Identification of differentially expressed genes in clear cell renal cell
carcinoma by analysis of full-length enriched cDNA library

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

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Genome-Wide Analysis of Differentially Expressed Genes and Splicing Isoforms in Clear Cell Renal Cell Carcinoma

PLoS ONE ◽

10.1371/journal.pone.0078452 ◽

2013 ◽

Vol 8 (10) ◽

pp. e78452 ◽

Cited By ~ 11

Author(s):

Alessio Valletti ◽

Margherita Gigante ◽

Orazio Palumbo ◽

Massimo Carella ◽

Chiara Divella ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Differentially Expressed Genes ◽

Renal Cell ◽

Clear Cell ◽

Differentially Expressed ◽

Cell Renal Cell Carcinoma ◽

Genome Wide Analysis ◽

Genome Wide ◽

Splicing Isoforms

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Expression of AOX1 Predicts Prognosis of Clear Cell Renal Cell Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.683173 ◽

2021 ◽

Vol 12 ◽

Author(s):

Luyang Xiong ◽

Yuchen Feng ◽

Wei Hu ◽

Jiahong Tan ◽

Shusheng Li ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Differentially Expressed Genes ◽

Renal Cell ◽

Clear Cell ◽

Metabolic Reprogramming ◽

Differentially Expressed ◽

Normal Kidney ◽

Cell Renal Cell Carcinoma ◽

Cancer Management

Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer worldwide, and appropriate cancer biomarkers facilitate early diagnosis, treatment, and prognosis prediction in cancer management. However, an accurate biomarker for ccRCC is lacking. This study identified 356 differentially expressed genes in ccRCC tissues compared with normal kidney tissues by integrative analysis of eight ccRCC datasets. Enrichment analysis of the differentially expressed genes unveiled improved adaptation to hypoxia and metabolic reprogramming of the tumor cells. Aldehyde oxidase 1 (AOX1) gene was identified as a biomarker for ccRCC among all the differentially expressed genes. ccRCC tissues expressed significantly lower AOX1 than normal kidney tissues, which was further validated by immunohistochemistry at the protein level and The Cancer Genome Atlas (TCGA) data mining at the mRNA level. Higher AOX1 expression predicted better overall survival in ccRCC patients. Furthermore, AOX1 DNA copy number deletion and hypermethylation were negatively correlated with AOX1 expression, which might be the potential mechanism for its dysregulation in ccRCC. Finally, we illustrated that the effect of AOX1 as a tumor suppressor gene is not restricted to ccRCC but universally exists in many other cancer types. Hence, AOX1 may act as a potential prognostic biomarker and therapeutic target for ccRCC.

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Differentially Expressed Genes in Clear Cell Renal Cell Carcinoma as a Potential Marker for Prognostic and Immune Signatures

Frontiers in Oncology ◽

10.3389/fonc.2021.776824 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ying Tong ◽

Yiwen Yu ◽

Hui Zheng ◽

Yanchun Wang ◽

Suhong Xie ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Differentially Expressed Genes ◽

Renal Cell ◽

Clear Cell ◽

Differentially Expressed ◽

High Expression ◽

Molecular Networks ◽

Cell Renal Cell Carcinoma ◽

Potential Marker

Clear cell renal cell carcinoma (ccRCC) is characterized by the inactivation of the von Hippel–Lindau (VHL) gene. Of note, no other gene is mutated as frequently as VHL in ccRCC, turning out that patients with inactivated VHL constitute the majority of ccRCC-related character. Thus, differentially expressed genes (DEGs) and their molecular networks caused by VHL mutation were considered as important factors for influencing the prognosis of ccRCC. Here, we first screened out six DEGs (GSTA1, GSTA2, NAT8, FABP7, SLC17A3, and SLC17A4) which downregulated in ccRCC patients with VHL non-mutation than with the mutation. Generally, most DEGs with high expression were associated with a favorable prognosis and low-risk score. Meanwhile, we spotted transcription factors and their kinases as hubs of DEGs. Finally, we clustered ccRCC patients into three subgroups according to the expression of hub proteins, and analyzed these subgroups with clinical profile, outcome, immune infiltration, and potential Immune checkpoint blockade (ICB) response. Herein, DEGs might be a promising biomarker panel for immunotherapy and prognosis in ccRCC. Moreover, the ccRCC subtype associated with high expression of hubs fit better for ICB therapy.

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Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/6699808 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Yan Zhang ◽

Mingying Chen ◽

Meihong Liu ◽

Yingkun Xu ◽

Guangzhen Wu

Keyword(s):

Renal Cell Carcinoma ◽

Overall Survival ◽

Cell Carcinoma ◽

Predictive Model ◽

Differentially Expressed Genes ◽

Renal Cell ◽

Drug Sensitivity ◽

Clear Cell ◽

Differentially Expressed ◽

Cell Renal Cell Carcinoma

Metabolic rearrangement is a marker of cancer that has been widely studied in recent years. One of the major metabolic characteristics of tumor cells is the high levels of glycolysis, even under aerobic conditions, a phenomenon that is called the “Warburg effect.” We investigated the expression and copy number variation (CNV) frequency of all glycolysis-related genes in multiple cancer types and found many differentially expressed genes, particularly in clear cell renal cell carcinoma (ccRCC). Single nucleotide variants (SNVs) showed that the overall average mutation frequency for all genes was low. The purpose of this study was to establish a predictive model by studying glycolysis-related genes in ccRCC. We compared the expression of glycolysis-related genes in 539 ccRCC tissues and 72 normal renal tissues from The Cancer Genome Atlas dataset and identified 17 upregulated and 26 downregulated genes. Pathway analysis revealed that PSAT1 and SDHB could activate the cell cycle, RPIA could activate the DNA damage response, and HK3 could activate apoptosis and EMT signaling, while PDK2 could inhibit apoptosis. The results of the drug sensitivity analysis suggested that some of these differentially expressed genes were positively correlated with drug sensitivity. Thirteen genes were selected from the gene coexpression network and the LASSO regression analysis. The Kaplan-Meier overall survival curves showed that the expression of upregulated genes in ccRCC patients was associated with lower overall survival. We established a predictive model consisting of 13 genes (RPIA, G6PD, PSAT1, ENO2, HK3, IDH1, PDK4, PGM2, PGK1, FBP1, OGDH, SUCLA2, and SUCLG2). This predictive model correlated well with the development and progression of ccRCC. Thus, it is of great value in the diagnosis and prognostic evaluation of ccRCC and may aid the identification of potential prognostic biomarkers and drug targets.

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Proteomic analysis in clear cell renal cell carcinoma: identification of differentially expressed protein by 2-D DIGE

Molecular BioSystems ◽

10.1039/c2mb05390j ◽

2012 ◽

Vol 8 (4) ◽

pp. 1040 ◽

Cited By ~ 21

Author(s):

Francesca Raimondo ◽

Claudia Salemi ◽

Clizia Chinello ◽

Daniela Fumagalli ◽

Lavinia Morosi ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Proteomic Analysis ◽

Renal Cell ◽

Clear Cell ◽

Differentially Expressed ◽

Cell Renal Cell Carcinoma ◽

Differentially Expressed Protein

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Transcription Factors BARX1 and DLX4 Contribute to Progression of Clear Cell Renal Cell Carcinoma via Promoting Proliferation and Epithelial–Mesenchymal Transition

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.626328 ◽

2021 ◽

Vol 8 ◽

Author(s):

Guoliang Sun ◽

Yue Ge ◽

Yangjun Zhang ◽

Libin Yan ◽

Xiaoliang Wu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Transcription Factors ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Epithelial Mesenchymal Transition ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Cell Renal Cell Carcinoma ◽

Mesenchymal Transition

Dysregulation of transcription factors contributes to the carcinogenesis and progression of cancers. However, their roles in clear cell renal cell carcinoma remain largely unknown. This study aimed to evaluate the clinical significance of TFs and investigate their potential molecular mechanisms in ccRCC. Data were accessed from the cancer genome atlas kidney clear cell carcinoma cohort. Bioinformatics algorithm was used in copy number alterations mutations, and differentially expressed TFs’ analysis. Univariate and multivariate Cox regression analyses were performed to identify clinically significant TFs and construct a six-TF prognostic panel. TFs’ expression was validated in human tissues. Gene set enrichment analysis (GSEA) was utilized to find enriched cancer hallmark pathways. Functional experiments were conducted to verify the cancer-promoting effect of BARX homeobox 1 (BARX1) and distal-less homeobox 4 (DLX4) in ccRCC, and Western blot was performed to explore their downstream pathways. As for results, many CNAs and mutations were identified in transcription factor genes. TFs were differentially expressed in ccRCC. An applicable predictive panel of six-TF genes was constructed to predict the overall survival for ccRCC patients, and its diagnostic efficiency was evaluated by the area under the curve (AUC). BARX1 and DLX4 were associated with poor prognosis, and they could promote the proliferation and migration of ccRCC. In conclusion, the six-TF panel can be used as a prognostic biomarker for ccRCC patients. BARX1 and DLX4 play oncogenic roles in ccRCC via promoting proliferation and epithelial–mesenchymal transition. They have the potential to be novel therapeutic targets for ccRCC.

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Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

Journal of Oncology ◽

10.1155/2021/2042114 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Yusa Chen ◽

Yumei Liang ◽

Ying Chen ◽

Shaxi Ouyang ◽

Kanghan Liu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Random Forest ◽

Cell Carcinoma ◽

Differentially Expressed Genes ◽

Renal Cell ◽

Multivariate Regression ◽

Clinical Characteristics ◽

Clear Cell ◽

Cell Renal Cell Carcinoma ◽

Immune Infiltration

Background. Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients. Methods. The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics. Results. According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (MDK, SLC1A1, SGCB, C4orf3, MALAT1, PILRB, IGHG1, FZD1, IFITM1, MUC20, KRT80, and SALL1) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage. Conclusion. We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.

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FCER1G Positively Relates To Macrophage Infiltration In Clear Cell Renal Cell Carcinoma And Contributes To Unfavorable Prognosis By Regulating Tumor Immunity

10.21203/rs.3.rs-665259/v1 ◽

2021 ◽

Author(s):

Keqin Dong ◽

Wenjin Chen ◽

Xiuwu Pan ◽

Hongru Wang ◽

Ye Sun ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

T Cell ◽

Differentially Expressed Genes ◽

Renal Cell ◽

Clear Cell ◽

Immunohistochemical Analysis ◽

Differentially Expressed ◽

High Expression ◽

Cell Renal Cell Carcinoma ◽

Tumor Tissues

Abstract Background: Tumor-associated macrophages (TAMs) are closely related to unfavorable prognosis of patients with clear cell renal cell carcinoma (ccRCC). However, the important molecules in the interaction between ccRCC and TAMs are unclear.Methods: TCGA-KIRC gene expression data of tumor tissues and normal tissues adjacent to tumor were compared to identify differentially expressed genes in ccRCC. TAMs related genes were discovered by analyzing the correlation between these differentially expressed genes and common macrophage biomarkers. Gene set enrichment analysis was performed to predict functions of TAMs related gene. The findings were further validated using RNA sequencing data obtained from the CheckMate 025 study and immunohistochemical analysis of samples from 350 patients with ccRCC. Kaplan–Meier survival curve, Cox regression analysis and Harrell’s concordance index analysis were used to determine the prognostic significance.Results: In this study, we applied bioinformatic analysis to explore TAMs related differentially expressed genes in ccRCC and identified 5 genes strongly correlated with all selected macrophage biomarkers: STAC3, LGALS9, TREM2, FCER1G, and PILRA. Among them, FCER1G was abundantly expressed in tumor tissues and showed prognostic importance in patients with ccRCC who received treatment with Nivolumab; however, it did not exhibit prognostic value in those treated with Everolimus. We also discovered that high expression levels of FCER1G are related to T cell suppression. Moreover, combination of FCER1G and macrophage biomarker CD68 can improve the prognostic stratification of patients with ccRCC from TCGA-KIRC. Based on the immunohistochemical analysis of samples from patients with ccRCC, we further validated that FCER1G and CD68 are both highly expressed in tumor tissue and correlate with each other. Higher expression of CD68 or FCER1G in ccRCC tissue indicates shorter overall survival and progression-free survival; patients with high expression of both CD68 and FCER1G have the worst outcome. Combining CD68 and FCER1G facilitates the screening of patients with a worse prognosis from the same TNM stage group.Conclusions: High expression of FCER1G in ccRCC is closely related to TAMs infiltration and suppression of T cell function. Combining the expression levels of FCER1G and macrophage biomarker CD68 may be a promising postoperative prognostic index for patients with ccRCC.

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