scholarly journals Identification of biomarkers and pathways associated with immune infiltration and prognosis in melanoma patients

2020 ◽  
Author(s):  
Biao Huang ◽  
Wei Han ◽  
Zu-Feng Sheng ◽  
Guo-Liang Shen
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cell ◽  
Primary Melanoma ◽  
Roc Curves ◽  
Cell Infiltration ◽  
Peptide Ligand ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Go Enrichment

Abstract Background Skin cutaneous melanoma (SKCM) is one of the most malignancy and aggressive cancers, causing about 72% of deaths in skin carcinoma. Although extensive study has explored the mechanism of recurrence and metastasis, the tumorigenesis of melanoma remains unclear. Exploring the tumorigenesis mechanism may help to identify prognostic biomarkers that could serve to guide cancer therapy . Methods We identified differentially expressed genes (DEGs) between patients with primary melanoma and normal skin in three data sets including GSE46517, GSE15605, GSE114445. Functional annotation including KEGG pathway and gene ontology (GO) enrichment analysis was performed by DAVID. Subsequently, protein ‐ protein interaction network of DEGs was developed and the most significant module was constructed as hub genes for further study. ClueGO was used to investigate significant pathways in hub genes. The Kaplan–Meier method was performed to assess prognostic genes. ROC curves were used to describe diagnosis value of hub genes. Then, genes expression and clinical characteristics were validated by TCGA profiles. In addition, we explored the correlation between prognostic genes and immune cell infiltration. Results A total of 308 DEGs and 12 hub genes were identified. GO enrichment results demonstrated a correlation between DEGs and the immune response, inflammatory response and transcriptional control. Hallmark pathways of hub genes including interleukin-10 signaling, chemokine receptors bind chemokine signaling ， peptide ligand-binding receptors. Survival analysis and ROC curves suggested that CCL4, CCL5, NMU, CXCL9, CXCL10, CXCL13 were independent prognostic factors (except GAL). Furthermore, prognostic genes were highly expressed in tumor tissue and related to pathological stages and Breslow depth. In addition, the expressions of CCL4, CCL5, CXCL9, CXCL10, CXCL13 were positively correlated with six immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells) and 28 types of TILs such as activated CD8 T cells, regulatory T cells, natural killer T cells while NMU and GAL were negatively correlated with it. Conclusion In summary, this study identified significant hub genes and pathways closely associated with immune infiltrations and prognosis in SKCM patients, which might help us evaluate underlying carcinogenesis progress from skin to melanoma.

scholarly journals Identification of new biomarkers and pathways associated with treatment and prognosis in melanoma patients

2020 ◽  
Author(s):  
Biao Huang ◽  
Wei Han ◽  
Zu-Feng Sheng ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Prognostic Value ◽  
Immune Cell ◽  
Gene Interaction ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Strong Positive Correlation ◽  
Hub Genes

Abstract Background Skin cutaneous melanoma (SKCM) is known as the most malignancy and treatment-resistant in human tumor, causing about 72% of deaths in skin carcinoma. However, the potential mechanism and new effective targets remain to be further elucidated. Available datasets such as Gene Expression Omnibus (GEO) can be utilized to search for novel therapeutic targets and prognostic biomarkers. Methods Three data sets were downloaded from GEO database . The differentially expressed genes (DEGs) were identified via Venn software. Protein‐protein interaction network of DEGs was developed and the module hub genes analysis was constructed by Cytoscape. Subsequently, multiple online tools and Kaplan-Meier survival curves were analyzed to detect underlying signaling pathways, gene expression, drug-gene interaction and prognostic value of hub genes. In addition, we explored the correlation between hub genes and immune cell infiltration. At last, the related miRNA, lncRNA networks were constructed by R software. Results A total of 308 DEGs and 12 hub genes were identified. Function and pathway enrichment results demonstrated a correlation between DEGs and the tumor microenvironment, immune response and melanoma tumorigenesis. Subsequently, we focused on assessing potential value of 12 hub genes. Seven hub genes ( CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 ) were identified with significant overall survival for prognosis. What’s more, five of these seven hub genes were found to be related to clinical stages (P values<0.05). In addition, the most important pathways of hub genes include interleukin-10 signaling, peptide ligand-binding receptors, which play important roles in tumor microenvironment for immune activation or immunosuppressive by regulating the infiltration of immune cells. Our results revealed a strong positive correlation between gene expression (CCL4, CCL5, CXCL9, CXCL10 and CXCL13) and immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells). Interestingly, 8 of 12 hub genes (CXCL10, CCL4, CCL5, IL6, CXCL2, PTGER3, GAL, NPY1R) were also found in the predicted drug-gene interaction. The related miRNA, lncRNA for diagnosis and prognosis were found in networks. Conclusion In conclusion, CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 were of high prognostic value and may be potential targets for the diagnosis and therapy of patients with melanoma.

scholarly journals Identification of the Key Serum Biomarkers to Diagnose and Predict Metastasis of Osteosarcoma Based on the Analysis of Immune Cell Infiltration Characteristics

2021 ◽  
Author(s):  
Zhihao Chen ◽  
Liubing Li ◽  
Ziyuan Li ◽  
Xi Wang ◽  
Mingxiao Han ◽  
...  
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Immune Cell ◽  
Serum Biomarkers ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Diagnostic Model ◽  
Immune Cell Infiltration ◽  
Differentially Expressed Mirnas

Abstract Background: The potential functions of circular RNAs (circRNAs) and micro RNAs (miRNAs) in osteosarcoma (OS) have not been fully elucidated. Especially, the behavior and mechanism of immune responses in OS development and progression have not been fully demonstrated. It was reported that circRNAs and miRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of many cancers. This study aimed to identify novel key serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics.Methods: The differentially-expressed circRNAs (DEcircRNAs), differentially-expressed miRNAs (DEmiRNAs),and differentially-expressed mRNAs (DEmRNAs) of human OS were investigated based on the microarray data downloaded from Gene Expression Omnibus (GEO) datasets. Then, we analyzed immune characteristics pattern of tumor-infiltrating immune cells in OS. On this basis, we identified statistically-significant transcription factors and performed pathway enrichment analysis. Subsequently, we constructed protein-protein interaction (PPI) and competitive endogenous RNA (ceRNA) networks. Moreover, the biological characteristic of targets in ceRNA networks was proposed. Finally, the expression and diagnostic capability of these potential biomarkers from ceRNA network were confirmed by RT-qPCR in patients’ serum.Results: Seven differentially-expressed circRNAs (DEcircRNAs), 166 differentially-expressed miRNAs (DEmiRNAs) and 175 differentially-expressed mRNAs (DEmRNAs) were identified in total. The highest level of infiltration in OS patients were M0 macrophages, M2 macrophages and CD8+ T cells. Further, M0 macrophages and CD8+ T cells were showed the largest negative correlation coefficients. These significant immune characteristics pattern of tumor-infiltrating immune cells were revealed by the principal component analysis in OS. Moreover, we found 185 statistically-significant transcription factors in which the main significant molecules show the potential in immunotherapy of OS. Hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A from ceRNA networks associated with immune cell infiltration were confirmed as the potential novel biomarkers for OS diagnosis, of which FAM98A could distinguish and predict metastasis. Most importantly, a novel diagnostic model consisting of the four promising biomarkers (hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A) was highlighted with 0.928 AUC value.Conclusions: In summary, the potenial serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics were found, and a novel diagnostic model consisting of four promising serum biomarkers was proposed firstly. These results provided a new perspective for the immunotherapy of OS.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we aimed to characterize infiltrating immune cells and genes associated with the immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Gens and Genomes (KEGG) analysis were applied using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, which suggest novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma 

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Clinical and Prognostic Significance of Tumor-Infiltrating CD8+ T Cells and PD-L1 Expression in Primary Gastrointestinal Stromal Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangfei Sun ◽  
Ping Shu ◽  
Yong Fang ◽  
Wei Yuan ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Gastrointestinal Stromal Tumors ◽  
Immune Cell ◽  
Prognostic Significance ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Wild Type ◽  
Stromal Tumors ◽  
Tissue Specimens

PurposeImmunotherapy for gastrointestinal stromal tumors (GISTs) remains a clinical challenge. The present study aimed to explore the clinical and prognostic significance of immune cell infiltration and PD-L1 expression in GISTs.MethodsA total of 507 clinical tissue specimens of primary GISTs were collected for immunohistochemical analysis of immune cell infiltration and PD-L1 expression. Influencing factors of survival were evaluated by Kaplan–Meier analysis. Univariate and multivariate analyses were performed using the Cox regression model.ResultsThere were significant differences in sex, tumor location, size, mitotic index, NIH risk grade, and cell morphology between different gene mutation types of GISTs. Immune cell infiltration in GISTs mainly involved macrophages and T cells. PD-1 was expressed in 48.5% of the tissue specimens, and PD-L1 expression was detected in 46.0% of the samples. PD-L1 expression was negatively correlated with the tumor size and mitotic index but positively correlated with the number of CD8+ T cells. There were significant differences in the number of CD8+ T cells between different gene mutation types. Wild type-mutant GISTs were enriched with CD8+ T cells as compared with KIT- and PDGFRA-mutant GISTs. The number of CD8+ T cells was higher in non-gastric GISTs. PD-L1 and CD8+ T cells were independent predictors for better relapse-free survival of GISTs.ConclusionsPD-L1 expression is a predictive biomarker for better prognosis of GISTs. Non-gastric GIST patients with wild-type mutations may be the beneficiaries of PD-1/PD-L1 inhibitors.

scholarly journals The Immune Cell Infiltration Patterns and Characterization Score in Bladder Cancer to Identify Prognostic.

2022 ◽  
Author(s):  
Yongsheng Zhang ◽  
Yunlong Wang ◽  
Jichuang Wang ◽  
Kaixiang Zhang
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
T Cells ◽  
Gene Expression Data ◽  
Cd8 T Cells ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Expression Data ◽  
Common Gene

Abstract Bladder cancer (BLCA) is among the most frequent types of cancer. Patients with BLCA have a significant recurrence rate and a poor post-surgery survival rate. Recent research has found a link between tumor immune cell infiltration (ICI) and the prognosis of BLCA patients. However, the ICI picture of BLCA remains unclear. Common gene expression data was obtained by combining the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) expression databases. Two computational algorithms were proposed to unravel the ICI landscape of BLCA patients. The R package "limma" was applied to find differentially expressed genes (DEGs). Principal-component analysis (PCA) was used to calculate the ICI score. A total of 569 common gene expression data were retrieved from TCGA and GEO cohorts. CD8+ T cells were found to have a substantial positive connection with activated memory CD4+ T cells and immune score. On the contrary, CD8+ T cells were found to have a substantial negative connection with Macrophages M0. Thirty-eight DEGs were selected. Two ICI patterns were defined by unsupervised clustering method. Patients of BLCA were separated into two groups. The high ICI score group exhibits better outcome than the low one (p < 0.001). Finally, the group with a high tumor mutation burden (TMB) as well as a high ICI score had the best outcome. (p <0.001). Combining TMB and ICI score resulted in a more accurate survival prediction, suggesting that ICI score could be used as a prognostic marker for BLCA patients.

The Landscape of Immune Cell Infiltration in the Glomerulus of Diabetic Nephropathy: Evidence Based on Bioinformatics

2021 ◽  
Author(s):  
Wei ZHOU ◽  
Yaoyu LIU ◽  
Qinghong HU ◽  
Jiuyao ZHOU ◽  
Hua LIN
Keyword(s):  
T Cells ◽  
Diabetic Nephropathy ◽  
Mast Cells ◽  
Protein Interactions ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Immune Infiltration

Abstract BackgroundIncreasing evidence suggests that immune cell infiltration contributes to the pathogenesis and progression of diabetic nephropathy (DN). We aim to unveil the immune infiltration pattern in the glomerulus of DN and provide potential targets for immunotherapy. MethodsInfiltrating percentage of 22 types of immune cell in the glomerulus tissues were estimated by the CIBERSORT algorithm based on three transcriptome datasets mined from the GEO database. Differentially expressed genes (DEGs) were identified by the “limma” package. Then immune-related DEGs were identified by intersecting DEGs with immune-related genes (downloaded from Immport database). The protein-protein interactions of Immune-related DEGs were explored using the STRING database and visualized by Cytoscape. The enrichment analyses for KEGG pathways and GO terms were carried out by the gene set enrichment analysis (GSEA) method. Results9 types of immune cell were revealed to be significantly altered in the glomerulus tissues of DN (Up: B cells memory, T cells CD4 naive, Macrophages M2, Dendritic cells resting, Mast cells resting, Mast cells activated; Down: NK cells resting, Monocytes, Neutrophils). Correlation analysis revealed that immune infiltration act as a complicated and tightly regulated network, among which T cells gamma delta and T cells CD4 naive show the most synergistic effect (r = 0.58, p ＜ 0.001); meanwhile, T cells CD8 and T cells CD4 memory resting show the most competitive effect (r = - 0.67, p ＜ 0.001). Several pathways related to immune were significantly activated. Moreover, 6 hub genes with a medium to strong correlation with renal function (eGFR) were identified (ALB, EGF, FOS, CXCR1, CXCR2, CCL2). ConclusionIn the glomerulus of DN, the immune infiltration pattern changed significantly. A complicated and tightly regulated network of immune cells exists in the pathological of DN. The hub genes identified here will facilitate the development of immunotherapy.

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