scholarly journals Systematic analyses of the role of prognostic and immunological EIF3A, a reader protein, in clear cell renal cell carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yi Zhang ◽  
Xiaoliang Hua ◽  
Haoqiang Shi ◽  
Li Zhang ◽  
Haibing Xiao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Multivariate Analyses ◽  
Enrichment Analysis ◽  
Marker Genes ◽  
Cell Renal Cell Carcinoma ◽  
Coexpressed Genes

Abstract Background Eukaryotic initiation factor 3a (EIF3A), a “reader” protein for RNA methylation, has been found to be involved in promoting tumorigenesis in a variety of cancers. The impact of EIF3A in clear cell renal cell carcinoma (ccRCC) has yet to be reported. This study aimed to identify the prognostic value of EIF3A in ccRCC and investigate the relationship between EIF3A expression and immune infiltration. Methods We collected 29 m6A-related mRNA data and clinicopathological parameters from The Cancer Genome Atlas (TCGA) database. Logistic regression analyses were used to analyse the correlation between EIF3A expression and clinical characteristics. Immunohistochemistry (IHC) was applied to examine EIF3A levels in normal and ccRCC tissues. Univariate and multivariate analyses were conducted to recognize independent factors associated with overall survival (OS) and disease-free survival (DFS). The nomogram aimed to predict the 1-, 3- and 5-year survival probabilities. Gene set enrichment analysis (GSEA) was carried out to determine the potential functions and related signalling pathways of EIF3A expression. To investigate EIF3A of coexpressed genes, we used LinkedOmics, and the results were subjected to enrichment analysis. Simultaneously, LinkedOmics and STRING datasets were used to identify EIF3A coexpressed genes that were visualized via Cytoscape. Finally, we evaluated whether EIF3A expression correlated with the infiltration of immune cells and the expression of marker genes in ccRCC by Tumour Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA). Result EIF3A expression was significantly different between ccRCC tissues and normal tissues. EIF3A expression was correlated with poor prognostic clinicopathological factors, and K–M analyses revealed that low EIF3A expression was correlated with a poor prognosis. The results of univariate and multivariate analyses proved that EIF3A was a prognostic factor in ccRCC patients. GSEA results indicated that EIF3A high expression was enriched in the renal cell carcinoma pathway. EIF3A expression was significantly positively correlated with B cells, CD8 + T cells, CD4 + T cells, neutrophils, macrophages, and dendritic cells. Furthermore, EIF3A expression was associated with most marker genes of immune cells. Conclusions EIF3A could serve as a potential biomarker for prognostic and diagnostic stratification of ccRCC and is related to immune cell infiltrates.

Systematic Analyses of The Role of Prognostic And Immunological of EIF3A, A Reader Protein, In Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Yi Zhang ◽  
Xiaoliang Hua ◽  
Haoqiang Shi ◽  
Li Zhang ◽  
HaiBing Xiao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Multivariate Analyses ◽  
Enrichment Analysis ◽  
Marker Genes ◽  
Cell Renal Cell Carcinoma

Abstract Background: Eukaryotic initiation factor 3a, EIF3A, as a “reader” protein for RNA methylation, has been found to be related to promote tumorigenesis in different variety of cancers. The impaction of EIF3A in clear cell renal cell carcinoma (ccRCC) has yet to be expounded. This study aimed to identify the prognostic value of EIF3A in ccRCC and investigate the relationship between expression and immune infiltration.Methods: We collected 29 m6a related mRNA data and clinicopathological parameters from Cancer Genmoe Atlas (TCGA) database. Logistic regression analyses were used to analyze the correlation between EIF3A expression and clinical characteristics. Immunohistochemistry (IHC) were applied to examine EIF3A levels in normal and ccRCC tissues. Univariate and multivariate analyses were conducted to recognize forcefully independent factor in associated with overall survival (OS) and diseases free survival (DFS). Nomogram was aim at predicting the 1-, 3-and 5-year survival probabilities. Gene set enrichment analysis (GSEA) was carried out to the potential function and related signaling pathways of EIF3A expression. To investigate EIF3A of co-expressed genes, we used LinkedOmics and its result was undertaken enrichment analysis. Simultaneously, to employ LinkedOmics and STRING dataset drew a conclusion that EIF3A co-expressed genes and visualized via Cytoscape. Finally, we evaluated that EIF3A expression correlated between with infiltration of immune cells and the expression of marker genes in ccRCC by Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA).Result: EIF3A expression was significantly different between ccRCC tissues and normal tissues. EIF3A expression was connected with poor prognostic clinicopathological factors, and K–M analyses revealed that low EIF3A expression was correlated with poor prognosis. The result of univariate and multivariate analyses proved that EIF3A was a prognostic factor in ccRCC patients. GSEA results indicated that high expression was enriched in renal cell carcinoma pathway and so on. EIF3A expression was significantly positively correlated with B cells, CD8+T cells, CD4+T cells, neutrophils, macrophages, and dendritic cells. Furthermore, EIF3A expression was associated with most of marker genes of immune cells.Conclusions: EIF3A could serve as potential biomarkers for prognostic and diagnostic stratification factor for ccRCC and is related with immune cells infiltrates.

scholarly journals Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Huiying Yang ◽  
Xiaoling Xiong ◽  
Hua Li
Keyword(s):  
Renal Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Genomic Instability ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Prognosis Prediction

BackgroundClear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.MethodsWe decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.ResultsWe developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.ConclusionOur study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.

Prognostic value of infiltrating immune cells in clear cell renal cell carcinoma (ccRCC)

2019 ◽  
Vol 121 (3) ◽  
pp. 2571-2581 ◽  
Author(s):  
Shiyi Liu ◽  
Saijiao Li ◽  
Yanqing Wang ◽  
Feiyan Wang ◽  
Li Zhang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

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.

Atezolizumab plus bevacizumab in non-clear cell renal cell carcinoma (NccRCC) and clear cell renal cell carcinoma with sarcomatoid differentiation (ccRCCsd): Updated results of activity and predictive biomarkers from a phase II study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 4583-4583 ◽  
Author(s):  
Ronan Flippot ◽  
Bradley Alexander McGregor ◽  
Abdallah Flaifel ◽  
Kathryn P. Gray ◽  
Sabina Signoretti ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Phase Ii ◽  
Immune Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Biomarkers ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltrate ◽  
Sarcomatoid Differentiation

4583 Background: NccRCC and ccRCCsd are aggressive tumors associated with poor prognosis and response to therapy. Combination strategies co-targeting VEGF signaling and inhibitory immune checkpoints are highly active in clear-cell renal cell carcinoma, but data is lacking in NccRCC and ccRCCsd. We conducted a multicenter, open-label, single arm phase II trial of atezolizumab plus bevacizumab in NccRCC and ccRCCsd. Methods: Patients with NccRCC and ccRCCsd ( > 20% sarcomatoid differentiation), and ECOG performance status of 0-2 were eligible. Prior systemic treatment was allowed with the exception of prior PD-1/PD-L1-directed therapy. Atezolizumab 1200mg and bevacizumab 15mg/kg were administered every 3 weeks until progression, unacceptable toxicity, or patient withdrawal. Primary endpoint was objective response rate (ORR) per RECIST 1.1. Exploratory biomarker analyses included PD-L1 expression on tumor (TC) and immune cells (IC), and spatial analysis of the immune infiltrate. Results: Sixty patients received at least 1 cycle of treatment, among whom 56 were evaluable for response (17 ccRCCsd and 39 NccRCC). ORR was 34% in the overall population, 53% in ccRCCsd and 26% in NccRCC. Median progression-free survival was 8.4 months (95%CI, 6.9-16.5). Baseline tumor tissue was available for 36 patients. TC PD-L1 expression ≥1% was associated with improved ORR (9/14, 64%) compared to patients with PD-L1 expression < 1% (4/20, 20%). Patients with TC PD-L1 expression ≥1% who experienced progressive disease as best response had shorter average distance between tumor cells and nearest neighboring immune cells at baseline. Further analysis of the immune tumor microenvironment on an expanded cohort, including IC PD-L1 expression and correlation with clinical outcomes, is ongoing and will be updated. Conclusions: The combination of atezolizumab plus bevacizumab is active in NccRCC and ccRCCsd. Candidate predictive biomarkers include PD-L1 expression in TC and topological analysis of the immune infiltrate. Clinical trial information: NCT02724878.

scholarly journals IGLL5 is correlated with tumor‐infiltrating immune cells in clear cell renal cell carcinoma

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
Zhi‐Nan Xia ◽  
Xing‐Yuan Wang ◽  
Li‐Cheng Cai ◽  
Wen‐Gang Jian ◽  
Cheng Zhang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

scholarly journals Construction and Validation of a Prognostic Nomogram for Clear Cell Renal Cell Carcinoma Based on DNA Methylation-driven Differentially Expressed Genes

2020 ◽  
Author(s):  
Zheng Wang ◽  
Yanlong Zhang ◽  
Shuaishuai Fan ◽  
Yuan Ji ◽  
Jianchao Ren ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Score Model

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent type of kidney cancer. This study aimed to establish a nomogram to predict ccRCC prognosis.Methods: By integrating DNA methylation (DNAm) data and gene expression profiles of ccRCC obtained from The Cancer Genome Atlas (TCGA), DNAm-driven genes were identified by differential and correlation analyses. Next, risk genes were selected by multiple algorithms (univariate Cox and Kaplan-Meier survival analyses) and various databases (TCGA, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and The Human Protein Atlas (HPA)). A risk score model was established by multivariate Cox analyses. ConsensusPathDB and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functions of the selected genes. After comprehensively evaluating the clinical data, we established and assessed a dynamic nomogram available on a webserver.Results: In total, 220 differentially expressed DNAm-driven genes were identified, and five-gene signature (EPB41L4B, HHLA2, IFI16, CMTM3, and XAF1) was related to overall survival (OS). Next, we integrated the DNAm-driven genes into the prognostic risk score model and found that age, histologic grade, pathological stage, and risk level were correlated with OS in ccRCC patients. Based on these variables, a dynamic nomogram was established to predict the ccRCC prognosis. Finally, Functional enrichment analysis showed that the functions of these genes were relevant to immune reactions.Conclusions: We identified a 5 DNAm-driven gene signature whose altered status was highly correlated with ccRCC patient OS. We constructed a dynamic nomogram to provide individualized survival predictions for ccRCC patients.

scholarly journals A novel five-zinc finger-related-gene signature related to tumor immune microenvironment allows for treatment stratification and predicts prognosis in clear cell renal cell carcinoma patients

2021 ◽  
Author(s):  
Feilong Zhang ◽  
Jiyue Wu ◽  
Jiandong Zhang ◽  
Peng Cao ◽  
Zejia Sun ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Zinc Finger ◽  
Renal Cell ◽  
Drug Sensitivity ◽  
Cox Regression ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cell Renal Cell Carcinoma

Abstract Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent renal malignant tumors, which survival rate and quality of life of ccRCC patients are not satisfactory. Therefore, identification of prognostic biomarkers of ccRCC patients will contribute to early and accurate clinical intervention and treatment, and then improve their prognosis. Methods We downloaded the original expression data of mRNAs from The Cancer Genome Atlas database and the zinc finger(ZNF)-related genes (ZRGs) from UniProt online database. Differentially expressed ZRGs (DE-ZRGs) was screened from tumor and adjacent nontumor tissues and functional enrichment analysis was conducted out. A five-ZRG signature were constructed by univariate Cox regression, least absolute shrinkage and selection operator and multivariate Cox regression. Furthermore, we screened out independent prognosis-related factors to build a nomogram by univariate and multivariate Cox regression. Potential biological pathways of five ZRGs were analyzed by Gene Set Enrichment Analysis (GSEA). Then, we further quantitatively analyze immune infiltration and evaluate tumor microenvironment by single sample GSEA. Finally, drug sensitivity of ccRCC patients was analyzed by the Genomics of Drug Sensitivity in Cancer database. Results TRIM59, VAV3, ZNF189, AGAP9 and PYGO1 were screened to be significantly associated with the prognosis of ccRCC patients. Through incorporated risk score and clinical parameters, we constructed a nomogram, which showed a good prognostic performance for ccRCC patients.

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