scholarly journals Functional Assessment of Four Novel Immune-Related Biomarkers in the Pathogenesis of Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Daojun Lv ◽  
Xiangkun Wu ◽  
Ming Wang ◽  
Wenzhe Chen ◽  
Shuxin Yang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Rank Aggregation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Network Analyses

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma whose pathogenesis is not well understood. We aimed at identifying novel immune-related biomarkers that could be valuable in the diagnosis and prognosis of ccRCC.MethodsThe Robust Rank Aggregation (RRA) method was used to integrate differently expressed genes (DEGs) of 7 Gene Expression Omnibus (GEO) datasets and obtain robust DEGs. Weighted gene co-expression network analyses (WGCNA) were performed to identify hub genes associated with clinical traits in The Cancer Genome Atlas (TCGA) database. Comprehensive bioinformatic analyses were used to explore the role of hub genes in ccRCC.ResultsFour hub genes IFI16, LMNB1, RHBDF2 and TACC3 were screened by the RRA method and WGCNA. These genes were found to be up-regulated in ccRCC, an upregulation that could be due to their associations with late TNM stages and tumor grades. The Receiver Operating Characteristic (ROC) curve and Kaplan-Meier survival analysis showed that the four hub genes had great diagnostic and prognostic values for ccRCC, while Gene Set Enrichment Analysis (GSEA) showed that they were involved in immune signaling pathways. They were also found to be closely associated with multiple tumor-infiltrating lymphocytes and critical immune checkpoint expressions. The results of Quantitative Real-time PCR (qRT-PCR) and immunohistochemical staining (IHC) analysis were consistent with bioinformatics analysis results.ConclusionThe four hub genes were shown to have great diagnostic and prognostic values and played key roles in the tumor microenvironment of ccRCC.

scholarly journals NDUFA4L2 is associated with clear cell renal cell carcinoma malignancy and is regulated by ELK1

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4065 ◽  
Author(s):  
Lei Wang ◽  
Zhiqiang Peng ◽  
Kaizhen Wang ◽  
Yijun Qi ◽  
Ying Yang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Proliferation ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Clinical Samples ◽  
Cell Renal Cell Carcinoma

Background Clear cell renal cell carcinoma (ccRCC) is the most common and lethal cancer of the adult kidney. However, its pathogenesis has not been fully understood till now, which hinders the therapeutic development of ccRCC. NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 4-like 2 (NDUFA4L2) was found to be upregulated and play an important role in ccRCC. We aimed to further investigate the underlying mechanisms by which NDUFA4L2 exerted function and its expression level was upregulated. Methods The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data were mined to verify the change of NDUFA4L2 expression level in ccRCC tissues. The correlation between expression level of NDUFA4L2 and cell proliferation/apoptosis was explored by Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) network of NDUFA4L2 was constructed. Biological process and involved pathways of NDUFA4L2 were analyzed by gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The transcription factors (TFs) which can induce the expression of NDUFA4L2 were explored in clinical samples by correlation analysis and its regulation on the expression of NDUFA4L2 was verified by knockdown experiment. Results NDUFA4L2 was verified to be overexpressed in ccRCC tissues and its expression level was increased accordingly as the American Joint Committee on Cancer (AJCC) stage progressed. A high NDUFA4L2 level predicted the poor prognosis of ccRCC patients and correlated with enhanced cell proliferation and anti-apoptosis. NDUFA4L2 may interact with 14 tumor-related proteins, participate in growth and death processes and be involved in ccRCC-related pathways, such as insulin-like growth factor 1 (IGF-1), mammalian target of Rapamycin (mTOR) and phosphoinositide 3 kinase serine/threonine protein kinase (PI3K/AKT). ETS domain-containing protein ELK1 level positively correlated with the level of NDUFA4L2 in ccRCC tissues and ELK1 could regulate the expression of NDUFA4L2 in ccRCC cells. Discussion NDUFA4L2 upregulation was associated with ccRCC malignancy. NDUFA4L2 expression was regulated by ELK1 in ccRCC cells. Our study provided potential mechanisms by which NDUFA4L2 affected ccRCC occurrence and progression.

scholarly journals Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Chuanchuan Zhan ◽  
Zichu Wang ◽  
Chao Xu ◽  
Xiao Huang ◽  
Junzhou Su ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.

scholarly journals Immune Infiltration Landscape in Clear Cell Renal Cell Carcinoma Implications

2021 ◽  
Vol 10 ◽  
Author(s):  
Yongfeng Wang ◽  
Ci Yin ◽  
Lele Geng ◽  
Weiyang Cai
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Activity Level ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

The malignant phenotypes of cancer are defined not only by its intrinsic tumor cells but also by the tumor infiltrating immune cells (TIICs) recruited to the cancer microenvironment. Clear cell renal cell carcinoma (ccRCC) immune microenvironment plays an important role in the tumorigenesis. This research investigated the characteristics of immune cell invasion of renal cell carcinoma and provided clues for future clinical implementation. Retrospectively, ccRCC gene expression was analyzed with appropriate clinicopathological data from the Cancer Genome Atlas (TCGA) and GEO database up to December 2019. The CIBERSORT algorithm, meta-analysis, principal component analysis (PCA), Single-Sample Gene Set Enrichment Analysis (ssGSEA) and hierarchical agglomerative clustering were used to measure and evaluate the respective proportions of 22 cell types of immune infiltration using normalized gene expression data. We also focused on evaluating the association with TIICs subpopulations and clinical features and molecular subtypes. TIICs subpopulation, especially Macrophages subgroup, T follicular helper (Tfh) cells and CD8 T cells, all contribute to tumorigenesis. Unsupervised clustering analysis revealed that there existed two distinct TIICs subgroups with different survival patterns. TIICs are extensively involved in the pathogenesis and development of the ccRCC. Characterizing the composition of TIICs influences the metabolism of tumors, activity, level, stage, and survival of patients. Collectively, the TIIC analysis has the potential to assist in the assessment and selection of ccRCC prognosis and treatment.

scholarly journals Pyroptosis Regulators and Tumor Microenvironment Infiltration Characterization in Clear Cell Renal Cell Carcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Xi Zhang ◽  
Xiyi Wei ◽  
Yichun Wang ◽  
Shuai Wang ◽  
Chengjian Ji ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Set Enrichment ◽  
Cell Renal Cell Carcinoma ◽  
Gene Set

BackgroundIt is well known that chronic inflammation can promote the occurrence and progression of cancer. As a type of proinflammatory death, pyroptosis can recast a suitable microenvironment to promote tumor growth. However, the potential role of pyroptosis in clear cell renal cell carcinoma (ccRCC) remains unclear.MethodsThe transcriptome expression profile and mutation profile data of ccRCC with clinical characteristics included in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Consensus clustering was used for clustering. Gene set enrichment analysis (GSEA) analysis were applied to evaluate the biological mechanisms. Single sample gene set enrichment analysis (ssGSEA) was applied for evaluating the proportion of various immune infiltrating cells. The ESTIMATE algorithm was involved to compute the immune microenvironment scores.ResultsAmong the 17 pyroptosis regulators, a total of 15 pyroptosis regulators were differential expressed between tumor and normal tissues, in which 12 of them emerged strong correlations with prognoses. According to the pyroptosis components, the ccRCC patients were divided into four pyroptosis subtypes with different clinical, molecular, and pathway characteristics. Compared with other clusters, cluster B showed the pyroptosis heat phenotype, while cluster D represented the pyroptosis cold phenotype with poor overall survival. In addition, we performed principal component analysis (PCA) on the differential genes between clusters to construct the pyroptosis index. Furthermore, the pyroptosis index was significantly correlated with survival in different tumor mutation statuses and different grades and stages. Besides, the expression of pyroptosis-related regulators was related to the infiltration of immune cells and the expression of immune checkpoints, among which AIM2 was considered as the most significant immune-related pyroptosis regulator. Ultimately, we found that AIM2 was related to the immune activation pathway and was significantly overexpressed in tumor tissues.ConclusionThis study revealed that pyroptosis regulators and pyroptosis index played an important role in the development and prognoses of ccRCC. Moreover, AIM2 can be used as a predictor of the response of immunotherapy. Assessing the pyroptosis patterns may help evaluate the tumor status and guide immunotherapy strategies.

scholarly journals Analyzing and validating the prognostic value and immune microenvironment of clear cell renal cell carcinoma

2021 ◽  
Author(s):  
Jingwei Ke ◽  
Jie Chen ◽  
Xin Liu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Significant Difference

Abstract Background: There is still controversy regarding immunotherapy biomarkers. Therefore, we aimed to identify prognostic biomarkers related to immunotherapy for clear cell renal cell carcinoma (ccRCC).Methods: Fragments Per Kilobase Million (FPKM) data and clinical characteristics for ccRCC patients from The Cancer Genome Atlas (TCGA) database were downloaded. Unsupervised consensus clustering analysis was performed to divide patients into different immune subgroups according to their single-sample gene set enrichment analysis (ssGSEA) scores. Then, we validated the differences in immune cell infiltration, prognosis, clinical characteristics and expression levels of HLA and immune checkpoint genes between different immune subgroups. Weighted gene coexpression network analysis (WGCNA) was used to identify the significant modules and hub genes that were related to the immune subgroups. A nomogram was established to predict the overall survival (OS) outcomes after independent prognostic factors were identified by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses.Results: Five clusters (immune subgroups) were identified. There was no significant difference in age, sex or N stage. And there were significant differences in race, T stage, M stage, grade, prognosis and tumor microenvironment. HLA gene families and CTLA4 showed significant differences between the five clusters, while PD1 and PDL1 did not. The red module was significant, and 14 hub genes were obtained. In addition, the nomogram containing LAG3 and GZMK accurately predicted OS outcomes of ccRCC patients.Conclusion: LAG3 and GZMK are strongly related to immunity and may provide suggestions for ccRCC immunotherapy.

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

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao Huang ◽  
Ling Zhu ◽  
Chao Huang ◽  
Yi Dong ◽  
Liangliang Fan ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Clear Cell ◽  
Single Gene ◽  
Gene Set Enrichment Analysis ◽  
Cancer Type ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma

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.

scholarly journals Bioinformatics analysis of C3 and CXCR4 demonstrates their potential as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Quan ◽  
Yuchen Bai ◽  
Yunbei Yang ◽  
Er Lei Han ◽  
Hong Bai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Therapeutic Targets ◽  
Disease Free Survival ◽  
Prognostic Biomarkers ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Upregulated Genes

Abstract Background The molecular prognostic biomarkers of clear cell renal cell carcinoma (ccRCC) are still unknown. We aimed at researching the candidate biomarkers and potential therapeutic targets of ccRCC. Methods Three ccRCC expression microarray datasets (include GSE14762, GSE66270 and GSE53757) were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) between ccRCC and normal tissues were explored. The potential functions of identified DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). And then the protein - protein interaction network (PPI) was established to screen the hub genes. After that, the expressions of hub genes were identified by the oncomine database. The hub genes’ prognostic values of patients with ccRCC were analyzed by GEPIA database. Results A total of 137 DEGs were identified by utilizing the limma package and RRA method, including 63 upregulated genes and 74 downregulated genes. It is found that 137 DEGs were mainly enriched in 82 functional terms and 24 pathways in accordance with the research results. Thirteen highest-scoring genes were screened as hub genes (include 10 upregulated genes and 3 downregulated candidate genes) by utilizing the PPI network and module analysis. Through integrating the oncoming database and GEPIA database, the author found that C3 and CXCR4 are not only overexpressed in ccRCC, but also associated with the prognosis of ccRCC. Further results could reveal that patients with high C3 expression had a poor overall survival (OS), while patients with high CTSS and TLR3 expressions had a good OS; patients with high C3 and CXCR4 expressions had a poor disease-free survival (DFS), while ccRCC patients with high TLR3 expression had a good DFS. Conclusion These findings suggested that C3 and CXCR4 were the candidate biomarkers and potential therapeutic targets of ccRCC patients.

scholarly journals Identification of Six Potentially Long Noncoding RNAs as Biomarkers Involved Competitive Endogenous RNA in Clear Cell Renal Cell Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Kang Yang ◽  
Xiao-fan Lu ◽  
Peng-cheng Luo ◽  
Jie Zhang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cerna Network

Background. Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma (RCC), usually is representative of metastatic heterogeneous neoplasm that links with poor prognosis, but the pathogenesis of ccRCC remains unclear. Currently, numerous evidences prove that long noncoding RNAs (lncRNAs) are considered as competing endogenous RNA (ceRNA) to participate in cellular processes of tumors. Therefore, to investigate the underlying mechanisms of ccRCC, the expression profiles of lncRNAs, miRNAs, and mRNAs were downloaded from the Cancer Genome Atlas (TCGA) database. A total of 1526 differentially expressed lncRNAs (DElncRNAs), 54 DEmiRNAs, and 2352 DEmRNAs were identified. To determine the connection of them, all DElncRNAs were input to the miRcode database. The results indicated that 85 DElncRNAs could connect with 9 DEmiRNAs in relation to our study. Then, databases of TargetScan and miRDB were used to search for targeted genes with reference to DEmiRNAs. The results showed that 203 out of 2352 targeted genes were identified in our TCGA set. Subsequently, ceRNA network was constructed according to Cytoscape and the targeted genes were functionally analyzed to elucidate the mechanisms of DEmRNAs. The results of survival analysis and regression analysis indicated that 6 DElncRNAs named COL18A1-AS1, WT1-AS, LINC00443, TCL6, AL356356.1, and SLC25A5-AS1 were significantly correlative with the clinical traits of ccRCC patients and could be served as predictors for ccRCC. Finally, these findings were validated by quantitative RT-PCR (qRT-PCR). Based on these discoveries, we believe that this identified ceRNA network will provide a novel perspective to elucidate ccRCC pathogenesis.

scholarly journals COL6A2/3 can serve as potential therapeutic targets and prognostic biomarkers for clear cell renal cell carcinoma

2020 ◽  
Author(s):  
Wingkeung Yiu ◽  
Can-Xuan Li ◽  
Jie Chen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Mrna Expression ◽  
Renal Cell ◽  
Clear Cell ◽  
Therapeutic Targets ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Biomarkers ◽  
Cell Renal Cell Carcinoma ◽  
Tumorigenesis And Progression

Abstract Background: Growing evidence has shown that the type VI collagen alpha chain (COL6A) family involved in the tumorigenesis and progression of diverse malignancies; however, its biological roles and potential mechanisms in clear cell renal cell carcinoma (ccRCC) remain unknown. The study was designed to explore the potential mechanisms and functions of COL6As in ccRCC.Methods: ONCOMINE and GEPIA databases were used to compare the transcriptional expression data of COL6As in ccRCC samples and normal renal samples. UALCAN database was utilized to determine the association between clinicopathological features and COL6As expression. Kaplan–Meier method was employed to determine the prognostic value of COL6As mRNA expression in ccRCC. CBioPortal database was used to investigate the genetic alterations of COL6As in ccRCC. Co-expression analyses, functional enrichment analyses, and gene set enrichment analysis (GSEA) were utilized to explore the potential action mechanisms of COL6As in ccRCC. Finally, we estimated the relationship between COL6As expression with immune cell infiltrates.Results: Upregulated transcriptional COL6A2/COL6A3 expression was observed in ccRCC specimens by comparison with noncancerous renal specimens. Patients with increased COL6A2/COL6A3 mRNA expression have a poor clinical outcome and unfavorable prognosis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA analyses showed that COL6A2/COL6A3 might promote the tumorigenesis and progression of ccRCC by involving in several cancer-related pathways, such as axon guidance, focal adhesion, ECM receptor interaction. Besides, we found that COL6A2/COL6A3 expression was significantly associated with immune infiltration levels in ccRCC.Conclusions: COL6A2 and COL6A3 could act as candidate prognostic biomarkers and therapeutic targets in ccRCC. However, further experimental work was required to validate the conclusions.

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