scholarly journals Identification of candidate RNA signatures in triple‑negative breast cancer by the construction of a competing endogenous RNA network with integrative analyses of Gene Expression Omnibus and The Cancer Genome Atlas data

2020 ◽  
Author(s):  
Ping Yan ◽  
Lingfeng Tang ◽  
Li Liu ◽  
Gang Tu
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Competing Endogenous Rna ◽  
Atlas Data ◽  
Integrative Analyses ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Identification of novel mRNA-miRNA-lncRNA competing endogenous RNA network associated with prognosis of breast cancer

Epigenomics ◽  
2019 ◽  
Vol 11 (13) ◽  
pp. 1501-1518 ◽  
Author(s):  
Guansheng Zhong ◽  
Weiyang Lou ◽  
Minya Yao ◽  
Chengyong Du ◽  
Haiyan Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: To identify novel competing endogenous RNA (ceRNA) network related to patients prognosis in breast cancer. Materials & methods: Dysregulated mRNA based on intersection of three Gene Expression Omnibus and The Cancer Genome Atlas datasets were analyzed by bioinformatics. Results: In total 72 upregulated and 208 downregulated genes were identified. Functional analysis showed that some pathways related to cancer were significantly enriched. By means of stepwise reverse prediction and validation from mRNA to lncRNA, 19 hub genes, nine key miRNA and four key lncRNAs were identified by expression and survival analysis. Ultimately, the coexpression analysis identified RRM2-let-7a-5p- SNHG16/ MAL2 as key ceRNA subnetwork associated with prognosis of breast cancer. Conclusion: We successfully constructed a novel ceRNA network, among which each component was significantly associated with breast cancer prognosis.

scholarly journals The construction and analysis of ceRNA networks in invasive breast cancer: a study based on The Cancer Genome Atlas

2018 ◽  
Vol Volume 11 ◽  
pp. 1-11 ◽  
Author(s):  
Chundi Gao ◽  
Huayao Li ◽  
Jing Zhuang ◽  
HongXiu Zhang ◽  
Kejia Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Clinical Significance of miR-210 and its Prospective Signaling Pathways in Non-Small Cell Lung Cancer: Evidence from Gene Expression Omnibus and the Cancer Genome Atlas Data Mining with 2763 Samples and Validation via Real-Time Quantitative PCR

2018 ◽  
Vol 46 (3) ◽  
pp. 925-952 ◽  
Author(s):  
Rong-quan He ◽  
Wei-luan Cen ◽  
Jie-mei Cen ◽  
Wei-ning Cen ◽  
Jia-yi Li ◽  
...  
Keyword(s):  
Clinical Significance ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Small Cell ◽  
The Cancer Genome Atlas ◽  
Small Cell Lung ◽  
Free Survival ◽  
Real Time Quantitative Pcr ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background/Aims: Since the function of microRNA (miR)-210 in non-small cell lung cancer (NSCLC) remains unclear, we aimed to explore the clinical significance of miR-210 in NSCLC. Methods: NSCLC-related data from 1673 samples on Gene Expression Omnibus and 1090 samples on The Cancer Genome Atlas were obtained and analyzed. The expression level of miR-210 was validated via real-time quantitative PCR analysis with 125 paired clinical samples. A meta-analysis was performed to generate a comprehensive understanding of miR-210 expression and its clinical significance in NSCLC. In addition, bioinformatics analysis was also conducted to reveal the potential underlying mechanism of miR-210 action in NSCLC. Results: miR-210 expression was consistently elevated in NSCLC solid tissue samples. However, its expression was controversial in easily obtained body fluids (i.e., blood, plasma, and serum). Moreover, an overall pooled meta-analysis implied a comparatively higher level of miR-210 expression in NSCLC cancerous tissue than in normal control tissue (P < 0.001). In addition, a meta-analysis of outcome revealed a significant diagnostic capacity of miR-210 in NSCLC by detecting its expression in serum and sputum (area under the summary receiver operating characteristic curve 0.82 and 0.81, respectively). miR-210 overexpression was associated with poor progression-free survival (PFS) in NSCLC and was negatively related to overall survival and disease-free survival. Bioinformatic gene enrichment and annotation analyses showed that the target genes of miR-210 were greatly enriched in cell adhesion and plasma membrane, and three pathways were considered to be the main functional circuits of miR-210: renin secretion, the cGMP-PKG signaling pathway, and cell adhesion molecules. Conclusion: In NSCLC, miR-210 expression was elevated and overexpression indicated poor PFS. Expression level of miR-210 in serum and sputum showed significant diagnostic value for NSCLC.

Prediction of transcription factors binding events based on epigenetic modifications in different human cells

Epigenomics ◽  
2020 ◽  
Vol 12 (16) ◽  
pp. 1443-1456
Author(s):  
Yan Huang ◽  
Dianshuang Zhou ◽  
Yihan Wang ◽  
Xingda Zhang ◽  
Mu Su ◽  
...  
Keyword(s):  
Cell Lines ◽  
Predictive Models ◽  
High Performance ◽  
Epigenetic Modifications ◽  
The Cancer Genome Atlas ◽  
Encode Project ◽  
Atlas Data ◽  
Random Forest Method ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: We aim to predict transcription factor (TF) binding events from knowledge of gene expression and epigenetic modifications. Materials & methods: TF-binding events based on the Encode project and The Cancer Genome Atlas data were analyzed by the random forest method. Results: We showed the high performance of TF-binding predictive models in GM12878, HeLa, HepG2 and K562 cell lines and applied them to other cell lines and tissues. The genes bound by the top TFs ( MAX and MAZ) were significantly associated with cancer-related processes such as cell proliferation and DNA repair. Conclusion: We successfully constructed TF-binding predictive models in cell lines and applied them in tissues.

scholarly journals High expression levels of pyrimidine metabolic rate–limiting enzymes are adverse prognostic factors in lung adenocarcinoma: a study based on The Cancer Genome Atlas and Gene Expression Omnibus datasets

2020 ◽  
Vol 16 (3) ◽  
pp. 347-366 ◽  
Author(s):  
Haiwei Wang ◽  
Xinrui Wang ◽  
Liangpu Xu ◽  
Ji Zhang ◽  
Hua Cao
Keyword(s):  
Lung Cancer ◽  
Metabolic Rate ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Pyrimidine Metabolism ◽  
Expression Levels ◽  
Cancer Genome Atlas ◽  
Rate Limiting ◽  
Genome Atlas

Abstract Reprogramming of metabolism is described in many types of cancer and is associated with the clinical outcomes. However, the prognostic significance of pyrimidine metabolism signaling pathway in lung adenocarcinoma (LUAD) is unclear. Using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, we found that the pyrimidine metabolism signaling pathway was significantly enriched in LUAD. Compared with normal lung tissues, the pyrimidine metabolic rate–limiting enzymes were highly expressed in lung tumor tissues. The high expression levels of pyrimidine metabolic–rate limiting enzymes were associated with unfavorable prognosis. However, purinergic receptors P2RX1, P2RX7, P2RY12, P2RY13, and P2RY14 were relatively downregulated in lung cancer tissues and were associated with favorable prognosis. Moreover, we found that hypo-DNA methylation, DNA amplification, and TP53 mutation were contributing to the high expression levels of pyrimidine metabolic rate–limiting enzymes in lung cancer cells. Furthermore, combined pyrimidine metabolic rate–limiting enzymes had significant prognostic effects in LUAD. Comprehensively, the pyrimidine metabolic rate–limiting enzymes were highly expressed in bladder cancer, breast cancer, colon cancer, liver cancer, and stomach cancer. And the high expression levels of pyrimidine metabolic rate–limiting enzymes were associated with unfavorable prognosis in liver cancer. Overall, our results suggested the mRNA levels of pyrimidine metabolic rate–limiting enzymes CAD, DTYMK, RRM1, RRM2, TK1, TYMS, UCK2, NR5C2, and TK2 were predictive of lung cancer as well as other cancers.

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