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

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.

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

10.21203/rs.3.rs-119445/v1 ◽

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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Identification of prognostic related hub genes in clear-cell renal cell carcinoma via bioinformatical analysis

Chinese Medical Sciences Journal ◽

10.24920/003651 ◽

2019 ◽

Vol 0 (0) ◽

pp. 1

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Hub Genes ◽

Cell Renal Cell Carcinoma ◽

Bioinformatical Analysis

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Downregulation of the lncRNA ASB16-AS1 Decreases LARP1 Expression and Promotes Clear Cell Renal Cell Carcinoma Progression via miR-185-5p/miR-214-3p

Frontiers in Oncology ◽

10.3389/fonc.2020.617105 ◽

2021 ◽

Vol 10 ◽

Author(s):

Mingzi Li ◽

Bingde Yin ◽

Mulin Chen ◽

Jingtao Peng ◽

Xinyu Mu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Noncoding Rna ◽

Clear Cell ◽

Therapeutic Targets ◽

Luciferase Reporter ◽

Advanced Tumor Stage ◽

Cell Renal Cell Carcinoma ◽

Advanced Tumor

Clear cell renal cell carcinoma (ccRCC) comprises approximately 75% of renal cell carcinomas, which is one of the most common and lethal urologic cancers, with poor quality of life for patients and is a huge economic burden to health care systems. It is imperative we find novel prognostic and therapeutic targets for ccRCC clinical intervention. In this study, we found that the expression of the long noncoding RNA (lncRNA) ASB16-AS1 was downregulated in ccRCC tissues compared with non-diseased tissues and was also associated with advanced tumor stage and larger tumors. By constructing cell and mouse models, it was found that downregulated lncRNA ASB16-AS1 enhanced cell proliferation, migration, invasion, and promoted tumor growth and metastasis. Furthermore, by performing bioinformatics analysis, biotinylated RNA pull-downs, AGO2-RIP, and luciferase reporter assays, our findings showed that downregulated ASB16-AS1 decreased La-related protein 1 (LARP1) expression by inhibiting miR-185-5p and miR-214-3p. Furthermore, it was found that overexpression of LARP1 reversed the promotive effects of downregulated ASB16-AS1 on ccRCC cellular progression. Our results revealed that downregulated ASB16-AS1 promotes ccRCC progression via a miR-185-5p-miR-214-3p-LARP1 pathway. We suggest that this pathway could be used to monitor prognosis and presents therapeutic targets for ccRCC clinical management.

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

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.621618 ◽

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.

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Corrigendum to “The SLC Family Are Candidate Diagnostic and Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma”

BioMed Research International ◽

10.1155/2020/1025178 ◽

2020 ◽

Vol 2020 ◽

pp. 1-1

Author(s):

Weiting Kang ◽

Meng Zhang ◽

Qiang Wang ◽

Da Gu ◽

Zhilong Huang ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Prognostic Biomarkers ◽

Cell Renal Cell Carcinoma

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Comprehensive landscape of immune-checkpoints uncovered in clear cell renal cell carcinoma reveals new and emerging therapeutic targets

Cancer Immunology Immunotherapy ◽

10.1007/s00262-020-02530-x ◽

2020 ◽

Vol 69 (7) ◽

pp. 1237-1252 ◽

Cited By ~ 1

Author(s):

Diana Tronik-Le Roux ◽

Mathilde Sautreuil ◽

Mahmoud Bentriou ◽

Jérôme Vérine ◽

Maria Belén Palma ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Therapeutic Targets ◽

Immune Checkpoints ◽

Cell Renal Cell Carcinoma

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Identification of Four Pathological Stage-Relevant Genes in Association with Progression and Prognosis in Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

BioMed Research International ◽

10.1155/2020/2137319 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Dengyong Xu ◽

Yuzi Xu ◽

Yiming Lv ◽

Fei Wu ◽

Yunlong Liu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Early Diagnosis ◽

Cell Carcinoma ◽

Renal Cell ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Clear Cell ◽

Clinical Stage ◽

Hub Genes ◽

Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a major histological subtype of renal cell carcinoma and can be clinically divided into four stages according to the TNM criteria. Identifying clinical stage-related genes is beneficial for improving the early diagnosis and prognosis of ccRCC. By using bioinformatics analysis, we aim to identify clinical stage-relevant genes that are significantly associated with the development of ccRCC. First, we analyzed the gene expression microarray data sets: GSE53757 and GSE73731. We divided these data into five groups by staging information—normal tissue and ccRCC stages I, II, III, and IV—and eventually identified 500 differentially expressed genes (DEGs). To obtain precise stage-relevant genes, we subsequently applied weighted gene coexpression network analysis (WGCNA) to the GSE73731 dataset and KIRC data from The Cancer Genome Atlas (TCGA). Two modules from each dataset were identified to be related to the tumor TNM stage. Several genes with high inner connection inside the modules were considered hub genes. The intersection results between hub genes of key modules and 500 DEGs revealed UBE2C, BUB1B, RRM2, and TPX2 as highly associated with the stage of ccRCC. In addition, the candidate genes were validated at both the RNA expression level and the protein level. Survival analysis also showed that 4 genes were significantly correlated with overall survival. In conclusion, our study affords a deeper understanding of the molecular mechanisms associated with the development of ccRCC and provides potential biomarkers for early diagnosis and individualized treatment for patients at different stages of ccRCC.

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