In Silico Analysis of Ferroptosis-Related Genes and Its Implication in Drug Prediction against Fluorosis

Liu, Bin; Fu, Xiaoli; Du, Yuhui; Feng, Zichen; Liu, Xiaoxue; Li, Zhiyuan; Yu, Fangfang; Zhou, Guoyu; Ba, Yue

doi:10.3390/ijms24044221

Cited by 6 publications

(2 citation statements)

References 77 publications

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“…The Search Tool for the Retrieval of Interacting Genes database (STRING, https://string-db.org/) was used for PPI analysis of CRs-related differentially expressed genes [21], and Cytosscape software was used to visualize the STRING analysis data [22]. The MCC algorithm of Cytohubba plug-in in Cytoscape was used to identify hub genes, and genes with top 10 scores were considered as potential hub genes [23].…”

Section: Construction Of Ppi Network and Identi Cation Of Hub Genesmentioning

confidence: 99%

Identification of a novel diagnostic biomarker for osteoarthritis associated with chromatin regulators and its association with immune cell infiltration

Zhang,

Guo

et al. 2024

Preprint

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Background: Osteoarthritis (OA) is considered to be a common chronic degenerative joint disease characterized by cartilage destruction and extracellular matrix degeneration. However, its specific pathogenesis is still unclear. This study aimed to find a novel diagnostic biomarker for osteoarthritis associated with chromatin regulators (CRs) and analyze possible associations between signature genes and immune cell infiltration. Methods: OA datasets were downloaded from the Gene Expression Omnibus (GEO) database and integrated them with the CRs dataset to create a new dataset. Differentially expressed genes (DEGs) associated with CRs were obtained by differential expression analysis, and functional enrichment analysis of these genes was performed. The CRs-related hub genes were further identified by protein-protein interaction (PPI) analysis. The infiltration level of immune cells in OA was analyzed by Single-sample GSEA (ssGSEA) to explore the correlation between the expression level of hub genes and immune infiltration, and the potential targeted therapeutic drugs of hub genes were predicted by Drug SIGnatures DataBase (DSigDB). Hub genes were used to construct OA diagnostic risk model, identify OA diagnostic biomarkers, and verify its diagnostic value by ROC curve and correction curve. Finally, signature genes were validated in vitro and in vivo by quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blotting (WB), and mouse models of destabilization of the medial meniscus (DMM). Results: A total of 86 CRs-related DEGs were obtained. These DEGs were enriched in the histone binding and modification, cell cycle, and FoxO signaling pathways. After PPI analysis, we obtained 10 CRs-related hub genes, and finally determined Aurora kinase B (AURKB) as a diagnostic biomarker for OA by constructing a OA risk prediction model. In mouse DMM models and IL-β-induced chondrocytes, qRT-PCR and WB results showed significantly high expression of AURKB in diseased joints and cells. Moreover, ssGSEA analysis showed that there were significant differences in the infiltration levels of 11 immune cells and 12 immune infiltrating functions in the OA samples. Infiltration analysis of immune cells showed that AURKB expression was positively correlated with tumor infiltrating lymphocyte (TIL), Th1 cells, T cell co-inhibition and check-point. Finally, based on the obtained 10 CRs-related hub genes, we found that dasatinib, enterolactone and genistein may be potential targeted therapies for OA. Conclusion: Overall, our study shows that AURKB is an important biomarker in the development and progression of OA and is significantly correlated with immune infiltration, providing a new way to explore the pathogenesis of OA.

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Section: Construction Of Ppi Network and Identi Cation Of Hub Genesmentioning

confidence: 99%

Identification of a novel diagnostic biomarker for osteoarthritis associated with chromatin regulators and its association with immune cell infiltration

Zhang,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…These interactions were reflected by the comprehensive score. With a confidence score (0.4) set as the minimum interaction score, the autophagy-related target genes were used to construct a PPI network [62]. These results were presented and analyzed using Cytoscape v3.9.0 (https://cytoscape.org/, accessed on 12 May 2023) [63].…”

Section: Ppi Network Construction and Validation Of Hub Genesmentioning

confidence: 99%

Expression Profiles of Long Non-Coding RNAs in the Articular Cartilage of Rats Exposed to T-2 Toxin

Yu,

Wang,

Luo

et al. 2023

IJMS

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T-2 toxin could induce bone damage. But there is no specific mechanism about the long non-coding RNAs (lncRNAs) involved in T-2 toxin-induced articular cartilage injury. In this study, 24 SD rats were randomly divided into a control group and a T-2 group, which were administered 4% absolute ethanol and 100 ng/g · bw/day of T-2 toxin, respectively. After treatment for 4 weeks, safranin O/fast green staining identified the pathological changes in the articular cartilage of rats, and immunofluorescence verified the autophagy level increase in the T-2 group. Total RNA was isolated, and high-throughput sequencing was performed. A total of 620 differentially expressed lncRNAs (DE-lncRNAs) were identified, and 326 target genes were predicted. Enrichment analyses showed that the target genes of DE-lncRNAs were enriched in the autophagy-related biological processes and pathways. According to the autophagy database, a total of 23 autophagy-related genes were identified, and five hub genes (Foxo3, Foxo1, Stk11, Hdac4, and Rela) were screened using the Maximal Clique Centrality algorithm. The Human Protein Atlas database indicated that Rela and Hdac4 proteins were highly expressed in the bone marrow tissue, while Foxo3, Foxo1, and Stk11 proteins were reduced. According to Enrichr, etoposide and diatrizoic acid were identified as the key drugs. The real-time quantitative PCR results were consistent with the RNA sequencing (RNA-Seq) results. These results suggested that autophagy was involved in the rat articular cartilage lesions induced by T-2 toxin. The lncRNAs of NONRATG014223.2, NONRATG012484.2, NONRATG021591.2, NONRATG024691.2, and NONRATG002808.2, and their target genes of Foxo3, Foxo1, Stk11, Hdac4, and Rela, respectively, were the key regulator factors of autophagy.

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Proteomics Sequencing Reveals the Role of TGF-β Signaling Pathway in the Peripheral Blood of Offspring Rats Exposed to Fluoride

Duan

et al. 2023

Biol Trace Elem Res

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In Silico Analysis of Ferroptosis-Related Genes and Its Implication in Drug Prediction against Fluorosis

Cited by 6 publications

References 77 publications

Identification of a novel diagnostic biomarker for osteoarthritis associated with chromatin regulators and its association with immune cell infiltration

Identification of a novel diagnostic biomarker for osteoarthritis associated with chromatin regulators and its association with immune cell infiltration

Expression Profiles of Long Non-Coding RNAs in the Articular Cartilage of Rats Exposed to T-2 Toxin

Proteomics Sequencing Reveals the Role of TGF-β Signaling Pathway in the Peripheral Blood of Offspring Rats Exposed to Fluoride

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