Dataset Information

Integrative Analysis Revealing Human Heart-Specific Genes and Consolidating Heart-Related Phenotypes.

ABSTRACT: Elucidating expression patterns of heart-specific genes is crucial for understanding developmental, physiological, and pathological processes of the heart. The aim of the present study is to identify functionally and pathologically important heart-specific genes by performing the Ingenuity Pathway Analysis (IPA). Through a median-based analysis of tissue-specific gene expression based on the Genotype-Tissue Expression (GTEx) data, we identified 56 genes with heart-specific or elevated expressions in the heart (heart-specific/enhanced), among which three common heart-specific/enhanced genes and four atrial appendage-specific/enhanced genes were unreported regarding the heart. Differential expression analysis further revealed 225 differentially expressed genes (DEGs) between atrial appendage and left ventricle. Our integrative analyses of those heart-specific/enhanced genes and DEGs with IPA revealed enriched heart-related traits and diseases, consolidating evidence of relationships between these genes and heart function. Our reports on comprehensive identification of heart-specific/enhanced genes and DEGs and their relation to pathways associated with heart-related traits and diseases provided molecular insights into essential regulators of cardiac physiology and pathophysiology and potential new therapeutic targets for heart diseases.

SUBMITTER: Ahn J

PROVIDER: S-EPMC7438927 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrative Analysis Revealing Human Heart-Specific Genes and Consolidating Heart-Related Phenotypes.

Ahn Jinsoo J Wu Huiguang H Lee Kichoon K

Frontiers in genetics 20200730

Elucidating expression patterns of heart-specific genes is crucial for understanding developmental, physiological, and pathological processes of the heart. The aim of the present study is to identify functionally and pathologically important heart-specific genes by performing the Ingenuity Pathway Analysis (IPA). Through a median-based analysis of tissue-specific gene expression based on the Genotype-Tissue Expression (GTEx) data, we identified 56 genes with heart-specific or elevated expression ...[more]

PMID: 32903789

Similar Datasets

Project description:Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC. Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5'-C-phosphate-G- 3' (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes. Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients. Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

Dataset Information

Integrative Analysis Revealing Human Heart-Specific Genes and Consolidating Heart-Related Phenotypes.

Publications

Integrative Analysis Revealing Human Heart-Specific Genes and Consolidating Heart-Related Phenotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure