A method and system for targeted gene editing of lung cancer cells

Abstract

This invention relates to the field of gene editing technology, specifically to a gene editing method and system targeting lung cancer cells. The method includes the following steps: obtaining lung cancer tissue sections; extracting gene expression data of lung cancer cells under various environmental variables using high-throughput RNA sequencing technology; screening and removing genes whose expression levels fluctuate beyond a set threshold; and obtaining the expression variation values ​​of the lung cancer microenvironment. In this invention, through dual screening of gene expression level fluctuations and mutation stability, the target values ​​for stable oncogene regulation are precisely located, ensuring the stability and reliability of gene targets. A precise off-target prediction mechanism is constructed using lung cancer-specific mutation data. The target gene splicing region is dynamically adjusted in real time through genome matching analysis and sequence mismatch scoring to avoid the risk of accidental editing at potential off-target sites. Furthermore, the splicing window boundary is set according to the dynamic matching threshold to optimize the splicing region range, comprehensively reducing potential off-target risks and forming a dual guarantee strategy for lung cancer specificity and stability.

Description

Technical Field

[0001] This invention relates to the field of gene editing technology, and in particular to a gene editing method and system for targeting lung cancer cells. Background Technology

[0002] Gene editing technology encompasses methods for precisely modifying the genome of organisms, primarily including technologies such as the CRISPR-Cas system, TALENs, and ZFNs. These technologies enable the insertion, deletion, substitution, or regulation of specific genes at specific sites in the genome, thus finding applications in basic biological research, disease model construction, gene therapy, and agricultural improvement. Among these, CRISPR-Cas9 dominates gene editing technology due to its high efficiency, simplicity, and programmability. In the medical field, gene editing technology is used in the treatment of single-gene genetic diseases, correction of cancer gene mutations, modification of immune cells, and gene function research, providing an important tool for precision medicine.

[0003] Gene editing targeting lung cancer cells is a lung cancer treatment strategy based on gene editing technology. It aims to modify or knock out lung cancer-related genes using specific gene editing tools to inhibit cancer cell proliferation, enhance apoptosis, or improve sensitivity to targeted therapy. This method can be used to correct driver gene mutations, regulate cancer cell metabolic pathways, restore tumor suppressor gene function, or improve the ability of immune cells to recognize tumors, thereby enhancing treatment efficacy. This method has significant application value in precision medicine, personalized medicine, and research on drug resistance in lung cancer, providing a new strategy for lung cancer treatment.

[0004] Traditional editing methods in lung cancer cell gene editing target selection focus primarily on the gene mutation characteristics and expression differences of cancer cells themselves, neglecting the influence of external tumor microenvironment factors on the dynamic changes in gene expression. This leads to insufficient stability of the selected targets, resulting in drastic fluctuations in gene expression under different microenvironmental conditions, affecting gene editing stability. For example, under hypoxic conditions and fluctuating immune factors, the expression of previously selected stable target genes in lung cancer cells changes, increasing the risk of off-target editing. Current targeted editing risk assessment processes rely solely on the mutation frequency to judge risk, ignoring the cross-regulatory effects between oncogenes and normal lung cell functions. This can easily cause conflicts between the editing target and normal gene pathway functions, resulting in unexpected functional disorders, reducing the potential safety of editing therapy, and limiting the application scope of targeted gene editing therapy in precision medicine for lung cancer. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a gene editing method and system for targeting lung cancer cells.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a gene editing method targeting lung cancer cells, comprising the following steps:

[0007] S1: Obtain lung cancer tissue sections, use high-throughput RNA sequencing technology to extract gene expression data of lung cancer cells under multiple environmental variables, screen and remove genes whose expression level changes exceed a set threshold, and obtain the expression change value of lung cancer microenvironment;

[0008] S2: Based on the expression change values ​​of the lung cancer microenvironment, calculate the regulatory influence coefficient of the mutated gene on the tumor suppressor genes related to cell proliferation, apoptosis, and immune escape, extract genes with high regulatory capacity, and obtain the target values ​​for stable oncogene regulation.

[0009] S3: Based on the stable oncogene regulation target value, calculate the degree of influence of the gene on normal lung cells, screen genes whose influence range is lower than the set risk threshold, and obtain information on low-risk genes for targeted editing;

[0010] S4: Based on the information on low-risk genes targeted for editing, calculate the sequence matching degree between the editing site and potential off-target regions of the whole genome, remove genes with high sequence similarity but no functional impact, and obtain lung cancer-specific off-target prediction values;

[0011] S5: Based on the lung cancer-specific off-target prediction value, adjust the CRISPR-Cas cutting window for regions with high mismatch scores, remove regions where the cutting window overlaps with off-target sites, perform gene editing, and obtain optimized target editing information.

[0012] As a further aspect of the present invention, the lung cancer microenvironment expression variation values ​​include gene expression fluctuation range, environmental variable influence coefficient, and lung cancer-related gene expression stability; the stable oncogene regulation target values ​​include oncogene regulation influence coefficient, tumor suppressor gene co-expression relationship, and gene interaction network score; the targeted editing low-risk gene information specifically includes signaling pathway influence range, gene function intervention risk assessment, and editing potential impact distribution; the lung cancer-specific off-target prediction values ​​include mutation region sequence characteristics, genome matching score, and off-target site specificity index; and the optimized target editing information specifically refers to cut window matching degree, mismatch score adjustment range, and off-target overlapping region removal parameters.

[0013] As a further aspect of the present invention, the step of obtaining the expression variation value of the lung cancer microenvironment specifically includes:

[0014] S111: Obtain lung cancer tissue sections, set oxygen concentration, inflammatory factor concentration and immune cell infiltration under various experimental conditions, extract lung cancer cell RNA under each experimental condition, and use high-throughput RNA sequencing technology to determine gene expression levels to obtain a gene expression dataset.

[0015] S112: Based on the aforementioned gene expression dataset, compare the expression values ​​of the same gene under multiple conditions using the following formula:

[0016]

[0017] The calculation retrieves the expression variation values ​​of genes;

[0018] Where V represents the gene expression variation value, E j The expression value of the representative gene under experimental condition j. S represents the mean expression of the gene under all experimental conditions, n represents the number of experimental conditions, α is the expression variation regulation parameter, β is the environmental factor amplification factor, and S j This represents the value of the environmental variable under experimental condition j;

[0019] S113: Call the expression variation value of the gene, filter the variation range of the gene, remove genes whose expression variation value exceeds the set threshold, and obtain the expression variation value of the lung cancer microenvironment.

[0020] As a further aspect of the present invention, the step of obtaining the stable oncogene regulatory target value specifically includes:

[0021] S211: Based on the expression variation values ​​of the lung cancer microenvironment, combined with the lung cancer gene mutation database, a set of mutated gene data is screened, the mutation frequency and stability of mutated genes are compared, genes with mutation instability are removed, and a set of stable mutated genes is generated.

[0022] S212: Using the aforementioned stable mutant gene set, extract the interaction relationships between mutant genes and tumor suppressor genes, calculate the regulatory effects of mutant genes on tumor suppressor genes related to cell proliferation, apoptosis, and immune escape, using the following formula:

[0023]

[0024] The regulatory influence coefficient of the mutated gene is obtained through calculation;

[0025] Among them, C k I represents the regulatory influence coefficient of the mutant gene k. km T represents the strength of the effect of the mutated gene k on the tumor suppressor gene m. m D represents the expression level of tumor suppressor gene m in cancer cells. m γ represents the complexity of the pathway in which tumor suppressor gene m is located, γ represents the pathway complexity adjustment factor, and M represents the number of tumor suppressor genes associated with mutated gene k.

[0026] S213: Based on the regulatory influence coefficient of the mutated gene, sort them by numerical value, extract genes whose regulatory capacity exceeds the set threshold, and obtain the target value for stable oncogene regulation.

[0027] As a further aspect of the present invention, the step of obtaining the targeted editing low-risk gene information specifically includes:

[0028] S311: Based on the stable oncogene regulation target value, call the lung cancer gene functional pathway database, extract the node information of the target gene in the signaling pathway, establish a signaling pathway association set between target genes, and generate gene pathway node information;

[0029] S312: Based on the aforementioned gene pathway node information, combined with the regulatory intensity and co-expression relationship of the target gene, the following formula is used:

[0030]

[0031] The calculation yields the value of the gene's influence.

[0032] Where I represents the degree of influence of the gene on normal lung cells, and R... p W represents the regulatory strength of a gene at signaling pathway node p. p G represents the weight of node p in the signal path. p δ represents the number of genes associated with signaling pathway node p, δ represents the gene pathway complexity correction parameter, and P represents the total number of nodes in the signaling pathway where the target gene is located.

[0033] S313: Based on the gene influence level value, screen genes whose influence range is lower than the set risk threshold to obtain information on low-risk genes for targeted editing.

[0034] As a further aspect of the present invention, the step of obtaining the lung cancer-specific off-target prediction value specifically includes:

[0035] S411: Based on the targeted editing of low-risk gene information, obtain lung cancer-specific mutation data, analyze the mutation region sequence information of the mutated gene, extract the sequence features of the mutation region, and generate mutation region sequence feature data;

[0036] S412: Based on the sequence feature data of the mutation region, the following formula is used:

[0037]

[0038] The sequence matching degree between the edit site and potential off-target regions of the whole genome is calculated to obtain the sequence matching degree of the edit site;

[0039] Among them, S A M represents the sequence match between the edit site and potential off-target regions across the entire genome.A,i B represents the sequence matching score at edit site i. A,i D represents the control score of the genomic background sequence at site i. A,i λ represents the degree of sequence conservation at site i. A G represents the conservative correction parameter. A,i θ represents the functional relevance score at site i. A Represents the functional relevance weight, N A The total number of edit sites;

[0040] S413: Based on the sequence matching degree of the edit site, genes with high sequence similarity but no functional impact are removed, and the specific matching score of the edit site in the background of the mutation region is calculated to obtain the lung cancer specific off-target prediction value.

[0041] As a further aspect of the present invention, the step of obtaining the optimized target editing information specifically includes:

[0042] S511: Based on the lung cancer-specific off-target prediction value, a dynamic matching threshold is set, off-target sites with a matching degree exceeding the dynamic matching threshold are extracted, and high-matching off-target site data are generated.

[0043] S512: Based on the aforementioned highly matched off-target site data, the following formula is used:

[0044]

[0045] Calculate the mismatch score between the target site and the off-target site to obtain mismatch score information;

[0046] Among them, E C Indicates mismatch rating, ω B,i It is the weight of site i, γ B,i It is the position sensitivity parameter, d B,i It is the distance between the target site and the off-target site, σ B,i It is a sensitivity correction factor for mismatch, c B,i It is a parameter related to the type of mismatched bases, and K is the total number of sites.

[0047] S513: Based on the mismatch score information, adjust the CRISPR-Cas cutting window for high mismatch score regions, optimize the cutting region boundary, remove the overlapping region between the cutting window and off-target sites, and perform gene editing to obtain optimized target site editing information.

[0048] A gene editing system targeting lung cancer cells, the gene editing system targeting lung cancer cells being used to perform the above-described gene editing method targeting lung cancer cells, the system comprising:

[0049] The expression change analysis module obtains lung cancer tissue sections, filters and removes genes whose expression level changes exceed a set threshold, and obtains the expression change values ​​of the lung cancer microenvironment.

[0050] Based on the expression variation values ​​of the lung cancer microenvironment, the regulatory influence analysis module calculates the regulatory influence coefficient of mutated genes on tumor suppressor genes related to cell proliferation, apoptosis, and immune escape, extracts genes with high regulatory capacity, and obtains the target values ​​for stable oncogene regulation.

[0051] The gene impact analysis module calculates the degree of gene impact on normal lung cells based on the stable oncogene regulation target value, screens genes whose impact range is lower than the set risk threshold, and obtains information on low-risk genes for targeted editing.

[0052] Based on the information on low-risk genes for targeted editing, the specificity analysis module removes genes with high sequence similarity but no functional impact to obtain lung cancer-specific off-target prediction values.

[0053] Based on the lung cancer-specific off-target prediction value, the optimization editing module adjusts the CRISPR-Cas cutting window for regions with high mismatch scores, removes regions where the cutting window overlaps with off-target sites, and performs gene editing to obtain optimized target site editing information.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0055] In this invention, by acquiring gene expression variation values, the target gene expression measurement under a single condition is avoided, enhancing the data specificity of the expression level parameter screening process. Through dual screening of gene expression level fluctuations and mutation stability, the stable oncogene regulatory target value is accurately located, ensuring the stability and reliability of gene targets. Combined with signaling pathway influence and normal lung cell function risk analysis, information on low-risk genes for targeted editing is obtained, avoiding blind selection. A precise off-target prediction mechanism is constructed using lung cancer-specific mutation data. Through genome matching analysis and sequence mismatch scoring, the target gene splicing region is dynamically adjusted in real time to avoid the risk of mis-editing at potential off-target sites. This ensures the accuracy and reliability of the targeted action of the lung cancer cell gene editing process in the context of mutations, forming lung cancer-specific off-target prediction values. The splicing window boundary is set according to the dynamic matching threshold to optimize the splicing region range, comprehensively reducing potential off-target risks, and forming a dual guarantee strategy for lung cancer specificity and stability. Attached Figure Description

[0056] Figure 1 This is a schematic flowchart of the method of the present invention;

[0057] Figure 2 This is a flowchart illustrating the process of obtaining expression variation values ​​in the lung cancer microenvironment according to the present invention;

[0058] Figure 3This is a flowchart illustrating the process of obtaining stable oncogene regulatory target values ​​according to the present invention;

[0059] Figure 4 This is a flowchart illustrating the process of obtaining information on low-risk genes for targeted editing in this invention;

[0060] Figure 5 This is a flowchart illustrating the process of obtaining lung cancer-specific off-target prediction values ​​according to the present invention.

[0061] Figure 6 This is a flowchart illustrating the process of obtaining optimized target editing information for this invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0063] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0064] Example 1

[0065] Please see Figure 1 This invention provides a technical solution: a gene editing method targeting lung cancer cells, comprising the following steps:

[0066] S1: Obtain lung cancer tissue sections, set oxygen concentration, inflammatory factor concentration and immune cell infiltration as key variables, use high-throughput RNA sequencing technology to extract gene expression data of lung cancer cells under multiple environmental variables, calculate the expression level of lung cancer-related genes under multiple conditions, screen and remove genes whose expression level changes exceed the set threshold, and obtain the expression change value of lung cancer microenvironment.

[0067] S2: Based on the expression variation values ​​of lung cancer microenvironment, combined with the lung cancer gene mutation database, genes with mutation instability are removed, the interaction relationship between mutated genes and tumor suppressor genes is extracted, the regulatory influence coefficient of mutated genes on tumor suppressor genes related to cell proliferation, apoptosis, and immune escape is calculated, and genes with high regulatory capacity are extracted according to the ranking of regulatory influence coefficients to obtain the target value of stable oncogene regulation.

[0068] S3: Based on the stable oncogene regulatory target value, the lung cancer gene functional pathway database is called to extract the signal pathway nodes of the target gene. Combined with the regulatory strength and co-expression relationship between genes, the degree of gene influence on normal lung cells is calculated, and genes with an influence range lower than the set risk threshold are screened to obtain information on low-risk genes for targeted editing.

[0069] S4: Based on the information of low-risk genes targeted for editing, lung cancer-specific mutation data is obtained. The sequence characteristics of the mutation region are extracted by calling the genome alignment analysis. The sequence matching degree between the editing site and the potential off-target region of the whole genome is calculated. Genes with high sequence similarity but no functional impact are removed. The specific matching score of the editing site in the context of the mutation region is calculated to obtain the lung cancer-specific off-target prediction value.

[0070] S5: Based on lung cancer-specific off-target prediction values, a dynamic matching threshold is set, high-matching off-target sites are extracted, mismatch scores between target sites and off-target sites are calculated, CRISPR-Cas splicing windows are adjusted for high mismatch score regions, splicing region boundaries are optimized, overlapping regions between splicing windows and off-target sites are removed, gene editing is performed, and optimized target site editing information is obtained.

[0071] The expression variation values ​​of the lung cancer microenvironment include the range of gene expression fluctuations, the influence coefficient of environmental variables, and the expression stability of lung cancer-related genes. The target values ​​for stabilizing oncogene regulation include the influence coefficient of oncogene regulation, the co-expression relationship of tumor suppressor genes, and the gene interaction network score. The information on targeted editing of low-risk genes specifically includes the range of signaling pathway influence, gene function intervention risk assessment, and the distribution of potential editing effects. The lung cancer-specific off-target prediction values ​​include mutation region sequence characteristics, genome matching score, and off-target site specificity index. The information on optimizing target editing specifically refers to the splicing window matching degree, the mismatch score adjustment range, and the parameters for removing off-target overlapping regions.

[0072] Please see Figure 2 The specific steps for obtaining the expression variation values ​​of the lung cancer microenvironment are as follows:

[0073] S111: Obtain lung cancer tissue sections, set oxygen concentration, inflammatory factor concentration and immune cell infiltration under various experimental conditions, extract lung cancer cell RNA under each experimental condition, and use high-throughput RNA sequencing technology to determine gene expression levels to obtain a gene expression dataset.

[0074] After obtaining lung cancer tissue sections, the sample source was confirmed to ensure that the samples came from different patients or different lesions of the same patient, thus guaranteeing data diversity and reproducibility of experimental results. Each sample was numbered, recorded, and stored in liquid nitrogen to prevent RNA degradation. Subsequently, different oxygen concentrations (e.g., 5%, 10%, 20%), inflammatory factor concentrations (e.g., IL-6, TNF-α, set at 10 pg / mL, 50 pg / mL, 100 pg / mL), and immune cell infiltration (T cell percentage 5%, 15%, 30%) were set according to experimental requirements. For each experimental condition, lung cancer cells were cultured separately, and oxygen levels, inflammatory factor concentrations, and immune cell percentages were monitored during culture to ensure that each experimental condition strictly adhered to the set values. After 48 hours of culture, total RNA was extracted from lung cancer cells under each condition. Cells were lysed using TRIzol reagent, and the RNA layer was separated by centrifugation and purified. The RNA concentration and purity were detected using NanoDrop. The purity index required an A260 / A280 ratio between 1.8 and 2.1. Further RNA integrity analysis was performed using an Agilent 2100 scanner. Samples with an RNA integrity value (RIN) > 7 were selected for subsequent sequencing. The RIN threshold of 7 was set based on empirical data from RNA integrity analysis. In high-throughput sequencing, RNA with a RIN value below 7 is generally considered to be degraded or fragmented, affecting the quality of subsequent data. The rationality of this value can be determined by comparing the sequencing quality of samples with low and high RIN values. Experimental statistics showed that when the RIN value < 7, the Q30 value of the sequencing data was below 85%, while when the RIN value ≥ 7, the Q30 value was usually above 90%. Therefore, 7 was used as the minimum threshold for RIN. After the RNA was qualified, a cDNA library was constructed, and sequencing was performed using RNA-Seq high-throughput sequencing technology to obtain gene expression level data of lung cancer cells under various experimental conditions. The gene expression data sets under each experimental condition were recorded.

[0075] S112: Based on gene expression datasets, compare the expression values ​​of the same gene under multiple conditions using the following formula:

[0076]

[0077] The calculation retrieves the expression variation values ​​of genes;

[0078] Where V represents the gene expression variation value, E j The expression value of the representative gene under experimental condition j. S represents the mean expression of the gene under all experimental conditions, n represents the number of experimental conditions, α is the expression variation regulation parameter, β is the environmental factor amplification factor, and S j This represents the value of the environmental variable under experimental condition j;

[0079] Based on the measured gene expression data set, the expression values ​​of each gene under different experimental conditions were organized to construct a data matrix. The rows of the matrix represent genes, and the columns represent experimental conditions. For example, the expression values ​​of gene X1 under experimental conditions 1 to n are E... X1,1 E X1,2 ,...,E X1,n Calculate the mean expression of each gene under all experimental conditions. If the expression values ​​of gene X1 under the five experimental conditions are 10, 20, 30, 25, and 15, then... Subsequently, the absolute deviation of the expression value of each gene from the mean under each experimental condition was calculated. If the expression value of gene X1 under experimental condition 2 is 20, then its bias value |20-20|=0. Calculate the cumulative bias value under all conditions, and simultaneously calculate the comprehensive value S of the environmental variables. j Using a weighted approach, with oxygen concentration, inflammatory factor concentration, and immune cell infiltration assigned weights of 0.4, 0.3, and 0.3 respectively, the environmental variable value is: S j = 0.4 × Oxygen Concentration + 0.3 × Inflammatory Factor Concentration + 0.3 × Immune Cell Infiltration. The weights of 0.4, 0.3, and 0.3 are based on research findings on the impact of the lung cancer microenvironment on gene expression. Changes in oxygen concentration can directly regulate tumor metabolic pathways, hence the highest weight of 0.4. Both inflammatory factors and immune cell infiltration affect the immune microenvironment, thus influencing gene expression, therefore they are each set to 0.3. The rationality of the weights can be verified by calculating the variance of gene expression changes under different weight settings. In experimental statistics, the scheme with weights of 0.4, 0.3, and 0.3 resulted in the smallest variance in gene expression changes; therefore, this scheme was selected. If the oxygen concentration in experimental condition 2 is 10%, the inflammatory factor concentration is 50 pg / mL, and the immune cell infiltration is 15%, then...

[0080] S2=0.4×10+0.3×50+0.3×15=19.5;

[0081] Substitute into the formula to calculate the gene expression change value:

[0082]

[0083] Let α = 0.5 and β = 0.2. The α value is set based on the sensitivity of gene expression deviation to environmental variables. Generally, a smaller α value means a weaker response to environmental variables, while a larger α value amplifies the expression deviation. Therefore, based on experimental data, the calculated range of gene expression variation values ​​when α = 0.5 best reflects the actual biological significance. The β = 0.2 value is set based on the amplification effect of environmental variables on gene expression. When β > 0.2, the gene expression variation values ​​exhibit excessively large non-linear changes. Therefore, 0.2 is selected to maintain a reasonable range. The correction factors for each condition are calculated, for example, condition 2:

[0084] 1 + 0.5 × log(1 + 0.2 × 19.5) = 1 + 0.5 × log(4.9) ≈ 1.345;

[0085] The expression variation values ​​under all experimental conditions were calculated sequentially, and the expression variation value matrix of the genes was finally obtained.

[0086] S113: Call the gene expression variation value, filter the gene variation range, remove genes whose expression variation value exceeds the set threshold, and obtain the lung cancer microenvironment expression variation value.

[0087] Based on the calculated gene expression variation matrix, a screening threshold T is first set. The threshold can be set according to the statistical distribution, for example, by taking the mean of the data distribution plus twice the standard deviation as the upper limit of screening. The mean variation value V of all genes is then calculated. mean The threshold is calculated using the standard deviation σ. For example, if the mean expression variation of all genes is 2.5 and the standard deviation is 1.2, then T = 2.5 + 2 × 1.2 = 4.9. This threshold is based on the normal distribution characteristics of gene expression variation. Generally, the natural range of gene expression variation follows a normal distribution. Setting the mean plus twice the standard deviation as the upper limit of screening can effectively remove genes with extreme variations while retaining most of the biologically significant genes. Experimental data shows that this threshold setting can remove about 5% of highly variable genes while retaining 95% of stable expression genes. All genes with V>T are screened and removed. The retained genes are those that conform to the expression variation range in the lung cancer microenvironment. The filtered gene expression dataset is constructed and stored for subsequent biological analysis.

[0088] Please see Figure 3 The specific steps for obtaining the target values ​​for stable oncogene regulation are as follows:

[0089] S211: Based on the expression variation values ​​of lung cancer microenvironment, combined with the lung cancer gene mutation database, a set of mutated gene data is screened, the mutation frequency and stability of mutated genes are compared, genes with mutation instability are removed, and a set of stable mutated genes is generated.

[0090] Based on the expression variation values ​​of the lung cancer microenvironment, we first compiled the expression variations of all genes under different experimental conditions. Then, combined with a lung cancer gene mutation database, we extracted gene data containing mutations in known lung cancer patients, ensuring the database source was reliable and including authoritative databases such as TCGA (Cancer Genome Atlas) and ICGC (International Cancer Genome Consortium). We also matched the mutation frequency data of the mutated genes and calculated the mutation frequency of each gene in different patient samples. For example, the mutation frequency of gene TP53 in 1000 lung cancer patients was 60%, i.e., a mutation frequency value of 0.6. Simultaneously, we recorded the mutation types of each gene (missense mutations, nonsense mutations, frameshift mutations, splice site mutations, etc.). The stability of different types of mutations was evaluated. Stability was screened based on criteria such as whether the gene mutation persisted long-term in different patient groups and whether it caused functional changes. The mutation stability threshold was set as the low-frequency mutation exclusion threshold T. f This value is set based on the overall distribution of gene mutations, generally at the lower quartile of all gene mutation frequencies. If the mutation frequency distribution range of a mutated gene in different patients is 0.01, 0.04, 0.07, 0.15, 0.23, 0.34, 0.48, 0.52, 0.68, 0.75, then the lower quartile is 0.07. Therefore, T is set... f =0.07, meaning genes with a mutation frequency below 7% are removed. At the same time, genes with a mutation frequency below 10% and showing high variability in different databases are also removed. For example, if a gene has a mutation frequency of 5% in the TCGA database and 8% in the ICGC database, such low-frequency unstable genes are removed. The set of all screened mutant genes is calculated, and finally a set of stable mutant genes is formed.

[0091] S212: This step involves accessing a stable set of mutant genes, extracting the interactions between mutant genes and tumor suppressor genes, and calculating the regulatory effects of mutant genes on tumor suppressor genes related to cell proliferation, apoptosis, and immune escape. The formula used is:

[0092]

[0093] The regulatory influence coefficient of the mutated gene is obtained through calculation;

[0094] Among them, C k I represents the regulatory influence coefficient of the mutant gene k. km T represents the strength of the effect of the mutated gene k on the tumor suppressor gene m. m D represents the expression level of tumor suppressor gene m in cancer cells. m γ represents the complexity of the pathway in which tumor suppressor gene m is located, γ represents the pathway complexity adjustment factor, and M represents the number of tumor suppressor genes associated with mutated gene k.

[0095] After accessing the stable mutant gene set, the interaction data between mutant genes and tumor suppressor genes are first extracted. Gene pairs that interact in known cancer signaling pathways are screened, such as TP53 and MDM2, RB1 and E2F1, etc., and their interaction strength parameter I is extracted. km The intensity of action can be determined based on gene interaction scores provided in gene regulation databases (such as STRING and BioGRID). For example, TP53 has an effect score of 0.85 on MDM2 and 0.72 on RB1. Then, the expression levels of tumor suppressor genes (T0) are extracted. m Assuming the expression value of the MDM2 gene in cancer cells is 12.5 and the expression value of the RB1 gene is 15.3, then obtain the complexity parameter D of the signaling pathway in which each tumor suppressor gene is located. m Pathway complexity can be measured by the number of nodes in the pathway where a gene directly acts. For example, MDM2 involves 5 nodes, and RB1 involves 8 nodes. The pathway complexity adjustment factor γ is set based on the hierarchical relationship of the signaling pathway. The standard is that when the number of pathway nodes in which the gene is located is less than 5, γ = 0.2; when the number of pathway nodes is between 5 and 10, γ = 0.3; and when the number of pathway nodes exceeds 10, γ = 0.4. In this case, MDM2 has 5 pathway nodes and RB1 has 8 pathway nodes. Therefore, MDM2 corresponds to γ ​​= 0.3, and RB1 corresponds to γ ​​= 0.3. Substituting these values ​​into the formula, the regulatory influence coefficient of the mutant gene TP53 is calculated.

[0096]

[0097] Calculate the MDM2 gene portion:

[0098]

[0099] 0.85 × 7.91 = 6.72;

[0100] Calculate the RB1 gene portion:

[0101]

[0102] 0.72 × 8.32 = 5.99;

[0103] C TP53 =6.72 + 5.99 = 12.71;

[0104] Calculate the regulatory influence coefficients of all mutated genes sequentially to obtain a complete matrix of regulatory influence coefficients of mutated genes.

[0105] S213: Based on the regulatory influence coefficient of mutated genes, genes with regulatory capacity exceeding a set threshold are extracted and stable oncogene regulatory target values ​​are obtained.

[0106] Based on the calculated regulatory influence coefficient matrix of mutant genes, mutant genes with significant regulatory abilities are screened according to their numerical values, and a regulatory ability screening threshold T is set. c This value is set based on the statistical distribution of the regulatory influence coefficients of all mutated genes, and is generally calculated using the mean plus two standard deviations, i.e.:

[0107] T c =C mean +2σ C ;

[0108] Assuming the mean regulatory influence coefficient of all genes is 8.5 and the standard deviation is 2.1, the threshold for calculation is:

[0109] T c =8.5 + 2 × 2.1 = 12.7;

[0110] Filter all C k >T c The gene is retained as a stable oncogene regulatory target value.

[0111] Please see Figure 4 The specific steps for obtaining information on low-risk genes for targeted editing are as follows:

[0112] S311: Based on stable oncogene regulatory target values, call the lung cancer gene functional pathway database, extract the node information of target genes in signaling pathways, establish a signaling pathway association set between target genes, and generate gene pathway node information;

[0113] Based on stable oncogene regulatory target values, the lung cancer gene functional pathway database is first accessed to screen signaling pathway information related to the target genes. Node information for each gene within the signaling pathway is extracted. For different signaling pathways (such as the PI3K-Akt pathway, p53 pathway, and Wnt pathway), the specific roles of the target genes within these pathways are identified. For example, the TP53 gene is a transcription factor regulatory node in the p53 pathway, and the KRAS gene acts as an upstream signaling molecule in the MAPK signaling pathway. The positions of the target genes in the signaling pathway map are sequentially marked, and the pathway connections between target genes are calculated to determine if direct regulatory relationships exist. For example, the EGFR gene regulates the AKT gene through the PI3K pathway, thus establishing the EGFR→AKT association pathway. The pathway associations of all target genes are further calculated, the number of direct connections for each gene is counted, and a complete set of gene pathway node information is generated. The signaling pathway positions of all target genes, the number of genes directly regulated, and their pathway distribution are recorded. The weights of the signaling pathway nodes are set based on the gene's centrality within the pathway. Centrality is determined by the gene's out-degree (the number of other genes it regulates) and in-degree (the number of genes it is regulated by). The weight W... pThe calculation method can be set as W p = 1 + 0.1 × (out-degree + in-degree). If a gene regulates 6 genes in the PI3K-Akt pathway and is regulated by 4 genes, then its weight is calculated as W. p =1+0.1×(6+4)=2.0.

[0114] S312: Based on gene pathway node information, combined with the regulatory strength and co-expression relationship of target genes, the following formula is used:

[0115]

[0116] The calculation yields the value of the gene's influence.

[0117] Where I represents the degree of influence of the gene on normal lung cells, and R... p W represents the regulatory strength of a gene at signaling pathway node p. p G represents the weight of node p in the signal path. p δ represents the number of genes associated with signaling pathway node p, δ represents the gene pathway complexity correction parameter, and P represents the total number of nodes in the signaling pathway where the target gene is located.

[0118] Based on gene pathway node information, regulatory strength data of target genes are screened, and the regulatory capacity of genes at each node of the signaling pathway is extracted. For example, the regulatory strength of the TP53 gene in multiple signaling pathway nodes (such as DNA damage repair and cell cycle regulation) is calculated, and the regulatory strength parameter R is used. p Based on literature or database data, such as the regulatory strength of TP53 at DNA repair nodes being 0.85 and at cell cycle regulation nodes being 0.78, the weighting parameter W of the signaling pathway nodes was further extracted. p This weight is given by formula W. p =1 + 0.1 × (out-degree + in-degree) Calculated as follows: If a gene regulates 5 genes and is regulated by 3 genes in a cell cycle regulatory node, then W p =1 + 0.1 × (5 + 3) = 1.8, and simultaneously extract the number of associated genes G for each node. p This represents the number of other genes directly connected to the node. For example, a key node in the DNA repair pathway connects to 4 genes, and a node in the cell cycle regulation pathway connects to 6 genes. A gene pathway complexity correction parameter δ = 0.25 is set. This parameter is used to reduce the impact of overly complex pathways and avoid the amplification effect of nodes with high connection numbers on the calculation results. It is set based on the standardized calculation of pathway complexity, using δ = 0.05 × average number of associated genes. If the average number of associated genes in this signaling pathway is 5, then...

[0119] δ = 0.05 × 5 = 0.25. Substitute these values ​​into the formula to calculate the degree of influence of the TP53 gene on normal lung cells.

[0120]

[0121] Calculating DNA repair nodes:

[0122]

[0123] Calculating cell cycle regulatory nodes:

[0124]

[0125] I = 0.72 + 0.89 = 1.61;

[0126] The influence values ​​of all target genes are calculated sequentially to form a complete set of gene influence value data.

[0127] S313: Based on the gene impact value, genes with an impact range below the set risk threshold are screened to obtain information on low-risk genes for targeted editing;

[0128] Based on the calculated gene impact values, the data are sorted according to their numerical values, and a risk screening threshold I is set. c The threshold is set based on the distribution of the influence values ​​of all genes. For example, the mean plus twice the standard deviation is taken as the upper limit of screening. The mean I of all genes is calculated. mean and standard deviation σ I The mean is calculated as follows: The standard deviation is calculated as follows: If the mean influence of all genes is 1.2 and the standard deviation is 0.3, then the screening threshold is calculated as I. c =I mean +2×σ I =1.2 + 2 × 0.3 = 1.8, filter all I c Genes with excessive impact are removed, while genes with an impact range below a set threshold are retained as low-risk gene information for targeted editing.

[0129] Please see Figure 5 The specific steps for obtaining lung cancer-specific off-target prediction values ​​are as follows:

[0130] S411: Based on targeted editing of low-risk gene information, obtain lung cancer-specific mutation data, analyze the mutation region sequence information of mutated genes, extract the sequence features of the mutation region, and generate mutation region sequence feature data;

[0131] ​Based on targeted editing of low-risk gene information, we first acquire lung cancer-specific mutation data, screen for mutation sites of known lung cancer-related genes, and extract their corresponding DNA sequence information. For example, we search for genes with high-frequency mutations in lung cancer patients in the TCGA and ICGC databases, and screen for mutations whose mutation sites are distributed in exon regions and may affect gene function, such as the R175H mutation in the TP53 gene and the G12C mutation in the KRAS gene. We obtain the sequence information of 20 bp upstream and downstream of these mutation sites and establish a mutation region sequence library. For each mutation region, we analyze its nucleotide composition, GC content, secondary structure characteristics, and calculate its conservation index, screening for highly conserved sequences or structurally variable sequences. Mutation regions are identified by their GC content. For example, a mutation region with a GC content of 55% and a sequence conservation score of 0.8 is considered relatively stable, while a region with a GC content of 35% and a conservation score of 0.4 may have significant variations. By analyzing the sequence characteristics of all mutation regions, a complete set of mutation region sequence characteristics data is generated. The conservation score is set based on cross-species comparison data. If a mutation region is completely identical in at least five mammal species (such as humans, mice, dogs, rats, and rhesus monkeys), the conservation score is set to 1.0. If the region has less than 60% identity in mammals, the score is set to 0.5, and so on. This score directly affects the subsequent calculation of off-target risk.

[0132] S412: Based on sequence feature data of mutation regions, the formula is used:

[0133]

[0134] The sequence matching degree between the edit site and potential off-target regions of the whole genome is calculated to obtain the sequence matching degree of the edit site;

[0135] Among them, S A M represents the sequence match between the edit site and potential off-target regions across the entire genome. A,i B represents the sequence matching score at edit site i. A,i D represents the control score of the genomic background sequence at site i. A,i λ represents the degree of sequence conservation at site i. A G represents the conservative correction parameter. A,i θ represents the functional relevance score at site i. A Represents the functional relevance weight, N A The total number of edit sites;

[0136] Based on the sequence feature data of the mutation region, the sequence matching score M of the edit site is first extracted. A,iThis parameter measures the degree of match between the edit site and potential off-target regions. For example, it is calculated using a base matching score, with each edit site having a matching score ranging from 0 to 1. A perfectly matched site scores 1, a site with only one base difference scores 0.9, and so on. Subsequently, the control score B of the genomic background sequence at the corresponding site is extracted. A,i This parameter measures whether there is a variation in the background sequence at this site. For example, if the probability of a variation at this site in a certain gene is 5% in a normal population, then B... A,i =0.05, then calculate the sequence conservation degree D of the site. A,i This parameter can be determined through multi-species sequence alignment. For example, if a certain locus is completely identical in humans, mice, and rats, then D... A,i =1, if the similarity only exists between humans and primates, then D A,i =0.8, conservative correction parameter λ A The value is set to 0.3. This value is determined based on the evolutionary stability of different genomic regions. For example, exon regions have higher conservation, so their values ​​are usually between 0.2 and 0.4, while non-coding regions have lower conservation and may be assigned values ​​between 0.1 and 0.3. The current setting of 0.3 is based on statistical data on the evolutionary conservation of lung cancer-related genes, which is consistent with the characteristics of most lung cancer mutation hotspot regions. A further functional relevance score G is set. A,i This is used to assess whether a site involves a key functional gene; for example, if the site falls within a transcription factor binding region or an exonuclear splicing site, then G... A,i =1, if it is located in a non-encoded region, then G A,i =0.5, functional relevance weight θ A The value is set to 0.2. This value is calculated based on the influence weights of known functional regions in gene function databases (such as GENCODE and Ensembl). For example, mutations at RNA splicing sites or protein binding sites usually have a greater impact, therefore θ... A The value ranges from 0.15 to 0.25. The value of 0.2 was chosen in this study based on the distribution of the impact of functional mutations in lung cancer-related genes. This value was then substituted into the formula to calculate the sequence matching degree of the edited site.

[0137]

[0138] Suppose the parameters of a certain edit site are as follows: M A,1 =0.9, B A,1 =0.05D A,1 =0.8G A,1 =1

[0139]

[0140] The matching degree of all edit sites is calculated sequentially, and finally a set of edit site sequence matching degree data is formed.

[0141] S413: Based on the sequence matching degree of the edit site, genes with high sequence similarity but no functional impact are removed, and the specific matching score of the edit site in the context of the mutation region is calculated to obtain the lung cancer specific off-target prediction value.

[0142] Based on the sequence matching data of the edit sites, genes were sorted according to their matching scores, and genes with high matching scores but no functional impact were removed. A selection threshold T was set. S This threshold can be calculated based on the mean of the matching degree of all sites plus twice the standard deviation. For example, if the mean matching degree of all sites is 0.5 and the standard deviation is 0.15, then the screening threshold T is... S =0.5 + 2 × 0.15 = 0.8. This threshold is set according to statistical standards, usually by selecting the mean of a normal distribution plus two standard deviations, to ensure that the gene matching degree of the removed genes exceeds approximately 95% of the distribution range, thereby screening out irrelevant genes with high matching degree but no functional influence, and removing S. A >T S However, for genes with a functional relevance score below 0.6, the edit sites with specific matching in the mutated region background are retained, and their specific matching scores are calculated to obtain the lung cancer-specific off-target prediction value.

[0143] Please see Figure 6 The specific steps for optimizing the acquisition of target editing information are as follows:

[0144] S511: Based on lung cancer-specific off-target prediction values, a dynamic matching threshold is set, off-target sites with matching degrees exceeding the dynamic matching threshold are extracted, and high-matching off-target site data are generated.

[0145] Based on lung cancer-specific off-target prediction values, a dynamic matching threshold is first set. This threshold is calculated based on the distribution of matching data for all off-target sites. The threshold is determined using a normal distribution model in statistics, taking the mean of the matching degree of all off-target sites plus twice the standard deviation as the dynamic matching threshold. This ensures that the selected high-matching off-target sites cover more than 95% of the high-matching regions, avoiding the omission of key off-target sites. For example, if the mean matching degree of all off-target sites is 0.45 and the standard deviation is 0.12, then the dynamic matching threshold is calculated as follows:

[0146] T D =0.45 + 2 × 0.12 = 0.69;

[0147] The threshold tends to stabilize as the number of samples increases. When the amount of data is large, the fluctuation range of the mean and standard deviation decreases. Therefore, when the amount of data reaches more than 1,000 off-target sites, the fluctuation range of the threshold is less than 0.02. Then, all off-target sites are traversed, and sites with a matching degree higher than the set threshold are screened. For example, if the matching degree of a certain off-target site is 0.72, then the site meets the screening condition and is extracted into the high-matching off-target site dataset. At the same time, the chromosomal location, base sequence and functional information of the site are recorded. If a certain off-target site is located in the exon region of a gene, it is marked as potentially affecting gene expression. Finally, a high-matching off-target site dataset is generated.

[0148] S512: Based on highly matched off-target site data, the formula is:

[0149]

[0150] Calculate the mismatch score between the target site and the off-target site to obtain mismatch score information;

[0151] Among them, E C Indicates mismatch rating, ω B,i It is the weight of site i, γ B,i It is the position sensitivity parameter, d B,i It is the distance between the target site and the off-target site, σ B,i It is a sensitivity correction factor for mismatch, c B,i It is a parameter related to the type of mismatched bases, and K is the total number of sites.

[0152] Based on highly matched off-target site data, the distance d between the target site and the off-target site is first extracted. B,i This parameter measures the relative positions of the target site and off-target sites in the genome. For example, if the target site is located in the p21 region of chromosome 7 and the off-target site is located in the q34 region of chromosome 7, with a 5Mb interval between them, then d B,i =5, then extract the site weight parameter ω B,i This parameter is set based on the functional importance of the site in the genome. Weight scores for different gene regions are extracted from known gene databases (such as NCBIRefSeq). If the site is located in an enhancer region, the weight is set to 1.2; if it is located in a promoter region, the weight is set to 1.5; and if it is located in an exon coding region and affects protein translation, the weight is set to 2.0. For example, if an off-target site is located in an exon coding region, a value of ω is assigned. B,1 =2.0, and simultaneously calculate the position sensitivity parameter γ. B,i This parameter measures the impact of inter-site distance on mismatch scores and is typically set at 0.05 ≤ γ. B,i ≤0.2, based on genomic structure, if the target site and off-target site are 5Mb apart, then γ B,iSetting it to 0.1 means that for every 1Mb increase, the mismatch score decreases by 10%. Then, the mismatch sensitivity correction factor σ is extracted. B,i This parameter is used to adjust the impact of different mismatch types on the score, such as the σ of the G→A mismatch. B,i =0.2, σ of C→T mismatch B,i =0.3, this value is set based on biological experimental data, statistically analyzing the effects of different types of base mutations on RNA transcription and protein translation, and further calculating the type-related parameter c of mismatched bases. B,i This parameter is used to measure the impact of mismatch sites on protein structural stability. It is set based on GC content; if the GC content is greater than 50%, then c B,i =0.8, if the GC content is less than 40%, then c B,i =0.5. After setting the parameters, substitute them into the formula to calculate the mismatch score between the target site and the off-target site:

[0153]

[0154] Suppose the off-target matching parameters for a certain target site are as follows: ω B,1 =2.0 (located in the exon coding region)γ B,1 =0.1 (distance between target site and off-target site 5Mb)d B,1 =5(chromosome 7p21-q34)σ B,1 =0.2(G→A mismatch)c B,1 =0.8 (GC content 55%)

[0155] Calculate the mismatch score:

[0156]

[0157] This mismatch score is used to assess the impact of off-target sites. Sites with scores higher than 1.0 are usually located in highly conserved regions and require further optimization of gene editing strategies. The mismatch scores of all off-target sites are calculated sequentially, and finally a mismatch score dataset is formed.

[0158] S513: Based on mismatch score information, the CRISPR-Cas splicing window is adjusted for regions with high mismatch scores, the splicing region boundary is optimized, the overlapping area between the splicing window and off-target sites is removed, gene editing is performed, and optimized target site editing information is obtained.

[0159] Based on mismatch scoring information, regions with high mismatch scores are extracted and sorted according to their numerical values. The CRISPR-Cas cutoff window is then adjusted, and a cutoff window optimization rule is set. First, sites with mismatch scores greater than a set threshold are selected. This threshold is based on the distribution of all mismatch score data, using the mean plus twice the standard deviation as the selection criterion to ensure that over 95% of high-risk off-target regions are eliminated. For example, if the mean of all mismatch scores is 0.55 and the standard deviation is 0.18, then the cutoff window adjustment threshold is set as follows:

[0160] T C =0.55 + 2 × 0.18 = 0.91;

[0161] The threshold tends to stabilize as the number of samples increases. After calculation with more than 500 target sites, the fluctuation range does not exceed 0.03. For sites with a mismatch score greater than 0.91, the cut window boundary is adjusted to keep it away from the high mismatch score region. The cut window size is set according to the GC content of the target sequence. If the GC content is higher than 50%, the cut window is set to 30bp. If the GC content is lower than 40%, the cut window is set to 40bp to reduce the risk of non-specific binding. Suppose that the original range of a cut window is 30bp, and it is expanded to 40bp after adjustment, but the region with high mismatch score is avoided. At the same time, cut windows that overlap with off-target sites are removed. Finally, gene editing is performed to obtain optimized target site editing information.

[0162] A gene editing system targeting lung cancer cells, the gene editing system targeting lung cancer cells is used to perform the above-described gene editing method targeting lung cancer cells, the system comprising:

[0163] The expression change analysis module obtains lung cancer tissue sections, uses high-throughput RNA sequencing technology to extract gene expression data of lung cancer cells under multiple environmental variables, screens and removes genes whose expression level changes exceed a set threshold, and obtains the expression change value of the lung cancer microenvironment.

[0164] The regulatory impact analysis module calculates the regulatory impact coefficient of mutated genes on tumor suppressor genes related to cell proliferation, apoptosis, and immune escape based on the expression change values ​​of lung cancer microenvironment, extracts genes with high regulatory capacity, and obtains the target values ​​for stable oncogene regulation.

[0165] The gene impact analysis module calculates the degree of gene impact on normal lung cells based on stable oncogene regulatory target values, screens genes whose impact range is below the set risk threshold, and obtains information on low-risk genes for targeted editing.

[0166] The specificity analysis module calculates the sequence matching degree between the editing site and potential off-target regions of the whole genome based on the information of low-risk genes targeted for editing, removes genes with high sequence similarity but no functional impact, and obtains lung cancer-specific off-target prediction values.

[0167] The optimized editing module adjusts the CRISPR-Cas cutting window for regions with high mismatch scores based on lung cancer-specific off-target prediction values, removes regions where the cutting window overlaps with off-target sites, and performs gene editing to obtain optimized target site editing information.

[0168] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A gene editing method targeting lung cancer cells, characterized in that, Includes the following steps: S1: Obtain lung cancer tissue sections, use high-throughput RNA sequencing technology to extract gene expression data of lung cancer cells under multiple environmental variables, screen and remove genes whose expression level changes exceed a set threshold, and obtain the expression change value of lung cancer microenvironment; S2: Based on the expression change values ​​of the lung cancer microenvironment, calculate the regulatory influence coefficient of the mutated gene on the tumor suppressor genes related to cell proliferation, apoptosis, and immune escape, extract genes with high regulatory capacity, and obtain the target values ​​for stable oncogene regulation. S3: Based on the stable oncogene regulation target value, calculate the degree of influence of the gene on normal lung cells, screen genes whose influence range is lower than the set risk threshold, and obtain information on low-risk genes for targeted editing; S4: Based on the information on low-risk genes targeted for editing, calculate the sequence matching degree between the editing site and potential off-target regions of the whole genome, remove genes with high sequence similarity but no functional impact, and obtain lung cancer-specific off-target prediction values; S5: Based on the lung cancer-specific off-target prediction value, adjust the CRISPR-Cas cutting window for regions with high mismatch scores, remove regions where the cutting window overlaps with off-target sites, perform gene editing, and obtain optimized target editing information.

2. The gene editing method for targeting lung cancer cells according to claim 1, characterized in that, The lung cancer microenvironment expression variation values ​​include gene expression fluctuation range, environmental variable influence coefficient, and lung cancer-related gene expression stability. The stable oncogene regulation target values ​​include oncogene regulation influence coefficient, tumor suppressor gene co-expression relationship, and gene interaction network score. The targeted editing low-risk gene information specifically includes signaling pathway influence range, gene function intervention risk assessment, and editing potential impact distribution. The lung cancer-specific off-target prediction values ​​include mutation region sequence characteristics, genome matching score, and off-target site specificity index. The optimized target editing information specifically refers to cut window matching degree, mismatch score adjustment range, and off-target overlapping region removal parameters.

3. The gene editing method for targeting lung cancer cells according to claim 1, characterized in that, The specific steps for obtaining the expression variation values ​​of the lung cancer microenvironment are as follows: S111: Obtain lung cancer tissue sections, set oxygen concentration, inflammatory factor concentration and immune cell infiltration under various experimental conditions, extract lung cancer cell RNA under each experimental condition, and use high-throughput RNA sequencing technology to determine gene expression levels to obtain a gene expression dataset. S112: Based on the aforementioned gene expression dataset, compare the expression values ​​of the same gene under multiple conditions using the following formula: The calculation retrieves the expression variation values ​​of genes; Where V represents the gene expression variation value, E j The expression value of the representative gene under experimental condition j. S represents the mean expression of the gene under all experimental conditions, n represents the number of experimental conditions, α is the expression variation regulation parameter, β is the environmental factor amplification factor, and S j This represents the value of the environmental variable under experimental condition j; S113: Call the expression variation value of the gene, filter the variation range of the gene, remove genes whose expression variation value exceeds the set threshold, and obtain the expression variation value of the lung cancer microenvironment.

4. The gene editing method for targeting lung cancer cells according to claim 1, characterized in that, The specific steps for obtaining the target value for stable oncogene regulation are as follows: S211: Based on the expression variation values ​​of the lung cancer microenvironment, combined with the lung cancer gene mutation database, a set of mutated gene data is screened, the mutation frequency and stability of mutated genes are compared, genes with mutation instability are removed, and a set of stable mutated genes is generated. S212: Using the aforementioned stable mutant gene set, extract the interaction relationships between mutant genes and tumor suppressor genes, calculate the regulatory effects of mutant genes on tumor suppressor genes related to cell proliferation, apoptosis, and immune escape, using the following formula: The regulatory influence coefficient of the mutated gene is obtained through calculation; Among them, C k I represents the regulatory influence coefficient of the mutant gene k. km T represents the strength of the effect of the mutated gene k on the tumor suppressor gene m. m D represents the expression level of tumor suppressor gene m in cancer cells. m γ represents the complexity of the pathway in which tumor suppressor gene m is located, γ represents the pathway complexity adjustment factor, and M represents the number of tumor suppressor genes associated with mutated gene k. S213: Based on the regulatory influence coefficient of the mutated gene, sort them by numerical value, extract genes whose regulatory capacity exceeds the set threshold, and obtain the target value for stable oncogene regulation.

5. The gene editing method targeting lung cancer cells according to claim 1, characterized in that, The specific steps for obtaining the targeted editing low-risk gene information are as follows: S311: Based on the stable oncogene regulation target value, call the lung cancer gene functional pathway database, extract the node information of the target gene in the signaling pathway, establish a signaling pathway association set between target genes, and generate gene pathway node information; S312: Based on the aforementioned gene pathway node information, combined with the regulatory intensity and co-expression relationship of the target gene, the following formula is used: The calculation yields the value of the gene's influence. Where I represents the degree of influence of the gene on normal lung cells, and R... p W represents the regulatory strength of a gene at signaling pathway node p. p G represents the weight of node p in the signal path. p δ represents the number of genes associated with signaling pathway node p, δ represents the gene pathway complexity correction parameter, and P represents the total number of nodes in the signaling pathway where the target gene is located. S313: Based on the gene influence level value, screen genes whose influence range is lower than the set risk threshold to obtain information on low-risk genes for targeted editing.

6. The gene editing method for targeting lung cancer cells according to claim 1, characterized in that, The specific steps for obtaining the lung cancer-specific off-target prediction value are as follows: S411: Based on the targeted editing of low-risk gene information, obtain lung cancer-specific mutation data, analyze the mutation region sequence information of the mutated gene, extract the sequence features of the mutation region, and generate mutation region sequence feature data; S412: Based on the sequence feature data of the mutation region, the following formula is used: The sequence matching degree between the edit site and potential off-target regions of the whole genome is calculated to obtain the sequence matching degree of the edit site; Among them, S A M represents the sequence match between the edit site and potential off-target regions across the entire genome. A,i B represents the sequence matching score at edit site i. A,i D represents the control score of the genomic background sequence at site i. A,i λ represents the degree of sequence conservation at site i. A G represents the conservative correction parameter. A,i θ represents the functional relevance score at site i. A Represents the functional relevance weight, N A The total number of edit sites; S413: Based on the sequence matching degree of the edit site, genes with high sequence similarity but no functional impact are removed, and the specific matching score of the edit site in the background of the mutation region is calculated to obtain the lung cancer specific off-target prediction value.

7. The gene editing method for targeting lung cancer cells according to claim 1, characterized in that, The specific steps for obtaining the optimized target editing information are as follows: S511: Based on the lung cancer-specific off-target prediction value, a dynamic matching threshold is set, off-target sites with a matching degree exceeding the dynamic matching threshold are extracted, and high-matching off-target site data are generated. S512: Based on the aforementioned highly matched off-target site data, the following formula is used: Calculate the mismatch score between the target site and the off-target site to obtain mismatch score information; Among them, E C Indicates mismatch rating, ω B,i It is the weight of site i, γ B,i It is the position sensitivity parameter, d B,i It is the distance between the target site and the off-target site, σ B,i It is a sensitivity correction factor for mismatch, c B,i These are parameters related to the type of mismatched bases, where K is the total number of sites; S513: Based on the mismatch score information, adjust the CRISPR-Cas cutting window for high mismatch score regions, optimize the cutting region boundary, remove the overlapping region between the cutting window and off-target sites, and perform gene editing to obtain optimized target site editing information.

8. A gene editing system targeting lung cancer cells, characterized in that, The gene editing method for targeting lung cancer cells according to any one of claims 1-7, wherein the system comprises: The expression change analysis module obtains lung cancer tissue sections, filters and removes genes whose expression level changes exceed a set threshold, and obtains the expression change values ​​of the lung cancer microenvironment. Based on the expression variation values ​​of the lung cancer microenvironment, the regulatory influence analysis module calculates the regulatory influence coefficient of mutated genes on tumor suppressor genes related to cell proliferation, apoptosis, and immune escape, extracts genes with high regulatory capacity, and obtains the target values ​​for stable oncogene regulation. The gene impact analysis module calculates the degree of gene impact on normal lung cells based on the stable oncogene regulation target value, screens genes whose impact range is lower than the set risk threshold, and obtains information on low-risk genes for targeted editing. Based on the information on low-risk genes for targeted editing, the specificity analysis module removes genes with high sequence similarity but no functional impact to obtain lung cancer-specific off-target prediction values. Based on the lung cancer-specific off-target prediction value, the optimization editing module adjusts the CRISPR-Cas cutting window for regions with high mismatch scores, removes regions where the cutting window overlaps with off-target sites, and performs gene editing to obtain optimized target site editing information.

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