Therapeutic target and small molecule drug screening method of ferroptosis and autophagy genes in ARDS pathogenesis and application of therapeutic target and small molecule drug screening method

Abstract

The invention discloses a therapeutic target of ferroptosis and autophagy genes in ARDS pathogenesis, a small molecule drug screening method and application. A bioinformatics method is adopted to explore potential therapeutic targets of ferroptosis and autophagy related genes in acute respiratory distress syndrome (ARDS). By analyzing an ARDS data set in a GEO database and combining a FerrDb database and an HADb database, 66 common targets related to ferroptosis and 59 common targets related to autophagy are screened out. The method comprises the following steps: constructing a protein interaction network by utilizing an STRING database, and screening and performing enrichment analysis by combining a Cytoscape database and a Metascape database to obtain core targets of TP53, PTEN and MYC. In addition, the cMAP database is used for screening small molecule drugs which may treat ARDS by intervening ferroptosis and autophagy, and a new target and a drug discovery direction are provided for precise treatment of ARDS.

Description

Technical Field

[0001] The present invention belongs to the technical field of genomics, and specifically relates to a therapeutic target and a small molecule drug screening method and application of ferroptosis and autophagy genes in the pathogenesis of ARDS. Background Art

[0002] Acute respiratory distress syndrome (ARDS) is an acute inflammatory lung injury associated with increased pulmonary vascular permeability, increased lung weight, and loss of ventilated lung tissue. ARDS occurs when pulmonary or extrapulmonary damage leads to the release of inflammatory mediators, promoting the accumulation of inflammatory cells in the alveoli and pulmonary microcirculation. Studies have found that ARDS accounts for 10% of ICU admissions and 23% of mechanical ventilation. The in-hospital mortality rates of mild, moderate, and severe ARDS cases are 27%-35%, 32%-40%, and 46%-60%, respectively. It can be seen that ARDS has become a risk factor for patient death. However, the current targeted prevention and treatment methods for ARDS are relatively limited. Therefore, it is particularly urgent and important to explore effective therapeutic targets and strategies.

[0003] Ferroptosis is a new type of cell death discovered in recent years. During the cell death process, it is usually accompanied by a large amount of iron accumulation and lipid peroxidation. In recent years, studies have found that ferroptosis is involved in the regulation of ARDS. Ferroptosis is involved in the pathogenesis of lung injury, providing a new theoretical basis for the clinical treatment of ARDS. Therefore, strategies to inhibit ferroptosis may have important therapeutic prospects.

[0004] It is worth noting that autophagy plays a particularly important role in ARDS. Autophagy in alveolar epithelial cells plays an important role in the development of ARDS. Autophagy limited to alveolar epithelial cells can reduce ARDS lung damage, but uncontrolled autophagy in alveolar epithelial cells may lead to aggravated lung damage in ARDS. The degree of autophagy in alveolar epithelial cells is closely related to ARDS lung damage. In addition, the expression of autophagy-related genes in alveolar epithelial cells increases during ARDS, and the level of autophagy-related proteins increases, indicating increased autophagy. Some researchers have found that the expression of the autophagy-related gene Atg5 can be detected in alveolar epithelial cells in ARDS mouse models. Some researchers have also observed that the level of autophagy-related protein LC3B in alveolar epithelial cells in ARDS mouse models is significantly higher than that in normal mice. Therefore, the level of autophagy in alveolar epithelial cells is significantly increased during ARDS.

[0005] However, few studies have explored whether ferroptosis and autophagy are involved in ARDS. Therefore, our research aims to screen the core targets of ferroptosis and autophagy in ARDS and the corresponding small molecule drugs through bioinformatics analysis, so as to provide a scientific basis for the development of new prevention and treatment strategies. Summary of the invention

[0006] The present invention provides a method and application for screening therapeutic targets and small molecule drugs of ferroptosis and autophagy genes in the pathogenesis of ARDS, with the aim of screening out the core targets of ferroptosis and autophagy in ARDS and the corresponding small molecule drugs through bioinformatics analysis, so as to provide a scientific basis for the development of new prevention and treatment strategies.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] Biomarkers for the diagnosis and treatment of ARDS, wherein the biomarkers are: TP53, PTEN, MYC, TLR4, SNCA, ACSL4, BCL2, GAPDH, HSP90AB1, NFE2L2, FOXO1, FOXO3, and CDKN1A.

[0009] The biomarkers are: TP53, PTEN, and MYC.

[0010] The combination includes: at least one small molecule drug targeting TP53, at least one small molecule drug targeting PTEN, and at least one small molecule drug targeting MYC.

[0011] The small molecule drug targeting TP53 is pifithrin-alpha;

[0012] The small molecule drug targeting PTEN is temsirolimus;

[0013] The small molecule drug targeting MYC is TWS-119.

[0014] A composition for treating ARDS, comprising at least one active ingredient targeting TP53, PTEN, MYC, TLR4, SNCA, ACSL4, BCL2, GAPDH, HSP90AB1, NFE2L2, FOXO1, FOXO3, and CDKN1A.

[0015] A method for identifying biomarkers for diagnosis and treatment of ARDS comprises the following steps:

[0016] a. Collect gene expression data related to ARDS: obtain data through the GSE243066 dataset of the GEO online database;

[0017] b. Use bioinformatics tools to perform differentially expressed gene analysis to identify target genes associated with ARDS: PCA analysis and edgR differential analysis were performed on the data set; P < 0.05, expression difference fold FC > 1 were set to obtain differentially expressed gene DEGs;

[0018] c. Collect ferroptosis- and autophagy-related targets through the ferroptosis-related database FerrDb and the autophagy-related database HADb;

[0019] d. Compare the target gene obtained in step b with the target collected in step c to find the common target;

[0020] e. Constructing a PPI network using the STRING database: Importing common targets into the STRING database, and obtaining a PPI network between ARDS disease targets related to ferroptosis and autophagy;

[0021] f. Perform network analysis using Cytoscape software to screen out potential core targets: Use the MCODE plug-in in Cytoscape v3.7.2 to extract the core subnetwork embedded in the network system, and set the parameters as follows: Degree Cutoff: 2, K-core: 2, to obtain the core PPIs of potential core targets related to ferroptosis and the core PPIs of potential core targets related to autophagy;

[0022] The two PPI networks were analyzed using the MCC plug-in in the Cytoscape v3.7.2 software, and the top 10 potential core targets in the two network systems were extracted respectively;

[0023] The results of the MCODE plug-in screening were intersected with the results of the MCC plug-in screening to obtain potential core targets related to ferroptosis and potential core targets related to autophagy.

[0024] The beneficial effects obtained by the present invention are:

[0025] We screened out 13 potential core targets closely related to ARDS patients, providing a molecular basis for the development of targeted treatment strategies. We found that TP53, PTEN, and MYC are core targets shared by ferroptosis and autophagy, and screened out three small molecule drugs targeting TP53, PTEN, and MYC, which may play a key role in the ferroptosis and autophagy regulatory network in ARDS. The small molecule drugs screened out through the cMAP database provide new uses for approved drugs for ARDS treatment and accelerate the drug marketing process. The diagnostic potential evaluation based on biomarkers will help realize personalized medicine for ARDS and provide customized treatment plans for different patient groups. Through GO / KEGG enrichment analysis, we have enhanced our understanding of the biological processes of ferroptosis and autophagy in ARDS, providing a new perspective for revealing the disease mechanism. Analysis of the level of immune cell infiltration will help understand the characteristics of immune response in ARDS and may guide the development of immune regulation treatment strategies.

[0026] Through molecular docking verification, the binding ability of small molecule drugs to targets is predicted, providing a theoretical basis for the drug's mechanism of action. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the present invention.

[0028] Figure 2A This is the PCA analysis diagram of the GSE243066 dataset; Figure 2B It is the volcano map of GSE243066DEGs; Figure 2C is the correlation heat map of GSE243066DEGs; Figure 2D is the Venn diagram of GSE243066DEGs and ferroptosis genes; Figure 2E is a Venn diagram of GSE243066 DEGs and autophagy genes.

[0029] Figure 3A is the MCODE analysis result of ferroptosis-related DEGs, red is up-regulated genes; blue is down-regulated genes; Figure 3B is the MCC analysis result of ferroptosis-related DEGs; Figure 3C is the Venn diagram of the MCODE and MCC analysis results of ferroptosis-related DEGs; Figure 3D MCODE1 analysis of autophagy-related DEGs (red is up-regulated genes; blue is down-regulated genes), Figure 3E MCODE2 analysis of autophagy-related DEGs (red is up-regulated genes; blue is down-regulated genes); Figure 3F is the MCC analysis result of autophagy-related DEGs; Figure 3G is the Venn diagram of the MCODE and MCC analysis results of autophagy-related DEGs.

[0030] Figure 4 It is the GO / KEGG enrichment analysis of ferroptosis-related DEGs, in which the upper part is the pathway network of ferroptosis-related DEGs, and the lower part is the pathway enrichment analysis of ferroptosis-related DEGs.

[0031] Figure 5 It is the pathway enrichment analysis of autophagy-related DEGs, where the upper part is the pathway network of autophagy-related DEGs; the lower part is the pathway enrichment analysis of autophagy-related DEGs.

[0032] Figure 6 It is a GO / KEGG enrichment analysis of 13 potential core targets, of which the upper part is the pathway network of the 13 potential core targets, and the lower part is the pathway enrichment analysis of the 13 potential core targets.

[0033] Fig. 7A is a bubble plot of ferroptosis-related DEGs; Figure 7B is the peak diagram of ferroptosis-related DEGs; Figure 7C It is a line graph of ferroptosis-related DEGs.

[0034] Figure 8 It is the immune infiltration analysis result of genes. The upper part is the immune infiltration analysis result of GSE243066 dataset. The lower part is the correlation heat map of 13 potential core targets and 22 immune cells. Red represents positive correlation and blue represents negative correlation.

[0035] Fig. 9 This is the ROC curve diagram of 13 potential core targets, among which AM are ACSL4, BCL2, CDKN1A, FOXO1, FOXO3, GAPDH, HSP90AB1, MYC, NFE2L2, PTEN, SNCA, TLR4, and TP53.

[0036] Fig.10 The structures of three potential small molecule drugs for treating ARDS through ferroptosis and autophagy pathways.

[0037] Fig.11 This is a schematic diagram of molecular docking of three small molecule compounds with relevant core targets. The black dotted circle represents the binding pocket, and the dotted lines of different colors represent potential weak bond interactions between the ligand and the amino acid site. DETAILED DESCRIPTION

[0038] The present invention will be described below in conjunction with the accompanying drawings.

[0039] The therapeutic targets of ferroptosis and autophagy genes in the pathogenesis of ARDS and the screening process of small molecule drugs are as follows: Figure 1 The specific steps are as follows:

[0040] ARDS dataset collection and screening and differential analysis of related disease targets

[0041] The ARDS-related dataset (GSE243066) was retrieved through the GEO online database (https: / / www.ncbi.nlm.nih.gov / gds / ). The GSE26566 dataset contains a total of 49 samples, and the title is "T cell dysfunction inARDS based on miRNA and mRNA integration analysis (RNA-Seq)". Among them, the dataset contains 34 ARDS-related disease samples and 15 normal control samples. It was published on September 13, 2023 and last updated on June 06, 2024.

[0042] After the above-screened data sets were downloaded and exported, the data sets were deduplicated and missing values ​​were deleted using R 4.4.0 software, and then PCA analysis was performed ( Figure 2A) and edgR differential analysis ( Figure 2B ~C), set P < 0.05, expression difference fold (FC) > 1, and obtained 2548 differentially expressed genes.

[0043] Ferroptosis-related targets were collected through the FerrDb ferroptosis-related analysis database (http: / / www.zhounan.org / ferrdb / ), which focused on three types of ferroptosis-related factors: ferroptosis-driving factors, inhibitory factors, and marker factors. A total of 467 ferroptosis-related targets were collected.

[0044] Autophagy-related targets were collected through the HADb human autophagy database (http: / / www.autophagy.lu / index.html), and a total of 232 autophagy-related targets were collected.

[0045] The collected ferroptosis-related and autophagy-related targets were intersected with the ARDS-related disease targets obtained by differential analysis to obtain common targets. The common targets were imported into the STRING 12.0 (https: / / string-db.org / ) database to obtain the PPI between ferroptosis-related and autophagy-related ARDS disease targets.

[0046] The specific steps are to enter the page and first select the "multiple proteins" entry, enter the relevant target name obtained in the previous step, and select "Homo sapiens" for retrieval. The confidence score is selected as 0.400, and the PPI result (.tsv format file) is downloaded, and then imported into Cytoscape v3.7.2 software for MCODE and MCC analysis of the network. The MCODE plug-in is used to screen and extract the core subnetworks existing in the network system to obtain relevant potential core targets, and the MCC plug-in is used to screen and extract the top 10 potential core targets in the network system. The results of the MCODE plug-in screening are intersected with the results of the MCC plug-in screening to obtain relevant potential core targets. The intersection of it and the above-mentioned 2548 ARDS-related targets was respectively processed, and a Venn diagram was drawn online on the BioLadder platform https: / / www.bioladder.cn / web / # / pro / index. A total of 66 common targets related to ferroptosis and ARDS disease, and 59 common targets related to autophagy and ARDS disease were obtained ( Figure 2D ~E), suggesting that these targets may be involved in both the ferroptosis process and the occurrence and progression of ARDS disease, and the autophagy process and the occurrence and progression of ARDS disease.

[0047] The 66 targets and 59 targets obtained above were imported into the STRING 12.0 database to obtain the PPI networks constructed by each of them (.csv format files). The result files (.csv format) were downloaded and imported into the Cytoscape v3.7.2 software to conduct in-depth analysis of the total PPI network. The MCODE plug-in in the Cytoscape v3.7.2 software was used to extract the core subnetwork embedded in the network system (specific parameter settings: Degree Cutoff: 2, K-core: 2), and a core subnetwork related to ferroptosis containing 11 potential core targets (MCODE score: 4.8) was obtained. For details, see Figure 3A . Two autophagy-related core subnetworks contain 18 and 15 potential core targets respectively (MCODE1 score: 8.9; MCODE2 score: 8.4), see Figure 3D ~E.

[0048] The MCC plug-in in Cytoscape v3.7.2 software was used to analyze the two total PPI networks and extract the top 10 potential core targets in the two network systems. The results of the MCODE plug-in screening were intersected with the results of the MCC plug-in screening to obtain 6 potential core targets related to ferroptosis: TP53, PTEN, MYC, TLR4, SNCA, ACSL4 ( Figure 3C ); 10 potential core targets related to autophagy: TP53, BCL2, GAPDH, HSP90AB1, PTEN, NFE2L2, FOXO1, MYC, FOXO3, CDKN1A ( Figure 3F ). Among them, we found that TP53, PTEN, and MYC are the core targets shared by ferroptosis and autophagy. Therefore, we preliminarily believe that TP53, PTEN, and MYC affect the progression of ARDS through the ferroptosis and autophagy regulatory network.

[0049] GOKEGG pathway enrichment analysis of common targets of ferroptosis and ARDS

[0050] The 66 potential common targets and 59 potential common targets obtained above were uploaded to the Metascape online database (https: / / metascape.org / gp / index.html) for enrichment analysis of biological functions and signaling pathways.

[0051] The DEGs associated with ferroptosis involved ferroptosis, cellular response to oxidative stress, positive regulation of cellular reactive oxygen metabolism, and regulation of the cell cycle ( Figure 4A). DEGs related to autophagy involved the regulation of autophagy, cellular response to oxygen levels, positive regulation of programmed cell death, and apoptosis ( Figure 5 A). In addition, KEGG enrichment analysis showed that the pathways associated with ferroptosis-related DEGs mainly involved ferroptosis pathways, longevity regulation pathways ( Figure 4 B), the pathways associated with autophagy-related DEGs mainly involved apoptosis pathway, necroptosis pathway, FoxO signaling pathway ( Figure 5 B). The GO analysis results of 13 potential core targets showed that they were closely related to the regulation of reactive oxygen metabolism, positive regulation of programmed cell death, cell response to external stimuli, and negative regulation of intracellular signal transduction. Figure 6 A), KEGG analysis results showed that it was closely related to PI3K-Akt signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, etc. ( Figure 6 B).

[0052] GSEA enrichment analysis: GSEA pathway enrichment analysis was performed on 66 potential common targets using R software. The corrected P<0.05 was considered statistically significant. The results showed that these targets were mainly related to metabolic pathways ( Fig. 7A -C).

[0053] Immune infiltration correlation analysis: A comprehensive immune cell infiltration analysis was performed on samples from the GSE243066 dataset using the advanced CIBERSORT algorithm in the CIBERSORT online database (https: / / cibersortx.stanford.edu / ). The CIBERSORT algorithm can estimate the relative abundance of 22 major immune cell subsets in samples based on gene expression data and reveal differences in immune cell composition between different samples. In addition, spearman correlation analysis was performed on 13 potential core targets and 22 major immune cell subsets using R 4.4.0 software.

[0054] After a comprehensive immune cell infiltration analysis of samples from the GSE243066 dataset was performed using the CIBERSORT database, the analysis results were downloaded and box plots were drawn using R software ( Figure 8 A). Compared with the control group, samples from the acute respiratory distress syndrome group showed higher infiltration in Nuetrophils and lower infiltration in NK.cells.resting, Monocytes, T.cells.CD8, and T.cells.CD4.naive. In addition, a heat map of the correlation between 13 potential core targets and 22 immune cell subsets was drawn using R 4.4.0 software ( Figure 8B) The results showed that BCL2, TP53, MYC, and HSP90AB1 genes showed a strong positive correlation with NK cells and resting cells. In addition, BCL2 and TP53 genes showed a strong negative correlation with Neutrophils cells.

[0055] Prognostic analysis: In order to evaluate the diagnostic value of these 13 potential core targets, we used the pROC package of R 4.4.0 software to draw the ROC curve. The results showed that except for SNCA, whose AUC value was 0.507, the AUC values ​​of other genes were all above 0.7, indicating that these genes have good diagnostic potential in ARDS ( Fig. 9 ).

[0056] cMAP database high-throughput screening for the treatment of ARDS via ferroptosis and autophagy pathways

[0057] Potential small molecule drugs for treating ARDS were screened through the cMAP database (https: / / clue.io / query) for potential small molecule drugs for treating ARDS through ferroptosis and autophagy pathways.

[0058] The 13 potential core targets obtained from the above analysis were imported into the cMAP database platform in the format of "up-regulated" and "down-regulated" genes, and small molecule compound drugs with potential therapeutic effects were screened through systematic high-throughput analysis (screening started from candidate components with negative scores). The specific acquisition criteria were based on the target genes of the drugs, and drug components that separately targeted TP53, PTEN, and MYC were selected. Small molecule compounds with clear chemical structures were determined through screening, and a total of 3 components were found. The names of the 3 small molecule compounds are: pifithrin-alpha, temsirolimus, and TWS-119; see the specific structures. Fig.10 These three small molecule compounds are all known therapeutic drugs, and their specific application scenarios are shown in Table 1.

[0059] Table 1 Information on small molecule drugs that can potentially treat ARDS through ferroptosis and autophagy pathways screened by high-throughput screening in the cMAP database

[0060]

[0061] Docking verification of drug small molecule components and core common target molecules

[0062] Use the PDB database (https: / / www.rcsb.org / ) to download the 3D structure of the core common target (.pdb format file), and then use the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / ) to collect and analyze the small molecule drug structure (.sdf format file). Use the CB-Dock2 online database (https: / / cadd.labshare.cn / cb-dock2 / php / blinddock.php) to upload small molecule compounds and target proteins to the system for molecular docking. The larger the absolute value of the Vina score, the stronger the binding ability of the ligand molecule to the receptor protein. The specific parameter setting principle is to use the coordinates corresponding to the compound originally bound to the protein target pocket as the center to build the lattice.

[0063] Among the 13 potential core targets obtained, TP53, PTEN and MYC targets have the highest core degree (ranked from high to low according to the MCC algorithm score), and they also appear in the potential core target sub-network obtained after MCODE analysis. Therefore, they are taken as key targets for molecular docking research. The molecular docking results show that the obtained small molecule drugs and their corresponding targets have negative Vina scores, indicating that the drugs and targets can spontaneously bind. It is generally believed that the larger the absolute value of Vinascore, the more stable the conformation of ligand and receptor binding, and the greater the possibility of action. The molecular docking results show that these three small molecule compound drugs have strong binding ability to their respective potential core targets, suggesting that they can treat ARDS diseases through ferroptosis and autophagy pathways. The specific molecular docking results are shown in Fig.11 And Table 2.

[0064] Table 2 Vina scores of molecular docking between three small molecule drugs and three core targets

[0065]

[0066] The pathogenesis of ARDS is complex, treatment options are limited, and the hospital mortality rate of moderate to severe ARDS is still over 40%. Therefore, it is particularly necessary to discover differences between patients, accurately judge the prognosis of patients, and take individualized treatment measures. However, currently, the targeted prevention and treatment of ARDS through ferroptosis and autophagy regulatory networks are relatively limited. Therefore, in-depth research and exploration of new therapeutic targets for ARDS and finding new treatment options are of great significance for its future prevention and treatment.

[0067] In this study, we aimed to analyze the role of ferroptosis and autophagy genes in the treatment of ARDS patients based on bioinformatics. Recent studies have revealed the key role of ferroptosis in regulating ARDS. In addition, studies have shown that autophagy plays an important role in aggravating alveolar epithelial cell damage. These findings emphasize the involvement of autophagy and ferroptosis in the regulation of ARDS. In addition, studies have shown that the occurrence of ferroptosis requires the execution of autophagy, a theory that has been confirmed in a variety of disease models. Ferritin autophagy has been found to promote the occurrence of ferroptosis in diseases such as Parkinson's disease, liver fibrosis, and chronic obstructive pulmonary disease. However, there is currently a lack of research exploring whether ferroptosis and autophagy are jointly involved in the mechanism of ARDS. To fill this knowledge gap, this study aims to analyze the expression characteristics of related genes involved in ARDS through bioinformatics, and screen out the core targets of ferroptosis and autophagy in ARDS, providing a scientific basis for the development of new prevention and treatment strategies.

[0068] First, we performed differential gene expression analysis on 34 ARDS patients and 15 healthy subjects in GSE243066, and successfully identified 2548 differentially expressed genes. In order to further explore the differences in the distribution of immune cells in healthy and ARDS states, we evaluated the distribution of 22 immune cells between the two groups, and the results showed that the difference in the infiltration amount of immune cells between the two groups was not statistically significant. This shows that although ARDS is an immune-mediated disease, there is no significant quantitative difference between healthy people and ARDS patients in the overall distribution of immune cells. However, this does not mean that the role of immune cells in ARDS can be ignored. In fact, this may reveal the complex interactions and fine regulatory mechanisms between different cell types in the immune response. At the same time, the results of these correlation analyses indicate that although no significant statistical differences were observed at the macro level, the pathological process of ARDS may involve more fine regulation at the cell subset level.

[0069] Through bioinformatics analysis and screening, the potential core targets for treating ARDS from the ferroptosis pathway and autophagy pathway are TP53, PTEN, MYC, TLR4, SNCA, ACSL4, BCL2, GAPDH, HSP90AB1, NFE2L2, FOXO1, FOXO3, and CDKN1A. The biological functions and signal pathway enrichment analysis results mainly include PI3K-Akt signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, regulation of reactive oxygen metabolism, positive regulation of programmed cell death, cell response to external stimuli, and negative regulation of intracellular signal transduction. Among them, we found that TP53, PTEN, and MYC are the core targets shared by ferroptosis and autophagy pathways. The tumor suppressor gene Tp53 encodes p53, which is a key transcription factor with a wide target gene library. p53 is induced and stabilized during DNA damage and oncogene activation to induce cell cycle arrest, apoptosis, or senescence. At the same time, Tp53 also plays a key role in regulating different aspects of cell metabolism. PTEN is a tumor suppressor gene whose expression is often lost in tumors. PTEN is also a negative regulator of the PI3K / AKT signaling pathway. Recent studies have also identified the role of PTEN in metabolic regulation. PTEN is a key regulator of cell growth / survival and metabolism.

[0070] To further evaluate the diagnostic value of these 13 potential core targets, we drew ROC curves. The results showed that except for SNCA, whose AUC value was 0.507, the AUCs of other genes were all above 0.7, indicating that these genes have high accuracy and diagnostic potential in distinguishing ARDS patients from non-ARDS individuals. We also further explored the correlation between the 13 potential core targets and 22 immune cells. Through correlation analysis, we found that BCL2, TP53, MYC, and HSP90AB1 genes showed a strong positive correlation with NK.cells.resting cells, indicating that BCL2, TP53, MYC, and HSP90AB1 may play an important role in the activation and function of NK.cells.resting cells. In addition, BCL2 and TP53 genes showed a strong negative correlation with Neutrophils cells. The negative correlation of BCL2 and TP53 genes may indicate that it plays an inhibitory role in the maturation and antigen presentation function of Neutrophils cells. The results of these correlation analyses suggest that BCL2, TP53, MYC, and HSP90AB1 may participate in the immune regulation of ARDS by affecting the behavior of specific immune cells.

[0071] The cMAP database (https: / / clue.io / query) is a gene expression database. This study used the idea of ​​"drug repositioning" to screen potential small molecule drugs for the treatment of ARDS by intervening in the ferroptosis and autophagy regulatory network using the cMAP database. Drug repositioning refers to the process of expanding the scope of use of existing drugs and applying therapies originally used to treat a certain disease to the treatment of other diseases. This strategy has become increasingly important in recent years. Compared with the traditional drug development process, this method has a significant advantage: the drugs used for repositioning have undergone a large number of toxicity and clinical trials, their safety is recognized, and the risk of failure caused by adverse reactions is greatly reduced. According to the gene targets of the drug molecules, this study screened out three small molecule compounds with clear chemical structures and targeting TP53, PTEN and MYC respectively: pifithrin-alpha, temsirolimus and TWS-119. The above drugs all play an important role in regulating cell apoptosis. They have potential therapeutic effects on ARDS. The molecular docking results showed that the binding between the three potential core common targets and the three small molecule drugs was generally strong, which reflects the potential correlation between the small molecule and the screened ferroptosis and autophagy-related targets and pathways. It is expected to treat ARDS disease by intervening in the ferroptosis and autophagy regulatory network.

[0072] This study obtained targets and related drug components for the treatment of ARDS from the ferroptosis and autophagy pathways through bioinformatics analysis, and screened out 3 small molecule drugs and 13 key targets, among which TP53, PTEN and MYC have the potential to intervene in ferroptosis and autophagy, thereby playing a role in the treatment of ARDS. This study used bioinformatics methods to cut in from the perspective of ferroptosis and autophagy, screened potential drug components and targets for the treatment of ARDS, and provided a new perspective and direction for the development of ARDS treatment drugs. It is also suitable for promotion and application in other diseases. Discovering new uses for old drugs whose safety has been confirmed will develop new drugs for the treatment of refractory diseases more quickly, which has important guiding significance for the clinical research and application of drugs.

Claims

1. A biomarker for the diagnosis and treatment of ARDS, characterized by: The biomarkers are: TP53, PTEN, MYC, TLR4, SNCA, ACSL4, BCL2, GAPDH, HSP90AB1, NFE2L2, FOXO1, FOXO3, CDKN1A.

2. The biomarker for diagnosis and treatment of ARDS according to claim 1, characterized in that: The biomarkers are: TP53, PTEN, and MYC.

3. A small molecule drug combination for the treatment of ARDS, characterized in that: The combination includes: at least one small molecule drug targeting TP53, at least one small molecule drug targeting PTEN, and at least one small molecule drug targeting MYC.

4. The small molecule drug combination for treating ARDS according to claim 2, characterized in that: The small molecule drug targeting TP53 is pifithrin-alpha; The small molecule drug targeting PTEN is temsirolimus; The small molecule drug targeting MYC is TWS-119.

5. A composition for treating ARDS, characterized in that: The composition for treating ARDS comprises at least one active ingredient targeting the biomarker determined for diagnosis and treatment of ARDS according to claim 1 or 2.

6. A method for identifying biomarkers for diagnosis and treatment of ARDS, characterized in that: The following steps are involved: a. Collect gene expression data related to ARDS: obtain data through the GEO online database; b. Use bioinformatics tools to perform differentially expressed gene analysis to identify target genes associated with ARDS: PCA analysis and edgR differential analysis were performed on the data set; P < 0.05, expression difference fold FC > 1 were set to obtain differentially expressed gene DEGs; c. Collect ferroptosis- and autophagy-related targets through the ferroptosis-related database FerrDb and the autophagy-related database HADb; d. Compare the target gene obtained in step b with the target collected in step c to find the common target; e. Constructing a PPI network using the STRING database: Importing common targets into the STRING database, and obtaining a PPI network between ARDS disease targets related to ferroptosis and autophagy; f. Network analysis was performed using Cytoscape software to screen out potential core targets: the core subnetwork embedded in the network system was extracted using the MCODE plug-in in Cytoscape v3.7.2 to obtain the core PPIs of potential core targets related to ferroptosis and the core PPIs of potential core targets related to autophagy. The parameter settings were: Degree Cutoff: 2, K-core: 2; The two PPI networks were analyzed using the MCC plug-in in the Cytoscape v3.7.2 software, and the top 10 potential core targets in the two network systems were extracted respectively; The results of the MCODE plug-in screening were intersected with the results of the MCC plug-in screening to obtain potential core targets related to ferroptosis and potential core targets related to autophagy.

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