GADD45A as a Potential Biomarker Associated with Endoplasmic Reticulum Stress in Focal Segmental Glomerulosclerosis

GADD45A as a Potential Biomarker Associated with Endoplasmic Reticulum Stress in Focal Segmental Glomerulosclerosis | 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 GADD45A as a Potential Biomarker Associated with Endoplasmic Reticulum Stress in Focal Segmental Glomerulosclerosis Hongyu Wang, Jun Zou, hongbing li, Gengru Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6731959/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 Objective: This study aimed to identify and validate potential endoplasmic reticulum stress-related biomarkers of FSGS. Methods: Five microarray datasets were downloaded from the GEO database. The endoplasmic reticulum stress-related genes were extracted from GeneCards database. GSE104948, GSE129973, and GSE121233 datasets were used to identified DEGs by limma R package. WGCNA was performed to obtain hub gene modules. We intersected the DEGs, genes from hub module of WGCNA, and ERSRGs. GO and KEGG pathway enrichment analyses were performed. LASSO, SVM-RFE, and RF algorithms were used to screen characteristic genes. Further, GSE108109 and GSE104066 were used as validated datasets. Box plots, ROC curves, and AUC were created to identify potential biomarkers. A novel nomogram model was constructed using potential biomarkers. Immunohistochemistry was used to validate the expression of the potential biomarkers. Results: Intersecting DEGs, the brown module genes, and ERSRGs, we identified 15 hub ERSRGs of FSGS. AGO2, CCND1, GADD45A, TRAM2, and PTPN1 genes were screened using Lasso, SVM-RFE, and RF. Further, CCND1, GADD45A, and TRAM2 were validated as significant in training and validation datasets. A nomogram based on CCND1, GADD45A, and TRAM2 expression was constructed. The calibration curves, decision curve, and clinical impact curve showed that the nomogram had good consistency and clinical practical benefit. The expression of GADD45A is higher in FSGS patients than control patients in IHC results ( P < 0.0001). Conclusions: GADD45A may be a potential biomarker associated with endoplasmic reticulum stress in FSGS. Focal segmental glomerulosclerosis Endoplasmic reticulum stress WGCNA Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Focal segmental glomerulosclerosis (FSGS) accounts for approximately 20% of pediatric nephrotic syndromes and 40% of adult nephrotic syndromes [ 1 ] . FSGS is a pathologic diagnosis that refers to a histologic pattern of kidney injury, and is a common cause of end-stage renal disease (ESRD). FSGS is characterized by sclerotic lesions in glomeruli that are focal (< 50% of the total number of glomeruli) and segmental (< 50% of the glomerular capillary surface) under light microscopy [ 2 – 4 ] . Mounting evidence has demonstrated that the endoplasmic reticulum stress is closely related to the progression of renal diseases, and it has been found that the pathogenesis of FSGS is also related to endoplasmic reticulum stress, although the exact mechanism remains unclear. The endoplasmic reticulum (ER) is an organelle that plays an important role in protein production, metabolic homeostasis, cell signaling, et al. Changes in the state of microenvironment or mutations in specific genes can give limitations to the cellular ability to properly synthesize protein and perform other functions of the endoplasmic reticulum, leading to a state known as the endoplasmic reticulum stress (ERS) [ 5 ] . ER stress activates three major signaling pathways: the unfolded protein response (UPR), ER overload response (EOR), and sterol regulatory element binding protein (SREBP). The UPR comprises three signaling pathways initiated by three proteins: PKR-like ER like kinase (PERK), activating transcription 6 (ATF6), and inositol-requiring enzyme 1α (IRE1α ) [ 6 ] . Accumulating evidence suggests that ERS plays an important role in the pathogenesis of FSGS [ 7 – 9 ] . In cell culture studies, Cybulsky AV et al. cultured podocytes with and without the extracellular matrix (ECM) respectively. The results showed that podocytes lacking the ECM showed ERS and apoptosis, increased expression of ER chaperone proteins glucose-regulated protein 78 (GRP78), and increased phosphorylation of eukaryotic translation initiation factor-2α (eIF2α) [ 10 – 11 ] . In animal models studies, Cybulsky AV et al. observed the effects of exacerbating ER stress in a murine model of FSGS. In this model, they found that the experimental FSGS mice appeared to upregulate markers of ER stress. This finding raises the possibility that the the pathogenesis of FSGS may involve ER stress [ 12 ] . Mutations in human canonical transient receptor potential-6 (TRPC6) gene have been found to cause inherited focal segmental glomerulosclerosis. Chen S et al. reported that albumin overload could upregulate the expression of GRP78, activate caspase-12 and promote apoptosis in podocytes through TRPC6 [ 13 – 14 ] . In this study, we herein aimed to explore the role of endoplasmic reticulum stress-related potential biomarkers, immune infiltration landscape, and subtype classifications of FSGS patients according to integrated bioinformatics and machine learning analysis. First, five datasets from GEO database were downloaded with three datasets chose as training datasets and two external datasets selected as validation datasets. Subsequently, WGCNA and three machine learning algorithms including Lasso, SVM-RFE, and RF were used to identify characteristic genes. Box plots, ROC curves and AUC were used to validate the diagnostic value of characteristic genes to get potential biomarkers. Our findings are expected to provide new insights into diagnosis of the FSGS patients from the perspective of endoplasmic reticulum stress. 2. Methods 2.1Data source acquisition and processing Five microarray datasets were screened using the GEO database [ 15 ] . GSE104948, GSE129973, and GSE121233 were used as training datasets and GSE108109, and GSE104066 were used as validation datasets. ERS-related genes were obtained from the GeneCards database, and genes with relevance scores ≥ 5 were extracted for this study. The “sva” R package containing “combat” function was used to get rid of the batch effects and make datasets unified. The effect of removing the batch effect was assessed by using principal component analysis (PCA), and the distribution of FSGS and the normal group samples was visualized. Details of the datasets are listed in Table 1 and a flowchart of the data processing and analysis is shown in Fig. 1 . Table 1 Microarray datasets of this study Gene series Platform FSGS Origin Organism FSGS Normal GSE129973 GPL17586 20 20 Glomerular Homo sapiens GSE104948 GPL22945 GPL24120 18 21 Glomerular Homo sapiens GSE121233 GPL17586 5 5 Glomerular Homo sapiens GSE108109 GPL19983 30 6 Glomerular Homo sapiens GSE104066 GPL19983 48 6 Glomerular Homo sapiens 2.2Differential expression analysis The “limma” R package was used to detect differentially expressed genes (DEGs) with a threshold of |Log2 Fold Change (FC)| >0.4 and adjusted p -value < 0.05 [ 16 ] . The results were visualized using “ggplot2” and “pheatmap” R packages. 2.3Weighted gene co-expression network construction The‘WGCNA’ R package was used to construct a weighted gene co-expression network for all genes in FSGS and normal groups. The soft threshold parameter ( β ) was used to estimate links between genes. Then, an adjacency matrix and scale-free network features were constructed. Network connectivity was used to evaluate whether the adjacency matrix could be successfully transformed into a topological overlap metric (TOM). A clustering tree graph for the TOM matrix by average linkage hierarchical clustering was constructed. The main manifestations were different branches and colors, establishing module relationships and calculating the linkage between gene modules and FSGS. The hub genes in the most relevant modules were identified by module membership (MM) > 0.7 and gene significance (GS) > 0.25 [ 17 ] . The intersection of differentially expressed genes, ERSRGs, and hub genes in the most relevant modules was performed using the“VennDiagram”R package, which was used for subsequent analysis. 2.4Functional enrichment analysis The R packages “clusterProfiler” and “enrichplot” were used to perform GO analysis and KEGG analysis [ 18 – 19 ] . Three categories enrichments, biological process (BP), cellular component (CC), molecular function (MF), are involved in GO functional enrichment. KEGG analysis was used to explore potential pathways. 2.5Machine learning algorithms Three machine-learning algorithms: LASSO, RF, and SVM-RFE were used to screen for characteristic genes [ 20 – 21 ] . LASSO regression analysis was used to perform variable screening and complexity adjustment constructed by using the “cv.glmnet” function in R package ‘glmnet’. The degree of LASSO regression complexity adjustment was controlled by the parameter lambda (λ), and lambda.min was selected as the optimal lambda. The R package “e1071” was used for the SVM-RFE algorithm and the features were sorted by recursion. The random forest analysis was performed by using the “RandomForest” function. Genes within the intersection of three machine learning algorithms were selected for further study as characteristic genes. 2.6Validation of characteristic genes GSE108109 and GSE104066 were used as the validated datasets. Box plots were created and verified to confirm the significance of characteristic genes. Receiver operating characteristic (ROC) curves [ 22 – 24 ] , and the area under the curve (AUC) were calculated to measure the predictive capability of the algorithms. The “pROC” package was used for ROC curve analysis. AUC > 0.75 was considered the ideal diagnostic value. Above all, we tested the diagnostic value of charactersitic genes using box plots, ROC curves and AUC to identify potential biomarkers. 2.7Construction of a nomogram Based on the potential biomarkers, the “rms” R package was used to construct a nomogram. “Points” represents the score of potential biomarkers, and “Total Points” indicates the sum of scores of potential biomarkers above [ 25 ] . A calibration curve was constructed to assess the consistency between predicted probabilities and the observed frequencies. The clinical impact curve (CIC) and the decision curve analysis (DCA) were used to assess the clinical net benefit of the nomogram. 2.8 Patient selection and human kidney samples collection All patients with FSGS were diagnosed based on renal biopsy results at Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. The study included six patients diagnosed with focal segmental glomerulosclerosis disease and five control patients (with renal carcinoma). Control patients have no symptoms of impaired kidney function. Written informed consent was obtained from all patients. The study protocol was approved by the Human Ethics Committee of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine(No.XHEC-D-2024-218). This study is in accordance with the Declaration of Helsinki. 2.9Immunohistochemical (IHC) staining analysis To further verify the protein expression of potential markers, we used IHC staining assay to detect the expression levels of these genes in paraffin-embedded tissues of FSGS and adjacent non-tumor renal tissues. The paraffin embedded tissues were stained in 4 µm continuous sections. Primary antibodies against GADD45A were purchased from Thermo Fisher Scientific (Catalog no. PA588248, USA). The universal ZSBio Rabbit/ Mouse Hrp-conjugated Polymer Detection kit (Catalog no. PV-600, China) was reacted with primary body, followed by Diaminobenzidin (DAB) was used to dye. All experiments were conducted independently for at least three times. Image Fiji software was used to analyze and quantify the IHC results. 2.10Statistical analysis All statistical analyses were conducted using the R software (version 4.2.2). Wilcoxon or Student’s t-test was used to analyze the difference between the two groups based on data type and characteristics. Correlations were assessed using Pearson’ s correlation analysis [ 26 ] . All statistical p -values were two-sided, and p < 0.05 was regarded as statistically significant. 3. Results 3.1Identification of DEGs Principal component analysis was used to evaluate whether the batch effect was removed showed in Fig. 2 (A) and 2 (B). A total of 342 DEGs (186 upregulated and 156 downregulated) were identified. The expression patterns of the DEGs are illustrated using a heatmap and volcano plot in Fig. 2 (C) and 2 (D) . 3.2Construction of WGCNA We chose β = 9 (scale-free R 2 = 0.85) as the “soft” threshold based on the scale independence and average connectivity displayed in Fig. 3 (A) and 3 (B). A gene dendrogram is respectively shown in Fig. 3 (C). Using WGCNA for module classification, we identified sixteen modules. Based on the correlation coefficient between modules and FSGS, we selected the brown module (cor = 0.72, p = 1e-15) with the highest correlation with FSGS as the key module. A close relationship was observed between module membership and gene significance in the brown module (cor = 0.68; p 0.25 and MM > 0.7 [ 27 – 28 ] . In addition, we further intersected DEGs, ERS-related genes, and genes from the hub brown module of WGCNA in FSGS, obtaining a total of 15 genes, which were further subjected to further analysis (Fig. 3 (F)). 3.3Functional enrichment analysis GO and KEGG analyses were performed to understand the biological functions and signaling pathways of the 15 hub ERSRGs associated with FSGS. In terms of biological processes (Fig. 4 (A)), CC analysis showed that these genes were enriched in the membrane, endomembrane system, endoplasmic reticulum, etc. MF analysis showed that genes were involved in enzyme binding, kinase binding, cell adhesion molecular binding, etc. BP analysis revealed that these genes primarily are primarily involved in tube morphogenesis, tube development, and circulatory system development, etc. KEGG pathway analysis revealed that genes were mainly involved in pathways in cancer, small cell lung cancer, focal adhesion, gastric cancer, etc (Fig. 4 (B)). 3.4Machine learning algorithms Three machine learning algorithms: LASSO regression, SVM-RFE, and RF were applied to screen characteristic genes for further analysis. AGO2, CCND1, FN1, GADD45A, TRAM2, and PTPN1 were identified by LASSO regression (Fig. 5 (A) and Fig. 5 (B)). AGO2, CCND1, PTPN1, ATP2A2, JUP, CLIC1, TRAM2, GADD45A, WDR83OS, and BMP2 were identified using SVM-RFE (Fig. 5 (C)) [ 29 ] . RF algorithm ranked the genes based on their importance: AGO2, PTPN1, CCND1, ATP2A2, JUP, CLIC1, TRAM2, GADD45A, and WDR83OS (Fig. 5 (D) and Fig. 5 (E)). After the intersection, five overlapping characteristic genes, AGO2, CCND1, GADD45A, PTPN1, and TRAM2, were finally identified for final validation (Fig. 5 (F)). 3.5Validation of characteristic genes The expression levels of AGO2, CCND1, GADD45A, TRAM2, and PTPN1 were verified using boxplots. AGO2, CCND1, GADD45A, TRAM2, and PTPN1 expression levels ( p < 0.05) were higher in the glomeruli of FSGS than in those of normal group in training datasets (GSE104948, GSE129973, and GSE121233) (Fig. 6 (A)). Then GSE108109 and GSE104066 were used to verify that the expression of these genes. CCND1, GADD45A, and TRAM2 expression levels ( p < 0.05) was were greater than the normal group in both GSE108109 and GSE104066 datasets, whereas PTPN1 and AGO2 exhibited no significant expression levels between FSGS and the normal group (Fig. 6 (B) and 6 (C)). The AUC of all five genes was > 0.75 in the training datasets. The AUC was 1.000 for CCND1, 0.933 for TRAM2, 0.656 for PTPN1, 0.717 for AGO2, and 0.950 for GADD45A in GSE108109 dataset. The AUC were 0.997 for CCND1, 0.938 for TRAM2, 0.656 for PTPN1, 0.698 for AGO2, and 0.969 for GADD45A in GSE104066 dataset (Fig. 6 (D)). The AUC of PTPN1 and AGO2 were < 0.75 both in GSE108109 and GSE104066 datasets. CCND1, GADD45A, and TRAM2 were identified as potential biomarkers. 3.6Construction of a nomogram Based on these results, we constructed a nomogram based on the three potential biomarkers to better diagnose the risk of FSGS patients (Fig. 7 (A)). Each potential biomarker in the nomogram represents a point, and the total points of the three potential biomarkers were translated into individual disease risk. The higher the total points is, the greater is the disease risk. The results of the calibration curve showed that the apparent calibration curve and the bias-corrected calibration curve did not deviate significantly from the ideal curve. (Fig. 7 (B)). The clinical impact curve indicated that the nomogram had a significant predictive ability (Fig. 7 (C)). In addition, the red line ranging from 0 to 1 in the DCA curve was higher than the black and gray lines, indicating that the decision based on this nomogram may benefit FSGS patients (Fig. 7 (D)). 3.7Expression of GADD45A were increased in FSGS patients Based on literatures of GADD45A in podocyte injury, we chose GADD45A gene for further validation. IHC method was used to verify the protein expression levels of GADD45A. The results revealed that GADD45A ( P < 0.0001) was more highly expressed in FSGS patients (n = 6) than in normal patients (n = 5). The IHC results are shown in Fig. 8 . 4. Discussion FSGS has the highest diagnostic rate in North America including the USA and Canada, and the fifth highest rate in Asia [ 30 ] . FSGS is associated with hyperproteinuria and ESRD. This is because of its relatively long treatment cycle, coupled with the poor efficacy of some patients after treatment or frequent relapses, and even into end-stage renal disease. A huge economic burden has been placed on the national healthcare system and the families of patients. Therefore, there is an urgent need to better understand the detailed mechanisms of FSGS so as to develop novel diagnostic and treatment strategies. Endoplasmic reticulum stress, a state of dysregulated ER homeostasis, plays an important role in multiple renal diseases, including FSGS [ 31 ] . Emerging evidence has shown that ER stress could be a novel and potential pathway for exploring FSGS. It has been widely reported that Apolipoprotein-L1 (APOL1) varriants G1 and G2 increase the risk of FSGS. Gerstner L and al. demonstrated that inhibition of ER stress signaling could rescue cytotoxicity of human APOL1 risk variants, which indirectly indicated that FSGS may be associated with ER stress [ 32 – 33 ] . Ren G et al. reported that podocytes showed ER-associated degradation in FSGS [ 34 ] . ER stress is also reported as a trigger for apoptosis. Many studies have demonstrated that podocyte apoptosis is an important mechanism in the pathogenesis of FSGS [ 35 – 38 ] . Therefore, the ER stress-mediated apoptotic pathway may be a novel therapeutic target for FSGS-induced apoptosis. Our study firstly provides a comprehensive analysis of the role of ERS-related genes in FSGS. We chose three microarray datasets including 43 FSGS and 46 normal patients as training datasets, another two external datasets as validation datasets. Firstly, 342 DEGs were identified, of which 186 genes were upregulated and 156 genes were downregulated. The brown module included 200 hub genes was found to have the highest correlation with FSGS. We then intersected DEGs, hub genes from the brown module, and ERSRGs, and finally obtained 15 hub ERSRGs. GO and KEGG analysis of the hub ERSRGs indicated significant enrichment for ER stress, focal adhesion, and small lung cancer, et al. Three machine learning algorithms including Lasso, SVM-RFE, and RF selected five characteristic genes: CCND1, AGO2, GADD45A, TRAM2, and PTPN1 [ 39 ] . The results of the box plots, ROC curves, and AUC showed three potential biomarkers (CCND1, GADD45A, and PTPN1) have great diagnostic value. Our study may provide a valuable reference for further elucidation of the pathogenesis of FSGS and provide directions for immunotherapy for FSGS. GADD45A, CCND1, and TRAM2 were selected as three potential biomarkers associated with endoplasmic reticulum stress in FSGS. GADD45A is a member of the GADD45 protein family [ 40 ] . The functions of GADD45A include regulating cell cycle arrest, inducing apoptosis, inhibiting autophagy, promoting endoplasmic reticulum stress, et al [ 41 – 43 ] . CCND1 is a CDK-regulating protein that plays an important role in cell-cycle progression. CCND1 has been reported to be a G1 phase cell cycle regulator [ 44 – 45 ] . TRAM2 is a part of the translocon, which is a gated channel of the endoplasmic reticulum membrane for calcium concentration regulation [ 46 ] . GADD45A is mainly located mainly in the nucleus and may be a potential marker of FSGS or podocyte injury. GADD45A is the first found p53-effector gene with a function similar to that of p53. Loss or inactivation of p53 can promote abnormal podocyte growth and metastasis, whereas persistant activation of p53 can promote apoptosis or cell senescence. Optimized inhibition of p53 can protect podocytes from apoptosis [ 47 – 49 ] . Shi S et al. [ 50 ] found pod-Dicer −/− mice developed proteinuria and glomerulorsclerosis, with the up-regulation expression of GADD45B and down-regulation expression of GADD45A, which indicated that GADD45A and GADD45B may participate in podocyte injury. In other chip-seq and RNA-seq results, Ettou S et al. [ 51 ] found that the WT1 binding gene GADD45A was increased in doxorubicin-induced podocyte injury. Overall, GADD45A has great value to explore in modulation of podocyte injury and may be a good target of podocytopathies therapies. In conclusion, a nomogram based on three potential biomarkers of FSGS related to ER stress was established to diagnose FSGS patients and GADD45A was identified as a potential biomarker. However, our study had several limitations in our study. First, although we selected five FSGS datasets, the sample size was small. This result should be confirmed by larger and more diverse studies. Second, in vitro and in vivo experiments are necessary to validate these three novel biomarkers and the state of immune infiltration in future. Therefore, further studies of endoplasmic reticulum stress with FSGS are needed. 5. Conclusions This study showed that the endoplasmic reticulum stress-related potential biomarker, GADD45A has great value in the diagnosis of FSGS and provides a nomogram for diagnosing FSGS from the perspective of ERS. Declarations Ethics approval and consent to participate All the experiment protocol for involving humans was in accordance to declaration of Helsinki in the manuscript and was approved by the Human Ethics Committee of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine. Consent for publication Not applicable Data availability statement All data during this study are included in this published article or Supplementary information files. Author contributions HY W performed the study concept, research and writing of the manuscript. J Z helped with the pathological work and HB L collected the clinical data of this study. GR J helped to conceptualize and supervise this study. All authors have read and approved the final manuscript. Funding This study was supported by the National Natural Science Foundation of China (Grant No.82070697) . Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interests. References D'Agati VD, Kaskel FJ, Falk RJ. Focal segmental glomerulosclerosis. N Engl J Med. 2011;365(25):2398 – 411. 10.1056/NEJMra1106556 . PMID: 22187987. 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Shi S, Yu L, Chiu C, Sun Y, Chen J, Khitrov G, Merkenschlager M, Holzman LB, Zhang W, Mundel P, Bottinger EP. Podocyte-selective deletion of dicer induces proteinuria and glomerulosclerosis. J Am Soc Nephrol. 2008;19(11):2159–69. 10.1681/ASN.2008030312 . Epub 2008 Sep 5. PMID: 18776119; PMCID: PMC2573016. Ettou S, Jung YL, Miyoshi T, Jain D, Hiratsuka K, Schumacher V, Taglienti ME, Morizane R, Park PJ, Kreidberg JA. Epigenetic transcriptional reprogramming by WT1 mediates a repair response during podocyte injury. Sci Adv. 2020;6(30):eabb5460. 10.1126/sciadv.abb5460 . PMID: 32754639; PMCID: PMC7380960. Additional Declarations No competing interests reported. Supplementary Files IHCfiguresupplementarymaterials.zip 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. 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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-6731959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486368846,"identity":"05149037-5a6d-4f77-bdcc-696ef59ee6bf","order_by":0,"name":"Hongyu Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Wang","suffix":""},{"id":486368847,"identity":"f4de3c69-1643-49f3-a24c-9d1523f85d1e","order_by":1,"name":"Jun Zou","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zou","suffix":""},{"id":486368848,"identity":"bbacf86b-fada-4803-970b-32216513e63d","order_by":2,"name":"hongbing li","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"hongbing","middleName":"","lastName":"li","suffix":""},{"id":486368849,"identity":"02d0101a-feb7-4d4d-891e-5f74b26b24a4","order_by":3,"name":"Gengru Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYPACNh4gwfiAwYAhAcznIVILswEpWiD6JIAEYS3y7r2HX1e28cnwz26/Vs1TcCfP4PjpxAdvGOzkdBuwazE8cy7N8mwbG4/EnTNlN2cYPCs2OJO72XAOQ7Kx2QEcWmbkmBk2ArUw3MhJu/HB4HDihgO526R5GA4kbsOlZf4biBZ5oJaCBJCW82+3/8anRV6Cx/ghSIvBjfRjDGBbbuRuY8anxYAnx4yx4Rwbj+GNHGbJGUAtM2+83Sw5xwC3X+Tbzxh/bCg7Zi93I/3hZ54/hxP7zudu/PCmwk4OlxaDA+DoOAZk8hggi2NXDralgYH5AwNDDZDJ/gC3slEwCkbBKBjRAACXlWTrdqlaUgAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Gengru","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-05-23 10:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6731959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6731959/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87267556,"identity":"3a5d3e20-228b-42ec-a82a-ab220b863374","added_by":"auto","created_at":"2025-07-22 08:07:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":446833,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of data processing and analysis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/91633de9e84d6554d090eb42.png"},{"id":87267558,"identity":"71c26ac2-02e8-437c-9f56-2db730fe49cf","added_by":"auto","created_at":"2025-07-22 08:07:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":814327,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of gene expression profiles. (A) Principal component analysis (PCA) plot showing the expression distribution of training datasets (GSE121233, GSE129973, and GSE104948) before removal of batch effect. (B) Principal component analysis (PCA) plot showing the expression distribution of training datasets (GSE121233, GSE129973, and GSE104948) after removal of batch effect. (C) Heatmap displaying a differential expression of 186 upregulated genes and 156 downregulated genes. (D) Volcano plot of DEGs. Red dots represent upregulated genes and blue dots represent downregulated genes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/50535e4651d1817c73e3470f.png"},{"id":87270870,"identity":"daf6927a-6ff9-4df9-b51d-e338a33efcd8","added_by":"auto","created_at":"2025-07-22 08:23:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":419389,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of hub module by WGCNA. (A) Soft threshold non-scale fitting index analysis. (B) Soft threshold average connectivity analysis. (C) Heatmap of module-trait relationships. (D) Gene dendrogram andmodule colors. (E) Scatter plot between gene significance (GS) and module membership (MM) in brown. (F) Venn diagram showing 15 overlapping genes of DEGs, ERS-related genes, and genes from hub brown module of WGCNA.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/a3db7be4c34286fbd5d7fe35.png"},{"id":87267561,"identity":"247e438d-c1ab-45d4-9d63-63f7a104df87","added_by":"auto","created_at":"2025-07-22 08:07:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224612,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of the 15 hub ERSRGs in FSGS. (A) GO analysis including biological process, cellular component, and molecular function. The y-axis represents the top 10 GO terms, the x-axis represents gene ratio enriched in relative GO terms. (B) Histogram of KEGG analysis. The y-axis represents the top 20 KEGG terms, the x-axis represents the count of genes enriched in relative KEGG terms.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/b339d1ff700e156b95d08f53.png"},{"id":87269434,"identity":"6f9df5c7-c542-4932-b419-494ab7e3f21b","added_by":"auto","created_at":"2025-07-22 08:15:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":269580,"visible":true,"origin":"","legend":"\u003cp\u003eThe selection of five charateristic genes by three machine learning algorithms: LASSO, SVM-RFE, and RF. (A, B) Characteristic genes selected by the LASSO regression. (C) Characteristic genes chosed by the SVM-RFE algorithm. (D, E) Characteristic genes selected by the RF algorithm. (F) Venn diagram demonstrating five characteristic genes shared by the LASSO, SVM-RFE and RF algorithms.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/6095b6a1b456e1c92aa6ba42.png"},{"id":87270878,"identity":"56c5045a-be13-40ca-a2c9-638780ed7f61","added_by":"auto","created_at":"2025-07-22 08:23:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1769080,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of characteristic genes. (A) Validation of characteristic genes in the training datasets (GSE104948, GSE129973, and GSE121233). CCND1, AGO2, TRAM2, GADD45A, and PTPN1 were significantly higher expression in FSGS compared with normal group. (B) Validation of characteristic genes in the GSE104066. CCND1, TRAM2, and GADD45A are significantly higher expression in FSGS compared with normal group, while PTPN1 and AGO2 are not significant compared with normal group. (C) Validation of characteristic genes in the GSE108109 and the results were the same as the results of the GSE104066. (D) ROC curves of training datasets, GSE104066 datasets, and GSE108109 for estimating the diagnostic value of the characteristic genes.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/1f7623daab6578a55d30a34a.png"},{"id":87272800,"identity":"0ad1e788-a8ff-43d0-bed3-519aac9012b6","added_by":"auto","created_at":"2025-07-22 08:31:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":367715,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nomogram. (A) The nomogram for the diagnosis of focal segmental glomerulosclerosis (FSGS) based on CCND1, TRAM2, and GADD45A. (B) Calibration curve for nomogram validation. (C) Clinical impact curve of the nomogram. (D) Decision curve analysis based on the nomogram.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/9ddd68e8ec1a7543b2e1e9b8.png"},{"id":87269437,"identity":"459d6e54-70ed-46f5-b1a7-3ba62719b32a","added_by":"auto","created_at":"2025-07-22 08:15:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":363085,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the expression of GADD45A by immunohistochemistry analysis. (A) and (B) are the IHC results of control patients and FSGS patients (n = 5 in control group, n = 6 in FSGS group). (C)The integrated optical density of GADD45A expression in IHC results (n = 5 in control group, n = 6 in FSGS group). Scale bar, 200 μm (×200). Error bars: SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/4617f2f253ee538504714959.png"},{"id":87482057,"identity":"779eea90-d2da-4c0f-a88f-1b7fbf978622","added_by":"auto","created_at":"2025-07-24 10:17:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5522922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/44598bc1-a2ce-45c4-9fb5-442cf8d82426.pdf"},{"id":87267603,"identity":"373cfebf-f172-4569-833f-3f18cd9e852f","added_by":"auto","created_at":"2025-07-22 08:07:14","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":51497679,"visible":true,"origin":"","legend":"","description":"","filename":"IHCfiguresupplementarymaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-6731959/v1/0baddd2ac12bb0760c6919f7.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"GADD45A as a Potential Biomarker Associated with Endoplasmic Reticulum Stress in Focal Segmental Glomerulosclerosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFocal segmental glomerulosclerosis (FSGS) accounts for approximately 20% of pediatric nephrotic syndromes and 40% of adult nephrotic syndromes\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. FSGS is a pathologic diagnosis that refers to a histologic pattern of kidney injury, and is a common cause of end-stage renal disease (ESRD). FSGS is characterized by sclerotic lesions in glomeruli that are focal (\u0026lt;\u0026thinsp;50% of the total number of glomeruli) and segmental (\u0026lt;\u0026thinsp;50% of the glomerular capillary surface) under light microscopy\u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Mounting evidence has demonstrated that the endoplasmic reticulum stress is closely related to the progression of renal diseases, and it has been found that the pathogenesis of FSGS is also related to endoplasmic reticulum stress, although the exact mechanism remains unclear.\u003c/p\u003e\u003cp\u003eThe endoplasmic reticulum (ER) is an organelle that plays an important role in protein production, metabolic homeostasis, cell signaling, et al. Changes in the state of microenvironment or mutations in specific genes can give limitations to the cellular ability to properly synthesize protein and perform other functions of the endoplasmic reticulum, leading to a state known as the endoplasmic reticulum stress (ERS)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. ER stress activates three major signaling pathways: the unfolded protein response (UPR), ER overload response (EOR), and sterol regulatory element binding protein (SREBP). The UPR comprises three signaling pathways initiated by three proteins: PKR-like ER like kinase (PERK), activating transcription 6 (ATF6), and inositol-requiring enzyme 1α (IRE1α ) \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccumulating evidence suggests that ERS plays an important role in the pathogenesis of FSGS\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In cell culture studies, Cybulsky AV et al. cultured podocytes with and without the extracellular matrix (ECM) respectively. The results showed that podocytes lacking the ECM showed ERS and apoptosis, increased expression of ER chaperone proteins glucose-regulated protein 78 (GRP78), and increased phosphorylation of eukaryotic translation initiation factor-2α (eIF2α)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In animal models studies, Cybulsky AV et al. observed the effects of exacerbating ER stress in a murine model of FSGS. In this model, they found that the experimental FSGS mice appeared to upregulate markers of ER stress. This finding raises the possibility that the the pathogenesis of FSGS may involve ER stress\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Mutations in human canonical transient receptor potential-6 (TRPC6) gene have been found to cause inherited focal segmental glomerulosclerosis. Chen S et al. reported that albumin overload could upregulate the expression of GRP78, activate caspase-12 and promote apoptosis in podocytes through TRPC6 \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we herein aimed to explore the role of endoplasmic reticulum stress-related potential biomarkers, immune infiltration landscape, and subtype classifications of FSGS patients according to integrated bioinformatics and machine learning analysis. First, five datasets from GEO database were downloaded with three datasets chose as training datasets and two external datasets selected as validation datasets. Subsequently, WGCNA and three machine learning algorithms including Lasso, SVM-RFE, and RF were used to identify characteristic genes. Box plots, ROC curves and AUC were used to validate the diagnostic value of characteristic genes to get potential biomarkers. Our findings are expected to provide new insights into diagnosis of the FSGS patients from the perspective of endoplasmic reticulum stress.\u003c/p\u003e"},{"header":"2. Methods","content":"\n\u003ch3\u003e2.1Data source acquisition and processing\u003c/h3\u003e\n\u003cp\u003eFive microarray datasets were screened using the GEO database\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. GSE104948, GSE129973, and GSE121233 were used as training datasets and GSE108109, and GSE104066 were used as validation datasets. ERS-related genes were obtained from the GeneCards database, and genes with relevance scores\u0026thinsp;\u0026ge;\u0026thinsp;5 were extracted for this study. The \u0026ldquo;sva\u0026rdquo; R package containing \u0026ldquo;combat\u0026rdquo; function was used to get rid of the batch effects and make datasets unified. The effect of removing the batch effect was assessed by using principal component analysis (PCA), and the distribution of FSGS and the normal group samples was visualized. Details of the datasets are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and a flowchart of the data processing and analysis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMicroarray datasets of this study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGene series\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eFSGS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOrigin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOrganism\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFSGS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSE129973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL17586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlomerular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSE104948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL22945\u003c/p\u003e\u003cp\u003eGPL24120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlomerular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSE121233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL17586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlomerular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSE108109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL19983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlomerular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSE104066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL19983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlomerular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2Differential expression analysis\u003c/h2\u003e\u003cp\u003eThe \u0026ldquo;limma\u0026rdquo; R package was used to detect differentially expressed genes (DEGs) with a threshold of |Log2 Fold Change (FC)| \u0026gt;0.4 and adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The results were visualized using \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; R packages.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2.3Weighted gene co-expression network construction\u003c/h3\u003e\n\u003cp\u003eThe\u0026lsquo;WGCNA\u0026rsquo; R package was used to construct a weighted gene co-expression network for all genes in FSGS and normal groups. The soft threshold parameter (\u003cem\u003eβ\u003c/em\u003e) was used to estimate links between genes. Then, an adjacency matrix and scale-free network features were constructed. Network connectivity was used to evaluate whether the adjacency matrix could be successfully transformed into a topological overlap metric (TOM). A clustering tree graph for the TOM matrix by average linkage hierarchical clustering was constructed. The main manifestations were different branches and colors, establishing module relationships and calculating the linkage between gene modules and FSGS. The hub genes in the most relevant modules were identified by module membership (MM)\u0026thinsp;\u0026gt;\u0026thinsp;0.7 and gene significance (GS)\u0026thinsp;\u0026gt;\u0026thinsp;0.25\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The intersection of differentially expressed genes, ERSRGs, and hub genes in the most relevant modules was performed using the\u0026ldquo;VennDiagram\u0026rdquo;R package, which was used for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003e2.4Functional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe R packages \u0026ldquo;clusterProfiler\u0026rdquo; and \u0026ldquo;enrichplot\u0026rdquo; were used to perform GO analysis and KEGG analysis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Three categories enrichments, biological process (BP), cellular component (CC), molecular function (MF), are involved in GO functional enrichment. KEGG analysis was used to explore potential pathways.\u003c/p\u003e\n\u003ch3\u003e2.5Machine learning algorithms\u003c/h3\u003e\n\u003cp\u003eThree machine-learning algorithms: LASSO, RF, and SVM-RFE were used to screen for characteristic genes\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. LASSO regression analysis was used to perform variable screening and complexity adjustment constructed by using the \u0026ldquo;cv.glmnet\u0026rdquo; function in R package \u0026lsquo;glmnet\u0026rsquo;. The degree of LASSO regression complexity adjustment was controlled by the parameter lambda (λ), and lambda.min was selected as the optimal lambda. The R package \u0026ldquo;e1071\u0026rdquo; was used for the SVM-RFE algorithm and the features were sorted by recursion. The random forest analysis was performed by using the \u0026ldquo;RandomForest\u0026rdquo; function. Genes within the intersection of three machine learning algorithms were selected for further study as characteristic genes.\u003c/p\u003e\n\u003ch3\u003e2.6Validation of characteristic genes\u003c/h3\u003e\n\u003cp\u003eGSE108109 and GSE104066 were used as the validated datasets. Box plots were created and verified to confirm the significance of characteristic genes. Receiver operating characteristic (ROC) curves\u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, and the area under the curve (AUC) were calculated to measure the predictive capability of the algorithms. The \u0026ldquo;pROC\u0026rdquo; package was used for ROC curve analysis. AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 was considered the ideal diagnostic value. Above all, we tested the diagnostic value of charactersitic genes using box plots, ROC curves and AUC to identify potential biomarkers.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.7Construction of a nomogram\u003c/h2\u003e\u003cp\u003eBased on the potential biomarkers, the \u0026ldquo;rms\u0026rdquo; R package was used to construct a nomogram. \u0026ldquo;Points\u0026rdquo; represents the score of potential biomarkers, and \u0026ldquo;Total Points\u0026rdquo; indicates the sum of scores of potential biomarkers above\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. A calibration curve was constructed to assess the consistency between predicted probabilities and the observed frequencies. The clinical impact curve (CIC) and the decision curve analysis (DCA) were used to assess the clinical net benefit of the nomogram.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2.8 Patient selection and human kidney samples collection\u003c/h3\u003e\n\u003cp\u003eAll patients with FSGS were diagnosed based on renal biopsy results at Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. The study included six patients diagnosed with focal segmental glomerulosclerosis disease and five control patients (with renal carcinoma). Control patients have no symptoms of impaired kidney function. Written informed consent was obtained from all patients. The study protocol was approved by the Human Ethics Committee of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine(No.XHEC-D-2024-218). This study is in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch3\u003e2.9Immunohistochemical (IHC) staining analysis\u003c/h3\u003e\n\u003cp\u003eTo further verify the protein expression of potential markers, we used IHC staining assay to detect the expression levels of these genes in paraffin-embedded tissues of FSGS and adjacent non-tumor renal tissues. The paraffin embedded tissues were stained in 4 \u0026micro;m continuous sections. Primary antibodies against GADD45A were purchased from Thermo Fisher Scientific (Catalog no. PA588248, USA). The universal ZSBio Rabbit/ Mouse Hrp-conjugated Polymer Detection kit (Catalog no. PV-600, China) was reacted with primary body, followed by Diaminobenzidin (DAB) was used to dye. All experiments were conducted independently for at least three times. Image Fiji software was used to analyze and quantify the IHC results.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.10Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using the R software (version 4.2.2). Wilcoxon or Student\u0026rsquo;s t-test was used to analyze the difference between the two groups based on data type and characteristics. Correlations were assessed using Pearson\u0026rsquo; s correlation analysis\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. All statistical \u003cem\u003ep\u003c/em\u003e-values were two-sided, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.1Identification of DEGs\u003c/h2\u003e\u003cp\u003ePrincipal component analysis was used to evaluate whether the batch effect was removed showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (A) and 2 (B). A total of 342 DEGs (186 upregulated and 156 downregulated) were identified. The expression patterns of the DEGs are illustrated using a heatmap and volcano plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (C) and 2 (D) .\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2Construction of WGCNA\u003c/h2\u003e\u003cp\u003eWe chose β\u0026thinsp;=\u0026thinsp;9 (scale-free R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85) as the \u0026ldquo;soft\u0026rdquo; threshold based on the scale independence and average connectivity displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (A) and 3 (B). A gene dendrogram is respectively shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (C). Using WGCNA for module classification, we identified sixteen modules. Based on the correlation coefficient between modules and FSGS, we selected the brown module (cor\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;=\u0026thinsp;1e-15) with the highest correlation with FSGS as the key module. A close relationship was observed between module membership and gene significance in the brown module (cor\u0026thinsp;=\u0026thinsp;0.68; p\u0026thinsp;\u0026lt;\u0026thinsp;8.2e-117) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (E)). We screened 200 hub genes from the brown module for subsequent analysis, based on GS\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and MM\u0026thinsp;\u0026gt;\u0026thinsp;0.7 \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In addition, we further intersected DEGs, ERS-related genes, and genes from the hub brown module of WGCNA in FSGS, obtaining a total of 15 genes, which were further subjected to further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (F)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3Functional enrichment analysis\u003c/h2\u003e\u003cp\u003eGO and KEGG analyses were performed to understand the biological functions and signaling pathways of the 15 hub ERSRGs associated with FSGS. In terms of biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (A)), CC analysis showed that these genes were enriched in the membrane, endomembrane system, endoplasmic reticulum, etc. MF analysis showed that genes were involved in enzyme binding, kinase binding, cell adhesion molecular binding, etc. BP analysis revealed that these genes primarily are primarily involved in tube morphogenesis, tube development, and circulatory system development, etc. KEGG pathway analysis revealed that genes were mainly involved in pathways in cancer, small cell lung cancer, focal adhesion, gastric cancer, etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (B)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4Machine learning algorithms\u003c/h2\u003e\u003cp\u003eThree machine learning algorithms: LASSO regression, SVM-RFE, and RF were applied to screen characteristic genes for further analysis. AGO2, CCND1, FN1, GADD45A, TRAM2, and PTPN1 were identified by LASSO regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (A) and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (B)). AGO2, CCND1, PTPN1, ATP2A2, JUP, CLIC1, TRAM2, GADD45A, WDR83OS, and BMP2 were identified using SVM-RFE (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (C)) \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. RF algorithm ranked the genes based on their importance: AGO2, PTPN1, CCND1, ATP2A2, JUP, CLIC1, TRAM2, GADD45A, and WDR83OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (D) and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (E)). After the intersection, five overlapping characteristic genes, AGO2, CCND1, GADD45A, PTPN1, and TRAM2, were finally identified for final validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (F)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5Validation of characteristic genes\u003c/h2\u003e\u003cp\u003eThe expression levels of AGO2, CCND1, GADD45A, TRAM2, and PTPN1 were verified using boxplots. AGO2, CCND1, GADD45A, TRAM2, and PTPN1 expression levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were higher in the glomeruli of FSGS than in those of normal group in training datasets (GSE104948, GSE129973, and GSE121233) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(A)). Then GSE108109 and GSE104066 were used to verify that the expression of these genes. CCND1, GADD45A, and TRAM2 expression levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was were greater than the normal group in both GSE108109 and GSE104066 datasets, whereas PTPN1 and AGO2 exhibited no significant expression levels between FSGS and the normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (B) and 6 (C)). The AUC of all five genes was \u0026gt;\u0026thinsp;0.75 in the training datasets. The AUC was 1.000 for CCND1, 0.933 for TRAM2, 0.656 for PTPN1, 0.717 for AGO2, and 0.950 for GADD45A in GSE108109 dataset. The AUC were 0.997 for CCND1, 0.938 for TRAM2, 0.656 for PTPN1, 0.698 for AGO2, and 0.969 for GADD45A in GSE104066 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(D)). The AUC of PTPN1 and AGO2 were \u0026lt;\u0026thinsp;0.75 both in GSE108109 and GSE104066 datasets. CCND1, GADD45A, and TRAM2 were identified as potential biomarkers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6Construction of a nomogram\u003c/h2\u003e\u003cp\u003eBased on these results, we constructed a nomogram based on the three potential biomarkers to better diagnose the risk of FSGS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (A)). Each potential biomarker in the nomogram represents a point, and the total points of the three potential biomarkers were translated into individual disease risk. The higher the total points is, the greater is the disease risk. The results of the calibration curve showed that the apparent calibration curve and the bias-corrected calibration curve did not deviate significantly from the ideal curve. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (B)). The clinical impact curve indicated that the nomogram had a significant predictive ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (C)). In addition, the red line ranging from 0 to 1 in the DCA curve was higher than the black and gray lines, indicating that the decision based on this nomogram may benefit FSGS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (D)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.7Expression of GADD45A were increased in FSGS patients\u003c/h2\u003e\u003cp\u003eBased on literatures of GADD45A in podocyte injury, we chose GADD45A gene for further validation. IHC method was used to verify the protein expression levels of GADD45A. The results revealed that GADD45A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was more highly expressed in FSGS patients (n\u0026thinsp;=\u0026thinsp;6) than in normal patients (n\u0026thinsp;=\u0026thinsp;5). The IHC results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFSGS has the highest diagnostic rate in North America including the USA and Canada, and the fifth highest rate in Asia\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. FSGS is associated with hyperproteinuria and ESRD. This is because of its relatively long treatment cycle, coupled with the poor efficacy of some patients after treatment or frequent relapses, and even into end-stage renal disease. A huge economic burden has been placed on the national healthcare system and the families of patients. Therefore, there is an urgent need to better understand the detailed mechanisms of FSGS so as to develop novel diagnostic and treatment strategies.\u003c/p\u003e\u003cp\u003eEndoplasmic reticulum stress, a state of dysregulated ER homeostasis, plays an important role in multiple renal diseases, including FSGS\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Emerging evidence has shown that ER stress could be a novel and potential pathway for exploring FSGS. It has been widely reported that Apolipoprotein-L1 (APOL1) varriants G1 and G2 increase the risk of FSGS. Gerstner L and al. demonstrated that inhibition of ER stress signaling could rescue cytotoxicity of human APOL1 risk variants, which indirectly indicated that FSGS may be associated with ER stress\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Ren G et al. reported that podocytes showed ER-associated degradation in FSGS\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. ER stress is also reported as a trigger for apoptosis. Many studies have demonstrated that podocyte apoptosis is an important mechanism in the pathogenesis of FSGS\u003csup\u003e[\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Therefore, the ER stress-mediated apoptotic pathway may be a novel therapeutic target for FSGS-induced apoptosis.\u003c/p\u003e\u003cp\u003eOur study firstly provides a comprehensive analysis of the role of ERS-related genes in FSGS. We chose three microarray datasets including 43 FSGS and 46 normal patients as training datasets, another two external datasets as validation datasets. Firstly, 342 DEGs were identified, of which 186 genes were upregulated and 156 genes were downregulated. The brown module included 200 hub genes was found to have the highest correlation with FSGS. We then intersected DEGs, hub genes from the brown module, and ERSRGs, and finally obtained 15 hub ERSRGs. GO and KEGG analysis of the hub ERSRGs indicated significant enrichment for ER stress, focal adhesion, and small lung cancer, et al. Three machine learning algorithms including Lasso, SVM-RFE, and RF selected five characteristic genes: CCND1, AGO2, GADD45A, TRAM2, and PTPN1\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The results of the box plots, ROC curves, and AUC showed three potential biomarkers (CCND1, GADD45A, and PTPN1) have great diagnostic value. Our study may provide a valuable reference for further elucidation of the pathogenesis of FSGS and provide directions for immunotherapy for FSGS.\u003c/p\u003e\u003cp\u003eGADD45A, CCND1, and TRAM2 were selected as three potential biomarkers associated with endoplasmic reticulum stress in FSGS. GADD45A is a member of the GADD45 protein family\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. The functions of GADD45A include regulating cell cycle arrest, inducing apoptosis, inhibiting autophagy, promoting endoplasmic reticulum stress, et al\u003csup\u003e[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. CCND1 is a CDK-regulating protein that plays an important role in cell-cycle progression. CCND1 has been reported to be a G1 phase cell cycle regulator\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. TRAM2 is a part of the translocon, which is a gated channel of the endoplasmic reticulum membrane for calcium concentration regulation\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGADD45A is mainly located mainly in the nucleus and may be a potential marker of FSGS or podocyte injury. GADD45A is the first found p53-effector gene with a function similar to that of p53. Loss or inactivation of p53 can promote abnormal podocyte growth and metastasis, whereas persistant activation of p53 can promote apoptosis or cell senescence. Optimized inhibition of p53 can protect podocytes from apoptosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Shi S et al.\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e found pod-Dicer\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice developed proteinuria and glomerulorsclerosis, with the up-regulation expression of GADD45B and down-regulation expression of GADD45A, which indicated that GADD45A and GADD45B may participate in podocyte injury. In other chip-seq and RNA-seq results, Ettou S et al.\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e found that the WT1 binding gene GADD45A was increased in doxorubicin-induced podocyte injury. Overall, GADD45A has great value to explore in modulation of podocyte injury and may be a good target of podocytopathies therapies.\u003c/p\u003e\u003cp\u003eIn conclusion, a nomogram based on three potential biomarkers of FSGS related to ER stress was established to diagnose FSGS patients and GADD45A was identified as a potential biomarker. However, our study had several limitations in our study. First, although we selected five FSGS datasets, the sample size was small. This result should be confirmed by larger and more diverse studies. Second, in vitro and in vivo experiments are necessary to validate these three novel biomarkers and the state of immune infiltration in future. Therefore, further studies of endoplasmic reticulum stress with FSGS are needed.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study showed that the endoplasmic reticulum stress-related potential biomarker, GADD45A has great value in the diagnosis of FSGS and provides a nomogram for diagnosing FSGS from the perspective of ERS.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the experiment protocol for involving humans was in accordance to declaration of Helsinki in the manuscript and was approved by the Human Ethics Committee of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine.\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\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data during this study are included in this published article or Supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHY W performed the study concept, research and writing of the manuscript. J Z helped with the pathological work and HB L collected the clinical data of this study. GR J helped to conceptualize and supervise this study. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No.82070697) .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eD'Agati VD, Kaskel FJ, Falk RJ. Focal segmental glomerulosclerosis. 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PMID: 32754639; PMCID: PMC7380960.\u003c/span\u003e\u003c/li\u003e\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":"Focal segmental glomerulosclerosis, Endoplasmic reticulum stress, WGCNA, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6731959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6731959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to identify and\u003cstrong\u003e \u003c/strong\u003evalidate potential endoplasmic reticulum stress-related biomarkers of FSGS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eFive microarray datasets were downloaded from the GEO database. The endoplasmic reticulum stress-related genes were extracted from GeneCards database. GSE104948, GSE129973, and GSE121233 datasets were used to identified DEGs by limma R package. WGCNA was performed to obtain hub gene modules. We intersected the DEGs, genes from hub module of WGCNA, and ERSRGs. GO and KEGG pathway enrichment analyses were performed. LASSO, SVM-RFE, and RF algorithms were used to screen characteristic genes. Further, GSE108109 and GSE104066 were used as validated datasets. Box plots, ROC curves, and AUC were created to identify potential biomarkers. A novel nomogram model was constructed using potential biomarkers. Immunohistochemistry was used to validate the expression of the potential biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIntersecting DEGs, the brown module genes, and ERSRGs, we identified 15 hub ERSRGs of FSGS. AGO2, CCND1, GADD45A, TRAM2, and PTPN1 genes were screened using Lasso, SVM-RFE, and RF. Further, CCND1, GADD45A, and TRAM2 were validated as significant in training and validation datasets. A nomogram based on CCND1, GADD45A, and TRAM2 expression was constructed. The calibration curves, decision curve, and clinical impact curve showed that the nomogram had good consistency and clinical practical benefit. The expression of GADD45A is higher in FSGS patients than control patients in IHC results (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eGADD45A may be a potential biomarker associated with endoplasmic reticulum stress in FSGS.\u003c/p\u003e","manuscriptTitle":"GADD45A as a Potential Biomarker Associated with Endoplasmic Reticulum Stress in Focal Segmental Glomerulosclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 08:07:07","doi":"10.21203/rs.3.rs-6731959/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":"9e0fed44-c8f5-4489-b53c-a8e31158765d","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-24T10:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 08:07:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6731959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6731959","identity":"rs-6731959","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}