NT5E Gene Expression Data for Pulmonary Hypertension Biomarker Analysis

Supplementary data from a bioinformatics study identifies NT5E as a potential diagnostic biomarker for pulmonary hypertension. The analysis includes gene expression data from the GSE113439 and GSE53408 datasets, with NT5E achieving area under the ROC curve values of 0.891 and 0.894, respectively. The dataset was published by Yanan Chu on the figshare platform in April 2026.

Use Cases

• Validate NT5E expression as a diagnostic biomarker using ROC curve analysis metrics from the GSE113439 and GSE53408 datasets.
• Analyze the association between NT5E expression levels and immune cell infiltration, specifically plasma cells and eosinophils, in the pulmonary hypertension microenvironment.
• Correlate high NT5E expression with enriched biological pathways from Gene Set Enrichment Analysis (GSEA) results.
• Compare expression levels of candidate NAD+ metabolism-related genes (NMRGs) like NT5E and CD38 identified from overlapping differentially expressed genes.

Strengths

• Includes validated diagnostic performance metrics for NT5E, with AUC values of 0.891 and 0.894 across two independent datasets.
• Data is derived from established public repositories (Gene Expression Omnibus, KEGG, Reactome) and analyzed with standard bioinformatics tools (Limma, CIBERSORT).
• Findings are supported by in vitro cellular experiments demonstrating NT5E's functional role in pulmonary arterial smooth muscle cells.

Limitations

• The dataset is very small at 9.1 KB, indicating it contains summary or supplementary results rather than raw expression data.
• The specific row count, column names, and sample-level data are not provided, limiting direct analytical reuse.
• The temporal coverage of the underlying gene expression datasets is not specified.

Provenance

Source

Gene Expression Omnibus (GEO) databases GSE113439 and GSE53408, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome.

Collection Method

Bioinformatics analysis using the Limma R package for differential expression, weighted gene co-expression network analysis (WGCNA), and the CIBERSORT algorithm for immune infiltration.

Freshness

Last updated on April 6, 2026.