Evidence map›Paper›PMID 35722091›Full record

ArticleFrontiers in cardiovascular medicine2022

Transcriptome Sequencing Data Reveal LncRNA-miRNA-mRNA Regulatory Network in Calcified Aortic Valve Disease.

Kai Huang, Lujia Wu, Yuan Gao, Qin Li, Hao Wu, Xiaohong Liu, Lin Han

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
11.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. BMPR2 affects valve development via ECM-receptor interaction in zebrafish.Frontiers in cell and developmental biology · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Kai HuangDepartment of Cardiovascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
Lujia WuDepartment of Cardiovascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
Yuan GaoDepartment of Cardiovascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
Qin LiDepartment of Cardiovascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
Hao WuDepartment of Cardiovascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
Xiaohong LiuDepartment of Cardiovascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
Lin HanDepartment of Cardiovascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
Changhai Hospital · CNSecond Military Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Calcified aortic valve disease (CAVD) is one of the most common valvular heart diseases in the elderly population. However, no effective medical treatments have been found to interfere with the progression of CAVD, and specific molecular mechanisms of CAVD remain unclear. Materials and Methods: Transcriptome sequencing data of GSE55492 and GSE148219 were downloaded from the European Nucleotide Archive, and the microarray dataset, GSE12644 was acquired from the Gene Expression Omnibus database. Software, including FastQC, HISAT2, samtools, and featureCounts was applied to generate the read count matrix. The "Limma" package in R was utilized to analyze differentially expressed genes (DEGs). Thereafter, weighted gene co-expression network analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and the protein-protein interaction (PPI) network were used to identify hub genes associated with CAVD, which were further validated by receiver operating characteristic curve (ROC) analysis using GSE12644. The long non-coding RNA (LncRNA)-mediated regulatory network was established based on the differentially expressed LncRNAs and hub genes, which were detected using quantitative real-time PCR (qRT-PCR) in clinical samples and valve interstitial cells. Moreover, CIBERSORT was used to calculate the expression distribution of immune cell infiltration in CAVD. Results: A total of 126 DEGs were included in the PPI network. PI3K-Akt signaling pathway, ECM-receptor interaction, hematopoietic cell lineage, cell adhesion molecules, and focal adhesion were the most enriched pathways revealed by KEGG. Four LncRNAs, including TRHDE-AS1, LINC00092, LINC01094, and LINC00702 were considered the differentially expressed LncRNA. SPP1, TREM1, GPM6A, CCL19, CR1, NCAM1, CNTN1, TLR8, SDC1, and COL6A6 were the 10 hub genes identified to be associated with CAVD. Moreover, the calcified aortic valve samples had a greater level of Tregs, naïve B cells, and M0 macrophages than the noncalcified ones, whereas CAVD samples had a lower M2 macrophage expression compared to the noncalcified valve tissues. Conclusion: The current study identified SPP1, TREM1, TLR8, SDC1, GPM6A, and CNTN1 as hub genes that could potentially be associated with CAVD. The LINC00702-miR-181b-5p-SPP1 axis might participate in the development of CAVD. Additionally, M2 macrophages, Tregs, naïve B cells, and M0 macrophages might possibly play a role in the initiation of CAVD.

Indexed as

bioinformatics analysiscalcified aortic valve disease (CAVD)ceRNAimmune cellsLncRNA – long noncoding RNA

Identifiers

PMID35722091
PMCPMC9204424
OpenAlexW4282043625

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.