Finding Genetic Contributors to Parkinson’s Disease via Weighted Gene Co- expression Network Analysis

Finding Genetic Contributors to Parkinson’s Disease via Weighted Gene Co- expression Network Analysis | 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 Finding Genetic Contributors to Parkinson’s Disease via Weighted Gene Co- expression Network Analysis Mengrui Zhao, Saeid Afshar, Irina Dinu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8090106/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: As the second most prevalent progressive neurodegenerative disorder, Parkinson’s disease (PD) involves complex pathological processes and lacks definitive diagnostic biomarkers. This study aimed to explore molecular signatures associated with PD to support improved diagnostic and therapeutic strategies for PD patients. Methods: Gene expression profiles from GSE202667 dataset (platform: GPL20844) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between PD and control samples was assessed using the “limma” package in R software. We used Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks and evaluate their correlation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed to explore biological correlations. Hub genes were validated using an independent dataset (GSE54536). Results: A total of 365 common DEGs were identified. WGCNA revealed 4 co-expression modules, with the eigenvalues of the blue module most significantly with PD stages (r=0.78, P<0.001). Key genes from this module were analyzed using protein-protein interaction (PPI) networks and CytoHubba algorithm. Eight potential hub genes, including LILRB4, LILRB2, FCGR2A, FCGR2B, CCR1, TLR4, ITGAX, and NCAM1 , were identified, of which FCGR2A and FCGR2B were validated as consistently upregulated in PD across datasets. Conclusions: FCGR2A and FCGR2B may serve as immune-related molecular indicators of PD, suggesting a potential role in disease progression and as candidates for further clinical diagnostic and therapeutic development. Further studies with larger and experimentally enriched datasets are recommended. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction As the second most common progressive neurodegenerative disorder, Parkinson’s disease (PD) is typified by tremors, stiffness, and slowness of movements. It is associated with gradual loss of neurons in the substantia nigra and other brain structures (1). As population age, the prevalence of PD continues to grow, affecting approximately 1% of individuals over 60 and up to 4% in those over age 85 ( 2 ). Epidemiological studies have revealed a rapid growth in the global burden of PD over recent decades. The number of people affected by the PD has more than doubled — from 2.5 million (95% uncertainty interval [UI] 2.0–3.0) patients in 1990 to 6.1 million (95% UI 5.0–7.3) patients in 2016 around the world ( 3 ) — placing an ever-growing strain on healthcare systems worldwide. Many studies showed that most PD cases are sporadic with no identifiable genetic cause. It is now widely accepted that PD results from complex interactions between genetic variables and environmental variables. According to the statistics, nearly 15% of PD patients have a positive PD family history, while 5–10% respond to Mendelian inheritance ( 4 ). To date, evidence indicates that around 25% of the chance of developing PD is genetic ( 5 ). A 2018 genetic review showed that at least 23 loci and 19 genes are associated with PD, while many more are linked to sporadic cases( 2 ). Although a comprehensive clinical examination and neuroimaging techniques are necessary for the accuracy of a clinical diagnosis, there are currently no objective genetic or biochemical testing available for PD ( 6 ). Additionally, the high failure rate in the development of PD drugs is likely due to the many unknowns in disease pathogenesis, highlighting the urgent need to find novel pathogenic mechanisms and therapeutic targets. Several molecular pathways have been implicated in PD pathogenesis, including α-synuclein aggregation, mitochondrial dysfunction, oxidative stress, ferroptosis, gut dysbiosis, and neuroinflammation ( 7 ). All of these pathogenic mechanisms are believed to play a role in neurodegeneration. The identification of various biomarkers - including cerebrospinal fluid (CSF) biomarkers (e.g. altered levels of α-synuclein, lowered levels of tau), serum biomarkers (e.g. high levels of glial fibrillary acidic protein and its breakdown products), genetics biomarkers (e.g., mutations in SNCA and LRRK2 ), and imaging biomarkers (e.g. using neuromelanin-sensitive magnetic resonance imaging for assessing neuromelanin levels) - holds promise for earlier and more precise PD diagnosis ( 8 ). Furthermore, these biomarkers can also be used to track disease progression and evaluate patient responses to treatment. The discovery of effective biomarkers will also support the development of much-needed disease-modifying treatments for PD, which are currently lacking ( 8 ). Among computational approaches, weighted Gene Co-expression Network Analysis (WGCNA) stands out as a powerful systems biology tool that identifies modules of co-expression genes and relates them to clinical phenotypes. This networks-based method offers advantages over traditional single-gene analyses by highlighting hub genes that may serve as central regulators of disease-relevant pathways. It considers the variability of gene expression across different biological conditions, the potential for identifying higher-order relationships among genes, and managing multiple tests by assessing correlations between the eigengenes of important modules and variables of interest ( 9 , 10 ). In our study, we applied WGCNA to microarray data derived from PD patients and health controls to identify key differentially expressed genes (DEGs), construct gene co-expression networks and find modules most strongly associated with PD. We further examined the biological significance of these modules using functional enrichment analyses and validated the most promising hub genes in an independent dataset. Our goal was to reveal hub genes that may contribute to PD pathophysiology, enhancing diagnostic precision and fostering the development of personalized prevention and therapeutic intervention strategies. 2. Materials and methods 2.1. Data acquisition Our study used gene expression data from GEO database ( www.ncbi.nlm.nih.gov/geo/ ). To be more specifically, we retrieved dataset GSE202667 (platform: GPL20844, Agilent-072363 SurePrint G3 Human GE v3 8x60K Microarray 039494) from the study by Caroline Diener et al ( 11 ). This dataset comprises a total 62,976 expression profiles of 59 RNA samples, including 29 samples from patients diagnosed with Parkinson’s disease and 30 samples from age-matched health controls. 2.2. Identification of DEGs DEGs between PD and health control samples were identified using the limma package in R (version 4.1.3), a well-established tool within the Bioconductor suite. DEGs were defined using an adjusted p-value 1 to ensure biological relevance. 2.3 Construction of co-expression network using WGCNA WGCNA package in R was used to construct a gene co-expression network via analyzing the gene expression data of GSE202667 (platform: GPL20844) ( 12 , 13 ). This package was stated by Langfelder and Horvath (2008) which involves computing pairwise Pearson correlation coefficients between all gene expression profiles to generate a similarity matrix( 13 ). This similarity matrix was transformed into an adjacency matrix using a power function. The optimal soft-thresholding power was determined using the pickSoftThreshold function, which assesses scale-free topology fit. This power value was selected based on a fit index (R 2 ) greater than 0.85 in this study. Genes were subsequently grouped into modules using hierarchical clustering and the dynamic tree cut algorithm, with a minimum module size set to 30. Module eigengenes (MEs) were computed and correlated with clinical traits (i.e., disease stages, PD vs. healthy control) to identify the most relevant modules. Both high gene significance (GS) for clinical traits and high module membership (MM) within the selected module define the hub genes. All analyses were conducted by version 4.1.3 of the R statistical programming language. The significance level for the two-sided statistical test was P < 0.05. 2.4 Functional enrichment analysis To interpret the biological roles of genes the selected gene module, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. These analyses were performed using the DAVID database ( https://david.ncifcrf.gov ), covering three GO domains: biological processes (BP), cellular components (CC), and molecular functions (MF) ( 14 ). Pathways within adjusted p-values < 0.05 were considered significantly enriched. 2.5 Protein-protein interaction (PPI) network analysis, identification of hub genes and transcription factors A PPI network was constructed for genes in the most relevant module using STRING (version 12.0), with a confidence score cutoff set at 0.4 to ensure reliable interactions( 15 ). The constructed network was visualized and analyzed using Cytoscape software (version 3.10.2) and CytoHubba plugin was employed to identify the key genes based on four topological algorithms including Degree, maximum neighborhood component (MNC), Edge Percolated Component (EPC), and EcCentricity ( 16 ). Genes that were consistently ranked in the top by all four algorithms were considered robust hub genes. 2.6 Validation in an independent dataset To validate the expression pattern of selected hub genes, we used dataset GSE54536 (platform GPL10558), which contains a whole-transcriptome analysis of the peripheral blood from four untreated stage 1 PD patients and four neurologically normal age-matched controls from the same population ( 17 , 18 ). Differential expression of hub genes in this validation set was evaluated using the “limma” package, and violin plots were generated to illustrate expression differences. 3. Results 3.1. Differential expression analysis Out of the 35,200 unique genes examined in the GSE202667 dataset, 365 were identified as differential expressed based on the predefined thresholds. Among these, 213 genes were significantly upregulated (LogFc>1) and 152 were downregulated (LogFc<-1) in PD samples compared to healthy controls. A Volcano plot was generated by GEO2R on the GEO website by plotting the -log 10 (adjusted P -value) versus log 2 (FC) (Fig 1) to visually represent the distribution of DEGs. In this plot, red and blue points highlight genes with significant upregulation and downregulation, respectively, while non-significant genes are marked in black. 3.2. Co-expression network construction and module detection After confirming data quality (Fig 2), we applied WGCNA to the 365 DEGs to build a gene co-expression network. The soft-thresholding power were determined using scale-free topology criteria, with power=12 selected to ensure a network with optimal fit (R 2 > 0.85), as shown in Fig 3. Using this power setting, WGCNA identified four distinct co-expression modules, each labeled by color: turquoise, blue, brown and grey (Fig 4). These modules represent cluster of genes sharing similar expression patterns. Module-trait correlation analysis showed that the blue module exhibited the strongest correlation with PD disease stage (r = 0.78; P -Value = 6e-7), as visualized in a module trait heatmap (Fig 5). This result suggest that the gene set in the blue module may contribute significantly to disease progression. Further supporting this relationship, we observed a strong correlation between GS for PD stage and MM within blue module (r = 0.79; P -Value = 3e-21), illustrated in Fig 6. This suggests that the most connected genes in the module are also the most associated with disease severity. 3.3. Functional annotation of blue modules genes Functional enrichment analysis was conducted for genes in the blue module. GO analysis revealed enrichment in biological processes (BP) such as cytokine-mediated signaling pathway, antibody-dependent cellular cytotoxicity, positive regulation of tumor necrosis factor production, and cell surface receptor signaling pathway. For cellular component (CC) GO terms, DEGs are enriched in plasma membrane, membrane and external side of plasma membrane. For molecular function (MF), the selected genes are enriched in IgG binding and IgG receptor activity. KEGG analysis indicated involvement in B cell receptor signaling and osteoclast differentiation pathways. These findings are summarized in Fig 7 (13). 3.4. Identification of central hub genes A graphical rendering of the PPI network, highlighting high-ranking genes, is shown in Fig 8a. To enhance robustness, we focused on genes that appeared consistently across all four ranking methods, including Degree, EcCentricity, EPC and MNC. The intersection of results from each algorithm is illustrated in the Venn diagram (Fig 8b), which revealed eight overlapping genes: leukocyte immunoglobulin like receptor B4 ( LILRB4 ), leukocyte immunoglobulin like receptor B2 ( LILRB2 ), Fc gamma receptor IIa ( FCGR2A ), Fc gamma receptor IIa ( FCGR2B ), C-C motif chemokine receptor 1 ( CCR1 ), toll like receptor 4 ( TLR4 ), integrin subunit alpha X ( ITGAX ), and neural cell adhesion molecule 1 ( NCAM1 ). These genes were designated as final hub genes due to their topological prominence and potential biological relevance to PD. 3.5. Validation in an independent dataset To confirm the reliability of the selected hub genes, we examined their expression dataset GSE54536. Among the eight candidates, FCGR2A and FCGR2B were found to be consistently upregulated in PD patients compared to controls in both datasets. Violin plots in Fig 9a and 9b display the expression patterns of these genes, supporting their potential as robust biomarkers. 4. Discussion PD presents an increasing clinical and societal challenge, particularly in aging populations. Although significant efforts have been made to characterize its pathological mechanisms, effective disease-modifying therapies remain elusive. Recent genetic studies have broadened our understanding of the hereditary contributions to PD, identifying more than 80 risk loci through large-scale meta-analyses across multiple populations ( 19 – 21 ). However, the translation of these associations into biologically meaningful insights remains limited by the complex interplay among the identified genes and their regulatory networks. In this study, we applied WGCNA to explore the gene co-expression network of PD, focusing on co-regulated gene modules rather than individual gene changes. Using WGCNA, we identified a co-expression module (refer to as the blue module) that showed a strong correlation with disease stage. Functional enrichment analyses of this module pointed toward a substantial involvement of immune-related pathways, including cytokine signaling, immunoglobulin binding, and B cell receptor activity. These findings are consistent with accumulating evidence that implicates immune dysregulation as both a contributor to and consequence of PD progression. Our study identified eight hub genes within the blue module. Among them, FCGR2A and FCGR2B emerged as the most robust candidates due to their consistent upregulation in both the discovery and independent validation datasets. These genes encode low-affinity Fc gamma receptors involved in the regulation immune responses ( 22 ), particularly phagocytosis and antibody-mediated clearance( 23 ). FCGR2A functions as an activating receptor that can enhance inflammatory signaling, whereas FCGR2B contains an immunoreceptor tyrosine-based inhibitory motif (ITIM) that attenuates immune activation ( 24 , 25 ). The potential role of FCGR family genes in neuroinflammation has been discussed in the prior literature. Wang et al. ( 26 ) discussed FCGR2B’s role in nerve injury-induced neuropathic pain, and Qingqing Ye et al. ( 27 ) highlighted its involvement in neuropathic pain and aging. Caixiu Lin et al. ( 28 ) showed that M2 macrophage infiltration in AD was associated with five hub genes, including TLR2, FCGR2A, ITGB2, NCKAP1L , and CYBA. Several previous studies utilized other methodologies to identify the key genes associated with PD or related neurological disorders. Huiqing Wang et al. ( 29 ) explored the potential genes and molecular mechanisms of PD with major depressive disorder (MDD) by using GSE6613 and GSE98793 gene expression profiles in GEO. They utilized the limma package in R to obtain DEGs, as well as utilized GO and KEGG enrichment analyses to explore the function of these common DEGs. Key genes were identified using PPI network and least absolute shrinkage and selection operator (LASSO) regression; AQP9, SPI1, and RPH3A were validated as significant in PD with MDD. Similarly, Yajun Yang et al. ( 30 ) used WGCNA to screen for key genes linked to PD and then used GO, KEGG, and Disease Ontology (DO) to analyze enrichment of target genes. Eight hub genes were discovered, including RPL3L, PLEK2, PYCRL, CD99P1, LOC100133130, MELK, LINC01101 , and DLG3-AS1 , suggesting a new direction for PD diagnosis and treatment. Yongxing Lai et al. ( 31 ) employed ssGSEA, LASSO regression, and WGCNA algorithms to identify immune microenvironment subtypes and signature genes in Alzheimer’s disease (AD). Six machine-learning algorithms was created for identifying distinctive genes. Finally, they found 5 hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD. Lin Chen et al. ( 32 ) focused on immune-related hub genes in PD by analyzing datasets GSE7621, GSE20141, and GSE49036. They applied WGCNA to screen key module genes and intersect immune-related genes to obtain immune-key genes. Using LASSO analysis, they identified immune-related hub genes, including SLC18A2, L1CAM, S100A12, and CXCR4 , as being associated with the pathogenesis of PD. Min Wang et al. ( 33 ) used bioinformatics analysis, including gene set enrichment analysis, KEGG, and PPI network, to identify the potential DEGs, three hub genes ( PIK3CA, BRD4, ATM ) and the key lncRNA ( NEAT1 ) were verified in neurotoxic PD models. Guanghao Xin et al. ( 34 ) downloaded the GSE8397 dataset from the GEO database, using WGCNA combined with two machine learning algorithms for feature screening and combined with immune cell infiltration analysis to identify PD immune-related hub genes. They identified four hub genes: DLK1, IARS, DLD and TTC19. Shuang Liu et al. ( 35 ) emphasized the significance of pathways like the neuronal system and Calcium signaling pathway in PD progression, with DEGs of SYN1, GRIN1, GRIN2D , and DLGAP3 as potential therapeutic targets of PD. Our study used the most recently published GEO dataset and applied strict DEG threshold criteria combined with WGCNA and other bioinformatics analyses to identify key genes, which are significantly associated with different PD stages. The additional step of validating hub gene expression in an external dataset also increases the credibility and reproducibility of our findings. Both of FCGR2A and FCGR2B were significantly upregulated in PD patients and closely linked to immune-related pathways. This study also has limitations. The reliance on peripheral blood transcriptomic data may not fully capture central nervous system-specific gene regulation. Additionally, the relatively modest sample size in the validation dataset limits statistical power. The study is also based on transcriptomic inference, lacking direct protein-level or functional validation of gene activity. Furthermore, platform discrepancies between datasets may introduce confounding effects, despite normalization procedures. Further studies should use a large number of samples, conduct experimental studies to confirm the role of FCGR2A and FCGR2B , and assess the effects of genetic mutations alongside personal and/or environmental variables. 5. Conclusion In summary, this study identified FCGR2A and FCGR2B as potential biomarkers for PD, offering new opportunities for the development of diagnostic tools and therapeutic strategies. Through integrating immune-related mechanisms in PD research, this work provides a foundation for exploring novel treatments targeting immune modulation in this complex neurodegenerative disorder. Declarations Data availability status The datasets generated and/or analyzed during the current study are available on the GEO website. Further inquiries are available from the corresponding author on reasonable request. Declaration availability statement The authors declare no conflict of interest related to this work. Funding Declaration This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Clinical Trial Number Not applicable. References Tolosa E, Wenning G, Poewe W. The diagnosis of Parkinson’s disease. Lancet Neurol. 2006 Jan;5(1):75–86. Deng H, Wang P, Jankovic J. The genetics of Parkinson disease. Ageing Res Rev. 2018 Mar 1;42:72–85. Ding C, Wu Y, Chen X, Chen Y, Wu Z, Lin Z, et al. Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990–2019. Front Public Health. 2022 Nov 29;10:952161. Elsayed I, Martinez-Carrasco A, Cornejo-Olivas M, Bandres-Ciga S. Mapping the Diverse and Inclusive Future of Parkinson’s Disease Genetics and Its Widespread Impact. Genes. 2021 Oct 23;12(11):1681. Day JO, Mullin S. The Genetics of Parkinson’s Disease and Implications for Clinical Practice. Genes. 2021 Jun 30;12(7):1006. Rai S, Bharti PS, Singh R, Rastogi S, Rani K, Sharma V, et al. Circulating plasma miR-23b-3p as a biomarker target for idiopathic Parkinson’s disease: comparison with small extracellular vesicle miRNA. Front Neurosci. 2023 Nov 15;17:1174951. Dong-Chen X, Yong C, Yang X, Chen-Yu S, Li-Hua P. Signaling pathways in Parkinson’s disease: molecular mechanisms and therapeutic interventions. Signal Transduct Target Ther. 2023 Feb 21;8(1):1–18. Yamashita KY, Bhoopatiraju S, Silverglate BD, Grossberg GT. Biomarkers in Parkinson’s disease: A state of the art review. Biomark Neuropsychiatry. 2023 Dec 1;9:100074. Darvish Z, Ghanbari S, Afshar S, Tapak L, Amini P. Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis. Cell J Yakhteh. 2023 Jun;25(6):418–26. Zhao W, Langfelder P, Fuller T, Dong J, Li A, Hovarth S. Weighted gene coexpression network analysis: state of the art. J Biopharm Stat. 2010 Mar;20(2):281–300. Diener C, Hart M, Kehl T, Becker-Dorison A, Tänzer T, Schub D, et al. Time-resolved RNA signatures of CD4+ T cells in Parkinson’s disease. Cell Death Discov. 2023 Jan 21;9(1):18. Chen L, Yuan L, Wang Y, Wang G, Zhu Y, Cao R, et al. Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma. Int J Biol Sci. 2017 Nov 1;13(11):1361–72. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9:559. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009 Jan;4(1):44–57. STRING: functional protein association networks [Internet]. [cited 2024 Jun 18]. Available from: https://string-db.org/cgi/input?sessionId=bj13GKDjfMB5&input_page_show_search=on Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014 Dec 8;8(Suppl 4):S11. Alieva AKh, Shadrina MI, Filatova EV, Karabanov AV, Illarioshkin SN, Limborska SA, et al. Involvement of Endocytosis and Alternative Splicing in the Formation of the Pathological Process in the Early Stages of Parkinson’s Disease. BioMed Res Int. 2014;2014:718732. GEO Accession viewer [Internet]. [cited 2024 Jun 17]. Available from: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54536 Coukos R, Krainc D. Key genes and convergent pathogenic mechanisms in Parkinson disease. Nat Rev Neurosci. 2024 Jun;25(6):393–413. Kim JJ, Vitale D, Otani DV, Lian MM, Heilbron K, Iwaki H, et al. Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease. Nat Genet. 2024;56(1):27–36. Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 2019 Dec;18(12):1091–102. Nimmerjahn F, Ravetch JV. Fcγ receptors as regulators of immune responses. Nat Rev Immunol. 2008 Jan;8(1):34–47. Anania JC, Chenoweth AM, Wines BD, Hogarth PM. The Human FcγRII (CD32) Family of Leukocyte FcR in Health and Disease. Front Immunol. 2019 Mar 19;10:464. Herik-Oudijk IEV den, Capel PJA, Bruggen T van der, Winkel JGJV de. Identification of Signaling Motifs Within Human FcγRIIa and FcγRllb Isoforms. Blood. 1995 Apr 15;85(8):2202–11. Sun Z, Sun X, Yuan Y, Li H, Li X, Yao Z. FCGR2B as a prognostic and immune microenvironmental marker for gliomas based on transcriptomic analysis. Medicine (Baltimore). 2023 Sep 15;102(37):e35084. Wang K, Yi D, Yu Z, Zhu B, Li S, Liu X. Identification of the Hub Genes Related to Nerve Injury-Induced Neuropathic Pain. Front Neurosci. 2020 May 20;14:488. Ye Q, Huang Z, Lu W, Yan F, Zeng W, Xie J, et al. Identification of the common differentially expressed genes and pathogenesis between neuropathic pain and aging. Front Neurosci. 2022 Oct 19;16:994575. Lin C, Xu C, Zhou Y, Chen A, Jin B. Identification of Biomarkers Related to M2 Macrophage Infiltration in Alzheimer’s Disease. Cells. 2022 Aug 1;11(15):2365. Wang H, Dou S, Wang C, Gao W, Cheng B, Yan F. Identification and Experimental Validation of Parkinson’s Disease with Major Depressive Disorder Common Genes. Mol Neurobiol. 2023 Oct 1;60(10):6092–108. Yang Y, Wang Y, Wang C, Xu X, Liu C, Huang X. Identification of hub genes of Parkinson’s disease through bioinformatics analysis. Front Neurosci. 2022 Nov 8;16:974838. Lai Y, Lin P, Lin F, Chen M, Lin C, Lin X, et al. Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning. Front Immunol. 2022;13:1046410. Chen L, Wang Y, Huang J, Hu B, Huang W. Identification of Immune-Related Hub Genes in Parkinson’s Disease. Front Genet. 2022 Jul 22;13:914645. Wang M, Li T, Gao R, Zhang Y, Han Y. Identifying the potential genes in alpha synuclein driving ferroptosis of Parkinson’s disease. Sci Rep [Internet]. 2023 [cited 2024 Jun 13];13. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558439/ Xin G, Niu J, Tian Q, Fu Y, Chen L, Yi T, et al. Identification of potential immune-related hub genes in Parkinson’s disease based on machine learning and development and validation of a diagnostic classification model. PLOS ONE. 2023 Dec 5;18(12):e0294984. Liu S, Zhang Y, Bian H, Li X. Gene expression profiling predicts pathways and genes associated with Parkinson’s disease. Neurol Sci. 2016 Jan 1;37(1):73–9. Additional Declarations No competing interests reported. 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-8090106","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551161209,"identity":"a45fbd47-250b-408e-8278-961b4af8f97a","order_by":0,"name":"Mengrui Zhao","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Mengrui","middleName":"","lastName":"Zhao","suffix":""},{"id":551161210,"identity":"c73dcc3d-212c-48cf-bc5d-e37c5f73b2e7","order_by":1,"name":"Saeid Afshar","email":"","orcid":"","institution":"Hamadan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Saeid","middleName":"","lastName":"Afshar","suffix":""},{"id":551161211,"identity":"415b820f-b648-43c5-82cd-79439c3c7dd0","order_by":2,"name":"Irina Dinu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACNobEBgaGCgYGPgnStJwBMkBaDhCnKYGBgbGNFC187Mltj3nnHZZnk24+9vlDDYM8fwMhh/E8bDfm3XbYsE3mWPKMA8cYDGcQsopNIrFNGqiFsU0ix5jhABvQncRpmXPYvk0i/zPDgX8MCfLEaWk4nAi0hZnhYBtDggFBLTwP2yTnHEtPBvrFmOFsn4ThRkJa5NvTn0m8qbG27ZdufsxQ8c1GXo6QFnRAdBoYBaNgFIyCUYAPAADIyjvGS7iurAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Alberta","correspondingAuthor":true,"prefix":"","firstName":"Irina","middleName":"","lastName":"Dinu","suffix":""}],"badges":[],"createdAt":"2025-11-11 20:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8090106/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8090106/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97141871,"identity":"e63f2885-7c9b-4f40-95c7-7f94d342df2e","added_by":"auto","created_at":"2025-12-01 10:07:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1130562,"visible":true,"origin":"","legend":"","description":"","filename":"251116ManuscriptMengruiZhaoNeurobiology.docx","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/42d0b29215a73cff17bd6164.docx"},{"id":97141042,"identity":"251d560e-4b15-4259-ae67-d103ba711383","added_by":"auto","created_at":"2025-12-01 10:06:09","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5391,"visible":true,"origin":"","legend":"","description":"","filename":"f0d8d95b24a341218b6340ae8d36eb55.json","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/b7c3558601b154133435c1d8.json"},{"id":97120598,"identity":"8d219f6f-d8db-4f57-9f87-4c0b5cebe30d","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79261,"visible":true,"origin":"","legend":"","description":"","filename":"f0d8d95b24a341218b6340ae8d36eb551enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/7e696e70e15f4a2854ce98d0.xml"},{"id":97141052,"identity":"7f5e14d1-4b41-41aa-96ed-9929242e18e6","added_by":"auto","created_at":"2025-12-01 10:06:09","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":193472,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/abd32c6e3c0b58fff0516646.png"},{"id":97141957,"identity":"1122708e-ec94-4130-93b0-638e83d6fd4f","added_by":"auto","created_at":"2025-12-01 10:07:13","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148540,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/3938effeb85134128a9d9832.jpeg"},{"id":97120627,"identity":"a3f9fe63-cb3a-406a-b8d2-660e0659b0f4","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149188,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/b0a577a25d4a06b7efcd87d0.jpeg"},{"id":97142632,"identity":"48d4ba7f-a3ca-467b-95b7-66bbb0ec844e","added_by":"auto","created_at":"2025-12-01 10:07:46","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95849,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/7265eb89218a90f7c479f5eb.png"},{"id":97120602,"identity":"f9eb765d-4c6a-4e5b-b727-5f807ea88a43","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69140,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/d04d3bf11ae057c055c219fd.png"},{"id":97142752,"identity":"90ad3097-fc2c-4f9f-b456-4bc283184805","added_by":"auto","created_at":"2025-12-01 10:07:56","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160755,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/0ff97bb1dd04052a4827aea2.png"},{"id":97120613,"identity":"d51495da-7c8d-4e2b-a7ec-a925c1b8ff4f","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70276,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/0cab0a452b0492b64db1ada3.png"},{"id":97120615,"identity":"1c43f9c0-6687-46cf-b7ab-c653d5033f22","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88098,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/8eaa6aaf3d41799da6314e56.png"},{"id":97142252,"identity":"6dadaaa1-598d-489a-9c26-f18216940916","added_by":"auto","created_at":"2025-12-01 10:07:27","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83453,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/f4ea6f1fe32005c532d1380c.png"},{"id":97120603,"identity":"34e1027f-7035-458d-a1c7-4507ab0162c7","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54876,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/2fdbe8d743296f0c1e21d715.png"},{"id":97120623,"identity":"2015f4d9-d37d-4288-98a7-1fb806bb1f0d","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79143,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/8dc865da5ebea733f3053ac5.png"},{"id":97120614,"identity":"cf1eac82-5bd8-4a05-9d86-b9bb7ff09b2e","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42117,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/7fbe92a5cdf476e4aecc58d8.png"},{"id":97141185,"identity":"950c1cf4-ec2d-4d7e-bc52-13de2407b0ff","added_by":"auto","created_at":"2025-12-01 10:06:23","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27112,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/8f92aab48e45c0bd54661182.png"},{"id":97141336,"identity":"dfd4f778-95c7-4885-978a-b390ea0881e9","added_by":"auto","created_at":"2025-12-01 10:06:35","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26779,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/9beee92d35e1ff661bcc82e4.png"},{"id":97142225,"identity":"622f4ddc-06d7-4796-b1da-05fd8d753eb7","added_by":"auto","created_at":"2025-12-01 10:07:26","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22771,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/f51e6ded309bcd45cdce2244.png"},{"id":97120611,"identity":"fbefadcd-c168-4b0f-b8a5-04f181c7133a","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17177,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/17001f27e556ed1650cbb5b5.png"},{"id":97120625,"identity":"87ef0a00-2254-4fbe-9557-a0fde1d6f0a3","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35851,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/8aa1200e8db562f7e6f02232.png"},{"id":97120628,"identity":"fbd3f927-001b-498f-a6e3-5e2cc6003876","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15378,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/7a5edfce073788e3474242d8.png"},{"id":97120621,"identity":"b9bcf09f-4fd9-45f2-a43a-3960fb2e0c1e","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20668,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/7f98def560207451715e8af1.png"},{"id":97120618,"identity":"5962211d-e82a-42a0-8ce7-743c4e3a44e9","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35917,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/5d139461282cf5157286faaf.png"},{"id":97120617,"identity":"74590e98-6533-4ddb-8bea-4a7f602d266b","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10878,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/dc329ce34039a9f696c2c5dc.png"},{"id":97120619,"identity":"d3b8af4f-5739-47e7-84ba-7435a9f180e7","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14882,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/ef7aeebb600e24c26d037872.png"},{"id":97141747,"identity":"2115fa7c-7d33-4c9b-8975-61755c62c5e3","added_by":"auto","created_at":"2025-12-01 10:06:56","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78614,"visible":true,"origin":"","legend":"","description":"","filename":"f0d8d95b24a341218b6340ae8d36eb551structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/bb3b8d40c30d6b5aef399290.xml"},{"id":97120620,"identity":"1359b22f-11d4-4a54-a820-dcffaa1d2957","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86504,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/a895ef40ab6e250d3a3e6929.html"},{"id":97120594,"identity":"054800dd-9a48-4e43-b76e-fb74c0ce76f9","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140534,"visible":true,"origin":"","legend":"\u003cp\u003eThe Volcano plot showing differentially expressed genes between PD and control samples.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/6983db5da68e949a4eb81632.png"},{"id":97120597,"identity":"d0ce584d-d740-4d38-a760-ca91d4a8bd27","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75029,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering dendrogram of all PD and control RNA samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/371c13a9fdddd9d2d99675ca.png"},{"id":97120595,"identity":"13be3edc-d6da-4f4d-8de9-00e9e07a7c77","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40057,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of soft-thresholding power for network construction.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/373675eb22e961609e3194ac.png"},{"id":97120600,"identity":"021b871d-4e4f-4182-b604-c4027204f3b3","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139316,"visible":true,"origin":"","legend":"\u003cp\u003eGene dendrogram with assigned module colors.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/457c45701e6d4984adf6fcd6.png"},{"id":97142172,"identity":"2966f3c0-e057-4b83-9d6b-a7b2eea65998","added_by":"auto","created_at":"2025-12-01 10:07:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38263,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of correlations between gene modules and clinical phenotypes.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/fc9e4938eb3c8593af5c8ce5.png"},{"id":97120608,"identity":"9b86c167-db5f-4114-bb4d-5c96d5bbc5f7","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":55774,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of gene significance versus module membership for the blue module.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/2fb8e8e8530e705d2fc5bdb5.png"},{"id":97142150,"identity":"5af9dc77-0d31-4e8e-9abe-a80d33d3af7f","added_by":"auto","created_at":"2025-12-01 10:07:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":105224,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of genes in the blue module.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/a6a602db1504c32e2a04d6fa.png"},{"id":97120607,"identity":"66f75d7b-18eb-44db-9a2f-0c8e8d46d80b","added_by":"auto","created_at":"2025-12-01 07:50:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":88891,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork construction and hub genes identification for the blue module. (a) Co-expression network for the blue module visualized using Cytoscape. (b) Venn diagram illustrating convergence across four centrality metrics (Degree, EcCentricity, EPC and MNC) for hub gene selection\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/77892ec7a97f4e3f47276647.png"},{"id":97120610,"identity":"bba61d3f-6c6f-40a2-99f0-bb12d2686390","added_by":"auto","created_at":"2025-12-01 07:50:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":102637,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot of \u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B\u003c/em\u003e expression in PD vs. control samples from GSE202667 and GSE54536.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/e445c1804b94ee032e717615.png"},{"id":97812901,"identity":"083caf31-3252-4289-8c79-dcb10fa800d9","added_by":"auto","created_at":"2025-12-09 16:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1302109,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8090106/v1/f49c74be-7f72-4114-9b25-ca6b75a39ea0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Finding Genetic Contributors to Parkinson’s Disease via Weighted Gene Co- expression Network Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs the second most common progressive neurodegenerative disorder, Parkinson\u0026rsquo;s disease (PD) is typified by tremors, stiffness, and slowness of movements. It is associated with gradual loss of neurons in the substantia nigra and other brain structures (1). As population age, the prevalence of PD continues to grow, affecting approximately 1% of individuals over 60 and up to 4% in those over age 85 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Epidemiological studies have revealed a rapid growth in the global burden of PD over recent decades. The number of people affected by the PD has more than doubled \u0026mdash; from 2.5\u0026nbsp;million (95% uncertainty interval [UI] 2.0\u0026ndash;3.0) patients in 1990 to 6.1\u0026nbsp;million (95% UI 5.0\u0026ndash;7.3) patients in 2016 around the world (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u0026mdash; placing an ever-growing strain on healthcare systems worldwide. Many studies showed that most PD cases are sporadic with no identifiable genetic cause. It is now widely accepted that PD results from complex interactions between genetic variables and environmental variables. According to the statistics, nearly 15% of PD patients have a positive PD family history, while 5\u0026ndash;10% respond to Mendelian inheritance (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). To date, evidence indicates that around 25% of the chance of developing PD is genetic (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). A 2018 genetic review showed that at least 23 loci and 19 genes are associated with PD, while many more are linked to sporadic cases(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although a comprehensive clinical examination and neuroimaging techniques are necessary for the accuracy of a clinical diagnosis, there are currently no objective genetic or biochemical testing available for PD (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, the high failure rate in the development of PD drugs is likely due to the many unknowns in disease pathogenesis, highlighting the urgent need to find novel pathogenic mechanisms and therapeutic targets.\u003c/p\u003e\u003cp\u003eSeveral molecular pathways have been implicated in PD pathogenesis, including α-synuclein aggregation, mitochondrial dysfunction, oxidative stress, ferroptosis, gut dysbiosis, and neuroinflammation (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). All of these pathogenic mechanisms are believed to play a role in neurodegeneration.\u003c/p\u003e\u003cp\u003eThe identification of various biomarkers - including cerebrospinal fluid (CSF) biomarkers (e.g. altered levels of α-synuclein, lowered levels of tau), serum biomarkers (e.g. high levels of glial fibrillary acidic protein and its breakdown products), genetics biomarkers (e.g., mutations in \u003cem\u003eSNCA\u003c/em\u003e and \u003cem\u003eLRRK2\u003c/em\u003e), and imaging biomarkers (e.g. using neuromelanin-sensitive magnetic resonance imaging for assessing neuromelanin levels) - holds promise for earlier and more precise PD diagnosis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Furthermore, these biomarkers can also be used to track disease progression and evaluate patient responses to treatment. The discovery of effective biomarkers will also support the development of much-needed disease-modifying treatments for PD, which are currently lacking (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong computational approaches, weighted Gene Co-expression Network Analysis (WGCNA) stands out as a powerful systems biology tool that identifies modules of co-expression genes and relates them to clinical phenotypes. This networks-based method offers advantages over traditional single-gene analyses by highlighting hub genes that may serve as central regulators of disease-relevant pathways. It considers the variability of gene expression across different biological conditions, the potential for identifying higher-order relationships among genes, and managing multiple tests by assessing correlations between the eigengenes of important modules and variables of interest (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, we applied WGCNA to microarray data derived from PD patients and health controls to identify key differentially expressed genes (DEGs), construct gene co-expression networks and find modules most strongly associated with PD. We further examined the biological significance of these modules using functional enrichment analyses and validated the most promising hub genes in an independent dataset. Our goal was to reveal hub genes that may contribute to PD pathophysiology, enhancing diagnostic precision and fostering the development of personalized prevention and therapeutic intervention strategies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data acquisition\u003c/h2\u003e\u003cp\u003eOur study used gene expression data from GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo/\" target=\"_blank\"\u003ewww.ncbi.nlm.nih.gov/geo/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To be more specifically, we retrieved dataset GSE202667 (platform: GPL20844, Agilent-072363 SurePrint G3 Human GE v3 8x60K Microarray 039494) from the study by Caroline Diener et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This dataset comprises a total 62,976 expression profiles of 59 RNA samples, including 29 samples from patients diagnosed with Parkinson\u0026rsquo;s disease and 30 samples from age-matched health controls.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Identification of DEGs\u003c/h2\u003e\u003cp\u003eDEGs between PD and health control samples were identified using the limma package in R (version 4.1.3), a well-established tool within the Bioconductor suite. DEGs were defined using an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 to ensure biological relevance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Construction of co-expression network using WGCNA\u003c/h2\u003e\u003cp\u003eWGCNA package in R was used to construct a gene co-expression network via analyzing the gene expression data of GSE202667 (platform: GPL20844) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This package was stated by Langfelder and Horvath (2008) which involves computing pairwise Pearson correlation coefficients between all gene expression profiles to generate a similarity matrix(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This similarity matrix was transformed into an adjacency matrix using a power function. The optimal soft-thresholding power was determined using the pickSoftThreshold function, which assesses scale-free topology fit. This power value was selected based on a fit index (R\u003csup\u003e2\u003c/sup\u003e) greater than 0.85 in this study.\u003c/p\u003e\u003cp\u003eGenes were subsequently grouped into modules using hierarchical clustering and the dynamic tree cut algorithm, with a minimum module size set to 30. Module eigengenes (MEs) were computed and correlated with clinical traits (i.e., disease stages, PD vs. healthy control) to identify the most relevant modules. Both high gene significance (GS) for clinical traits and high module membership (MM) within the selected module define the hub genes.\u003c/p\u003e\u003cp\u003eAll analyses were conducted by version 4.1.3 of the R statistical programming language. The significance level for the two-sided statistical test was P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Functional enrichment analysis\u003c/h2\u003e\u003cp\u003eTo interpret the biological roles of genes the selected gene module, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. These analyses were performed using the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), covering three GO domains: biological processes (BP), cellular components (CC), and molecular functions (MF) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Pathways within adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Protein-protein interaction (PPI) network analysis, identification of hub genes and transcription factors\u003c/h2\u003e\u003cp\u003eA PPI network was constructed for genes in the most relevant module using STRING (version 12.0), with a confidence score cutoff set at 0.4 to ensure reliable interactions(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The constructed network was visualized and analyzed using Cytoscape software (version 3.10.2) and CytoHubba plugin was employed to identify the key genes based on four topological algorithms including Degree, maximum neighborhood component (MNC), Edge Percolated Component (EPC), and EcCentricity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Genes that were consistently ranked in the top by all four algorithms were considered robust hub genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Validation in an independent dataset\u003c/h2\u003e\u003cp\u003eTo validate the expression pattern of selected hub genes, we used dataset GSE54536 (platform GPL10558), which contains a whole-transcriptome analysis of the peripheral blood from four untreated stage 1 PD patients and four neurologically normal age-matched controls from the same population (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Differential expression of hub genes in this validation set was evaluated using the \u0026ldquo;limma\u0026rdquo; package, and violin plots were generated to illustrate expression differences.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003ch3\u003e3.1. Differential expression analysis\u003c/h3\u003e\n\u003cp\u003eOut of the 35,200 unique genes examined in the GSE202667 dataset, 365 were identified as differential expressed based on the predefined thresholds. Among these, 213 genes were significantly upregulated (LogFc\u0026gt;1) and 152 were downregulated (LogFc\u0026lt;-1) in PD samples compared to healthy controls. A Volcano plot was generated by GEO2R on the GEO website by plotting the -log\u003csub\u003e10\u003c/sub\u003e (adjusted \u003cem\u003eP\u003c/em\u003e-value) versus log\u003csub\u003e2\u003c/sub\u003e(FC) (Fig 1) to visually represent the distribution of DEGs. In this plot, red and blue points highlight genes with significant upregulation and downregulation, respectively, while non-significant genes are marked in black.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3.2. Co-expression network construction and module detection\u003c/h3\u003e\n\u003cp\u003eAfter confirming data quality (Fig 2), we applied WGCNA to the 365 DEGs to build a gene co-expression network. The soft-thresholding power were determined using scale-free topology criteria, with power=12 selected to ensure a network with optimal fit (R\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.85), as shown in Fig 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing this power setting, WGCNA identified four distinct co-expression modules, each labeled by color: turquoise, blue, brown and grey (Fig 4). These modules represent cluster of genes sharing similar expression patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModule-trait correlation analysis showed that the blue module exhibited the strongest correlation with PD disease stage (r = 0.78; \u003cem\u003eP\u003c/em\u003e-Value\u0026nbsp;=\u0026nbsp;6e-7), as visualized in a module trait heatmap (Fig 5). This result suggest that the gene set in the blue module may contribute significantly to disease progression.\u003c/p\u003e\n\u003cp\u003eFurther supporting this relationship, we observed a strong correlation between GS for PD stage and MM within blue module (r = 0.79; \u003cem\u003eP\u003c/em\u003e-Value = 3e-21), illustrated in Fig 6. This suggests that the most connected genes in the module are also the most associated with disease severity.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3.3. Functional annotation of blue modules genes\u003c/h3\u003e\n\u003cp\u003eFunctional enrichment analysis was conducted for genes in the blue module. GO analysis revealed enrichment in biological processes (BP) such as cytokine-mediated signaling pathway, antibody-dependent cellular cytotoxicity, positive regulation of tumor necrosis factor production, and cell surface receptor signaling pathway. For cellular component (CC) GO terms, DEGs are enriched in plasma membrane, membrane and external side of plasma membrane. For molecular function (MF), the selected genes are enriched in IgG binding and IgG receptor activity. KEGG analysis indicated involvement in B cell receptor signaling and osteoclast differentiation pathways. These findings are summarized in Fig 7 (13).\u003c/p\u003e\n\u003ch3\u003e3.4. Identification of central hub genes\u003c/h3\u003e\n\u003cp\u003eA graphical rendering of the PPI network, highlighting high-ranking genes, is shown in Fig 8a. To enhance robustness, we focused on genes that appeared consistently across all four ranking methods, including Degree, EcCentricity, EPC and MNC. The intersection of results from each algorithm is illustrated in the Venn diagram (Fig 8b), which revealed eight overlapping genes: leukocyte immunoglobulin like receptor B4 (\u003cem\u003eLILRB4\u003c/em\u003e), leukocyte immunoglobulin like receptor B2 (\u003cem\u003eLILRB2\u003c/em\u003e), Fc gamma receptor IIa\u0026nbsp;(\u003cem\u003eFCGR2A\u003c/em\u003e), Fc gamma receptor IIa (\u003cem\u003eFCGR2B\u003c/em\u003e), C-C motif chemokine receptor 1 (\u003cem\u003eCCR1\u003c/em\u003e), toll like receptor 4 (\u003cem\u003eTLR4\u003c/em\u003e), integrin subunit alpha X (\u003cem\u003eITGAX\u003c/em\u003e), and neural cell adhesion molecule 1 (\u003cem\u003eNCAM1\u003c/em\u003e). These genes were designated as final hub genes due to their topological prominence and potential biological relevance to PD.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3.5. Validation in an independent dataset\u003c/h3\u003e\n\u003cp\u003eTo confirm the reliability of the selected hub genes, we examined their expression dataset GSE54536. Among the eight candidates, \u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B\u003c/em\u003e were found to be consistently upregulated in PD patients compared to controls in both datasets. Violin plots in Fig 9a and 9b display the expression patterns of these genes, supporting their potential as robust biomarkers.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePD presents an increasing clinical and societal challenge, particularly in aging populations. Although significant efforts have been made to characterize its pathological mechanisms, effective disease-modifying therapies remain elusive. Recent genetic studies have broadened our understanding of the hereditary contributions to PD, identifying more than 80 risk loci through large-scale meta-analyses across multiple populations (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, the translation of these associations into biologically meaningful insights remains limited by the complex interplay among the identified genes and their regulatory networks.\u003c/p\u003e\u003cp\u003eIn this study, we applied WGCNA to explore the gene co-expression network of PD, focusing on co-regulated gene modules rather than individual gene changes. Using WGCNA, we identified a co-expression module (refer to as the blue module) that showed a strong correlation with disease stage. Functional enrichment analyses of this module pointed toward a substantial involvement of immune-related pathways, including cytokine signaling, immunoglobulin binding, and B cell receptor activity. These findings are consistent with accumulating evidence that implicates immune dysregulation as both a contributor to and consequence of PD progression.\u003c/p\u003e\u003cp\u003eOur study identified eight hub genes within the blue module. Among them, \u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B\u003c/em\u003e emerged as the most robust candidates due to their consistent upregulation in both the discovery and independent validation datasets. These genes encode low-affinity Fc gamma receptors involved in the regulation immune responses (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), particularly phagocytosis and antibody-mediated clearance(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). \u003cem\u003eFCGR2A\u003c/em\u003e functions as an activating receptor that can enhance inflammatory signaling, whereas \u003cem\u003eFCGR2B\u003c/em\u003e contains an immunoreceptor tyrosine-based inhibitory motif (ITIM) that attenuates immune activation (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe potential role of FCGR family genes in neuroinflammation has been discussed in the prior literature. Wang et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) discussed FCGR2B\u0026rsquo;s role in nerve injury-induced neuropathic pain, and Qingqing Ye et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) highlighted its involvement in neuropathic pain and aging. Caixiu Lin et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) showed that M2 macrophage infiltration in AD was associated with five hub genes, including \u003cem\u003eTLR2, FCGR2A, ITGB2, NCKAP1L\u003c/em\u003e, and \u003cem\u003eCYBA.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSeveral previous studies utilized other methodologies to identify the key genes associated with PD or related neurological disorders.\u003c/p\u003e\u003cp\u003eHuiqing Wang et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) explored the potential genes and molecular mechanisms of PD with major depressive disorder (MDD) by using GSE6613 and GSE98793 gene expression profiles in GEO. They utilized the limma package in R to obtain DEGs, as well as utilized GO and KEGG enrichment analyses to explore the function of these common DEGs. Key genes were identified using PPI network and least absolute shrinkage and selection operator (LASSO) regression; AQP9, SPI1, and RPH3A were validated as significant in PD with MDD. Similarly, Yajun Yang et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) used WGCNA to screen for key genes linked to PD and then used GO, KEGG, and Disease Ontology (DO) to analyze enrichment of target genes. Eight hub genes were discovered, including \u003cem\u003eRPL3L, PLEK2, PYCRL, CD99P1, LOC100133130, MELK, LINC01101\u003c/em\u003e, and \u003cem\u003eDLG3-AS1\u003c/em\u003e, suggesting a new direction for PD diagnosis and treatment. Yongxing Lai et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) employed ssGSEA, LASSO regression, and WGCNA algorithms to identify immune microenvironment subtypes and signature genes in Alzheimer\u0026rsquo;s disease (AD). Six machine-learning algorithms was created for identifying distinctive genes. Finally, they found 5 hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD.\u003c/p\u003e\u003cp\u003eLin Chen et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) focused on immune-related hub genes in PD by analyzing datasets GSE7621, GSE20141, and GSE49036. They applied WGCNA to screen key module genes and intersect immune-related genes to obtain immune-key genes. Using LASSO analysis, they identified immune-related hub genes, including \u003cem\u003eSLC18A2, L1CAM, S100A12, and CXCR4\u003c/em\u003e, as being associated with the pathogenesis of PD. Min Wang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) used bioinformatics analysis, including gene set enrichment analysis, KEGG, and PPI network, to identify the potential DEGs, three hub genes (\u003cem\u003ePIK3CA, BRD4, ATM\u003c/em\u003e) and the key lncRNA (\u003cem\u003eNEAT1\u003c/em\u003e) were verified in neurotoxic PD models. Guanghao Xin et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) downloaded the GSE8397 dataset from the GEO database, using WGCNA combined with two machine learning algorithms for feature screening and combined with immune cell infiltration analysis to identify PD immune-related hub genes. They identified four hub genes: \u003cem\u003eDLK1, IARS, DLD\u003c/em\u003e and \u003cem\u003eTTC19.\u003c/em\u003e Shuang Liu et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) emphasized the significance of pathways like the neuronal system and Calcium signaling pathway in PD progression, with DEGs of \u003cem\u003eSYN1, GRIN1, GRIN2D\u003c/em\u003e, and \u003cem\u003eDLGAP3\u003c/em\u003e as potential therapeutic targets of PD.\u003c/p\u003e\u003cp\u003eOur study used the most recently published GEO dataset and applied strict DEG threshold criteria combined with WGCNA and other bioinformatics analyses to identify key genes, which are significantly associated with different PD stages. The additional step of validating hub gene expression in an external dataset also increases the credibility and reproducibility of our findings. Both of \u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B\u003c/em\u003e were significantly upregulated in PD patients and closely linked to immune-related pathways.\u003c/p\u003e\u003cp\u003eThis study also has limitations. The reliance on peripheral blood transcriptomic data may not fully capture central nervous system-specific gene regulation. Additionally, the relatively modest sample size in the validation dataset limits statistical power. The study is also based on transcriptomic inference, lacking direct protein-level or functional validation of gene activity. Furthermore, platform discrepancies between datasets may introduce confounding effects, despite normalization procedures.\u003c/p\u003e\u003cp\u003eFurther studies should use a large number of samples, conduct experimental studies to confirm the role of \u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B\u003c/em\u003e, and assess the effects of genetic mutations alongside personal and/or environmental variables.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this study identified \u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B\u003c/em\u003e as potential biomarkers for PD, offering new opportunities for the development of diagnostic tools and therapeutic strategies. Through integrating immune-related mechanisms in PD research, this work provides a foundation for exploring novel treatments targeting immune modulation in this complex neurodegenerative disorder.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available on the GEO website. Further inquiries are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest related to this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTolosa E, Wenning G, Poewe W. The diagnosis of Parkinson\u0026rsquo;s disease. Lancet Neurol. 2006 Jan;5(1):75\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003eDeng H, Wang P, Jankovic J. The genetics of Parkinson disease. Ageing Res Rev. 2018 Mar 1;42:72\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eDing C, Wu Y, Chen X, Chen Y, Wu Z, Lin Z, et al. Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990\u0026ndash;2019. Front Public Health. 2022 Nov 29;10:952161. \u003c/li\u003e\n\u003cli\u003eElsayed I, Martinez-Carrasco A, Cornejo-Olivas M, Bandres-Ciga S. Mapping the Diverse and Inclusive Future of Parkinson\u0026rsquo;s Disease Genetics and Its Widespread Impact. Genes. 2021 Oct 23;12(11):1681. \u003c/li\u003e\n\u003cli\u003eDay JO, Mullin S. The Genetics of Parkinson\u0026rsquo;s Disease and Implications for Clinical Practice. Genes. 2021 Jun 30;12(7):1006. \u003c/li\u003e\n\u003cli\u003eRai S, Bharti PS, Singh R, Rastogi S, Rani K, Sharma V, et al. Circulating plasma miR-23b-3p as a biomarker target for idiopathic Parkinson\u0026rsquo;s disease: comparison with small extracellular vesicle miRNA. Front Neurosci. 2023 Nov 15;17:1174951. \u003c/li\u003e\n\u003cli\u003eDong-Chen X, Yong C, Yang X, Chen-Yu S, Li-Hua P. Signaling pathways in Parkinson\u0026rsquo;s disease: molecular mechanisms and therapeutic interventions. Signal Transduct Target Ther. 2023 Feb 21;8(1):1\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eYamashita KY, Bhoopatiraju S, Silverglate BD, Grossberg GT. Biomarkers in Parkinson\u0026rsquo;s disease: A state of the art review. Biomark Neuropsychiatry. 2023 Dec 1;9:100074. \u003c/li\u003e\n\u003cli\u003eDarvish Z, Ghanbari S, Afshar S, Tapak L, Amini P. Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis. Cell J Yakhteh. 2023 Jun;25(6):418\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eZhao W, Langfelder P, Fuller T, Dong J, Li A, Hovarth S. Weighted gene coexpression network analysis: state of the art. J Biopharm Stat. 2010 Mar;20(2):281\u0026ndash;300. \u003c/li\u003e\n\u003cli\u003eDiener C, Hart M, Kehl T, Becker-Dorison A, T\u0026auml;nzer T, Schub D, et al. Time-resolved RNA signatures of CD4+ T cells in Parkinson\u0026rsquo;s disease. Cell Death Discov. 2023 Jan 21;9(1):18. \u003c/li\u003e\n\u003cli\u003eChen L, Yuan L, Wang Y, Wang G, Zhu Y, Cao R, et al. Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma. Int J Biol Sci. 2017 Nov 1;13(11):1361\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9:559. \u003c/li\u003e\n\u003cli\u003eHuang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009 Jan;4(1):44\u0026ndash;57. \u003c/li\u003e\n\u003cli\u003eSTRING: functional protein association networks [Internet]. [cited 2024 Jun 18]. Available from: https://string-db.org/cgi/input?sessionId=bj13GKDjfMB5\u0026amp;input_page_show_search=on\u003c/li\u003e\n\u003cli\u003eChin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014 Dec 8;8(Suppl 4):S11. \u003c/li\u003e\n\u003cli\u003eAlieva AKh, Shadrina MI, Filatova EV, Karabanov AV, Illarioshkin SN, Limborska SA, et al. Involvement of Endocytosis and Alternative Splicing in the Formation of the Pathological Process in the Early Stages of Parkinson\u0026rsquo;s Disease. BioMed Res Int. 2014;2014:718732. \u003c/li\u003e\n\u003cli\u003eGEO Accession viewer [Internet]. [cited 2024 Jun 17]. Available from: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54536\u003c/li\u003e\n\u003cli\u003eCoukos R, Krainc D. Key genes and convergent pathogenic mechanisms in Parkinson disease. Nat Rev Neurosci. 2024 Jun;25(6):393\u0026ndash;413. \u003c/li\u003e\n\u003cli\u003eKim JJ, Vitale D, Otani DV, Lian MM, Heilbron K, Iwaki H, et al. Multi-ancestry genome-wide association meta-analysis of Parkinson\u0026rsquo;s disease. Nat Genet. 2024;56(1):27\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eNalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson\u0026rsquo;s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 2019 Dec;18(12):1091\u0026ndash;102. \u003c/li\u003e\n\u003cli\u003eNimmerjahn F, Ravetch JV. Fc\u0026gamma; receptors as regulators of immune responses. Nat Rev Immunol. 2008 Jan;8(1):34\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eAnania JC, Chenoweth AM, Wines BD, Hogarth PM. The Human Fc\u0026gamma;RII (CD32) Family of Leukocyte FcR in Health and Disease. Front Immunol. 2019 Mar 19;10:464. \u003c/li\u003e\n\u003cli\u003eHerik-Oudijk IEV den, Capel PJA, Bruggen T van der, Winkel JGJV de. Identification of Signaling Motifs Within Human Fc\u0026gamma;RIIa and Fc\u0026gamma;Rllb Isoforms. Blood. 1995 Apr 15;85(8):2202\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eSun Z, Sun X, Yuan Y, Li H, Li X, Yao Z. FCGR2B as a prognostic and immune microenvironmental marker for gliomas based on transcriptomic analysis. Medicine (Baltimore). 2023 Sep 15;102(37):e35084. \u003c/li\u003e\n\u003cli\u003eWang K, Yi D, Yu Z, Zhu B, Li S, Liu X. Identification of the Hub Genes Related to Nerve Injury-Induced Neuropathic Pain. Front Neurosci. 2020 May 20;14:488. \u003c/li\u003e\n\u003cli\u003eYe Q, Huang Z, Lu W, Yan F, Zeng W, Xie J, et al. Identification of the common differentially expressed genes and pathogenesis between neuropathic pain and aging. Front Neurosci. 2022 Oct 19;16:994575. \u003c/li\u003e\n\u003cli\u003eLin C, Xu C, Zhou Y, Chen A, Jin B. Identification of Biomarkers Related to M2 Macrophage Infiltration in Alzheimer\u0026rsquo;s Disease. Cells. 2022 Aug 1;11(15):2365. \u003c/li\u003e\n\u003cli\u003eWang H, Dou S, Wang C, Gao W, Cheng B, Yan F. Identification and Experimental Validation of Parkinson\u0026rsquo;s Disease with Major Depressive Disorder Common Genes. Mol Neurobiol. 2023 Oct 1;60(10):6092\u0026ndash;108. \u003c/li\u003e\n\u003cli\u003eYang Y, Wang Y, Wang C, Xu X, Liu C, Huang X. Identification of hub genes of Parkinson\u0026rsquo;s disease through bioinformatics analysis. Front Neurosci. 2022 Nov 8;16:974838. \u003c/li\u003e\n\u003cli\u003eLai Y, Lin P, Lin F, Chen M, Lin C, Lin X, et al. Identification of immune microenvironment subtypes and signature genes for Alzheimer\u0026rsquo;s disease diagnosis and risk prediction based on explainable machine learning. Front Immunol. 2022;13:1046410. \u003c/li\u003e\n\u003cli\u003eChen L, Wang Y, Huang J, Hu B, Huang W. Identification of Immune-Related Hub Genes in Parkinson\u0026rsquo;s Disease. Front Genet. 2022 Jul 22;13:914645. \u003c/li\u003e\n\u003cli\u003eWang M, Li T, Gao R, Zhang Y, Han Y. Identifying the potential genes in alpha synuclein driving ferroptosis of Parkinson\u0026rsquo;s disease. Sci Rep [Internet]. 2023 [cited 2024 Jun 13];13. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558439/\u003c/li\u003e\n\u003cli\u003eXin G, Niu J, Tian Q, Fu Y, Chen L, Yi T, et al. Identification of potential immune-related hub genes in Parkinson\u0026rsquo;s disease based on machine learning and development and validation of a diagnostic classification model. PLOS ONE. 2023 Dec 5;18(12):e0294984. \u003c/li\u003e\n\u003cli\u003eLiu S, Zhang Y, Bian H, Li X. Gene expression profiling predicts pathways and genes associated with Parkinson\u0026rsquo;s disease. Neurol Sci. 2016 Jan 1;37(1):73\u0026ndash;9. \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":"","lastPublishedDoi":"10.21203/rs.3.rs-8090106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8090106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the second most prevalent progressive neurodegenerative disorder, Parkinson’s disease (PD) involves complex pathological processes and lacks definitive diagnostic biomarkers. This study aimed to explore molecular signatures associated with PD to support improved diagnostic and therapeutic strategies for PD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression profiles from GSE202667 dataset (platform: GPL20844) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between PD and control samples was assessed using the “limma” package in R software. We used Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks and evaluate their correlation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed to explore biological correlations. Hub genes were validated using an independent dataset (GSE54536).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 365 common DEGs were identified. WGCNA revealed 4 co-expression modules, with the eigenvalues of the blue module most significantly with PD stages (r=0.78, P\u0026lt;0.001). Key genes from this module were analyzed using protein-protein interaction (PPI) networks and CytoHubba algorithm. Eight potential hub genes, including \u003cem\u003eLILRB4, LILRB2, FCGR2A, FCGR2B, CCR1, TLR4, ITGAX,\u003c/em\u003e and \u003cem\u003eNCAM1\u003c/em\u003e, were identified, of which \u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B\u003c/em\u003e were validated as consistently upregulated in PD across datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFCGR2A\u003c/em\u003e and \u003cem\u003eFCGR2B \u003c/em\u003emay serve as immune-related molecular indicators of PD, suggesting a potential role in disease progression and as candidates for further clinical diagnostic and therapeutic development. Further studies with larger and experimentally enriched datasets are recommended.\u003c/p\u003e","manuscriptTitle":"Finding Genetic Contributors to Parkinson’s Disease via Weighted Gene Co- expression Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 07:50:13","doi":"10.21203/rs.3.rs-8090106/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":"1500711e-c8c0-419c-9850-718eeed03801","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T16:08:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 07:50:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8090106","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8090106","identity":"rs-8090106","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}