Identification of GMFG as a novel biomarker in IgA nephropathy based on comprehensive bioinformatics analysis

© 2024 The Authors..

Background: IgA nephropathy (IgAN) stands as the most prevalent form of glomerulonephritis and ranks among the leading causes of end-stage renal disease worldwide. Regrettably, we continue to grapple with the absence of dependable diagnostic markers and specific therapeutic agents for IgAN. Therefore, this study endeavors to explore novel biomarkers and potential therapeutic targets in IgAN, while also considering their relevance in the context of tumors.

Methods: We gathered IgAN datasets from the Gene Expression Omnibus (GEO) database. Subsequently, leveraging these datasets, we conducted an array of analyses, encompassing differential gene expression, weighted gene co-expression network analysis (WGCNA), machine learning, receiver operator characteristic (ROC) curve analysis, gene expression validation, clinical correlations, and immune infiltration. Finally, we carried out pan-cancer analysis based on hub gene.

Results: We obtained 1391 differentially expressed genes (DEGs) in GSE93798 and 783 DGEs in GSE14795, respectively. identifying 69 common genes for further investigation. Subsequently, GMFG was identified the hub gene based on machine learning. In the verification set and the training set, the GMFG was higher in the IgAN group than in the healthy group and all of the GMFG area under the curve (AUC) was more 0.8. In addition, GMFG has a close relationship with the prognosis of malignancies and a range of immune cells.

Conclusions: Our study suggests that GMFG could serve as a promising novel biomarker and potential therapeutic target for both IgAN and certain types of tumors.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:10

Enthalten in:

Heliyon - 10(2024), 7 vom: 15. Apr., Seite e28997

Sprache:

Englisch

Beteiligte Personen:

Deng, Xiaoqi [VerfasserIn]
Luo, Yu [VerfasserIn]
Lu, Meiqi [VerfasserIn]
Lin, Yun [VerfasserIn]
Ma, Li [VerfasserIn]

Links:

Volltext

Themen:

Biomarker
GMFG
IgA nephropathy (IgAN)
Journal Article
Machine learning
Pan-cancer analysis
Weighted gene co-expression network (WGCNA)

Anmerkungen:

Date Revised 25.04.2024

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.1016/j.heliyon.2024.e28997

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM370909380