Evidence map›Paper›PMID 36238494›Full record

ArticleComputational and mathematical methods in medicine2022

Identification of the Immune Status of Hypertrophic Cardiomyopathy by Integrated Analysis of Bulk- and Single-Cell RNA Sequencing Data.

Wei Zhao, Tianyu Wu, Jian Zhan, Zishuang Dong

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.6field-weighted citation impact, top 15% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. The inflammatory spectrum of cardiomyopathies.Frontiers in cardiovascular medicine · 2024
    Review
  7. Article
  8. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Wei ZhaoDivision of Cardiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0002-3228-1578
Tianyu WuDivision of Cardiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210000, China.
Jian ZhanDivision of Cardiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210000, China.ORCID https://orcid.org/0000-0001-7992-3083
Zishuang DongDepartment of Cardiology, Xuzhou Central Hospital, Xuzhou, Jiangsu 221000, China.ORCID https://orcid.org/0000-0001-5133-6709
Nanjing Medical University · CNXuzhou Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Hypertrophic cardiomyopathy (HCM) is the most common hereditary cardiomyopathy and immune infiltration is considered an indispensable factor involved in its pathogenesis. In this study, we attempted to combine bulk sequencing and single-cell sequencing to map the immune infiltration-related genes in hypertrophic cardiomyopathy. Methods: The GSE36961, GSE160997, and GSE122930 datasets were obtained from the Gene Expression Omnibus database. The compositional patterns of the 18 types of immune cell fraction and pathway enrichment score in control and HCM patients were estimated based on the GSE36961 cohort using xCell algorithm. The Weighted Gene Coexpression Network Analysis (WGCNA) was performed to identify genes associated with immune infiltration for hypertrophic cardiomyopathy. The area under the curve (AUC) value was obtained and used to evaluate the discriminatory ability of common immune-related DEGs. "NetworkAnalyst" platform was used to identify TF-gene and TF-miRNA interaction with identified common genes. Heat map was used to determine the association between common DEGs and various immune cells. Results: Immune infiltration analysis by the xCell algorithm showed a higher level of CD8+ naive T cells, CD8+ T cells, as well as a lower level of activated dendritic cells (aDC), dendritic cells (DC), immature dendritic cells (iDC), conventional dendritic cells (cDC), macrophages, M1 macrophages, monocytes, and NKT cell in HCM compared with the control group in GSE36961 dataset. aDC, macrophages, and M1 macrophages were the top three discriminators between HCM and control groups with the area under the curve (AUC) of 0.907, 0.867, and 0.941. WGCNA analysis showed that 1258 immune-related genes were included in four different modules. Of these modules, the turquoise module showed a pivotal correlation with HCM. 13 common immune-related DEGs were found by intersecting common DEGs in GSE36961 and GSE160997 datasets with genes from the genes in turquoise module. 5 hub immune-related genes (S100A9, TYROBP, FCER1G, CD14, and S100A8) were identified by protein interaction network. Through analysis of single-cell sequencing data, S100a9, TYROBP, FCER1G, and S100a8 were mainly expressed by infiltrated M1 proinflammatory cells, especially Ccr2-M1 proinflammatory macrophage cells in the heart immune microenvironment while Cd14 was expressed by infiltrated M1 proinflammatory macrophage cells and M2 macrophages in transverse aortic constriction (TAC) mice at 1 week. Higher M2 macrophage and M1 proinflammatory macrophage infiltration as well as lower Ccr2-M1 proinflammatory macrophage and dendritic cells were shown in TAC 1week mice compared with sham mice. Conclusions: There was a difference in immune infiltration between HCM patients and normal groups. aDC, macrophages, and M1 macrophages were the top three discriminator immune cell subsets between HCM and control groups. S100A9, TYROBP, FCER1G, CD14, and S100A8 were identified as potential biomarkers to discriminate HCM from the control group. S100a9, TYROBP, FCER1G, and S100a8 were mainly expressed by infiltrated M1 proinflammatory cells, especially Ccr2-M1 proinflammatory cells in the heart immune microenvironment while Cd14 was expressed by M2 macrophages in transverse aortic constriction (TAC) mice at 1 week.

Indexed as

Cardiomyopathy, HypertrophicMicroRNAsAnimalsBiomarkersGene Regulatory NetworksMiceSequence Analysis, RNABiomarkersMicroRNAs

Identifiers

PMID36238494
PMCPMC9553329
OpenAlexW4301179840

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.