Evidence map›Paper›PMID 36568106›Full record

ArticleFrontiers in endocrinology2022

Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy.

Kunna Lu, Li Wang, Yan Fu, Guanghong Li, Xinhuan Zhang, Mingfeng Cao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 6% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 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

6 authors at 2 institutions in 1 country.

Kunna LuDepartment of Endocrinology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
Li WangDepartment of Pharmacy, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
Yan FuThe Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
Guanghong LiDepartment of Endocrinology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
Xinhuan ZhangDepartment of Endocrinology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
Mingfeng CaoDepartment of Endocrinology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
Affiliated Hospital of Taishan Medical University · CNShandong First Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Systemic inflammation and immune response are involved in the pathogenesis of diabetic nephropathy (DN). However, the specific immune-associated signature during DN development is unclear. Our study aimed to reveal the roles of immune-related genes during DN progression. Methods: The GSE30529 and GSE30528 datasets were acquired from the Gene Expression Omnibus (GEO) database. Then, the intersection between differentially expressed genes (DEGs) and immune score-related genes (ISRGs) was screened. Subsequently, functional enrichment analyses were performed. The different immune phenotype-related subgroups were finally divided using unsupervised clustering. The core genes were identified by WGCNA and the protein-protein interaction (PPI) network. xCell algorithm was applied to assess the proportion of immune cell infiltration. Results: 92 immune score-related DEGs (ISRDEGs) were identified, and these genes were enriched in inflammation- and immune-associated pathways. Furthermore, two distinct immune-associated subgroups (C1 and C2) were identified, and the C1 subgroup exhibited activated immune pathways and a higher percentage of immune cells compared to the C2 subgroup. Two core genes (LCK and HCK) were identified and all up-regulated in DN, and the expressions were verified using GSE30122, GSE142025, and GSE104954 datasets. GSEA indicated the core genes were mainly enriched in immune-related pathways. Correlation analysis indicated LCK and HCK expressions were positively correlated with aDC, CD4+ Tem, CD8+T cells, CD8+ Tem, and mast cells. Conclusions: We identified two immune-related genes and two immune-associated subgroups, which might help to design more precise tailored immunotherapy for DN patients.

Indexed as

Diabetes MellitusDiabetic NephropathiesAlgorithmsCD8-Positive T-LymphocytesComputational BiologyHumansInflammationbioinformaticsdiabetic kidney diseaseGSEAimmune infiltrationxCell

Identifiers

PMID36568106
PMCPMC9768367
OpenAlexW4311707590

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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.