NOX4-mediated astrocyte ferroptosis in Alzheimer's disease.

This study investigates NADPH oxidase 4 (NOX4) involvement in iron-mediated astrocyte cell death in Alzheimer's Disease (AD) using single-cell sequencing data and transcriptomes. We analyzed AD single-cell RNA sequencing data, identified astrocyte marker genes, and explored biological processes in astrocytes. We integrated AD-related chip data with ferroptosis-related genes, highlighting NOX4. We validated NOX4's role in ferroptosis and AD in vitro and in vivo. Astrocyte marker genes were enriched in AD, emphasizing their role. NOX4 emerged as a crucial player in astrocytic ferroptosis in AD. Silencing NOX4 mitigated ferroptosis, improved cognition, reduced Aß and p-Tau levels, and alleviated mitochondrial abnormalities. NOX4 promotes astrocytic ferroptosis, underscoring its significance in AD progression.

A comprehensive workflow for optimizing RNA-seq data analysis.

BACKGROUND: Current RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science, determining how to construct an analysis workflow that meets their specific needs from the array of complex analytical tools available poses a significant challenge. RESULTS: By utilizing RNA-seq data from plants, animals, and fungi, it was observed that different analytical tools demonstrate some variations in performance when applied to different species. A comprehensive experiment was conducted specifically for analyzing plant pathogenic fungal data, focusing on differential gene analysis as the ultimate goal. In this study, 288 pipelines using different tools were applied to analyze five fungal RNA-seq datasets, and the performance of their results was evaluated based on simulation. This led to the establishment of a relatively universal and superior fungal RNA-seq analysis pipeline that can serve as a reference, and certain standards for selecting analysis tools were derived for reference. Additionally, we compared various tools for alternative splicing analysis. The results based on simulated data indicated that rMATS remained the optimal choice, although consideration could be given to supplementing with tools such as SpliceWiz. CONCLUSION: The experimental results demonstrate that, in comparison to the default software parameter configurations, the analysis combination results after tuning can provide more accurate biological insights. It is beneficial to carefully select suitable analysis software based on the data, rather than indiscriminately choosing tools, in order to achieve high-quality analysis results more efficiently.

Shared Genetic Features of Psoriasis and Myocardial Infarction: Insights From a Weighted Gene Coexpression Network Analysis.

BACKGROUND: Increasing evidence suggests a higher propensity for acute myocardial infarction (MI) in patients with psoriasis. However, the shared mechanisms underlying this comorbidity in these patients remain unclear. This study aimed to explore the shared genetic features of psoriasis and MI and to identify potential biomarkers indicating their coexistence. METHODS AND RESULTS: Data sets obtained from the gene expression omnibus were examined using a weighted gene coexpression network analysis approach. Hub genes were identified using coexpression modules and validated in other data sets and through in vitro cellular experiments. Bioinformatics tools, including the Human microRNA Disease Database, StarBase, and miRNet databases, were used to construct a ceRNA network and predict potential regulatory mechanisms. By applying weighted gene coexpression network analysis, we identified 2 distinct modules that were significant for both MI and psoriasis. Inflammatory and immune pathways were highlighted by gene ontology enrichment analysis of the overlapping genes. Three pivotal genes-Src homology and collagen 1, disruptor of telomeric silencing 1-like, and feline leukemia virus subgroup C cellular receptor family member 2-were identified as potential biomarkers. We constructed a ceRNA network that suggested the upstream regulatory roles of these genes in the coexistence of psoriasis and MI. CONCLUSIONS: As potential therapeutic targets, Src homology and collagen 1, feline leukemia virus subgroup C cellular receptor family member 2, and disruptor of telomeric silencing 1-like provide novel insights into the shared genetic features between psoriasis and MI. This study paves the way for future studies focusing on the prevention of MI in patients with psoriasis.

Identifying the natural products in the treatment of atherosclerosis by increasing HDL-C level based on bioinformatics analysis, molecular docking, and in vitro experiment.

BACKGROUND: Previous studies have demonstrated that high-density lipoprotein cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse cholesterol transport. Several studies have validated the efficacy and safety of natural products in treating atherosclerosis (AS). However, the study of raising HDL-C levels through natural products to treat AS still needs to be explored. METHODS: The gene sets associated with AS were collected and identified by differential gene analysis and database query. By constructing a protein-protein interaction (PPI) network, the core submodules in the network are screened out. At the same time, by calculating node importance (Nim) in the PPI network of AS disease and combining it with Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment analysis, the key target proteins of AS were obtained. Molecular docking is used to screen out small natural drug molecules with potential therapeutic effects. By constructing an in vitro foam cell model, the effects of small molecules on lipid metabolism and key target expression of foam cells were investigated. RESULTS: By differential gene analysis, 451 differential genes were obtained, and a total of 313 disease genes were obtained from 6 kind of databases, then 758 AS-related genes were obtained. The enrichment analysis of the KEGG pathway showed that the enhancement of HDL-C level against AS was related to Lipid and atherosclerosis, Cholesterol metabolism, Fluid shear stress and atherosclerosis, PPAR signaling pathway, and other pathways. Then we intersected 31 genes in the core module of the PPI network, the top 30 genes in Nims, and 32 genes in the cholesterol metabolism pathway, and finally found 3 genes. After the above analysis and literature collection, we focused on the following three related gene targets: APOA1, LIPC, and CETP. Molecular docking showed that Genistein has a good binding affinity for APOA1, CETP, and LIPC. In vitro, experiments showed that Genistein can up-regulated APOA1, LIPC, and CETP levels. CONCLUSIONS: Based on our research, Genistein may have the effects of regulating HDL-C and anti-atherosclerosis. Its mechanism of action may be related to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.

Identification of ADAM23 as a Potential Signature for Psoriasis Using Integrative Machine-Learning and Experimental Verification.

Background: Psoriasis is a common chronic, recurrent, and inflammatory skin disease. Identifying novel and potential biomarkers is valuable in the treatment and diagnosis of psoriasis. The goal of this study was to identify novel key biomarkers of psoriasis and analyze the potential underlying mechanisms. Methods: Psoriasis-related datasets were downloaded from the Gene Expression Omnibus database to screen differential genes in the datasets. Functional and pathway enrichment analyses were performed on the differentially expressed genes (DEGs). Candidate biomarkers for psoriasis were identified from the GSE30999 and GSE6710 datasets using four machine learning algorithms, namely, random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE), and were validated using the GSE41662 dataset. Next, we used CIBERSORT and single-cell RNA analysis to explore the relationship between ADAM23 and immune cells. Finally, we validated the expression of the identified biomarkers expressions in human and mouse experiments. Results: A total of 709 overlapping DEGs were identified, including 426 upregulated and 283 downregulated genes. Enhanced by enrichment analysis, the differentially expressed genes (DEGs) were spatially arranged in relation to immune cell involvement, immune-activating processes, and inflammatory signals. Based on the enrichment analysis, the DEGs were mapped to immune cell involvement, immune-activating processes, and inflammatory signals. Four machine learning strategies and single-cell RNA sequencing analysis showed that ADAM23, a disintegrin and metalloprotease, may be a unique, critical biomarker with high diagnostic accuracy for psoriasis. Based on CIBERSORT analysis, ADAM23 was found to be associated with a variety of immune cells, such as macrophages and mast cells, and it was upregulated in the macrophages of psoriatic lesions in patients and mice. Conclusion: ADAM23 may be a potential biomarker in the diagnosis of psoriasis and may contribute to the pathogenesis by regulating immunological activity in psoriatic lesions.

Identification of the shared gene signatures between pulmonary fibrosis and pulmonary hypertension using bioinformatics analysis.

Pulmonary fibrosis (PF) and pulmonary hypertension (PH) have common pathophysiological features, such as the significant remodeling of pulmonary parenchyma and vascular wall. There is no effective specific drug in clinical treatment for these two diseases, resulting in a worse prognosis and higher mortality. This study aimed to screen the common key genes and immune characteristics of PF and PH by means of bioinformatics to find new common therapeutic targets. Expression profiles are downloaded from the Gene Expression Database. Weighted gene co-expression network analysis is used to identify the co-expression modules related to PF and PH. We used the ClueGO software to enrich and analyze the common genes in PF and PH and obtained the protein-protein interaction (PPI) network. Then, the differential genes were screened out in another cohort of PF and PH, and the shared genes were crossed. Finally, RT-PCR verification and immune infiltration analysis were performed on the intersection genes. In the result, the positive correlation module with the highest correlation between PF and PH was determined, and it was found that lymphocyte activation is a common feature of the pathophysiology of PF and PH. Eight common characteristic genes (ACTR2, COL5A2, COL6A3, CYSLTR1, IGF1, RSPO3, SCARNA17 and SEL1L) were gained. Immune infiltration showed that compared with the control group, resting CD4 memory T cells were upregulated in PF and PH. Combining the results of crossing characteristic genes in ImmPort database and RT-PCR, the important gene IGF1 was obtained. Knocking down IGF1 could significantly reduce the proliferation and apoptosis resistance in pulmonary microvascular endothelial cells, pulmonary smooth muscle cells, and fibroblasts induced by hypoxia, platelet-derived growth factor-BB (PDGF-BB), and transforming growth factor-ß1 (TGF-ß1), respectively. Our work identified the common biomarkers of PF and PH and provided a new candidate gene for the potential therapeutic targets of PF and PH in the future.

Exploring the key genetic association between chronic pancreatitis and pancreatic ductal adenocarcinoma through integrated bioinformatics.

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma. Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis. Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME. Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.

Identification of unique transcriptomic signatures and key genes through RNA sequencing and integrated WGCNA and PPI network analysis in HIV infected lung cancer.

With the widespread use of highly active antiretroviral therapy (HARRT), the survival time of AIDS patients has been greatly extended. However, the incidence of lung cancer in HIV-infected patients is increasing and has become a major problem threatening the survival of AIDS patients. The aim of this study is to use Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene analysis to find possible key genes involved in HIV-infected lung cancer. In this study, using lung tissue samples from five pairs of HIV-infected lung cancer patients, second-generation sequencing was performed and transcriptomic data were obtained. A total of 132 HIV-infected lung cancer-related genes were screened out by WGCNA and differential gene expression analysis methods. Based on gene annotation analysis, these genes were mainly enriched in mitosis-related functions and pathways. In addition, in protein-protein interaction (PPI) analysis, a total of 39 hub genes were identified. Among them, five genes (ASPM, CDCA8, CENPF, CEP55, and PLK1) were present in both three hub gene lists (intersection gene, DEGs, and WCGNA module) suggesting that these five genes may become key genes involved in HIV-infected lung cancer.

Exploring the common gene signatures and pathogeneses of obesity with Alzheimer's disease via transcriptome data.

Background: Obesity is a complex condition that influences several organ systems and physiologic systems. Obesity (OB) is closely linked to Alzheimer's disease (AD). However, the interrelationship between them remains unclear. The purpose of this study is to explore the key genes and potential molecular mechanisms in obesity and AD. Methods: The microarray data for OB and AD were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) was used to delineate the co-expression modules related to OB and AD. The shared genes existing in obesity and AD were identified through biological process analyses using the DAVID website, which then constructed the Protein-Protein Interaction (PPI) Network and selected the hub genes by Cytoscape. The results were validated in other microarray data by differential gene analysis. Moreover, the hub gene expressions were further determined in mice by qPCR. Results: The WGCNA identifies five modules and four modules as significant modules with OB and AD, respectively. Functional analysis of shared genes emphasized that inflammation response and mitochondrial functionality were common features in the pathophysiology of OB and AD. The results of differential gene analysis in other microarray data were extremely similar to them. Then six important hub genes were selected and identified using cytoHubba, including MMP9, PECAM1, C3AR1, IL1R1, PPARGC1α, and COQ3. Finally, we validated the hub gene expressions via qPCR. Conclusions: Our work revealed the high inflammation/immune response and mitochondrial impairment in OB patients, which might be a crucial susceptibility factor for AD. Meanwhile, we identified novel gene candidates such as MMP9, PECAM1, C3AR1, IL1R1, PPARGC1α, and COQ3 that could be used as biomarkers or potential therapeutic targets for OB with AD.

Exploration of differentially expressed mRNAs and miRNAs for pediatric acute myeloid leukemia.

Background: To establish a comprehensive differential gene profile for pediatric acute myeloid leukemia patients (pAML) based on two independent databases and verify the differentially expressed genes using in vitro and in vivo analyses. Methods: The mRNA and miRNA sequencing information of GSE2191 and GSE35320, clinically recruited pAML individuals, and human AML cell line (NB4 cells) were utilized in the study. Results: Compared with the control sample, pAML patients demonstrated a total of 778 differentially expressed genes, including 565 upregulated genes and 213 downregulated genes. The genes including ZC3H15, BCLAF1, PPIG, DNTTIP2, SRSF11, KTN1, UBE3A, PRPF40A, TMED5, and GNL2 were the top 10 potential hub genes. At the same time, 12 miRNAs demonstrated remarkable differential expressions in pAML individuals compared with control individuals, as five upregulated and seven downregulated miRNAs. The hsa-miR-133, hsa-miR-181, and hsa-miR-195 were significantly downregulated. Building a miRNA-mRNA regulatory network, hsa-miR-133 regulated ZC3H15, BCLAF1, SRSF11, KTN1, PRPF40A, and GNL2. Using the NB4 cell model, hsa-miR-133 treatment inhibited cell proliferation capacity, which could be attenuated by a single mRNA transfection or a combination of ZC3H15 and BCLAF1. At the same time, hsa-miR-133 mimic treatment could significantly accelerate cell apoptosis in NB4 cells, which was also ZC3H15- and BCLAF1-dependent. The concentrations of ZC3H15 and BCLAF1 were investigated in peripheral blood using the ELISA method for the clinical control and pAML samples. In pAML samples, the expression levels of ZC3H15 and BCLAF1 were significantly enhanced (p < 0.01), regardless of the classification. Conclusion: Collectively, this study hypothesized several promising candidates for pAML formation.

Osteoarticular Involvement-Associated Biomarkers and Pathways in Psoriasis: The Shared Pathway With Ankylosing Spondylitis.

Psoriatic arthritis (PsA) is a unique immune-mediated disease with cutaneous and osteoarticular involvement. However, only a few studies have explored the susceptibility of osteoarticular involvement in psoriasis (Ps) at the genetic level. This study investigated the biomarkers associated with osteoarticular participation and potential shared molecular mechanisms for PsA and ankylosing spondylitis (AS). Methods: The RNA-seq data of Ps, PsA, and AS in the Gene Expression Omnibus (GEO) database were obtained. First, we used the limma package and the weighted gene co-expression network analysis (WGCNA) to identify the potential genes related to PsA and AS. Then, the shared genes in PsA and AS were performed using the GO, KEGG, and GSEA analyses. We also used machine learning to screen hub genes. The results were validated using external datasets and native cohorts. Finally, we used the CIBERSORT algorithm to estimate the correlation between hub genes and the abundance of immune cells in tissues. Results: An overlap was observed between the PsA and AS-related modules as 9 genes. For differentially expressed genes in AS and PsA, only one overlapping gene was found (COX7B). Gene enrichment analysis showed that the above 9 genes might be related to the mRNA surveillance pathway. The GSEA analyses showed that COX7B was involved in adaptive immune response, cell activation, etc. The PUM1 and ZFP91, identified from the support vector machine, had preferable values as diagnostic markers for osteoarticular involvement in Ps and AS (AUC > 0.7). Finally, CIBERSORT results showed PUM1 and ZFP91 involvement in changes of the immune microenvironment. Conclusion: For the first time, this study showed that the osteoarticular involvement in psoriasis and AS could be mediated by the mRNA surveillance pathway-mediated abnormal immunologic process. The biological processes may represent the cross talk between PsA and AS. Therefore, PUM1 and ZFP91 could be used as potential biomarkers or therapeutic targets for AS and Ps patients.

Differential gene expression analysis for osteosarcoma lung metastases.

PURPOSE: To explore the exact molecular mechanisms underline osteosarcoma (OS) patients with lung metastases. METHODS: The differentially expressed gene (DEG) as well as differentially expressed miRNAs (DEMs) for OS lung metastases were deeply investigated with two independent sources of databases (GEO dataset and clinical participants); The enriched biological processes and signaling pathways were explored; the miRNAs-mRNAs network was constructed; the functions of potential DEGs and DEMs were also verified with external analysis. RESULTS: The OS patients with lung metastases displayed 323 DEGs as C-C motif chemokine ligand 3 (CCL3), sorting nexin 10 (SNX10), alpha-2-macroglobulin (A2M), carboxypeptidase E (CPE), Rap guanine nucleotide exchange factor 4 (RAPGEF4), PDZ domain containing 2 (PDZD2), calpain 10 (CAPN10), four and a half LIM domains 2 (FHL2), alkaline phosphatase, biomineralization associated (ALPL), interleukin 6 (IL6), solute carrier family 26 member 1 (SLC26A1) as well as smoothened, frizzled class receptor (SMO) were significant differentially expressed. At the same time, 21 DEMs were potential for the progress of OS lung metastasis with hsa-miR-638, hsa-miR-451, hsa-miR-486-5p, hsa-miR-134 and hsa-miR-648 were significant distinct. It could been shown that hsa-miR-638 manipulated the largest number of target genes. The functions of hsa-miR-638 and target mRNAs for the development of lung metastasis in OS could be confirmed by quantitative Real-time PCR analysis. CONCLUSION: This integrated study hypothesized several miRNA dependent signaling pathway for OS patients with lung metastases and initiated a potential strategy for better understanding the lung metastases in clinic.

Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Systemic Lupus Erythematosus and Pulmonary Arterial Hypertension: Evidence From Transcriptome Data.

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease that can affect multiple systems. Pulmonary arterial hypertension (PAH) has a close linkage with SLE. However, the inter-relational mechanisms between them are still unclear. This article aimed to explore the shared gene signatures and potential molecular mechanisms in SLE and PAH. Methods: The microarray data of SLE and PAH in the Gene Expression Omnibus (GEO) database were downloaded. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify the co-expression modules related to SLE and PAH. The shared genes existing in the SLE and PAH were performed an enrichment analysis by ClueGO software, and their unique genes were also performed with biological processes analyses using the DAVID website. The results were validated in another cohort by differential gene analysis. Moreover, the common microRNAs (miRNAs) in SLE and PAH were obtained from the Human microRNA Disease Database (HMDD) and the target genes of whom were predicted through the miRTarbase. Finally, we constructed the common miRNAs-mRNAs network with the overlapped genes in target and shared genes. Results: Using WGCNA, four modules and one module were identified as the significant modules with SLE and PAH, respectively. A ClueGO enrichment analysis of shared genes reported that highly activated type I IFN response was a common feature in the pathophysiology of SLE and PAH. The results of differential analysis in another cohort were extremely similar to them. We also proposed a disease road model for the possible mechanism of PAH secondary to SLE according to the shared and unique gene signatures in SLE and PAH. The miRNA-mRNA network showed that hsa-miR-146a might regulate the shared IFN-induced genes, which might play an important role in PAH secondary to SLE. Conclusion: Our work firstly revealed the high IFN response in SLE patients might be a crucial susceptible factor for PAH and identified novel gene candidates that could be used as biomarkers or potential therapeutic targets.

Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus.

Background and aims: Patients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population. Studies on atherosclerosis prediction models specific for SLE patients are very limited. This study aimed to build a risk prediction model for atherosclerosis in SLE. Methods: RNA sequencing was performed on 67 SLE patients. Subsequently, differential expression analysis was carried out on 19 pairs of age-matched SLE patients with (AT group) or without (Non-AT group) atherosclerosis using peripheral venous blood. We used logistic least absolute shrinkage and selection operator regression to select variables among differentially expressed (DE) genes and clinical features and utilized backward stepwise logistic regression to build an atherosclerosis risk prediction model with all 67 patients. The performance of the prediction model was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses. Results: The 67 patients had a median age of 42.7 (Q1-Q3: 36.6-51.2) years, and 20 (29.9%) had atherosclerosis. A total of 106 DE genes were identified between the age-matched AT and Non-AT groups. Pathway analyses revealed that the AT group had upregulated atherosclerosis signaling, oxidative phosphorylation, and interleukin (IL)-17-related pathways but downregulated T cell and B cell receptor signaling. Keratin 10, age, and hyperlipidemia were selected as variables for the risk prediction model. The AUC and Hosmer-Lemeshow test p-value of the model were 0.922 and 0.666, respectively, suggesting a relatively high discrimination and calibration performance. The prediction model had a higher net benefit in the decision curve analysis than that when predicting with age or hyperlipidemia only. Conclusions: We built an atherosclerotic risk prediction model with one gene and two clinical factors. This model may greatly assist clinicians to identify SLE patients with atherosclerosis, especially asymptomatic atherosclerosis.

Exploring the Differences in Molecular Mechanisms and Key Biomarkers Between Membranous Nephropathy and Lupus Nephritis Using Integrated Bioinformatics Analysis.

Background: Both membranous nephropathy (MN) and lupus nephritis (LN) are autoimmune kidney disease. In recent years, with the deepening of research, some similarities have been found in the pathogenesis of these two diseases. However, the mechanism of their interrelationship is not clear. The purpose of this study was to investigate the differences in molecular mechanisms and key biomarkers between MN and LN. Method: The expression profiles of GSE99325, GSE99339, GSE104948 and GSE104954 were downloaded from GEO database, and the differentially expressed genes (DEGs) of MN and LN samples were obtained. We used Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for enrichment analysis of DEGs. A protein-protein interaction (PPI) network of DEGs was constructed using Metascape. We filtered DEGs with NetworkAnalyst. Finally, we used receiver operating characteristic (ROC) analysis to identify the most significant DEGs for MN and LN. Result: Compared with LN in the glomerulus, 14 DEGs were up-regulated and 77 DEGs were down-regulated in MN. Compared with LN in renal tubules, 21 DEGs were down-regulated, but no up-regulated genes were found in MN. According to the result of GO and KEGG enrichment, PPI network and Networkanalyst, we screened out six genes (IFI6, MX1, XAF1, HERC6, IFI44L, IFI44). Interestingly, among PLA2R, THSD7A and NELL1, which are the target antigens of podocyte in MN, the expression level of NELL1 in MN glomerulus is significantly higher than that of LN, while there is no significant difference in the expression level of PLA2R and THSD7A. Conclusion: Our study provides new insights into the pathogenesis of MN and LN by analyzing the differences in gene expression levels between MN and LN kidney samples, and is expected to be used to prepare an animal model of MN that is more similar to human.