Recognition method of key mitochondrial autophagy and ferroptosis genes in sepsis-induced ARDS and immunoregulation mechanism thereof

Abstract

The invention discloses a method for identifying key mitochondrial autophagy and ferroptosis genes in sepsis-induced ARDS and an immunoregulation mechanism of the key mitochondrial autophagy and ferroptosis genes. The method comprises the following steps: screening differential expression genes DEGs related to the sepsis-induced ARDS from a GEO gene expression database; and screening the DEGs by using LASSO and SVM + RFE, and identifying 12 key DEGs. The diagnostic potential of the genes in sepsis-induced ARDS is verified through an ROC curve, and the effect of the genes in immune response is disclosed through gene enrichment analysis and immune infiltration analysis. According to the invention, a key gene FTH1 is identified, a miRNA gene [miR-224-5p] targeting the FTH1 and a pathologic process in which the miRNA gene [miR-224-5p] participates in regulation and control of sepsis-induced ARDS are found, and a close relationship between the FTH1 and E3 ubiquitin ligase SMURF1 is found.

Description

Technical Field

[0001] The present invention belongs to the technical field of genomics, and specifically relates to a method for identifying key mitochondrial autophagy and ferroptosis genes in sepsis-induced ARDS and an immune regulation mechanism thereof. Background Art

[0002] Sepsis is a life-threatening organ dysfunction syndrome caused by the body's dysregulated immune response to infection and extensive inflammatory damage. It is extremely susceptible to infection and other factors, leading to severe organ failure and acute lung injury (ALI), and even progressing to acute respiratory distress syndrome (ARDS), ultimately leading to severe respiratory failure and death. It is one of the main causes of death in clinical critically ill patients, and ARDS has become a risk factor for death in patients with sepsis. However, the current targeted prevention and treatment methods for ARDS caused by sepsis are relatively limited. Therefore, it is particularly urgent and important to explore effective therapeutic targets and strategies.

[0003] Ferroptosis is a regulated cell death that depends on iron and oxidative stress, characterized by increased iron content and lipid peroxidation. In recent years, studies have found that ferroptosis is involved in regulating ARDS caused by sepsis. For example, STAT3 expression in patients with ARDS caused by sepsis is positively correlated with monocyte and neutrophil levels, and STAT3 phosphorylation activation may promote LPS-induced ferroptosis, suggesting that ferroptosis may promote bronchial epithelial cell death in sepsis-induced ARDS. Other studies have found that rosmarinic acid alleviates sepsis-induced ARDS in mice by inhibiting bronchial epithelial renin-angiotensin system-mediated ferroptosis. These findings indicate that in sepsis-induced ARDS, ferroptosis may play a role in promoting bronchial epithelial cell death and exacerbating the patient's condition. Therefore, strategies to inhibit ferroptosis may have important therapeutic prospects.

[0004] It is worth noting that during ferroptosis, mitochondrial morphology changes, including mitochondrial fragmentation and cristae enlargement. This change in mitochondrial morphology may be related to mitochondrial dysfunction, which is a key factor in many diseases. In order to maintain cell health, cells have developed a mechanism to cope with this situation, namely mitophagy. Mitophagy is a selective form of autophagy that regulates mitochondrial turnover and recycling by removing long-lived or damaged mitochondria. This mechanism is essential for maintaining the stability of the internal environment of the cell. In particular, the role of mitophagy is particularly important in sepsis-induced ARDS. For example, studies have found that resveratrol can play a protective role and reduce the severity of sepsis-induced ARDS by regulating PLSCR-3 mediation, reducing mitochondrial dysfunction and mitophagy. In addition, the lack of HDAC3 in type II alveolar epithelial cells reduces mitophagy through the FOXO1-ROCK1 axis, thereby alleviating sepsis-induced ALI. These results suggest that by reducing the number of damaged mitochondria, mitophagy helps to attenuate inflammatory responses and cellular damage, thereby preserving lung function, particularly in sepsis-induced lung disease.

[0005] Ferroptosis is closely linked to mitochondrial dysfunction and oxidative stress, and mitophagy, as a cleanup mechanism within the cell, can alleviate oxidative stress caused by mitochondrial damage, which may inhibit the occurrence of ferroptosis. In addition, since mitochondria play a key role in cellular iron metabolism and are responsible for the storage and utilization of iron, mitophagy may also affect ferroptosis by regulating the level of iron in mitochondria. At present, many studies have explored the complex relationship between ferroptosis and mitophagy. Some studies have pointed out that mitophagy can inhibit ferroptosis. For example, mitophagy alleviates cisplatin-induced ferroptosis of renal tubular epithelial cells through the ROS / HO-1 / GPX4 signaling axis. In addition, the activation of PPARγ can restore Pink1 / Parkin-dependent mitophagy, improve mitochondrial function, inhibit chondrocyte ferroptosis, and delay the development of osteoarthritis. However, some studies have put forward different views that mitophagy may promote ferroptosis. For example, in human umbilical vein endothelial cells and human fibrosarcoma cell lines, dynamic O-GlcNAcylation coordinates mitophagy, leading to the generation of ROS and accumulation of iron ions, activating ferroptosis. Ferritin targeting triggers mitophagy, which induces ferroptosis in pancreatic cancer cells by generating ROS and accumulating iron ions. These results suggest that mitophagy and ferroptosis are closely related and may participate in regulating the progression of the disease through mutual promotion or inhibition. However, no studies have yet explored whether mitophagy and ferroptosis are jointly involved in sepsis-induced ARDS.

[0006] However, there is a lack of research exploring whether mitophagy and ferroptosis are jointly involved in the mechanism of sepsis-induced ARDS. To fill this knowledge gap, this study aims to analyze the expression characteristics of genes involved in sepsis-induced ARDS through machine learning algorithms, and screen out the key genes of mitophagy and ferroptosis in sepsis-induced ARDS, providing a scientific basis for the development of new prevention and treatment strategies. Summary of the invention

[0007] The purpose of the present invention is to provide a method for identifying key mitophagy and ferroptosis genes in sepsis-induced ARDS and its immune regulation mechanism, analyze the immune characteristics of related genes involved in sepsis-induced ARDS through a machine learning algorithm, and screen out the key genes of mitophagy and ferroptosis in sepsis-induced ARDS, so as to provide a scientific basis for the development of new prevention and treatment strategies.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A drug gene target for treating key mitochondrial autophagy and ferroptosis genes in sepsis-induced ARDS, wherein the drug gene target is FTH1.

[0010] The method for identifying the drug gene target FTH1 comprises the following steps:

[0011] (1) The gene expression data of sepsis patients were analyzed using the machine learning algorithm LASSO and support vector machine combined with recursive feature elimination (SVM+RFE);

[0012] (2) Screening the differentially expressed genes DEGs closely related to sepsis-induced ARDS;

[0013] (3) ROC curve analysis was used to evaluate the diagnostic potential of the DEGs;

[0014] (4) Gene enrichment analysis and immune infiltration analysis were performed to reveal the role of the genes in immune response.

[0015] The immune infiltration was assessed using the CIBERSORT algorithm.

[0016] The drug gene target FTH1 is used in the preparation of a pharmaceutical composition for treating sepsis-induced ARDS.

[0017] The application of the drug gene target FTH1 in the preparation of a pharmaceutical composition for treating sepsis-induced ARDS is to target the FTH1 gene by regulating the expression level of miR-224-5p.

[0018] The role of ubiquitination modification of the drug gene target FTH1 gene in sepsis-induced acute respiratory distress syndrome.

[0019] A pharmaceutical composition for regulating ubiquitination modification of FTH1 gene in sepsis-induced ARDS to treat ARDS comprises at least one active ingredient capable of affecting the ubiquitination level of FTH1.

[0020] The active ingredient is an agonist or antagonist of the E3 ubiquitin ligase SMURF1.

[0021] Non-therapeutic use of FTH1 gene ubiquitination modification as a biomarker for disease progression and treatment response in patients with sepsis-induced ARDS.

[0022] Application of FTH1 gene ubiquitination modification as a biomarker in the disease progression and treatment response panel of patients with sepsis-induced ARDS.

[0023] The beneficial effects obtained by the present invention are:

[0024] By identifying key genes associated with sepsis-induced ARDS, new biomarkers are provided to help with early diagnosis and precision medicine of the disease. The mechanism of action of mitochondrial autophagy and ferroptosis in sepsis-induced ARDS is revealed, providing a theoretical basis for the development of new treatment strategies.

[0025] The identified key genes FTH1 and miR-224-5p serve as potential drug targets, which will help develop targeted therapeutic drugs. Risk assessment using the screened key genes will help predict disease progression and prognosis in patients with sepsis-induced ARDS. Based on the patient's specific gene expression profile, the treatment plan can be adjusted to achieve personalized medicine. A deep understanding of the role of immune cells in sepsis-induced ARDS provides a new perspective for immunomodulatory therapy. Improving the treatment effect of sepsis-induced ARDS will help reduce the mortality and medical burden caused by the disease and have a positive impact on public health. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a process flow chart of the present invention.

[0027] Figure 2A It is the volcano plot of 576 differentially expressed genes between sepsis-induced ARDS and control groups; Figure 2B Heat map of the top 100 differentially expressed genes screened from the 76 differentially expressed genes; Figure 2C It is a differential diagram of immune infiltration of 22 immune cells; Figure 2D The difference in the infiltration amount of immune cells between the two groups of patients; Figure 2ECorrelation diagram between resting CD4+ memory T cells and CD8+ T cells, and activated CD4+ memory T cells and plasma cells.

[0028] Figure 3A It is a cross-validation for tuning parameter screening in the LASSO regression model. Figure 3B It is the coefficient distribution diagram of the LASSO regression model. Figure 3C -D is the important feature selection diagram obtained by the SVM-RFE algorithm, screening 24 key genes.

[0029] Figure 4 This is a graph showing the differential expression of 12 genes related to sepsis-induced ARDS.

[0030] Figure 5 This is the ROC curve of 12 differentially expressed genes related to sepsis-induced ARDS.

[0031] Figure 6 shows the results of GSEA enrichment analysis of 12 key DEGs.

[0032] Figure 7 shows the immune function analysis results of 12 key DEGs.

[0033] Figure 8 is a correlation diagram of 12 key DEGs and immune cells.

[0034] Fig. 9 This is the regulatory network analysis diagram of the FTH1 gene. DETAILED DESCRIPTION

[0035] The present invention will be described below in conjunction with the accompanying drawings.

[0036] The research process of the method for identifying key mitochondrial autophagy and ferroptosis genes in sepsis-induced ARDS of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0037] (1) Using "sepsis" as the keyword, the gene expression profile dataset GSE32707, whose species was human and whose chip platform was GPL10558, was downloaded from the GEO database. The gene chip dataset contained gene expression array data of 32 patients with sepsis-induced ARDS and 34 healthy subjects, and the gene chip data in the gene chip dataset were divided into sepsis-induced ARDS group and healthy control group.

[0038] (2) Identification of DEGs and immune cell infiltration characteristics of sepsis-induced ARDS.

[0039] The differentially expressed genes of sepsis-induced ARDS samples and healthy control samples in GSE32707 chip were analyzed using the limma package in R software. DEGs were obtained based on LogFC absolute value>1 and corrected P value (FDR)<0.05, and 576 differentially expressed genes were obtained. The volcano plot of differentially expressed genes between sepsis-induced ARDS and control groups is shown in Figure 2. Figure 2A The heat map analysis of the top 100 differentially expressed genes is shown in Figure 2B To explore the differences in immune infiltration between the healthy population and sepsis-induced ARDS group, the CIBERSORT method was used to evaluate the distribution of 22 immune cells between the two groups. Figure 2C There was no statistically significant difference in the infiltration amount of immune cells between the two groups of patients ( Figure 2D ). The correlation analysis of 22 immune cells showed that the negative correlation ratios of monocytes and neutrophils, and resting NK cells and γδT cells were the largest; the positive correlation ratios of resting CD4+ memory T cells and CD8+ T cells, and activated CD4+ memory T cells and plasma cells were the highest ( Figure 2E ), blue indicates negative correlation, and red indicates positive correlation. The stronger the correlation, the darker the color.

[0040] (3) We used the IOBR package in the R language environment and combined it with the advanced CIBERSORT algorithm to conduct a comprehensive immune cell infiltration analysis on samples from the GSE32707 dataset. The CIBERSORT algorithm can estimate the relative abundance of 22 major immune cell subsets in samples based on gene expression data and reveal the differences in immune cell composition between different samples.

[0041] (4) Pearson correlation analysis was used to further explore the potential relationship between differentially expressed genes in the GSE32707 dataset and specific immune cell subsets. These immune cell subsets include but are not limited to key types of immune cells such as plasma cells and T cells, which play a vital role in immune response and disease progression.

[0042] (5) Identify the biomarkers most relevant to sepsis-induced ARDS through machine learning algorithms.

[0043] A statistical method using LASSO regression and support vector machine (SVM) combined with recursive feature elimination (RFE). LASSO regression is a regression method that achieves variable selection by minimizing a loss function with an L1 regularization term. In the calculation, we determine the regularization parameter through cross-validation to balance the complexity and goodness of fit of the model. Next, we use the method of support vector machine (SVM) combined with recursive feature elimination (RFE). The SVM model is first trained on the full feature set, and then RFE optimizes the feature set by iteratively removing the features that contribute the least to the model. In each iteration, we retrain the SVM and evaluate the model performance until the predetermined number of features is reached or the performance no longer improves. In this way, we ensure that the model is optimized in terms of feature selection and classification accuracy.

[0044] To identify the most relevant biomarkers for sepsis-induced ARDS, we further analyzed and screened the DEGs using machine learning algorithms (LASSO and SVM+RFE).

[0045] By using LASSO analysis, a total of 22 differentially expressed genes related to sepsis-induced ARDS were obtained, namely: ZNF438, ZNF431, ZNF594, ZNF561, ZNF366, ZNF329, ZNF409, ZNF714, SLC2A5, FBXO45, USP11, SLC39A9, FTH1, SLC35E2, UBE2E2, SLC22A11, SLC15A1, SLC35A2, SLC22A14, SLC28A1, SLC45A1, and USP46; Figure 3A -B.

[0046] Next, we used another machine learning algorithm, SVM+RFE, to obtain a total of 24 differentially expressed genes related to sepsis-induced ARDS: SLC35A2, ZNF594, SLC47A1, SLC15A1, SLC38A9, ZNF366, USP11, SLC39A14, FBXO31, UBE2D4, FBXO45, SLC39A9, ZNF664, UBE2E2, ZNF609, SLC35F2, BNIP3, FTH1, ZNF409, ZNF561, SLC4A8, SLC22A11, ZNF621, and ATG9A; Figure 3C -D as shown.

[0047] The differentially expressed genes obtained by the two algorithms were intersected, and 12 differentially expressed genes related to sepsis-induced ARDS were obtained, namely FBXO45, FTH1, SLC15A1, SLC22A11, SLC35A2, SLC39A9, UBE2E2, USP11, ZNF366, ZNF409, ZNF561, and ZNF594.

[0048] The results showed that compared with the control group, FBXO45, FTH1, SLC35A2, SLC39A9, UBE2E2, USP11, ZNF561, and ZNF594 were down-expressed in sepsis-induced ARDS, while SLC15A1, SLC22A11, ZNF366, and ZNF409 were over-expressed in sepsis-induced ARDS; Figure 4 shown.

[0049] (6) Evaluation of the diagnostic value of 12 key differentially expressed genes

[0050] The pROC package of R language was used to draw ROC curves to evaluate the diagnostic potential of key DEGs. We further confirmed the expression of these candidate biomarkers and their diagnostic effects on the independent GSE32707 dataset. The rms package was used to construct a nomogram to comprehensively evaluate the risk score, and a calibration plot was drawn to test the predictive accuracy of the model. In addition, the rmda package was used to draw decision curves and influence curves to evaluate the application value of the model in clinical decision-making. An area under the ROC curve of more than 0.7 usually indicates that the biomarker has a high diagnostic discrimination ability.

[0051] In order to evaluate the diagnostic value of these 12 differentially expressed genes, we drew ROC curves. The results showed that except for ZNF366 with an AUC value of 0.699, the AUC values ​​of other genes were all above 0.7, indicating that these genes have good diagnostic potential in sepsis-induced ARDS ( Figure 5 AL).

[0052] GSEA enrichment analysis of 12 key differentially expressed genes

[0053] We obtained the software version 3.0 from the GSEA official website (https: / / www.gsea-msigdb.org / gsea / index.jsp) and divided the samples into high expression (>50%) and low expression (<50%) groups based on the hub gene expression. The c2.cp.kegg.v7.4.symbols.gmt gene set was downloaded from the Molecular Signature Database for the analysis of biological pathways and molecular mechanisms. Through GSEA analysis, the minimum gene set was set to 5 and the maximum was set to 5000, and a thousand simulated samplings were performed. Pathways with P<0.05 and NES> thresholds were screened, classified, and visualized.

[0054] GSEA is a method used to identify whether a gene set is significantly expressed in a specific biological state, which can help us understand how genes work together and the biological processes or pathways they may be involved in. GSEA enrichment analysis of the 12 key DEGs in sepsis-induced ARDS showed that FBXO45, ZNF366, and ZNF561 were marked as high-expression groups, all enriched in the oxidative phosphorylation pathway ( Fig. 6A -C). SLC15A1 and SLC39A9 were marked as low expression groups, and the results showed that these genes tended to be enriched in Fcγ receptor-mediated phagocytosis and ribosomes in the low expression state ( Fig.6D -E). SLC22A11, SLC35A2, FTH1, UBE2E2, USP11, ZNF409, and ZNF594 were enriched in both the high expression group and the low expression group ( Fig. 6F -S), in the high expression group, these genes are mainly enriched in Fcγ receptor-mediated phagocytosis and natural killer cell-mediated cytotoxicity in immune-related aspects; in metabolism and signal transduction, they are enriched in oxidative phosphorylation and cytokine-cytokine receptor interaction; in cell structure and function, they tend to be enriched in ribosomes, actin cytoskeleton regulation, etc. In the low expression group, these genes tend to be enriched in primary immunodeficiency diseases, ribosomes, proteasomes, etc.

[0055] Immune function analysis of 12 key genes

[0056] In addition, we also performed immune function analysis on these 12 genes. The results showed that the expression level of FBXO45 was associated with iDCs; the expression level of FTH1 was associated with CCR and neutrophils; SLC35A2 was associated with T cell co-inhibition and T helper cells, SLC39A9 was associated with DCs and NK cells; UBE2E2 was associated with B cells and Tfh, USP11 was associated with DCs and macrophages; ZNF366 was associated with type I MHC and parainflammatory response; ZNF561 was associated with APC co-inhibition, APC co-activation, CD8+ T cells, immune checkpoints, cytokine activity, HLA, type I MHC, parainflammatory response, T cell co-inhibition, T cell co-activation, T helper cells, Th1 cells, TIL, Treg cells; and ZNF594 was associated with neutrophils. However, for SLC15A1, SLC22A11, and ZNF409, our analysis did not find that they were statistically significantly associated with immune function. The results of immune function analysis of the 12 genes are shown in FIG7 .

[0057] Correlation between 12 key genes and immune cells

[0058] We also analyzed the correlation between these 12 genes and immune cells, and the results are shown in Figure 8. The results in Figure 8 show that SLC22A11 with a correlation |R| value greater than 0.5 is strongly positively correlated with M0 macrophages, and UBE2E2 is strongly negatively correlated with activated dendritic cells.

[0059] miR-224-5p targets FTH1 and interacts with SMURF1 to regulate ferroptosis and mitophagy in sepsis-induced ARDS

[0060] Thirty-five genes related to mitophagy were identified through literature review, and nine ferroptosis-related biomarkers were screened from the FerrDb database ( Fig. 9 A). We further intersected LASSO, SVM+RFE, mitochondrial autophagy-related genes, and ferroptosis-related markers and successfully identified a key gene FTH1 ( Fig. 9 B). In order to further explore the regulatory network of FTH1 gene, we used miRWalk, StarBase and TargetScan databases to analyze the miRNAs that may target FTH1. 1074 miRNAs were identified from miRWalk database, 6 from StarBase database, and 151 from TargetScan database. Finally, one common miRNA was identified through intersection analysis, namely miR-224-5p( Fig. 9 C). miRNA can induce gene silencing by specifically binding to mRNA, thereby reducing the expression level of the target gene. In addition, considering the central role of the ubiquitin-proteasome system in maintaining protein homeostasis, we used the UbiBrowser database to conduct an in-depth analysis of the ubiquitination modification of the FTH1 gene and its interaction network. The results showed that FTH1 is closely related to the E3 ubiquitin ligase SMURF1 (see Fig. 9 D).

[0061] Through literature review, 35 genes related to mitophagy were identified, which are widely believed to play a central role in the mitophagy process. We also accessed the FerrDb database (http: / / www.zhounan.org / ferrdb / current / ), a public database dedicated to ferroptosis-related biomarkers and regulatory factors, from which we selected specific ferroptosis markers.

[0062] miRWalk database (http: / / mirwalk.umm.uni-heidelberg.de / ), StarBase database (https: / / rnasysu.com / encori / ), and TargetScan database (https: / / www.targetscan.org / vert_80 / ) were used for miRNA prediction. We further performed a comprehensive comparison and intersection analysis of the miRNA data provided by these three databases to screen out the miRNAs that are most likely to interact with our research genes.

[0063] Ubiquitination analysis

[0064] We accessed the UbiBrowser database (http: / / ubibrowser.bio-it.cn / ubibrowser_v3 / ), which has the ability to map ubiquitination sites of specific genes and identify key ubiquitin ligases and deubiquitinases involved in this modification process. In addition, we also used the network analysis tools in the UbiBrowser database to construct an interaction network between the FTH1 gene and other proteins, revealing the potential functional connections and regulatory mechanisms of FTH1 in cells.

[0065] (10) Ubiquitination analysis

[0066] We accessed the UbiBrowser database (http: / / ubibrowser.bio-it.cn / ubibrowser_v3 / ), which has the ability to map ubiquitination sites of specific genes and identify key ubiquitin ligases and deubiquitinases involved in this modification process. In addition, we also used the network analysis tools in the UbiBrowser database to construct an interaction network between the FTH1 gene and other proteins, revealing the potential functional connections and regulatory mechanisms of FTH1 in cells.

[0067] In the present invention, firstly, differential gene expression analysis was performed on 32 sepsis-induced ARDS patients and 34 healthy subjects in GSE32707, and 576 differentially expressed genes were successfully identified. In order to further explore the distribution differences of immune cells in healthy and sepsis-induced 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 sepsis-induced 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, by further analyzing the correlation between immune cells, we revealed some suggestive correlation patterns. In particular, the significant negative correlation between monocytes and neutrophils, and between resting NK cells and γδT cells reveals the complex interactions and fine regulatory mechanisms between different cell types in the immune response. At the same time, we also found a high positive correlation between resting CD4+ memory T cells and CD8+ T cells, and between activated CD4+ memory T cells and plasma cells, suggesting the synergistic effect between specific cell subsets during the immune response. The results of these correlation analyses indicate that, although no significant statistical differences were observed at the macroscopic level, the pathological process of ARDS involves more sophisticated regulation at the cell subset level.

[0068] To identify the most relevant biomarkers for sepsis-induced ARDS, we further analyzed and screened DEGs using advanced machine learning algorithms. By applying the LASSO algorithm, we identified 22 differentially expressed genes associated with sepsis-induced ARDS. Similarly, the SVM+RFE algorithm also identified 24 relevant differentially expressed genes. By finding the intersection of these two result sets, we finally identified 12 genes with significant differential expression in sepsis-induced ARDS. To evaluate the diagnostic value of these 12 differentially expressed genes, we plotted ROC curves, and the results showed that except for ZNF366 with an AUC value of 0.699, the AUCs of other genes were all above 0.7, indicating that these genes have high accuracy and diagnostic potential in distinguishing sepsis-induced ARDS patients from non-ARDS individuals.

[0069] The biological significance of 12 key DEGs in sepsis-induced ARDS was deeply explored by GSEA enrichment analysis. The results revealed specific enrichment patterns of biological processes and pathways of specific genes at different expression levels. In particular, FBXO45, ZNF366, and ZNF561 were significantly enriched in the oxidative phosphorylation pathway in the high expression group. This finding is closely related to the regulation of energy metabolism in sepsis-induced ARDS. Studies have shown that imbalance in the oxidative phosphorylation system is prominent in non-surviving patients with sepsis. SLC15A1 and SLC39A9 were associated with Fcγ receptor-mediated phagocytosis and ribosome function in the low expression group. These results suggest that the patient's immune regulation and protein synthesis may be inhibited, thereby affecting the immune response and repair process of ARDS. It is noteworthy that SLC22A11, SLC35A2, FTH1, UBE2E2, USP11, ZNF409, and ZNF594 showed enrichment in both high and low expression groups, indicating that these genes may play a role in multiple pathological processes of ARDS in sepsis. In the high expression state, they are associated with the regulation of immune response, metabolic pathways, and cell structure; while in the low expression state, they are associated with immune deficiency, protein degradation, and other processes.

[0070] In addition, immune function analysis of these genes was further performed to reveal their potential role in immune response. The analysis results suggest that the expression levels of specific genes are correlated with different types of immune cells. For example, the expression of FBXO45 is associated with immature dendritic cells, while the expression of FTH1 is associated with CCR and neutrophils. These results indicate that these genes may play an important role in the immune regulation of ARDS. By identifying the interactions between these genes and immune cells, we can have a deeper understanding of the immunopathological mechanisms of ARDS and provide potential targets for the development of new therapeutic strategies.

[0071] We further explored the correlation between these 12 genes and immune cells. Through correlation analysis, we found that the SLC22A11 gene showed a strong positive correlation with M0 macrophages, indicating that SLC22A11 may play an important role in the activation and function of M0 macrophages. M0 macrophages are a type of undifferentiated macrophages that play a key role in the early stages of inflammatory response and can respond to pathogens and damage signals. However, current research on SLC22A11 is mainly focused on kidney disease and metabolic diseases, and no studies have explored the relationship between SLC22A11 and immune response. In addition, the UBE2E2 gene showed a strong negative correlation with activated dendritic cells. Dendritic cells are key antigen-presenting cells in the immune system, and they play a vital role in activating T cell-mediated immune responses. The negative correlation of the UBE2E2 gene may indicate that it plays an inhibitory role in the maturation and antigen presentation function of dendritic cells. At present, there is a lack of research on the role of UBE2E2 in sepsis-induced ARDS or immune response. In conclusion, the results of these correlation analyses suggest that SLC22A11 and UBE2E2 may be involved in the immune regulation of sepsis-induced ARDS by affecting the behavior of specific immune cells.

[0072] Next, our study identified 35 mitophagy-related genes and 9 ferroptosis biomarkers through literature review and database screening. Through comprehensive analysis, we identified the key gene FTH1. Existing studies have pointed out that FTH1, as a negative regulator of ferroptosis, has a negative correlation between decreased expression levels and enhanced mitophagy, which will lead to further deterioration of kidney damage. The results of this study support FTH1 as a key gene involved in the regulation of ferroptosis and mitophagy, which is consistent with our results. In addition, some studies have pointed out that in the case of sepsis-induced acute lung injury, the interaction between FTH1 and NCOA4 and its subsequent degradation through the autophagy pathway will lead to increased intracellular iron ion levels, triggering the Fenton reaction, producing a large amount of reactive oxygen species, and further promoting lipid peroxidation and ferroptosis. This indicates that FTH1 plays a key role in regulating ferroptosis in sepsis-induced acute lung injury. It is worth noting that no study has yet explored in depth whether FTH1 is involved in regulating the mitophagy process in sepsis-induced ARDS. Our study identified for the first time that FTH1 is a key gene involved in the regulation of ferroptosis and mitophagy in sepsis-induced ARDS. This discovery opens up new horizons for future research in this field.

[0073] In addition, we predicted through online databases that miR-224-5p may target and regulate the expression of FTH1. Studies have shown that miR-224-5p is involved in regulating a variety of inflammatory diseases by targeting mRNA, including asthma, osteoarthritis, and inflammatory response in acute childhood liver failure. However, no studies have yet explored whether miR-224-5p is involved in regulating sepsis-induced ARDS. Our results identified that miR-224-5p is involved in regulating ferroptosis and mitochondrial autophagy in sepsis-induced ARDS by targeting FTH1.

[0074] It is worth noting that ubiquitination modification is an important post-translational modification of proteins that can participate in the degradation of mitochondrial proteins and help remove damaged or dysfunctional mitochondrial proteins, thereby maintaining mitochondrial function, which is essential for the process of mitophagy. For example, Parkin is an E3 ubiquitin ligase that is recruited to damaged mitochondria by PINK1 and marks damaged mitochondria by ubiquitination, thereby triggering mitophagy and preventing the decline of immune cell function in patients with sepsis. In addition, ubiquitination modification may affect the metabolism and storage proteins of iron ions, thereby affecting the availability of iron ions and the occurrence of ferroptosis. For example, ubiquitination-mediated degradation of RSL1D1 protein leads to decreased FTH1 expression, which in turn causes the accumulation of intracellular ferrous ions and triggers the process of ferroptosis. It can be seen that ubiquitination modification may play an important role in ferroptosis and mitophagy. Therefore, our study used the UbiBrowser database to deeply analyze the ubiquitination modification of FTH1 and its interaction network. The results showed that FTH1 is closely related to the E3 ubiquitin ligase SMURF1. Previous studies have shown that SMURF1 is involved in mitophagy in acute kidney injury, indicating its important role in regulating mitophagy. Although there is currently a lack of direct research on the role of SMURF1 in ferroptosis, our study points out that FTH1 may play a key role in the ferroptosis and mitophagy regulatory network of sepsis-induced ARDS through its interaction with SMURF1. This discovery not only provides a new perspective for understanding these complex biological processes, but also provides new targets for future research and potential therapeutic strategies in this field.

[0075] We identified 12 differentially expressed genes closely related to the immune status of patients with sepsis-induced ARDS by using LASSO and SVM+RFE algorithms. Secondly, we identified FTH1 as a key gene involved in regulating mitochondrial autophagy and ferroptosis in sepsis-induced ARDS. Thirdly, we predicted miR-224-5p as a miRNA targeting FTH1 through an online database. Finally, we analyzed the ubiquitination modification of the FTH1 gene and its interaction network and found that FTH1 was closely related to the E3 ubiquitin ligase SMURF1.

Claims

1. A drug gene target for treating key mitochondrial autophagy and ferroptosis genes in sepsis-induced ARDS, characterized by: The drug gene target is FTH1.

2. The method for identifying the drug gene target FTH1 according to claim 1, characterized in that: The following steps are involved: (1) The gene expression data of sepsis patients were analyzed using the machine learning algorithm LASSO and support vector machine combined with recursive feature elimination (SVM+RFE); (2) The differentially expressed genes obtained by the two algorithms were intersected to screen out genes related to sepsis-induced ARDS Closely related differentially expressed gene DEGs; (3) ROC curve analysis was used to evaluate the diagnostic potential of the DEGs; (4) Gene enrichment analysis and immune infiltration analysis were performed to reveal the role of the genes in immune response.

3. The method for identifying the drug gene target FTH1 according to claim 2, characterized in that: The immune infiltration was assessed using the CIBERSORT algorithm.

4. Use of the drug gene target FTH1 according to claim 1 in the preparation of a pharmaceutical composition for treating sepsis-induced ARDS.

5. Use of the drug gene target FTH1 according to claim 5 in the preparation of a pharmaceutical composition for treating sepsis-induced ARDS, characterized in that: Targeting the FTH1 gene by regulating the expression level of miR-224-5p.

6. The role of ubiquitination modification of the drug gene target FTH1 gene according to claim 1 in sepsis-induced acute respiratory distress syndrome.

7. A pharmaceutical composition for regulating ubiquitination modification of FTH1 gene in sepsis-induced ARDS to treat ARDS, characterized in that: Contains at least one active ingredient capable of affecting the ubiquitination level of FTH1.

8. The pharmaceutical composition according to claim 7, characterized in that: The active ingredient is an agonist or antagonist of the E3 ubiquitin ligase SMURF1.

9. Non-therapeutic use of FTH1 gene ubiquitination modification as a biomarker for disease progression and treatment response in patients with sepsis-induced ARDS.

10. Application of FTH1 gene ubiquitination modification as a biomarker in a disease progression and treatment response kit for patients with sepsis-induced ARDS.

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