A method of analyzing and identifying autophagy biomarkers associated with atherosclerotic plaque progression

Abstract

The application provides a method for identifying and analyzing autophagy biomarkers related to atherosclerotic plaque progression, and belongs to the technical field of analysis. The method comprises the following steps: (1) data acquisition and processing; (2) identification of differentially expressed genes and autophagy-related genes; (3) key gene verification and prediction performance evaluation; (4) immune infiltration analysis; (5) single-cell RNA sequencing analysis; (6) expression of three genes in macrophages. The application identifies autophagy-related genes associated with AS, and uses multi-omics methods to clarify the expression pattern, spatial distribution and immune infiltration characteristics of key differentially expressed genes. The research results of the application provide new insights into the molecular mechanisms of AS and potential intervention and treatment targets.

Description

Technical Field

[0001] This invention relates to the field of analytical technology, and more specifically to a method for analyzing and identifying autophagy biomarkers associated with the progression of atherosclerotic plaques. Background Technology

[0002] Atherosclerosis (AS) is a leading cause of coronary artery disease and the most common form of cardiovascular disease. It is the leading cause of myocardial infarction and stroke, resulting in significant morbidity and mortality worldwide. Studies show that the global prevalence of ischemic heart disease has increased by more than 80% in the past thirty years. Economic development, accompanied by changes in traditional dietary patterns, continued smoking, and the increasing burden of obesity, diabetes, and hypertension, has brought significant public health challenges and increased the risk of atherosclerotic cardiovascular events. Myocardial infarction and cerebral infarction caused by AS continue to seriously impact human health. Addressing the increasing prevalence of AS in young people has become an urgent research focus.

[0003] The pathological essence of atherosclerosis (AS) lies in a vicious cycle of lipid deposition and dysregulation of the immune response. Atherosclerotic plaques consist of a necrotic core (containing lipids, cellular debris, cholesterol crystals, macrophages, and foam cells) and a fibrous cap covering it. Oxidized low-density lipoprotein (LDL) phagocytosis by macrophages activates Toll-like receptor 4, triggering the NF-κB signaling pathway. This promotes the release of pro-inflammatory factors such as IL-1β and TNF-α and recruits more monocytes to the site. Foam cell apoptosis activates the NLRP3 inflammasome, leading to the maturation of IL-18 and IL-1β and enhanced infiltration of neutrophils and mast cells. The continuous recruitment of immune cells amplifies local inflammation and disrupts the structural integrity of the fibrous cap. Persistent inflammation further inhibits collagen synthesis and promotes matrix metalloproteinase-9-mediated cap degradation, ultimately leading to plaque rupture. Macrophages are considered the dominant immune cell type in atherosclerotic lesions. Their metabolic homeostasis is a key determinant of plaque progression and stability. Therefore, promoting lipid clearance and regulating inflammatory responses are key therapeutic strategies for restoring macrophage metabolic balance and stabilizing atherosclerotic plaques. Macrophage autophagy plays an important role in metabolic and inflammatory regulation.

[0004] Autophagy primarily comprises three forms: macroautophagy, microautophagy, and molecular chaperone-mediated autophagy. Macroautophagy (hereinafter referred to as autophagy) is an intracellular degradation process that promotes the turnover of proteins, organelles, and cytoplasmic components during periods of nutrient deficiency or cellular stress. It enables cells to adapt to fluctuating nutrient and energy demands, maintaining metabolic homeostasis and promoting survival by reclaiming cellular material. Furthermore, autophagy modulates inflammatory responses by interacting with innate immune signaling pathways, clearing endogenous inflammasome agonists, and regulating the secretion of immune mediators. Increasing evidence suggests that autophagic flux plays a crucial role in the pathogenesis of various diseases, including cardiovascular disease, diabetes, inflammatory diseases, infections, and cancer. Moreover, autophagy is closely associated with ankylosing spondylitis (AS).

[0005] Autophagy in macrophages is widely believed to be involved in the pathogenesis of vascular diseases. In the apoE- / - mouse model of atherosclerosis (AS), autophagy markers such as p62 and LC3 were observed within atherosclerotic lesions and significantly co-localized with monocytes / macrophages and leukocytes. Notably, p62 expression was found to increase with aging and plaque accumulation in the atherosclerotic aorta, suggesting that autophagic flux may be impaired with disease progression, leading to the accumulation of this adaptor protein. Importantly, impaired macrophage autophagy accelerates atherosclerotic progression by promoting foam cell necrosis and plaque instability. The specific mechanisms by which macrophage autophagy regulates and spatially distributes the immune microenvironment in atherosclerosis remain unclear. Summary of the Invention

[0006] The purpose of this invention is to propose a method for analyzing and identifying autophagy biomarkers associated with the progression of atherosclerotic plaques. This method identifies autophagy-related genes associated with atherosclerosis (AS) and uses a multi-omics approach to elucidate the expression patterns, spatial distribution, and immune infiltration characteristics of key differentially expressed genes. The results of this invention provide new insights into the molecular mechanisms of AS and potential therapeutic targets.

[0007] The technical solution of this invention is implemented as follows: This invention provides a method for analyzing and identifying autophagy biomarkers associated with the progression of atherosclerotic plaques, comprising the following steps: (1) Retrieve atherosclerosis-related datasets from the database to obtain autophagy-related genes; (2) Identify differentially expressed genes and overlapping genes with the autophagy gene set, screen for effective overlapping genes, and obtain the target differentially expressed genes through the intersection; (3) Extract the expression of key genes, test and visualize the expression differences of the control group, extract significantly expressed genes, construct a nomogram model to predict the occurrence of atherosclerotic plaques, and analyze and evaluate the discrimination ability and predictive effect of the nomogram. (4) Analyze the differences and correlations of immune infiltration, visualize the abundance of immune cells in plaque patients, and study the association between the level of immune infiltration and the expression of autophagy-related core genes; (5) Extract the expressed genes from the cells, perform principal component analysis to remove batch effects between samples, and then perform analysis; (6) THP-1 macrophages were transformed into foam cells by culturing in a medium with added ox-LDL. The effect of ox-LDL on the expression of key genes was detected by PCR, and the relative expression level of the genes was calculated.

[0008] As a further improvement of the present invention, the database mentioned in step (1) is the Gene Expression Omnibus database, the dataset includes whole tissue mRNA sequence data and single-cell RNA sequencing data, and the autophagy-related genes are obtained from the KEGG Pathway database.

[0009] As a further improvement of the present invention, in step (2), differentially expressed genes are identified by volcano plots, overlapping genes between DEGs and autophagy gene sets are identified by Venn diagrams, and two machine learning algorithms, LASS, are employed. O Further screening for effective overlapping genes was conducted using RF-SVM.

[0010] As a further improvement of the present invention, in step (3), the expression difference of the control group is calculated and visualized using the Wilcoxon test, and the discrimination ability and predictive effect of the nomogram are evaluated using receiver operating characteristic curves and decision curves.

[0011] As a further improvement of the present invention, box plots are used in step (4) to visualize the abundance of immune cells in patients with plaques.

[0012] As a further improvement of the present invention, in step (5), principal component analysis is performed using hypervariable genes; batch effects between samples are removed using the Harmony algorithm; and the extracted subpopulations are processed using Monocle2 for analysis, including re-embedding, batch effect correction and re-clustering.

[0013] As a further improvement of the present invention, the culture medium in step (6) is serum-free RPMI 1640 culture medium supplemented with 50 μg / ml ox-LDL.

[0014] As a further improvement of the present invention, the PCR detection cycle conditions in step (6) are 90-100°C for 30 seconds, followed by 30-50 cycles: 90-100°C for 5 seconds, 55-65°C for 30 seconds.

[0015] The present invention has the following beneficial effects: This invention explores the role of macrophage autophagy-related genes in atherosclerosis through multi-omics integrated analysis. Based on the GEO database (GSE270260, GSE100927, GSE193533) and machine learning algorithms (LASSO / RF-SVM), five differentially expressed autophagy-related genes were screened. Among them, SNX5 and SMG1 were significantly upregulated in AS, while GSK3A was downregulated. The SNX5 / SMG1 / GSK3A prediction model showed high diagnostic efficacy for plaque formation (AUC=0.844). Immune infiltration analysis showed that these three factors were significantly associated with B cell and macrophage infiltration (P<0.05). Single-cell spatial localization showed that SMG1 was highly expressed in the plaque core / adventitia, GSK3A was distributed in the vessel wall, and SNX5 was lowly expressed in the plaque core. In in vitro experiments, after ox-LDL induced macrophage foaming, the expression of SNX5 and SMG1 significantly increased (P<0.05). Conclusion: SNX5, SMG1, and GSK3A may participate in AS progression by regulating lipid transport and the macrophage immune microenvironment, and may be potential intervention targets.

[0016] Given the crucial role of autophagy in the pathogenesis of atherosclerotic plaques (AS), this invention integrates bioinformatics analysis to identify autophagy biomarkers associated with the progression of atherosclerotic plaques and experimental validation to explore the function of autophagy-related genes within macrophages during AS progression. This invention identifies AS-related autophagy genes and employs multi-omics methods to elucidate the expression patterns, spatial distribution, and immune infiltration characteristics of key differentially expressed genes. The findings of this invention provide new insights into the molecular mechanisms of AS and potential therapeutic targets. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 Identification of differentially expressed genes and autophagy-related genes. (A) Volcano plot showing the expression patterns of differentially expressed genes in atherosclerosis. (B) Venn diagram showing common genes between the autophagy gene set and differentially expressed genes. (C) Screening of candidate genes in atherosclerosis using a LASSO regression model and a random forest-support vector machine joint model. (D) Venn diagram showing common genes screened by the LASSO regression model and the random forest-support vector machine joint model.

[0019] Figure 2Key gene validation and predictive efficacy assessment. (A) Volcano plot of differentially expressed genes. (B) Box plot of differential expression of key genes. (C) Nonodal plot of key genes for the diagnosis of atherosclerosis. (D) Receiver operating characteristic curve of key genes for diagnosing atherosclerosis. (E) Decision curve analysis of key genes for diagnosing atherosclerosis.

[0020] Figure 3 Correlation analysis between differentially expressed genes and immune cells. (A) Bar chart showing the proportion of immune cells. (B) Box plot showing the difference in the proportion of immune cells. (C) Heatmap of interactions between immune cells. (D) Heatmap showing the correlation between key genes and differentially expressed immune cells. In the figures, *P < 0.05, **P < 0.01, ***P < 0.001; purple represents positive correlation, and pink represents negative correlation.

[0021] Figure 4 Expression of autophagy-related genes at the single-cell level. (A) Density map of autophagy gene set scores in atherosclerotic cell populations. (B) Density map of autophagy gene set scores in six different cell populations. (C) Cluster diagram of the six cell populations. (D) Box plot of key gene expression levels in four cell types. (E) Density map showing significant co-expression of three autophagy genes in macrophages; lighter green indicates higher density.

[0022] Figure 5 Spatial transcriptomic profiles of three key autophagy-related genes in atherosclerotic plaques. (A) SMG1 is widely expressed in the plaque core, periphery, and vascular smooth muscle cells; GSK3A is mainly located in the core; SNX5 is expressed at very low levels in the core. (B) SMG1 expression remains significant in the periphery and vascular smooth muscle cells, while SNX5 expression is low. (C) The spatial expression pattern of GSK3A is similar to that of SMG1, and expression can be detected in the periphery and vascular smooth muscle cells, but at low levels. (D) SNX5 and GSK3A have the same expression pattern, both being enriched in vascular smooth muscle cell-rich regions.

[0023] Figure 6 Expression detection of key genes in atherosclerotic macrophages. Statistical graphs of SNX5, SMG1, and GSK3A mRNA expression levels. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1 1. Materials and Methods (1) Data acquisition and processing AS-related datasets were retrieved from the Gene Expression Omnibus database (https: / / www.ncbi.nlm.nih.gov / geo / ), including whole-cell mRNA sequence data (GSE270260, GSE100927) and single-cell RNA sequencing data (GSE260657). A total of 15 patients with carotid artery stenosis were included (n=7, asymptomatic; n=8, symptomatic). 580 autophagy-related genes were obtained from the KEGG Pathway database.

[0026] (2) Identification of differentially expressed genes and autophagy-related genes The dataset GSE270260 was analyzed using the R package "DESeq2" (P < 0.05 and |log2(fold change)| > 1). Differentially expressed genes were identified using volcano plots. Overlapping genes between DEGs and the autophagy gene set were identified using Venn diagrams. Two machine learning algorithms, LASSO and RF-SVM, were used to further screen for effective overlapping genes, and the target differentially expressed genes were obtained through the intersection of the two algorithms.

[0027] (3) Validation of key genes and evaluation of predictive efficacy The dataset GSE270260 was analyzed using the R package "DESeq2". The expression of key genes was extracted from the dataset GSE100927, and the expression differences between the AS group and the control group were calculated and visualized using the Wilcoxon test. Significantly expressed genes were extracted. Based on the results of LASSO-Cox regression analysis, a nomogram model was constructed using the R package "rms" (v 6.3-0) to predict the occurrence of atherosclerotic plaques. Receiver operating characteristic (ROC) curves and decision curve analyses were used to evaluate the discriminative power and predictive performance of the nomogram.

[0028] (4) Immune infiltration analysis Based on gene sets of 28 immune-related cell types, the ssGSEA algorithm of the R package "GSVA" was used to assess the immune activity of each sample. Differences and correlations in immune infiltration between the two groups were analyzed. Box plots were used to visualize the abundance of immune cells in plaque patients. Furthermore, the association between immune infiltration levels and the expression of autophagy-related core genes was investigated.

[0029] (5) Single-cell RNA sequencing analysis To conduct single-cell profiling, this invention analyzed the scRNA-seq dataset GSE260657, following the "Seurat" standard procedure. This dataset underwent mitochondrial gene removal and diploid removal. Genes expressed in more than three cells or with a cumulative read count exceeding 300 were extracted. Principal component analysis was performed using the top 2000 hypervariable genes. The Harmony algorithm was applied to remove batch effects between samples. Dimensionality reduction visualization (15 dimensions) was performed using a uniform manifold approximation and projection. After iterative subpopulation visualization at different resolutions, a resolution of 0.01 was selected, resulting in six distinct subpopulations. Cluster labeling and annotation were performed using AddModuleScore visualization, which aggregates gene expression patterns. Simultaneously, the autophagy activity of each cluster was scored using the AddModuleScore function with the autophagy gene set. Box plots were generated to visualize the expression patterns of autophagy-related genes in different cell clusters.

[0030] (6) Expression of three genes in macrophages THP-1 macrophages were transformed into foam cells by culturing them in serum-free RPMI 1640 medium supplemented with 50 μg / ml ox-LDL for 18 hours. The effects of ox-LDL on the expression of key genes were then detected by PCR.

[0031] PCR: Total RNA was extracted from cells using TRIzol reagent, and cDNA was synthesized using TB Green® 2×Universal Blue SYBRGreen qPCR Master Mix (Servicebio, G3326-05). Quantitative PCR was performed on a 9600 PCR system using TB Green® Premix Ex Taq™ II (TaKaRa, RR820A). Cycling conditions were 95°C for 30 seconds, followed by 40 cycles: 95°C for 5 seconds, 60°C for 30 seconds. Gene expression levels were normalized to the internal control gene GAPDH. Relative gene expression levels were calculated using the 2−ΔΔCT method. Primer sequences are shown in Table 1.

[0032] Table 1 Primer sequences used in PCR experiments

[0033] 2. Results (1) Identification of differentially expressed genes and autophagy-related genes Based on the expression profile of GSE270260, differential expression analysis revealed 16,019 DEGs between the HFD group and the control group, of which 1,534 genes were upregulated and 2,170 genes were downregulated. The DEGs were visualized using a volcano plot created with 'EnhancedVolcano'. Figure 1 A). The Venn diagram shows 180 overlapping genes between DEGs and the autophagy gene set ( Figure 1 B). To screen for the predictive value of overlapping genes, machine learning algorithms were applied. Furthermore, LASSO regression and RF-SVM algorithms were used to identify key differentially expressed genes. Figure 1 C). The LASSO regression model identified 9 key differentially expressed genes, and the RF-SVM algorithm identified 10 key differentially expressed genes. After taking the intersection, 5 overlapping genes were obtained (SNX5, SMG1, RNF41, KDM4A, GSK3A). Figure 1 D). The expression levels of these five autophagy-related genes may change during the process of atherosclerosis.

[0034] (2) Validation of key genes and evaluation of predictive efficacy External validation was performed using the independent dataset GSE100927. Volcano plots revealed differences in 29,986 genes between atherosclerotic and normal tissues, with 200 significantly upregulated and 300 significantly downregulated (|log2FC|>1, adj.P<0.05). The volcano plots were visualized using 'EnhancedVolcano'. Figure 2 A). The expression of five differentially expressed genes (SNX5, SMG1, RNF41, KDM4A, GSK3A) was compared among the groups. In the atherosclerosis group, the expression of SNX5 and SMG1 was significantly upregulated, while the expression of GSK3A was significantly downregulated. Figure 2 B). The nomogram constructed using these three autophagy-related genes shows that the lower the values ​​of SNX5 and SMG1, and the higher the value of GSK3A, the greater the probability of forming atherosclerotic plaques. Furthermore, changes in SNX5 have a significant impact on the total number of points and the final probability. Figure 2 C). The present invention also plotted ROC curves for the three genes separately. The results showed that SNX5 had the best predictive ability (AUC = 0.826), SMG1 had an AUC of 0.657, and GSK3A had the lowest AUC (0.641). However, the predictive ability of the combination of the three genes (AUC = 0.844) was better than that of a single gene. Figure 2 D). DCA showed that when the threshold probability was between 0.1 and 0.8, the three biomarkers, alone or in combination, could predict plaque occurrence. Furthermore, the combination of SNX5, SMG1, and GSK3A was superior to using SNX5, SMG1, or GSK3A alone in predicting plaque occurrence. Figure 2 E).

[0035] (3) Immune-related autophagy in AS To elucidate the subtypes of various immune cells, the relative abundance of 28 immune cell types in 16 samples was quantitatively analyzed using the ssGSEA algorithm. Stacked area plots showed the abundance of different immune cell types. Stacked bar plots visualized the abundance comparisons, revealing significant differences among CD56 natural killer cells, central memory CD8 T cells, immature dendritic cells, mast cells, memory B cells, regulatory T cells, and follicular helper T cells. Figure 3 A, B). The heatmap reveals the interactions between immune cells during atherosclerosis. Figure 3 C). The darker the color and the larger the square, the stronger the correlation between the two cell types. This invention assessed the correlation between the expression of key differentially expressed genes (SNX5, SMG1, and GSK3A) and immune cell infiltration. Figure 3 (D) These autophagy-related differentially expressed genes are moderately associated with immune cells such as immune B cells, memory B cells, macrophages, and plasmacytoid dendritic cells. Autophagy in immune cells is crucial for cellular immunity, differentiation, and survival.

[0036] (4) Single-cell level expression of autophagy To investigate the single-cell resolution of autophagy within human atherosclerotic plaques, this invention performed single-cell RNA sequencing analysis. After standard quality control and filtering, 7690 high-quality cells were retained. Dimensionality reduction and clustering revealed six distinct cell populations: smooth muscle cells / myofibroblasts, macrophages, endothelial cells, and T cells / B cells. Cell type annotation was performed using established marker genes: B cells (CD19, CD79A, MS4A1, CD22, CD24, IGKC), endothelial cells (PECAM1, VWF, CD34, CLDN5, FLT1, PLVAP), macrophages (CD68, CD163, FCGR3A, FCGR1A, ITGAM, ITGAX, MRC1), myofibroblasts (ACTA2, COL1A1, COL3A1, FN1, POSTN, FAP, THY1), smooth muscle cells (ACTA2, MYH11, TAGLN, CNN1, DES, TPM2), and T cells (CD3D, CD3E, CD3G, CD4, CD8A, CD88, IL7R).

[0037] The marker gene set scores were calculated using Seurat's AddModuleScore feature. Density plots visualizing these scores showed higher pathway activity in the corresponding cell types, indicated by a darker green. Figure 4A, B). Subsequent deconvolution analysis resolved the expression patterns of three autophagy-related genes (SNX5, SMG1, GSK3A) in the cell population: SNX5 was significantly downregulated in endothelial cells but upregulated in macrophages; SMG1 was upregulated in macrophages, smooth muscle cells / myofibroblasts, and T cells; GSK3A was downregulated in endothelial cells but upregulated in macrophages. Figure 4 C, D). Density map analysis confirmed significant co-expression of all three autophagy genes in macrophages, suggesting their isolation for subsequent subset characterization analysis. Figure 4 E).

[0038] (5) Spatial single-cell transcriptome analysis Spatial transcriptomic analysis revealed distinct expression patterns across plaque subregions. SMG1 was widely expressed in the plaque periphery (outer layer), core region, and vascular smooth muscle cells. In contrast, GSK3A expression was primarily enriched in the core region, while SNX5 showed very low expression in the core region. Figure 5 A). SMG1 expression remained significant in the periphery and VSMCs, while SNX5 expression remained low. Figure 5 B). GSK3A exhibited a similar spatial localization pattern to SMG1 (periphery and VSMCs), although at lower expression levels. SNX5 and GSK3A showed consistent expression patterns. Notably, SNX5 expression levels were enriched in the VSMC regions of these slices. Figure 5 C, D).

[0039] (6) Validation of differential gene expression in vitro An in vitro AS model was constructed using ox-LDL-induced Raw264.7 cells, and the expression levels of key differentially expressed genes at the mRNA level were quantified. In ox-LDL-induced Raw264.7 cells, the expression levels of SNX5, SMG1, and GSK3A were all significantly upregulated (P < 0.05). Figure 6 (Table 2).

[0040] Table 2. Statistics on mRNA expression levels of key genes

[0041] AS is a chronic inflammatory disease driven by lipid metabolism disorders, primarily involving endothelial cells, vascular smooth muscle cells, and macrophages. Autophagy is a crucial mechanism for degrading cytoplasmic components and maintaining cellular homeostasis. Macroautophagy enables cells to adapt to changes in nutritional and energy conditions by reclaiming intracellular components, thereby supporting metabolic homeostasis and cell survival. While the role of autophagy in AS has been extensively studied in vascular smooth muscle cells and endothelial cells, its function in macrophages—key immune cells in atherosclerotic lesions—remains not fully understood. A key event in the pathogenesis of AS is the accumulation of ox-LDL, which has been shown to induce macrophage autophagy. Evidence suggests that autophagy plays a stage-dependent role in AS. In the intermediate stage, macrophage autophagy exerts a protective effect by inhibiting foam cell formation. Ox-LDL promotes autophagy through endoplasmic reticulum stress, facilitating the clearance of damaged organelles and proteins and enhancing macrophage survival. Furthermore, autophagy promotes cholesterol efflux through lysosomal degradation of lipid droplets, thereby reducing lipid accumulation. It also promotes polarization towards the M2 anti-inflammatory macrophage phenotype, supporting plaque stability. Conversely, autophagy activity gradually declines as atherosclerosis (AS) progresses. This dysfunction leads to mitochondrial clearance defects, increased lipid accumulation, increased macrophage death, and enlargement of the necrotic core, ultimately accelerating plaque instability. Against this backdrop, this invention aims to characterize the spatial expression patterns of autophagy-related genes in atherosclerotic tissues using an integrated multi-omics approach and to elucidate their immunomodulatory roles. The findings of this invention provide mechanistic insights into how autophagy regulates macrophage metabolic homeostasis, offering new perspectives for the development of diagnostic biomarkers and immunotargeted therapies for AS.

[0042] Multiple screening and multidimensional evaluation enhance the rigor of differential gene screening. This invention uses two machine learning algorithms to identify differentially expressed genes and validates the results on other datasets, avoiding model overfitting and effectively improving the rigor of differential gene screening. Screening of key differentially expressed genes enriches the biomarker atlas for the clinical diagnosis of atherosclerosis. This invention provides stronger support for the clinical application of diagnostic biomarkers through multidimensional evaluation of the nomogram visualization prediction model using ROC and DAC curves.

[0043] This invention provides a multi-faceted analysis of the correlation between differentially expressed genes and ankylosing spondylitis (AS). Using KEGG and ssGSEA, it reveals various cellular activities, metabolic processes, signaling pathways, and the immune microenvironment associated with different genes. SNX5, SMG1, and GSK3A have not yet been studied in AS. This invention is the first to demonstrate that these biomarkers may participate in the development and progression of AS by coordinating lipid transport in macrophages and the immune microenvironment, providing new targets for AS treatment.

[0044] Transcriptome screening (whole-cell RNA-seq), single-cell spatial analysis (scRNA-seq), and experimental validation mapped the expression profiles of key differentially expressed genes at the cellular level, avoiding false positives caused by single analyses or validations. Whole-cell RNA-seq showed that, compared with the control group, the atherosclerosis group exhibited high expression of SNX5 and SMG1, and low expression of GSK3A. In in vitro cell culture PCR validation, this invention found that the expression of SNX5, SMG1, and GSK3A in ox-LDL-induced macrophages was higher than that in the control group. The bioinformatics prediction results for SNX5 and SMG1 were consistent with the in vitro validation.

[0045] This invention pioneered the exploration of the association between autophagy-related genes and the immune microenvironment of atherosclerosis through comprehensive bioinformatics analysis. The findings suggest that SNX5, SMG1, and GSK3A may serve as potential therapeutic targets for regulating the immune microenvironment of AS. The expression of these key genes has been confirmed at the cellular level.

[0046] Studies have shown that autophagy-related genes SNX5, SMG1, and GSK3A may participate in AS progression by regulating macrophage lipid transport and the immune microenvironment, and are potential therapeutic targets. The strategy of integrating multi-omics analysis with experimental validation provides a new direction for the study of AS mechanisms and clinical diagnosis.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing and identifying autophagy biomarkers associated with the progression of atherosclerotic plaques, characterized in that, Includes the following steps: (1) Retrieve atherosclerosis-related datasets from the database and obtain autophagy-related genes; the database mentioned in step (1) is the Gene Expression Omnibus database, the dataset includes whole tissue mRNA sequence data and single-cell RNA sequencing data, and the autophagy-related genes are obtained from the KEGG Pathway database; (2) Identify differentially expressed genes and overlapping genes with the autophagy gene set, screen for effective overlapping genes, and obtain the target differentially expressed genes through the intersection; (3) Extract the expression of key genes, verify and visualize the expression differences of the control group, extract significantly expressed genes, determine the target autophagy biomarker combination composed of SNX5, SMG1 and GSK3A; construct a nomogram model to predict the occurrence of atherosclerotic plaques, and evaluate the discrimination ability and predictive effect of the nomogram. (4) Analyze and identify the differences and correlations of autophagy biomarkers and immune infiltration associated with the progression of atherosclerotic plaques, visualize the abundance of immune cells in plaque patients, and study the association between the level of immune infiltration and the expression of autophagy-related core genes; (5) Extract genes expressed in cells, remove batch effects between samples, and perform principal component analysis to identify autophagy biomarkers associated with the progression of atherosclerotic plaques; (6) THP-1 macrophages were transformed into foam cells by culturing in a medium with added ox-LDL. The effect of ox-LDL on the expression of key genes was detected by PCR, and the relative expression level of the genes was calculated.

2. The method for analyzing and identifying autophagy biomarkers related to the progression of atherosclerotic plaques according to claim 1, characterized in that, In step (2), differentially expressed genes are identified by volcano plots, overlapping genes between DEGs and autophagy gene sets are identified by Venn diagrams, and two machine learning algorithms, LASSO and RF-SVM, are used to further screen for effective overlapping genes.

3. The method for analyzing and identifying autophagy biomarkers related to the progression of atherosclerotic plaques according to claim 1, characterized in that, In step (3), the expression differences in the control group were calculated and visualized using the Wilcoxon test. Receiver operating characteristic curves and decision curves were used to analyze and identify autophagy biomarkers associated with the progression of atherosclerotic plaques. The discriminative power and predictive effect of the nomogram were evaluated.

4. The method for analyzing and identifying autophagy biomarkers related to the progression of atherosclerotic plaques according to claim 1, characterized in that, In step (4), box plots are used to visualize the abundance of immune cells in patients with plaques.

5. The method for analyzing and identifying autophagy biomarkers related to the progression of atherosclerotic plaques according to claim 1, characterized in that, In step (5), principal component analysis was performed using hypervariable genes to identify autophagy biomarkers associated with the progression of atherosclerotic plaques; the Harmony algorithm was applied to remove batch effects between samples, and the extracted subpopulations were processed using Monocle2 for analysis, including re-embedding, batch effect correction and re-clustering.

6. The method for analyzing and identifying autophagy biomarkers associated with the progression of atherosclerotic plaques according to claim 1, characterized in that, The culture medium mentioned in step (6) is serum-free RPMI 1640 medium supplemented with 50 μg / ml ox-LDL.

7. The method for analyzing and identifying autophagy biomarkers associated with the progression of atherosclerotic plaques according to claim 1, characterized in that, The PCR detection cycle conditions described in step (6) are 90-100°C for 30 seconds, followed by 30-50 cycles: 90-100°C for 5 seconds, 55-65°C for 30 seconds.

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