Evidence map›Paper›PMID 34124269›Full record

ArticleJournal of diabetes research2021

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis.

Bojun Xu, Lei Wang, Huakui Zhan, Liangbin Zhao, Yuehan Wang, Meng Shen, Keyang Xu, Li Li, Xu Luo, Shasha Zhou and 4 more

Open access · goldAbstract read
In one paragraph

Article in Journal of diabetes research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

23 citing papers in PubMed, 28 citations in OpenAlex.

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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

14 authors at 4 institutions in 2 countries.

Bojun XuHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Lei WangKey Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
Huakui ZhanHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.ORCID https://orcid.org/0000-0003-2210-8728
Liangbin ZhaoHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Yuehan WangHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Meng ShenChengdu Seventh People's Hospital, Chengdu, 610213 Sichuan, China.
Keyang XuCentre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong.
Li LiHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Xu LuoThe First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning 530023, China.
Shasha ZhouHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Anqi TangHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Gang LiuHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Lu SongHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.
Yan LiHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China.ORCID https://orcid.org/0000-0003-3020-6424
Beijing University of Chinese Medicine · CNGuangxi University of Chinese Medicine · CNHong Kong Baptist University · HKSeventh People's Hospital of Shanghai · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesDiabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients.

methodsGSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN.

resultsThere were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN.

conclusionsWe confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

Indexed as

Computational BiologyComplement ActivationComplement System ProteinsDatabases, GeneticDiabetic NephropathiesGene Expression ProfilingGene Regulatory NetworksHumansOligonucleotide Array Sequence AnalysisPrognosisProtein Interaction MapsTranscriptomeComplement System Proteins

Identifiers

PMID34124269
PMCPMC8169258
OpenAlexW3164616447

What OpenQuestion holds

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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.