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pmid:39567703

Integrated analysis ceRNA network of autophagy-related gene RNF144B in steroid-induced necrosis of the femoral head.

Zeng-Ying Xing · Wei Liu · Ri-Jin Xing · Jian-Fei Chen · Jun Xiong

Scientific reports · 2024 doi:10.1038/s41598-024-79923-w

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

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title
Integrated analysis ceRNA network of autophagy-related gene RNF144B in steroid-induced necrosis of the femoral head.
doi
10.1038/s41598-024-79923-w
pmid
39567703
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Zeng-Ying Xing
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Wei Liu
[2]
Ri-Jin Xing
[3]
Jian-Fei Chen
[4]
Jun Xiong
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Scientific reports
year
2024
abstract
This study aimed to investigate the regulatory mechanisms that influenced autophagy in Steroid-induced necrosis of the femoral head (SONFH) by constructing a competing endogenous RNA (ceRNA) network. Blood sample data from the SONFH patients were obtained from the Gene Expression Omnibus (GEO) database under the accession number GSE123568. Autophagy-related genes were identified from the Human Autophagy Database (HADb). Differential analysis and weighted gene co-expression network analysis (WGCNA) were performed on the GSE123568 dataset to screen for core genes and validation was performed with the validation set. Based on the GEO dataset (GSE74089), we performed differential lncRNA analysis. Meanwhile, we utilized three databases, namely miRDB, TargetScan, and StarBase, to predict the miRNAs of target genes and corresponding lncRNAs. Cytoscape software was used to construct and visualize the ceRNA networks. We also employed reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to quantify their expression levels. A total of 1692 differentially expressed genes (DEGs) were identified in the GSE123568 dataset. By intersecting with the HADb database, 47 autophagy-related genes were identified from these DEGs. Furthermore, we found the significant correlation between RNF144B and 37 autophagy genes. Importantly, we established a regulatory axis involving TUG1, hsa-miR-31-5p, and RNF144B., and both TUG1 and RNF144B were upregulated, while hsa-miR-31-5p was downregulated in the SONFH cell model. A TUG1-hsa-miR-31-5p-RNF144B axis was related to autophagy genes, which potentially provided insights into the RNA interactions triggering autophagy in SONFH.
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By Spearman correlation analysis, we found that 37 autophagy genes were significantly correlated with RNF144B (Fig. 7).
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By Spearman correlation analysis, we found that 37 autophagy genes were significantly correlated with RNF144B (Fig. 7).
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Furthermore, we found the significant correlation between RNF144B and 37 autophagy genes.
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Blood sample data from the SONFH patients were obtained from the Gene Expression Omnibus (GEO) database under the accession number GSE123568. Autophagy-related genes were identified from the Human Autophagy Database (HADb). Differential analysis and weighted gene co-expression network analysis (WGCNA) were performed on the GSE123568 dataset to screen for core genes and validation was performed with the validation set. Based on the GEO dataset (GSE74089), we performed differential lncRNA analysis. Meanwhile, we utilized three databases, namely miRDB, TargetScan, and StarBase, to predict the miRNAs of target genes and corresponding lncRNAs. Cytoscape software was used to construct and visualize the ceRNA networks. We also employed reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to quantify their expression levels. A total of 1692 differentially expressed genes (DEGs) were identified in the GSE123568 dataset. 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