Identification and Validation of Autophagy-Related Genes in Osteoarthritis through Bioinformatics and Machine Learning

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Although increasing evidence suggests a close relationship between autophagy and OA, its pathogenesis remains unclear. This study aimed to identify autophagy-related genes in OA using bioinformatics and machine learning methods. Three OA datasets (GSE55235, GSE55457 and GSE12021) were retrieved from the GEO database for differential analysis. Subsequently, differentially expressed genes (DEGs) were intersected with autophagy-related genes to identify differentially expressed autophagy-related genes (DEARGs), which were then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Subsequently, potential key genes were selected using three machine learning algorithms (LASSO, SVM and RF) and their diagnostic accuracy was validated using an external dataset (GSE114007) to determine the key genes. Next, potential interactions between the key genes were predicted using the GeneMANIA database. Additionally, immune cell infiltration analysis was performed to explore the correlation between the key genes and immune cells. Finally, the expression levels of the key genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). In this study, a total of 27 DEARGs were identified. GO and KEGG enrichment analyses indicated that these DEARGs might be associated with pathways related to cellular immunity, autophagy, and inflammation. Four potential key genes were selected through the use of three machine learning algorithms. Notably, validation with the external dataset revealed that the expression levels of PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA and exhibited strong diagnostic performance. Immune infiltration analysis showed that PPP1R15A, GABARAPL1 and FOXO3 were positively correlated with activated mast cells and resting memory CD4 + T cells, but negatively correlated with plasma cells and M0 macrophages. Finally, qRT-PCR confirmed these results, which were consistent with the bioinformatics analysis.In conclusion, this study identifies PPP1R15A, GABARAPL1 and FOXO3 as autophagy key genes in OA, providing potential targets for the diagnosis and treatment of OA. Health sciences/Medical research/Biomarkers/Diagnostic markers Health sciences/Pathogenesis/Inflammation/Chronic inflammation Health sciences/Biomarkers/Predictive markers osteoarthritis autophagy differentially expressed genes machine learning key genes immune cell infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Osteoarthritis (OA) is a common chronic degenerative joint disease characterized by the degeneration of articular cartilage, subchondral bone damage, and synovial inflammation [1, 2]. OA is primarily characterized by joint pain and limited mobility, significantly reducing the quality of life for patients and imposing a substantial burden on individuals and society [3]. With the aging global population, the prevalence of OA is projected to increase from 26.6–29.5% by 2032[4]. In recent years, OA has become one of the leading causes of disability[5]. Therefore, identifying new diagnostic biomarkers is crucial for exploring the pathogenesis of OA and advancing treatment strategies. Autophagy is an essential metabolic pathway that maintains cellular homeostasis by eliminating damaged proteins, organelles, and pathogens, thereby preserving normal cellular function[6, 7]. In recent years, increasing evidence has demonstrated that autophagy plays a crucial role in the onset and progression of OA, including involvement in joint cartilage repair and inhibition of chondrocyte apoptosis[8, 9]. A recent study indicated that autophagy-related genes such as CDKN1A, DDIT3, MAP1LC3B and MYC may serve as potential biomarkers for OA and could modulate the progression of OA by influencing immune cell infiltration[10]. Another study suggested that SNORC knockout may reduce the expression of autophagy-related proteins, thereby promoting chondrocyte proliferation and delaying the progression of OA[11]. Despite the close association between autophagy and OA, the specific mechanisms remain unclear and require further investigation[12]. Therefore, exploring ARGs in OA may help us gain deeper insights into disease pathogenesis and identify new therapeutic targets. In this study, we downloaded OA datasets from the GEO database for differential analysis and intersected the DEGs with ARGs to obtain DEARGs. Then, potential key genes were selected using three machine learning algorithms, and their accuracy was validated using both the training set and the validation set to identify the final key genes. Additionally, we investigated the correlation between key genes and immune cell infiltration using the CIBERSORT algorithm. Finally, these results were validated through qRT-PCR experiments. Therefore, this study provides new insights into the pathogenesis of autophagy in OA and offers novel directions for the diagnosis and treatment of OA. Methods Data Collection and Processing In this study, four datasets (GSE55235, GSE55457, GSE12021 and GSE114007) were downloaded from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE55235 is based on the GPL96 platform and consists of 10 control synovial tissue samples and 10 OA synovial tissue samples. GSE55457, which is based on the GPL96 platform, includes 10 control synovial tissue samples and 10 OA synovial tissue samples. GSE12021, which is based on the GPL96 platform, consists of 9 control synovial tissue samples and 10 OA synovial tissue samples. GSE114007, which is based on the GPL11154 and GPL18573 platforms, contains 18 control articular cartilage samples and 20 OA articular cartilage samples. To eliminate batch effects, the "sva" package in R software was used to integrate the three datasets from the same platform as the training set[13], with GSE114007 used as the validation set. Additionally, ARGs were downloaded from the human autophagy-related gene database HADb ( http://www.autophagy.lu/index.html ), a total of 222 ARGs were obtained. The detailed analysis process is shown in Fig. 1 . Identification of DEARGs To identify DEGs, we performed normalization and differential analysis on the merged datasets using the "Limma" package in R software[14]. The screening criteria were set as |log2FC| > 0.585 and adjusted P < 0.05. The "ggplot2" and "pheatmap" packages in R were used for visualizing the DEGs. Subsequently, the intersection of DEGs and ARGs was used to obtain DEARGs, and a Venn diagram was drawn using the "VennDiagram" package in R. Finally, DEARGs were visualized using the "pheatmap" package in R. Functional Enrichment Analysis of DEARGs To further explore the biological significance of DEARGs, GO and KEGG pathway enrichment analyses were performed using the "clusterProfiler" package in R software[15]. A P value < 0.05 was considered statistically significant for enrichment. The results were visualized using the "ggplot2" package in R software. Machine learning to screen biomarkers To further screen DEARGs, three machine learning algorithms were used: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF). The LASSO algorithm was performed using the "glmnet" package in R software[16]. The SVM-RFE algorithm was executed using the "e1071" package in R software[17]. The RF algorithm was implemented using the "randomForest" package in R software [18], and genes with an importance score > 1 were included in the selection criteria. Finally, the genes selected by all three machine learning algorithms were intersected for subsequent analysis. Validation of Key Genes To further validate the reliability of the results, the expression levels of the genes were assessed in both the training and validation sets (GSE114007). Boxplots were generated using the "ggpubr" package in R software. Additionally, ROC curves were plotted using the "pROC" package in R, and the results were visualized using the "ggplot2" package to evaluate the diagnostic value of the key genes. Prediction of Key Gene Mechanisms GeneMANIA ( http://genemania.org ) is a web-based database that identifies functionally similar genes using genomic and proteomic data. It can also generate protein-protein interaction (PPI) networks and predict functional and interaction relationships between genes[19]. In this study, we used the GeneMANIA database to construct an interaction network of key genes to explore their mechanisms in OA. Immune Cell Infiltration Analysis To further explore the relationship between key genes and immune infiltrating cells, we used the CIBERSORT algorithm to assess the relative abundance of 22 immune cell types in the training set[20]. A correlation heatmap of immune cells was then generated using the "corrplot" package in R. Additionally, boxplots were drawn using the "corrplot" package in R to explore the differences in immune cell types between healthy and OA samples. Correlation Analysis Between Key Genes and Immune Cells Gene expression data were processed using the "reshape2" package in R to obtain the expression levels of key genes. A correlation filter with P < 0.05 was applied, and correlation lollipop plots were generated using the "ggpubr" and "ggExtra" packages in R to explore the relationships between key genes and immune cells. qRT-PCR Validation To further verify the accuracy of the key genes, we collected 12 articular cartilage samples from the Eighth Medical Center of the Chinese PLA General Hospital, including 6 OA samples and 6 healthy control samples. The study was approved by the Ethics Committee of the Eighth Medical Center of the Chinese PLA General Hospital (Ethical Approval No.309202406063213). All research methods strictly adhered to the Declaration of Helsinki and relevant ethical guidelines and regulations. All participants in this study signed informed consent forms. The 12 cartilage samples were divided into two groups: 6 OA samples and 6 healthy samples. Total RNA was extracted from the cartilage tissue using the AG Prime Script® RT Master Mix reagent. The total RNA was then reverse-transcribed into cDNA using a PCR instrument, and qRT-PCR was performed using 2× SYBR Green qPCR Master Mix (Service). The key genes were analyzed with GAPDH as the internal control. Relative mRNA expression was calculated using the 2 −ΔΔCt method. Primer sequences are listed in Table 1 . Table 1 Primer Sequences Information. Gene Forward Primer (5’-3’) Reverse Primer (5’-3’) GAPDH CATGTACGTTGCTATCCAGGC CTCCTTAATGTCACGCACGAT PPP1R15A ATGATGGCATGTATGGTGAGC AACCTTGCAGTGTCCTTATCAG GABARAPL1 GGTGCATCATGAAGTTCCAGTAC CAGGTCTCAGGTGGATTCTCTTC FOXO3 GCGAACGGACAGGAGTACAT GCTCTTGCCAGTTCCCTCAT Statistical Analysis Statistical analyses were performed using R software (version 4.2.1) and GraphPad Prism 9 software. Gene expression differences between the two groups were compared using an unpaired Student's t-test or Wilcoxon test. A P value < 0.05 was considered statistically significant. Data are presented as the mean ± standard deviation (SD). Results Identification of DEARGs Differential analysis was performed using R software, identifying a total of 1,747 differentially expressed genes (DEGs), including 909 downregulated genes and 837 upregulated genes. The results were visualized using a volcano plot (Fig. 2 A) and a heatmap (Fig. 2 B). Additionally, the intersection of the 1,747 DEGs and 222 ARGs revealed 27 differentially expressed autophagy-related genes (DEARGs), consisting of 5 upregulated genes and 22 downregulated genes (Fig. 2 C). The expression of the 27 DEARGs is shown in a heatmap (Fig. 2 D). GO and KEGG Enrichment Analysis of DEARGs GO enrichment analysis showed that DEARGs were primarily enriched in biological processes (BP) such as cellular response to external stimulus, cellular response to nutrient levels, cellular response to extracellular stimulus, regulation of autophagy, and cellular response to starvation. In cellular components (CC), DEARGs were mainly enriched in focal adhesion, cell-substrate junction, endoplasmic reticulum lumen, early endosome, and endoplasmic reticulum-Golgi intermediate compartment. In molecular functions (MF), DEARGs were primarily enriched in DNA-binding transcription factor binding, phosphatase binding, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, and heat shock protein binding (Figs. 3 A, B).KEGG enrichment analysis revealed that DEARGs were predominantly enriched in pathways related to Human cytomegalovirus infection, PI3K-Akt signaling pathway, Kaposi sarcoma-associated herpesvirus infection, Human papillomavirus infection, and Autophagy-Animal (Figs. 3 C, D). Machine Learning for Potential Key Genes Selection To identify OA autophagy-related key genes, three machine learning algorithms were used for screening. The LASSO algorithm, using 10-fold cross-validation, selected 14 DEARGs (Figs. 4 A, B). The SVM-RFE algorithm identified 8 DEARGs (Figs. 4 C, D). Additionally, the RF algorithm selected 8 DEARGs (Figs. 4 E, F). Finally, 4 potential key genes (PPP1R15A, GABARAPL1, FOXO3 and P4HB) were identified (Fig. 4 G). Validation of Key Genes To further validate the accuracy of the 4 potential key genes, we evaluated their diagnostic value in both the training and validation sets. In the training set, the expression levels of biomarkers showed that PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA patients, while P4HB was significantly upregulated in OA patients (Fig. 5 A). ROC curve analysis revealed that the area under the curve (AUC) for PPP1R15A, GABARAPL1, FOXO3 and P4HB were 0.956, 0.902, 0.906, and 0.868, respectively (Fig. 5 B). Notably, in the validation set, the expression levels of PPP1R15A, GABARAPL1 and FOXO3 were also significantly downregulated in OA patients, consistent with the training set results, while P4HB showed no significant difference in OA patients (Fig. 5 C). ROC curve analysis showed AUC values of 0.967, 0.864, 0.875, and 0.553 for PPP1R15A, GABARAPL1, FOXO3 and P4HB, respectively (Fig. 5 D). Therefore, the results indicate that PPP1R15A, GABARAPL1 and FOXO3 are key autophagy-related genes in OA. Prediction of Key Gene Mechanisms Protein-protein interaction (PPI) network analysis was performed using the GeneMANIA database to explore the interactions of the three key genes—PPP1R15A, GABARAPL1 and FOXO3—along with their corresponding 20 interacting genes (Fig. 6 ). The results revealed that PPP1R15A, GABARAPL1 and FOXO3 primarily exhibit physical interactions (77.64%), and these three key genes are mainly enriched in pathways related to the regulation of translational initiation in response to stress, protein serine/threonine phosphatase complex, phosphatase complex, regulation of translation in response to stress, SMAD binding, response to BMP, and regulation of muscle adaptation. Immune Cell Infiltration Analysis The correlation between immune cells is displayed in the heatmap, showing that Macrophages M1 are positively correlated with B cells naive and T cells follicular helper. Additionally, Macrophages M2 are negatively correlated with NK cells activated and Plasma cells (Fig. 7 A). Boxplot analysis revealed that nine immune cell types showed differential expression between OA and normal samples. Plasma cells, Macrophages M0, and Mast cells resting were highly expressed in OA samples, while T cells CD4 memory resting, NK cells activated, Monocytes, Dendritic cells activated, Mast cells activated, and Eosinophils were lowly expressed in OA samples (Fig. 7 B). Correlation Analysis of Key Genes with Immune Cell Infiltration The correlation analysis of immune cell infiltration revealed that PPP1R15A was positively correlated with Mast cells activated, T cells CD4 memory resting, and NK cells activated, whereas it was negatively correlated with Mast cells resting, Plasma cells, Macrophages M0, and T cells CD4 naive (Fig. 8 A). GABARAPL1 showed a positive correlation with Mast cells activated, T cells CD4 memory resting, Monocytes, and Dendritic cells activated, while it was negatively correlated with Mast cells resting, Macrophages M0, and Plasma cells (Fig. 8 B). FOXO3 was positively correlated with T cells CD4 memory resting, Mast cells activated, and Dendritic cells activated, but negatively correlated with Plasma cells, Dendritic cells activated, Macrophages M0, and T cells CD4 naive (Fig. 8 C). qRT-PCR Validation According to the qRT-PCR results, PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA cartilage compared to normal cartilage tissue (Fig. 9 A-C), which is consistent with the aforementioned analysis results. Discussion OA is an age-related chronic degenerative joint disease, and it has become a leading cause of pain and disability, severely affecting the quality of life of middle-aged and elderly individuals[21, 22]. Currently, the treatment for OA mainly focuses on symptom relief, and no studies have yet demonstrated that treatment can prevent or reverse the onset and progression of OA [23]. Despite significant progress in OA research, the exact pathogenesis remains unclear [24]. Recent studies have shown that autophagy may help slow the progression of OA by regulating processes such as cell apoptosis, but the precise role and mechanisms of autophagy in OA are not yet fully understood[9, 25].Therefore, exploring autophagy-related key genes in OA through bioinformatics and machine learning will help us better understand its pathogenesis and identify potential therapeutic targets for the disease. In this study, we identified 27 DEARGs through bioinformatics. Subsequently, GO and KEGG enrichment analyses of the DEARGs were performed, and the results indicated that the DEARGs were mainly enriched in processes such as cell immunity, autophagy, and inflammation, highlighting the close relationship between autophagy and OA. Furthermore, four potential key genes were selected using three machine learning algorithms, and their accuracy was evaluated using both the training and validation sets. Ultimately, PPP1R15A, GABARAPL1 and FOXO3 were identified as key autophagy-related genes in OA. Among the three identified key genes, PPP1R15A (Protein Phosphatase 1 Regulatory Subunit 15A), also known as Growth Arrest and DNA Damage-Inducible Protein 34 (GADD34), is a critical factor in the mammalian integrated stress response (ISR) [26]. Studies have shown that PPP1R15A plays a crucial role in regulating cell death by promoting protein synthesis and activating death-related pathways such as ER stress, ROS production, and autophagy [27]. Additionally, PPP1R15A has been shown to be associated with both OA and type 2 diabetes mellitus (T2DM) [28]. However, the exact role of PPP1R15A in the pathogenesis of OA remains unclear and warrants further investigation. GABARAPL1, a member of the ATG8 family, plays a critical role in the development of autophagosomal vesicles [29, 30].Additionally, GABARAPL1 facilitates the fusion of autophagosomes with lysosomes[31]. Research has shown that inhibiting GABARAPL1 expression can regulate the autophagic process in OA chondrocytes[32]. Another study suggests that GABARAPL1 may modulate the immune microenvironment of OA by affecting immune cell function, thus contributing to the onset and progression of the disease [33]. Although there is a strong association between GABARAPL1 and OA, further investigation is needed to fully understand its role in the pathogenesis of OA. FOXO3 is a member of the FoxO transcription factor family, primarily involved in regulating cellular senescence, apoptosis, autophagy, and oxidative stress [34]. Additionally, FOXO3 regulates osteocyte function and intercellular signaling, influencing bone development and bone mass, thereby contributing to the development of osteoarthritis and osteoporosis[35]. Moreover, FOXO3 plays a critical role in maintaining systemic homeostasis and preventing meniscal injury [36]. Although the importance of FOXO3 in OA is well-established, its specific role in the molecular mechanisms of OA-related autophagy remains unclear and warrants further research. Although this study identified PPP1R15A, GABARAPL1 and FOXO3 as key autophagy-related genes in OA through bioinformatics and machine learning, there are still some limitations. First, the sample size is relatively small, and increasing the sample size is needed to enhance the reliability of the experimental results. Second, the validation methods are limited; further validation using gene knockout, cell models, and animal models is needed to confirm the accuracy of the results. Finally, this study has not explored the immune mechanisms between OA and the key genes in depth, and further research is required to elucidate their molecular mechanisms. Conclusion In this study, we found that PPP1R15A, GABARAPL1 and FOXO3 could serve as key autophagy-related genes in OA. Furthermore, the correlation analysis between these three key genes and immune cell infiltration suggests that they may be involved in regulating the progression of OA, providing potential targets for the diagnosis and treatment of OA. Declarations Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding No financial support. Author Contribution Conception and design: DJ, XCQ, PW. Collection and assembly of data: DJ, XCQ, ZT. Sample collection: PW, XCQ. Analyzed and interpreted the data: DJ, XCQ, ZT, PW. Gene validation and statistical analysis: DJ, ZT, PW. Manuscript writing: DJ, XCQ, PW. All authors reviewed the manuscript. Acknowledgement We sincerely thank all the patients and researchers for their contributions to this study. 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Additional Declarations No competing interests reported. Supplementary Files DEARGs.txt Intersectiongenesofthethreemachinelearningalgorithms.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5617353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392950186,"identity":"e73a5d54-87b3-4030-b1a6-c5d3394e335a","order_by":0,"name":"Jian Du","email":"","orcid":"","institution":"Senior Department of Orthopedics, the Fourth Medical Center of PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Du","suffix":""},{"id":392950187,"identity":"75a049cb-cfe1-48e0-ad20-38ee0f69b97e","order_by":1,"name":"Congqin Xie","email":"","orcid":"","institution":"Senior Department of Orthopedics, the Eighth Medical Center of PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Congqin","middleName":"","lastName":"Xie","suffix":""},{"id":392950188,"identity":"ee6f569c-8d86-4218-b09c-866c56115345","order_by":2,"name":"Tian Zhou","email":"","orcid":"","institution":"Graduate School of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Zhou","suffix":""},{"id":392950189,"identity":"e5f6d67d-1c7c-4e40-b927-db975ce51394","order_by":3,"name":"Wei Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYPACCR5+9uYDBz5UkKBFRrLnWOLBGWdIsMbG4EaO8WHeFiKUmrP3Hn7NU2HBw3Aj58MB3gYGeX6xA/i1WPacS7PmOSPBw9jzdsMByR0MhjNnJ+DXAnSPmXFumwQPM3vuhgOGZxgSDG4T0nL/DVDLPwkeNoacBwcS24jRcoPH+HFugwQPD0cOw4GDxGix7MkxY/5zTIJHgueYwcGGMxKE/WLOfsb444yaOnv7482PP/+psJHnlybkMAYGNgkkvgROlchamD8QVjYKRsEoGAUjGgAAeqVG80ZHU2oAAAAASUVORK5CYII=","orcid":"","institution":"Senior Department of Orthopedics, the Fourth Medical Center of PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-12-10 14:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5617353/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5617353/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72208266,"identity":"970a4dcc-8f3c-48b7-915e-6733acdcfe29","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65029,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of the analyses.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/fc993202c04e828cb9fbae76.png"},{"id":72208276,"identity":"82d64891-1924-4f36-9c2e-ac8684069532","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":335893,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEARGs. (A) Volcano plot of DEGs between OA and normal samples. (B) Heatmap of DEGs between OA and control samples. (C) Venn diagram showing the intersection of DEGs and ARGs. (D) Heatmap of the 27 DEARGs.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/729f38e101a5a17e8c4f08ff.png"},{"id":72208802,"identity":"93f0b9ae-7632-4425-be7d-badad62492a3","added_by":"auto","created_at":"2024-12-23 17:15:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":555369,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment Analysis of DEARGs. (A) GO enrichment analysis bubble plot. (B) GO enrichment analysis Circor chart. (C) KEGG enrichment analysis bubble plot. (D) KEGG enrichment analysis Circor chart.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/49aa723703d0c81e6febc472.png"},{"id":72208288,"identity":"9179b1f0-5452-40e0-99d9-684a19834ed5","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80819,"visible":true,"origin":"","legend":"\u003cp\u003eFeature Gene Selection by Three Machine Learning Algorithms. (A) Distribution of LASSO logistic regression coefficients. (B) Confidence interval of LASSO logistic regression log(λ). (C) Accuracy plot of the SVM algorithm. (D) Error plot of the SVM algorithm. (E) Relationship between the number of decision trees and error rate in the random forest algorithm. (F) RF importance score results. (G) Venn diagram showing the 4 biomarkers identified by the three machine learning algorithms.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/00e2b122ba1e121f1c9fbf42.png"},{"id":72208281,"identity":"ada03a77-f1ba-47fe-8d48-5c564b920436","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":214714,"visible":true,"origin":"","legend":"\u003cp\u003eExternal Dataset Validation. (A) Boxplot of the expression levels of the four biomarkers between Control and OA in the training set. (B) ROC curve analysis results of the biomarkers in the training set. (C) Boxplot of the expression levels of the four biomarkers between Control and OA in the validation set. (D) ROC curve analysis results of the biomarkers in the validation set. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ns, not significant.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/491b7cade3ff9e7cd4049cfb.png"},{"id":72208271,"identity":"95ff289e-1ca9-4ad1-b7e0-2cc04929198e","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":349038,"visible":true,"origin":"","legend":"\u003cp\u003ePPI Network of PPP1R15A, GABARAPL1 and FOXO3 Constructed Using the GeneMANIA Database.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/ff8bc5c4286b6afce6d4d793.png"},{"id":72208805,"identity":"2f1854c2-d9be-402c-8a25-60fe52f9342a","added_by":"auto","created_at":"2024-12-23 17:15:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":197867,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Cell Infiltration Analysis. (A) Correlation analysis of immune cells. Red indicates positive correlation, while blue indicates negative correlation. (B) Boxplot of 22 different immune cell types between OA and normal samples. ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05. ns, not significant.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/c66a1360d716377454e70592.png"},{"id":72208290,"identity":"d11f181a-919a-4dc7-a694-202c78e32735","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":105244,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Analysis of Immune Cell Infiltration. (A) Correlation between PPP1R15A and immune cell infiltration. (B) Correlation between GABARAPL1 and immune cell infiltration. (C) Correlation between FOXO3 and immune cell infiltration. A p-value \u0026lt; 0.05 is considered statistically significant.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/2d3cfba9bc5767b7e179f7a5.png"},{"id":72208278,"identity":"182fe3a4-cd7b-437a-b056-22b6f3bb193a","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":62084,"visible":true,"origin":"","legend":"\u003cp\u003eqRT-PCR validation. (A) mRNA expression level of PPP1R15A in OA cartilage. (B) mRNA expression level of GABARAPL1 in OA cartilage. (C) mRNA expression level of FOXO3 in OA cartilage. *P\u0026lt;0.05, **P\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/cfbf21f413166f4d69f71774.png"},{"id":81797482,"identity":"0f165844-f613-4b01-8cb2-bfb468c0005d","added_by":"auto","created_at":"2025-05-02 04:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2768733,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/9ca1b3d5-b234-4779-a9ca-75003a33a387.pdf"},{"id":72208267,"identity":"91f03fae-4fce-4fba-ba05-1fb93cb17632","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":197,"visible":true,"origin":"","legend":"","description":"","filename":"DEARGs.txt","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/ae480b40af29570262d5e2f4.txt"},{"id":72208270,"identity":"6b30fae3-3e06-4403-8ee5-147d7be83535","added_by":"auto","created_at":"2024-12-23 17:07:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10961,"visible":true,"origin":"","legend":"","description":"","filename":"Intersectiongenesofthethreemachinelearningalgorithms.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5617353/v1/19fefc5f99d524691349a015.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and Validation of Autophagy-Related Genes in Osteoarthritis through Bioinformatics and Machine Learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoarthritis (OA) is a common chronic degenerative joint disease characterized by the degeneration of articular cartilage, subchondral bone damage, and synovial inflammation [1, 2]. OA is primarily characterized by joint pain and limited mobility, significantly reducing the quality of life for patients and imposing a substantial burden on individuals and society [3]. With the aging global population, the prevalence of OA is projected to increase from 26.6\u0026ndash;29.5% by 2032[4]. In recent years, OA has become one of the leading causes of disability[5]. Therefore, identifying new diagnostic biomarkers is crucial for exploring the pathogenesis of OA and advancing treatment strategies.\u003c/p\u003e \u003cp\u003eAutophagy is an essential metabolic pathway that maintains cellular homeostasis by eliminating damaged proteins, organelles, and pathogens, thereby preserving normal cellular function[6, 7]. In recent years, increasing evidence has demonstrated that autophagy plays a crucial role in the onset and progression of OA, including involvement in joint cartilage repair and inhibition of chondrocyte apoptosis[8, 9]. A recent study indicated that autophagy-related genes such as CDKN1A, DDIT3, MAP1LC3B and MYC may serve as potential biomarkers for OA and could modulate the progression of OA by influencing immune cell infiltration[10]. Another study suggested that SNORC knockout may reduce the expression of autophagy-related proteins, thereby promoting chondrocyte proliferation and delaying the progression of OA[11]. Despite the close association between autophagy and OA, the specific mechanisms remain unclear and require further investigation[12]. Therefore, exploring ARGs in OA may help us gain deeper insights into disease pathogenesis and identify new therapeutic targets.\u003c/p\u003e \u003cp\u003eIn this study, we downloaded OA datasets from the GEO database for differential analysis and intersected the DEGs with ARGs to obtain DEARGs. Then, potential key genes were selected using three machine learning algorithms, and their accuracy was validated using both the training set and the validation set to identify the final key genes. Additionally, we investigated the correlation between key genes and immune cell infiltration using the CIBERSORT algorithm. Finally, these results were validated through qRT-PCR experiments. Therefore, this study provides new insights into the pathogenesis of autophagy in OA and offers novel directions for the diagnosis and treatment of OA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Processing\u003c/h2\u003e \u003cp\u003eIn this study, four datasets (GSE55235, GSE55457, GSE12021 and GSE114007) were downloaded from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GSE55235 is based on the GPL96 platform and consists of 10 control synovial tissue samples and 10 OA synovial tissue samples. GSE55457, which is based on the GPL96 platform, includes 10 control synovial tissue samples and 10 OA synovial tissue samples. GSE12021, which is based on the GPL96 platform, consists of 9 control synovial tissue samples and 10 OA synovial tissue samples. GSE114007, which is based on the GPL11154 and GPL18573 platforms, contains 18 control articular cartilage samples and 20 OA articular cartilage samples. To eliminate batch effects, the \"sva\" package in R software was used to integrate the three datasets from the same platform as the training set[13], with GSE114007 used as the validation set. Additionally, ARGs were downloaded from the human autophagy-related gene database HADb (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.autophagy.lu/index.html\u003c/span\u003e\u003cspan address=\"http://www.autophagy.lu/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a total of 222 ARGs were obtained. The detailed analysis process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of DEARGs\u003c/h3\u003e\n\u003cp\u003eTo identify DEGs, we performed normalization and differential analysis on the merged datasets using the \"Limma\" package in R software[14]. The screening criteria were set as |log2FC| \u0026gt; 0.585 and adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The \"ggplot2\" and \"pheatmap\" packages in R were used for visualizing the DEGs. Subsequently, the intersection of DEGs and ARGs was used to obtain DEARGs, and a Venn diagram was drawn using the \"VennDiagram\" package in R. Finally, DEARGs were visualized using the \"pheatmap\" package in R.\u003c/p\u003e\n\u003ch3\u003eFunctional Enrichment Analysis of DEARGs\u003c/h3\u003e\n\u003cp\u003eTo further explore the biological significance of DEARGs, GO and KEGG pathway enrichment analyses were performed using the \"clusterProfiler\" package in R software[15]. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for enrichment. The results were visualized using the \"ggplot2\" package in R software.\u003c/p\u003e\n\u003ch3\u003eMachine learning to screen biomarkers\u003c/h3\u003e\n\u003cp\u003eTo further screen DEARGs, three machine learning algorithms were used: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF). The LASSO algorithm was performed using the \"glmnet\" package in R software[16]. The SVM-RFE algorithm was executed using the \"e1071\" package in R software[17]. The RF algorithm was implemented using the \"randomForest\" package in R software [18], and genes with an importance score\u0026thinsp;\u0026gt;\u0026thinsp;1 were included in the selection criteria. Finally, the genes selected by all three machine learning algorithms were intersected for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eValidation of Key Genes\u003c/h3\u003e\n\u003cp\u003eTo further validate the reliability of the results, the expression levels of the genes were assessed in both the training and validation sets (GSE114007). Boxplots were generated using the \"ggpubr\" package in R software. Additionally, ROC curves were plotted using the \"pROC\" package in R, and the results were visualized using the \"ggplot2\" package to evaluate the diagnostic value of the key genes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of Key Gene Mechanisms\u003c/h2\u003e \u003cp\u003eGeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org\u003c/span\u003e\u003cspan address=\"http://genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a web-based database that identifies functionally similar genes using genomic and proteomic data. It can also generate protein-protein interaction (PPI) networks and predict functional and interaction relationships between genes[19]. In this study, we used the GeneMANIA database to construct an interaction network of key genes to explore their mechanisms in OA.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmune Cell Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eTo further explore the relationship between key genes and immune infiltrating cells, we used the CIBERSORT algorithm to assess the relative abundance of 22 immune cell types in the training set[20]. A correlation heatmap of immune cells was then generated using the \"corrplot\" package in R. Additionally, boxplots were drawn using the \"corrplot\" package in R to explore the differences in immune cell types between healthy and OA samples.\u003c/p\u003e\n\u003ch3\u003eCorrelation Analysis Between Key Genes and Immune Cells\u003c/h3\u003e\n\u003cp\u003eGene expression data were processed using the \"reshape2\" package in R to obtain the expression levels of key genes. A correlation filter with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied, and correlation lollipop plots were generated using the \"ggpubr\" and \"ggExtra\" packages in R to explore the relationships between key genes and immune cells.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR Validation\u003c/h2\u003e \u003cp\u003eTo further verify the accuracy of the key genes, we collected 12 articular cartilage samples from the Eighth Medical Center of the Chinese PLA General Hospital, including 6 OA samples and 6 healthy control samples. The study was approved by the Ethics Committee of the Eighth Medical Center of the Chinese PLA General Hospital (Ethical Approval No.309202406063213). All research methods strictly adhered to the Declaration of Helsinki and relevant ethical guidelines and regulations. All participants in this study signed informed consent forms.\u003c/p\u003e \u003cp\u003eThe 12 cartilage samples were divided into two groups: 6 OA samples and 6 healthy samples. Total RNA was extracted from the cartilage tissue using the AG Prime Script\u0026reg; RT Master Mix reagent. The total RNA was then reverse-transcribed into cDNA using a PCR instrument, and qRT-PCR was performed using 2\u0026times; SYBR Green qPCR Master Mix (Service). The key genes were analyzed with GAPDH as the internal control. Relative mRNA expression was calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. Primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer Sequences Information.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primer (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse Primer (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCATGTACGTTGCTATCCAGGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTCCTTAATGTCACGCACGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPP1R15A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGATGGCATGTATGGTGAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAACCTTGCAGTGTCCTTATCAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGABARAPL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGTGCATCATGAAGTTCCAGTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAGGTCTCAGGTGGATTCTCTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCGAACGGACAGGAGTACAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCTCTTGCCAGTTCCCTCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R software (version 4.2.1) and GraphPad Prism 9 software. Gene expression differences between the two groups were compared using an unpaired Student's t-test or Wilcoxon test. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEARGs\u003c/h2\u003e \u003cp\u003eDifferential analysis was performed using R software, identifying a total of 1,747 differentially expressed genes (DEGs), including 909 downregulated genes and 837 upregulated genes. The results were visualized using a volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Additionally, the intersection of the 1,747 DEGs and 222 ARGs revealed 27 differentially expressed autophagy-related genes (DEARGs), consisting of 5 upregulated genes and 22 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The expression of the 27 DEARGs is shown in a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG Enrichment Analysis of DEARGs\u003c/h2\u003e \u003cp\u003eGO enrichment analysis showed that DEARGs were primarily enriched in biological processes (BP) such as cellular response to external stimulus, cellular response to nutrient levels, cellular response to extracellular stimulus, regulation of autophagy, and cellular response to starvation. In cellular components (CC), DEARGs were mainly enriched in focal adhesion, cell-substrate junction, endoplasmic reticulum lumen, early endosome, and endoplasmic reticulum-Golgi intermediate compartment. In molecular functions (MF), DEARGs were primarily enriched in DNA-binding transcription factor binding, phosphatase binding, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, and heat shock protein binding (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B).KEGG enrichment analysis revealed that DEARGs were predominantly enriched in pathways related to Human cytomegalovirus infection, PI3K-Akt signaling pathway, Kaposi sarcoma-associated herpesvirus infection, Human papillomavirus infection, and Autophagy-Animal (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMachine Learning for Potential Key Genes Selection\u003c/h2\u003e \u003cp\u003eTo identify OA autophagy-related key genes, three machine learning algorithms were used for screening. The LASSO algorithm, using 10-fold cross-validation, selected 14 DEARGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). The SVM-RFE algorithm identified 8 DEARGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Additionally, the RF algorithm selected 8 DEARGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F). Finally, 4 potential key genes (PPP1R15A, GABARAPL1, FOXO3 and P4HB) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation of Key Genes\u003c/h2\u003e \u003cp\u003eTo further validate the accuracy of the 4 potential key genes, we evaluated their diagnostic value in both the training and validation sets. In the training set, the expression levels of biomarkers showed that PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA patients, while P4HB was significantly upregulated in OA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). ROC curve analysis revealed that the area under the curve (AUC) for PPP1R15A, GABARAPL1, FOXO3 and P4HB were 0.956, 0.902, 0.906, and 0.868, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Notably, in the validation set, the expression levels of PPP1R15A, GABARAPL1 and FOXO3 were also significantly downregulated in OA patients, consistent with the training set results, while P4HB showed no significant difference in OA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). ROC curve analysis showed AUC values of 0.967, 0.864, 0.875, and 0.553 for PPP1R15A, GABARAPL1, FOXO3 and P4HB, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Therefore, the results indicate that PPP1R15A, GABARAPL1 and FOXO3 are key autophagy-related genes in OA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of Key Gene Mechanisms\u003c/h2\u003e \u003cp\u003eProtein-protein interaction (PPI) network analysis was performed using the GeneMANIA database to explore the interactions of the three key genes\u0026mdash;PPP1R15A, GABARAPL1 and FOXO3\u0026mdash;along with their corresponding 20 interacting genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results revealed that PPP1R15A, GABARAPL1 and FOXO3 primarily exhibit physical interactions (77.64%), and these three key genes are mainly enriched in pathways related to the regulation of translational initiation in response to stress, protein serine/threonine phosphatase complex, phosphatase complex, regulation of translation in response to stress, SMAD binding, response to BMP, and regulation of muscle adaptation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImmune Cell Infiltration Analysis\u003c/h2\u003e \u003cp\u003eThe correlation between immune cells is displayed in the heatmap, showing that Macrophages M1 are positively correlated with B cells naive and T cells follicular helper. Additionally, Macrophages M2 are negatively correlated with NK cells activated and Plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Boxplot analysis revealed that nine immune cell types showed differential expression between OA and normal samples. Plasma cells, Macrophages M0, and Mast cells resting were highly expressed in OA samples, while T cells CD4 memory resting, NK cells activated, Monocytes, Dendritic cells activated, Mast cells activated, and Eosinophils were lowly expressed in OA samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis of Key Genes with Immune Cell Infiltration\u003c/h2\u003e \u003cp\u003eThe correlation analysis of immune cell infiltration revealed that PPP1R15A was positively correlated with Mast cells activated, T cells CD4 memory resting, and NK cells activated, whereas it was negatively correlated with Mast cells resting, Plasma cells, Macrophages M0, and T cells CD4 naive (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). GABARAPL1 showed a positive correlation with Mast cells activated, T cells CD4 memory resting, Monocytes, and Dendritic cells activated, while it was negatively correlated with Mast cells resting, Macrophages M0, and Plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). FOXO3 was positively correlated with T cells CD4 memory resting, Mast cells activated, and Dendritic cells activated, but negatively correlated with Plasma cells, Dendritic cells activated, Macrophages M0, and T cells CD4 naive (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR Validation\u003c/h2\u003e \u003cp\u003eAccording to the qRT-PCR results, PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA cartilage compared to normal cartilage tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-C), which is consistent with the aforementioned analysis results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOA is an age-related chronic degenerative joint disease, and it has become a leading cause of pain and disability, severely affecting the quality of life of middle-aged and elderly individuals[21, 22]. Currently, the treatment for OA mainly focuses on symptom relief, and no studies have yet demonstrated that treatment can prevent or reverse the onset and progression of OA [23]. Despite significant progress in OA research, the exact pathogenesis remains unclear [24]. Recent studies have shown that autophagy may help slow the progression of OA by regulating processes such as cell apoptosis, but the precise role and mechanisms of autophagy in OA are not yet fully understood[9, 25].Therefore, exploring autophagy-related key genes in OA through bioinformatics and machine learning will help us better understand its pathogenesis and identify potential therapeutic targets for the disease.\u003c/p\u003e \u003cp\u003eIn this study, we identified 27 DEARGs through bioinformatics. Subsequently, GO and KEGG enrichment analyses of the DEARGs were performed, and the results indicated that the DEARGs were mainly enriched in processes such as cell immunity, autophagy, and inflammation, highlighting the close relationship between autophagy and OA. Furthermore, four potential key genes were selected using three machine learning algorithms, and their accuracy was evaluated using both the training and validation sets. Ultimately, PPP1R15A, GABARAPL1 and FOXO3 were identified as key autophagy-related genes in OA.\u003c/p\u003e \u003cp\u003eAmong the three identified key genes, PPP1R15A (Protein Phosphatase 1 Regulatory Subunit 15A), also known as Growth Arrest and DNA Damage-Inducible Protein 34 (GADD34), is a critical factor in the mammalian integrated stress response (ISR) [26]. Studies have shown that PPP1R15A plays a crucial role in regulating cell death by promoting protein synthesis and activating death-related pathways such as ER stress, ROS production, and autophagy [27]. Additionally, PPP1R15A has been shown to be associated with both OA and type 2 diabetes mellitus (T2DM) [28]. However, the exact role of PPP1R15A in the pathogenesis of OA remains unclear and warrants further investigation.\u003c/p\u003e \u003cp\u003eGABARAPL1, a member of the ATG8 family, plays a critical role in the development of autophagosomal vesicles [29, 30].Additionally, GABARAPL1 facilitates the fusion of autophagosomes with lysosomes[31]. Research has shown that inhibiting GABARAPL1 expression can regulate the autophagic process in OA chondrocytes[32]. Another study suggests that GABARAPL1 may modulate the immune microenvironment of OA by affecting immune cell function, thus contributing to the onset and progression of the disease [33]. Although there is a strong association between GABARAPL1 and OA, further investigation is needed to fully understand its role in the pathogenesis of OA.\u003c/p\u003e \u003cp\u003eFOXO3 is a member of the FoxO transcription factor family, primarily involved in regulating cellular senescence, apoptosis, autophagy, and oxidative stress [34]. Additionally, FOXO3 regulates osteocyte function and intercellular signaling, influencing bone development and bone mass, thereby contributing to the development of osteoarthritis and osteoporosis[35]. Moreover, FOXO3 plays a critical role in maintaining systemic homeostasis and preventing meniscal injury [36]. Although the importance of FOXO3 in OA is well-established, its specific role in the molecular mechanisms of OA-related autophagy remains unclear and warrants further research.\u003c/p\u003e \u003cp\u003eAlthough this study identified PPP1R15A, GABARAPL1 and FOXO3 as key autophagy-related genes in OA through bioinformatics and machine learning, there are still some limitations. First, the sample size is relatively small, and increasing the sample size is needed to enhance the reliability of the experimental results. Second, the validation methods are limited; further validation using gene knockout, cell models, and animal models is needed to confirm the accuracy of the results. Finally, this study has not explored the immune mechanisms between OA and the key genes in depth, and further research is required to elucidate their molecular mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we found that PPP1R15A, GABARAPL1 and FOXO3 could serve as key autophagy-related genes in OA. Furthermore, the correlation analysis between these three key genes and immune cell infiltration suggests that they may be involved in regulating the progression of OA, providing potential targets for the diagnosis and treatment of OA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo financial support.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design: DJ, XCQ, PW. Collection and assembly of data: DJ, XCQ, ZT. Sample collection: PW, XCQ. Analyzed and interpreted the data: DJ, XCQ, ZT, PW. Gene validation and statistical analysis: DJ, ZT, PW. Manuscript writing: DJ, XCQ, PW. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank all the patients and researchers for their contributions to this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset used in this study is sourced from the GEO database(http://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChe, J., et al., \u003cem\u003eRisk factor prediction and immune correlation analysis of cuproptosis-related gene in osteoarthritis.\u003c/em\u003e J Cell Mol Med, 2024. \u003cstrong\u003e28\u003c/strong\u003e(15): p. e18574.\u003c/li\u003e\n\u003cli\u003eGrandi, F.C. and N. 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Wong, \u003cem\u003eMicroRNAs-associated with FOXO3 in cellular senescence and other stress responses.\u003c/em\u003e Biogerontology, 2024. \u003cstrong\u003e25\u003c/strong\u003e(1): p. 23-51.\u003c/li\u003e\n\u003cli\u003eMa, X., et al., \u003cem\u003eThe Roles of FoxO Transcription Factors in Regulation of Bone Cells Function.\u003c/em\u003e Int J Mol Sci, 2020. \u003cstrong\u003e21\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eLee, K.I., et al., \u003cem\u003eFOXO1 and FOXO3 transcription factors have unique functions in meniscus development and homeostasis during aging and osteoarthritis.\u003c/em\u003e Proc Natl Acad Sci U S A, 2020. \u003cstrong\u003e117\u003c/strong\u003e(6): p. 3135-3143.\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":"osteoarthritis, autophagy, differentially expressed genes, machine learning, key genes, immune cell infiltration","lastPublishedDoi":"10.21203/rs.3.rs-5617353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5617353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOsteoarthritis (OA) is a common degenerative joint disease affecting the elderly worldwide. Although increasing evidence suggests a close relationship between autophagy and OA, its pathogenesis remains unclear. This study aimed to identify autophagy-related genes in OA using bioinformatics and machine learning methods. Three OA datasets (GSE55235, GSE55457 and GSE12021) were retrieved from the GEO database for differential analysis. Subsequently, differentially expressed genes (DEGs) were intersected with autophagy-related genes to identify differentially expressed autophagy-related genes (DEARGs), which were then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Subsequently, potential key genes were selected using three machine learning algorithms (LASSO, SVM and RF) and their diagnostic accuracy was validated using an external dataset (GSE114007) to determine the key genes. Next, potential interactions between the key genes were predicted using the GeneMANIA database. Additionally, immune cell infiltration analysis was performed to explore the correlation between the key genes and immune cells. Finally, the expression levels of the key genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). In this study, a total of 27 DEARGs were identified. GO and KEGG enrichment analyses indicated that these DEARGs might be associated with pathways related to cellular immunity, autophagy, and inflammation. Four potential key genes were selected through the use of three machine learning algorithms. Notably, validation with the external dataset revealed that the expression levels of PPP1R15A, GABARAPL1 and FOXO3 were significantly downregulated in OA and exhibited strong diagnostic performance. Immune infiltration analysis showed that PPP1R15A, GABARAPL1 and FOXO3 were positively correlated with activated mast cells and resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, but negatively correlated with plasma cells and M0 macrophages. Finally, qRT-PCR confirmed these results, which were consistent with the bioinformatics analysis.In conclusion, this study identifies PPP1R15A, GABARAPL1 and FOXO3 as autophagy key genes in OA, providing potential targets for the diagnosis and treatment of OA.\u003c/p\u003e","manuscriptTitle":"Identification and Validation of Autophagy-Related Genes in Osteoarthritis through Bioinformatics and Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-23 17:07:24","doi":"10.21203/rs.3.rs-5617353/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":"d9eede83-619d-4be3-a1f3-39750ec6ca97","owner":[],"postedDate":"December 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41894993,"name":"Health sciences/Medical research/Biomarkers/Diagnostic markers"},{"id":41894994,"name":"Health sciences/Pathogenesis/Inflammation/Chronic inflammation"},{"id":41894995,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-05-02T04:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-23 17:07:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5617353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5617353","identity":"rs-5617353","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}