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Identification of novel biomarkers of ferroptosis involved in keloid based on bioinformatics analysis
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AbstractKeloid is a fibroproliferative disease of unknown aetiology, which has a significant impact the quality of life of patients. Ferroptosis plays an important role in the occurrence and development of fibrosis, but there is still a lack of research related to keloids. The objective of this work was to identify the hub genes related to ferroptosis in keloid to better understand the keloid process. The microarray data (GSE7890 GSE145725, and GSE44270) (23 keloid and 22 normal fibroblast) were analysed via the gene expression comprehensive database (GEO). Only GSE7890 met the FerrDB database. Cell cycle and pathway analysis were performed with gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to differentially expressed genes (DEG). The differential genes were confirmed in other GEO datasets (GSE145725 and GSE44270), and multi‐fibrosis‐gene correlation analysed. To validate these hub genes, quantitative real‐time PCR (qRT‐PCR) was conducted. A total of 581 DEGs were screened, with 417 genes down‐regulated and 164 genes up‐regulated, with 11 ferroptosis genes significantly up‐regulated in both keloid and normal tissue, and 6 genes are consistent with our findings and are associated with multiple fibrosis genes. The qRT‐PCR results and tissues of normal skin and keloid agreed with our predictions. Our findings provide new evidence for the ferroptosis‐related molecular pathways and biomarker of keloid.
Title: Identification of novel biomarkers of ferroptosis involved in keloid based on bioinformatics analysis
Description:
AbstractKeloid is a fibroproliferative disease of unknown aetiology, which has a significant impact the quality of life of patients.
Ferroptosis plays an important role in the occurrence and development of fibrosis, but there is still a lack of research related to keloids.
The objective of this work was to identify the hub genes related to ferroptosis in keloid to better understand the keloid process.
The microarray data (GSE7890 GSE145725, and GSE44270) (23 keloid and 22 normal fibroblast) were analysed via the gene expression comprehensive database (GEO).
Only GSE7890 met the FerrDB database.
Cell cycle and pathway analysis were performed with gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to differentially expressed genes (DEG).
The differential genes were confirmed in other GEO datasets (GSE145725 and GSE44270), and multi‐fibrosis‐gene correlation analysed.
To validate these hub genes, quantitative real‐time PCR (qRT‐PCR) was conducted.
A total of 581 DEGs were screened, with 417 genes down‐regulated and 164 genes up‐regulated, with 11 ferroptosis genes significantly up‐regulated in both keloid and normal tissue, and 6 genes are consistent with our findings and are associated with multiple fibrosis genes.
The qRT‐PCR results and tissues of normal skin and keloid agreed with our predictions.
Our findings provide new evidence for the ferroptosis‐related molecular pathways and biomarker of keloid.
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