Abstract
BackgroundClear cell renal cell carcinoma (ccRCC) is a urinary disease with high incidence. The high incidence of metastasis is the leading cause of death in patients with ccRCC. This study was aimed to identify the gene signatures during the metastasis of ccRCC.MethodsTwo datasets, including one gene expression profile dataset and one microRNA (miRNA) expression profile dataset, were downloaded from Gene Expression Omnibus (GEO) database. The integrated bioinformatics analysis was performed using the (limma) R package, miRWalk, DAVID, STRING, Kaplan-Meier plotter databases. Quantitative real-time polymerase chain reaction (qPCR) was conducted to validate the expression of differentially expressed genes (DEGs) and DE-miRNAs.ResultsIn total, 84 DEGs (68 up-regulated and 16 down-regulated) and 41 DE-miRNAs (24 up-regulated and 17 down-regulated) were screened from GSE22541 and GSE37989 datasets, respectively. Furthermore, 11 hub genes and 3 key miRNAs were identified from the PPI network, including FBLN1, THBS2, SCGB1A1, NKX2-1, COL11A1, DCN, LUM, COL1A1, COL6A3, SFTPC, SFTPB, miR-328, miR-502, and miR-504. The qPCR data showed that most of the selected genes and miRNAs were consistent with that in our integrated analysis. A novel mRNA-miRNA network, SFTPB-miR-328-miR-502-miR-504-NKX2-1 was found in metastatic ccRCC after the combination of data from expression, survival analysis, and experiment validation.ConclusionIn conclusion, key candidate genes and miRNAs were identified and a novel mRNA-miRNA network was constructed in ccRCC metastasis using integrated bioinformatics analysis and qPCR validation, which might be utilized as diagnostic biomarkers and molecular targets of metastatic ccRCC.
Highlights
Renal cell carcinoma (RCC) is a common cancer worldwide, representing approximately 2–3% of all malignant tumors in adults
To search the potential differential expression genes (DEGs) and differential expression miRNAs (DE-miRNAs) in metastatic Clear cell RCC (ccRCC) tissues compared with primary ccRCC tissues, we conducted a data mining in Gene Expression Omnibus (GEO) database and two datasets (GSE22541 for mRNAs, GSE37989 for miRNAs) were included in our study
84 significant DEGs were found in GSE22541 dataset with 68 up-regulated and 16 down-regulated genes, and 41 significant DE-miRNAs were obtained in GSE37989 dataset with 24 up-regulated and 17 down-regulated miRNAs (Supplementary Tables 2–4)
Summary
Renal cell carcinoma (RCC) is a common cancer worldwide, representing approximately 2–3% of all malignant tumors in adults. It was reported that approximately 63,990 new cases of RCC were diagnosed and more that 14,400 kidney cancer related deaths were found in the United States in 2017 [1, 2]. In China, RCC has a rising incidence with about 68,300 new cases of RCC and 25,600 kidney cancer related deaths in 2014 [3]. Metastasis of ccRCC is the cause of its high incidence and poor prognosis. More than 80% of patients survived less than 5 years after the diagnosis of distant metastasis [4, 7]. Clear cell renal cell carcinoma (ccRCC) is a urinary disease with high incidence. The high incidence of metastasis is the leading cause of death in patients with ccRCC. This study was aimed to identify the gene signatures during the metastasis of ccRCC
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