Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies

BackgroundRheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA’s diagnostic signatures and the effect of immune cell infiltration in this pathology.MethodsGene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA’s diagnostic signatures and further verify by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. Moreover, this study employed cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) website for assessing the inflammatory state of RA, and an investigation was conducted on the relationship of diagnostic signatures and infiltrating immune cells.ResultsOn the whole, 54 robust DEGs received the recognition. Lymphocyte-specific protein 1 (LSP1), Granulysin (GNLY), and Mesenchymal homobox 2 (MEOX2) (AUC = 0.955) were regarded as RA’s diagnostic markers and showed their statistically significant difference by qRT-PCR. As indicated from the immune cell infiltration analysis, resting NK cells, neutrophils, activated NK cells, T cells CD8, memory B cells, and M0 macrophages may be involved in the development of RA. Additionally, all diagnostic signatures might be different degrees of correlation with immune cells.ConclusionsIn conclusion, LSP1, GNLY, and MEOX2 are likely to be available in terms of diagnosing and treating RA, and the infiltration of immune cells mentioned above may critically impact RA development and occurrence.

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Immune Cell Infiltration as Signatures for the Diagnosis and Prognosis of Malignant Gynecological Tumors

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.702451 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qi-Fang Liu ◽

Zi-Yi Feng ◽

Li-Li Jiang ◽

Tong-Tong Xu ◽

Si-Man Li ◽

...

Keyword(s):

Nk Cells ◽

Immune Cells ◽

Immune Cell ◽

Cell Infiltration ◽

Diagnostic Model ◽

Immune Cell Infiltration ◽

Consensus Clustering ◽

Strong Basis ◽

Gynecological Tumors ◽

Diagnosis And Prognosis

Background Malignant gynecological tumors are the main cause of cancer-related deaths in women worldwide and include uterine carcinosarcomas, endometrial cancer, cervical cancer, ovarian cancer, and breast cancer. This study aims to determine the association between immune cell infiltration and malignant gynecological tumors and construct signatures for diagnosis and prognosis.Methods We acquired malignant gynecological tumor RNA-seq transcriptome data from the TCGA database. Next, the “CIBERSORT” algorithm calculated the infiltration of 22 immune cells in malignant gynecological tumors. To construct diagnosis and prognosis signatures, step-wise regression and LASSO analyses were applied, and nomogram and immune subtypes were further identified.Results Notably, Immune cell infiltration plays a significant role in tumorigenesis and development. There are obvious differences in the distribution of immune cells in normal, and tumor tissues. Resting NK cells, M0 Macrophages, and M1 Macrophages participated in the construction of the diagnostic model, with an AUC value of 0.898. LASSO analyses identified a risk signature including T cells CD8, activated NK cells, Monocytes, M2 Macrophages, resting Mast cells, and Neutrophils, proving the prognostic value for the risk signature. We identified two subtypes according to consensus clustering, where immune subtype 3 presented the highest risk.Conclusion We identified diagnostic and prognostic signatures based on immune cell infiltration. Thus, this study provided a strong basis for the early diagnosis and effective treatment of malignant gynecological tumors.

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Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis

Disease Markers ◽

10.1155/2021/9911184 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Fei Sun ◽

Jian lin Zhou ◽

Pu ji Peng ◽

Chen Qiu ◽

Jia rui Cao ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Immune Cells ◽

Bioinformatics Analysis ◽

Immune Cell ◽

Relative Proportion ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Synovial Tissues ◽

Disease Specific

Background. Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive. Objective. This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion. Methods. Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the “LIMMA” package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA. Results. We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion ( P < 0.05 ). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA. Conclusions. We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

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Immune cell infiltration characteristics and related core genes of bioinformatic analysis in multiple sclerosis

10.21203/rs.3.rs-540699/v1 ◽

2021 ◽

Author(s):

jingyu zhao ◽

Jianyong Zheng ◽

Qun Wang ◽

Qian Li ◽

Nan zhang

Keyword(s):

Multiple Sclerosis ◽

Immune Cells ◽

Immune Cell ◽

Clinical Information ◽

Bioinformatic Analysis ◽

Differentially Expressed ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Systematic Understanding ◽

Core Genes

Abstract Background Introduction Multiple sclerosis(MS) is a common complication of uncontrolled or excessive neuroinflammation and autoimmunity disease. Advances in high-throughput technologies and available bioinformatics tools make it possible to evaluate different expressions in the whole genome instead of focusing on a limited number of genes. MethodsMaterials and methods Two public available databases GSE81279 and GSE21942 of multiple sclerosis samples were downloaded analyzed by CIBERSORT. Gene Ontology (GO) and KEGG pathway analysis based on GSEA was performed by cluster profile software to reveal the regulatory relations among genes and provided a systematic understanding of the functional differentially expressed genes at the transcriptional level.GSE81279 was used to validate the association between core genes and clinical information. ResultsFor immune cells, T-cell gamma delta and monocyte showed a trend toward reduction. The connection between the most prominent GO terms showed HBB, GATA2, NAA35, TCL1A, SECISBP2L, CLC, AGPAT5, CCR3, LTF, MALAT1, MS4A3 were significantly differentially expressed in MS. Gene set enrichment result was presented CDKN1A, DDB2, MME HMGN1, XPC, RELA for subsequent analysis.GSE81279 showed five types of immune cells revealed important links with MS. GSEA and layered KEGG analyses revealed that enrichment of immune response-related in primary immunodeficiency, it also consistent with previous studies. We got 10 genes, including HLA-DR, IL7R, HBB, TNFRSF1A, CYP27B1, NR1H3, IL2RA, TNFR1, BAFF, and CYP2R1 had close connections to clinical features. ConclusionsOur study identifies immune cell infiltration with microarray data of the plasma in MS by using CIBERSORT analysis, we also provide novel information for further study of genes of multiple sclerosis.

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Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis

Frontiers in Immunology ◽

10.3389/fimmu.2021.726747 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sheng Zhou ◽

Hongcheng Lu ◽

Min Xiong

Keyword(s):

Rheumatoid Arthritis ◽

T Cells ◽

Immune Cells ◽

Cd4 T Cells ◽

Bioinformatics Analysis ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

M1 Macrophages ◽

Tfh Cells

BackgroundRheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis.MethodsFive microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by “Batch correction” and “RobustRankAggreg” methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman’s rank correlation analysis in R software.ResultsTen significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package “UpSet”. CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells.ConclusionsCCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.

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Identification of immune-based prostate cancer subtypes using mRNA expression

10.21203/rs.2.23038/v1 ◽

2020 ◽

Author(s):

Jukun Song ◽

Song He ◽

Wei Wang ◽

Jiaming Su ◽

Dongbo Yuan ◽

...

Keyword(s):

Prostate Cancer ◽

Immune Response ◽

Immune Cells ◽

Immune Cell ◽

Molecular Classification ◽

Cell Types ◽

Functional Enrichment ◽

Cell Infiltration ◽

Cell Subpopulation ◽

Immune Cell Infiltration

Abstract Background Immune infiltration of Prostate cancer (PCa) was highly related to clinical outcomes. However, previous works failed to elucidate the diversity of different immune cell types that make up the function of the immune response system. The aim of the study was to uncover the composition of TIICs in PCa utilizing the CIBERSORT algorithm and further reveal the molecular characteristics of PCa subtypes. Method In the present work, we employed the CIBERSORT method to evaluate the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We analyzed the correlation between immune cell infiltration and clinical information. The tumor-infiltrating immune cells of the TCGA PCa cohort were analyzed for the first time. The fractions of 22 immune cell types were imputed to determine the correlation between each immune cell subpopulation and clinical feature. Three types of molecular classification were identified via R-package of “CancerSubtypes”. The functional enrichment was analyzed in each subtype. The submap and TIDE algorithm were used to predict the clinical response to immune checkpoint blockade, and GDSC was employed to screen chemotherapeutic targets for the potential treatment of PCa. Results In current work, we utilized the CIBERSORT algorithm to assess the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We investigated the correlation between immune cell infiltration and clinical data. The tumor-infiltrating immune cells in the TCGA PCa cohort were analyzed. The 22 immune cells were also calculated to determine the correlation between each immune cell subpopulation and survival and response to chemotherapy. Three types of molecular classification were identified. Each subtype has specific molecular and clinical characteristics. Meanwhile, Cluster I is defined as advanced PCa, and is more likely to respond to immunotherapy. Conclusions Our results demonstrated that differences in immune response may be important drivers of PCa progression and response to treatment. The deconvolution algorithm of gene expression microarray data by CIBERSOFT provides useful information about the immune cell composition of PCa patients. In addition, we have found a subtype of immunopositive PCa subtype and will help to explore the reasons for the poor effect of PCa on immunotherapy, and it is expected that immunotherapy will be used to guide the individualized management and treatment of PCa patients.

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Correlation Between 18F-FDG Uptake and Immune Cell Infiltration in Metastatic Brain Lesions

Frontiers in Oncology ◽

10.3389/fonc.2021.618705 ◽

2021 ◽

Vol 11 ◽

Author(s):

Young-Sil An ◽

Se-Hyuk Kim ◽

Tae Hoon Roh ◽

So Hyun Park ◽

Tae-Gyu Kim ◽

...

Keyword(s):

Breast Cancer ◽

Immune Cells ◽

Immune Cell ◽

Standardized Uptake Value ◽

Brain Lesions ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Ki 67 ◽

Glucose Transporter 1 ◽

Fdg Uptake

BackgroundThe purpose of this study was to investigate the correlation between 18F-fluorodeoxyglucose (FDG) uptake and infiltrating immune cells in metastatic brain lesions.MethodsThis retrospective study included 34 patients with metastatic brain lesions who underwent brain 18F-FDG positron emission tomography (PET)/computed tomography (CT) followed by surgery. 18F-FDG uptake ratio was calculated by dividing the standardized uptake value (SUV) of the metastatic brain lesion by the contralateral normal white matter uptake value. We investigated the clinicopathological characteristics of the patients and analyzed the correlation between 18F-FDG uptake and infiltration of various immune cells. In addition, we evaluated immune-expression levels of glucose transporter 1 (GLUT1), hexokinase 2 (HK2), and Ki-67 in metastatic brain lesions.ResultsThe degree of 18F-FDG uptake of metastatic brain lesions was not significantly correlated with clinical parameters. There was no significant relationship between the 18F-FDG uptake and degree of immune cell infiltration in brain metastasis. Furthermore, other markers, such as GLUT1, HK2, and Ki-67, were not correlated with degree of 18F-FDG uptake. In metastatic brain lesions that originated from breast cancer, a higher degree of 18F-FDG uptake was observed in those with high expression of CD68.ConclusionsIn metastatic brain lesions, the degree of 18F-FDG uptake was not significantly associated with infiltration of immune cells. The 18F-FDG uptake of metastatic brain lesions from breast cancer, however, might be associated with macrophage activity.

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BATF2 and PDK4 As Diagnostic Molecular Markers of Sarcoidosis and Their Relationship With Immune Infiltration

10.21203/rs.3.rs-557951/v1 ◽

2021 ◽

Author(s):

Xiaoyan Li ◽

Jing Zhou ◽

Jie He

Keyword(s):

Immune Cells ◽

Immune Cell ◽

External Validation ◽

Pulmonary Sarcoidosis ◽

Gene Expression Omnibus ◽

Diagnostic Markers ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Disorder ◽

Qpcr Method

Abstract Background: Sarcoidosis (SA) is an immune disorder disease featured with granulomas formation. The work purposed to uncover potential markers for sarcoidosis (SA) diagnosis and explore how immune cell infiltration contributes to the pathogenesis of SA.Methods: Sarcoidosis GSE83456 samples and GSE42834 from Gene Expression Omnibus (GEO) were analyzed as the training and external validation sets, respectively. R statistical software was employed to uncover the differentially expressed genes (DEGs) of GSE83456. SVM algorithms and LASSO logistic regression were applied for screening and verification of the diagnostic markers for key module genes. The infiltration of immune cells in sarcoidosis patients’ blood samples was assessed by CIBERSORT. The expression of serum BATF2 and PDK4 was detected by RT-qPCR method, and the value of BATF2 and PDK4 mRNA expression in the diagnosis of pulmonary sarcoidosis was analyzed.Results: In total, 580 DEGs were identified from the key module. PDK4 (AUC=0.942) and BATF4 (AUC=0.980) were revealed as diagnostic markers of sarcoidosis. We found that monocytes, T cells regulatory (Tregs), mast cells, macrophages，NK cells, and dendritic cells may contribute to sarcoidosis development. In addition, PDK4 and BATF4 were closely associated with these immune cells. BATF2 and PDK4 were highly expressed in pulmonary sarcoidosis. BATF2 and PDK4 combined to predict the area under the ROC curve of pulmonary sarcoidosis was 0.922.Conclusions: PDK4 and BATF4 could be used as diagnostic markers of sarcoidosis, and immune cell infiltration severs an important role in sarcoidosis.

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Prognostic and predictive value of tumor-infiltrating immune cells in Japanese patients with stage II/III gastric cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.3_suppl.46 ◽

2014 ◽

Vol 32 (3_suppl) ◽

pp. 46-46

Author(s):

Sophie Earle ◽

Toru Aoyama ◽

Alexander I. Wright ◽

Darren Treanor ◽

Yohei Miyagi ◽

...

Keyword(s):

Gastric Cancer ◽

Adjuvant Chemotherapy ◽

Immune Cells ◽

Prognostic Markers ◽

Immune Cell ◽

Japanese Patients ◽

Stage Ii ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Treatment Interaction

46 Background: Since the ACTS-GC trial, Japanese patients with stage II/III gastric cancer (GC) receive adjuvant S1 chemotherapy. However, selection of patients (pts) by TNM stage does not predict benefit from adjuvant S1 with certainty. Thus, there is an urgent clinical need to identify predictive biomarkers. Increasing evidence suggests tumor immune cell infiltration may be related to GC pts prognosis. We tested the hypothesis that extent and type of immune cell infiltration in GC is related to benefit from adjuvant chemotherapy. Methods: Tissue microarrays from 252 GC resections (109 pts treated by surgery alone (S), 143 pts treated by surgery and adjuvant S1 chemotherapy (SC)) from the Kanagawa Cancer Center Hospital (Yokohama, Japan) were investigated by immunohistochemistry for common leucocytes antigen (CD45), neutrophils (CD66b), macrophages (CD68 and CD163), T-cell subtypes (CD45R0, CD8, CD3), B-cells (CD20) and Treg cells (FOXP3). Staining was quantified as percentage immunoreactivity/area by automated image analysis. Relationship with overall survival was analyzed. A Cox regression model was used to identify independent prognostic markers and treatment interaction effect. Results: The hazard ratio of S1 was 0.694 in this GC cohort which is similar to the results of the ACTS-GC trial. CD45 and CD45R0 were independent prognostic markers in the S group only (CD45 p=0.032, CD45R0 p=0.003). A treatment interaction effect was seen for CD45, CD45R0, and CD68 (p value for test of interaction: CD45 p=0.062, CD45R0 p=0.082, CD68 p=0.057). Survival in the SC group was significantly poorer compared to the S group for CD45>56% or CD68>7% (p<0.05). Conclusions: This is the first study to investigate the relationship between tumor immune cell infiltration at time of surgery and benefit from adjuvant chemotherapy. Our results indicate that GC patients with high intratumoral levels of CD68, CD45, or CD45R0 positive immune cells might not benefit from adjuvant S1 chemotherapy. These findings require validation in a second independent dataset before conducting a prospective study stratifying patients with stage II/III GC based upon extent of CD45, CD45R0, or CD68 immune cell infiltration for adjuvant treatment.

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The prognostic value of a tumor microenvironment-based immune cell infiltration score model in colon cancer

10.21203/rs.3.rs-496500/v1 ◽

2021 ◽

Author(s):

Xiaofen Pan ◽

Xingkui Tang ◽

Minling Liu ◽

Xijun Luo ◽

Mengyuan Zhu ◽

...

Keyword(s):

Colon Cancer ◽

Signaling Pathway ◽

Immune Cells ◽

Evaluation System ◽

Prognostic Value ◽

Immune Cell ◽

Wnt Signaling Pathway ◽

Mapk Signaling ◽

Cell Infiltration ◽

Immune Cell Infiltration

Abstract BackgroundTumor microenvironment consists of tumor cells, immune cells and other matric components. Tumor infiltration immune cells are associated with prognosis. But all the current prognosis evaluation system dose not take tumor immune cells other matrix component into consideration. In the current study, we aimed to construct a prognosis predictive model based on tumor microenvironment.MethodCIBERSORT and ESTIMATE algorithms were used to reveal the immune cell infiltration landscape of colon cancer. Patients were classified into three clusters by ConsensusClusterPlus algorithm. Immune cell infiltration (ICI) scores of each patient were determine by principal-component analysis. Patients were divided to high and low ICI score groups. Survival, gene expression and somatic mutation of the two groups were compared.ResultsPatients with no lymph node invasion, no metastasis, T1-2 disease and stage I-II had higher ICI scores. Calcium signaling pathway, leukocyte transendothelial migration pathway, MAPK signaling pathway, TGF β pathway, and WNT signaling pathway were enriched in high ICI score group. Immune-checkpoint genes and immune-activity associated genes were significantly decreased in high ICI score. Patients in high ICI score group had better survival than low ICI score group. Prognostic value of ICI score was independent of TMB.ConclusionICI score might serve as an independent prognostic biomarker in colon cancer.

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Identification of Methylated Differentially Expressed Hub Genes and Immune Infiltration in Diabetic Retinopathy

10.21203/rs.3.rs-1136469/v1 ◽

2021 ◽

Author(s):

Qi Zhou ◽

Xin Xiong ◽

Min Tang ◽

Yingqing Lei ◽

Hongbin Lv

Keyword(s):

Gene Expression ◽

Extracellular Matrix ◽

Immune Cell ◽

Interaction Network ◽

Bioinformatic Analysis ◽

Differentially Expressed ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Critical Pathways

Abstract BackgroundDiabetic retinopathy (DR), a severe complication of diabetes mellitus (DM), is a global social and economic burden. However, the pathological mechanisms mediating DR are not well-understood. This study aimed to identify differentially methylated and differentially expressed hub genes (DMGs and DEGs, respectively) and associated signaling pathways, and to evaluate immune cell infiltration involved in DR. MethodsTwo publicly available datasets were downloaded from the Gene Expression Omnibus database. Transcriptome and epigenome microarray data and multi-component weighted gene coexpression network analysis (WGCNA) were utilized to determine hub genes within DR. One dataset was utilized to screen DEGs and to further explore their potential biological functions using functional annotation analysis. A protein-protein interaction network was constructed. Gene set enrichment and variation analyses (GSVA and GSEA, respectively) were utilized to identify the potential mechanisms mediating the function of hub genes in DR. Infiltrating immune cells were evaluated in one dataset using CIBERSORT. The Connectivity Map (CMap) database was used to predict potential therapeutic agents. ResultsIn total, 673 DEGs (151 upregulated and 522 downregulated genes) were detected. Gene expression was significantly enriched in the extracellular matrix and sensory organ development, extracellular matrix organization, and glial cell differentiation pathways. Through WGCNA, one module was found to be significantly related with DR (r=0.34, P =0.002), and 979 hub genes were identified. By comparing DMGs, DEGs, and genes in WGCNA, we identified eight hub genes in DR ( AKAP13, BOC, ACSS1, ARNT2, TGFB2, LHFPL2, GFPT2, TNFRSF1A ), which were significantly enriched in critical pathways involving coagulation, angiogenesis, TGF-β, and TNF-α-NF-κB signaling via GSVA and GSEA. Immune cell infiltration analysis revealed that activated natural killer cells, M0 macrophages, resting mast cells, and CD8 + T cells may be involved in DR. ARNT2, TGFB2, LHFPL2 , and AKAP13 expression were correlated with immune cell processes, and ZG-10, JNK-9L, chromomycin-a3, and calyculin were identified as potential drugs against DR. Finally, TNFRSF1A , GFPT2 , and LHFPL2 expression levels were consistent with the bioinformatic analysis. ConclusionsOur results are informative with respect to correlations between differentially methylated and expressed hub genes and immune cell infiltration in DR, providing new insight towards DR drug development and treatment.

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