Mapping the immune environment in clear cell renal carcinoma by single-cell genomics

Borcherding, Nicholas; Vishwakarma, Ajaykumar; Voigt, Andrew P.; Bellizzi, Andrew M.; Kaplan, Jacob; Nepple, Kenneth G.; Salem, Aliasger K.; Jenkins, Russell W.; Zakharia, Yousef; Zhang, Weizhou

doi:10.1038/s42003-020-01625-6

Cited by 221 publications

(186 citation statements)

References 62 publications

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“… 17 It has been found that KIRC tissue is often infiltrated by lymphocytes (T cells, B cells, and natural killer cells), macrophages, and dendritic cells. 33 , 34 The activated CD8+ T cells and activated dendritic cells were associated with the poor prognosis of KIRC, and the infiltrating CD4+ T cells could enhance KIRC cell proliferation. Infiltrating macrophages increased KIRC cell invasion via alteration of EMT and increasing the stem-cell-like population.…”

Section: Discussionmentioning

confidence: 99%

MMP25-AS1/hsa-miR-10a-5p/SERPINE1 axis as a novel prognostic biomarker associated with immune cell infiltration in KIRC

Tan

Chen

Huang

et al. 2021

Molecular Therapy - Oncolytics

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Long non-coding RNAs (lncRNAs) play a significant role in multiple human cancers as competing endogenous RNAs (ceRNAs). However, a systematic mRNA-microRNA (miRNA)-lncRNA network linked to kidney renal clear cell carcinoma (KIRC) prognosis has not been described. In this study, we aimed to identify the prognosis-related ceRNA regulatory network and analyzed its relationship with immune cell infiltration to predict KIRC patient survival. The MMP25-AS1/hsa-miR-10a-5p/SERPINE1 ceRNA network related to the prognosis of KIRC was obtained through bioinformatics analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Meanwhile, we constructed a three-gene-based survival predictor model, which could be referential for future clinical research. Methylation analyses suggested that the abnormal upregulation of the SERPINE1 likely resulted from hypomethylation. Furthermore, the immune infiltration analysis showed that the MMP25-AS1/hsa-miR-10a-5p/SERPINE1 axis could affect the changes in the tumor immune microenvironment and the development of KIRC by affecting the expression of chemokines (CCL4, CCL5, CXCL13, and XCL2). Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that the high expression of SERPINE1 might be related to tumor immune evasion in KIRC. In summary, the current study constructing the MMP25-AS1/hsa-miR-10a-5p/SERPINE1 ceRNA network might be a novel significant prognostic factor associated with the diagnosis and prognosis of KIRC.

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Section: Discussionmentioning

confidence: 99%

MMP25-AS1/hsa-miR-10a-5p/SERPINE1 axis as a novel prognostic biomarker associated with immune cell infiltration in KIRC

Tan

Chen

Huang

et al. 2021

Molecular Therapy - Oncolytics

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“…The single-cell RNA-seq data processing was described previously ( 27 ). The cell clusters were annotated manually based on previous knowledge and information from literatures ( 28 , 29 ). Expression profiling of the genes were depicted by heatmap and violin plot using the function “FeaturePlot” and “VlnPlot.”…”

Section: Methodsmentioning

confidence: 99%

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

et al. 2021

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BackgroundClear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.MethodsTranscriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.ResultsIn the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.ConclusionsThe present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

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“…D. Prediction of cell types. Cell types were predicted using singleR R package 92 ; normalized correlation values for predicted immune cell phenotypes are shown. Cluster of columns based on Ward.D2 distance between normalized correlation values across all pure immune cell populations in the Immgen database 17 .…”

Section: Resultsmentioning

confidence: 99%

“…Results were visualized using the ggplot2 R package (version 3.2.1). Single-cell normalized enrichment scores were calculated using the escape (v1.0.1) R package 92 . From this analysis, differentially expressed ligand and receptor between myocarditic and healthy controls for indicated cell types were extracted to use for the size/count of the dot plot.…”

Section: Methodsmentioning

confidence: 99%

Dissecting the Cellular Landscape and Transcriptome Network in Viral Myocarditis by Single-Cell RNA Sequencing

Lasrado

Borcherding

Arumugam

et al. 2021

Preprint

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Myocarditis induced with Coxsackievirus B3 (CVB3) is commonly employed to study viral pathogenesis in mice. Although infectious virus is cleared after the acute phase, affected animals chronically develop the features of dilated cardiomyopathy, which may involve the mediation of immune and non-immune cells. To dissect this complexity, we performed single-cell RNA sequencing on heart cells obtained from healthy and myocarditic mice, leading us to note that myocarditic mice had significantly higher proportions of myeloid cells, CD4 and CD8 T cells, and fibroblasts, whereas NK cells, ILCs and B cells were low. While the transcriptome profiles of myeloid cells revealed detection of monocytes and macrophages of M2 phenotype with pathways important in immune metabolism and inflammation, T cells consisted of Th17 cells, CTLs, and Treg cells with transcriptome signatures critical for cytotoxic functions. Although fibroblasts detected in myocarditic mice were phenotypically heterogeneous, their transcriptomes played roles in fibrosis and regulation of inflammation and immune responses. Additionally, analysis of intercellular communication networks revealed unique interactions and signaling pathways in the cardiac cellulome, whereas myeloid cells and T cells in myocarditic mice revealed uniquely upregulated transcription factors modulating cardiac remodeling functions. Taken together, our data suggest that M2 cells, T cells, and fibroblasts may cooperatively or independently participate in the pathogenesis of viral myocarditis.

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Mapping the immune environment in clear cell renal carcinoma by single-cell genomics

Cited by 221 publications

References 62 publications

MMP25-AS1/hsa-miR-10a-5p/SERPINE1 axis as a novel prognostic biomarker associated with immune cell infiltration in KIRC

MMP25-AS1/hsa-miR-10a-5p/SERPINE1 axis as a novel prognostic biomarker associated with immune cell infiltration in KIRC

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

Dissecting the Cellular Landscape and Transcriptome Network in Viral Myocarditis by Single-Cell RNA Sequencing

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