Dynamic PAH-Related Changes: A Dataset of tRNA-Derived Small RNA Transcriptome Across Multiple Organs

Pulmonary arterial hypertension (PAH) is a severe cardiopulmonary disease characterized by elevated pulmonary artery pressure and cardiac dysfunction, often leading to heart failure and even death. Despite the complexity of its mechanisms, the molecular basis of PAH remains unclear. tRNA-derived small RNAs (tsRNAs) are noncoding RNAs that play regulatory roles in various diseases. We collected plasma samples from PAH patients and healthy controls for small RNA microarray, and we established a monocrotaline-induced PAH rat model to collect right ventricular and lung tissues for tsRNA sequencing. Our analysis revealed 2,716 unique tsRNAs in human plasma and 4,733 in rat tissues, with a 7.84% overlap. Additionally, 204 tsRNAs were highly conserved across plasma, lung tissue, and right ventricle samples. The reproducibility of the expression profiles was confirmed through Pearson correlation and principal component analysis. KEGG pathway enrichment analysis indicated that tsRNAs are involved in key pathways, such as the MAPK and cancer signalling pathways. These datasets offer valuable insights for researching the epigenetic mechanisms underlying PAH and potential therapeutic targets.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2024-11-20
DOI
https://doi.org/10.1038/s41597-024-04115-0
Citations
1
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.23
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic PAH-Related Changes: A Dataset of tRNA-Derived Small RNA Transcriptome Across Multiple Organs

Yi Tang, Jianqiang Peng, Jun Luo, Jiang Li et al.
1 citations
Scientific Data
RNA modifications and cancer
0.23
article

Dynamic PAH-Related Changes: A Dataset of tRNA-Derived Small RNA Transcriptome Across Multiple Organs

Yi Tang, Jianqiang Peng, Jun Luo, Jiang Li, Tianyu Wang, Fang Li, Jingyuan Chen, Yingjie Tan, Tengteng Zhu, Gang Jiang, Yusi Chen
article en
1 citations

Abstract

Pulmonary arterial hypertension (PAH) is a severe cardiopulmonary disease characterized by elevated pulmonary artery pressure and cardiac dysfunction, often leading to heart failure and even death. Despite the complexity of its mechanisms, the molecular basis of PAH remains unclear. tRNA-derived small RNAs (tsRNAs) are noncoding RNAs that play regulatory roles in various diseases. We collected plasma samples from PAH patients and healthy controls for small RNA microarray, and we established a monocrotaline-induced PAH rat model to collect right ventricular and lung tissues for tsRNA sequencing. Our analysis revealed 2,716 unique tsRNAs in human plasma and 4,733 in rat tissues, with a 7.84% overlap. Additionally, 204 tsRNAs were highly conserved across plasma, lung tissue, and right ventricle samples. The reproducibility of the expression profiles was confirmed through Pearson correlation and principal component analysis. KEGG pathway enrichment analysis indicated that tsRNAs are involved in key pathways, such as the MAPK and cancer signalling pathways. These datasets offer valuable insights for researching the epigenetic mechanisms underlying PAH and potential therapeutic targets.

Scientific DataVol. 11(1)
Central South University (CN), Hunan Normal University (CN), Zhuzhou Central Hospital (CN), Hunan Provincial People's Hospital (CN), Shandong Provincial QianFoShan Hospital (CN), Second Xiangya Hospital of Central South University (CN), Shandong First Medical University (CN)
Zero hunger
Openalex Percentile: Top 41%
RNA modifications and cancer
0.23
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.