Evidence map›Paper›PMID 37872956›Full record

ArticleJournal of inflammation research2023

Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction.

Qiang Yang, Jing Gao, Tian-Yu Wang, Jun-Can Ding, Peng-Fei Hu

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 72% 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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. 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

5 authors at 2 institutions in 1 country.

Qiang Yang *Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.ORCID 0009-0007-1493-2852
Jing Gao *Department of Cardiology, Sir Run Run Shaw Hospital, College of Medicine Zhejiang University, Hangzhou, Zhejiang Province, 310018, People's Republic of China.
Tian-Yu WangSecond Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.
Jun-Can DingSecond Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.
Peng-Fei HuDepartment of Cardiology, the Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310005, People's Republic of China.
Zhejiang Chinese Medical University · CNSir Run Run Shaw Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to analyze the hub genes of heart failure with reduced ejection fraction (HFrEF) treated with Empagliflozin using RNA sequencing (RNA-seq) and bioinformatics methods, including machine learning. Methods: From February 2021 to February 2023, nine patients with HFrEF were enrolled from our hospital's cardiovascular department. In addition to routine drug treatment, these patients received 10 mg of Empagliflozin once daily for two months. Efficacy was assessed and RNA-seq was performed on peripheral blood before and after treatment with empagliflozin. HFrEF-related hub genes were identified through bioinformatics analyses including differential gene expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, immune infiltration analysis, machine learning, immune cell correlation analysis and clinical indicator correlation analysis. Results: The nine patients included in this study completed a two-month treatment regimen, with an average age of 62.11 ± 6.36 years. By performing bioinformatics analysis on the transcriptome from the treatment groups, 42 differentially expressed genes were identified, with six being up-regulated and 36 being down-regulated (|log2FC|>1 and adj.pvalue<0.05). Immune infiltration analysis of these genes demonstrated a significant difference in the proportion of plasma between the pre-treatment and post-treatment groups ( Conclusion: Through RNA-seq and bioinformatics analysis, this study identified GTF2IP14 and MTLN as the hub genes of HFrEF, and their mechanisms may be related to immune inflammatory responses and various immune cells.

Indexed as

bioinformaticsempagliflozinHFrEFmachine learningRNA-seq

Identifiers

PMID37872956
PMCPMC10590560
OpenAlexW4387745356

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.