Bioinformatic analysis of related immune cell infiltration and key genes in the progression of osteonecrosis of the femoral head
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PROVIDER: S-EPMC10811953 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Osteonecrosis of the femoral head (ONFH) is a elaborate hip disease characterized by collapse of femoral head and osteoarthritis. RNA N6-methyladenosine (m6A) plays a crucial role in a lot of biological processes within eukaryotic cells. However, the role of m6A in the regulation of ONFH remains unclear. In this study, we identified the m6A regulators in ONFH and performed subtype classification. We identified 7 significantly differentially expressed m6A regulators through the analysis of differences between ONFH and normal samples in the Gene Expression Omnibus (GEO) database. A random forest algorithm was employed to monitor these regulators to assess the risk of developing ONFH. We constructed a nomogram based on these 7 regulators. The decision curve analysis suggested that patients can benefit from the nomogram model. We classified the ONFH samples into two m6A models according to these 7 regulators through consensus clustering algorithm. After that, we evaluated those two m6A patterns using principal component analysis. We assessed the scores of those two m6A patterns and their relationship with immune infiltration. We observed a higher m6A score of type A than that of type B. Finally, we performed a cross-validation of crucial m6A regulatory factors in ONFH using external datasets and femoral head bone samples. In conclusion, we believed that the m6A pattern could provide a novel diagnostic strategy and offer new insights for molecularly targeted therapy of ONFH.
| S-EPMC10943872 | biostudies-literature
Project description:ObjectiveThis study aimed to identify key diagnostic markers and immune infiltration of (SONFH) by bioinformatics analysis.MethodsRelated SONFH datasets were downloaded from the Gene Expression Omnibus (GEO) database. First, we identified the differentially expressed genes (DEGs) and performed the functional enrichment analysis. Then weighted correlation network analysis (WGCNA) and the MCODE plug-in in Cytoscape were used to identify the diagnostic markers of SONFH. Finally, CIBERSORT was used to analyze the immune infiltration between SONFH and healthy controls, and the correlation between infiltrating immune cells and diagnostic markers was analyzed.ResultsTYROBP, TLR2, P2RY13, TLR8, HCK, MNDA, and NCF2 may be key diagnostic markers of SONFH. Immune cell infiltration analysis revealed that Memory B cells and activated dendritic cells may be related to the SONFH process. Moreover, HCK was negatively correlated with CD8 T cells, and neutrophils were positively correlated with those key diagnostic markers.ConclusionsTYROBP, TLR2, P2RY13, TLR8, HCK, MNDA, and NCF2 may be used as diagnostic markers of SONFH, and immune-related mechanism of SONFH and the potential immunotherapy are worthy of further study.
| S-EPMC8767711 | biostudies-literature
Project description:Osteonecrosis of the femoral head (ONFH) is a condition caused by a disruption or damage to the femoral head's blood supply, which causes the death of bone cells and bone marrow components and prevents future regeneration. Ferroptosis, a type of controlled cell death, is caused by iron-dependent lipid peroxidation. Here, we identified ferroptosis-related genes and infiltrating immune cells involved in ONFH and predicted the underlying molecular mechanisms. The GSE123568 dataset was subjected to differential expression analysis to identify genes related to ferroptosis. Subsequently, GO and KEGG pathway enrichment analyses, as well as protein-protein interaction (PPI) network analysis, were conducted. Hub genes involved in ferroptosis were identified using machine learning and other techniques. Additionally, immune infiltration analysis and lncRNA-miRNA-mRNA network prediction analysis were performed. Finally, we determined whether ferroptosis occurred by measuring iron content. The hub genes were validated by ROC curve analysis and qRT-PCR. Four ferroptosis-related hub genes (MAPK3, PTGS2, STK11, and SLC2A1) were identified. Additionally, immune infiltration analysis revealed a strong correlation among ONFH, hub genes, and various immune cells. Finally, we predicted the network relationship between differentially expressed lncRNAs and hub genes in the lncRNA-miRNA-mRNA network. MAPK3, PTGS2, STK11, and SLC2A1 have been identified as potential ferroptosis-related biomarkers and drug targets for the diagnosis and prognosis of ONFH, while some immune cells, as well as the interaction between lncRNA, miRNA, and mRNA, have also been identified as potential pathogenesis markers and therapeutic targets.
| S-EPMC10909969 | biostudies-literature
Project description:ObjectiveThis study aimed to identify and evaluate genes associated with cellular autophagy in steroid hormonal femoral head necrosis.MethodsAutophagy-related differentially expressed genes (ARDEGs) in steroid-induced osteonecrosis of the femoral head (SONFH) were identified by obtaining the intersection of differentially expressed genes (DEGs) and autophagy-related genes in a SONFH Gene Expression Omnibus dataset. The ARDEGs were screened, and correlations between gene expression and immune cell infiltration were evaluated. Finally, the validation of hub genes was undertaken through quantitative real-time-PCR.ResultsA comparison of peripheral blood samples from patients with and without SONFH revealed 189 DEGs. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses showed that the DEGs were related to various biologic processes (e.g., neutrophil activation) and pathways (e.g., hematopoietic cell lineage). The expression levels of these genes were correlated with the infiltration of multiple immune cell types. Among the 189 putative autophagy-related genes associated with SONFH, three genes, RPL6, RPL9, and RPL23, were identified as candidate biomarkers or therapeutic targets based on network analysis and their correlations with immune cell subtypes. The quantitative real-time polymerase chain reaction results confirmed our prediction regarding the mRNA expression of RPL9 and RPS6.ConclusionIn this study, we identified 189 putative autophagy-related genes associated with SONFH, and the prediction of down-regulated genes RPL9 and RPS6 was validated using PCR, thereby expanding our understanding of the contribution of autophagy to SONFH.
| S-EPMC10579027 | biostudies-literature
Project description:Steroid-induced osteonecrosis of the femoral head (SONFH) is a progressive disease that leads to an increased disability rate. This study aimed to ascertain biomarkers, infiltrating immune cells, and therapeutic drugs for SONFH. The gene expression profile of the GSE123568 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the NetworkAnalyst platform. Functional enrichment, protein-protein interaction network (PPI), and module analyses were performed using Metascape tools. An immune cell abundance identifier was used to explore immune cell infiltration. Furthermore, hub genes were identified based on maximal clique centrality (MCC) evaluation using cytoHubba application and confirmed by qRT-PCR using clinical samples. Finally, the L1000 platform was used to determine potential drugs for SONFH treatment. The SONFH mouse model was used to determine the therapeutic effects of aspirin. In total, 429 DEGs were identified in SONFH samples. Functional enrichment analysis showed that these DEGs were enriched in myeloid leukocyte activation and osteoclast differentiation processes. A set of nine immune cell types was confirmed to be markedly different between the SONFH and control samples. All 10 hub genes were significantly highly expressed in the serum of SONFH patients, as shown by qRT-PCR. Finally, the therapeutic effect of aspirin on SONFH was examined in animal experiments. Taken together, our data revealed the hub genes and infiltrating immune cells in SONFH, and we also screened potential drugs for use in SONFH treatment.
| S-EPMC8815624 | biostudies-literature
Project description:Background and Objectives. Glucocorticoid-induced osteonecrosis of the femoral head (GIONFH) is a serve complication of long-term administration of glucocorticoids. Previous experimental studies have shown that ferroptosis might be involved in the pathological process of GIONFH. The purpose of this study is to identify the ferroptosis-related genes and pathways of GIONFH by bioinformatics to further illustrate the mechanism of ferroptosis in SONFH through bioinformatics analysis. Materials and Methods. The GSE123568 mRNA expression profile dataset, including 30 GIONFH samples and 10 non-GIONFH samples, was downloaded from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were obtained from the FerrDb database. First, differentially expressed genes (DEGs) were identified between the serum samples from GIONFH cases and those from controls. Ferroptosis-related DEGs were obtained from the intersection of ferroptosis-related genes and DEGs. Only ferroptosis DEGs were used for all analyses. Then, we conducted a Kyoto encyclopedia of genome (KEGG) and gene ontology (GO) pathway enrichment analysis. We constructed a protein-protein interaction (PPI) network to screen out hub genes. Additionally, the expression levels of the hub genes were validated in an independent dataset GSE10311. Results. A total of 27 ferroptosis-related DEGs were obtained between the peripheral blood samples of GIONFH cases and non-GIONFH controls. Then, GO, and KEGG pathway enrichment analysis revealed that ferroptosis-related DEGs were mainly enriched in the regulation of the apoptotic process, oxidation-reduction process, and cell redox homeostasis, as well as HIF-1, TNF, FoxO signaling pathways, and osteoclast differentiation. Eight hub genes, including TLR4, PTGS2, SNCA, MAPK1, CYBB, SLC2A1, TXNIP, and MAP3K5, were identified by PPI network analysis. The expression levels of TLR4, TXNIP and MAP3K5 were further validated in the dataset GSE10311. Conclusion. A total of 27 ferroptosis-related DEGs involved in GIONFH were identified via bioinformatics analysis. TLR4, TXNIP, and MAP3K5 might serve as potential biomarkers and drug targets for GIONFH.
| S-EPMC9962586 | biostudies-literature
Project description:PurposeSteroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people, but the pathogenesis of SONFH remained unclear. We aimed to identify the potential genes and screen potential therapeutic compounds for SONFH.MethodsThe microarray was obtained for blood tissue from the GEO database, and then it identifies differentially expressed genes (DEGs). The DEGs were analyzed to obtain the differences in immune cell infiltration. The gene functional enrichment analysis of SONFH was analyzed. The PPI of DEGs was identified through the STRING database, and the cluster modules and hub genes were ascertained using MCODE and CytoHubba, and the ROC curve of hub genes was analyzed, and the tissues distribution of hub genes was understood by the HPA, Bgee and BioGPS databases. The hub genes and target miRNAs and corresponding upstream lncRNAs were predicted by TargetScan, miRDB and ENCORI database. Subsequently, we used CMap, DGIdb and L1000FWD databases to identify several potential therapeutic molecular compounds for SONFH. Finally, the AutoDockTools Vina, PyMOL and Discovery Studio were employed for molecular docking analyses between compounds and hub genes.ResultsThe microarray dataset GSE123568 was obtained related to SONFH. There were 372 DEGs including 197 upregulated genes and 175 downregulated genes by adjusted P value < 0.01 and |log2FC|> 1. Several significant GSEA enrichment analysis and biological processes and KEGG pathway associated with SONFH were identified, which were significantly related to cytoskeleton organization, nucleobase-containing compound catabolic process, NOD-like receptor signaling pathway, MAPK signaling pathway, FoxO signaling pathway, neutrophil-mediated immunity, neutrophil degranulation and neutrophil activation involved in immune response. Activated T cells CD4 memory, B cells naïve, B cells memory, T cells CD8 and T cells gamma delta might be involved in the occurrence and development of SONFH. Three cluster modules were identified in the PPI network, and eleven hub genes including FPR2, LILRB2, MNDA, CCR1, IRF8, TYROBP, TLR1, HCK, TLR8, TLR2 and CCR2 were identified by Cytohubba, which were differed in bone marrow, adipose tissue and blood, and which had good diagnostic performance in SONFH. We identified IRF8 and 10 target miRNAs that was utilized including Targetsan, miRDB and ENCORI databases and 8 corresponding upstream lncRNAs that was revealed by ENCORI database. IRF8 was detected with consistent expression by qRT-PCR. Based on the CMap, DGIdb and L1000FWD databases, the 11 small molecular compounds that were most strongly therapeutic correlated with SONFH were estradiol, genistein, domperidone, lovastatin, myricetin, fenbufen, rosiglitazone, sirolimus, phenformin, vorinostat and vinblastine. All of 11 small molecules had good binding affinity with the IRF8 in molecular docking.ConclusionThe occurrence of SONFH was associated with a "multi-target" and "multi-pathway" pattern, especially related to immunity, and IRF8 and its noncoding RNA were closely related to the development of SONFH. The CMap, DGIdb and L1000FWD databases could be effectively used in a systematic manner to predict potential drugs for the prevention and treatment of SONFH. However, additional clinical and experimental research is warranted.
| S-EPMC9832881 | biostudies-literature
Project description:BackgroundThis study was aimed to identify key ferroptosis-related biomarkers in steroid-induced osteonecrosis of the femoral head (SONFH) based on machine learning algorithm.MethodsThe SONFH dataset GSE123568 (including 30 SONFH patients and 10 controls) was used in this study. The differentially expressed genes (DEGs) were selected between SONFH and control groups, which were subjected to WGCNA. Ferroptosis-related genes were downloaded from FerrDb V2, which were then compared with DEGs and module genes. Two machine learning algorithms were utilized to identify key ferroptosis-related genes, and the underlying mechanisms were analyzed by GSEA. Correlation analysis between key ferroptosis-related genes and immune cells was analyzed by Spearman method. The drug-gene relationships were predicted in CTD.ResultsTotal 2030 DEGs were obtained. WGCNA identified two key modules and obtained 1561 module genes. Finally, 43 intersection genes were identified as disease-related ferroptosis-related genes. After LASSO regression and RFE-SVM algorithms, 4 intersection genes (AKT1S1, BACH1, MGST1 and SETD1B) were considered as key ferroptosis-related gene. The 4 genes were correlated with osteoclast differentiation pathway. Twenty immune cells with significant differences were obtained between the groups, and the 4 key ferroptosis-related genes were correlated with most immune cells. In CTD, 41 drug-gene relationship pairs were finally obtained.ConclusionsThe 4 key ferroptosis-related genes, AKT1S1, BACH1, MGST1 and SETD1B, were identified to play a critical role in SONFH progression through osteoclast differentiation and immunologic mechanisms. Additionally, all the 4 genes had good disease prediction effect and could act as biomarkers for the diagnosis and treatment of SONFH.
| S-EPMC10148479 | biostudies-literature
Project description:BackgroundThe diagnosis for steroid-induced osteonecrosis of the femoral head (SONFH) is hard to achieve at the early stage, which results in patients receiving ineffective treatment options and a poor prognosis for most cases. The present study aimed to find potential diagnostic markers of SONFH and analyze the effect exerted by infiltration of immune cells in this pathology.Materials and methodsR software was adopted for identifying differentially expressed genes (DEGs) and conducting functional investigation based on the microarray dataset. Then we combined SVM-RFE, WGCNA, LASSO logistic regression, and random forest (RF) algorithms for screening the diagnostic markers of SONFH and further verification by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. CIBERSORT was then adopted for assessing the infiltration of immune cells and the relationship of infiltration-related immune cells and diagnostic markers.ResultsWe identified 383 DEGs overall. This study found ARG2, MAP4K5, and TSTA3 (AUC = 0.980) to be diagnostic markers of SONFH. The results of qRT-PCR showed a statistically significant difference in all markers. Analysis of infiltration of immune cells indicated that neutrophils, activated dendritic cells and memory B cells were likely to show the relationship with SONFH occurrence and progress. Additionally, all diagnostic markers had different degrees of correlation with T cell follicular helper, neutrophils, memory B cells, and activated dendritic cells.ConclusionARG2, MAP4K5, and TSTA3 are potential diagnostic genes for SONFH, and infiltration of immune cells may critically impact SONFH occurrence and progression.
| S-EPMC8350574 | biostudies-literature
Project description: Not available
| S-EPMC11803313 | biostudies-literature