ArticleAnnals of translational medicine2020
Investigation of hub genes involved in diabetic nephropathy using biological informatics methods.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- Exploring TGFBR3 in disease pathogenesis: Mechanisms, clinical implications, and pharmacological modulation.Journal of pharmaceutical analysis · 2026Review
- Reduced FGF9 Leads to Kidney Injury Through Regulating Renal Tubular Epithelial Cell EMT in Diabetes.Journal of cellular and molecular medicine · 2025Article
- Overexpression of TGFBR3 Aggravates Cognitive Impairment and Neuroinflammation by Promoting Microglia M1 Polarization in the APP/PS1 Mouse Model of Alzheimer's Disease.Molecular neurobiology · 2025Article
- Celastrol protects against diabetic nephropathy by modulating immune-related pathways: a bioinformatics and experimental validation.American journal of translational research · 2025Article
- Identification of hub genes associated with diabetic cardiomyopathy using integrated bioinformatics analysis.Scientific reports · 2024Article
- Landscape of infiltrating immune cells and related genes in diabetic kidney disease.Clinical and experimental nephrology · 2024Article
- Exploring early DNA methylation alterations in type 1 diabetes: implications of glycemic control.Frontiers in endocrinology · 2024Article
- Comprehensive analysis of key genes and pathways for biological and clinical implications in thyroid-associated ophthalmopathy.BMC genomics · 2022Article
- BMP-7 ameliorates partial epithelial-mesenchymal transition by restoring SnoN protein level via Smad1/5 pathway in diabetic kidney disease.Cell death & disease · 2022Article
- AKT3 and related molecules as potential biomarkers responsible for cryptorchidism and cryptorchidism-induced azoospermia.Translational pediatrics · 2021Article
- The Multiple Roles of Fibroblast Growth Factor in Diabetic Nephropathy.Journal of inflammation research · 2021Review
- Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets ofEvidence-based complementary and alternative medicine : eCAM · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to find genes with significantly aberrant expression in diabetic nephropathy (DN) and determine their underlying mechanisms.
methodsGSE30528 and GSE1009 were obtained by querying the Gene Expression Omnibus (GEO) database. The difference in target gene expression between normal renal tissues and kidney tissues in patients with DN was screened by using the GEO2R tool. Using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database, differentially expressed genes (DEGs) were analysed by Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Then, the protein-protein interactions (PPIs) of DEGs were analyzed by Cytoscape with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the hub genes in this PPI network were recognized by centrality analysis.
resultsThere were 110 genes with significant expression differences between normal and DN tissues. The differences in gene expression involved many functions and expression pathways, such as the formation of the extracellular matrix and the construction of the extracellular domain. The correlation analysis and subgroup analysis of 14 hub genes and the clinical characteristics of DN showed that CTGF, ALB, PDPN, FLT1, IGF1, WT1, GJA1, IGFBP2, FGF9, BMP2, FGF1, BMP7, VEGFA, and TGFBR3 may be involved in the progression of DN.
conclusionsWe confirmed the differentially expressed hub genes and other genes which may be the novel biomarker and target candidates in DN.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.