtained through mass spectrometry analysis;

[0062] b7) Data analysis: The data obtained from the mass spectrometry analysis are processed and analyzed, including data preprocessing, peak identification, quantitative analysis and differential metabolite screening. These analyses are used to understand the changes in lipid metabolism during the development of lung cancer, as well as the regulatory effects of piRNA on lipid metabolism. Specifically, by comparing with the control group, lipid metabolites with significantly changed levels in the lung cancer cell model are screened out, and their correlation with piRNA expression is analyzed.

[0063] Bioinformatics analysis is detailed in the following steps:

[0064] c1) Data Integration:

[0065] c11) Data collection: Integrate RNA-seq (transcriptome sequencing), small RNA-seq (small RNA sequencing, mainly used to detect small RNA molecules such as miRNA and piRNA) and lipid metabolomics data resources; RNA-seq data can provide gene expression level information, small RNA-seq data can be used to identify and analyze the expression of piRNA, and lipid metabolomics data can reflect the types and contents of lipid metabolites in cells;

[0066] c12) Data preprocessing: Quality control and preprocessing of the collected raw data, including removal of low-quality sequencing reads, correction of sequencing errors, and standardization and normalization of the data to ensure the accuracy and reliability of the data;

[0067] c2) Differential expression analysis:

[0068] c21) Gene differential expression analysis: Use appropriate statistical methods and bioinformatics tools to perform differential expression analysis on RNA-seq data and screen out genes with significant differential expression between different experimental conditions or sample groups;

[0069] c22) piRNA differential expression analysis: Similarly, small molecule RNA-seq data were analyzed to screen out differentially expressed piRNAs. Considering the important role of piRNAs in gene regulation, special attention was paid to piRNAs related to lipid metabolism;

[0070] c3) Functional annotation and enrichment analysis:

[0071] c31) Gene function annotation: Functionally annotate differentially expressed genes to understand their biological functions, cellular localization, and signal pathways involved; use databases such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional annotation and enrichment analysis;

[0072] c32) piRNA target prediction: Use bioinformatics algorithms to predict potential targets of differentially expressed piRNAs. By binding to the targets, piRNAs can regulate gene expression.

[0073] c4) Construction of gene regulatory network:

[0074] c41) Integrative analysis: Integrate the information of differentially expressed genes and piRNAs to construct a gene regulatory network. In this network, genes and piRNAs are nodes, and their interactions, such as transcriptional regulation and RNA interference, are edges.

[0075] c42) Network analysis: Analyze the constructed gene regulatory network to identify key nodes and modules; these key nodes and modules can play a role in the regulation of lipid metabolism. By deeply analyzing the topological structure and functional modules of the network, the regulatory mechanism of lipid metabolism and the interaction between piR-27222 and lipid metabolism genes can be revealed.

[0076] Functional experimental verification specifically includes the following:

[0077] d1) piRNA overexpression and knockdown experiments: piRNA overexpression and knockdown vectors were constructed and transfected into lung cancer cell models to achieve piRNA overexpression and knockdown. Then, real-time fluorescence quantitative PCR (qPCR) and Northern blot methods were used to verify whether the expression level of piRNA was successfully changed.

[0078] d2) Detection of lipid metabolism-related indicators: In lung cancer cell models with piRNA overexpression and knockdown, detect lipid metabolism-related indicators, such as changes in the content of triglycerides (TG), cholesterol (CHOL), free fatty acids (FFA), etc.; at the same time, detect the activity of lipid metabolism-related enzymes, such as fatty acid synthase (FASN), acetyl-CoA carboxylase (ACC), etc., to understand the regulatory effect of piRNA on lipid metabolism;

[0079] d3) Cell function experiments: Through cell proliferation experiments, cell apoptosis experiments, cell migration and invasion experiments, the effects of piRNA on lung cancer cell proliferation, apoptosis, migration and invasion were detected; at the same time, the sensitivity of lung cancer cells to PAHs was detected to understand the role of piRNA in the process of PAH-induced lung cancer;

[0080] Animal experiment verification specifically includes the following:

[0081] d4) Establish an animal model of PAHs-induced lung cancer by selecting mice of a specific strain, administering the PAHs mixture by nasal instillation or tracheal instillation, and regularly observing the general conditions of the mice, such as weight, behavior, and appearance;

[0082] d5) During the model construction process, piRNA mimics or inhibitors were introduced into mice by tail vein injection, and control and experimental groups were set up;

[0083] d6) At different time points of the experiment (e.g., 4 weeks, 8 weeks, and 12 weeks), the mice were killed and lung tissue samples were collected;

[0084] d7) Observe the pathological morphological changes of lung tissue by hematoxylin-eosin (HE) staining to evaluate the occurrence and development of lung cancer;

[0085] d8) Immunohistochemistry (IHC) was used to detect the expression levels of lung cancer-related markers (such as Ki-67, p53, etc.) in lung tissue to further verify the effect of piRNA on PAHs-induced lung cancer in vivo;

[0086] d9) Use qRT-PCR and Western blot techniques to detect the expression level of piRNA in lung tissue and the expression changes of its potential target genes, and verify the regulatory effect of piRNA on lipid metabolism and the occurrence and development of lung cancer at the molecular level;

[0087] The mechanism research specifically includes the following:

[0088] e1) Target gene prediction: Use bioinformatics methods to predict the target genes of piRNAs, and screen out possible target genes by analyzing the complementarity between piRNAs and target genes, as well as the functions and biological pathways of target genes;

[0089] e2) Verify target genes: Use luciferase reporter gene experiments and RNA immunoprecipitation (RIP) experiments to verify the interaction between piRNA and predicted target genes; at the same time, use qPCR and Western blot methods to detect whether the expression level of the target gene is regulated by piRNA;

[0090] e3) Signaling pathway research: By detecting the expression and activity changes of related signaling pathway molecules, such as PI3K / Akt and MAPK, the signaling pathway mechanism of piRNA regulating lipid metabolism and affecting PAH-induced lung cancer is studied.

[0091] Specific embodiments: The technical scheme of the present invention will be described in detail below in conjunction with specific embodiments. It should be noted that these embodiments are only used to illustrate the principles and methods of the present invention and do not constitute any limitation to the scope of protection of the present invention. In practical applications, appropriate adjustments and optimizations should be made according to specific circumstances.

[0092] Example 1: Cell culture and PAHs treatment

[0093] 1. Culture of BEAS-2B cells:

[0094] a. Take out the frozen BEAS-2B cells from liquid nitrogen and quickly thaw them in a 37°C water bath. After the cell suspension is completely thawed, transfer it to a 15 mL centrifuge tube, add 5 mL of RPMI 1640 medium (containing 10% fetal bovine serum and 1% double antibody), mix gently, and centrifuge at 1000 rpm for 5 minutes.

[0095] b. Discard the supernatant, add 5 mL of fresh RPMI 1640 medium, resuspend the cells, inoculate the cells into a culture flask, and culture in an incubator at 37°C and 5% CO2.

[0096] c. Observe the growth of cells every day, and carry out subculture when the cell fusion degree reaches about 80%.

[0097] 2.PAHs treatment:

[0098] a. Prepare a treatment solution containing a mixture of PAHs. The type and concentration of PAHs are selected and optimized according to the actual situation. Dissolve the PAHs in DMSO to prepare a high-concentration stock solution, which is then diluted to the required working concentration using RPMI 1640 culture medium so that the final concentration of DMSO does not exceed 0.1%.

[0099] b. BEAS-2B cells in the logarithmic growth phase were seeded in 6-well plates, with 2×10 5 After culturing for 24 hours, when the cells adhered to the wall and reached 70%-80% confluence, the culture medium containing different concentrations of PAHs was replaced for treatment. A control group (RPMI 1640 culture medium containing only 0.1% DMSO) and a treatment group (RPMI 1640 culture medium containing different concentrations of PAHs) were set up, and 3 replicate wells were set up in each group.

[0100] c. Place the cells in an incubator at 37° C. and 5% CO 2 and culture them for different treatment times (such as 24 hours, 48 ​​hours, or 72 hours) according to experimental requirements. After the treatment, collect the cells for subsequent experiments.

[0101] Example 2: RNA extraction and sequencing analysis

[0102] 1. RNA extraction:

[0103] a. After the PAHs treatment, carefully remove the culture medium and gently wash the cells twice with cold PBS.

[0104] b. Add 1 mL of Trizol reagent to each well and leave at room temperature for 5 minutes to allow the cells to fully lyse.

[0105] c. Transfer the cell lysate to a 1.5 mL RNase-free centrifuge tube, add 200 μL of chloroform, shake vigorously for 15 seconds, and leave at room temperature for 3 minutes.

[0106] d. Centrifuge at 4°C and 12,000 rpm for 15 minutes. Transfer the supernatant to a new centrifuge tube, add an equal volume of isopropanol, mix gently, and leave at room temperature for 10 minutes.

[0107] e. Centrifuge at 4°C and 12,000 rpm for 10 minutes, discard the supernatant, add 1 mL of 75% ethanol (prepared with DEPC water), and gently wash the precipitate.

[0108] f. Centrifuge at 4°C and 7500 rpm for 5 minutes, discard the supernatant, dry the precipitate at room temperature for 5-10 minutes, and add an appropriate amount of DEPC water to dissolve the RNA.

[0109] g. Use Nanodrop spectrophotometer to measure RNA concentration and purity. RNA with A260 / A280 ratio between 1.8-2.0 is considered qualified. At the same time, take 2 μL RNA for agarose gel electrophoresis to detect RNA integrity.

[0110] 2. RNA-seq library construction and sequencing:

[0111] a. Take 1 μg of qualified total RNA and use Illumina TruSeq Stranded mRNA LT Sample PrepKit to construct RNA-seq library. The specific steps are as follows:

[0112] i. Use oligo(dT) magnetic beads to enrich mRNA, and then add fragmentation buffer to break the mRNA into 200-300 bp fragments.

[0113] ii. Using the disrupted mRNA as a template, SuperScript II Reverse Transcriptase was used to synthesize the first-strand cDNA, and then DNA Polymerase I and RNase H were used to synthesize the second-strand cDNA.

[0114] iii. Repair and add A-tail to the cDNA ends, and then connect to Illumina sequencing adapters.

[0115] iv. Purify the ligation product using AMPure XP beads, select cDNA with a fragment size between 300-500 bp for PCR amplification to obtain the RNA-seq library.

[0116] b. The RNA-seq library was sequenced using the Illumina NovaSeq 6000 sequencing platform with a sequencing read length of 2×150 bp, and at least 6 Gb of data was obtained for each sample.

[0117] 3. Small RNA-seq library construction and sequencing:

[0118] a. Take 1 μg of qualified total RNA and use Illumina TruSeq Small RNA Sample Prep Kit to construct a small RNA-seq library. The specific steps are as follows:

[0119] i. First, use 3' and 5' adapter ligases to ligate adapters to both ends of the small RNA.

[0120] ii. Use the small RNA after connecting the adapter as a template and use SuperScript II ReverseTranscriptase to perform reverse transcription to synthesize the first chain of cDNA.

[0121] iii. Then, DNA Polymerase I and RNase H were used to synthesize the second strand of cDNA.

[0122] iv. Perform PCR amplification to obtain a small RNA-seq library.

[0123] b. The small RNA-seq library was sequenced using the Illumina NovaSeq 6000 sequencing platform with a sequencing read length of 1×50bp, and at least 10M reads were obtained for each sample.

[0124] 4. Sequencing data analysis:

[0125] a. Perform quality assessment on the raw sequencing data of RNA-seq and small RNA-seq, and use FastQC software to check the quality parameters of the sequencing data, such as base quality distribution, GC content, sequence repetition rate, etc. Remove low-quality reads (such as reads containing too many unknown bases N or a high proportion of bases with a quality value lower than 20) and adapter sequences to obtain high-quality clean reads.

[0126] b. Use TopHat2 software to align clean reads to the human reference genome (hg38), allowing up to 2 base mismatches. The alignment results are saved in BAM format files.

[0127] c. Use Cuffdiff software to analyze the gene expression level of the aligned RNA-seq data, calculate the FPKM value (Fragments Per Kilobase of exon per Million fragments mapped) of each gene, and screen out differentially expressed mRNAs. The screening criteria are |log2FC|≥1 and p<0.05.

[0128] d. For small RNA-seq data, miRDeep2 software was used for analysis. First, clean reads were compared with known miRNA databases (such as miRBase) to identify known miRNAs. Then, secondary structure prediction and sequence alignment were performed on reads that did not match known miRNAs to discover new miRNAs and other types of small RNAs, such as piRNAs. Finally, the expression level of each small RNA was calculated, and differentially expressed piRNAs were screened out, with the screening criteria of |log2FC|≥1 and p<0.05.

[0129] Example 3: Lipid metabolomics analysis

[0130] 1. Extraction of lipid metabolites: Collect BEAS-2B cells treated with PAHs, wash twice with cold PBS, add 1 mL of methanol / chloroform (2:1, v / v) mixture, vortex for 30 seconds, and ultrasonically disrupt cells on ice for 5 minutes (working time 2 seconds, interval time 3 seconds, power 300W). Transfer the cell lysate to a 2 mL centrifuge tube, add 0.2 mL of water, vortex for 30 seconds, and centrifuge at 4°C and 12000 rpm for 10 minutes. Collect the lower organic phase, transfer it to a new centrifuge tube, and blow dry it in a nitrogen blow dryer to obtain a lipid metabolite extract.

[0131] 2. UPLC-MS / MS analysis: Weigh an appropriate amount of lipid metabolite extract, dissolve it in 100 μL of isopropanol / acetonitrile (1:1, v / v) solution, vortex for 30 seconds, centrifuge at 12000 rpm for 10 minutes, and take the supernatant for UPLC-MS / MS analysis. The UPLC conditions are as follows:

[0132] Chromatographic column: Waters ACQUITY UPLC BEH C18 column (2.1×100 mm, 1.7 μm);

[0133] Mobile phase: Phase A is 0.1% formic acid aqueous solution, phase B is acetonitrile;

[0134] Gradient elution program: 0-2 min, 30% B; 2-5 min, 30%-50% B; 5-9 min, 50%-70% B; 9-12 min, 70%-90% B; 12-13 min, 90% B; 13-13.1 min, 90%-30% B; 13.1-15 min, 30% B;

[0135] Flow rate: 0.4 mL / min;

[0136] Column temperature: 50°C;

[0137] Injection volume: 5 μL;

[0138] MS / MS conditions are as follows:

[0139] Ion source: electrospray ionization (ESI);

[0140] Scanning mode: positive ion and negative ion modes scan simultaneously;

[0141] Detection mode: multiple reaction monitoring (MRM);

[0142] Spray voltage: 3500 V in positive ion mode and -3000 V in negative ion mode;

[0143] Ion source temperature: 150°C;

[0144] Desolvation temperature: 500°C;

[0145] Desolvation gas flow rate: 1000L / h;

[0146] Cone hole gas flow: 150L / h.

[0147] 3. Data processing and analysis: Waters MassLynx software was used to collect and process UPLC-MS / MS raw data, including peak detection, peak integration, retention time correction, and mass spectrum analysis. The processed data were imported into SIMCA-P 14.1 software for multivariate statistical analysis. First, principal component analysis (PCA) was performed to observe the overall differences between the treatment group and the control group. Then, partial least squares discriminant analysis (PLS-DA) was performed to screen out variables (lipid metabolites) that contributed more to the grouping. According to the variable importance projection (VIP) value and p value, lipid metabolites with significant differences were screened. Lipid metabolites with VIP values ​​greater than 1 and p<0.05 were considered to be significantly changed metabolites. According to the analysis results, biological processes and signaling pathways related to lipid metabolism, as well as differentially expressed genes that were significantly enriched in these processes and pathways were screened.

[0148] Example 4: Bioinformatics analysis

[0149] 1. Functional annotation and enrichment analysis of differentially expressed genes: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on differentially expressed mRNA using the DAVID online tool. GO analysis includes three aspects: biological process, cellular component, and molecular function. KEGG pathway analysis is used to reveal the signal pathways in which differentially expressed genes participate. Based on the analysis results, biological processes and signal pathways related to lipid metabolism, as well as differentially expressed genes that are significantly enriched in these processes and pathways, were screened.

[0150] 2. Prediction of potential target genes of piRNA:

[0151] a. Use software such as TargetScan, miRanda and RNAhybrid to predict potential target genes of differentially expressed piRNAs. These software predict possible targets based on factors such as sequence complementarity and thermodynamic stability between piRNA and mRNA.

[0152] b. Compare the predicted target genes with the differentially expressed mRNAs to screen out lipid metabolism-related genes that may be regulated by piRNAs.

[0153] 3. Construction of piRNA-mRNA regulatory network:

[0154] a. Based on the prediction results of piRNA and potential target genes, a piRNA-mRNA regulatory network is constructed. In the network, piRNA is a node and its potential target gene is an edge connected to it.

[0155] b. Use software such as Cytoscape to visualize the regulatory network, analyze the regulatory relationship between piRNA and lipid metabolism-related genes, and reveal the regulatory mechanism of piRNA on lipid metabolism.

[0156] Example 5: Functional verification experiment

[0157] Cell level validation:

[0158] 1. Transfection of piRNA mimics and inhibitors: Design and synthesize mimics and inhibitors of the selected key piRNAs (such as piR-12345 and piR-67890). BEAS-2B cells were inoculated in 6-well plates and cultured until the cell confluence reached 60%-70%. According to the instructions of the liposome transfection reagent, piRNA mimics or inhibitors were transfected into the cells. Negative controls (transfection of irrelevant sequences) and blank controls (no transfection) were set up.

[0159] 2.qRT-PCR detection: 24 hours, 48 ​​hours and 72 hours after transfection, cells were collected, total RNA was extracted and reverse transcribed into cDNA. Real-time fluorescence quantitative PCR technology was used to detect the expression level of piRNA and the mRNA expression level of its potential target genes. GAPDH was used as the internal reference gene, and the 2^(-ΔΔCt) method was used to calculate the relative expression of genes.

[0160] 3. Western blot detection: - 48 hours after transfection, cells were collected and total protein was extracted. Proteins were separated by SDS-PAGE electrophoresis and transferred to a PVDF membrane. After blocking the membrane with 5% skim milk, primary antibody (for the protein encoded by the target gene) was added and incubated overnight at 4°C. After washing the membrane, secondary antibody was added and incubated at room temperature for 1 hour. Finally, the protein signal was detected using an ECL chemiluminescence kit, and β-actin was used as an internal reference protein to analyze the expression level of the protein encoded by the target gene.

[0161] 4. Detection of lipid metabolite content: 48 hours after transfection, cells were collected and lipid metabolites in the cells were extracted according to the method in Example 3. UPLC-MS / MS technology was used to quantitatively detect the content of lipid metabolites such as triglycerides, cholesterol, and phospholipids, and the differences in lipid metabolites between the transfection group and the control group were compared.

[0162] 5. Cell proliferation, apoptosis and cell cycle detection: Cell proliferation detection: MTT method was used. The transfected cells were inoculated in a 96-well plate. After culturing for 24 hours, 48 ​​hours and 72 hours, 20 μL MTT solution (5 mg / mL) was added to each well and cultured for 4 hours. Then, the culture medium was aspirated, 150 μL DMSO was added, and the crystals were fully dissolved. The absorbance value (OD value) of each well was measured at a wavelength of 490 nm using an enzyme reader to reflect the proliferation of the cells. Cell apoptosis detection: Annexin V-FITC / PI double staining method was used. The transfected cells were collected in a centrifuge tube, washed twice with cold PBS, and 100 μL Binding Buffer was added to resuspend the cells. Then, 5 μL Annexin V-FITC and 5 μL PI were added respectively, gently mixed, and incubated at room temperature in the dark for 15 minutes. Finally, 400 μL Binding Buffer was added, and cell apoptosis was detected using a flow cytometer.

[0163] 6. Cell cycle analysis: PI staining was used. The transfected cells were washed twice with cold PBS and fixed overnight with 70% ethanol. After centrifugation to remove the ethanol, the cells were washed with PBS, 500 μL PI staining solution (containing 50 μg / mL PI and 100 μg / mL RNaseA) was added, and incubated at room temperature in the dark for 30 minutes. The cell cycle distribution was detected using a flow cytometer.

[0164] Example 6: Animal Experiment Verification

[0165] 1. Construction of PAHs-induced lung cancer animal model: 6-8 week old C57BL / 6 mice were selected and adaptively raised for 1 week before the lung cancer model was constructed. During the model construction process, the general conditions of the mice, such as weight, behavior, and appearance, were regularly observed. The PAHs mixture was administered by nasal instillation or tracheal instillation once a week for 8-10 weeks.

[0166] 2. piRNA intervention in vivo: While PAHs treatment was in progress, piRNA mimics or inhibitors were introduced into mice by tail vein injection. The injection was performed once a week, and the dose was optimized based on previous experimental results and literature reports. A control group (given saline), a PAHs treatment group, and a PAHs treatment + piRNA intervention group were set up.

[0167] 3. Sample collection and analysis: At the 12th week of the experiment, mice were killed and lung tissue samples were collected. A portion of the lung tissue was used to prepare pathological sections and stained with hematoxylin-eosin (HE) to observe the pathological morphological changes of the lung tissue and evaluate the occurrence and development of lung cancer. Another portion of the lung tissue was used to extract RNA and protein, and qRT-PCR and Western blot techniques were used to detect the expression level of piRNA and the expression changes of its potential target genes, respectively. At the same time, immunohistochemistry (IHC) technology was used to detect the expression levels of lung cancer-related markers (such as Ki-67, p53, etc.) in lung tissue to further verify the effect of piRNA on PAHs-induced lung cancer in vivo.

[0168] The mechanism of action of piRNA regulating lipid metabolism and affecting PAH-induced lung cancer revealed by this invention provides new ideas and targets for the diagnosis and treatment of lung cancer. Based on the research results, the following applications can be developed:

[0169] 1. Diagnostic markers: Detect the expression level of specific piRNAs and changes in the content of related lipid metabolites in the patient's blood or tissue as potential markers for the early diagnosis of lung cancer.

[0170] 2. Therapeutic targets: Develop inhibitors or agonists targeting specific piRNAs, as well as drugs that regulate lipid metabolism, to provide new strategies for the treatment of lung cancer.

[0171] 3. Personalized treatment: Develop personalized treatment plans based on the expression of piRNA and lipid metabolism characteristics in the patient to improve treatment efficacy and patient survival rate.

[0172] In addition, with the support of this project, based on the concept of systems toxicology, integrating mechanism research and computational toxicology methods, using the five key toxic pathways disturbed by the typical PAH toxicant benzo(a)pyrene as the molecular skeleton and lung damage as the final outcome, we successfully constructed seven adverse outcome pathways (AOPs), all of which were recognized and included in the AOP-Wiki official website (numbered AOP 414-420). Subsequently, artificial intelligence algorithms such as machine learning were used to predict the key events of 2,629 new chemicals that disturbed the AOP framework, and 166 lung injury poisons were successfully identified. Targeting the key toxic pathways disturbed by benzo(a)pyrene, the Bottomup research method was used to discover multiple "driving small molecule RNAs" that can target toxic pathways and induce lung injury outcomes; whole genome sequencing was used to identify somatic mutations in the DNA repair gene APOBEC3 as PM 2.5 These results provide new ideas for further clarifying the genetic and epigenetic events of chemical carcinogenesis.

[0173] Experimental verification: This project conducted piRNA sequencing analysis on the PAH-induced malignant cell model, and verified the most significantly differentially expressed piRNAs by qRT-PCR. It was found that the expression of piR-27222 and piR-7327 in lung tumor cell lines and malignant cells was significantly higher than that in control cells, suggesting that piR-27222 and piR-7327 are key response factors in the process of PAH-induced lung cancer. The results of fluorescence in situ hybridization experiments showed that the increase of piR-27222 and piR-7327 was mainly reflected in the cytoplasm. The lung tumor tissues and adjacent tissues removed by lung cancer patients were further collected, and the expression of piR-27222 and piR-7327 in them was analyzed. It was found that the expression of piR-27222 and piR-7327 in tumor tissues was significantly higher than that in adjacent tissues.

[0174] 1) Validation of the biological mechanism by which piR-27222 promotes lung cancer progression

[0175] refer to Figure 1 The cell phenotype results showed that transfection of piR-27222 mimics could promote cell proliferation, invasion and migration, and inhibit PM 2.5 The cell death caused by piR-27222 was observed, while the opposite result was obtained by transfection of piR-27222 inhibitor, suggesting that piR-27222 has the ability to promote the occurrence and development of lung cancer. 6 The results of A level detection showed that m 6 A modification level was significantly increased ( Figure 1 a), while transfection of piR-27222 mimics can also increase the m 6 A modification level ( Figure 1 b), indicating that piR-27222 may be involved in the carcinogenic process of PAHs by changing the m6A metabolic level in cells. 6 The expression of three key methyltransferases in A modification, WTAP, METTL3 and METTL4, in transfected cells was detected, and the results showed that only WTAP expression was significantly higher than that in control cells. After transfection of piR-27222 mimics and inhibitors in cells, the protein expression of WTAP, METTL3 and METTL14 was analyzed. It was also found that only WTAP expression was upregulated after transfection of piR-27222 mimics and downregulated after transfection of piR-27222 inhibitors. Further research results showed that piR-27222 can promote the accumulation of WTAP protein in the cytoplasm, and when WTAP was knocked down, the regulatory ability of piR-27222 on cell phenotype was reversed, indicating that WTAP is a necessary pathway for piR-27222 to exert its biological function.

[0176] refer to Figure 2 , a pull-down experiment was performed using a biotin-labeled piR-27222 probe to obtain protein molecules bound to piR-27222, and then the obtained proteins were identified by mass spectrometry analysis ( Figure 2 a). Among the proteins with high abundance binding to piR-27222, eIF4B was reported to be a translation initiation factor that promotes WTAP expression. Protein quantitative analysis ( Figure 2 a) RNA immunoprecipitation experiment using eIF4B antibody ( Figure 2 b) and RNA-protein molecular docking prediction model ( Figure 2 c) It was also confirmed that piR-27222 can directly bind to eIF4B protein. Further studies found that piR-27222 had no significant regulatory effect on the mRNA level of eIF4B. piR-27222 mimics can promote the expression of eIF4B protein, while piR-27222 inhibitors can inhibit the expression of eIF4B protein. The results of nuclear cytoplasm separation experiments showed that piR-27222 mainly promoted the accumulation of eIF4B in the cytoplasm.

[0177] 2) Verification of the biological mechanism of piR-7327 affecting the carcinogenic process of PAHs

[0178] Reference Figure 3 Functional studies have shown that piR_7327 overexpression can significantly promote the proliferation, metastasis and invasion of malignant cells and lung cancer cells. In addition, inhibiting the expression of piR_7327 can promote cell apoptosis and increase the sensitivity of lung cancer cells to anti-tumor drugs. After overexpression of piR-7327, transcriptome sequencing revealed that 1128 mRNAs were upregulated and 566 mRNAs were downregulated (fold change > 2, P < 0.05). Pathway enrichment analysis found that piR-7327 can regulate the small cell lung cancer pathway, and further verification found that piR-7327 can significantly upregulate the expression of the oncogene ITGB1.

[0179] Reference Figure 4 To further explore the mechanism by which piR-7327 affects ITGB1 expression, a pull-down experiment was performed using a biotin-labeled piR-7327 probe to obtain protein molecules bound to piR-7327, and then the obtained proteins were identified by mass spectrometry analysis ( Figure 4 a). Among them, NCL has the highest binding score with piR-7327. NCL has been reported to be an important protein affecting mRNA stability. RNA immunoprecipitation experiment using NCL antibody ( Figure 4 b, c) and RNA-protein molecular docking prediction model ( Figure 4d) It was also confirmed that piR-7327 can directly bind to NCL protein. Further studies found that piR-7327 had no significant regulatory effect on the mRNA and protein levels of NCL. Therefore, we speculated whether piR-7327 affects the expression of ITGB1 by affecting the binding of NCL to ITGB1. To verify this hypothesis, we transfected piR-7327 mimics into cells, and RIP analysis showed that overexpression of piR-7327 significantly reduced the binding of NCL to ITGB1 mRNA, while knocking down NCL could rescue the upregulation of ITGB1 caused by piR-7327, confirming that piR-7327 can interact with NCL, reduce the binding of NCL to ITGB1, and thus increase the expression of ITGB1.

[0180] It should be noted that the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusions, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method of the present invention and its core idea. The above are only preferred implementation methods of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements, modifications or changes can be made, and the above technical features can be combined in an appropriate manner; these improvements, modifications, combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement should be regarded as the scope of protection of the present invention.

Claims

1. A method for regulating lipid metabolism and affecting the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer by piRNA, characterized in that: The following steps are involved: a) Construction of cell model: Human bronchial epithelial cells were selected as the research basis. By exposing them to coke oven exhaust containing polycyclic aromatic hydrocarbons, the cells were induced to clone and metastasize into lung squamous cell carcinoma, thus constructing a cell model that can simulate the process of lung cancer occurrence; b) Multi-omics analysis: RNA-seq and small molecule RNA-seq sequencing were performed on the constructed lung cancer cell model to accurately identify the 2.5 The abnormal expression of piRNA (especially piR-27222) during the development of lung cancer induced by induced PD-1; lipid metabolomics analysis, using mass spectrometry technology to deeply analyze the changes in lipid metabolites in cell culture fluid and cell lysate, to reveal the specific role of lipid metabolism in the development of lung cancer; c) Bioinformatics analysis: Integrate RNA-seq, small molecule RNA-seq and lipid metabolomics data resources to screen out differentially expressed genes and piRNAs closely related to lipid metabolism, and then construct a gene regulatory network to deeply analyze the interaction between piR-27222 and lipid metabolism genes; d) Functional experimental verification: Through cell proliferation, apoptosis, migration and invasion experiments, the specific effects of piR-27222 on the malignant phenotype of cells were fully verified. The expression levels of piR-27222 and its target genes were precisely manipulated by gene knockout and overexpression techniques to further observe the actual effects of these changes on cell biological behavior and lipid metabolism; e) Mechanism study: In-depth study of the relationship between piR-27222 and m 6 The complex regulatory relationship between A modification and related proteins WTAP and eIF4B is clarified to clarify the specific mechanism of action of piR-27222 in regulating lipid metabolism genes and affecting the occurrence of lung cancer.

2. The method of claim 1 for regulating lipid metabolism and affecting the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer by piRNA, characterized in that: The construction of the cell model also includes the identification of the lung cancer cell model, specifically: identifying the constructed lung cancer cell model through morphological observation, cell proliferation experiment, cell cycle analysis and marker detection method to determine whether the constructed cell model meets the characteristics of lung cancer cells.

3. The method of piRNA regulating lipid metabolism and affecting the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer according to claim 1, characterized in that: The RNA-seq and small molecule RNA-seq sequencing comprises the following steps: b1) RNA extraction: Use an appropriate RNA extraction kit to extract total RNA from the constructed lung cancer cell model and control cells. Avoid RNA degradation and contamination during the extraction process to ensure the quality and integrity of RNA; b2) Library construction: The extracted RNA is reverse transcribed to synthesize cDNA, and then the cDNA is fragmented using appropriate kits and methods, and connected to adapters to construct a sequencing library; b3) Sequencing: Use a high-throughput sequencing platform to sequence the constructed library, and the sequencing depth and coverage are selected according to experimental requirements; b4) Data analysis: Analyze the data obtained from sequencing, including data quality assessment, sequence alignment, gene expression quantification and differential expression analysis; identify 2.5 The abnormal expression of piRNA, especially piR-27222, during the induction of lung cancer was investigated. Specifically, by comparing with the control group, piRNAs whose expression was significantly upregulated or downregulated in the lung cancer cell model were screened out, and further functional studies were performed on them.

4. The method of claim 1 for regulating lipid metabolism and affecting the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer by piRNA, characterized in that: The lipid metabolomics analysis comprises the following steps: b5) Sample preparation: Collect cell culture fluid and lysate, and use appropriate methods to extract lipid metabolites, avoiding oxidation and degradation of lipid metabolites to ensure the quality and stability of the samples; b6) Mass spectrometry analysis: Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) technology is used to analyze the extracted lipid metabolites, and the molecular weight, structural information and relative content of lipid metabolites are obtained through mass spectrometry analysis; b7) Data analysis: The data obtained from the mass spectrometry analysis are processed and analyzed, including data preprocessing, peak identification, quantitative analysis and differential metabolite screening. These analyses are used to understand the changes in lipid metabolism during the development of lung cancer, as well as the regulatory effects of piRNA on lipid metabolism. Specifically, by comparing with the control group, lipid metabolites with significantly changed levels in the lung cancer cell model are screened out, and their correlation with piRNA expression is analyzed.

5. The method of claim 1 for regulating lipid metabolism and affecting the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer by piRNA, characterized in that: The bioinformatics analysis includes the following detailed steps: c1) Data Integration: c11) Data collection: Integrate RNA-seq (transcriptome sequencing), small RNA-seq (smallRNA sequencing, mainly used to detect small RNA molecules such as miRNA and piRNA) and lipid metabolomics data resources; RNA-seq data can provide information on gene expression levels, small RNA-seq data can be used to identify and analyze the expression of piRNA, and lipid metabolomics data can reflect the types and contents of lipid metabolites in cells; c12) Data preprocessing: Quality control and preprocessing of the collected raw data, including removal of low-quality sequencing reads, correction of sequencing errors, and standardization and normalization of the data to ensure the accuracy and reliability of the data; c2) Differential expression analysis: c21) Gene differential expression analysis: Use appropriate statistical methods and bioinformatics tools to perform differential expression analysis on RNA-seq data and screen out genes with significant differential expression between different experimental conditions or sample groups; c22) piRNA differential expression analysis: Similarly, small molecule RNA-seq data were analyzed to screen out differentially expressed piRNAs. Considering the important role of piRNAs in gene regulation, special attention was paid to piRNAs related to lipid metabolism; c3) Functional annotation and enrichment analysis: c31) Gene function annotation: Functionally annotate differentially expressed genes to understand their biological functions, cellular localization, and signal pathways involved; use the databases Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional annotation and enrichment analysis; c32) piRNA target prediction: Use bioinformatics algorithms to predict potential targets of differentially expressed piRNAs. By binding to the targets, piRNAs can regulate gene expression. c4) Construction of gene regulatory network: c41) Integrative analysis: Integrate the information of differentially expressed genes and piRNAs to construct a gene regulatory network. In this network, genes and piRNAs are nodes, and their interactions, such as transcriptional regulation and RNA interference, are edges. c42) Network analysis: Analyze the constructed gene regulatory network to identify key nodes and modules; these key nodes and modules can play a role in the regulation of lipid metabolism. By deeply analyzing the topological structure and functional modules of the network, the regulatory mechanism of lipid metabolism and the interaction between piR-27222 and lipid metabolism genes can be revealed.

6. The method of claim 1 for regulating lipid metabolism and affecting the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer by piRNA, characterized in that: The functional experimental verification specifically includes the following: d1) piRNA overexpression and knockdown experiments: piRNA overexpression and knockdown vectors were constructed and transfected into lung cancer cell models to achieve piRNA overexpression and knockdown. Then, real-time fluorescence quantitative PCR (qPCR) and Northern blot methods were used to verify whether the expression level of piRNA was successfully changed. d2) Detection of lipid metabolism-related indicators: In lung cancer cell models with piRNA overexpression and knockdown, the changes in the content of lipid metabolism-related indicators triglyceride (TG), cholesterol (CHOL), and free fatty acid (FFA) were detected; at the same time, the activities of lipid metabolism-related enzymes fatty acid synthase (FASN) and acetyl-CoA carboxylase (ACC) were detected to understand the regulatory effect of piRNA on lipid metabolism; d3) Cell function experiments: Through cell proliferation experiments, cell apoptosis experiments, cell migration and invasion experiments, the effects of piRNA on lung cancer cell proliferation, apoptosis, migration and invasion were detected; at the same time, the sensitivity of lung cancer cells to PAHs was detected to understand the role of piRNA in the process of PAH-induced lung cancer; d4) Animal experiments: Construct an animal model of lung cancer, inoculate lung cancer cells with piRNA overexpression or knockdown into the animal, and observe the growth and metastasis of the tumor; at the same time, detect changes in the content of lipid metabolites in the animal to further verify the regulatory effect of piRNA on lipid metabolism and the occurrence and development of lung cancer.

7. The method of claim 1 for regulating lipid metabolism and affecting the mechanism of action of polycyclic aromatic hydrocarbons in causing lung cancer by piRNA, characterized in that: The mechanism studies specifically include the following: e1) Target gene prediction: Use bioinformatics methods to predict the target genes of piRNAs, and screen out possible target genes by analyzing the complementarity between piRNAs and target genes, as well as the functions and biological pathways of target genes; e2) Verify target genes: Use luciferase reporter gene experiments and RNA immunoprecipitation (RIP) experiments to verify the interaction between piRNA and predicted target genes; at the same time, use qPCR and Western blot methods to detect whether the expression level of the target gene is regulated by piRNA; e3) Signaling pathway research: By detecting the expression and activity changes of related signaling pathway molecules, the signaling pathway mechanism of piRNA regulating lipid metabolism and affecting PAH-induced lung cancer is studied.

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