Dataset Information

Identifying a Novel Biomarker TOP2A of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis.

ABSTRACT: Although it is well known that smoking is one of pathogenesis of clear cell renal cell carcinoma (ccRCC), the underlying molecular mechanism is still unclear. In our study, the microarray dataset GSE46699 is analyzed by weighted gene co-expression network analysis (WGCNA). Then we identify 15 co-expressed gene modules in which the lightcyan module (R2 = 0.30) is the most significant. Combined with the protein-protein interaction (PPI) network and WGCNA, two hub genes are identified. Meanwhile, linear regression analyses indicate that TOP2A has a higher connection with smoking in ccRCC, survival analysis proved that overexpression of TOP2A in ccRCC could lead to shorter survival time. Furthermore, bioinformatical analyses based on GSE46699 and GSE2109 as well as qRT-PCR experiment show similar results that TOP2A is significantly up-regulated in smoking ccRCC compared to non-smoking ccRCC samples. In addition, Functional analysis, pathway enrichment analysis and gene set enrichment analysis (GSEA) indicate that high expression of TOP2A is related to cell cycle and p53 signaling pathway in ccRCC samples. Moreover, in vitro experiments revealed that TOP2A induced cell cycle arrest at G2 phase and proliferation inhibition via p53 phosphorylation. Taken together, by using WGCNA, we have identified a novel biomarker named TOP2A, which could affect the development of smoking-related ccRCC by regulating cell cycle and p53 signaling pathway.

SUBMITTER: Xiong Y

PROVIDER: S-EPMC6218786 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identifying a Novel Biomarker <i>TOP2A</i> of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis.

Xiong Yaoyi Y Yuan Lushun L Chen Liang L Zhu Yuan Y Zhang Shanshan S Liu Xuefeng X Xiao Yu Y Wang Xinghuan X

Journal of Cancer 20181010 21

Although it is well known that smoking is one of pathogenesis of clear cell renal cell carcinoma (ccRCC), the underlying molecular mechanism is still unclear. In our study, the microarray dataset GSE46699 is analyzed by weighted gene co-expression network analysis (WGCNA). Then we identify 15 co-expressed gene modules in which the lightcyan module (R<sup>2</sup> = 0.30) is the most significant. Combined with the protein-protein interaction (PPI) network and WGCNA, two hub genes are identified. M ...[more]

PMID: 30410595

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Project description:Kidney cancer ranks as one of the top 10 causes of cancer death; this cancer is difficult to detect, difficult to treat, and poorly understood. The most common subtype of kidney cancer is clear cell renal cell carcinoma (ccRCC) and its progression is influenced by complex gene interactions. Few clinical studies have investigated the molecular markers associated with the progression of ccRCC. In this study, we collected microarray profiles of 72 ccRCCs and matched normal samples to identify differentially expressed genes (DEGs). Then a weighted gene co-expression network analysis (WGCNA) was conducted to identify co-expressed gene modules. By relating all co-expressed modules to clinical features, we found that the brown module and Fuhrman grade had the highest correlation (r = -0.8, p = 1e-09). Thus, the brown module was regarded as a clinically significant module and subsequently analyzed. Functional annotation showed that the brown module focused on metabolism-related biological processes and pathways, such as fatty acid oxidation and amino acid metabolism. We then performed a protein-protein interaction (PPI) network to identify the hub nodes in the brown module. It is worth noting that only one candidate, acetyl-CoA acetyltransferase (ACAT1), was considered to be the final target most relevant to the Fuhrman grade of ccRCC, by applying the intersection of hub genes in the co-expressed network and the PPI network. ACAT1 was subsequently validated using another two external microarray datasets and the TCGA dataset. Intriguingly, validation results indicated that ACAT1 was negatively correlated with four grades of ccRCC, which was also consistent with our results from qRT-PCR analysis and immunohistochemistry staining of clinical samples. Overexpression of ACAT1 inhibited the proliferation and migration of human ccRCC cells in vitro. In addition, the Kaplan-Meier survival curve showed that patients with a lower expression of ACAT1 showed a significantly lower overall survival rate and disease-free survival rate, indicating that ACAT1 could act as a prognostic and recurrence/progression biomarker of ccRCC. In summary, we found and confirmed that ACAT1 might help to identify the progression of ccRCC, which might have important clinical implications for enhancing risk stratification, therapeutic decision, and prognosis prediction in ccRCC patients.

Project description:BackgroundHigh immunogenicity is an important feature of ccRCC, but its underlying immune-related molecular mechanisms remain unclear. This study aimed to investigate the effect of immune-related gene TEK on ccRCC and its prognostic value.MethodsThe immune-related differentially expressed genes (DEGs) and transcription factors (TFs) in ccRCC were screened based on The Cancer Genome Atlas (TCGA) database, and a regulatory network of TF was constructed. Prognostic-related immune genes were screened by univariate Cox regression analysis and functional annotation was performed. Univariate and multivariate Cox regression analyses were performed to construct the immune gene risk model and identify the hub gene TEK that independently affected the prognosis of ccRCC. The effectiveness of the TEK was verified by external microarray datasets. The relationship between TEK and immune cells in ccRCC was evaluated based on Tumor Immune Estimation Resource (TIMER). The expression of TEK in clinical specimens was verified by qRT-PCR and immunohistochemical (IHC) staining. MTT and cloning formation assay were used to evaluate cell proliferation. Transwell assays were used to assess cell migration. Apoptosis was assessed by flow cytometry, and the expression of related proteins was detected by Western blot and immunofluorescence.ResultsWe constructed a prognostic model consisting of 12 hub genes and performed risk scores to determine the relationship between these scores and prognosis. Through Cox regression analysis and survival analysis, TEK, an immune marker highly related to survival prognosis, was obtained and validated. In vitro experiments showed that knockdown of TEK promoted the proliferation and migration of ccRCC cells, and we found that TEK promoted apoptosis by regulating the phosphorylation of AKT, thereby inhibiting cell proliferation.ConclusionsTEK plays an important role in risk assessment and survival prediction for ccRCC patients as a new immune gene and maybe an emerging target for immunotherapy for ccRCC patients.

Project description:To discuss the dual energy spectral computer tomography (CT) imaging features of the pathological grading of clear cell renal cell carcinoma (ccRCC) and the correlation between spectral CT imaging features and pathology.We performed analyses of 62 patients with confirmed diagnosis of ccRCC. All patients underwent non-enhanced CT and dual-phase (cortex phase, CP and parenchyma phase, PP) contrast-enhanced CT with dual energy spectral mode. The subjects were pathologically divided into two groups: low-grade group (Fuhrman 1/2) and high-grade group (Fuhrman 3/4). The CT value of each lesion was measured on the monochromatic image at 70 keV. The normalized iodine concentrations (NIC) and the slope of the spectrum curve were calculated. The qualitative morphological parameters, including tumor shape, calcification, pseudocapsule, necrosis, and enhancement mode, were compared between the two groups. The quantitative data were compared using Student's t-test, and the enumeration data were analyzed using the chi-square test between low-grade and high-grade ccRCC.Receiver operating characteristic curve was used to determine the sensitivity and specificity of the quantitative parameters in two groups.The CT value, NIC, and mean slope of the low-grade group were increased compared with the high-grade group during CP (P = 0.001, P = 0.043, and P<0.001, respectively). The CT did not differ significantly during PP (P = 0.134); however, the NIC and mean slope varied considerably in the low grade compared with the high-grade group (P = 0.048, P = 0.017, respectively). The CT threshold value, NIC, and slope had high sensitivity and specificity in differentiating low-grade ccRCC from high-grade ccRCC. The tumor shape, pseudocapsule, and necrosis differed significantly between the two groups (P<0.05).Dual energy spectral CT with the quantitative analysis of iodine concentration and qualitative analysis of morphological characteristics increases the accuracy of diagnosing the pathological grade of ccRCC.

Dataset Information

Identifying a Novel Biomarker TOP2A of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis.

Publications

Identifying a Novel Biomarker <i>TOP2A</i> of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis.

Similar Datasets

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