Method for identifying and predicting repositioned drug for protection against γ-ray radiation damage

Abstract

The present invention relates to the technical field of drug repositioning, and provides a method for identifying and predicting a repositioned drug for protection against γ-ray radiation damage. The method comprises: extracting a target gene expression profile dataset of a target biological effect and a candidate gene expression profile dataset of a candidate repositioned drug; calculating an ES of the target gene expression profile dataset and an ES of the candidate gene expression profile dataset; and on the basis of an inverse biological effect and the ESs, selecting, from the candidate gene expression profile dataset, a corresponding candidate drug for protection against the target biological effect. The present invention further reduces the time and economic costs for discovery of the repositioned drug for protection against radiation, and also improves the success rate.

Description

A method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection Technical Field

[0001] This invention relates to the field of drug repositioning technology, and in particular to a method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage. Background Technology

[0002] Current research on radiation protection drugs mainly focuses on antioxidants, estrogens, and thiol derivatives, which suffer from limitations such as limited variety, short duration of effectiveness, significant adverse reactions, and unclear targets and mechanisms of action, and their efficacy needs improvement. Many compounds with potential radiation protection effects are still in the experimental stage, and there is a significant shortage of drugs for the treatment and management of nuclear radiation-related emergencies. There is an urgent need to develop next-generation radiation protection drugs with clear efficacy, well-defined mechanisms, and fewer side effects.

[0003] The development of novel structural drugs is a lengthy, costly, risky, and low-success-rate endeavor. Drug repositioning, a strategy for discovering new uses and indications for known drugs, is becoming increasingly important. However, the discovery of most representative repositioned drugs is accidental, not the result of rational design. Given the vast number of diseases and known drugs, solely relying on experimental screening for new uses of existing drugs is prohibitively expensive. Therefore, identifying and predicting drug repositioning through analytical and computational methods has become a hot topic in computational biology and systems biology research in recent years. Technical issues

[0004] This invention provides a method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage, thereby overcoming the shortcomings of existing technologies that cannot quickly reposition drugs. Technical solutions

[0005] This invention provides a method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection, comprising:

[0006] S1: Extract the target gene expression profile dataset for the target biological effect and the candidate gene expression profile dataset for the candidate repositioning drug;

[0007] S2: Calculate the ES scores of the target gene expression profile dataset and the candidate gene expression profile dataset;

[0008] S3: Based on the reversibility of biological effects, select candidate drugs from the candidate gene expression profile dataset for protection against the target biological effects according to the ES score.

[0009] According to the method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage provided by the present invention, the target biological effect in step S1 is the gamma-ray radiation biological effect.

[0010] According to the method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage provided by the present invention, the target gene expression profile dataset is selected from the GEO database, and the candidate gene expression profile dataset is selected from the LINCS database.

[0011] According to the method for identifying and predicting repositioning drugs for γ-ray radiation damage protection provided by the present invention, step S1, the step of obtaining the candidate gene expression profile dataset further includes:

[0012] S11: Introduce the LINCS database and select various compounds from the LINCS database;

[0013] S12: Using the LINCS database, the gene expression profiles generated by applying each selected compound at multiple concentrations to multiple cells were downloaded and analyzed to obtain the cell response expression profiles and the control group expression profiles.

[0014] S13: Output the cell response expression profile and the control group expression profile as a candidate gene expression profile dataset.

[0015] According to the method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage provided by the present invention, step S2 further includes:

[0016] S21: Align the target gene expression profile dataset and the candidate gene expression profile dataset according to cell type to obtain a related dataset;

[0017] S22: Convert all gene IDs in the relevant dataset into unified gene symbols to obtain a unified dataset;

[0018] S23: Using R language, calculate the ES scores of candidate repositioning drugs and target biological effects in the unified dataset.

[0019] According to the method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage provided by the present invention, the unified gene symbol in step S22 is an official gene symbol.

[0020] According to the identification and prediction method for repositioning drugs for gamma-ray radiation damage protection provided by the present invention, the range of the ES score in step S2 is as follows:

[0021]

[0022] in, To calculate the obtained ES score.

[0023] According to the method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection provided by the present invention, the range of ES scores for the candidate drugs selected in step S3 is as follows: .

[0024] According to the method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage provided by the present invention, step S3 further includes:

[0025] S31: Select candidate repositioning drugs with ES scores less than 0 and absolute values ​​of ES scores that meet a preset threshold based on the calculated ES scores, and obtain multiple candidate drugs.

[0026] S32: Take the intersection of multiple candidate drugs and select the candidate drugs that appear most frequently in multiple cell lines as candidate drugs for protecting against the target biological effects.

[0027] According to the method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage provided by the present invention, step S3 further includes:

[0028] S33: Perform physicochemical information analysis, pharmacological mechanism analysis, and mechanism of action analysis on the candidate drug to conduct experimental verification of the candidate drug. Beneficial effects

[0029] This invention provides a method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection. Based on a large-scale pharmacological database, it comprehensively utilizes gene expression profile data of radiation response and multiple drug effects to establish a method for rationally and accurately identifying potential radiation protection repositioning drugs through computational biology and systems biology approaches. This method is validated through in vivo experiments, laying the foundation for building a technology system for screening and evaluating radiation protection drugs. It not only further reduces the time and economic cost of repositioning drug discovery but also improves the success rate, enabling radiation damage protection drug development to enter a stage combining rational design and experimental screening. Furthermore, the potential radiation protection repositioning drugs proposed through this invention have guaranteed safety and bioavailability, are readily available and usable, and are more suitable for emergency response and treatment of related emergencies. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 is a schematic flowchart of a method for identifying and predicting repositioning drugs for protection against γ-ray radiation damage provided in an embodiment of the present invention.

[0032] Figure 2 is a schematic diagram of the steps for obtaining the candidate gene expression profile dataset provided in an embodiment of the present invention;

[0033] Figure 3 is a schematic diagram of the ES score calculation method for the target gene expression profile dataset and the candidate gene expression profile dataset provided in the embodiment of the present invention;

[0034] Figure 4 is a schematic flowchart of the candidate drug selection method provided in an embodiment of the present invention;

[0035] Figure 5 is a schematic diagram of the survival rate of experimental mice after intraperitoneal injection of lenalidomide and losartan to increase the dose of 9 Gy γ-ray irradiation according to the embodiment of the present invention.

[0036] Figure 6 is a schematic diagram showing the effect of intraperitoneal injection of lenalidomide and losartan on the change in body weight of experimental mice after 9 Gy dose of γ-ray irradiation, according to an embodiment of the present invention. Embodiments of the present invention

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0038] As shown in Figure 1, the present invention provides a method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage, comprising:

[0039] S1: Extract the target gene expression profile dataset for the target biological effect and the candidate gene expression profile dataset for the candidate repositioning drug.

[0040] The target biological effect in step S1 is the biological effect of gamma-ray radiation.

[0041] This embodiment uses the prediction of potential gamma-ray radiation damage protection repositioning drugs based on the reversibility of biological effects as an example, so the target biological effect in step S1 is the gamma-ray radiation biological effect.

[0042] The target gene expression profile dataset is selected from the GEO database, and the candidate gene expression profile dataset is selected from the LINCS database.

[0043] The gene expression profile dataset representing the biological effects of gamma-ray radiation is the set with ID GSE44245 in Gene Expression Omnibus (GEO), and the gene expression profiles of candidate repositioning drugs are from the Library of integrated network-based cellular signatures (LINCS) database.

[0044] As shown in Figure 2, step S1, the step of obtaining the candidate gene expression profile dataset, further includes:

[0045] S11: Introduce the LINCS database and select various compounds from the LINCS database;

[0046] S12: Using the LINCS database, the gene expression profiles generated by applying each selected compound at multiple concentrations to multiple cells were downloaded and analyzed to obtain the cell response expression profiles and the control group expression profiles.

[0047] S13: Output the cell response expression profile and the control group expression profile as a candidate gene expression profile dataset.

[0048] In steps S11 to S13, 4617 compounds in LINCS were first identified through manual screening. Subsequently, 55129 response expression profiles and 4388 control group cell expression profiles were obtained by extracting and integrating these compounds at different time points after they were applied to various cells at different concentrations. These compounds provided materials and targets for subsequent prediction of repositioning drugs for gamma-ray radiation protection.

[0049] S2: Calculate the ES scores of the target gene expression profile dataset and the candidate gene expression profile dataset.

[0050] As shown in Figure 3, step S2 further includes:

[0051] S21: Align the target gene expression profile dataset and the candidate gene expression profile dataset according to cell type to obtain a related dataset;

[0052] S22: Convert all gene IDs in the relevant dataset into unified gene symbols to obtain a unified dataset;

[0053] In step S22, the unified gene symbol is the official gene symbol.

[0054] In steps S21 to S22, the cell lines involved in the gene expression profiles in the GSE44245 and LINCS sets are first compared and aligned according to cell type. The gene expression profiles of the four most relevant cell lines in LINCS are selected for subsequent analysis. Then, gene ID conversion is performed, and the gene IDs in the GSE44245 and LINCS sets are converted into official gene symbols to facilitate subsequent comparison.

[0055] S23: Using R language, calculate the ES scores of candidate repositioning drugs and target biological effects in the unified dataset.

[0056] In step S23, data is processed using R language. The whole genome expression is analyzed based on the cell response data of the two input sets, and ES is calculated based on this. R language is a programming language used for statistical analysis and graphical representation, which can perform data analysis through statistical calculations, data manipulation, etc.

[0057] The range of values ​​for the ES score in step S2 is as follows:

[0058] ;

[0059] in, To calculate the obtained ES score.

[0060] Enrichment scores (ES) are used to characterize and measure the similarity of biological effects of cell responses to gamma-ray radiation and different perturbations. They range from 1 to -1, with a value greater than 0 indicating that the biological effects induced by the drug are similar to those induced by gamma-ray radiation, and vice versa. The absolute value characterizes the degree of similarity or incompatibility.

[0061] S3: Based on the reversibility of biological effects, select candidate drugs from the candidate gene expression profile dataset for protection against the target biological effects according to the ES score.

[0062] In step S3, the range of ES scores for the selected candidate drugs is as follows: .

[0063] As per step S1, this embodiment predicts potential repositioning drugs for protection against gamma-ray radiation damage based on the reversibility of biological effects. Therefore, when the target biological effect is selected as the biological effect of gamma-ray radiation, in order to obtain a repositioning drug that can protect against gamma-ray radiation damage, a drug with an ES score of less than 0 needs to be selected.

[0064] As shown in Figure 4, step S3 further includes:

[0065] S31: Select candidate repositioning drugs with ES scores less than 0 and absolute values ​​of ES scores that meet a preset threshold based on the calculated ES scores, and obtain multiple candidate drugs.

[0066] S32: Take the intersection of multiple candidate drugs and select the candidate drugs that appear most frequently in multiple cell lines as candidate drugs for protecting against the target biological effects.

[0067] For calculations of multiple cell lines, only FDA-approved drugs are considered. Results with ES < 0 indicate that the biological effects of the corresponding drugs on cells are opposite to those of gamma-ray radiation, and they may be candidate drugs for radiation protection. Selecting thresholds yields possible radiation protection drugs in multiple cell lines, and taking the intersection, compounds that appear in multiple cell lines are given priority as candidate drugs.

[0068] Step S3 further includes:

[0069] S33: Perform physicochemical information analysis, pharmacological mechanism analysis, and mechanism of action analysis on the candidate drug to conduct experimental verification of the candidate drug.

[0070] After obtaining the aforementioned candidate drugs, it is necessary to investigate their physicochemical information, pharmacological properties, mechanisms of action, and previous applications in the field of radiation response modulation. A comprehensive judgment should be made to determine the candidate drugs to be used for experimental verification. Published literature should be reviewed to report relevant studies on the use of candidate drugs for radiation response modulation, thereby verifying the feasibility and effectiveness of the above prediction method.

[0071] The following is another embodiment of the identification and prediction method for repositioning drugs for protection against gamma-ray radiation damage provided by the present invention.

[0072] First, the dataset involved in this invention will be described in detail.

[0073] Gene Expression Omnibus (GEO): GEO is a high-throughput functional database containing a large number of gene expression profiles from genome hybridization experiments. This includes expression profiles of DNA damage response proteins measured before and after different types and doses of ionizing radiation in various human cell lines. In GEO, the criteria "Series," "Expression profiling by array," and "Homo sapiens" are specified. To ensure accurate matching with LINCS, datasets on platforms GPL570 or GPL96 must be selected. Searches are conducted using keywords such as "ionizing radiation," "DNA damage response," and "radiosensitivity," and the results are manually screened. Finally, the GSE44245 dataset (https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE44245) is selected to characterize the biological effects of radiation. The "Series Matrix File(s)" in TXT format can be directly downloaded from the "Download family" table at the bottom of the page. This dataset contains gene expression data obtained using gene expression microarrays 24 hours after 5 Gy gamma ray irradiation of human lymphocytes, providing a view of the genome-wide changes in these cells under radiation conditions. The platform used was a GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array, containing a total of 6 samples, including 3 unirradiated control groups (GSM1081193-GSM1081195) and 3 experimental groups irradiated with 5 Gy gamma rays (GSM1081196-GSM1081198).

[0074] The Library of Integrated Network-Based Cellular Signatures (LINCS) is a comprehensive, large-scale pharmacology database for small molecule drugs. It contains gene expression profiles of various cell lines at different concentrations of existing drugs, representing gene expression data in response to a range of perturbations, including compounds, gene mutations, microenvironments, and diseases. Control and experimental group data are available for download. This study downloaded and integrated data from the "LINCS 1000 Chemical Perturbations" section for subsequent drug prediction.

[0075] Next, the reverse biological effects are calculated and compared.

[0076] First, cell line alignment and ID conversion were performed. Since the data in GSE44245 were obtained from experiments involving gamma-ray irradiation of human lymphocytes, cell line alignment and unification were necessary. Data from LINCS on drug effects on relevant cell types (artificial hematopoietic system) were screened. Comparison revealed that the most relevant cell lines in LINCS were multiple leukemia subtypes. Therefore, gene expression profiles from the following four cell lines were selected for subsequent analysis: human leukemia cell line NOMO1, human acute myeloid leukemia cell line PL21, human myelodysplastic syndrome cell line SKM1, and human diffuse large B-cell lymphoma cell line WSUDLCL2. Gene expression profile data obtained from perturbation experiments of compounds acting on the above four cell lines were screened from LINCS. Using the GPL96 platform annotation file, gene ID conversion of the LINCS expression profile data was performed, converting them into official gene symbols. Similarly, gamma-ray irradiation expression profile gene IDs were converted into official gene symbols based on the GPL570 platform annotation information.

[0077] Next, enrichment scores (ES) were calculated. Data processing was performed using R. Based on the cell response expression profiles before and after gamma irradiation, and in different drug treatment groups and control groups, the Limma package was used to analyze the genome-wide differential expression under gamma irradiation and different drug treatments, calculating the Fold change (FC) to obtain all differentially expressed genes under irradiation and all drug treatments. Using the Gene Set Enrichment Analysis (GSEA) method, the ES scores of various drugs on different cell lines compared to the radiation response were calculated based on FC matching. This score characterizes the similarity of biological effects between the two.

[0078] The next step is to identify the candidate repositioning radiation protection drugs to be validated.

[0079] The screening of candidate drugs for validation only considered FDA-approved drugs. For the prediction results of the four leukemia subtypes in cell lines, the results were sorted by ES (Expected Estimate) from smallest to largest, retaining entries with ES < 0. The threshold was set at -0.25. Drugs meeting the threshold criteria were extracted from each of the four cell lines and listed, as shown in Table 1, totaling 29 entries and 19 compounds. The number of cell lines each drug appeared in was counted, focusing on drugs appearing in multiple cell lines simultaneously. It was found that 7 drugs appeared in two or more cell lines: menadione, losartan, lenalidomide, rosiglitazone, pimozide, nimodipine, and fluspirilene.

[0080]

[0081] In Table 1, Drugbank ID is the drug's identifier in the DrugBank database, Drug name is the drug's name in the DrugBank database, Drug group is the drug's group in the DrugBank database, investigational indicates that the drug is in the approval stage for at least one indication, approved; investigational and approved; nutraceutical indicates that the drug is used for different indications and belongs to different categories, Cell line indicates the cell line to which the drug belongs, Enrichment score is the drug's ES value, and Count indicates the number of times the drug appears in different cell line lists.

[0082] Basic information on these drugs was obtained from the DrugBank database, including their chemical structure, molecular weight, original indications, mechanisms of action, and known targets. Research progress on the application of these drugs in various indications was reviewed through published literature, including previous reports and details of their use in radiation protection or radiosensitization. Lenalidomide and losartan were given particular attention. Lenalidomide is an immunomodulatory drug with anti-tumor, anti-angiogenic, and anti-inflammatory properties. It was originally used to treat multiple tumors and anemia in low- to intermediate-risk myelodysplastic syndromes. Known targets include CRBN, TNFSF11, CDH5, and PTGS2. Lenalidomide exerts its effects through immunomodulation, such as altering cytokine production (inhibiting the production of pro-inflammatory cytokines TNF-α and IL-1, and increasing the production of anti-inflammatory cytokines IL-10), regulating T cell function (promoting CD3T cell proliferation and increasing the production of IL-2 and IFN-γ in T lymphocytes), enhancing NK cell-mediated cytotoxicity, regulating the immune response, and inhibiting the NF-κB and MAPK signaling pathways. Losartan is a renin-angiotensin system inhibitor that blocks the binding of angiotensin II to the AT1 receptor. It was originally used to treat hypertension and diabetic nephropathy. Known targets include AGTR1 and AT1R. This compound inhibits oxidative stress-mediated cell damage and apoptosis, reducing oxidative stress and exhibiting significant systemic antioxidant potential. It upregulates PPARγ to inhibit the TGF-β1 pathway, while simultaneously regulating metabolism and the Smad signaling pathway, increasing the expression of Smurf2 and Smurf1, inhibiting the expression of TGF-β1, collagen, and Smad, and decreasing Smad2 and Smad3 phosphorylation. Based on this information, in vivo experiments were conducted to verify the efficacy of lenalidomide and losartan for gamma-ray radiation protection.

[0083] Finally, the potential radiation protection repositioning candidate drugs were validated.

[0084] Specifically, in vivo experiments were conducted to verify the protective effect of lenalidomide against gamma-ray radiation. Six-week-old male C57BL / 6J mice were selected and divided into four groups: control group, irradiation-only group, irradiation + lenalidomide group, and irradiation + losartan group, with nine mice in each group. The mice were administered the drug via a single intraperitoneal injection 2 hours before irradiation. The concentration of lenalidomide was 100 mg / kg, and the concentration of losartan was 80 mg / kg. A single whole-body irradiation with a dose of 9 Gy gamma rays was performed using a cobalt-60 (60Co) radioactive source at a dose rate of 66.04 cGy / min. The survival rate of mice in each group was observed and recorded 30 days after irradiation, and the weight of the mice was measured, as shown in Figures 5 and 6. In Figures 5 and 6, Control is the control group, IR is the irradiation-only group, lenalidomide is the irradiation + lenalidomide (100 mg / kg) group, and losartan is the irradiation + losartan (80 mg / kg) group. It can be seen that: all mice in the irradiation-only group died on day 13 after irradiation, while mice in the irradiation + lenalidomide group and the irradiation + losartan group survived to day 30 after irradiation. From day 11 post-irradiation, the number of surviving mice in the irradiation + lenalidomide group was higher than that in the control group; from day 13 post-irradiation, the number of surviving mice in the irradiation + losartan group was higher than that in the control group. Changes in mouse body weight showed that the average body weight of mice in both the irradiation + lenalidomide and irradiation + losartan groups increased from day 11 post-irradiation. These results indicate that intraperitoneal injection of 100 mg / kg lenalidomide and 80 mg / kg losartan provides some protection against high-dose (9 Gy) gamma-ray irradiation, further confirming the accuracy and practical significance of this identification and prediction method.

[0085] Due to the vast number of known compounds, drug repositioning studies relying solely on experimental screening remain prohibitively expensive. In recent years, the rapid development of sequencing technology and the accumulation of large amounts of multivariate expression profiling data have provided an opportunity for the introduction of computational analysis methods in drug discovery. This invention, based on a large-scale pharmacological database, comprehensively utilizes gene expression profiling data on radiation response and multiple drug effects to establish a method for rationally and accurately identifying potential radiation protection repositioning drugs using computational biology and systems biology approaches. Validation through in vivo experiments lays the foundation for constructing a technology system for screening and evaluating radiation protection drugs. This invention, combined with experimental techniques, can further reduce the time and economic costs of repositioning drug discovery, improve the success rate, and enable radiation damage protection drug development to enter a stage combining rational design and experimental screening. The proposed potential radiation protection repositioning drugs, lenalidomide and losartan, are already marketed drugs with guaranteed safety and bioavailability, and are readily available for use.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0087] Cross-reference to related applications

[0088] This application claims priority to Chinese patent application No. 202411199207.6, filed on August 29, 2024, the entire contents of which are incorporated herein by reference. Industrial applicability

[0089] This invention provides a method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection, belonging to the field of drug repositioning technology. The method includes: extracting a target gene expression profile dataset for the target biological effect and a candidate gene expression profile dataset for candidate repositioning drugs; calculating the ES score of the target gene expression profile dataset and the candidate gene expression profile dataset; and selecting, based on the biological effect inverse relationship, candidate drugs corresponding to the target biological effect in the candidate gene expression profile dataset according to the ES score. This invention not only further reduces the time and economic cost of discovering repositioning radiation protection drugs but also improves the success rate.

Claims

1. A method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection, characterized in that, include: S1: Extract the target gene expression profile dataset for the target biological effect and the candidate gene expression profile dataset for the candidate repositioning drug; S2: Calculate the ES scores of the target gene expression profile dataset and the candidate gene expression profile dataset; S3: Based on the reversibility of biological effects, select candidate drugs from the candidate gene expression profile dataset for protection against the target biological effects according to the ES score.

2. The method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection according to claim 1, characterized in that, The target biological effect mentioned in step S1 is the biological effect of gamma-ray radiation.

3. The method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection according to claim 1, characterized in that, The target gene expression profile dataset was selected from the GEO database, and the candidate gene expression profile dataset was selected from the LINCS database.

4. The method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage according to claim 3, characterized in that, In step S1, the step of obtaining the candidate gene expression profile dataset further includes: S11: Introduce the LINCS database and select various compounds from the LINCS database; S12: Using the LINCS database, the gene expression profiles generated by applying each selected compound at multiple concentrations to multiple cells were downloaded and analyzed to obtain the cell response expression profiles and the control group expression profiles. S13: Output the cell response expression profile and the control group expression profile as a candidate gene expression profile dataset.

5. The method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage according to claim 1, characterized in that, Step S2 further includes: S21: Align the target gene expression profile dataset and the candidate gene expression profile dataset according to cell type to obtain a related dataset; S22: Convert all gene IDs in the relevant dataset into unified gene symbols to obtain a unified dataset; S23: Using R language, calculate the ES scores of candidate repositioning drugs and target biological effects in the unified dataset.

6. The method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection according to claim 5, characterized in that, The unified gene symbols mentioned in step S22 are official gene symbols.

7. The method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection according to claim 1, characterized in that, The range of values ​​for the ES score in step S2 is as follows: ； in, To calculate the obtained ES score.

8. The method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection according to claim 7, characterized in that, The range of ES scores for the candidate drugs selected in step S3 is as follows: .

9. The method for identifying and predicting repositioning drugs for gamma-ray radiation damage protection according to claim 8, characterized in that, Step S3 further includes: S31: Select candidate repositioning drugs with ES scores less than 0 and absolute values ​​of ES scores that meet a preset threshold based on the calculated ES scores, and obtain multiple candidate drugs. S32: Take the intersection of multiple candidate drugs and select the candidate drugs that appear most frequently in multiple cell lines as candidate drugs for protecting against the target biological effects.

10. The method for identifying and predicting repositioning drugs for protection against gamma-ray radiation damage according to claim 9, characterized in that, Step S3 also includes: S33: Perform physicochemical information analysis, pharmacological mechanism analysis, and mechanism of action analysis on the candidate drug to conduct experimental verification of the candidate drug.

Images