scholarly journals LAG3(LAG-3,CD223) DNA methylation correlates with LAG3 expression by tumor and immune cells, immune cell infiltration, and overall survival in clear cell renal cell carcinoma

2020 ◽  
Vol 8 (1) ◽  
pp. e000552 ◽  
Author(s):  
Niklas Klümper ◽  
Damian J Ralser ◽  
Emma Grace Bawden ◽  
Jenny Landsberg ◽  
Romina Zarbl ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Overall Survival ◽  
Cell Lines ◽  
Immune Cells ◽  
Immune Cell ◽  
Clear Cell ◽  
Immune Cell Infiltration ◽  
Strongly Correlated ◽  
Cell Renal Cell Carcinoma ◽  
Immune Cell Infiltrates

BackgroundLymphocyte activating 3 (LAG3, LAG-3, CD223) is a promising target for immune checkpoint inhibition in clear cell renal cell carcinoma (KIRC). The aim of this study was to investigate the epigenetic regulation ofLAG3in KIRC by methylation.MethodsWe correlated quantitativeLAG3methylation levels with transcriptional activity, immune cell infiltration, and overall survival in a cohort of n=533 patients with KIRC and n=160 normal adjacent tissue (NAT) samples obtained from The Cancer Genome Atlas (TCGA). Furthermore, we analyzedLAG3methylation in peripheral blood mononuclear cells (PBMCs) and KIRC cell lines. We validated correlations between LAG3 expression, immune cell infiltrates, survival, and methylation in an independent KIRC cohort (University Hospital Bonn (UHB) cohort, n=118) by means of immunohistochemistry and quantitative methylation-specific PCR.ResultsWe found differential methylation profiles among PBMCs, NAT, KIRC cell lines, and KIRC tumor tissue. Methylation strongly correlated with LAG3 mRNA expression in KIRCs (TCGA cohort) and KIRC cell lines. In the UHB cohort, methylation correlated with LAG3-positive immune cells and tumor-intrinsic LAG3 protein expression. Furthermore,LAG3methylation strongly correlated with signatures of distinct immune cell infiltrates, an interferon-y signature (TCGA cohort), and immunohistochemically quantified CD45+, CD8+, and CD4+immune cell infiltrates (UHB cohort). LAG3 mRNA expression (TCGA cohort), methylation (both cohorts), and tumor cell-intrinsic protein expression (UHB cohort) was significantly associated with overall survival.ConclusionOur data suggest an epigenetic regulation of LAG3 expression in tumor and immune cells via DNA methylation. LAG3 expression and methylation is associated with a subset of KIRCs showing a distinct clinical course and immunogenicity. Our study provides rationale for further testingLAG3DNA methylation as a predictive biomarker for response to LAG3 immune checkpoint inhibitors.

scholarly journals Systematic Chromatin Accessibility Analysis Based on Different Immunological Subtypes of Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Shiqiang Zhang ◽  
Wenzhong Zheng ◽  
Donggen Jiang ◽  
Haiyun Xiong ◽  
Guolong Liao ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Chromatin Accessibility ◽  
Cell Infiltration ◽  
Tnf Alpha ◽  
Immune Cell Infiltration ◽  
Cell Renal Cell Carcinoma

BackgroundRecent research of clear cell renal cell carcinoma (ccRCC) is focused on the tumor immune microenvironment (TIME). Chromatin accessibility is critical for regulation of gene expression. However, its role in different immunological subtypes of ccRCC based on immune cell infiltration has not been systematically studied.MethodsFive hundred thirty patient data from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) were adopted to estimate immune cell infiltration. Twenty-four types of immune cells were evaluated with single-sample Gene Set Enrichment Analysis (ssGSEA). Patients were divided into two clusters based on immune cell infiltration. Systematic chromatin accessibility analysis was conducted based on the two clusters.ResultsWe compared the relative expression of the immune gene signatures among 530 patients of TCGA-KIRC using ssGSEA. Overall survival (OS) analysis revealed 10 types of immune cells were significantly associated with prognosis. Patients were divided into two clusters based on 24 types of immune cell infiltration. Immune cell signals as well as PD-1/PD-L1 signal were higher in cluster 1. Among the two clusters, 2,400 differential peaks were found in TCGA-KIRC Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) data. The distribution of differential peaks and prognosis-related immune cells in 23 chromosomes are essentially the same. There is no peak distribution downstream. The proportion of peaks upstream of the 5’ transcription start site decreases, and both sides of binding regions of the TSS 0.1-1 kb becomes smaller. Enrichment analysis of GO and KEGG of these differential peaks showed that they are remarkably related to the immune regulation in tumor microenvironment. Known motifs and de novo motifs were found by linking motif annotations to different peaks. Survival analysis of related motif transcription factors were prognostic. The GSEA enrichment analysis showed that high SP1 expression positively correlates with TGF-beta signaling and inflammatory response, while negatively correlates with TNF-alpha signaling via NFKB. High KLF12 expression negatively correlates with interferon gamma response, IL2-STAT5 signaling, TNF-alpha signaling via NFKB, IL6-JAK-STAT3 signaling.ConclusionThe abnormality of chromatin accessibility may play an important regulatory role in ccRCC immunity.

scholarly journals Identification and validation of dichotomous immune subtypes based on intratumoral immune cells infiltration in clear cell renal cell carcinoma patients

2020 ◽  
Vol 8 (1) ◽  
pp. e000447
Author(s):  
Ying Xiong ◽  
Zewei Wang ◽  
Quan Zhou ◽  
Han Zeng ◽  
Hongyu Zhang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Treatment Response ◽  
Immune Cells ◽  
Renal Cell ◽  
Immune Cell ◽  
Clear Cell ◽  
Biological Pathways ◽  
Cell Renal Cell Carcinoma ◽  
Free Survival

BackgroundIncreasing evidence has elucidated the clinical significance of tumor infiltrating immune cells in predicting outcomes and therapeutic efficacy. In this study, we comprehensively analyze the tumor microenvironment (TME) immune cell infiltrations in clear cell renal cell carcinoma (ccRCC) and correlated the infiltration patterns with anti-tumor immunity and clinical outcomes.MethodsWe analyzed immune cell infiltrations in four independent cohorts, including the KIRC cohort of 533 patients, the Zhongshan ccRCC cohorts of 259 patients, the Zhongshan fresh tumor sample cohorts of 20 patients and the Zhongshan metastatic ccRCC cohorts of 87 patients. Intrinsic patterns of immune cell infiltrations were evaluated for associations with clinicopathological characteristics, underlying biological pathways, genetic changes, oncological outcomes and treatment responses.ResultsUnsupervised clustering of tumor infiltrating immune cells identified two microenvironment subtypes, TMEcluster-A and TMEcluster-B. Gene markers and biological pathways referring to immune evasion were upregulated in TMEcluster-B. TMEcluster-B associated with poor overall survival (p<0.001; HR 2.629) and recurrence free survival (p=0.012; HR 1.870) in ccRCC validation cohort. TMEcluster-B cases had worse treatment response (p=0.009), overall survival (p<0.001; HR 2.223) and progression free survival (p=0.015; HR 2.7762) in metastatic ccRCC cohort. The predictive accuracy of International Metastatic Database Consortium risk score was improved after incorporation of TME clusters.ConclusionsTMEcluster-A featured increased mast cells infiltration, prolonged survival and better treatment response. TMEcluster-B was a heavily infiltrated but immunosuppressed phenotype enriched for macrophages, CD4+T cells, Tregs, CD8+T cells and B cells. TMEcluster-B predicted dismal survival and worse treatment response in clear cell renal cell carcinoma patients.

scholarly journals Epigenetic signature predicts overall survival clear cell renal cell carcinoma

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yejinpeng Wang ◽  
Liang Chen ◽  
Lingao Ju ◽  
Kaiyu Qian ◽  
Xinghuan Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Abstract Background Recently, increasing study have found that DNA methylation plays an important role in tumor, including clear cell renal cell carcinoma (ccRCC). Methods We used the DNA methylation dataset of The Cancer Genome Atlas (TCGA) database to construct a 31-CpG-based signature which could accurately predict the overall survival of ccRCC. Meanwhile, we constructed a nomogram to predict the prognosis of patients with ccRCC. Result Through LASSO Cox regression analysis, we obtained the 31-CpG-based epigenetic signature which were significantly related to the prognosis of ccRCC. According to the epigenetic signature, patients were divided into two groups with high and low risk, and the predictive value of the epigenetic signature was verified by other two sets. In the training set, hazard ratio (HR) = 13.0, 95% confidence interval (CI) 8.0–21.2, P < 0.0001; testing set: HR = 4.1, CI 2.2–7.7, P < 0.0001; entire set: HR = 7.2, CI 4.9–10.6, P < 0.0001, Moreover, combined with clinical indicators, the prediction of 5-year survival of ccRCC reached an AUC of 0.871. Conclusions Our study constructed a 31-CpG-based epigenetic signature that could accurately predicted overall survival of ccRCC and staging progression of ccRCC. At the same time, we constructed a nomogram, which may facilitate the prediction of prognosis for patients with ccRCC.

scholarly journals The Correlation Between Novel Survival-related Alternative Splicing Signature and Prognostic Prediction and Immune Microenvironment in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Wengang Jian ◽  
Gang Wang ◽  
Yipeng Yu ◽  
Licheng Cai ◽  
Yongchun Yu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Splicing Factors ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cell Renal Cell Carcinoma

Abstract BackgroundAlthough extensive researches related alternative splicing (AS) as the prognostic markers of patients in cancers, which remains unknown in clear cell renal cell carcinoma (ccRCC), especially in immunotherapy. Therefore, a novel survival-associated AS signature was established to predict prognosis of patients and explored its correlation with immune cell infiltration or immune checkpoint expression to explain the phenomenon of resistance to immunotherapy in ccRCC.Methodsccording to AS data, clinical information and gene expression data in ccRCC, overall survival-related AS events was identified and further the AS-related prognostic risk model established by LASSO regression and multi-Cox regression analysis was evaluated by Kaplan-Meier survival analysis, the ROC curves and a nomogram model. Then we clarified the biological processes and pathways by GSEA, and further measured the immune cell infiltration by ESTIMATE, CIBERSORT and ssGSEA. Finally we analysed the clinical features and immune features of different parental genes, and quested the splicing factors regulating riskScore-related AS events by Spearman correlation analysis.ResultsWe obtained the most significant 5 AS events, including C4orf19|69001|AT, UACA|31438|AP, FAM120C|89237|AT, TRIM16L|39629|AP and SEC31A|100881|ES, to establish the prognostic risk model, and further illustrated the stability and importance of the riskScore prognostic signatures. Then we found that in high risk group, most of the top 10 GO enrichments and the KEGG pathway were closely related to the immunity, and the higher immune cell infiltration, and higher expression of classic immune checkpoints such as PD1 and CTLA4. In addition, 6 different parental genes were obtained, including C4orf19, ARHGAP24 DNASE1L3, P4HA1, SLC39A14 and TAF1D. These 6 genes could not be the independent prognostic signatures, but the expression of these genes was closely related to immune cells infiltration and the expression of immune checkpoints. Finally, we got aberrant 52 splicing factors regulating riskScore-related AS events.ConclusionOur study discovered that overall survival-related AS events mediated by aberrant splicing factors can be constructed a prognostic risk model to predict prognosis of patiens and utilized to index the situation of immune cell infiltration and immune checkpoint expression that impact tumor immune microenvironment in ccRCC.

scholarly journals Integrated analysis of single‑cell and transcriptome based RNA‑seq multilayer network and WGCNA for construction and validation of an immune cell-related prognostic model in clear cell renal cell carcinoma

2021 ◽  
Author(s):  
Guanlin Wu ◽  
Miao Liu ◽  
Weifeng Yang ◽  
Shuai Zhu ◽  
Weiming Guo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Single Cell ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Marker Genes ◽  
Multilayer Network ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer (RCC). The increasing incidence and poor prognosis of ccRCC after tumour metastasis makes the study of its pathogenesis extremely important. Traditional studies mostly focus on the regulation of ccRCC by single gene, while ignoring the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database for providing help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. The DEGs and marker genes of the immune cell were combined and then intersected with the ligand-receptor gene data, and the 981 ligand-receptor relationship pairs obtained were intersected with the target gene of the transcription factor afterwards; 7,987 immune cell multilayer network relationship pairs were finally observed. Then, the genes in the network and the genes in TCGA were intersected to obtain 966 genes for constructing a co-expression network. Subsequently, 53 genes in black and magenta modules related to prognosis were screened by WGCNA for subsequent analysis. Whereafter, using the data of TCGA, 28 genes with significant prognostic differences were screened out through univariate Cox regression analysis. After that, LASSO regression analysis of these genes was performed to obtain a prognostic risk scoring model containing 16 genes, and CPTAC data showed that the effectiveness of this model was good. The results of correlation analysis between the risk score and other clinical factors showed that age, grade, M, T, stage and risk score were all significantly different (p < 0.05), and the results of prognostic accuracy also reached the threshold of qualification. Combined with clinical information, univariate and multivariate Cox regression analyses verified that risk score was an independent prognostic factor (p < 0.05). A nomogram constructed based on a predictive model for predicting the overall survival was established, and internal validation performed well. Our findings suggest that the predictive model built based on the immune cells scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

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