Exploratory Analysis of Serum Exosome Proteomics in the Pathogenesis and Early Biomarker Discovery of Non-traumatic Osteonecrosis of the Femoral Head

Exploratory Analysis of Serum Exosome Proteomics in the Pathogenesis and Early Biomarker Discovery of Non-traumatic Osteonecrosis of the Femoral Head | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploratory Analysis of Serum Exosome Proteomics in the Pathogenesis and Early Biomarker Discovery of Non-traumatic Osteonecrosis of the Femoral Head Gang Wang, Boyuan Kang, Qingsong Jia, Shiwei Jin, Zheng Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5150372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: In clinical practice, the treatment of osteonecrosis of the femoral head (ONFH) often lags behind diagnosis, resulting in delayed treatment and high disability rates. Therefore, identifying appropriate biomarkers for early diagnosis is crucial. Exosome-related functions offer promising avenues for understanding the pathogenesis and early diagnosis of non-traumatic osteonecrosis of the femoral head (NONFH). This study employed label-free quantitative proteomics and bioinformatics analysis to explore the molecular mechanisms underlying NONFH and identify potential new biomarkers. Methods: We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) and bioinformatics to analyze exosome proteins in serum samples from early-stage (n=11) and late-stage NONFH patients (n=9), as well as healthy controls (n=10). Our goal was to investigate the molecular mechanisms of NONFH at different stages. We utilized protein interaction networks and topological classification methods to pinpoint key markers in early-stage NONFH and healthy samples. Additionally, we used the Enrichr database to identify drugs that interact with differentially expressed proteins (DEPs). Results: Proteomic analysis revealed 76 differential proteins between early-stage NONFH and healthy controls, 26 between late-stage NONFH and healthy controls, and 60 between late-stage and early-stage NONFH. Early-stage NONFH compared to healthy controls, Early-stage NONFH mechanisms were associated with protein folding, blood coagulation, spliceosome-mediated mRNA splicing, lipid transport, superoxide radical clearance, and cholesterol esterification. Late-stage NONFH compared to healthy controls,Late-stage NONFH was linked to innate immune response, chondrocyte development, I-kappaB kinase/NF-kappaB signaling, and inflammatory responses to antigens. Late-stage NONFH compared to Early-stage NONFH, In late-stage NONFH, DEPs were primarily involved in lipid metabolism and angiotensin response. Notably, NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3 were significantly upregulated in early-stage NONFH, suggesting their potential as biomarkers. The Enrichr database identified artesunate, clindamycin, and disodium selenite as potentially effective therapeutic agents for NONFH. Conclusion: This study offers valuable insights into potential serum biomarkers for early NONFH and elucidates the molecular mechanisms at the plasma exosome protein level across different stages of the disease. These findings provide new perspectives for identifying potential therapeutic targets and advancing early diagnosis strategies. Non-traumatic osteonecrosis of the femoral head Exosomes Proteomics Bioinformatics Molecular mechanisms Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Osteonecrosis of the femoral head (ONFH) is a common and challenging orthopedic condition associated with high disability rates. It arises from various factors that cause venous stasis in the femoral head, impaired or interrupted blood supply and subsequent ischemia and hypoxia within the femoral head. These factors ultimately result in osteocyte apoptosis or necrosis, leading to structural changes or collapse of the femoral head[ 1 ]. ONFH is classified into traumatic osteonecrosis of the femoral head (TONFH) and non-traumatic osteonecrosis of the femoral head (NONFH) based on its etiology. NONFH is commonly caused by high-dose corticosteroid use and alcohol consumption. Epidemiological studies estimate approximately 8.12 million cases of NONFH in China, with a prevalence in men twice that of women and a higher incidence in northern regions compared to the south[ 2 ]. In the United States, the number of NONFH patients is estimated to be between 300,000 and 600,000, with about 20,000 new cases annually[ 3 ]. Early-stage NONFH often lacks significant imaging features, leading to missed or incorrect diagnoses and delayed treatment. Many patients miss the optimal treatment window. By the time imaging abnormalities become apparent, patients are frequently in the mid-to-late stages, with irreversible collapse of the femoral head[ 4 ]. Thus, early diagnosis of NONFH presents a significant challenge, compounded by unclear pathogenesis and treatment that often lags behind diagnosis. Effective early assessment and identification of biomarkers for NONFH are crucial for preventing femoral head collapse and slowing disease progression. Timely intervention and accurate diagnosis are critical for improving outcomes for NONFH patients. Exosomes (Exo) have gained significant attention as non-invasive biomarkers. These vesicles, ranging from 30 to 150 nm in diameter, are released into the extracellular space following the fusion of cytoplasmic membranes with multivesicular bodies[ 5 ]. Exosomes are secreted by various cells including osteoblasts, osteoclasts, and mesenchymal stem cells, and play a key role in cell-to-cell signal transduction and antigen presentation. They contain a diverse array of bioactive molecules, such as nucleic acids, lipids, and proteins[ 6 , 7 ]. Exosomes have been identified as biomarkers in numerous diseases; for instance, distinct miRNA profiles in bronchoalveolar lavage fluid differentiate healthy individuals from asthmatic patients [ 8 ], and serum levels of miR-192 can predict progression to heart failure following acute myocardial infarction[ 9 ]. Thus, exosomes are crucial for intercellular signaling and can have both positive and negative regulatory effects on target cells and organs[ 10 , 11 ]. In the context of NONFH, exosomes have shown promise for enhancing diagnosis and targeted therapy. Serum exosomes have demonstrated high accuracy and sensitivity in diagnosis and in reflecting the pathological stages of NONFH. Research into serum exosomes as biomarkers for NONFH is ongoing. For example, Zhu et al. investigated circulating exosome levels in steroid-induced osteonecrosis of the femoral head and found that healthy donors had higher exosome levels compared to patients with the condition[ 12 ]. They identified specific exosomal proteins and RNA associated with NONFH severity and occurrence. Consequently, the levels of exosomes and specific exosomal proteins and RNA in extracellular fluids like plasma can provide valuable insights into the disease presence and progression. This study evaluates serum exosomal protein expression levels for diagnosing NONFH, with promising results detailed below. 1. Materials and Methods 1.1 Study Design This clinical trial was registered with the Chinese Clinical Trial Registry (ChiCTR) under registration number ChiCTR2200056864 on February 21, 2022. The study protocol received approval from the Ethics Committee of the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine (Approval No.: ZYDEYL [2022] K08). We recruited NONFH patients diagnosed at the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine between March 2022 and May 2023. The experimental group comprised 11 patients with early-stage NONFH and 9 patients with late-stage NONFH. Additionally, serum samples from 10 healthy individuals were included as the control group. All NONFH patients met the clinical diagnostic criteria for osteonecrosis of the femoral head. Informed consent was obtained from all participants or their families prior to sample collection. 1.2 Main Instruments and Reagents High-speed centrifuge (5810R, Eppendorf); Nanoparticle tracking analyzer (NanoSight NS300, Malvern Panalytical); Transmission electron microscope (JEM-1230, JEOL);5200 fully automated chemiluminescence image analysis system (Shanghai Tianren Technology Co., Ltd.); Exosome extraction and purification kit (UR52136, Umibio); Protein gel electrophoresis instrument (EI0001, Thermo Fisher);Lysis solution for exosomal proteins (UR33101, Umibio);BCA Protein Assay Kit (WB0123, Weiao) 1.3 Detection of Blood Biomarkers Five milliliters of whole blood were collected from the peripheral veins of each participant and stored in polypropylene tubes containing EDTA. The samples were then analyzed at the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine. Biomarkers such as total cholesterol (CHO), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were measured using these blood samples. 1.4 Isolation and Characterization of Plasma Exosomes 1.4.1 Exosome Isolation Process Sample Pre-treatment : Plasma samples were transferred to centrifuge tubes and centrifuged at 3,000g for 10 minutes at 4°C to remove cell debris. The supernatant was carefully transferred to new centrifuge tubes. The supernatant was then centrifuged at 10,000g for 20 minutes at 4°C to remove additional impurities, and the resulting supernatant was transferred to another new centrifuge tube. Exosome Extraction : The supernatant was diluted by adding 16 ml of pre-cooled 1× PBS, followed by 4 ml of BPS. The centrifuge tube was sealed and mixed thoroughly using a vortex mixer for 1 minute. The mixture was incubated at 2°C to 8°C for 2 hours. It was then centrifuged at 10,000g for 60 minutes at 4°C. The supernatant was discarded, and the pellet containing exosome particles was resuspended in 0.8 ml of 1× PBS. This resuspension was transferred to a new 1.5 ml centrifuge tube and centrifuged at 12,000g for 2 minutes at 4°C. The supernatant, enriched with exosome particles, was retained. Exosome Purification : The crude exosome preparation was transferred to the upper chamber of the Exosome Purification Filter (EPF column) and centrifuged at 3,000g for 10 minutes at 4°C. The liquid collected at the bottom of the EPF column contained the purified exosome particles. 1.4.2 Transmission Electron Microscopy (TEM) for Exosome Morphology Fixing Exosomes on Copper Grid : The frozen exosomes were thawed and mixed with an equal volume of 4% PFA. 10μl of the exosome solution was placed on a copper grid. The copper grid was immersed in 100μl of PBS for washing and was then placed on 50μl of 1% glutaraldehyde for 5 minutes. It was washed with 100μl of ddH2O for 2 minutes, repeating this process 8 times. Negative Staining and Electron Microscopy : 50 μl of uranyl oxalate stain were added to the grid, which was then placed on 50 μl of methyl cellulose for 10 minutes. Excess fluid was removed, and the grid was dried for 2 minutes under an incandescent lamp. The exosomes were examined under a transmission electron microscope at 80 kV for imaging. 1.4.3 Particle Size Analysis Using Zeta Potential and Size Analyzer Exosomes were diluted with PBS to a concentration of 1 × 10 7 to 1 × 10 9 particles/ml. The diluted sample was injected into the sample cell after checking for air bubbles. The sample cell was then inserted into the instrument, and detection was initiated. The NTA software analyzed the particle motion, measured the size and concentration, and determined the exosome size and concentration. 1.4.4 Western Blot Analysis of Exosomal Marker Proteins Exosomal proteins were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to 0.45μm polyvinylidene fluoride (PVDF) membranes (Scolarbio, Beijing) via a wet transfer method. The membranes were blocked with 5% skim milk for 2 hours. Primary antibodies (anti-CD63, 1:1000; anti-TSG101, 1:500) were added and incubated overnight at 4°C. After washing with 0.1% PBST three times for 10 minutes each, the membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies (Bioworld, BS20241-Y, 1:3000) at room temperature for 1 hour. Following another round of washing with 0.1% PBST three times for 10 minutes each, protein bands were detected using an enhanced chemiluminescence (ECL) detection kit (Shenyang Wanlei Biotechnology Co., Ltd., Shenyang, China) and visualized with the Tanon 5200 automated chemiluminescence imaging analysis system (Tanon Technology Co., Ltd., Shanghai, China). 1.5 Exosomal Protein Extraction and Trypsin Digestion After removing the samples from −80°C storage, they were centrifuged at 12,000g for 15 minutes at 4°C, and the supernatant was transferred to a new centrifuge tube. The supernatant was then filtered through a 0.22μm microporous membrane, and exosomes were isolated using the qEVs kit from IZON according to the manufacturer's instructions, followed by SDS-PAGE. Equal amounts of protein from each sample were subjected to digestion. The volume was adjusted with lysis buffer, and dithiothreitol (DTT) was added to a final concentration of 5 mM, followed by reduction at 56°C for 30 minutes. Iodoacetamide (IAA) was subsequently added to a final concentration of 11 mM and incubated at room temperature in the dark for 15 minutes. Urea was diluted with TEAB to ensure a concentration below 2 M. Trypsin was added at a 1:50 ratio (enzyme, m/m) for overnight digestion, and a second trypsin digestion was performed at a 1:100 ratio (enzyme, m/m) for 4 hours. 1.6 Label-Free Quantitative Proteomics Peptides were dissolved in liquid chromatography mobile phase A and separated using the EASY-nLC 1200 ultra-high-performance liquid chromatography (UHPLC) system. Mobile phase A was an aqueous solution containing 0.1% formic acid and 2% acetonitrile, while mobile phase B contained 0.1% formic acid and 90% acetonitrile. The liquid chromatography gradient was set as follows: 0-22.5 minutes, 6% to 22% B; 22.5-26.5 minutes, 22% to 34% B; 26.5-28.5 minutes, 34% to 80% B; and 28.5-30 minutes, 80% B, with a flow rate of 700 nL/min. After separation by UHPLC, peptides were ionized by the NSI source and analyzed using an Orbitrap Exploris 480 mass spectrometer. The ion source voltage was set to 2300 V, and the FAIMS compensation voltage (CV) was set to −45 V. Both precursor ions and their fragments were detected and analyzed using a high-resolution Orbitrap. The MS1 scan range was set to 350-1400 m/z with a resolution of 60,000, while the MS2 scan range started at 120 m/z with a resolution of 15,000. Data acquisition was performed using a data-independent acquisition (DIA) mode, where peptide ions from multiple consecutive m/z windows were fragmented in the HCD collision cell with 27% collision energy, followed by MS2 analysis. To enhance mass spectrometry efficiency, the automatic gain control (AGC) was set to 1E6, and the maximum injection time was set to 22 ms. 1.7 Bioinformatics Analysis 1.7.1 Data Preprocessing and Differential Protein Identification We first used the "limma" package in R software to analyze differential proteins, applying the criteria of "P < 0.05" to identify DEPs. The "ggplot2" package in R was then employed to generate a volcano plot of the DEPs. 1.7.2 Functional Enrichment Analysis of Robust DEPs In this study, we conducted a comprehensive functional enrichment analysis of the DEPs identified in our dataset to gain insights into their potential biological roles and pathways. We used the DAVID database, a widely utilized bioinformatics resource, to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. 1.7.3 PPI Network Construction and Hub Protein Identification We input the identified DEPs into the STRING database to construct a protein-protein interaction (PPI) network, using a confidence score > 0.4. The network was then visualized with Cytoscape software (version 3.9.1). To identify the top 10 hub proteins, we performed PPI network analysis combined with 11 topological algorithms (Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC)), as well as six centrality measures (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress). 1.8 Protein-Drug Interactions We explored potential interactions between DEPs and various drug molecules using the Enrichr platform. The Drug Signature Database (DSigDB) in Enrichr was utilized to identify these interactions, providing insights into possible therapeutic targets and interventions. 2 Results 2.1 Characteristics of Study Subjects A one-way ANOVA was performed to analyze the clinical characteristics of subjects across healthy, early-stage, and late-stage groups, and their impact on non-traumatic osteonecrosis of the femoral head (NONFH). There were no significant differences in mean age, cholesterol (CHO), triglycerides (TG), LDL cholesterol (LDL-C), or alkaline phosphatase (ALP) among the three groups (p > 0.05). However, a significant difference was observed in the mean HDL cholesterol (HDL-C) levels among the groups (p < 0.05). Post hoc LSD comparisons revealed that HDL-C levels in the early-stage NONFH group differed significantly from those in the healthy group, and there was a significant difference in HDL-C between the late-stage and early-stage NONFH groups. No significant difference in HDL-C was found between the late-stage NONFH group and the healthy group. Table 1 summarizes the demographic and clinical data of the groups Variable health group (n=10) Early NONFH （ n=11 ） Advanced NONFH （ n=9 ） F P Continuous variables mean + SD ANOVA Age 44.80±11.70 53.09±8.68 48.44±11.34 1.627 0.215 CHO 5.12±1.38 4.60±1.55 5.17 ±0.76 0.603 0.554 TG 1.83±1.06 2.15±1.66 1.81±1.18 0.211 0.811 HDL-C 1.41±0.16 1.13±0.31 1.52±0.36 4.961 0.015 LDL-C 3.43±1.09 2.78±1.32 2.93±0.59 1.062 0.360 ALP 75.30±21.45 85.45±32.04 78.22±12.07 0.500 0.612 2.2 Isolation and Characterization of Plasma Exosomes TEM analysis of serum-derived exosomes revealed spherical nanoparticles with diameters ranging from 107.5 to 145 nm, consistent with previous reports(see Figure 3A)[13]. The size distribution of the exosomes was further confirmed using a nanoparticle tracking analyzer (NTA). Results showed that exosomes from the healthy group ranged from 109.6 to 145.1 nm, with an average diameter of 125.16 nm; those from the early-stage group ranged from 111.8 to 133.8 nm, with an average diameter of 125.15 nm; and those from the late-stage group ranged from 107.5 to 144.8 nm, with an average diameter of 123.43 nm (see Figure 2). The morphology and size of the extracellular vesicles isolated in this study are consistent with the definition of exosomes. Additionally, Western blotting confirmed the presence of exosomal markers CD63, and TSG101 in the isolated exosomes (see Figure 3B). 2.3 Mass Spectrometry Identification and Analysis In this experiment, a total of 1,962 proteins were identified, with 1,672 proteins subjected to quantitative comparison. Differential proteins were screened using a p-value cutoff of < 0.05. Between the early-stage and healthy groups, 76 differentially expressed proteins were identified, including 58 that were upregulated and 18 that were downregulated. Between the late-stage and healthy groups, 26 differentially expressed proteins were found, with 20 upregulated and 6 downregulated. Between the late-stage and early-stage groups, 60 differentially expressed proteins were identified, with 17 that were upregulated and 43 that were downregulated (see Figure 4). 2.4 GO and KEGG Enrichment Analysis Between Groups 2.4.1 Early-Stage Group vs. Healthy Group We performed GO and KEGG enrichment analyses on 76 differentially expressed proteins using the DAVID platform, focusing on three categories: cellular component, molecular function, and biological process. The biological processes were predominantly enriched in protein folding, blood coagulation, mRNA splicing via the spliceosome, lipid transport, superoxide radical removal, and positive regulation of cholesterol esterification. The cellular components were mainly associated with the cell membrane, extracellular exosome, extracellular space, endoplasmic reticulum lumen, and secretory granule lumen. The molecular functions primarily involved protein binding, RNA binding, actin binding, and superoxide dismutase activity. KEGG pathway analysis revealed significant enrichment in base excision repair, oxidative phosphorylation, nitrogen metabolism, ribosome biogenesis in eukaryotes, the spliceosome, and collecting duct acid secretion (see Figure 5A). 2.4.2 Late-Stage Group vs. Healthy Group We performed GO and KEGG enrichment analyses on 26 differentially expressed proteins using the DAVID platform, focusing on cellular components, molecular functions, and biological processes. Biological processes were primarily related to the innate immune response, recognition of apoptotic cells, complement activation via the lectin pathway, central nervous system development, inflammatory response to antigenic stimulus, positive regulation of I-kappaB kinase/NF-kappaB signaling, and chondral condensation. Cellular components included the extracellular region, extracellular space, extracellular exosome, phagocytic vesicle membrane, and perineuronal net. Molecular functions mainly involved serine hydrolase activity, antigen binding, cytokine activity, and serine-type endopeptidase activity. KEGG pathways were enriched in rheumatoid arthritis, Staphylococcus aureus infection, and alcoholic liver disease (see Figure 5B). 2.4.3 Late-Stage Group vs. Early-Stage Group We analyzed 60 differentially expressed proteins for GO and KEGG enrichment using the DAVID platform, focusing on cellular components, molecular functions, and biological processes. Biological processes were primarily involved in cytoplasmic translation, zymogen activation, acute-phase response, RNA splicing, very-low-density lipoprotein particle remodeling, lipid transport, response to angiotensin, positive regulation of interleukin-1 production, and angiotensin-activated signaling pathways. Cellular components mainly included extracellular exosome, focal adhesion, blood microparticle, cytosolic ribosome, extracellular region, ribosome, and cytosol. Molecular functions involved RNA binding, structural constituent of ribosome, cadherin binding, complement component C1q complex binding, and heparin binding. KEGG pathways were enriched in Coronavirus disease - COVID-19, ribosome, diabetic cardiomyopathy, and spliceosome (see Figure 5C). 2.5 Protein-Protein Interaction Network and Identification of Key Genes To identify biomarkers for early-stage non-traumatic osteonecrosis of the femoral head (NONFH), we conducted a protein-protein interaction (PPI) network analysis using the STRING platform(see Figure 6A). This analysis focused on differentially expressed proteins between the early-stage NONFH group and the healthy group, resulting in a PPI network consisting of 75 nodes and 91 edges. The data in TSV format were imported into Cytoscape, and the cytoHubba plugin was employed to perform 11 different topological analyses, identifying the top 10 key proteins for each method(see Figure 6B). Proteins that appeared in more than six of these analyses were selected as key proteins for further investigation. These key proteins included: T-complex protein 1 subunit eta (CCT7), Large ribosomal subunit protein uL30 (RPL7), Large ribosomal subunit protein uL3 (RPL3), T-complex protein 1 subunit beta (CCT2), Proteasome subunit alpha type-6 (PSMA6), Superoxide dismutase [Cu-Zn] (SOD1), Small nuclear ribonucleoprotein Sm D2 (SNRPD2), T-complex protein 1 subunit gamma (CCT3), and Nucleolar protein 58 (NOP58). Analysis of the expression levels of these ten key proteins revealed that they were significantly upregulated in the early-stage NONFH group compared to the healthy group. 2.6 Gene-Drug Interactions We utilized the Enrichr platform to investigate potential interactions between DEPs and various drugs, employing the DSigDB database. The top 10 ranked drugs, based on p-values, are illustrated in the figure(see Figure 6C). Discussion In this study, we performed a label-free quantitative proteomic analysis of exosomal proteins from patients with NONFH at various stages to uncover potential molecular mechanisms and biomarkers associated with the condition. Initially, we identified DEPs based on specific criteria. These proteins were then analyzed using GO enrichment analysis via the DAVID database, revealing distinct biological processes linked to NONFH at different stages. In the early stage of NONFH, DEPs were predominantly involved in coagulation, lipid transport, and scavenging of superoxide radicals compared to the healthy control group. In late-stage NONFH, DEPs were associated with immune responses, chondrocyte development, and inflammatory processes. Comparison between late and early stages highlighted DEPs involved mainly in lipid metabolism and responses to angiotensin. Notably upregulated proteins in early-stage NONFH included NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3, which may serve as novel biomarkers for NONFH. Additionally, using the Enrichr database, we identified potential therapeutic drugs such as artemether, clindamycin, and selenite, which may offer significant therapeutic benefits for treating NONFH. Among the differentially expressed proteins identified between early-stage NONFH patients and healthy controls, biological functions were primarily enriched in blood coagulation, lipid transport, and scavenging of superoxide radicals. Elevated intraosseous pressure, a major factor in femoral head necrosis and collapse, is largely driven by venous stasis due to blood coagulation[14,15]. In the early stage of osteonecrosis, venous return obstruction and microcirculation stasis occur, leading to impaired blood supply to the femoral head and accelerated disease progression. Exosomal lipid transport, involving the transfer of lipids such as cholesterol and fatty acids between cells, can impact inflammation, immunity, and metabolism[16,17,18]. Both corticosteroids and alcohol are known to induce osteocyte death by disrupting lipid metabolism. For example, high-dose steroid administration results in lipid accumulation in osteocytes, with expanding lipid droplets causing membrane integrity disruption and cell death [19]. In early-stage NONFH patients, proteins related to lipid transport, such as APOA4, CD36, and APOL1, were downregulated, indicating impaired exosomal lipid transport function. Superoxide radicals, reactive oxygen species generated in the body, can cause lipid peroxidation and accelerate aging[20]. In NONFH, oxidative stress can induce osteocyte apoptosis, particularly in cases of steroid-induced osteonecrosis[21]. Thus, suppressing oxidative stress is crucial for treating NONFH. Research suggests that mesenchymal stem cells, neural progenitor cells, and astrocytes can resist oxidative damage[22, 23]. Notably, exosomes derived from these cells also show resistance to oxidative damage. For instance, exosomes from mesenchymal stem cells can reduce oxidative stress and activate the PI3K/Akt pathway, enhancing cardiac health and preventing myocardial ischemia[24]. Similarly, exosomes from adipose stem cells can alleviate lipopolysaccharide-induced reactive oxygen species accumulation and decrease the expression of inflammatory cytokines IL-1β, TNF-α, and IL-6 in macrophages[25] . Exosomes from human umbilical cord mesenchymal stem cells can prevent cisplatin-induced renal oxidative stress and apoptosis[26]. Compared to healthy controls, early-stage NONFH patients exhibited increased expression of proteins related to scavenging superoxide radicals, suggesting that exosomes in NONFH patient serum may counteract oxidative damage by upregulating these proteins. Thus, the pathogenesis of early-stage NONFH is characterized by venous stasis, lipid accumulation, and oxidative stress response. Among the differentially expressed proteins identified between late-stage NONFH patients and healthy controls, biological functions were predominantly enriched in the innate immune response, chondrocyte development, positive regulation of the I-kappaB kinase/NF-kappaB signaling pathway, and inflammatory response to antigenic stimuli. In late-stage NONFH patients, the collapse of the femoral head alters joint stress and exacerbates degenerative changes in the articular cartilage, typically affecting the subchondral calcified layer and deep cartilage first, which eventually leads to the destruction of cartilage integrity. Studies have shown that exosomes derived from mesenchymal stem cells (MSCs) can facilitate cartilage repair and regeneration by modulating immune responses, reducing apoptosis, and promoting cell proliferation[27]. In this study, we observed that proteins associated with cartilage repair were upregulated in late-stage NONFH. This finding not only supports the cartilage reparative effects of exosomes at the protein level but also elucidates the pathological changes in cartilage damage observed in late-stage NONFH patients. During the progression of femoral head necrosis, hormones can activate the TLR4 signaling pathway, disrupt immune responses, and contribute to the development of NONFH[28]. This highlights the critical role of immune responses in the pathogenesis of NONFH. Fang et al. found that differential genes in exosomes derived from bone marrow mesenchymal stem cells were predominantly enriched in immune responses when comparing healthy mice with those having steroid-induced osteonecrosis of the femoral head[29]. This observation reflects the immune response-related pathological changes in late-stage NONFH. Our study also revealed that differential proteins were enriched in the NF-kappaB signaling pathway and inflammation responses. Anderson et al. suggested that exosomes secreted by mesenchymal stem cells can treat ischemic diseases by modulating the body's ischemic state, with proteomic analyses showing that exosome treatment for ischemic conditions primarily involves the NF-kappaB signaling pathway[30]. Other research indicated that exosomes derived from bone marrow mesenchymal stem cells can inhibit PEG2 expression, thereby reducing macrophage activation, inducing macrophage differentiation into an anti-inflammatory phenotype, and decreasing inflammatory response activation[31]. Additionally, we performed enrichment analysis on the differentially expressed proteins between late-stage and early-stage NONFH patients to elucidate the molecular mechanisms underlying disease progression. The biological functions of these differential proteins were primarily enriched in lipid transport, angiotensin-activated signaling pathways, and positive regulation of interleukin-1. Studies have demonstrated that corticosteroid administration in chickens leads to lipid metabolism disorders within one week, including adipocyte hypertrophy and triglyceride vesicle formation[32]. The pathology of late-stage NONFH is associated with fat infiltration, which disrupts microcirculation within the bone, reducing blood flow and altering the structure of the femoral head[19]. Glucocorticoids can activate the renin-angiotensin-aldosterone system (RAAS) in bone tissue, with angiotensin II (Ang II) serving as a key effector peptide in the ACE1/Ang II/AT1 receptor cascade. Ang II activates AT1 receptors, leading to increased expression of TNF-α and IL-6 [33], which accelerates bone resorption[34]. Therefore, the regulation of vascular function and lipid metabolism disorders are crucial factors influencing the pathological progression of late-stage osteonecrosis of the femoral head. We conducted a protein-protein interaction (PPI) network analysis on the differentially expressed proteins (DEPs) between early-stage NONFH patients and healthy controls. Utilizing 11 topological classification methods, we identified key proteins including NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3. NOP58 is a precursor of the small nucleolar ribonucleoprotein subunit and plays a vital role in assembling the SSU processome in the nucleolus. This process involves numerous ribosome biogenesis factors, RNA chaperones, and ribosomal proteins, which work together to facilitate RNA folding, modification, rearrangement, cleavage, and targeted degradation of pre-ribosomal RNA by the exosome[35]. SF3B1, a critical subunit of the U2 small nuclear ribonucleoprotein (snRNP), is essential for the proper assembly of the spliceosome at the branch point sequence[36]. Research has primarily focused on SF3B1's role in tumorigenesis and myelodysplastic syndromes, with recurrent mutations in SF3B1 being linked to human cancers and affecting patient prognosis[37]. CCT subunits are components of the T-complex protein (TRiC), a molecular chaperone complex that assists in protein folding during ATP hydrolysis[38]. Proteomic analyses of osteoblasts have shown that CCT2 is highly expressed in mineralized osteoblasts and may play a role in bone formation[39]. CCT3 has been associated with the diagnosis and prognosis of various cancers, including liver, bladder, and cervical cancers[40,41,42]. PSMA6 is a regulatory subunit of the proteasome, responsible for degrading misfolded or damaged proteins and proteins no longer needed by the cell, thus maintaining protein homeostasis[43]. SNRPD2 is a component of the minor spliceosome involved in splicing U12-type introns in pre-mRNA[44]. SOD1 is a crucial antioxidant enzyme that neutralizes free radicals within cells, which can be harmful to biological systems[45]. The identification of these proteins in the PPI network of NONFH underscores their potential as novel diagnostic and therapeutic biomarkers. Exosomes are increasingly recognized for their potential in drug delivery systems due to their inherent ability to transport molecules between cells. They can encapsulate therapeutic agents and target specific tissues, positioning them as promising vehicles for drug delivery. Our Enrichr analysis identified artesunate, clindamycin, and disodium selenite as potential therapeutic agents for treating NONFH. Artesunate, a semisynthetic derivative of artemisinin, is known for its anti-inflammatory and immunosuppressive effects, making it effective against autoimmune and inflammatory diseases[46]. It operates by inhibiting the PLCγ1-Ca2+-calcineurin-NFATc1 pathway, which reduces RANKL-induced osteoclastogenesis and bone loss. This mechanism suggests that artesunate could mitigate bone resorption in NONFH by targeting inflammatory pathways[47]. Clindamycin, primarily an antibiotic, has shown potential in promoting osteoblast growth and differentiation at a concentration of 150 mg/mL. This effect is linked to genes associated with TGF-β signaling—such as TGF-β1, TGF-βR1, TGF-βR2, TGF-βR3, and VEGF—as well as RUNX-2, Col-1, OSX, OSC, BMP-2, BMP-7, and ALP. These findings suggest that clindamycin could aid in bone formation and repair processes in NONFH[48]. Disodium selenite, an oral selenium supplement, has proven effective in enhancing the antioxidant capacity of stem cells. At safe doses, it protects mesenchymal stem cells from oxidative stress-induced inhibition of osteoblast differentiation by inhibiting oxidative stress and ERK activation. This protective effect could benefit NONFH patients by preserving bone cell function and promoting bone regeneration[49]. Thus, the potential application of artesunate, clindamycin, and disodium selenite in treating NONFH underscores the versatility of exosome-based drug delivery systems. These drugs offer targeted therapeutic benefits by modulating key pathways involved in bone metabolism and inflammation. Future research should focus on optimizing exosome formulations of these drugs and conducting clinical trials to assess their efficacy and safety in treating NONFH. This study investigated the pathogenesis of NONFH at various stages and identified potential early biomarkers through serum exosome proteomics. However, there were several limitations. First, the small sample size could impact the statistical significance and generalizability of the results. Second, the lack of experimental validation means that further studies are required to confirm the biological relevance of the findings. Future research should address these limitations by incorporating larger sample sizes and experimental validation to enhance the reliability and practical applicability of the insights into NONFH pathogenesis and potential biomarkers. Conclusion This study analyzed clinical data from patients with NONFH at various stages and employed bioinformatics approaches to investigate differentially expressed proteins, elucidating the pathogenesis of NONFH. We constructed a PPI network and identified key genes, including NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3. Drug predictions for these core genes suggested that artesunate, clindamycin, and disodium selenite could be promising candidates for treating NONFH. Our research advances the understanding of the molecular mechanisms associated with different stages of NONFH and identifies potential targets for diagnosis and treatment, especially from the perspective of exosome proteomics. Abbreviations NONFH Non-traumatic osteonecrosis of the femoral head ONFH Osteonecrosis of the femoral head TONFH traumatic osteonecrosis of the femoral head CHO cholesterol TG triglycerides LDL-C LDL cholesterol HDL-C HDL cholesterol ALP alkaline phosphatase TEM Transmission Electron Microscopy GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes DEPs Differently expressed proteins PPI protein-protein interaction network Declarations Acknowledgements: Not applicable Author Contributions: X.X. and K.B. conceived the experiment.G.W., S.J. and Q.J. completed the experiment.G.W.and Z.Z. performed the data analysis. G.W and X.X. wrote the original draft. All authors reviewed and approved the final manuscript. Funding: This research was supported by the National Natural Science Foundation of China (grant. no.8237153419); the Young Qi Huang Scholar Support Program of the National Administration of Traditional Chinese Medicine (No. Guo Zhong Yi Yao Ren Jiao Fa [2020] 7); the Key Research and Development Program of Heilongjiang Province (GZ20210136, GA21C006); the Traditional Chinese Medicine Research Project of Heilongjiang Province (ZHY2023-099); and the Key Laboratory of Basic and Clinical Research on Osteonecrosis of Heilongjiang Province. Data availability : No datasets were generated or analysed during the current study. Ethical statement and consent: The research protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine (Approval No.: ZYDEYL [2022] K08).The written consent of all participants or their legal guardians has been obtained. 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Artesunate suppresses RANKL-induced osteoclastogenesis through inhibition of PLCγ1-Ca2+-NFATc1 signaling pathway and prevents ovariectomy-induced bone loss. Biochem Pharmacol. 2017 Jan 15;124:57–68. Manzano-Moreno FJ, Gónzalez-Acedo A, de Luna-Bertos E, García-Recio E, Ruiz C, Reyes-Botella C. Effect of amoxicillin and clindamycin on the gene expression of markers involved in osteoblast physiology. J Dent Sci. 2024 Apr;19(2):990–7. Ebert R, Ulmer M, Zeck S, Meissner-Weigl J, Schneider D, Stopper H, et al. Selenium supplementation restores the antioxidative capacity and prevents cell damage in bone marrow stromal cells in vitro. Stem Cells. 2006 May;24(5):1226–35. Additional Declarations No competing interests reported. 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09:56:26","extension":"rar","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1260321280,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.rar","url":"https://assets-eu.researchsquare.com/files/rs-5150372/v1/a8ccc5e04d8438eb000d7a72.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploratory Analysis of Serum Exosome Proteomics in the Pathogenesis and Early Biomarker Discovery of Non-traumatic Osteonecrosis of the Femoral Head","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteonecrosis of the femoral head (ONFH) is a common and challenging orthopedic condition associated with high disability rates. It arises from various factors that cause venous stasis in the femoral head, impaired or interrupted blood supply and subsequent ischemia and hypoxia within the femoral head. These factors ultimately result in osteocyte apoptosis or necrosis, leading to structural changes or collapse of the femoral head[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. ONFH is classified into traumatic osteonecrosis of the femoral head (TONFH) and non-traumatic osteonecrosis of the femoral head (NONFH) based on its etiology. NONFH is commonly caused by high-dose corticosteroid use and alcohol consumption.\u003c/p\u003e \u003cp\u003eEpidemiological studies estimate approximately 8.12\u0026nbsp;million cases of NONFH in China, with a prevalence in men twice that of women and a higher incidence in northern regions compared to the south[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the United States, the number of NONFH patients is estimated to be between 300,000 and 600,000, with about 20,000 new cases annually[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Early-stage NONFH often lacks significant imaging features, leading to missed or incorrect diagnoses and delayed treatment. Many patients miss the optimal treatment window. By the time imaging abnormalities become apparent, patients are frequently in the mid-to-late stages, with irreversible collapse of the femoral head[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, early diagnosis of NONFH presents a significant challenge, compounded by unclear pathogenesis and treatment that often lags behind diagnosis. Effective early assessment and identification of biomarkers for NONFH are crucial for preventing femoral head collapse and slowing disease progression. Timely intervention and accurate diagnosis are critical for improving outcomes for NONFH patients.\u003c/p\u003e \u003cp\u003eExosomes (Exo) have gained significant attention as non-invasive biomarkers. These vesicles, ranging from 30 to 150 nm in diameter, are released into the extracellular space following the fusion of cytoplasmic membranes with multivesicular bodies[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Exosomes are secreted by various cells including osteoblasts, osteoclasts, and mesenchymal stem cells, and play a key role in cell-to-cell signal transduction and antigen presentation. They contain a diverse array of bioactive molecules, such as nucleic acids, lipids, and proteins[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Exosomes have been identified as biomarkers in numerous diseases; for instance, distinct miRNA profiles in bronchoalveolar lavage fluid differentiate healthy individuals from asthmatic patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and serum levels of miR-192 can predict progression to heart failure following acute myocardial infarction[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, exosomes are crucial for intercellular signaling and can have both positive and negative regulatory effects on target cells and organs[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of NONFH, exosomes have shown promise for enhancing diagnosis and targeted therapy. Serum exosomes have demonstrated high accuracy and sensitivity in diagnosis and in reflecting the pathological stages of NONFH. Research into serum exosomes as biomarkers for NONFH is ongoing. For example, Zhu et al. investigated circulating exosome levels in steroid-induced osteonecrosis of the femoral head and found that healthy donors had higher exosome levels compared to patients with the condition[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. They identified specific exosomal proteins and RNA associated with NONFH severity and occurrence. Consequently, the levels of exosomes and specific exosomal proteins and RNA in extracellular fluids like plasma can provide valuable insights into the disease presence and progression. This study evaluates serum exosomal protein expression levels for diagnosing NONFH, with promising results detailed below.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis clinical trial was registered with the Chinese Clinical Trial Registry (ChiCTR) under registration number ChiCTR2200056864 on February 21, 2022. The study protocol received approval from the Ethics Committee of the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine (Approval No.: ZYDEYL [2022] K08).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe recruited NONFH patients diagnosed at the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine between March 2022 and May 2023. The experimental group comprised 11 patients with early-stage NONFH and 9 patients with late-stage NONFH. Additionally, serum samples from 10 healthy individuals were included as the control group. All NONFH patients met the clinical diagnostic criteria for osteonecrosis of the femoral head. Informed consent was obtained from all participants or their families prior to sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Main Instruments and Reagents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-speed centrifuge (5810R, Eppendorf); Nanoparticle tracking analyzer (NanoSight NS300, Malvern Panalytical); Transmission electron microscope (JEM-1230, JEOL);5200 fully automated chemiluminescence image analysis system (Shanghai Tianren Technology Co., Ltd.); Exosome extraction and purification kit (UR52136, Umibio); Protein gel electrophoresis instrument (EI0001, Thermo Fisher);Lysis solution for exosomal proteins (UR33101, Umibio);BCA Protein Assay Kit (WB0123, Weiao)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Detection of Blood Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive milliliters of whole blood were collected from the peripheral veins of each participant and stored in polypropylene tubes containing EDTA. The samples were then analyzed at the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine. Biomarkers such as total cholesterol (CHO), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were measured using these blood samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Isolation and Characterization of Plasma Exosomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4.1 Exosome Isolation Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample Pre-treatment\u003cstrong\u003e:\u003c/strong\u003e Plasma samples were transferred to centrifuge tubes and centrifuged at 3,000g for 10 minutes at 4\u0026deg;C to remove cell debris. The supernatant was carefully transferred to new centrifuge tubes. The supernatant was then centrifuged at 10,000g for 20 minutes at 4\u0026deg;C to remove additional impurities, and the resulting supernatant was transferred to another new centrifuge tube.\u003c/p\u003e\n\u003cp\u003eExosome Extraction\u003cstrong\u003e:\u003c/strong\u003e The supernatant was diluted by adding 16 ml of pre-cooled 1\u0026times; PBS, followed by 4 ml of BPS. The centrifuge tube was sealed and mixed thoroughly using a vortex mixer for 1 minute. The mixture was incubated at 2\u0026deg;C to 8\u0026deg;C for 2 hours. It was then centrifuged at 10,000g for 60 minutes at 4\u0026deg;C. The supernatant was discarded, and the pellet containing exosome particles was resuspended in 0.8 ml of 1\u0026times; PBS. This resuspension was transferred to a new 1.5 ml centrifuge tube and centrifuged at 12,000g for 2 minutes at 4\u0026deg;C. The supernatant, enriched with exosome particles, was retained.\u003c/p\u003e\n\u003cp\u003eExosome Purification\u003cstrong\u003e:\u003c/strong\u003e The crude exosome preparation was transferred to the upper chamber of the Exosome Purification Filter (EPF column) and centrifuged at 3,000g for 10 minutes at 4\u0026deg;C. The liquid collected at the bottom of the EPF column contained the purified exosome particles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4.2 Transmission Electron Microscopy (TEM) for Exosome Morphology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFixing Exosomes on Copper Grid\u003cstrong\u003e:\u003c/strong\u003e The frozen exosomes were thawed and mixed with an equal volume of 4% PFA. 10\u0026mu;l of the exosome solution was placed on a copper grid. The copper grid was immersed in 100\u0026mu;l of PBS for washing and was then placed on 50\u0026mu;l of 1% glutaraldehyde for 5 minutes. It was washed with 100\u0026mu;l of ddH2O for 2 minutes, repeating this process 8 times.\u003c/p\u003e\n\u003cp\u003eNegative Staining and Electron Microscopy\u003cstrong\u003e:\u003c/strong\u003e 50 \u0026mu;l of uranyl oxalate stain were added to the grid, which was then placed on 50 \u0026mu;l of methyl cellulose for 10 minutes. Excess fluid was removed, and the grid was dried for 2 minutes under an incandescent lamp. The exosomes were examined under a transmission electron microscope at 80 kV for imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4.3 Particle Size Analysis Using Zeta Potential and Size Analyzer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExosomes were diluted with PBS to a concentration of 1 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e to 1 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e particles/ml. The diluted sample was injected into the sample cell after checking for air bubbles. The sample cell was then inserted into the instrument, and detection was initiated. The NTA software analyzed the particle motion, measured the size and concentration, and determined the exosome size and concentration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4.4 Western Blot Analysis of Exosomal Marker Proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExosomal proteins were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to 0.45\u0026mu;m polyvinylidene fluoride (PVDF) membranes (Scolarbio, Beijing) via a wet transfer method. The membranes were blocked with 5% skim milk for 2 hours. Primary antibodies (anti-CD63, 1:1000; anti-TSG101, 1:500) were added and incubated overnight at 4\u0026deg;C. After washing with 0.1% PBST three times for 10 minutes each, the membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies (Bioworld, BS20241-Y, 1:3000) at room temperature for 1 hour. Following another round of washing with 0.1% PBST three times for 10 minutes each, protein bands were detected using an enhanced chemiluminescence (ECL) detection kit (Shenyang Wanlei Biotechnology Co., Ltd., Shenyang, China) and visualized with the Tanon 5200 automated chemiluminescence imaging analysis system (Tanon Technology Co., Ltd., Shanghai, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Exosomal Protein Extraction and Trypsin Digestion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter removing the samples from \u0026minus;80\u0026deg;C storage, they were centrifuged at 12,000g for 15 minutes at 4\u0026deg;C, and the supernatant was transferred to a new centrifuge tube. The supernatant was then filtered through a 0.22\u0026mu;m microporous membrane, and exosomes were isolated using the qEVs kit from IZON according to the manufacturer\u0026apos;s instructions, followed by SDS-PAGE. Equal amounts of protein from each sample were subjected to digestion. The volume was adjusted with lysis buffer, and dithiothreitol (DTT) was added to a final concentration of 5 mM, followed by reduction at 56\u0026deg;C for 30 minutes. Iodoacetamide (IAA) was subsequently added to a final concentration of 11 mM and incubated at room temperature in the dark for 15 minutes. Urea was diluted with TEAB to ensure a concentration below 2 M. Trypsin was added at a 1:50 ratio (enzyme, m/m) for overnight digestion, and a second trypsin digestion was performed at a 1:100 ratio (enzyme, m/m) for 4 hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6 Label-Free Quantitative Proteomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeptides were dissolved in liquid chromatography mobile phase A and separated using the EASY-nLC 1200 ultra-high-performance liquid chromatography (UHPLC) system. Mobile phase A was an aqueous solution containing 0.1% formic acid and 2% acetonitrile, while mobile phase B contained 0.1% formic acid and 90% acetonitrile. The liquid chromatography gradient was set as follows: 0-22.5 minutes, 6% to 22% B; 22.5-26.5 minutes, 22% to 34% B; 26.5-28.5 minutes, 34% to 80% B; and 28.5-30 minutes, 80% B, with a flow rate of 700 nL/min. After separation by UHPLC, peptides were ionized by the NSI source and analyzed using an Orbitrap Exploris 480 mass spectrometer. The ion source voltage was set to 2300 V, and the FAIMS compensation voltage (CV) was set to \u0026minus;45 V. Both precursor ions and their fragments were detected and analyzed using a high-resolution Orbitrap. The MS1 scan range was set to 350-1400 m/z with a resolution of 60,000, while the MS2 scan range started at 120 m/z with a resolution of 15,000. Data acquisition was performed using a data-independent acquisition (DIA) mode, where peptide ions from multiple consecutive m/z windows were fragmented in the HCD collision cell with 27% collision energy, followed by MS2 analysis. To enhance mass spectrometry efficiency, the automatic gain control (AGC) was set to 1E6, and the maximum injection time was set to 22 ms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7 Bioinformatics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7.1 Data Preprocessing and Differential Protein Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first used the \u0026quot;limma\u0026quot; package in R software to analyze differential proteins, applying the criteria of \u0026quot;P \u0026lt; 0.05\u0026quot; to identify DEPs. The \u0026quot;ggplot2\u0026quot; package in R was then employed to generate a volcano plot of the DEPs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7.2 Functional Enrichment Analysis of Robust DEPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we conducted a comprehensive functional enrichment analysis of the DEPs identified in our dataset to gain insights into their potential biological roles and pathways. We used the DAVID database, a widely utilized bioinformatics resource, to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7.3 PPI Network Construction and Hub Protein Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe input the identified DEPs into the STRING database to construct a protein-protein interaction (PPI) network, using a confidence score \u0026gt; 0.4. The network was then visualized with Cytoscape software (version 3.9.1). To identify the top 10 hub proteins, we performed PPI network analysis combined with 11 topological algorithms (Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC)), as well as six centrality measures (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.8 Protein-Drug Interactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe explored potential interactions between DEPs and various drug molecules using the Enrichr platform. The Drug Signature Database (DSigDB) in Enrichr was utilized to identify these interactions, providing insights into possible therapeutic targets and interventions.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Characteristics of Study Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA one-way ANOVA was performed to analyze the clinical characteristics of subjects across healthy, early-stage, and late-stage groups, and their impact on non-traumatic osteonecrosis of the femoral head (NONFH). There were no significant differences in mean age, cholesterol (CHO), triglycerides (TG), LDL cholesterol (LDL-C), or alkaline phosphatase (ALP) among the three groups (p \u0026gt; 0.05). However, a significant difference was observed in the mean HDL cholesterol (HDL-C) levels among the groups (p \u0026lt; 0.05). Post hoc LSD comparisons revealed that HDL-C levels in the early-stage NONFH group differed significantly from those in the healthy group, and there was a significant difference in HDL-C between the late-stage and early-stage NONFH groups. No significant difference in HDL-C was found between the late-stage NONFH group and the healthy group.\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes the demographic and clinical data of the groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehealth group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly NONFH\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=11\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdvanced NONFH\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=9\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 647px;\"\u003e\n \u003cp\u003eContinuous variables mean + SD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e44.80\u0026plusmn;11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e53.09\u0026plusmn;8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e48.44\u0026plusmn;11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eCHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e5.12\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e4.60\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5.17 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1.83\u0026plusmn;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2.15\u0026plusmn;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1.81\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1.41\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1.52\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e3.43\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2.78\u0026plusmn;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2.93\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e75.30\u0026plusmn;21.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e85.45\u0026plusmn;32.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e78.22\u0026plusmn;12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Isolation and Characterization of Plasma Exosomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTEM analysis of serum-derived exosomes revealed spherical nanoparticles with diameters ranging from 107.5 to 145 nm, consistent with previous reports(see Figure 3A)[13]. The size distribution of the exosomes was further confirmed using a nanoparticle tracking analyzer (NTA). Results showed that exosomes from the healthy group ranged from 109.6 to 145.1 nm, with an average diameter of 125.16 nm; those from the early-stage group ranged from 111.8 to 133.8 nm, with an average diameter of 125.15 nm; and those from the late-stage group ranged from 107.5 to 144.8 nm, with an average diameter of 123.43 nm (see Figure 2). The morphology and size of the extracellular vesicles isolated in this study are consistent with the definition of exosomes. Additionally, Western blotting confirmed the presence of exosomal markers \u0026nbsp;CD63, and TSG101 in the isolated exosomes (see Figure 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Mass Spectrometry Identification and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this experiment, a total of 1,962 proteins were identified, with 1,672 proteins subjected to quantitative comparison. Differential proteins were screened using a p-value cutoff of \u0026lt; 0.05. Between the early-stage and healthy groups, 76 differentially expressed proteins were identified, including 58 that were upregulated and 18 that were downregulated. Between the late-stage and healthy groups, 26 differentially expressed proteins were found, with 20 upregulated and 6 downregulated. Between the late-stage and early-stage groups, 60 differentially expressed proteins were identified, with 17 that were upregulated and 43 that were downregulated (see Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 GO and KEGG Enrichment Analysis Between Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1 Early-Stage Group vs. Healthy Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed GO and KEGG enrichment analyses on 76 differentially expressed proteins using the DAVID platform, focusing on three categories: cellular component, molecular function, and biological process. The biological processes were predominantly enriched in protein folding, blood coagulation, mRNA splicing via the spliceosome, lipid transport, superoxide radical removal, and positive regulation of cholesterol esterification. The cellular components were mainly associated with the cell membrane, extracellular exosome, extracellular space, endoplasmic reticulum lumen, and secretory granule lumen. The molecular functions primarily involved protein binding, RNA binding, actin binding, and superoxide dismutase activity. KEGG pathway analysis revealed significant enrichment in base excision repair, oxidative phosphorylation, nitrogen metabolism, ribosome biogenesis in eukaryotes, the spliceosome, and collecting duct acid secretion\u0026nbsp;(see Figure 5A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2 Late-Stage Group vs. Healthy Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed GO and KEGG enrichment analyses on 26 differentially expressed proteins using the DAVID platform, focusing on cellular components, molecular functions, and biological processes. Biological processes were primarily related to the innate immune response, recognition of apoptotic cells, complement activation via the lectin pathway, central nervous system development, inflammatory response to antigenic stimulus, positive regulation of I-kappaB kinase/NF-kappaB signaling, and chondral condensation. Cellular components included the extracellular region, extracellular space, extracellular exosome, phagocytic vesicle membrane, and perineuronal net. Molecular functions mainly involved serine hydrolase activity, antigen binding, cytokine activity, and serine-type endopeptidase activity. KEGG pathways were enriched in rheumatoid arthritis, Staphylococcus aureus infection, and alcoholic liver disease\u0026nbsp;(see Figure 5B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.3 Late-Stage Group vs. Early-Stage Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed 60 differentially expressed proteins for GO and KEGG enrichment using the DAVID platform, focusing on cellular components, molecular functions, and biological processes. Biological processes were primarily involved in cytoplasmic translation, zymogen activation, acute-phase response, RNA splicing, very-low-density lipoprotein particle remodeling, lipid transport, response to angiotensin, positive regulation of interleukin-1 production, and angiotensin-activated signaling pathways. Cellular components mainly included extracellular exosome, focal adhesion, blood microparticle, cytosolic ribosome, extracellular region, ribosome, and cytosol. Molecular functions involved RNA binding, structural constituent of ribosome, cadherin binding, complement component C1q complex binding, and heparin binding. KEGG pathways were enriched in Coronavirus disease - COVID-19, ribosome, diabetic cardiomyopathy, and spliceosome (see Figure 5C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Protein-Protein Interaction Network and Identification of Key Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify biomarkers for early-stage non-traumatic osteonecrosis of the femoral head (NONFH), we conducted a protein-protein interaction (PPI) network analysis using the STRING platform(see Figure 6A). This analysis focused on differentially expressed proteins between the early-stage NONFH group and the healthy group, resulting in a PPI network consisting of 75 nodes and 91 edges. The data in TSV format were imported into Cytoscape, and the cytoHubba plugin was employed to perform 11 different topological analyses, identifying the top 10 key proteins for each method(see Figure 6B). Proteins that appeared in more than six of these analyses were selected as key proteins for further investigation. These key proteins included: T-complex protein 1 subunit eta (CCT7), Large ribosomal subunit protein uL30 (RPL7), Large ribosomal subunit protein uL3 (RPL3), T-complex protein 1 subunit beta (CCT2), Proteasome subunit alpha type-6 (PSMA6), Superoxide dismutase [Cu-Zn] (SOD1), Small nuclear ribonucleoprotein Sm D2 (SNRPD2), T-complex protein 1 subunit gamma (CCT3), and Nucleolar protein 58 (NOP58). Analysis of the expression levels of these ten key proteins revealed that they were significantly upregulated in the early-stage NONFH group compared to the healthy group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Gene-Drug Interactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the Enrichr platform to investigate potential interactions between DEPs and various drugs, employing the DSigDB database. The top 10 ranked drugs, based on p-values, are illustrated in the figure(see Figure 6C).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we performed a label-free quantitative proteomic analysis of exosomal proteins from patients with NONFH at various stages to uncover potential molecular mechanisms and biomarkers associated with the condition. Initially, we identified DEPs based on specific criteria. These proteins were then analyzed using GO enrichment analysis via the DAVID database, revealing distinct biological processes linked to NONFH at different stages.\u003c/p\u003e\n\u003cp\u003eIn the early stage of NONFH, DEPs were predominantly involved in coagulation, lipid transport, and scavenging of superoxide radicals compared to the healthy control group. In late-stage NONFH, DEPs were associated with immune responses, chondrocyte development, and inflammatory processes. Comparison between late and early stages highlighted DEPs involved mainly in lipid metabolism and responses to angiotensin. Notably upregulated proteins in early-stage NONFH included NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3, which may serve as novel biomarkers for NONFH. Additionally, using the Enrichr database, we identified potential therapeutic drugs such as artemether, clindamycin, and selenite, which may offer significant therapeutic benefits for treating NONFH.\u003c/p\u003e\n\u003cp\u003eAmong the differentially expressed proteins identified between early-stage NONFH patients and healthy controls, biological functions were primarily enriched in blood coagulation, lipid transport, and scavenging of superoxide radicals. Elevated intraosseous pressure, a major factor in femoral head necrosis and collapse, is largely driven by venous stasis due to blood coagulation[14,15]. In the early stage of osteonecrosis, venous return obstruction and microcirculation stasis occur, leading to impaired blood supply to the femoral head and accelerated disease progression.\u003c/p\u003e\n\u003cp\u003eExosomal lipid transport, involving the transfer of lipids such as cholesterol and fatty acids between cells, can impact inflammation, immunity, and metabolism[16,17,18]. Both corticosteroids and alcohol are known to induce osteocyte death by disrupting lipid metabolism. For example, high-dose steroid administration results in lipid accumulation in osteocytes, with expanding lipid droplets causing membrane integrity disruption and cell death [19]. In early-stage NONFH patients, proteins related to lipid transport, such as APOA4, CD36, and APOL1, were downregulated, indicating impaired exosomal lipid transport function.\u003c/p\u003e\n\u003cp\u003eSuperoxide radicals, reactive oxygen species generated in the body, can cause lipid peroxidation and accelerate aging[20]. In NONFH, oxidative stress can induce osteocyte apoptosis, particularly in cases of steroid-induced osteonecrosis[21]. Thus, suppressing oxidative stress is crucial for treating NONFH. Research suggests that mesenchymal stem cells, neural progenitor cells, and astrocytes can resist oxidative damage[22, 23]. Notably, exosomes derived from these cells also show resistance to oxidative damage. For instance, exosomes from mesenchymal stem cells can reduce oxidative stress and activate the PI3K/Akt pathway, enhancing cardiac health and preventing myocardial ischemia[24]. Similarly, exosomes from adipose stem cells can alleviate lipopolysaccharide-induced reactive oxygen species accumulation and decrease the expression of inflammatory cytokines IL-1\u0026beta;, TNF-\u0026alpha;, and IL-6 in macrophages[25] . Exosomes from human umbilical cord mesenchymal stem cells can prevent cisplatin-induced renal oxidative stress and apoptosis[26]. Compared to healthy controls, early-stage NONFH patients exhibited increased expression of proteins related to scavenging superoxide radicals, suggesting that exosomes in NONFH patient serum may counteract oxidative damage by upregulating these proteins. Thus, the pathogenesis of early-stage NONFH is characterized by venous stasis, lipid accumulation, and oxidative stress response.\u003c/p\u003e\n\u003cp\u003eAmong the differentially expressed proteins identified between late-stage NONFH patients and healthy controls, biological functions were predominantly enriched in the innate immune response, chondrocyte development, positive regulation of the I-kappaB kinase/NF-kappaB signaling pathway, and inflammatory response to antigenic stimuli. In late-stage NONFH patients, the collapse of the femoral head alters joint stress and exacerbates degenerative changes in the articular cartilage, typically affecting the subchondral calcified layer and deep cartilage first, which eventually leads to the destruction of cartilage integrity. Studies have shown that exosomes derived from mesenchymal stem cells (MSCs) can facilitate cartilage repair and regeneration by modulating immune responses, reducing apoptosis, and promoting cell proliferation[27]. In this study, we observed that proteins associated with cartilage repair were upregulated in late-stage NONFH. This finding not only supports the cartilage reparative effects of exosomes at the protein level but also elucidates the pathological changes in cartilage damage observed in late-stage NONFH patients.\u003c/p\u003e\n\u003cp\u003eDuring the progression of femoral head necrosis, hormones can activate the TLR4 signaling pathway, disrupt immune responses, and contribute to the development of NONFH[28]. This highlights the critical role of immune responses in the pathogenesis of NONFH. Fang et al. found that differential genes in exosomes derived from bone marrow mesenchymal stem cells were predominantly enriched in immune responses when comparing healthy mice with those having steroid-induced osteonecrosis of the femoral head[29]. This observation reflects the immune response-related pathological changes in late-stage NONFH. Our study also revealed that differential proteins were enriched in the NF-kappaB signaling pathway and inflammation responses. Anderson et al. suggested that exosomes secreted by mesenchymal stem cells can treat ischemic diseases by modulating the body\u0026apos;s ischemic state, with proteomic analyses showing that exosome treatment for ischemic conditions primarily involves the NF-kappaB signaling pathway[30]. Other research indicated that exosomes derived from bone marrow mesenchymal stem cells can inhibit PEG2 expression, thereby reducing macrophage activation, inducing macrophage differentiation into an anti-inflammatory phenotype, and decreasing inflammatory response activation[31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we performed enrichment analysis on the differentially expressed proteins between late-stage and early-stage NONFH patients to elucidate the molecular mechanisms underlying disease progression. The biological functions of these differential proteins were primarily enriched in lipid transport, angiotensin-activated signaling pathways, and positive regulation of interleukin-1. Studies have demonstrated that corticosteroid administration in chickens leads to lipid metabolism disorders within one week, including adipocyte hypertrophy and triglyceride vesicle formation[32]. The pathology of late-stage NONFH is associated with fat infiltration, which disrupts microcirculation within the bone, reducing blood flow and altering the structure of the femoral head[19]. Glucocorticoids can activate the renin-angiotensin-aldosterone system (RAAS) in bone tissue, with angiotensin II (Ang II) serving as a key effector peptide in the ACE1/Ang II/AT1 receptor cascade. Ang II activates AT1 receptors, leading to increased expression of TNF-\u0026alpha; and IL-6 [33], which accelerates bone resorption[34]. Therefore, the regulation of vascular function and lipid metabolism disorders are crucial factors influencing the pathological progression of late-stage osteonecrosis of the femoral head.\u003c/p\u003e\n\u003cp\u003eWe conducted a protein-protein interaction (PPI) network analysis on the differentially expressed proteins (DEPs) between early-stage NONFH patients and healthy controls. Utilizing 11 topological classification methods, we identified key proteins including NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3. NOP58 is a precursor of the small nucleolar ribonucleoprotein subunit and plays a vital role in assembling the SSU processome in the nucleolus. This process involves numerous ribosome biogenesis factors, RNA chaperones, and ribosomal proteins, which work together to facilitate RNA folding, modification, rearrangement, cleavage, and targeted degradation of pre-ribosomal RNA by the exosome[35]. SF3B1, a critical subunit of the U2 small nuclear ribonucleoprotein (snRNP), is essential for the proper assembly of the spliceosome at the branch point sequence[36]. Research has primarily focused on SF3B1\u0026apos;s role in tumorigenesis and myelodysplastic syndromes, with recurrent mutations in SF3B1 being linked to human cancers and affecting patient prognosis[37]. CCT subunits are components of the T-complex protein (TRiC), a molecular chaperone complex that assists in protein folding during ATP hydrolysis[38]. Proteomic analyses of osteoblasts have shown that CCT2 is highly expressed in mineralized osteoblasts and may play a role in bone formation[39]. CCT3 has been associated with the diagnosis and prognosis of various cancers, including liver, bladder, and cervical cancers[40,41,42]. PSMA6 is a regulatory subunit of the proteasome, responsible for degrading misfolded or damaged proteins and proteins no longer needed by the cell, thus maintaining protein homeostasis[43]. SNRPD2 is a component of the minor spliceosome involved in splicing U12-type introns in pre-mRNA[44]. SOD1 is a crucial antioxidant enzyme that neutralizes free radicals within cells, which can be harmful to biological systems[45]. The identification of these proteins in the PPI network of NONFH underscores their potential as novel diagnostic and therapeutic biomarkers.\u003c/p\u003e\n\u003cp\u003eExosomes are increasingly recognized for their potential in drug delivery systems due to their inherent ability to transport molecules between cells. They can encapsulate therapeutic agents and target specific tissues, positioning them as promising vehicles for drug delivery. Our Enrichr analysis identified artesunate, clindamycin, and disodium selenite as potential therapeutic agents for treating NONFH. Artesunate, a semisynthetic derivative of artemisinin, is known for its anti-inflammatory and immunosuppressive effects, making it effective against autoimmune and inflammatory diseases[46]. It operates by inhibiting the PLC\u0026gamma;1-Ca2+-calcineurin-NFATc1 pathway, which reduces RANKL-induced osteoclastogenesis and bone loss. This mechanism suggests that artesunate could mitigate bone resorption in NONFH by targeting inflammatory pathways[47]. Clindamycin, primarily an antibiotic, has shown potential in promoting osteoblast growth and differentiation at a concentration of 150 mg/mL. This effect is linked to genes associated with TGF-\u0026beta; signaling\u0026mdash;such as TGF-\u0026beta;1, TGF-\u0026beta;R1, TGF-\u0026beta;R2, TGF-\u0026beta;R3, and VEGF\u0026mdash;as well as RUNX-2, Col-1, OSX, OSC, BMP-2, BMP-7, and ALP. These findings suggest that clindamycin could aid in bone formation and repair processes in NONFH[48]. Disodium selenite, an oral selenium supplement, has proven effective in enhancing the antioxidant capacity of stem cells. At safe doses, it protects mesenchymal stem cells from oxidative stress-induced inhibition of osteoblast differentiation by inhibiting oxidative stress and ERK activation. This protective effect could benefit NONFH patients by preserving bone cell function and promoting bone regeneration[49]. Thus, the potential application of artesunate, clindamycin, and disodium selenite in treating NONFH underscores the versatility of exosome-based drug delivery systems. These drugs offer targeted therapeutic benefits by modulating key pathways involved in bone metabolism and inflammation. Future research should focus on optimizing exosome formulations of these drugs and conducting clinical trials to assess their efficacy and safety in treating NONFH.\u003c/p\u003e\n\u003cp\u003eThis study investigated the pathogenesis of NONFH at various stages and identified potential early biomarkers through serum exosome proteomics. However, there were several limitations. First, the small sample size could impact the statistical significance and generalizability of the results. Second, the lack of experimental validation means that further studies are required to confirm the biological relevance of the findings. Future research should address these limitations by incorporating larger sample sizes and experimental validation to enhance the reliability and practical applicability of the insights into NONFH pathogenesis and potential biomarkers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study analyzed clinical data from patients with NONFH at various stages and employed bioinformatics approaches to investigate differentially expressed proteins, elucidating the pathogenesis of NONFH. We constructed a PPI network and identified key genes, including NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3. Drug predictions for these core genes suggested that artesunate, clindamycin, and disodium selenite could be promising candidates for treating NONFH. Our research advances the understanding of the molecular mechanisms associated with different stages of NONFH and identifies potential targets for diagnosis and treatment, especially from the perspective of exosome proteomics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eNONFH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eNon-traumatic osteonecrosis of the femoral head\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eONFH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eOsteonecrosis of the femoral head\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eTONFH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003etraumatic osteonecrosis of the femoral head\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eCHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003echolesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003etriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eLDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eHDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003ealkaline phosphatase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eTEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eTransmission Electron Microscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003eDEPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eDifferently expressed proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9638%;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0362%;\"\u003e\n \u003cp\u003eprotein-protein interaction network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eX.X. and K.B. conceived the experiment.G.W., S.J. and Q.J. completed the experiment.G.W.and Z.Z. performed the data analysis. G.W and X.X. wrote the original draft. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003eThis research was supported by the National Natural Science Foundation of China (grant. no.8237153419); the Young Qi Huang Scholar Support Program of the National Administration of Traditional Chinese Medicine (No. Guo Zhong Yi Yao Ren Jiao Fa [2020] 7); the Key Research and Development Program of Heilongjiang Province (GZ20210136, GA21C006); the Traditional Chinese Medicine Research Project of Heilongjiang Province (ZHY2023-099); and the Key Laboratory of Basic and Clinical Research on Osteonecrosis of Heilongjiang Province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: No datasets were generated or analysed during the current study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement and consent:\u003c/strong\u003eThe research protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Heilongjiang University of Chinese Medicine (Approval No.: ZYDEYL [2022] K08).The written consent of all participants or their legal guardians has been obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: The authors affirm that human research participants provided informed consent for publication of the images in all Figures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interests:\u003c/strong\u003eThe authors declare no conflicts of interest related to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao D, Zhang F, Wang B, Liu B, Li L, Kim SY, et al. 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Stem Cells. 2006 May;24(5):1226\u0026ndash;35. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-traumatic osteonecrosis of the femoral head, Exosomes, Proteomics, Bioinformatics, Molecular mechanisms, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-5150372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5150372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eIn clinical practice, the treatment of osteonecrosis of the femoral head (ONFH) often lags behind diagnosis, resulting in delayed treatment and high disability rates. Therefore, identifying appropriate biomarkers for early diagnosis is crucial. Exosome-related functions offer promising avenues for understanding the pathogenesis and early diagnosis of non-traumatic osteonecrosis of the femoral head (NONFH). This study employed label-free quantitative proteomics and bioinformatics analysis to explore the molecular mechanisms underlying NONFH and identify potential new biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eWe used liquid chromatography-tandem mass spectrometry (LC-MS/MS) and bioinformatics to analyze exosome proteins in serum samples from early-stage (n=11) and late-stage NONFH patients (n=9), as well as healthy controls (n=10). Our goal was to investigate the molecular mechanisms of NONFH at different stages. We utilized protein interaction networks and topological classification methods to pinpoint key markers in early-stage NONFH and healthy samples. Additionally, we used the Enrichr database to identify drugs that interact with differentially expressed proteins (DEPs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Proteomic analysis revealed 76 differential proteins between early-stage NONFH and healthy controls, 26 between late-stage NONFH and healthy controls, and 60 between late-stage and early-stage NONFH. Early-stage NONFH compared to healthy controls, Early-stage NONFH mechanisms were associated with protein folding, blood coagulation, spliceosome-mediated mRNA splicing, lipid transport, superoxide radical clearance, and cholesterol esterification. Late-stage NONFH compared to healthy controls,Late-stage NONFH was linked to innate immune response, chondrocyte development, I-kappaB kinase/NF-kappaB signaling, and inflammatory responses to antigens. Late-stage NONFH compared to Early-stage NONFH, In late-stage NONFH, DEPs were primarily involved in lipid metabolism and angiotensin response. Notably, NOP58, SF3B1, RPL7, RPL3, CCT7, CCT2, PSMA6, SNRPD2, SOD1, and CCT3 were significantly upregulated in early-stage NONFH, suggesting their potential as biomarkers. The Enrichr database identified artesunate, clindamycin, and disodium selenite as potentially effective therapeutic agents for NONFH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study offers valuable insights into potential serum biomarkers for early NONFH and elucidates the molecular mechanisms at the plasma exosome protein level across different stages of the disease. These findings provide new perspectives for identifying potential therapeutic targets and advancing early diagnosis strategies.\u003c/p\u003e","manuscriptTitle":"Exploratory Analysis of Serum Exosome Proteomics in the Pathogenesis and Early Biomarker Discovery of Non-traumatic Osteonecrosis of the Femoral Head","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-30 09:55:48","doi":"10.21203/rs.3.rs-5150372/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebeff447-d489-46bb-8b1c-1efc5f4d64c9","owner":[],"postedDate":"October 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-25T07:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-30 09:55:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5150372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5150372","identity":"rs-5150372","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}