Evidence map›Paper›PMID 36157062›Full record

ArticlePeerJ2022

Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice.

Jing Zhao, Kaiying He, Hongxuan Du, Guohua Wei, Yuejia Wen, Jiaqi Wang, Xiaochun Zhou, Jianqin Wang

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.9field-weighted citation impact, top 5% 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

21 citing papers in PubMed, 31 citations in OpenAlex.

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  20. Network pharmacology based virtual screening of Flavonoids fromSaudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2023
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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

8 authors at 2 institutions in 1 country.

Jing Zhao *Lanzhou University, Lanzhou, China.
Kaiying He *Lanzhou University, Lanzhou, China.
Hongxuan DuLanzhou University, Lanzhou, China.
Guohua WeiLanzhou University Second Hospital, Lanzhou, China.
Yuejia WenLanzhou University, Lanzhou, China.
Jiaqi WangLanzhou University, Lanzhou, China.
Xiaochun ZhouLanzhou University Second Hospital, Lanzhou, China.
Jianqin WangLanzhou University Second Hospital, Lanzhou, China.
Lanzhou University Second Hospital · CNLanzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD. Materials and Methods: Two datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO) database were used, combined with our RNA sequencing (RNA-seq) results of DKD mice (C57 BLKS-32w db/db) and non-diabetic (db/m) mice for further analysis. After processing the expression matrix of the three sets of data using R software "Limma", differential expression analysis was performed. The significantly differentially expressed genes (DEGs) (-logFC- > 1, Results: Our RNA-seq results identified a total of 125 DEGs, including 59 up-regulated and 66 down-regulated DEGs. At the same time, 183 up-regulated and 153 down-regulated DEGs were obtained in GEO database GSE86300, and 76 up-regulated and 117 down-regulated DEGs were obtained in GSE184836. Venn diagram showed that 13 co-expression DEGs among the three groups of DEGs. GO analysis showed that biological processes (BP) were mainly enriched inresponse to stilbenoid, response to fatty acid, response to nutrient, positive regulation of macrophage derived foam cell differentiation, triglyceride metabolic process. KEGG pathway analysis showed that the three major enriched pathways were cholesterol metabolism, drug metabolism-cytochrome P450, PPAR signaling pathway. After qRT-PCR validation, we obtained 11 genes that were significant differentially expressed in the kidney tissues of DKD mice compared with control mice. (The mRNA expression levels of Aacs, Cpe, Cd36, Slc22a7, Slc1a4, Lpl, Cyp7b1, Akr1c14 and Apoh were declined, whereas Abcc4 and Gsta2 were elevated). Conclusion: Our study, based on RNA-seq results, GEO databases and qRT-PCR, identified 11 significant dysregulated DEGs, which play an important role in lipid metabolism and the PPAR signaling pathway, which provide novel targets for diagnosis and treatment of DKD.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesAnimalsBiomarkersComputational BiologyGene Expression ProfilingMicePeroxisome Proliferator-Activated ReceptorsTranscriptomeBiomarkersPeroxisome Proliferator-Activated ReceptorsBioinformatics analysisBiomarkerDiabetic kidney disease (DKD)Differentially expressed genes (DEGs)RNA-seq

Identifiers

PMID36157062
PMCPMC9504448
OpenAlexW4297252183

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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.