Exploration of Short-chain Fatty Acid-Associated Hub Genes and potential therapeutic targets in Primary Open-Angle Glaucoma

Exploration of Short-chain Fatty Acid-Associated Hub Genes and potential therapeutic targets in Primary Open-Angle Glaucoma | 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 Exploration of Short-chain Fatty Acid-Associated Hub Genes and potential therapeutic targets in Primary Open-Angle Glaucoma Wenbin Huang, Jifa Kuang, Ailing Li, Yan Liang, Feilan Chen, Yu Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4150868/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 Purpose Glaucoma is a progressive optic neuropathy with degeneration of retinal ganglion cells and retinal nerve fiber layer. Studies have shown that short chain fatty acids produced by gut microbiota can regulate intraocular inflammation. The aim of this research was to screen biomarkers associated with short chain fatty acids in glaucoma. Methods Firstly, WGCNA was performed for obtaining the key module genes associated with the primary open-angle glaucoma (POAG). We performed differential expression analysis (POAG samples vs normal samples) to obtain differentially expressed genes (DEGs) in GSE27276 dataset. The short chain fatty acids related differentially expressed genes (SCFAR-DEGs) were obtained by overlapping DEGs, short chain fatty acids related genes (SCFARGs) and key module genes. Three machine learning algorithms were implemented to select short chain fatty acids related biomarkers. We performed immune infiltration and GSEA based on biomarkers. Results A sum of 2433 key module genes associated with POAG were identified. We identified 615 DEGs between two groups. Soon afterwards, 10 SCFAR-DEGs were obtained through overlapping DEGs, SCFARGs and key module genes. Moreover, 5 biomarkers associated with short chain fatty acids, including HBB , ZFP36 , NFKBIA , TIMP2 and NAMPT , were screened via three machine learning algorithms. The immune infiltration and GSEA analysis suggested that these biomarkers were related to the function of antigen presentation and some differential immune cells. Conclusion Overall, we obtained five short chain fatty acids related biomarkers ( THBB , ZFP36 , NFKBIA , TIMP2 and NAMPT ) associated with POAG, which laid a theoretical foundation for the treatment of glaucoma. Glaucoma Primary open-angle glaucoma short chain fatty acids Biomarkers Machine learning Immune cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Figure 23 1. Introduction Glaucoma is a progressive optic neuropathy characterized by the degeneration of retinal ganglion cells and the retinal nerve fiber layer, leading to alterations in the optic nerve head. 1 It ranks as the second leading cause of blindness worldwide and constitutes a major contributor to irreversible blindness, accounting for 8% of all cases of blindness, affecting approximately 3.12 million individuals. 2 Primary open-angle glaucoma(POAG)is the most prevalent subtype, and its incidence increases with age. Glaucoma stands as a prominent cause of blindness on a global scale, particularly in developing countries where the rate of glaucoma-related blindness is elevated. 3 , 4 Although existing treatments are incapable of reversing glaucomatous damage to the visual system, early diagnosis and intervention can effectively manage disease progression. In most instances, glaucoma presents as a chronic condition necessitating lifelong management. The eye is traditionally considered an immune-privileged site. However, the immune privilege of the eye is compromised in certain diseases such as glaucoma, where alterations in blood-retinal barrier integrity and cytokine production occur 5 , resulting in the infiltration of autoimmune antibodies, inflammatory leukocytes, and macrophages into the retina, indicative of glaucomatous manifestations. 6 , 7 In glaucoma, both innate and adaptive immune responses are prominently activated. Neuroglial cells, including microglia, astrocytes, and Müller cells, undertake immunosurveillance within the retina and exhibit early activation during the initial stages of glaucoma. 8 Short-chain fatty acids (SCFAs), primarily acetate, propionate, and butyrate 9 , are microbial products of dietary fiber fermentation in the colon that contribute to maintaining human health. 10 SCFAs play a vital role not only in preserving intestinal barrier integrity 11 but also in regulating inflammation within extraintestinal tissues/organs, such as the lungs 12 , kidneys 13 , and brain. 14 Oral administration of SCFAs has been demonstrated to mitigate experimental autoimmune uveitis (EAU) induced by retinal antigens and Complete Freund's adjuvant (CFA) in mice, underscoring the capacity of gut microbiota-derived SCFAs to modulate intraocular inflammation. 15 However, the precise mechanism of SCFA action in glaucoma remains unclear. Emerging studies related to glaucoma suggest that SCFAs and their associated genes may contribute to the onset and progression of the disease. 16 For instance, specific SCFAs might influence intraocular pressure by regulating aqueous humor secretion and drainage, thereby affecting the pathogenesis of glaucoma. Furthermore, SCFA-associated genes may participate in ocular inflammatory responses and processes such as retinal cell apoptosis 17 , 18 , exerting influences on glaucoma development. Therefore, investigating the regulatory roles and functional disparities of SCFA-associated genes in glaucoma holds significant importance for comprehending the underlying mechanisms of glaucomatous pathogenesis and identifying novel therapeutic targets. In this study, we comprehensively analyze differentially expressed genes associated with SCFAs in glaucoma and delve into the underlying immunological mechanisms. We anticipate that our research will enhance the understanding of glaucoma and provide novel insights for therapeutic strategies. 2. Materials and methods 2.1 Data source Gene expression profile of GSE27276 dataset was collected from GEO, which included 19 normal samples and 17 POAG samples. In this study, it was applied as the training set. The GSE138125 dataset, as a validation set, consisted of 4 POAG samples and 4 normal samples. All samples were taken from the human trabecular meshwork organization. A sum of 344 short chain fatty acids related genes (SCFARGs) were derived from GeneCards database. 2.2 WGCNA The POAG/control was considered as clinical trait for WGCNA via ‘WGCNA’ (version 1.70-3) package. Firstly, we clustered all samples and removed outliers to ensure the accuracy of the analysis. Then, trait heat map and sample dendrogram were constructed, and the soft threshold was determined. The similarity between genes was calculated according to the adjacency, and the phylogenetic tree between genes was obtained. The modules were divided via dynamic tree cutting algorithm. Finally, the modules with the the highest correlation to POAG/control were used as key modules. 2.3 Differential analysis The ‘limma’ package (version 3.48.3) 19 was executed to obtain differentially expressed genes (DEGs) between POAG group and normal group in training set. The |log2FC| > 0.5 and adjust.p.value < 0.05 were determined as the liminal value. The volcano plot and heat map were applied to show DEGs via ‘ggplot2’ 20 . The top20 (top 10 down-regulated and top 10 up-regulated) of DEGs was displayed in heat map by ‘circlize’ package 21 . 2.4 Functional enrichment analysis The short chain fatty acids related differentially expressed genes (SCFAR-DEGs) were obtained through overlapping DEGs, SCFARGs and key module genes associated with POAG/control. GO and KEGG enrichment analysis of was SCFAR-DEGs conducted via ‘clusterProfiler’ package 22 . The p.adjust < 0.05 was selected as criteria. 2.5 Machine learning methods LASSO, Boruta algorithm and SVM-RFE were applied to screen important genes in GSE27276 dataset based on SCFAR-DEGs. The biomarkers were obtained through overlapping genes from three algorithms. Moreover, receiver operating characteristic (ROC) curve was plotted to evaluate the value of the biomarkers by ‘pROC’ package 23 . 2.6 Clinical nomogram model The nomogram containing diagnostic biomarkers were drawn via ‘rms’ to predict the risk of POAG. Evaluation of the predictive effect was done by the calibration and ROC curves. 2.7 Immune feature and GSEA The CIBERSORT algorithm was applied to calculate the relative abundance of 22 immune cells infiltrated in POAG microenvironment. Subsequently, correlation between biomarkers and differential immune cells were calculated and displayed. In addition, GSEA was conducted to explore the potential KEGG pathways associated with biomarkers through ‘enrichplot’ package 24 . The p.adjust < 0.05 was selected as criteria. 2.8 Potential drug prediction and molecular docking In order to explore the potential therapeutic drugs for diagnostic biomarkers in POAG, the targeting drugs were identified through DGIdb database. To evaluate the affinity of potential drugs for biomarkers, the molecular structure of the drugs was obtained from the PDB database. The 3D structure SDF format file of the therapeutic drug was from the NCBIPubChem compound database. Autodock Vina (v.1.2.2) was selected for molecular docking in CB-Dock . 3. Results 3.1 Identification of key module genes in GSE27276 dataset To seek out pivotal modules related to POAG, we conducted the WGCNA. The results of sample clustering indicated that there were one outlier sample ( sFig.1 ). The optimal soft threshold was 5. When the mean connectivity was tended to 0, the ordinate scale-free fit the index, and the sign R2 approached the threshold value of 0.85 (red line) (Fig. 1 A). A total of 20 modules were obtained by the dynamic tree cut algorithm (Fig. 1 B). MEbrown module was markedly positive correlated with POAG (R^2 = 0.8, P.value = 0.01), while MEyellow was negative related to POAG (R^2= -0.72, P.value = 0.01) (Fig. 1 C). Thus, 2433 key module genes were obtained for subsequent analyses. 3.2 Identification and functional enrichment analysis of SCFAR-DEGs A sum of 615 DEGs were identified between tumor and POAG groups, including 295 up-regulated and 320 down-regulated DEGs (Fig. 2 A). We selected the top 10 down-regulated and top 10 up-regulated genes to display in the heat map (Fig. 2 B). Thus, 10 SCFAR-DEGs were obtained through overlapping DEGs, SCFARGs and key module genes associated with POAG (Fig. 2 C). In order to uncover potential mechanisms for SCFAR-DEGs, we proceeded with functional enrichment analysis. The top10 GO items were shown in Fig. 2 D-E. We observed that the above genes were principally linked to ‘reactive oxygen species metabolic process’ and ‘negative regulation of cellular catabolic process’ (Fig. 2 D). In addition, the KEGG results suggested that these genes were mainly enriched in the ‘IL − 17 signaling pathway’, and ‘TNF signaling pathway’ (Fig. 2 E). 3.3 Identification of short chain fatty acids related biomarkers for POAG To further dig out the key genes, LASSO, Boruta and SVM-RFE algorithm were performed on SCFAR-DEGs with interaction to unearth the optima. LASSO regression analysis identified 9 significant genes, including HBB , ZFP36 , SCD , HBG1 , NFKBIA , TIMP2 , NAMPT , C5 and CEACAM7 (Fig. 3 A-B). Then, 5 feature genes were obtained via SVM-RFE, including HBB , NAMPT , ZFP36 , NFKBIA and TIMP2 (Fig. 3 C). Meanwhile, 9 feature genes were obtained via Boruta, including HBB , ZFP36 , SCD , HBG1 , NFKBIA , TIMP2 , NAMPT , C5 , MMP3 and CEACAM7 (Fig. 3 D). Eventually, 5 short chain fatty acids related diagnostic biomarkers ( HBB , ZFP36 , NFKBIA , TIMP2 and NAMPT ) were obtained by intersecting the genes obtained by the three machine learning algorithms (Fig. 3 E). The AUC value of 5 short chain fatty acids related biomarkers was greater than 0.9, indicating that the these genes had good accuracy (Fig. 3 F). 3.4 Construction and verification of nomogram The nomogram containing 5 diagnostic biomarkers was generated in GSE27276 dataset (Fig. 4 A). The calibration and ROC curves proved that the performance of the short chain fatty acids related POAG diagnostic model was effective (Fig. 4 B-C). 3.4 Immune infiltration and functional enrichment analysis To explore the immune microenvironment of POAG, we showed the abundance of 22 immune cells between two sample groups in GSE27276 (Fig. 5 A). Notably, there were 5 immune cell abundances that differed significantly, including M2 macrophages, monocytes, CD4 memory activated T cells, activated NK cells and plasma cells (Fig. 5 B). The correlation analysis revealed that TIMP2 was significantly positive associated with M2 macrophages. Meantime, ZFP36 was positive associated with Plasma cells (Fig. 5 C). To further study the potential roles of HBB , ZFP36 , NFKBIA , TIMP2 and NAMPT in POAG, we performed single-gene GSEA on biomarkers (Fig. 5 D). The results showed that HBB , NAMPT and TIMP2 were related to ‘antigen processing and presentation’, while ‘allograft rejection’ was associated with NFKBIA and ZFP36 . 3.5 Biomarkers-drug interaction network In order to look for potential drugs targeting biomarkers, we predict small molecule drugs through DGIdb database. There were 40 drugs with therapeutic potential on three biomarkers ( HBB , NFKBIA and NAMPT ) (Fig. 6 A). Drugs targeting HBB was AES-103, FLUOROURACIL and NEBULARINE etc. Drugs targeting NFKBIA was GEFITINIB and CHEMBL401565 etc. And drugs targeting NAMPT was CHS-828, TEGLARINAD and DAPORINAD. We selected the top five drugs with the largest query score and interaction score corresponding to the biomarkers for molecular docking. Molecular docking results showed that HBB and VOXELOTOR, EFAPROXIRAL, DEFERITAZOLE, NFKBIA and CHEMBL401565, CHEMBL256967, CHEMBL1940084, PEPEROMIN E had strong binding energy (Fig. 6 B; Table 1 ). 4. Discussion POAG is a multifactorial disorder with a complex etiology. Despite this complexity, the precise mechanisms of SCFA-related genes in POAG remain elusive, warranting the establishment of novel molecular pathways for therapeutic and diagnostic advancements. Currently, machine learning algorithms and WGCNA have matured and are extensively employed in predicting disease markers and therapeutic targets. In this study, we retrieved transcriptional data from the GEO database and utilized WGCNA in combination with machine learning to identify five SCFA-related hub genes in POAG: HBB, ZFP36, NFKBIA, TIMP2 , and NAMPT , which were further validated using additional datasets. ROC curve analysis revealed excellent diagnostic performance for these hub genes. Additionally, a heatmap model constructed from the collective action of these five hub genes effectively predicted the risk of POAG onset. The HBB gene encodes hemoglobin subunit beta, a major component of red blood cells responsible for oxygen transport. Recent findings indicate HBB expression in the human eye, not only in the retina and choroid but also with increased expression in ocular tissues of glaucoma patients, suggesting potential involvement in ocular inflammation and retinal cell apoptosis. 25 The ZFP36 gene encodes a transcription factor involved in RNA degradation and post-transcriptional regulation. Emerging research highlights ZFP36' s significance in VEGF-stimulated developmental retinal angiogenesis, underscoring its role in supporting retinal ganglion cell metabolic processes. 26 NFKBIA and TIMP2 genes play pivotal roles in extracellular matrix degradation and remodeling, maintaining trabecular meshwork tissue homeostasis. NFKBIA siRNA in trabecular meshwork cells enhances MMP-2 expression and activity through NF-κB pathway activation, potentially impacting uveoscleral outflow, a potential target for glaucoma modulation. 27 NAMPT , encoding nicotinamide phosphoribosyltransferase, is a critical enzyme in NAD + biosynthesis. NAD + is a pivotal intracellular coenzyme involved in various metabolic and stress responses. Studies suggest NAMPT 's involvement in cellular energy metabolism and stress responses within ocular tissues of glaucoma patients, contributing to disease progression. 28 Collectively, these genes play roles in multiple pathological processes, including ocular inflammation, retinal cell apoptosis, NF-κB pathway activation, abnormal extracellular matrix remodeling, cellular energy metabolism, and stress responses, thus potentially serving as biomarkers for glaucoma diagnosis and therapeutic targets. Functional enrichment analysis reveals HBB, NAMPT , and TIMP2 associations with the "Antigen processing and presentation" pathway, while NFKBIA and ZFP36 associate with the "Graft rejection" pathway, pathways potentially implicated in POAG development. These pathways are integral to immune systems, encompassing antigen-presenting cells, MHC molecules, T-cell receptors, and co-stimulatory molecules, orchestrating immune responses through antigen processing and presentation to activate or inhibit immunity. Aberrant immune and inflammatory responses in ocular tissues of glaucoma patients have been reported 29 , 30 , potentially linked to pathway alterations. However, the precise roles of these pathways and genes necessitate further exploration. Comparison of immune cell infiltration between POAG and control groups using the CIBERSORT algorithm reveals significant differences in five of 22 immune cell types. Furthermore, correlations between differentially expressed immune cells and hub genes are analyzed. These findings enhance our understanding of SCFA-related hub gene functions in POAG development. Notably, POAG patients exhibit differential expression of M2 macrophages 31 and plasma cells 32 , both potentially pivotal in ocular inflammation, apoptosis, and other pathological processes relevant to POAG. The interplay of these immune cells likely modulates the actions of genes such as HBB, NAMPT, TIMP2, NFKBIA , and ZFP36 in POAG, affecting inflammatory factor expression, matrix metalloproteinase regulation, and more. These findings offer novel perspectives for POAG diagnosis and treatment. Using the DGidb database, we predict potential therapeutic drugs for POAG, unveiling new directions for clinical interventions. Predicted drugs, including immune modulators, anti-inflammatory agents, and antioxidants, include Voxelotor, used for sickle cell anemia treatment 33 , with potential benefits arising from improved hemorheological characteristics, reduced cellular aggregation, and damage. Chembl401565 and Chembl256967, although currently lacking widespread clinical application, possess reported antioxidant and anti-inflammatory properties. Peperomin E, a natural compound found in plants like pepper, boasts antioxidant, anti-inflammatory, antibacterial, and anticancer effects. 34 These drugs may impact POAG through immune system modulation and ocular tissue inflammation reduction, aligned with the functions of some predicted POAG-related genes. However, further investigation is required to elucidate the effects of these predicted drugs in the context of POAG. This comprehensive study employs systematic gene expression profiling to reveal a set of SCFA-associated genes relevant to POAG, unraveling molecular mechanisms underlying POAG pathogenesis. Additionally, drug network and immune cell infiltration analyses uncover potential therapeutic avenues and characterize immune cell involvement, providing new directions for glaucoma treatment. Although limitations such as sample size, this study's innovative and practical findings highlight the potential roles of SCFA-associated diagnostic markers in POAG. Future research will continue exploring these markers to enhance glaucoma diagnosis and treatment strategies 5. Conclusions Overall, we obtained five short chain fatty acids related biomarkers ( THBB , ZFP36 , NFKBIA , TIMP2 and NAMPT ) associated with POAG, which laid a theoretical foundation for the treatment of glaucoma. List Of Abbreviations Primary open-angle glaucoma（POAG） Short-chain fatty acids (SCFAs) experimental autoimmune uveitis (EAU) Complete Freund's adjuvant (CFA) short chain fatty acids related genes (SCFARGs) differentially expressed genes (DEGs) short chain fatty acids related differentially expressed genes (SCFAR-DEGs) receiver operating characteristic (ROC) weighted gene co-expression network analysis (WGCNA) Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding The study was supported by the National Natural Science Foundation of China (82000890), Hainan Provincial Natural Science Foundation of China (820RC780), Young Talents’Science and Technology Innovation Project of Hainan Association for Science and Technology (QCXM202020), and Hainan Province Clinical Medical Center. Authors' contributions W.H. and Y.F.: conception and study design, J.K., A.L, Y.L, and F.C.: data analysis and interpretation and writing the manuscript. All authors read and approved the final manuscript Acknowledgements Not applicable References Stein JD, Khawaja AP, Weizer JS. Glaucoma in Adults-Screening, Diagnosis, and Management: A Review. JAMA. 2021;325(2):164–74. Pascolini D, Mariotti SP. Global estimates of visual impairment: 2010. Br J Ophthalmol. 2012;96(5):614–8. Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90(3):262–7. Tham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014;121(11):2081–90. Perez VL, Caspi RR. Immune mechanisms in inflammatory and degenerative eye disease. Trends Immunol. 2015;36(6):354–63. Gramlich OW, Beck S, von Thun UHN, et al. Enhanced insight into the autoimmune component of glaucoma: IgG autoantibody accumulation and pro-inflammatory conditions in human glaucomatous retina. PLoS ONE. 2013;8(2):e57557. Callahan MK, Ransohoff RM. Analysis of leukocyte extravasation across the blood-brain barrier: conceptual and technical aspects. Curr Allergy Asthma Rep. 2004;4(1):65–73. Wei X, Cho KS, Thee EF, et al. Neuroinflammation and microglia in glaucoma: time for a paradigm shift. J Neurosci Res. 2019;97(1):70–6. Lymperopoulos A, Suster MS, Borges JI. Short-Chain Fatty Acid Receptors and Cardiovascular Function. Int J Mol Sci 2022;23(6). Cong J, Zhou P, Zhang R. Intestinal Microbiota-Derived Short Chain Fatty Acids in Host Health and Disease. Nutrients 2022;14(9). Kim CH, Park J, Kim M. Gut microbiota-derived short-chain Fatty acids, T cells, and inflammation. Immune Netw. 2014;14(6):277–88. Theiler A, Barnthaler T, Platzer W, et al. Butyrate ameliorates allergic airway inflammation by limiting eosinophil trafficking and survival. J Allergy Clin Immunol. 2019;144(3):764–76. Andrade-Oliveira V, Amano MT, Correa-Costa M, et al. Gut Bacteria Products Prevent AKI Induced by Ischemia-Reperfusion. J Am Soc Nephrol. 2015;26(8):1877–88. Matt SM, Allen JM, Lawson MA, et al. Butyrate and Dietary Soluble Fiber Improve Neuroinflammation Associated With Aging in Mice. Front Immunol. 2018;9:1832. Nakamura YK, Janowitz C, Metea C, et al. Short chain fatty acids ameliorate immune-mediated uveitis partially by altering migration of lymphocytes from the intestine. Sci Rep. 2017;7(1):11745. Mahalingam K, Chaurasia AK, Gowtham L, et al. Therapeutic potential of valproic acid in advanced glaucoma: A pilot study. Indian J Ophthalmol. 2018;66(8):1104–8. Zysset-Burri DC, Morandi S, Herzog EL, et al. The role of the gut microbiome in eye diseases. Prog Retin Eye Res. 2023;92:101117. Napolitano P, Filippelli M, Davinelli S, et al. Influence of gut microbiota on eye diseases: an overview. Ann Med. 2021;53(1):750–61. Phipson B, Lee S, Majewski IJ, ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION, et al. Ann Appl Stat. 2016;10(2):946–63. Ito K, Murphy D. Application of ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst Pharmacol. 2013;2(10):e79. Gu Z, Gu L, Eils R, et al. circlize Implements enhances circular visualization R Bioinf. 2014;30(19):2811–2. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. Kumar L. Mfuzz: a software package for soft clustering of microarray data. Bioinformation. 2007;2(1):5–7. Feng J, Xu J. Identification of pathogenic genes and transcription factors in glaucoma. Mol Med Rep. 2019;20(1):216–24. Cicchetto AC, Jacobson EC, Sunshine H, et al. ZFP36-mediated mRNA decay regulates metabolism. Cell Rep. 2023;42(5):112411. Lan YQ, Zhang C, Xiao JH, et al. Suppression of IkappaBalpha increases the expression of matrix metalloproteinase-2 in human ciliary muscle cells. Mol Vis. 2009;15:1977–87. Dai M, Hu Z, Kang Z, Zheng Z. Based on multiple machine learning to identify the ENO2 as diagnosis biomarkers of glaucoma. BMC Ophthalmol. 2022;22(1):155. Jiang S, Kametani M, Chen DF. Adaptive Immunity: New Aspects of Pathogenesis Underlying Neurodegeneration in Glaucoma and Optic Neuropathy. Front Immunol. 2020;11:65. Stepp MA, Menko AS. Immune responses to injury and their links to eye disease. Transl Res. 2021;236:52–71. Shapouri-Moghaddam A, Mohammadian S, Vazini H, et al. Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol. 2018;233(9):6425–40. Bell K, Und HN, Teister J, Grus F. Modulation of the Immune System for the Treatment of Glaucoma. Curr Neuropharmacol. 2018;16(7):942–58. Blair HA, Voxelotor. First Approval Drugs. 2020;80(2):209–15. Lin M, Zhu Q, Li Y, Pan J, Peperomin E. Induces Apoptosis and Cytoprotective Autophagy in Human Prostate Cancer DU145 Cells In Vitro and In Vivo. Planta Med. 2021;87(8):620–30. Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files table1.xlsx sFig1.pdf sFig1. Sample Hierarchical Clustering with Phenotype Introduction (After Sample Removal). 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-4150868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283563250,"identity":"7cc39b36-c229-459a-bf17-530ade7a81e1","order_by":0,"name":"Wenbin Huang","email":"","orcid":"","institution":"Hainan Eye Hospital, Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Huang","suffix":""},{"id":283563251,"identity":"28172b01-499e-46a9-aca6-1731936aa591","order_by":1,"name":"Jifa Kuang","email":"","orcid":"","institution":"Hainan Eye Hospital, Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jifa","middleName":"","lastName":"Kuang","suffix":""},{"id":283563252,"identity":"c1be1677-77ef-48a7-80ef-434f25198456","order_by":2,"name":"Ailing Li","email":"","orcid":"","institution":"Hainan Eye Hospital, Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ailing","middleName":"","lastName":"Li","suffix":""},{"id":283563254,"identity":"0bc26f34-e555-43c5-b42f-2ec20d6d9782","order_by":3,"name":"Yan Liang","email":"","orcid":"","institution":"Hainan Eye Hospital, Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liang","suffix":""},{"id":283563256,"identity":"6e59dc55-2e88-409a-ac5e-baf6a91b1ad6","order_by":4,"name":"Feilan Chen","email":"","orcid":"","institution":"Hainan Eye Hospital, Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feilan","middleName":"","lastName":"Chen","suffix":""},{"id":283563258,"identity":"4850257a-a869-4157-b4c6-7ffa810da1d7","order_by":5,"name":"Yu Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIie3PsQrCMBCA4Qsn6RJox5O+RKBQOhR8EJeUQjdxdSgaEHTsWt/CR6gE6tjVseALtC8gKoJrMwrm3w7u4zgAl+sH8zTrIXvQrkK89FZENChh4Ak7HXkurQmr+YadOxEHdoRyHgpBGBmIAcp0aUmIeGyg6KEtVnqKLGjdhkKSeJGrZNpMk88VRRTt2YGsybxuSEpEbknEHZNRkyLDUSqbX4SXs1umt8qvurEfynSaQKBm9B3U5Po7v8HBatHlcrn+tyck+Te44XU2GwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University, Hainan Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2024-03-22 15:46:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4150868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4150868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53649149,"identity":"4b129286-7bc7-4e3f-b54f-a5431cfcc9c7","added_by":"auto","created_at":"2024-03-28 14:23:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 1A.\u003c/strong\u003e Soft Thresholding Selection. The optimal soft threshold value was determined primarily based on the left plot, which illustrates the scale-free fit index (y-axis) across different soft threshold values (x-axis). The red line indicates the chosen scale-free fit index value. As observed from the left plot, when the scale-free fit index reaches 0.85, the minimum soft threshold value for constructing a scale-free network is determined to be 5. Hence, 5 is selected as the optimal soft threshold value for subsequent analysis. The right plot presents network connectivity under different soft threshold values.\u003c/p\u003e","description":"","filename":"Fig1A.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/1b333f3f4df5d436fb8f7c0d.png"},{"id":53649159,"identity":"0f98ab9c-cde8-4c94-8721-5272cd9adecd","added_by":"auto","created_at":"2024-03-28 14:23:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 1B.\u003c/strong\u003e Co-expression Module Identification. This figure consists of two parts. The upper portion displays a hierarchical clustering dendrogram of genes, while the lower portion represents gene modules, corresponding to network modules. Notably, genes with closer distances (clustered on the same branch) are grouped into the same module.\u003c/p\u003e","description":"","filename":"Fig1B.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/a1061734051992f70729cb21.png"},{"id":53649152,"identity":"1167f572-7e39-4413-ae97-ac06f5b8b308","added_by":"auto","created_at":"2024-03-28 14:23:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 1C. \u003c/strong\u003eModule-Phenotype Correlation Heatmap. The leftmost color blocks represent modules, while the rightmost color bar represents the range of correlations. In the central heatmap section, darker colors indicate higher correlation, with red representing positive correlation and blue representing negative correlation. The numbers within each cell indicate the correlation and significance. In the example shown, the brown module exhibits the highest positive correlation with POAG, while the yellow module exhibits the highest negative correlation with POAG.\u003c/p\u003e","description":"","filename":"Fig1C.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/369a30f7612dfb56925fa3cc.png"},{"id":53649936,"identity":"d19d52f2-0552-4bb6-b50c-705e2df12237","added_by":"auto","created_at":"2024-03-28 14:31:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":476595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2A. \u003c/strong\u003eDifferential Expression Gene Distribution Volcano Plot between POAG and Control. Red points indicate upregulated genes, cyan points indicate downregulated genes, and gray points indicate genes with no significant difference.\u003c/p\u003e","description":"","filename":"Fig2A.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/4dddbead18aae5c2b0f6b9f4.png"},{"id":53649153,"identity":"07809f7f-6a14-4015-ac55-72623eee826a","added_by":"auto","created_at":"2024-03-28 14:23:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":738692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2B.\u003c/strong\u003e Heatmap of Differential Expression Genes between POAG and Control. Color changes represent expression level variations, with red indicating higher expression and blue indicating lower expression.\u003c/p\u003e","description":"","filename":"Fig2B.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/b2dd0ed398791f919cbe766f.png"},{"id":53649154,"identity":"7b2f402b-0521-4231-976a-1dcc95906654","added_by":"auto","created_at":"2024-03-28 14:23:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":121370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2C.\u003c/strong\u003e Venn Diagram of Candidate Genes Intersection. Green represents differentially expressed genes, purple represents short-chain fatty acid-related genes, and blue represents POAG-related module genes.\u003c/p\u003e","description":"","filename":"Fig2C.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/3e0e0e439a26b71358791af7.png"},{"id":53649151,"identity":"d3c503d9-b90f-4ba9-aafd-eb391ba23f89","added_by":"auto","created_at":"2024-03-28 14:23:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1017504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2D. \u003c/strong\u003eVisualization of Candidate Genes' GO Enrichment Results. This figure is divided into two parts. Left part: The x-axis represents the number of genes involved in GO functions, and the y-axis represents GO function names. Color changes indicate significance p.adjust variations, with red being more significant and green less significant. Right part: Different color bands on the right represent different functions, while the color bands on the left represent the logFC of genes.\u003c/p\u003e","description":"","filename":"Fig2D.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/926111799c6bd7f5964ba967.png"},{"id":53649933,"identity":"2a6fc0da-5108-431c-92eb-0039f7c51ca0","added_by":"auto","created_at":"2024-03-28 14:31:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":910686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2E. \u003c/strong\u003eVisualization of Candidate Genes' KEGG Enrichment Results. Similar to Fig 2D, this figure also has two parts. Left part: The x-axis represents the proportion of genes involved in KEGG pathways, and the y-axis represents KEGG pathway names. Color changes indicate significance p.adjust variations. Bubble sizes represent the number of genes involved in KEGG pathways. Right part: Different color bands represent different pathways, and the color of gene bands on the left represents the logFC of genes.\u003c/p\u003e","description":"","filename":"Fig2E.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/bf52405baaa598e6f627dd74.png"},{"id":53649934,"identity":"19e45256-4187-4849-bbc8-4e8fd3f348cf","added_by":"auto","created_at":"2024-03-28 14:31:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":88666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3A. \u003c/strong\u003eTen-fold Cross-validation of Adjusted Parameters in LASSO Analysis. The x-axis represents the logarithm of lambdas, and the y-axis represents the model error. The optimal lambda value is identified at the lowest point of the red curve, corresponding to a variable count of 9.\u003c/p\u003e","description":"","filename":"Fig3A.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/36de1b5ddf669e56db9ebc9a.png"},{"id":53649157,"identity":"92be478a-5f66-457e-bd45-995cf12212ea","added_by":"auto","created_at":"2024-03-28 14:23:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":132412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3B. \u003c/strong\u003eLasso Coefficient Spectrum. The x-axis represents the logarithm of lambdas, and the y-axis represents the variable coefficients. As lambdas increase, variable coefficients tend to approach 0. The optimal lambda value corresponds to the elimination of variables with coefficients equal to 0.\u003c/p\u003e","description":"","filename":"Fig3B.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/773d696d88819e645f082727.png"},{"id":53649937,"identity":"90aaa155-8b8e-4f21-9547-1da52fd5271f","added_by":"auto","created_at":"2024-03-28 14:31:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":66561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3C. \u003c/strong\u003ePrediction Accuracy of Different Feature Gene Counts. The x-axis represents the number of feature genes, and the y-axis represents the model's prediction accuracy.\u003c/p\u003e","description":"","filename":"Fig3C.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/33fad857e18ae7ad7da68f24.png"},{"id":53649935,"identity":"65cf852f-5078-4a46-8a84-310a2d2110ac","added_by":"auto","created_at":"2024-03-28 14:31:05","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":56281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3D. \u003c/strong\u003eImportance of Different Feature Genes. The x-axis represents feature genes, and the y-axis represents the importance of feature genes.\u003c/p\u003e","description":"","filename":"Fig3D.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/5febc7c491233e8b47e79f21.png"},{"id":53649168,"identity":"c7dd930b-cd7d-49bb-a934-e8b63cb2fd21","added_by":"auto","created_at":"2024-03-28 14:23:06","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":106429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3E. \u003c/strong\u003eVenn Diagram of Feature Genes Correlation.\u003c/p\u003e","description":"","filename":"Fig3E.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/81be99a956ca0efed6c2f3d1.png"},{"id":53649932,"identity":"1d4cdd34-17b9-4b4a-a8eb-c90013495417","added_by":"auto","created_at":"2024-03-28 14:31:03","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":119466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3F.\u003c/strong\u003e Hub Gene ROC Curves.\u003c/p\u003e","description":"","filename":"Fig3F.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/2e07514d2718f76fa5e05210.png"},{"id":53649171,"identity":"b82557ce-810d-49d7-bbf1-edbc2bb78207","added_by":"auto","created_at":"2024-03-28 14:23:06","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":90349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4A. \u003c/strong\u003eColumn Line Plot Model for Hub Genes. The variables in the predictive model are: \u003cem\u003eHBB, ZFP36, NFKBIA, TIMP2, NAMPT\u003c/em\u003e. Each variable's segment on the line represents its range of values, and the segment's length reflects the contribution of the factor to the outcome event. The single-item scores (Points) indicate the scores for each variable at different values. The Total Points represent the combined scores for all variable values. The \"Risk of POAG\" indicates the risk of POAG for the sample.\u003c/p\u003e","description":"","filename":"Fig4A.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/93a3e1e5fbc6ee77ce4aa4c8.png"},{"id":53649170,"identity":"c83d1464-ec78-4300-b093-06f94008bad4","added_by":"auto","created_at":"2024-03-28 14:23:06","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":179800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4B. \u003c/strong\u003eColumn Line Plot Model Calibration Curve. The x-axis represents the predicted probability of having POAG disease according to the nomogram, while the y-axis represents the actual probability of having POAG disease. The black dashed line represents a perfect prediction. The blue solid line represents the observed performance of the nomogram, while the black solid line represents the bias-corrected performance obtained through Bootstrapping (1000 repetitions).\u003c/p\u003e","description":"","filename":"Fig4B.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/31fabbb7d5bc979a7153c778.png"},{"id":53649165,"identity":"9d1b02ec-2859-4754-8c8f-4dbbfe67215e","added_by":"auto","created_at":"2024-03-28 14:23:05","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":59053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4C.\u003c/strong\u003e Column Line Plot Model ROC Curves.\u003c/p\u003e","description":"","filename":"Fig4C.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/4824d1586210d5c495c8089f.png"},{"id":53649162,"identity":"76eecd32-a374-484e-8e86-6ee0890d75be","added_by":"auto","created_at":"2024-03-28 14:23:05","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":254927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5A.\u003c/strong\u003e Histogram of the Abundance of 22 Immune Infiltrating Cells in Samples.\u003c/p\u003e","description":"","filename":"Fig5A.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/6aa77d251a082907fc9eabaa.png"},{"id":53649166,"identity":"9b9b8875-ddae-4ceb-9bd6-39a968294586","added_by":"auto","created_at":"2024-03-28 14:23:06","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":189681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5B. \u003c/strong\u003eBoxplot of Abundance of 22 Immune Infiltrating Cells between POAG and Control. The horizontal labels indicate that red annotations indicate significantly higher immune cell abundance in the POAG group, while cyan annotations indicate significantly higher immune cell abundance in the Control group.\u003c/p\u003e","description":"","filename":"Fig5B.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/85f3fbaea45a1a9ef0adad7a.png"},{"id":53649160,"identity":"7ae38920-0767-4449-8adf-95152193aad6","added_by":"auto","created_at":"2024-03-28 14:23:05","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":152919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5C. \u003c/strong\u003eLollipop Plot of Correlation between Differential Immune Infiltrating Cells and Hub Genes.\u003c/p\u003e","description":"","filename":"Fig5C.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/dc8f3ff2c365602bbaf85ce5.png"},{"id":53649938,"identity":"68aefc25-3992-4b58-806a-43a4c4504171","added_by":"auto","created_at":"2024-03-28 14:31:06","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":1032242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5D. \u003c/strong\u003eHub Gene GSEA Enrichment Analysis. The figure can be divided into two parts. The top part shows five lines representing the gene enrichment score. The y-axis represents the corresponding Running ES, and the peak value represents the enrichment score of the gene set. The bottom part resembles a barcode, with each vertical line representing a gene in the gene set.\u003c/p\u003e","description":"","filename":"Fig5D.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/0184679163ee2f75575098c6.png"},{"id":53649173,"identity":"3de390ac-e939-44a2-8962-04240f3d7efe","added_by":"auto","created_at":"2024-03-28 14:23:06","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":756602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 6A. \u003c/strong\u003eRelationship Between Hub Genes and Small Molecule Drugs. Orange circles represent hub genes, and light green elongated squares represent small molecule drugs.\u003c/p\u003e","description":"","filename":"Fig6A.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/78b41f917f0adf575644d783.png"},{"id":53649169,"identity":"3a3dd2a4-0ff6-4ba3-9656-aea6c2a3f0cc","added_by":"auto","created_at":"2024-03-28 14:23:06","extension":"png","order_by":23,"title":"Figure 23","display":"","copyAsset":false,"role":"figure","size":710048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 6B.\u003c/strong\u003e Docking of Hub Genes with Small Molecule Drugs. Cyan represents protein structure, purple represents drug structure, and peach-pink represents amino acid residue structure. Examples include the docking of HBB with different small molecule drugs.\u003c/p\u003e","description":"","filename":"fig6b.png","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/05fa02c79959f749691aca13.png"},{"id":54557200,"identity":"ad299c7c-7b37-42a9-b0e9-42409add4aab","added_by":"auto","created_at":"2024-04-12 08:52:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5343650,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/562036fd-fd06-4d6d-96e8-71de6ecba561.pdf"},{"id":53649155,"identity":"2f16e635-f337-4de2-b402-560300c520d7","added_by":"auto","created_at":"2024-03-28 14:23:04","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11111,"visible":true,"origin":"","legend":"","description":"","filename":"table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/77486586db6067f18fc4205b.xlsx"},{"id":53649163,"identity":"a517830f-550e-4bbf-abf6-948e1046e3d1","added_by":"auto","created_at":"2024-03-28 14:23:05","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":142753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esFig1. \u003c/strong\u003eSample Hierarchical Clustering with Phenotype Introduction (After Sample Removal).\u003c/p\u003e","description":"","filename":"sFig1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4150868/v1/aef1a1c9d141ae8eceab7005.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration of Short-chain Fatty Acid-Associated Hub Genes and potential therapeutic targets in Primary Open-Angle Glaucoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlaucoma is a progressive optic neuropathy characterized by the degeneration of retinal ganglion cells and the retinal nerve fiber layer, leading to alterations in the optic nerve head.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e It ranks as the second leading cause of blindness worldwide and constitutes a major contributor to irreversible blindness, accounting for 8% of all cases of blindness, affecting approximately 3.12\u0026nbsp;million individuals.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Primary open-angle glaucoma(POAG)is the most prevalent subtype, and its incidence increases with age. Glaucoma stands as a prominent cause of blindness on a global scale, particularly in developing countries where the rate of glaucoma-related blindness is elevated.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Although existing treatments are incapable of reversing glaucomatous damage to the visual system, early diagnosis and intervention can effectively manage disease progression. In most instances, glaucoma presents as a chronic condition necessitating lifelong management.\u003c/p\u003e \u003cp\u003eThe eye is traditionally considered an immune-privileged site. However, the immune privilege of the eye is compromised in certain diseases such as glaucoma, where alterations in blood-retinal barrier integrity and cytokine production occur \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, resulting in the infiltration of autoimmune antibodies, inflammatory leukocytes, and macrophages into the retina, indicative of glaucomatous manifestations.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In glaucoma, both innate and adaptive immune responses are prominently activated. Neuroglial cells, including microglia, astrocytes, and M\u0026uuml;ller cells, undertake immunosurveillance within the retina and exhibit early activation during the initial stages of glaucoma.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eShort-chain fatty acids (SCFAs), primarily acetate, propionate, and butyrate\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, are microbial products of dietary fiber fermentation in the colon that contribute to maintaining human health.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e SCFAs play a vital role not only in preserving intestinal barrier integrity\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e but also in regulating inflammation within extraintestinal tissues/organs, such as the lungs\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, kidneys\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and brain.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Oral administration of SCFAs has been demonstrated to mitigate experimental autoimmune uveitis (EAU) induced by retinal antigens and Complete Freund's adjuvant (CFA) in mice, underscoring the capacity of gut microbiota-derived SCFAs to modulate intraocular inflammation.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e However, the precise mechanism of SCFA action in glaucoma remains unclear. Emerging studies related to glaucoma suggest that SCFAs and their associated genes may contribute to the onset and progression of the disease.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e For instance, specific SCFAs might influence intraocular pressure by regulating aqueous humor secretion and drainage, thereby affecting the pathogenesis of glaucoma. Furthermore, SCFA-associated genes may participate in ocular inflammatory responses and processes such as retinal cell apoptosis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, exerting influences on glaucoma development. Therefore, investigating the regulatory roles and functional disparities of SCFA-associated genes in glaucoma holds significant importance for comprehending the underlying mechanisms of glaucomatous pathogenesis and identifying novel therapeutic targets.\u003c/p\u003e \u003cp\u003eIn this study, we comprehensively analyze differentially expressed genes associated with SCFAs in glaucoma and delve into the underlying immunological mechanisms. We anticipate that our research will enhance the understanding of glaucoma and provide novel insights for therapeutic strategies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eGene expression profile of GSE27276 dataset was collected from GEO, which included 19 normal samples and 17 POAG samples. In this study, it was applied as the training set. The GSE138125 dataset, as a validation set, consisted of 4 POAG samples and 4 normal samples. All samples were taken from the human trabecular meshwork organization. A sum of 344 short chain fatty acids related genes (SCFARGs) were derived from GeneCards database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 WGCNA\u003c/h2\u003e \u003cp\u003eThe POAG/control was considered as clinical trait for WGCNA via \u0026lsquo;WGCNA\u0026rsquo; (version 1.70-3) package. Firstly, we clustered all samples and removed outliers to ensure the accuracy of the analysis. Then, trait heat map and sample dendrogram were constructed, and the soft threshold was determined. The similarity between genes was calculated according to the adjacency, and the phylogenetic tree between genes was obtained. The modules were divided via dynamic tree cutting algorithm. Finally, the modules with the the highest correlation to POAG/control were used as key modules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Differential analysis\u003c/h2\u003e \u003cp\u003eThe \u0026lsquo;limma\u0026rsquo; package (version 3.48.3)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e was executed to obtain differentially expressed genes (DEGs) between POAG group and normal group in training set. The |log2FC| \u0026gt; 0.5 and adjust.p.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were determined as the liminal value. The volcano plot and heat map were applied to show DEGs via \u0026lsquo;ggplot2\u0026rsquo; \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The top20 (top 10 down-regulated and top 10 up-regulated) of DEGs was displayed in heat map by \u0026lsquo;circlize\u0026rsquo; package\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe short chain fatty acids related differentially expressed genes (SCFAR-DEGs) were obtained through overlapping DEGs, SCFARGs and key module genes associated with POAG/control. GO and KEGG enrichment analysis of was SCFAR-DEGs conducted via \u0026lsquo;clusterProfiler\u0026rsquo; package\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was selected as criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Machine learning methods\u003c/h2\u003e \u003cp\u003eLASSO, Boruta algorithm and SVM-RFE were applied to screen important genes in GSE27276 dataset based on SCFAR-DEGs. The biomarkers were obtained through overlapping genes from three algorithms. Moreover, receiver operating characteristic (ROC) curve was plotted to evaluate the value of the biomarkers by \u0026lsquo;pROC\u0026rsquo; package \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Clinical nomogram model\u003c/h2\u003e \u003cp\u003eThe nomogram containing diagnostic biomarkers were drawn via \u0026lsquo;rms\u0026rsquo; to predict the risk of POAG. Evaluation of the predictive effect was done by the calibration and ROC curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Immune feature and GSEA\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm was applied to calculate the relative abundance of 22 immune cells infiltrated in POAG microenvironment. Subsequently, correlation between biomarkers and differential immune cells were calculated and displayed. In addition, GSEA was conducted to explore the potential KEGG pathways associated with biomarkers through \u0026lsquo;enrichplot\u0026rsquo; package \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was selected as criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Potential drug prediction and molecular docking\u003c/h2\u003e \u003cp\u003eIn order to explore the potential therapeutic drugs for diagnostic biomarkers in POAG, the targeting drugs were identified through DGIdb database. To evaluate the affinity of potential drugs for biomarkers, the molecular structure of the drugs was obtained from the PDB database. The 3D structure SDF format file of the therapeutic drug was from the NCBIPubChem compound database. Autodock Vina (v.1.2.2) was selected for molecular docking in CB-Dock .\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Identification of key module genes in GSE27276 dataset\u003c/h2\u003e\n \u003cp\u003eTo seek out pivotal modules related to POAG, we conducted the WGCNA. The results of sample clustering indicated that there were one outlier sample (\u003cstrong\u003esFig.1\u003c/strong\u003e). The optimal soft threshold was 5. When the mean connectivity was tended to 0, the ordinate scale-free fit the index, and the sign R2 approached the threshold value of 0.85 (red line) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). A total of 20 modules were obtained by the dynamic tree cut algorithm (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). MEbrown module was markedly positive correlated with POAG (R^2\u0026thinsp;=\u0026thinsp;0.8, P.value\u0026thinsp;=\u0026thinsp;0.01), while MEyellow was negative related to POAG (R^2= -0.72, P.value\u0026thinsp;=\u0026thinsp;0.01) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Thus, 2433 key module genes were obtained for subsequent analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Identification and functional enrichment analysis of SCFAR-DEGs\u003c/h2\u003e\n \u003cp\u003eA sum of 615 DEGs were identified between tumor and POAG groups, including 295 up-regulated and 320 down-regulated DEGs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). We selected the top 10 down-regulated and top 10 up-regulated genes to display in the heat map (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Thus, 10 SCFAR-DEGs were obtained through overlapping DEGs, SCFARGs and key module genes associated with POAG (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). In order to uncover potential mechanisms for SCFAR-DEGs, we proceeded with functional enrichment analysis. The top10 GO items were shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD-E. We observed that the above genes were principally linked to \u0026lsquo;reactive oxygen species metabolic process\u0026rsquo; and \u0026lsquo;negative regulation of cellular catabolic process\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). In addition, the KEGG results suggested that these genes were mainly enriched in the \u0026lsquo;IL\u0026thinsp;\u0026minus;\u0026thinsp;17 signaling pathway\u0026rsquo;, and \u0026lsquo;TNF signaling pathway\u0026rsquo; (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Identification of short chain fatty acids related biomarkers for POAG\u003c/h2\u003e\n \u003cp\u003eTo further dig out the key genes, LASSO, Boruta and SVM-RFE algorithm were performed on SCFAR-DEGs with interaction to unearth the optima. LASSO regression analysis identified 9 significant genes, including \u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eSCD\u003c/em\u003e, \u003cem\u003eHBG1\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTIMP2\u003c/em\u003e, \u003cem\u003eNAMPT\u003c/em\u003e, \u003cem\u003eC5\u003c/em\u003e and \u003cem\u003eCEACAM7\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Then, 5 feature genes were obtained via SVM-RFE, including \u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eNAMPT\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eTIMP2\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Meanwhile, 9 feature genes were obtained via Boruta, including \u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eSCD\u003c/em\u003e, \u003cem\u003eHBG1\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTIMP2\u003c/em\u003e, \u003cem\u003eNAMPT\u003c/em\u003e, \u003cem\u003eC5\u003c/em\u003e, \u003cem\u003eMMP3\u003c/em\u003e and \u003cem\u003eCEACAM7\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). Eventually, 5 short chain fatty acids related diagnostic biomarkers (\u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTIMP2\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) were obtained by intersecting the genes obtained by the three machine learning algorithms (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). The AUC value of 5 short chain fatty acids related biomarkers was greater than 0.9, indicating that the these genes had good accuracy (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Construction and verification of nomogram\u003c/h2\u003e\n \u003cp\u003eThe nomogram containing 5 diagnostic biomarkers was generated in GSE27276 dataset (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The calibration and ROC curves proved that the performance of the short chain fatty acids related POAG diagnostic model was effective (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB-C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Immune infiltration and functional enrichment analysis\u003c/h2\u003e\n \u003cp\u003eTo explore the immune microenvironment of POAG, we showed the abundance of 22 immune cells between two sample groups in GSE27276 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, there were 5 immune cell abundances that differed significantly, including M2 macrophages, monocytes, CD4 memory activated T cells, activated NK cells and plasma cells (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). The correlation analysis revealed that \u003cem\u003eTIMP2\u003c/em\u003e was significantly positive associated with M2 macrophages. Meantime, \u003cem\u003eZFP36\u003c/em\u003e was positive associated with Plasma cells (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). To further study the potential roles of \u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTIMP2\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e in POAG, we performed single-gene GSEA on biomarkers (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). The results showed that \u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eNAMPT\u003c/em\u003e and \u003cem\u003eTIMP2\u003c/em\u003e were related to \u0026lsquo;antigen processing and presentation\u0026rsquo;, while \u0026lsquo;allograft rejection\u0026rsquo; was associated with \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eZFP36\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Biomarkers-drug interaction network\u003c/h2\u003e\n \u003cp\u003eIn order to look for potential drugs targeting biomarkers, we predict small molecule drugs through DGIdb database. There were 40 drugs with therapeutic potential on three biomarkers (\u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Drugs targeting \u003cem\u003eHBB\u003c/em\u003e was AES-103, FLUOROURACIL and NEBULARINE etc. Drugs targeting \u003cem\u003eNFKBIA\u003c/em\u003e was GEFITINIB and CHEMBL401565 etc. And drugs targeting \u003cem\u003eNAMPT\u003c/em\u003e was CHS-828, TEGLARINAD and DAPORINAD. We selected the top five drugs with the largest query score and interaction score corresponding to the biomarkers for molecular docking. Molecular docking results showed that \u003cem\u003eHBB\u003c/em\u003e and VOXELOTOR, EFAPROXIRAL, DEFERITAZOLE, \u003cem\u003eNFKBIA\u003c/em\u003e and CHEMBL401565, CHEMBL256967, CHEMBL1940084, PEPEROMIN E had strong binding energy (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB; \u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePOAG is a multifactorial disorder with a complex etiology. Despite this complexity, the precise mechanisms of SCFA-related genes in POAG remain elusive, warranting the establishment of novel molecular pathways for therapeutic and diagnostic advancements. Currently, machine learning algorithms and WGCNA have matured and are extensively employed in predicting disease markers and therapeutic targets. In this study, we retrieved transcriptional data from the GEO database and utilized WGCNA in combination with machine learning to identify five SCFA-related hub genes in POAG: \u003cem\u003eHBB, ZFP36, NFKBIA, TIMP2\u003c/em\u003e, and \u003cem\u003eNAMPT\u003c/em\u003e, which were further validated using additional datasets. ROC curve analysis revealed excellent diagnostic performance for these hub genes. Additionally, a heatmap model constructed from the collective action of these five hub genes effectively predicted the risk of POAG onset.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eHBB\u003c/em\u003e gene encodes hemoglobin subunit beta, a major component of red blood cells responsible for oxygen transport. Recent findings indicate \u003cem\u003eHBB\u003c/em\u003e expression in the human eye, not only in the retina and choroid but also with increased expression in ocular tissues of glaucoma patients, suggesting potential involvement in ocular inflammation and retinal cell apoptosis.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The \u003cem\u003eZFP36\u003c/em\u003e gene encodes a transcription factor involved in RNA degradation and post-transcriptional regulation. Emerging research highlights \u003cem\u003eZFP36'\u003c/em\u003es significance in VEGF-stimulated developmental retinal angiogenesis, underscoring its role in supporting retinal ganglion cell metabolic processes.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eTIMP2\u003c/em\u003e genes play pivotal roles in extracellular matrix degradation and remodeling, maintaining trabecular meshwork tissue homeostasis. \u003cem\u003eNFKBIA\u003c/em\u003e siRNA in trabecular meshwork cells enhances MMP-2 expression and activity through NF-κB pathway activation, potentially impacting uveoscleral outflow, a potential target for glaucoma modulation.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eNAMPT\u003c/em\u003e, encoding nicotinamide phosphoribosyltransferase, is a critical enzyme in NAD\u0026thinsp;+\u0026thinsp;biosynthesis. NAD\u0026thinsp;+\u0026thinsp;is a pivotal intracellular coenzyme involved in various metabolic and stress responses. Studies suggest \u003cem\u003eNAMPT\u003c/em\u003e's involvement in cellular energy metabolism and stress responses within ocular tissues of glaucoma patients, contributing to disease progression.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Collectively, these genes play roles in multiple pathological processes, including ocular inflammation, retinal cell apoptosis, NF-κB pathway activation, abnormal extracellular matrix remodeling, cellular energy metabolism, and stress responses, thus potentially serving as biomarkers for glaucoma diagnosis and therapeutic targets.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis reveals \u003cem\u003eHBB, NAMPT\u003c/em\u003e, and \u003cem\u003eTIMP2\u003c/em\u003e associations with the \"Antigen processing and presentation\" pathway, while \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eZFP36\u003c/em\u003e associate with the \"Graft rejection\" pathway, pathways potentially implicated in POAG development. These pathways are integral to immune systems, encompassing antigen-presenting cells, MHC molecules, T-cell receptors, and co-stimulatory molecules, orchestrating immune responses through antigen processing and presentation to activate or inhibit immunity. Aberrant immune and inflammatory responses in ocular tissues of glaucoma patients have been reported \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, potentially linked to pathway alterations. However, the precise roles of these pathways and genes necessitate further exploration.\u003c/p\u003e \u003cp\u003eComparison of immune cell infiltration between POAG and control groups using the CIBERSORT algorithm reveals significant differences in five of 22 immune cell types. Furthermore, correlations between differentially expressed immune cells and hub genes are analyzed. These findings enhance our understanding of SCFA-related hub gene functions in POAG development. Notably, POAG patients exhibit differential expression of M2 macrophages\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and plasma cells\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, both potentially pivotal in ocular inflammation, apoptosis, and other pathological processes relevant to POAG. The interplay of these immune cells likely modulates the actions of genes such as \u003cem\u003eHBB, NAMPT, TIMP2, NFKBIA\u003c/em\u003e, and \u003cem\u003eZFP36\u003c/em\u003e in POAG, affecting inflammatory factor expression, matrix metalloproteinase regulation, and more. These findings offer novel perspectives for POAG diagnosis and treatment.\u003c/p\u003e \u003cp\u003eUsing the DGidb database, we predict potential therapeutic drugs for POAG, unveiling new directions for clinical interventions. Predicted drugs, including immune modulators, anti-inflammatory agents, and antioxidants, include Voxelotor, used for sickle cell anemia treatment\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, with potential benefits arising from improved hemorheological characteristics, reduced cellular aggregation, and damage. Chembl401565 and Chembl256967, although currently lacking widespread clinical application, possess reported antioxidant and anti-inflammatory properties. Peperomin E, a natural compound found in plants like pepper, boasts antioxidant, anti-inflammatory, antibacterial, and anticancer effects.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e These drugs may impact POAG through immune system modulation and ocular tissue inflammation reduction, aligned with the functions of some predicted POAG-related genes. However, further investigation is required to elucidate the effects of these predicted drugs in the context of POAG.\u003c/p\u003e \u003cp\u003eThis comprehensive study employs systematic gene expression profiling to reveal a set of SCFA-associated genes relevant to POAG, unraveling molecular mechanisms underlying POAG pathogenesis. Additionally, drug network and immune cell infiltration analyses uncover potential therapeutic avenues and characterize immune cell involvement, providing new directions for glaucoma treatment. Although limitations such as sample size, this study's innovative and practical findings highlight the potential roles of SCFA-associated diagnostic markers in POAG. Future research will continue exploring these markers to enhance glaucoma diagnosis and treatment strategies\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOverall, we obtained five short chain fatty acids related biomarkers (\u003cem\u003eTHBB\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTIMP2\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) associated with POAG, which laid a theoretical foundation for the treatment of glaucoma.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003col\u003e\n \u003cli\u003ePrimary open-angle glaucoma（POAG）\u003c/li\u003e\n \u003cli\u003eShort-chain fatty acids (SCFAs)\u003c/li\u003e\n \u003cli\u003eexperimental autoimmune uveitis (EAU)\u003c/li\u003e\n \u003cli\u003eComplete Freund\u0026apos;s adjuvant (CFA)\u003c/li\u003e\n \u003cli\u003eshort chain fatty acids related genes (SCFARGs)\u003c/li\u003e\n \u003cli\u003edifferentially expressed genes (DEGs)\u003c/li\u003e\n \u003cli\u003eshort chain fatty acids related differentially expressed genes (SCFAR-DEGs)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ereceiver operating characteristic (ROC)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eweighted gene co-expression network analysis (WGCNA)\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the National Natural Science Foundation of China (82000890), Hainan Provincial Natural Science Foundation of China (820RC780), Young Talents\u0026rsquo;Science and Technology Innovation Project of Hainan Association for Science and Technology (QCXM202020), and Hainan Province Clinical Medical Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.H. and Y.F.: conception and study design, J.K., A.L, Y.L, and F.C.: data analysis and interpretation and writing the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStein JD, Khawaja AP, Weizer JS. Glaucoma in Adults-Screening, Diagnosis, and Management: A Review. JAMA. 2021;325(2):164\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePascolini D, Mariotti SP. Global estimates of visual impairment: 2010. Br J Ophthalmol. 2012;96(5):614\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90(3):262\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014;121(11):2081\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez VL, Caspi RR. Immune mechanisms in inflammatory and degenerative eye disease. Trends Immunol. 2015;36(6):354\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGramlich OW, Beck S, von Thun UHN, et al. Enhanced insight into the autoimmune component of glaucoma: IgG autoantibody accumulation and pro-inflammatory conditions in human glaucomatous retina. PLoS ONE. 2013;8(2):e57557.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan MK, Ransohoff RM. Analysis of leukocyte extravasation across the blood-brain barrier: conceptual and technical aspects. Curr Allergy Asthma Rep. 2004;4(1):65\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei X, Cho KS, Thee EF, et al. Neuroinflammation and microglia in glaucoma: time for a paradigm shift. J Neurosci Res. 2019;97(1):70\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLymperopoulos A, Suster MS, Borges JI. Short-Chain Fatty Acid Receptors and Cardiovascular Function. Int J Mol Sci 2022;23(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCong J, Zhou P, Zhang R. Intestinal Microbiota-Derived Short Chain Fatty Acids in Host Health and Disease. Nutrients 2022;14(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim CH, Park J, Kim M. Gut microbiota-derived short-chain Fatty acids, T cells, and inflammation. Immune Netw. 2014;14(6):277\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheiler A, Barnthaler T, Platzer W, et al. Butyrate ameliorates allergic airway inflammation by limiting eosinophil trafficking and survival. J Allergy Clin Immunol. 2019;144(3):764\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade-Oliveira V, Amano MT, Correa-Costa M, et al. Gut Bacteria Products Prevent AKI Induced by Ischemia-Reperfusion. J Am Soc Nephrol. 2015;26(8):1877\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatt SM, Allen JM, Lawson MA, et al. Butyrate and Dietary Soluble Fiber Improve Neuroinflammation Associated With Aging in Mice. Front Immunol. 2018;9:1832.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura YK, Janowitz C, Metea C, et al. Short chain fatty acids ameliorate immune-mediated uveitis partially by altering migration of lymphocytes from the intestine. Sci Rep. 2017;7(1):11745.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahalingam K, Chaurasia AK, Gowtham L, et al. Therapeutic potential of valproic acid in advanced glaucoma: A pilot study. Indian J Ophthalmol. 2018;66(8):1104\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZysset-Burri DC, Morandi S, Herzog EL, et al. The role of the gut microbiome in eye diseases. Prog Retin Eye Res. 2023;92:101117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNapolitano P, Filippelli M, Davinelli S, et al. Influence of gut microbiota on eye diseases: an overview. Ann Med. 2021;53(1):750\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhipson B, Lee S, Majewski IJ, ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION, et al. Ann Appl Stat. 2016;10(2):946\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto K, Murphy D. Application of ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst Pharmacol. 2013;2(10):e79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Z, Gu L, Eils R, et al. circlize Implements enhances circular visualization R Bioinf. 2014;30(19):2811\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S\u0026thinsp;+\u0026thinsp;to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar L. Mfuzz: a software package for soft clustering of microarray data. Bioinformation. 2007;2(1):5\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng J, Xu J. Identification of pathogenic genes and transcription factors in glaucoma. Mol Med Rep. 2019;20(1):216\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCicchetto AC, Jacobson EC, Sunshine H, et al. ZFP36-mediated mRNA decay regulates metabolism. Cell Rep. 2023;42(5):112411.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan YQ, Zhang C, Xiao JH, et al. Suppression of IkappaBalpha increases the expression of matrix metalloproteinase-2 in human ciliary muscle cells. Mol Vis. 2009;15:1977\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai M, Hu Z, Kang Z, Zheng Z. Based on multiple machine learning to identify the ENO2 as diagnosis biomarkers of glaucoma. BMC Ophthalmol. 2022;22(1):155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang S, Kametani M, Chen DF. Adaptive Immunity: New Aspects of Pathogenesis Underlying Neurodegeneration in Glaucoma and Optic Neuropathy. Front Immunol. 2020;11:65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStepp MA, Menko AS. Immune responses to injury and their links to eye disease. Transl Res. 2021;236:52\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapouri-Moghaddam A, Mohammadian S, Vazini H, et al. Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol. 2018;233(9):6425\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell K, Und HN, Teister J, Grus F. Modulation of the Immune System for the Treatment of Glaucoma. Curr Neuropharmacol. 2018;16(7):942\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlair HA, Voxelotor. First Approval Drugs. 2020;80(2):209\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin M, Zhu Q, Li Y, Pan J, Peperomin E. Induces Apoptosis and Cytoprotective Autophagy in Human Prostate Cancer DU145 Cells In Vitro and In Vivo. Planta Med. 2021;87(8):620\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"Glaucoma, Primary open-angle glaucoma, short chain fatty acids, Biomarkers, Machine learning, Immune cells","lastPublishedDoi":"10.21203/rs.3.rs-4150868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4150868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eGlaucoma is a progressive optic neuropathy with degeneration of retinal ganglion cells and retinal nerve fiber layer. Studies have shown that short chain fatty acids produced by gut microbiota can regulate intraocular inflammation. The aim of this research was to screen biomarkers associated with short chain fatty acids in glaucoma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFirstly, WGCNA was performed for obtaining the key module genes associated with the primary open-angle glaucoma (POAG). We performed differential expression analysis (POAG samples \u003cem\u003evs\u003c/em\u003e normal samples) to obtain differentially expressed genes (DEGs) in GSE27276 dataset. The short chain fatty acids related differentially expressed genes (SCFAR-DEGs) were obtained by overlapping DEGs, short chain fatty acids related genes (SCFARGs) and key module genes. Three machine learning algorithms were implemented to select short chain fatty acids related biomarkers. We performed immune infiltration and GSEA based on biomarkers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA sum of 2433 key module genes associated with POAG were identified. We identified 615 DEGs between two groups. Soon afterwards, 10 SCFAR-DEGs were obtained through overlapping DEGs, SCFARGs and key module genes. Moreover, 5 biomarkers associated with short chain fatty acids, including \u003cem\u003eHBB\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTIMP2\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e, were screened via three machine learning algorithms. The immune infiltration and GSEA analysis suggested that these biomarkers were related to the function of antigen presentation and some differential immune cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOverall, we obtained five short chain fatty acids related biomarkers (\u003cem\u003eTHBB\u003c/em\u003e, \u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTIMP2\u003c/em\u003e and \u003cem\u003eNAMPT\u003c/em\u003e) associated with POAG, which laid a theoretical foundation for the treatment of glaucoma.\u003c/p\u003e","manuscriptTitle":"Exploration of Short-chain Fatty Acid-Associated Hub Genes and potential therapeutic targets in Primary Open-Angle Glaucoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 14:22:57","doi":"10.21203/rs.3.rs-4150868/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":"0d3f6847-6681-42d4-ab31-f9af0d9da36a","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-30T13:33:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 14:22:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4150868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4150868","identity":"rs-4150868","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}