Bioinformatic analysis identifying FGF1 gene as a new prognostic indicator in clear cell Renal Cell Carcinoma

Abstract

Background Clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current clinical treatment situation is still challenging considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease Methods Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein–protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function of the gene and signaling mechanism of gene regulating ccRCC development was preliminary explored. Results Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed with ccRCC tissue microarray which contained 104 local samples and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan–Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data. Conclusions Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which statistical significantly correlated with patients overall survival, and the gene’s clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use.

Identification S100A9 as a potential biomarker in neuroblastoma

“…The expression levels of TAC1, PTGS2 and FGF1 in resected specimen of NB patients with or without metastasis were then validated by qRT-PCR. Although the expression of TAC1, PTGS2 and FGF1 were related with many kinds of tumorigenesis, such as non-small cell lung cancer, pancreatic ductal cancer, colorectal cancer, squamous cell carcinoma, gastric cancer and clear cell renal cell carcinoma [26][27][28][29][30][31][32][33], but these genes expression did not exhibit significant change as expected as the microarray results, indicating that these three genes may not the pivotal gene that participate in the metastasis of NB.…”

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

“…The GEO database is one of the most common public databases used by researchers worldwide to explore genetic abnormalities in various cancers [ 29 – 32 ]. In this study, we first selected four different cDNA expression profiles, GSE121248, GSE101685, GSE85598, and GSE62232, from the GEO database to analyze the DEGs in HCC compared with normal liver tissues and screened 145 genes (including 42 upregulated and 153 downregulated genes).…”

“…Herein, GSEA results showed that MAD2L1 was associated with DNA repair, G2M checkpoint, p53 signaling pathway, PI3K/AKT/mTOR signaling pathway, and Wnt/ β -catenin signaling pathway in cancer. It is widely acknowledged that DNA replication ensures that cellular genetic information is accurately copied and correctly transmitted to offspring cells [ 32 , 40 , 41 ]. However, DNA replication is prone to interference and damage under various pressures in the body, leading to stagnant DNA replication, affecting genome stability, and even inducing apoptosis [ 42 ], necrosis [ 43 ], and carcinogenesis [ 44 ].…”

Bioinformatic Analysis Identifying S100a9 Associated With Cell Proliferation and Metastasis in Neuroblastoma

“…Dysregulated TAC1, PTGS2 and FGF1 were the top 3 outstanding genes based on both six methods (Closeness, Degree, EPC, MNC, Radiality, and Stress centrality) evaluation. The expression levels of TAC1, PTGS2 and FGF1 in resected specimen of NB patients with or without metastasis were then validated by qRT-PCR, although the expression of TAC1, PTGS2 and FGF1 were related with many kinds of tumorigenesis, such as non-small cell lung cancer [22], pancreatic ductal cancer [23], colorectal cancer [24][25][26], squamous cell carcinoma [27], gastric cancer[28] and clear cell renal cell carcinoma [29] ,but these genes expression did not exhibit signi cant change as expected as the microarray results, indicating that these three genes may not the pivotal gene that participate in the metastasis of NB.…”