BackgroundThe study aimed to analyze aberrantly methylated genes, relevant pathways and transcription factors (TFs) in osteosarcoma (OS) development.MethodsBased on the DNA methylation microarray data GSE36002 that were downloaded from GEO database, the differentially methylated genes in promoter regions were identified between OS and normal samples. Pathway and function enrichment analyses of differentially methylated genes was performed. Subsequently, protein-protein interaction (PPI) network was constructed, followed by identification of cancer-associated differentially methylated genes and significant differentially methylated TFs.ResultsA total of 1379 hyper-methylation regions and 169 hypo-methylation regions in promoter regions were identified in OS samples compared to normal samples. The differentially hyper-methylated genes were significantly enriched in Neuroactive ligand-receptor interaction pathway, and Peroxisome proliferator activated receptor (PPAR) signaling pathway. The differentially hypo-methylated genes were significantly enriched in Toll-like receptor signaling pathway. In PPI network, signal transducers and activators of transcription (STAT3) had high degree (degree=21). MAX interactor 1, dimerization protein (MXI1), STAT3 and T-cell acute lymphocytic leukemia 1 (TAL1) were significant TFs enriched with target genes in OS samples. They were found to be cancer-associated and hyper-methylated in OS samples.ConclusionNeuroactive ligand-receptor interaction, PPAR signaling, Toll-like receptor signaling pathways are implicated in OS. MXI1, STAT3, and TAL1 may be important TFs involved in OS development.
Discoid lateral meniscus (DLM) is more prone to injury than a normally shaped meniscus. No study has compared the gene expression and cell heterogeneity between discoid and normal menisci. We aimed to identify specific cell clusters and their marker genes in discoid meniscus, thereby providing a theoretical basis for the treatment and etiology of DLM. ScRNA-seq was used in DLM and osteoarthritis lateral meniscus (OAM) cells to identify cell subsets and their gene signatures. Pseudo-time analysis and immunohistochemical staining were used to investigate the temporal and spatial distribution of DLM-specific clusters. ScRNA-seq identified nine clusters originating from DLM and OAM, composed of seven empirically defined populations and two novel populations specific to DLM, namely, the prehypertrophic chondrocyte 2 (PreHTC-2) and regulatory chondrocyte (RegC-2) populations. Single-cell trajectory showed that RegC-2 and PreHTC-2 were mainly distributed in a specific cell fate, with the PreHTC-2 marker gene HAPLN1 highly expressed at the end of this fate. Immunohistochemical staining showed that HAPLN1 + cells were mainly distributed in the white zone of DLM.Matrix metalloproteinase (MMP) variants were expressed in DLM and OAM, with MMP2 highly expressed in OAM-dominant cell clusters, while MMP3 was highly expressed in DLM-dominant cell clusters. We concluded that two novel cell clusters including PreHTC-2 were identified using single-cell sequencing, which were mainly distributed in the white areas of DLM. Differentiated MMP expression in the trajectory may be a possible mechanism of DLM formation.
Background Cross-table lateral (CL) radiography is a convenient and feasible method to assess cup version angle (VA) after total hip arthroplasty; However, pelvic tilt (PT) may contribute to its measurement inaccuracy. How PT affects CL radiographic measurements have not been well studied. We sought (1) to determine the effect of the PT on cup version measurement on CL radiography and (2) to develop a method for reducing measurement errors caused by the PT. Methods We used 3D technique to construct standard model and capture CL radiography simulation. A linear regression model was created to analyze the relationship between PT and VA. CL radiography and computed tomography (CT) were performed for the enrolled patients after surgery. The consistency between CL and CT measurements were verified by intra-class correlation coefficient (ICC). Results There was a high correlation between the VA and PT. For each 1-degree increased in the PT, the VA decreased by 0.76° (R2 = 0.995, p < 0.001). Based on the data, we created a corrective formula to convert the radiographic measurements into values approximating the actual VA under a natural pelvic position. The VA measurements corrected by our equation was in high agreement with the CT-measured values with reference to the corresponding PT (ICC = 0.988, p < 0.001), which was in sharp contrast to that without PT control (ICC = 0.454, p = 0.203). Conclusions The PT may contribute to cup version measurement inaccuracies on CL radiography. Our mathematical algorithm can serve as a reliable method to improve the accuracy of CL radiography.
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