Combinatorial chemistry & high throughput screening · 2023
Identification of Chemokines-Related miRNAs as Potential Biomarkers in Psoriasis Based on Integrated Bioinformatics Analysis.
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Zhuang Haojun, Wang Xiaoming, Guo Meiliang, Meng Qinqin, Liu Na, Wei Min, Shi Yuling, Deng Hui
Аннотация
BACKGROUND: Psoriasis is an immune-mediated skin disorder caused by the proliferation of keratinocytes. Although psoriasis is generally diagnosed based on clinical manifestations, sensitive biomarkers are needed to help diagnose psoriasis early with atypical presentations. MicroRNAs play a functional role in the development of psoriasis, and they are stable and suitable as biomarkers in psoriasis.
MATERIAL AND METHODS: The GSE50790 and GSE53552 datasets from the Gene Expression Omnibus (GEO) database were used to identify Differentially Expressed Genes (DEGs) between the control group and the lesional group. DEGs were processed for enrichment analysis to explore the functions, and a Protein-Protein Interaction (PPI) network was constructed to obtain gene clusters. The signalling pathway associated with gene cluster 1 was processed to further identify related genes. Hub genes were obtained through the intersection of cluster 1 and the related genes. Hub genes were used to predict the miRNAs through a gene-miRNA interaction network. The relative expression of miRNAs was measured by qRT-PCR to identify the suitability of miRNAs as biomarkers.
RESULTS: Bioinformatics analysis revealed that the chemokine signalling pathway is involved in the development of psoriasis. Five related miRNAs were mined from the datasets, and qRT-PCR showed that hsa-miR-612 (p=0.0015), hsa-miR-3194-5p (p=0.0078) and hsa-miR-4316 (p<0.0001) may be potential biomarkers in psoriasis.
MATERIAL AND METHODS: The GSE50790 and GSE53552 datasets from the Gene Expression Omnibus (GEO) database were used to identify Differentially Expressed Genes (DEGs) between the control group and the lesional group. DEGs were processed for enrichment analysis to explore the functions, and a Protein-Protein Interaction (PPI) network was constructed to obtain gene clusters. The signalling pathway associated with gene cluster 1 was processed to further identify related genes. Hub genes were obtained through the intersection of cluster 1 and the related genes. Hub genes were used to predict the miRNAs through a gene-miRNA interaction network. The relative expression of miRNAs was measured by qRT-PCR to identify the suitability of miRNAs as biomarkers.
RESULTS: Bioinformatics analysis revealed that the chemokine signalling pathway is involved in the development of psoriasis. Five related miRNAs were mined from the datasets, and qRT-PCR showed that hsa-miR-612 (p=0.0015), hsa-miR-3194-5p (p=0.0078) and hsa-miR-4316 (p<0.0001) may be potential biomarkers in psoriasis.