scholarly journals Candidate Gene and MicroRNA Biomarkers of Metastatic Renal Cell Carcinoma (MRCC) Response to Sunitinib

2022 ◽  
Author(s):  
qiwei yang ◽  
wei yang ◽  
yijun tian ◽  
da xu ◽  
chuanmin chu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Renal Cancer ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Differentially Expressed ◽  
Sphingolipid Metabolism ◽  
Microrna Target ◽  
Metastatic Renal Cancer

Abstract Backgrounds: The incidence of renal cancer is relatively insidious, and some patients have been metastatic renal cancer at the initial visit. Sunitinib is the first-line systemic therapy for patients with metastatic renal cell carcinoma, however, there is scant analysis of its effect on genes and microRNAs.Methods: In this study, 8 differentially expressed microRNAs and 112 differentially expressed genes were designated by analyzing mRNA and microRNA data sets and weighted correlation network analysis (WGCNA).Results: NIPSNAP1 gene showed the most co-expression with other genes. Through the intersection of the microRNA target gene with our differentially expressed genes, we got 26 genes. KEGG and GO analysis showed that these genes were predominantly concentrated in Pathways in cancer, Sphingolipid metabolism and Glycosaminoglycan degradation. After we set the 26 genes and gene of WGCNA do intersection, received six genes, respectively is NIPSNAP1, SDC4, TBC1D9, NEU1, STK40 and PLAUR. Conclusion: Through subsequent cell, molecular and flow cytometry experiments, we found the PLAUR would play a crucial role in renal cell carcinoma (RCC) resistant to sunitinib, which will be available for new ideas to forecast sunitinib resistance and reverse sunitinib resistance.

scholarly journals Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8096 ◽  
Author(s):  
Haiping Zhang ◽  
Jian Zou ◽  
Ying Yin ◽  
Bo Zhang ◽  
Yaling Hu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Renal Cell ◽  
Molecular Mechanisms ◽  
Clear Cell ◽  
Clinical Stage ◽  
Stage I ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma

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.

scholarly journals Identification and Classification of Differentially Expressed Genes in Renal Cell Carcinoma by Expression Profiling on a Global Human 31,500-Element cDNA Array

2001 ◽  
Vol 11 (11) ◽  
pp. 1861-1870 ◽  
Author(s):  
Judith M. Boer ◽  
Wolfgang K. Huber ◽  
Holger Sültmann ◽  
Friederike Wilmer ◽  
Anja von Heydebreck ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Renal Cell ◽  
Cdna Array ◽  
Differentially Expressed

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

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e78452 ◽  
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

scholarly journals Expression of AOX1 Predicts Prognosis of Clear Cell Renal Cell Carcinoma

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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