Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis

Xia, Lin; Gong, Ningji

doi:10.3389/fmolb.2022.992044

Cited by 24 publications

(27 citation statements)

References 30 publications

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“…Correspondingly, we demonstrated the correlations between FDEGs, which will facilitate further studies targeting FDEGs. Compared to previous studies, we used for the rst time LASSO and SVM-RFE machine learning algorithms to lter the FDEGs, which greatly improved the representativeness of the selected genes [33] . Later, using logistic regression analysis, univariate and multivariate conducted on the intersection genes between the two algorithms were performed.…”

Section: Discussionmentioning

confidence: 99%

Screening of diagnostic biomarkers for Ferroptosis-related osteoarthritis and construction of a risk-prognosis model

Yan

Cheng

et al. 2023

Preprint

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Background Osteoarthritis (OA) is the most prevalent and commonly chronic joint disease that frequently develops among the elderly population. It is not just a single tissue that is affected, but rather a pathology involving the entire joint. Among them, synovitis is a key pathological change in OA. Ferroptosis is a newly discovered form of cell death that results from the buildup of lipid peroxidation. However, the role and impact of it in OA are yet to be explored. Objective The key to this work is to uncover the mechanisms of ferroptosis-related OA pathogenesis and develop more novel diagnostic biomarkers to facilitate the diagnostic and therapeutic of OA. Materials and Methods Download FRGs and OA synovial chip datasets separately from the FerrDB and GEO databases. Identify FDEGs using R software, obtain the intersection genes through two machine learning algorithms, and obtain diagnostic biomarkers after logistic regression analysis. Verify the diagnostic and therapeutic efficacy of specific genes for OA through the construction of clinical risk prognostic models using ROC curves and nomogram. Simultaneously, correlations between specific genes and OA immune cell infiltration co-expression were constructed. Finally, verify the differential presentation of specific genes in OA and HC synovium. Results Obtain 38 FDEGs through screening. Based on machine learning algorithms and logistic regression analysis, select AGPS, BRD4, RBMS1, and EGR1 as diagnostic biomarker genes. The diagnostic and therapeutic efficacy of the four specific genes for OA has been validated by ROC curves and nomogram of clinical risk prognostic models. The analysis of immune cell infiltration and correlation suggests a close association between specific genes and OA immune cell infiltration. Further revealing the diagnostic value of specific genes for OA by the differential presentation analysis of their differential presentation in synovial tissue from OA and HC. Conclusion This study identified four diagnostic biomarkers for OA that are associated with iron death. The establishment of a risk-prognostic model is conducive to the premature diagnosis of OA, evaluating functional recovery during rehabilitation, and guidance for subsequent treatment.

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Section: Discussionmentioning

confidence: 99%

Screening of diagnostic biomarkers for Ferroptosis-related osteoarthritis and construction of a risk-prognosis model

Yan

Cheng

et al. 2023

Preprint

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“…Downregulation of IL1B was detected in advanced stage of T-cell lymphoma [26]. A previous study suggested that IL1B was a ferroptosis-related diagnostic marker in osteoarthritis [27]. The third molecule, YBX1, was a nucleic acid binding protein that regulated tumor cell proliferation and migration [28].…”

Section: Discussionmentioning

confidence: 99%

A Ferroptosis-Related Prognostic Model of Mantle Cell Lymphoma

Gao,

Wang,

Zhang

et al. 2023

Preprint

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Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin's lymphoma. Ferroptosis, an iron-dependent programmed cell death, is closely related to cancer prognosis. In this study, we established a model of ferroptosis-related genes for prognostic evaluation of patients with MCL. Using the single-cell RNA sequencing datasets GSE184031 and GSE32018 from the Gene Expression Omnibus, we identified 139 ferroptosis-related genes in MCL. Next, a prognostic model was constructed by Cox regression and LASSO regression analysis. In our model, the prognosis of MCL patients was assessed by risk scoring using 7 genes ANXA1, IL1B, YBX1, CCND1, MS4A1, MFHAS1 and RILPL2. The patients were divided into high-risk and low-risk groups based on our model, and the high-risk patients had inferior overall survival. Finally, according to our model and computational drug sensitivity analysis, four small molecule compounds, BMS-754807, SB216763, Doramapimod and Trametinib, were identified as potential therapeutic agents for patients with MCL. In summary, we provide a prognostic model with ferroptosis-related gene signature for MCL.

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“…Several previous studies have successfully used bioinformatics to identify potential target genes for osteoarthritis. Xia L et al [ 31 ] identified seven key DEFRGs (ATF3, IL6, CDKN1A, IL1B, EGR1, JUN, and CD44) in the synovial tissue of osteoarthritis. Hu Y et al [ 32 ] screened three hub genes (CX3CR1, MYC, and TLR7) and identified potential drugs for the treatment of OA.…”

Section: Discussionmentioning

confidence: 99%

Identification of SLC3A2 as a Potential Therapeutic Target of Osteoarthritis Involved in Ferroptosis by Integrating Bioinformatics, Clinical Factors and Experiments

Liu

Deng

et al. 2022

Cells

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Osteoarthritis (OA) is a type of arthritis that causes joint pain and limited mobility. In recent years, some studies have shown that the pathological process of OA chondrocytes is related to ferroptosis. Our study aims to identify and validate differentially expressed ferroptosis-related genes (DEFRGs) in OA chondrocytes and to investigate the potential molecular mechanisms. RNA-sequencing and microarray datasets were downloaded from Gene Expression Omnibus (GEO) data repository. Differentially expressed genes (DEGs) were screened by four methods: limma-voom, edgeR, DESeq2, and Wilcoxon rank-sum test. Weighted correlation network analysis (WGCNA), protein-protein interactions (PPI), and cytoHubba of Cytoscape were applied to identify hub genes. Clinical OA cartilage specimens were collected for quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis, western blotting (WB), histological staining, transmission electron microscopy (TEM), and transfection. Sankey diagram was used to visualize the relationships between the expression level of SLC3A2 in the damaged area and clinical factors. Based on bioinformatics analysis, clinical factors, and experiment validation, SLC3A2 was identified as a hub gene. It was down-regulated in OA cartilage compared to normal cartilage (p < 0.05). Functional enrichment analysis revealed that SLC3A2 was associated with ferroptosis-related functions. Spearman correlation analysis showed that the expression level of SLC3A2 in the OA cartilage-damaged area was closely related to BMI, obesity grade, and Kellgren-Lawrence grade. Furthermore, in vitro experiments validated that SLC3A2 inhibited ferroptosis and suppressed cartilage degeneration in OA. In summary, we demonstrated that SLC3A2 inhibited ferroptosis and suppressed cartilage degeneration in OA. These findings provide a new idea for the study of the pathogenesis of OA, thus providing new means for the clinical diagnosis and targeted therapy of OA.

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Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis

Cited by 24 publications

References 30 publications

Screening of diagnostic biomarkers for Ferroptosis-related osteoarthritis and construction of a risk-prognosis model

Screening of diagnostic biomarkers for Ferroptosis-related osteoarthritis and construction of a risk-prognosis model

A Ferroptosis-Related Prognostic Model of Mantle Cell Lymphoma

Identification of SLC3A2 as a Potential Therapeutic Target of Osteoarthritis Involved in Ferroptosis by Integrating Bioinformatics, Clinical Factors and Experiments

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