Identification of Up-Regulated ANXA3 Resulting in Fracture Non-Union in Patients With T2DM

Liu, Changjiang; Liu, Yuhang; Yu, Yifeng; Zhao, Yong; Zhang, Dong; Yu, Aixi

doi:10.3389/fendo.2022.890941

Cited by 13 publications

(10 citation statements)

References 66 publications

(68 reference statements)

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“…It is worth noting that studies have shown that osteoporosis and diabetes are risk factors for fracture nonunion 45,46 ; however, the results of this study suggest the lack of a significant association, possibly due to the study population. These factors are mostly observed in elderly individuals, whereas femoral shaft fractures are mostly caused by high-energy injuries.…”

Section: Discussioncontrasting

confidence: 77%

Development and validation of predictive nomogram for postoperative non-union of closed femoral shaft fracture

Li,

Wang,

Zhou

et al. 2024

Sci Rep

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Closed femoral shaft fracture is caused by high-energy injuries, and non-union exists after operation, which can significantly damage patients’ body and mind. This study aimed to explore the factors influencing postoperative non-union of closed femoral shaft fractures and establish a predictive nomogram. Patients with closed femoral shaft fractures treated at Hebei Medical University Third Hospital between January 2015 and December 2021 were retrospectively enrolled. A total of 729 patients met the inclusion criteria; of them, those treated in 2015–2019 comprised the training cohort (n = 617), while those treated in 2020–2021 comprised the external validation cohort (n = 112). According to multivariate logistic regression analysis, complex fractures, bone defects, smoking, and postoperative infection were independent risk factors. Based on the factors, a predictive nomogram was constructed and validated. The C-indices in training and external validation cohorts were 0.818 and 0.781, respectively; and the C-index of internal validation via bootstrap resampling was 0.804. The Hosmer–Lemeshow test showed good fit of the nomogram (P > 0.05) consistent with the calibration plot results. The clinical effectiveness was best at a threshold probability of 0.10–0.40 in decision curve analysis. The risk prediction for patients with fractures using this nomogram may aid targeted prevention and rehabilitation programs.

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Section: Discussioncontrasting

confidence: 77%

Development and validation of predictive nomogram for postoperative non-union of closed femoral shaft fracture

Li,

Wang,

Zhou

et al. 2024

Sci Rep

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“…Furthermore, GO analysis revealed that bosutinib mainly influences extracellular structure organization, extracellular matrix organization, sterol biosynthetic process, secondary alcohol biosynthetic process, and cholesterol biosynthetic process (Figure B). Pathways enrichment analysis using GO and KEGG revealed several pathways related with bone metabolism or bone-related disease, including extracellular matrix organization, steroid biosynthesis, TNF signaling pathway, as well as starch and sucrose metabolism. − PCA showed that the difference between groups is obvious (Figure S1D). Venn diagram analysis revealed that 382 and 297 genes were specifically expressed in hUCMSCs induced with and without bosutinib, respectively, with 10830 genes co-expressed between the two groups (Figure C).…”

Section: Resultsmentioning

confidence: 99%

Bosutinib Laden Three-Dimensional Printed Zein Scaffolds Promote Osteogenesis

Zou,

Yin,

Lei

et al. 2024

ACS Appl. Polym. Mater.

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Bone defect repair remains a challenge to be addressed in clinical practice, and existing bone grafting methods still have disadvantages and limitations. Drugs or cytokines laden into bioactive scaffolds to promote bone regeneration is a promising strategy to deal with the problem; thus, it is meaningful to develop drugs and scaffolds with osteogenic abilities. Drug repurposing is an admirable method to find unknown usages of old drugs for enhancing osteogenic differentiation. In this study, by screening an approved drug library consisting of 2040 compounds, bosutinib, a tyrosine kinase inhibitor, was identified to promote osteogenic differentiation in vitro. The observed effect was further confirmed using polymerase chain reaction (PCR), western blot, Alizarin Red staining, and alkaline phosphatase (ALP) activity staining. Subsequently, transcriptome sequencing was performed to elucidate the osteogenesis mechanism of bosutinib, and a potential target gene, Storkhead box 1 (STOX1), which was not reported to be associated with osteogenesis, was found. By a gene knockdown strategy, we provided preliminary evidence that bosutinib can promote osteogenic differentiation by mediating STOX1. Furthermore, in order to maximize the local effects of drug and minimize its side effects, a bosutinib laden three-dimensional (3D)-printed zein scaffold was constructed and characterized, and its ability of promoting osteogenesis was evaluated in vivo. Micro-CT analysis and histological staining demonstrated that the scaffold effectively promoted bone regeneration. All in all, our study provides a therapeutic option for the treatment of bone defects.

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“…MMP9 [262], IGF2 [273], SOCS3 [274], MMP8 [265], OSM (oncostatin M) [266], PLAU (plasminogen activator, urokinase) [275], ADAMDEC1 [269], IL1RN [270], TLR4 [271], CD80 [276] and TXNIP (thioredoxin interacting protein) [272] make great contributions to the progression of nasal polyps. MMP9 [277], S100A12 [278], IGF2 [279], GPR84 [280], SOCS3 [281], ANXA3 [282], IGFBP2 [283], NOS1AP [284], G0S2 [285], HP (haptoglobin) [286], MMP8 [287], SLC6A19 [288], OSM (oncostatin M) [289], S100A8 [290], FFAR3 [291], ARG1 [292], ADM (adrenomedullin) [293], S100A9 [294], NRG1 [295], IL1R1 [296], IL4R [297], TSPO (translocator protein) [298], TXN (thioredoxin) [299], PLAU (plasminogen activator, urokinase) [300], SLC11A1 [301], TRPM2 [302], RETN (resistin) [303], NLRC4 [304], CD55 [305], ALOX5AP [306], GPR160 [307], TRPM6 [308], ALOX5 [309], IL1RN [310], ADM2 [311], DGAT2 [312], TLR4 [313], ENTPD1 [314], GRB10 [315], IL17RA [316], IRS2 [317], MGAM (maltase-glucoamylase) [318], FADS2 [319], SLC22A4 [320], KCNJ15 [321], PPP1R3B [322], CTSD (cathepsin D) [323], SERPINB11 [324], COL4A3 [325], ADAM12 [326], SOX5 [327], CD80 [328], ERBB2 [329], POU2AF1 [330], FASLG (Fas ligand) [331], SOX13 [332], BANK1 [333], IL2RA [334], AQP3 [335], CCR4 [336], CD74 [337], FCRL3 [338], ITGB7 [339],...…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.

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Identification of Up-Regulated ANXA3 Resulting in Fracture Non-Union in Patients With T2DM

Cited by 13 publications

References 66 publications

Development and validation of predictive nomogram for postoperative non-union of closed femoral shaft fracture

Development and validation of predictive nomogram for postoperative non-union of closed femoral shaft fracture

Bosutinib Laden Three-Dimensional Printed Zein Scaffolds Promote Osteogenesis

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

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