Evidence map›Paper›PMID 38528189›Full record

ArticleScientific reports2024

Identification of HDAC9 and ARRDC4 as potential biomarkers and targets for treatment of type 2 diabetes.

Jing Liu, Lingzhen Meng, Zhihong Liu, Ming Lu, Ruiying Wang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. 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
0.5field-weighted citation impact, top 38% 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, 2 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 3 institutions in 1 country.

Jing LiuEndocrinology Department, The Second Hospital of Hebei Medical University, No.215 Heping West Road, Shijiazhuang, 050000, People's Republic of China.
Lingzhen MengGeneral Medical Department, The Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang, 050000, People's Republic of China.
Zhihong LiuEndocrinology Department, The Second Hospital of Hebei Medical University, No.215 Heping West Road, Shijiazhuang, 050000, People's Republic of China. mliuzhihong@163.com.
Ming LuMedical Department, The Second Hospital of Hebei Medical University, No.215 Heping West Road, Shijiazhuang, 050000, People's Republic of China.
Ruiying WangEndocrinology Department, The Second Hospital of Hebei Medical University, No.215 Heping West Road, Shijiazhuang, 050000, People's Republic of China.
Hebei Medical University · CNFourth Hospital of Hebei Medical University · CNSecond Hospital of Hebei Medical University · CN

Funding

Hebei Province medical science research project plan No. 20230458
6 · The paper itself

Abstract

We aimed to identify the key potential insulin resistance (IR)-related genes and investigate their correlation with immune cell infiltration in type 2 diabetes (T2D). The GSE78721 dataset (68 diabetic patients and 62 controls) was downloaded from the Gene Expression Omnibus database and utilized for single-sample gene set enrichment analysis. IR-related genes were obtained from the Comparative Toxicology Genetics Database, and the final IR-differentially expressed genes (DEGs) were screened by intersecting with the DEGs obtained from the GSE78721 datasets. Functional enrichment analysis was performed, and the networks of the target gene with microRNA, transcription factor, and drug were constructed. Hub genes were identified based on a protein-protein interaction network. Least absolute shrinkage and selection operator regression and Random Forest and Boruta analysis were combined to screen diagnostic biomarkers in T2D, which were validated using the GSE76894 (19 diabetic patients and 84 controls) and GSE9006 (12 diabetic patients and 24 controls) datasets. Quantitative real-time polymerase chain reaction was performed to validate the biomarker expression in IR mice and control mice. In addition, infiltration of immune cells in T2D and their correlation with the identified markers were computed using CIBERSORT. We identified differential immune gene set regulatory T-cells in the GSE78721 dataset, and T2D samples were assigned into three clusters based on immune infiltration. A total of 2094 IR-DEGs were primarily enriched in response to endoplasmic reticulum stress. Importantly, HDAC9 and ARRDC4 were identified as markers of T2D and associated with different levels of immune cell infiltration. HDAC9 mRNA level were higher in the IR mice than in control mice, while ARRDC4 showed the opposite trend. In summary, we discovered potential vital biomarkers that contribute to immune cell infiltration associated with IR, which offers a new sight of immunotherapy for T2D.

Indexed as

Diabetes Mellitus, Type 2Histone DeacetylasesInsulin ResistanceMicroRNAsAnimalsBiomarkersHumansImmunotherapyInsulinIntracellular Signaling Peptides and ProteinsMiceRepressor ProteinsARRDC4 protein, humanArrdc4 protein, mouseBiomarkersHDAC9 protein, humanHistone DeacetylasesInsulinIntracellular Signaling Peptides and ProteinsMicroRNAsRepressor ProteinsDiagnostic biomarkerImmunotherapyInsulin resistanceType 2 diabetes

Identifiers

PMID38528189
PMCPMC10963792
OpenAlexW4393139253

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.