Bioinformatics analysis and identification of genes and molecular pathways in steroid-induced osteonecrosis of the femoral head

Machine learning-mediated identification of ferroptosis-related genes in osteonecrosis of the femoral head

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.

Comprehensive analysis of femoral head necrosis based on machine learning and bioinformatics analysis

Osteonecrosis of the femoral head (ONFH) is a kind of disabling disease, given that the molecular mechanism of ONFH has not been elucidated, it is of significance to use bioinformatics analysis to understand the disease mechanism of ONFH and discover biomarkers. Gene set for ONFH GSE74089 was downloaded in the Gene Expression Omnibus, and "limma" package in R software was used to identify differentially expressed genes related to oxidative stress. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyze were performed for functional analysis. We constructed a protein interaction network and identified potential transcription factors and therapeutic drugs for the hub genes, and delineated the TF-hub genes network. Least absolute shrinkage and selection operator regression, support vector machine and cytoHubba were used to screen feature genes and key genes, which were validated by Receiver operating characteristic. CIBERSORT was used to explored the immune microenvironment. Subsequently, we identified the function of key genes using Gene set variation analysis and their relationship with each type of immune cell. Finally, molecular docking validated the binding association between molecules and validated genes. We detected 144 differentially expressed oxidative stress-related genes, and enrichment analysis showed that they were enriched in reactive oxygen species and AGE-RAGE signaling pathway. Protein-protein interaction and TF-hub genes network were conducted. Further exploration suggested that APOD and TMEM161A were feature genes, while TNF, NOS3 and CASP3 were key genes. Receiver operating characteristic analysis showed that APOD, CASP3, NOS3, and TNF have strong diagnostic ability. The key genes were enriched in oxidative phosphorylation. CIBERSORT analysis showed that 17 types immune cells were differentially relocated, and most of which were also closely related to key genes. In addition, genistein maybe potential therapeutic compound. In all, we identified that TNF, NOS3, and CASP3 played key roles on ONFH, and APOD, CASP3, NOS3, and TNF could serve as diagnostic biomarkers.

Astragalus polysaccharide ameliorates steroid-induced osteonecrosis of the femoral head by regulating miR-200b-3p-mediated Wnt/β-catenin signaling pathway via inhibiting SP1 expression : Astragalus polysaccharide regulates SONFH via SP1

BACKGROUND

Steroid-induced osteonecrosis of the femoral head (SONFH) is the necrosis of the femur bone caused by prolonged and massive use of corticosteroids. The present study probed into the significance of Astragalus polysaccharide (APS) in SONFH progression.

METHODS

SONFH cell model was constructed using murine long bone osteocyte Y4 (MLO-Y4) cells and then treated with APS. mRNA microarray analysis selected differentially expressed genes between control group and SONFH group. RT-qPCR determined SP1 and miR-200b-3p expression. Levels of SP1, β-catenin, autophagy-related proteins (LC3II/LC3I, Beclin1, p62) and apoptosis-related proteins (Bax, C-caspase3, C-caspase9, Bcl-2) were tested by Western blot. ChIP and luciferase reporter assays confirmed relationship between SP1 and miR-200b-3p. Fluorescence intensity of LC3 in cells was detected by immunofluorescence. Flow cytometry assessed cell apoptosis. Osteonecrosis tissues from SONFH mice were examined by HE and TRAP staining.

RESULTS

APS induced autophagy and suppressed apoptosis in SONFH cell model. APS inhibited SP1 expression and SP1 overexpression reversed effects of APS on SONFH cell model. Mechanistically, SP1 targeted miR-200b-3p to inhibit Wnt/β-catenin pathway. MiR-200b-3p depletion rescued the promoting effect of SP1 on SONFH cell model by activating Wnt/β-catenin pathway. HE staining showed that APS treatment reduced the empty lacunae and alleviated inflammation in trabecular bone of SONFH mice. TRAP staining revealed decreased osteoclasts number in SONFH mice after APS treatment.

CONCLUSION

APS regulated osteocyte autophagy and apoptosis via SP1/miR-200b-3p axis and activated Wnt/β-catenin signaling, thereby alleviating SONFH, shedding new insights for therapy of SONFH.

Identification and Validation of Potential Ferroptosis-Related Genes in Glucocorticoid-Induced Osteonecrosis of the Femoral Head

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.

Transcriptomic analysis reveals genetic factors regulating early steroid-induced osteonecrosis of the femoral head

The present study aimed to explore the signaling pathways involved in development of early steroid-induced osteonecrosis of the femoral head (SONFH) and identify diagnostic biomarkers regulating peripheral blood in SONFH patients. We downloaded transcriptome data and identified differentially expressed genes (DEGs) using the R software. We used ClusterProfiler to perform enrichment analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, and analyzed protein-protein interactions using the STRING database. Network X was used to visualize the networks in Python. A total of 584 DEGs were identified, of which 294 and 290 were upregulated and downregulated, respectively. Enrichment analysis showed that the DEGs were mainly involved in red blood cell differentiation, cell protein catabolism, gas transportation, activation of myeloid leukocytes, phagocytosis, and inflammatory response. Pathway analysis revealed that these DEGs were involved in regulation of mitophagy-animal, human T-cell leukemia virus-1 infection, Forkhead box O, phagocytosis, osteoclast differentiation, and cytokine-cytokine receptor interaction. Quantitative real-time polymerase chain reaction results were consistent with findings from protein-protein interaction network analysis. Several genes, including peroxiredoxin 2, haptoglobin, matrix metallopeptidase 8, formyl peptide receptor 2, and integrin subunit alpha X, promote SONFH occurrence by regulating the redox, inflammatory response, and osteoblast and osteoclast structure and function pathways. They may be important targets for designing approaches for early diagnosis and treatment of SONFH.

Identification of potential dilated cardiomyopathy-related targets by meta-analysis and co-expression analysis of human RNA-sequencing datasets

AIMS

Dilated cardiomyopathy (DCM) remains among the most refractory heart diseases because of its complicated pathogenesis, and the key molecules that cause it remain unclear.

MAIN METHODS

To elucidate the molecules and upstream pathways critical for DCM pathogenesis, we performed meta-analysis and co-expression analysis of RNA-sequencing (RNA-seq) datasets from publicly available databases. We analyzed three RNA-seq datasets containing comparisons of RNA expression in left ventricles between healthy controls and DCM patients. We extracted differentially expressed genes (DEGs) and clarified upstream regulators of cardiovascular disease-related DEGs by Ingenuity Pathway Analysis (IPA). Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) analysis were also used to identify the hub gene candidates strongly associated with DCM.

KEY FINDINGS

In total, 406 samples (184 healthy, 222 DCM) were used in this study. Overall, 391 DEGs [absolute fold change (FC) ≥ 1.5; P < 0.01], including 221 upregulated and 170 downregulated ones in DCM, were extracted. Seven common hub genes (LUM, COL1A2, CXCL10, FMOD, COL3A1, ADAMTS4, MRC1) were finally screened. IPA showed several upstream transcriptional regulators, including activating (NFKBIA, TP73, CALR, NFKB1, KLF4) and inhibiting (CEBPA, PPARGC1A) ones. We further validated increased expression of several common hub genes in the transverse aortic constriction-induced heart failure model.

SIGNIFICANCE

In conclusion, meta-analysis and WGCNA using RNA-seq databases of DCM patients identified seven hub genes and seven upstream transcriptional regulators.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Integrated Analysis of miRNA-mRNA Regulatory Networks Associated with Osteonecrosis of the Femoral Head

Osteonecrosis of the femoral head (ONFH) accounts for as many as 18% of total hip arthroplasties. Knowledge of genetic changes and molecular abnormalities could help identify individuals considered to be at a higher risk of developing ONFH. In this study, we sought to identify differentially expressed miRNAs (DEmiRs) and genes (DEGs) associated with ONFH by integrated bioinformatics analyses as well as to construct the miRNA-mRNA regulatory network involving in the pathogenesis of ONFH. We performed differential expression analysis using a gene expression profile GSE123568 and a miRNA expression profile GSE89587 deposited in the Gene Expression Omnibus and identified 47 DEmiRs (24 upregulated miRNAs and 23 downregulated miRNAs) and 529 DEGs (218 upregulated genes and 311 downregulated genes). Gene Ontology enrichment analyses of DEGs suggested that DEGs were significantly enriched in neutrophil activation, cytosol, and ubiquitin-protein transferase activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs revealed that DEGs were significantly enriched in transcriptional misregulation in cancer. DEGs-based miRNA-mRNA regulatory networks were obtained by searching miRNA-mRNA prediction databases, TargetScan, miTarBase, miRMap, miRDB, and miRanda databases. Then, overlapped miRNAs were selected between these putative miRNAs and DEmiRs between ONFH and non-ONFH, and pairs of the DEmiR-DEG regulatory network were finally depicted. There were 12 nodes and 64 interactions for upDEmiR-downDEG regulatory networks and 6 nodes and 16 interactions for downDEmiR-upDEG regulatory networks. Using the STRING database, we established a protein-protein interaction network based on the overlapped DEGs between ONFH and non-ONFH. C5AR1, CDC27, CDC34, KAT2B, CPPED1, TFDP1, and MX2 were identified as the hub genes. The present study characterizes the miRNA profile, gene profile, and miRNA-mRNA regulatory network in ONFH, which may contribute to the interpretation of the pathogenesis of ONFH and the identification of novel biomarkers and therapeutic targets for ONFH.