Identification of miRNA-mRNA network and immune-related gene signatures in IgA nephropathy by integrated bioinformatics analysis

Abstract Background IgA nephropathy (IgAN) is the most common form of primary glomerulonephritis worldwide, and its diagnosis depends mainly on renal biopsy. However, there is no specific treatment for IgAN. Moreover, its causes and underlying molecular events require further exploration. Methods The expression profiles of GSE64306 and GSE93798 were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differential expression of miRNAs and genes, respectively. The StarBase and TransmiR databases were employed to predict target genes and transcription factors of the differentially expressed miRNAs (DE-miRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to predict biological functions. A comprehensive analysis of the miRNA-mRNA regulatory network was constructed, and protein–protein interaction (PPI) networks and hub genes were identified. CIBERSORT was used to examine the immune cells in IgAN, and correlation analyses were performed between the hub genes and infiltrating immune cells. Results Four downregulated miRNAs and 16 upregulated miRNAs were identified. Forty-five and twelve target genes were identified for the upregulated and downregulated DE-miRNAs, respectively. CDKN1A, CDC23, EGR1, HIF1A, and TRIM28 were the hub genes with the highest degrees of connectivity. CIBERSORT revealed increases in the numbers of activated NK cells, M1 and M2 macrophages, CD4 naive T cells, and regulatory T cells in IgAN. Additionally, HIF1A, CDC23, TRIM28, and CDKN1A in IgAN patients were associated with immune cell infiltration. Conclusions A potential miRNA-mRNA regulatory network contributing to IgAN onset and progression was successfully established. The results of the present study may facilitate the diagnosis and treatment of IgAN by targeting established miRNA-mRNA interaction networks. Infiltrating immune cells may play significant roles in IgAN pathogenesis. Future studies on these immune cells may help guide immunotherapy for IgAN patients.

Download Full-text

Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents

BMC Nephrology ◽

10.1186/s12882-021-02356-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Alieh Gholaminejad ◽

Yousof Gheisari ◽

Sedigheh Jalali ◽

Amir Roointan

Keyword(s):

Iga Nephropathy ◽

Regulatory Network ◽

Target Genes ◽

Expression Profiles ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Integrative Approach ◽

Potential Factors ◽

Ngf Signaling ◽

Underlying Mechanisms

Abstract Background IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease are remained to be explored and still, there is an urgent need for the discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied to mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN. Methods Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database), and long-non coding RNAs (from LncTarD database) were used for the construction of a multilayer regulatory network via Cytoscape. Result “GSE25590” (miRNA) and “GSE73953” (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with “Innate immune system”, “Apoptosis”, as well as “NGF signaling” pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN. Conclusion Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

Download Full-text

Identification of Potential ceRNA Network and Patterns of Immune Cell Infiltration in Systemic Sclerosis-Associated Interstitial Lung Disease

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.622021 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qiuhong Wu ◽

Yang Liu ◽

Yan Xie ◽

Shixiong Wei ◽

Yi Liu

Keyword(s):

Systemic Sclerosis ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Immune Cells ◽

Regulatory Network ◽

Target Genes ◽

Immune Cell ◽

Differentially Expressed ◽

Immune Cell Infiltration ◽

Overlapping Genes

PurposeSystemic sclerosis-associated interstitial lung disease (SSc-ILD) is one of the most severe complications of systemic sclerosis (SSc) and is the leading cause of SSc-related deaths. However, the precise pathogenesis of pulmonary fibrosis in SSc-ILD remains unknown. This study aimed to evaluate the competing endogenous RNA (ceRNA) regulatory network and immune cell infiltration patterns in SSc-ILD.MethodsOne microRNA (miRNA) and three messenger RNA (mRNA) microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Then, the differentially expressed miRNAs (DEmiRs) and mRNAs (DEMs) between SSc-ILD patients and normal controls were identified, respectively, followed by the prediction of the target genes and target lncRNAs of DEmiRs. The overlapping genes between DEmiRs target genes and DEMs were identified as core mRNAs to construct the ceRNA network. In addition, the “Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)” algorithm was used to analyze the composition of infiltrating immune cells in lung tissues of SSc-ILD patients and controls, and differentially expressed immune cells were recognized. The correlation between immune cells and core mRNAs was evaluated by Pearson correlation analysis.ResultsTotally, 42 SSc-ILD lung tissues and 18 normal lung tissues were included in this study. We identified 35 DEmiRs and 142 DEMs and predicted 1,265 target genes of DEmiRs. Then, 9 core mRNAs related to SSc-ILD were recognized, which were the overlapping genes between DEmiRs target genes and DEMs. Meanwhile, 9 DEmiRs related to core mRNAs were identified reversely, and their target lncRNAs were predicted. In total, 9 DEmiRs, 9 core mRNAs, and 51 predicted lncRNAs were integrated to construct the ceRNA regulatory network of SSc-ILD. In addition, 9 types of immune cells were differentially expressed in lung tissues between SSc-ILD patients and controls. Some core mRNAs, such as COL1A1, FOS, and EDN1, were positively or negatively correlated with the number of infiltrating immune cells.ConclusionThis is the first comprehensive study to construct the potential ceRNA regulatory network and analyze the composition of infiltrating immune cells in lung tissues of SSc-ILD patients, which improves our understanding of the pathogenesis of SSc-ILD.

Download Full-text

CXCR4 Negatively Correlates with NKT Cell Infiltration During Gastric Precancerous Lesions Progression to Early Gastric Cancer

10.21203/rs.3.rs-88936/v1 ◽

2020 ◽

Author(s):

Xiaotao Jiang ◽

Kunhai Zhuang ◽

Kailin Jiang ◽

Yi Wen ◽

Linling Xie ◽

...

Keyword(s):

Gastric Cancer ◽

Early Gastric Cancer ◽

Immune Cells ◽

Immune Cell ◽

Precancerous Lesions ◽

Expression Profiles ◽

Cell Infiltration ◽

Hub Genes ◽

Nkt Cell ◽

Hub Gene

Abstract Background Immune microenvironment in gastric precancerous lesions (GPL) and early gastric cancer (EGC) still remain largely unknown. This study aims to identify key immune cells and hub genes associated with GPL progression to EGC. Methods Immune Cell Abundance Identifier (ImmuCellAI) algorithm was used to quantify the proportions of immune cells of GPL and GC samples based on gene expression profiles. Key immune cells associated with GPL progression to EGC were identified using one‐way analysis of variance (ANOVA) test and Spearman’s correlation test. Weighted gene co-expression analysis (WGCNA) and pathway enrichment were adopted to identify hub gene co-expression network and hub genes associated with the key immune cells infiltration. The pattern of key immune cells infiltration, hub genes expression and their correlation were verified in an independent GPL-EGC cohort and GC datasets.Results NKT cell was found gradually decreased during GPL progression to EGC and negatively correlated with tumorigenesis. According to WGCNA and hub genes screening, CXCR4, having a poor prognosis, increased with GPL progression, positively correlated with tumorigenesis and negatively correlated with NKT cell infiltration significantly, was identified as the real hub gene. The negative correlation between CXCR4 and NKT cell infiltration was successfully verified in an independent GPL-EGC cohort and GC datasets.Conclusion CXCR4 and NKT cell are possible to serve as biomarkers in monitoring GPL progression to EGC. Besides, CXCR4 may be involved in regulating NKT cell infiltration during GPL progression to EGC, which may provide a new immunotherapeutic target.

Download Full-text

Database Mining of Genes of Prognostic Value for the Prostate Adenocarcinoma Microenvironment Using the Cancer Gene Atlas

BioMed Research International ◽

10.1155/2020/5019793 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xin Zhao ◽

Daixing Hu ◽

Jia Li ◽

Guozhi Zhao ◽

Wei Tang ◽

...

Keyword(s):

Overall Survival ◽

Immune Cell ◽

Kegg Pathway ◽

Clinical Sample ◽

Expression Profiles ◽

Prostate Adenocarcinoma ◽

Cancer Gene ◽

Hub Genes ◽

Ppi Networks ◽

Pathway Analyses

Background. Prostate adenocarcinoma (PRAD) is a common malignant tumor in elderly men. Our research uses The Cancer Gene Atlas (TCGA) database to find potential related genes for predicting the prognosis of patients with PRAD. Methods. We downloaded gene expression profiles and clinical sample information from TCGA for 490 patients with PRAD (patient age: 41-78 years). We calculated stromal and immune scores using the ESTIMATE algorithm to predict the level of stromal and immune cell infiltration. We categorized patients with PRAD in TCGA into high and low score arrays according to their median immune/stromal scores and identified differentially expressed genes (DEGs) that were significantly correlated with the prognosis of PRAD. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. The association between DEGs and overall survival was investigated by weighted Kaplan–Meier survival analysis and multivariate analysis. Furthermore, the protein-protein interaction network (PPI) of DEGs was constructed using the STRING tool. Finally, the hub genes were identified by analyzing the degree of association of PPI networks. Results. We found that 8 individual DEGs, C6, S100A12, MLC1, PAX5, C7, FAM162B, CAMK1G, and TCEAL5, were significantly predictive of favorable overall survival and one DEG, EPYC, was associated with poor overall survival. GO and KEGG pathway analyses revealed that the DEGs were associated with immune responses. Moreover, 30 hub genes were obtained using the PPI network of DEGs: ITGAM, CD4, CD3E, IL-10, LCP2, ITGB2, ZAP-70, C3, CCL19, CXCL13, CXCL9, BTK, CCL21, CD247, CD28, CD3D, FCER1G, PTPRC, TYROBP, CCR5, ITK, CCL13, CCR1, CCR2, CD79B, CYBB, IL2RG, JAK3, PLCG2, and CD19. These prominent nodes had the most associations with other genes, indicating that they might play crucial roles in the prognosis of PRAD. Conclusions. We extracted a list of genes associated with the prostate adenocarcinoma microenvironment, which might contribute to the prediction and interpretation of PRAD prognosis.

Download Full-text

In silico Identification of 10 Hub Genes and an miRNA–mRNA Regulatory Network in Acute Kawasaki Disease

Frontiers in Genetics ◽

10.3389/fgene.2021.585058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jin Ma ◽

Huan Gui ◽

Yunjia Tang ◽

Yueyue Ding ◽

Guanghui Qian ◽

...

Keyword(s):

Kawasaki Disease ◽

Signaling Pathway ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Unknown Etiology ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

Kawasaki disease (KD) causes acute systemic vasculitis and has unknown etiology. Since the acute stage of KD is the most relevant, the aim of the present study was to identify hub genes in acute KD by bioinformatics analysis. We also aimed at constructing microRNA (miRNA)–messenger RNA (mRNA) regulatory networks associated with acute KD based on previously identified differentially expressed miRNAs (DE-miRNAs). DE-mRNAs in acute KD patients were screened using the mRNA expression profile data of GSE18606 from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DE-mRNAs were performed with the DAVID database. Target genes of DE-miRNAs were predicted using the miRWalk database and their intersection with DE-mRNAs was obtained. From a protein–protein interaction (PPI) network established by the STRING database, Cytoscape software identified hub genes with the two topological analysis methods maximal clique centrality and Degree algorithm to construct a miRNA-hub gene network. A total of 1,063 DE-mRNAs were identified between acute KD and healthy individuals, 472 upregulated and 591 downregulated. The constructed PPI network with these DE-mRNAs identified 38 hub genes mostly enriched in pathways related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, osteoclast differentiation, adipocytokine signaling pathway and tumor necrosis factor signaling pathway. Target genes were predicted for the up-regulated and down-regulated DE-miRNAs, 10,203, and 5,310, respectively. Subsequently, 355, and 130 overlapping target DE-mRNAs were obtained for upregulated and downregulated DE-miRNAs, respectively. PPI networks with these target DE-mRNAs produced 15 hub genes, six down-regulated and nine upregulated hub genes. Among these, ten genes (ATM, MDC1, CD59, CD177, TRPM2, FCAR, TSPAN14, LILRB2, SIRPA, and STAT3) were identified as hub genes in the PPI network of DE-mRNAs. Finally, we constructed the regulatory network of DE-miRNAs and hub genes, which suggested potential modulation of most hub genes by hsa-miR-4443 and hsa-miR-6510-5p. SP1 was predicted to potentially regulate most of DE-miRNAs. In conclusion, several hub genes are associated with acute KD. An miRNA–mRNA regulatory network potentially relevant for acute KD pathogenesis provides new insights into the underlying molecular mechanisms of acute KD. The latter may contribute to the diagnosis and treatment of acute KD.

Download Full-text

Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

10.21203/rs.3.rs-95029/v1 ◽

2020 ◽

Author(s):

Harish Joshi ◽

Basavaraj Mallikarjunayya Vastrad ◽

Nidhi Joshi ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Expression Profiles ◽

Therapeutic Targets ◽

Rank Aggregation ◽

Receiver Operating Curve ◽

Progression Rate ◽

Hub Genes

Abstract Background Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. ObjectivesTherefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. MethodsThe gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network.Results We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. The robust DEGs linked with the development of obesity were screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. ConclusionsOur study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

Download Full-text

Comprehensive Analysis of circRNA-associated Competing Endogenous RNA Networks and Immune Infiltration in Gastric Cancer

10.21203/rs.3.rs-805632/v1 ◽

2021 ◽

Author(s):

beibei xu ◽

Endian Zheng ◽

Yi Huang ◽

Liang Zheng ◽

Qiaoli Lan ◽

...

Keyword(s):

Gastric Cancer ◽

T Cells ◽

Immune Cell ◽

Clinical Stage ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Hub Genes ◽

Qrt Pcr ◽

Ppi Networks

Abstract BackgroundCircular RNA (circRNA) has been shown to be an important regulator in gastric cancer (GC). However, functions and regulatory mechanisms of circRNA-related competitive endogenous RNA (ceRNA) in GC have not been established.MethodsCircRNA data and clinical data were downloaded from the GEO and TCGA databases. The ceRNA and Protein-Protein Interaction (PPI) networks were constructed through bioinformatics analysis. Function enrichment analysis was performed. Additionally, correlations between expression levels of the top 10 hub genes and immune cell infiltration levels, histopathological grade and clinical stage were determined to establish their clinical values. The differentially expressed circRNA (DEcircRNA) was validated by quantitative real-time PCR (qRT-PCR).ResultsScreening of the GEO and TCGA databases revealed a total of 1627 DEcircRNAs, 6516 DEmRNAs, and 1451 DEmiRNAs. The ceRNA interaction network contained 2 circRNAs, 3 miRNAs and 55 mRNAs. Meanwhile, Gene Ontology (GO) analysis revealed a total of 323 biological processes (BP) terms, 53 cellular components (CC) terms, 51 molecular functions (MF) terms, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed 4 signaling pathways. Gene Set Enrichment Analysis (GSEA) analysis revealed that EPHA4, NCAM1 and NRXN1 were positively correlated with the axon guidan and adhesion molecules pathways. Most of top 10 hub genes were positively correlated with B cells, CD8+ T cells, CD4+ T cells, Neutrophils and Dendritic Cell infiltration. Correlation analysis between hub genes and clinical phenotypes revealed that elevated expressions of EPHA4 and KCNA1 indicated poor tissue differentiation and were associated with clinically advanced stages of GC. The qRT-PCR results revealed that the expression of has_circ_0002504 was significantly down-regulated in 3 GC cell lines which was consistent with the results of our bioinformatics analysis.ConclusionsHas_circ_0001998 and has_circ_0002504 are potential diagnostic biomarkers for GC, and the high expressions of both EPHA4 and KCNA1 may predict poor prognosis.

Download Full-text

Construction of miRNA-mRNA regulatory network and prognostic signature in endometrial cancer

10.21203/rs.2.23832/v1 ◽

2020 ◽

Author(s):

Jinhui Liu ◽

Rui Sun ◽

Sipei Nie ◽

Jing Yang ◽

Siyue Li ◽

...

Keyword(s):

Endometrial Cancer ◽

Regulatory Network ◽

Target Genes ◽

Immune Cell ◽

Cox Proportional Hazards ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Prognostic Signature ◽

Hub Genes ◽

Qrt Pcr

Abstract Background: Many studies have well supported the close relationship between miRNA and endometrial cancer (EC). This bioinformatic study, compared with other similar studies, confirmed a new miRNA-mRNA regulatory network to investigate the miRNA-mRNA regulatory network and the prognostic biomarkers in EC. Methods: We downloaded RNA-seq and miRNA-seq data of endometrial cancer from the TCGA database, and then we used EdegR package to screen differentially expressed miRNAs and mRNAs (DE-miRNAs and DE-mRNAs). The differentially expressed genes (DEGs) were identified and their functions were predicted using the functional and pathway enrichment analysis. Protein–protein interaction (PPI) network was established using STRING database, and the hub genes were verified by Gene Expression Proﬁling Interactive Analysis (GEPIA). Then, we constructed a regulatory network of EC-associated miRNAs and hub genes by Cytoscape, and determined the expression of unexplored miRNAs in EC tissues and normal adjacent tissues by quantitative Real-Time PCR (qRT-PCR). A prognostic signature model and a predictive nomogram were constructed. Finally, we explored the association between the prognostic model and the immune cell infiltration. Results: 11531 DE-mRNAs and 236 DE-miRNAs, as well as 275 and 118 candidate DEGs for upregulated and downregulated DE-miRNAs were screened out. These DEGs were significantly concentrated in FOXO signaling pathway, cell cycle and Focal adhesion. Among the 20 hub genes identified, 17 exhibited significantly different expression compared with normal tissues. The miRNA-mRNA network included 5 downregulated and 13 upregulated DE-miRNAs . qRT-PCR proved that the expression levels of miRNA-18a-5p, miRNA-18b-5p, miRNA-449c-5p and miRNA-1224-5p and their target genes, NR3C1, CTGF, MYC, and TNS1 were consistent with our predictions. Univariate and multivariate Cox proportional hazards regression analyses of the hub genes revealed that NR3C1, EZH2, and GATA4 showed a significant prognostic value. We identified the three-gene signature as an independent prognostic indicator for EC ( p =0.022，HR=1.321, 95% CI: 1.041-1.675) and these genes were closely related to eight types of immune infiltration cells. Conclusion: Our study revealed the mechanisms of the carcinogenesis and progression of EC.

Download Full-text

Immune Cell Infiltration in Bladder Cancer

10.21203/rs.2.11176/v1 ◽

2019 ◽

Author(s):

shanhui liu ◽

Lanlan Li ◽

Shan Wu ◽

Wei He ◽

Jianzhong Lu ◽

...

Keyword(s):

Bladder Cancer ◽

T Cells ◽

Immune Cells ◽

Immune Cell ◽

Expression Profiles ◽

Computational Method ◽

Low Grade ◽

Recurrence Rates ◽

Bladder Tissue ◽

Normal Bladder

Abstract Background Bladder cancer is one of the most common malignant diseases with high recurrence rates worldwide. Although immunotherapy has been applied in bladder cancer for a long period of time, the tumor-infiltrating immune cells (TIICs) in bladder cancer has not been systematical investigated. Methods CIBERSORT, a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs), was applied to calculate the TIICs fraction proportion in normal bladder tissues and in bladder cancer tissues with the TCGA data. Results compared to normal bladder tissue, B cells naïve, T cells CD4 memory resting and Mast cells resting fractions proportion decreased, and NK resting, macrophages M0 and macrophages M1 increased. In BLCAs tissue, pro-tumorigenic related immune cells were negatively correlated with anti-tumor immune cells. New tumor with locoregional had high fraction proportion of macrophages M0 and macrophages M1. Dendritic cells activated, Monocytes, macrophages M1, Tregs and T cells follicular helper significantly increased in low grade BLCAs. Tregs had a high proportion in BLCAs patients accepted low radiation dose. Conclusions This study indicates that macrophages M2 and Tregs could be the promising immunotherapy targets combined radiotherapy in BLCAs. The result provides valuable information to understand the immunity status in BLCAs.

Download Full-text

Identification of immune cell infiltration pattern and related critical genes in metastatic castration-resistant prostate cancer by bioinformatics analysis

Cancer Biomarkers ◽

10.3233/cbm-203222 ◽

2021 ◽

pp. 1-15

Author(s):

Caibin Fan ◽

Wei Lu ◽

Kai Li ◽

Chunchun Zhao ◽

Fei Wang ◽

...

Keyword(s):

Prostate Cancer ◽

T Cells ◽

Protein Interaction ◽

Immune Cells ◽

Immune Cell ◽

Immune Cell Infiltration ◽

Castration Resistant Prostate Cancer ◽

Hub Genes ◽

Protein Protein Interaction ◽

Castration Resistant

BACKGROUND: Metastatic castration-resistant prostate cancer (mCRPC) is the lethal stage of prostate cancer and the main cause of morbidity and mortality, which is also a potential target for immunotherapy. METHOD: In this study, using the Approximate Relative Subset of RNA Transcripts (CIBERSORT) online method, we analysed the immune cell abundance ratio of each sample in the mCRPC dataset. The EdgeR (an R package) was used to classify differentially expressed genes (DEGs). Using the Database for annotation, visualisation and interactive exploration (DAVID) online method, we performed functional enrichment analyses. STRING online database and Cytoscape tools have been used to analyse protein-protein interaction (PPI) and classify hub genes. RESULTS: The profiles of immune infiltration in mCRPC showed that Macrophages M2, Macrophages M0, T cells CD4 memory resting, T cells CD8 and Plasma cells were the main infiltration cell types in mCRPC samples. Macrophage M0 and T cell CD4 memory resting abundance ratios were correlated with clinical outcomes. We identified 1102 differentially expressed genes (DEGs) associated with the above two immune cells to further explore the underlying mechanisms. Enrichment analysis found that DEGs were substantially enriched in immune response, cell metastasis, and metabolism related categories. We identified 20 hub genes by the protein-protein interaction network analysis. Further analysis showed that three critical hub genes, CCR5, COL1A1 and CXCR3, were significantly associated with prostate cancer prognosis. CONCLUSION: Our findings revealed the pattern of immune cell infiltration in mCRPC, and identified the types and genes of immune cells correlated with clinical outcomes. A new theoretical basis for immunotherapy may be given by our results.

Download Full-text