Signature constructed by glycolysis-immune-related genes can predict the prognosis of osteosarcoma patients.

BACKGROUND: Glycolysis and tumor immunity were interrelated. In present study, we aimed to construct a prognostic model based on glycolysis-immune-related genes (GIGs) of osteosarcoma (OS) patients. METHODS: The mRNA expression data of OS patients were downloaded from GEO and TARGET databases. The hub genes were screened from 305 differentially expressed genes by univariate cox regression analysis and used to further establish a prognostic Risk Score. The independence of the Risk Score prognostic prediction model based on five genes was tested by multivariate Cox regression analysis. Finally, CIBERSORT and LM22 feature matrix were used to estimate the differences in immune infiltration of OS patients. RESULTS: A total of 141 OS patients' mRNA expression data and 296 glycolysis-associated genes were analyzed. Based on these 296 genes, all patients could be divided into two clusters: high glycolysis state and low glycolysis state. In the group with high glycolysis status, patients had low immune scores, indicating that glycolysis status was negatively correlated with immune function. The OS patients with high glycolysis and low immunity had the worst prognosis. Next, the Risk Score was constructed by 5 GIGs, including RAI14, MAF, CLEC5A, TIAL1 and CENPJ. Moreover, the Risk Score was shown to be an independent prognostic model, and high Risk Score patients had a greater risk of death. CONCLUSIONS: The Risk Score based on GIG could predict the prognosis of OS patients.

Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes.

Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients. Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool. Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis. Conclusion: Autophagy-related genes were identified as pivotal prognostic signatures, which could guide the clinical decision making in the treatment of osteosarcoma.

How to choose kinematic or mechanical alignment individually according to preoperative characteristics of patients?

BACKGROUND: Making decisions in alignment techniques in total knee arthroplasty (TKA) remains controversial. This study aims to identify the potential patients who were suitable for the kinematic (KA) or mechanical alignment (MA). METHODS: We reviewed 296 consecutive patients (296 TKAs, including 114 KA-TKAs and 182 MA-TKAs) who underwent unilateral TKA using a computer-assisted navigation from 2016 to 2018 in our prospectively maintained database. The minimum followup was 1 year. Clinical outcomes including the range of motion (ROM) and knee society score (KSS) were compared between KA-TKAs and MA-TKAs. Multiple regression models were used to evaluate the relationship between alignment techniques and KSS at the 1-year followup. Interaction and stratified analyses were conducted according to gender, age, body mass index (BMI), preoperative hip-knee-ankle (HKA) angle, ROM and KSS. RESULTS: ROM and KSS at the 1-year followup didn't differ between MA-TKAs and KA-TKAs (all p > 0.05). Alignment techniques did not associate with postoperative ROM (Adjusted ß = 0.4, 95% confidence interval [CI]: - 0.3, 1.6; p = 0.752) or 1-year KSS (Adjusted ß = 2.2, 95%CI: - 0.7, 5.6; p = 0.107). Patients with a BMI more than 30 kg/m^2 achieved better 1-year KSS when using MA than KA (p for interaction< 0.05). Additionally, patients with preoperative HKA angle more than 10 degrees varus benefited more from KA than MA (p for interaction< 0.05). CONCLUSIONS: Patients with severe varus deformity may be suitable for the KA technique, whereas MA should be used in obese patients.

Combined bioinformatics analysis reveals gene expression and DNA methylation patterns in osteoarthritis.

Osteoarthritis (OA) is a common type of arthritis, which may cause pain and disability. Alterations in gene expression and DNA methylation have been proven to be associated with the development of OA. The aim of the present study was to identify potential therapeutic targets and associated processes for OA via the combined analysis of gene expression and DNA methylation datasets. The gene expression and DNA methylation profiles were obtained from the Gene Expression Omnibus, and differentially expressed genes (DEGs) and differentially methylated sites (DMSs) were identified in the present study, using R programming software. The enriched functions of DEGs and DMSs were obtained via the Database for Annotation, Visualization and Integrated Discovery. Finally, cross analysis of DEGs and DMSs was performed to identify genes that exhibited differential expression and methylation simultaneously. The protein­protein interaction (PPI) network of overlaps between DEGs and DMSs was obtained using the Human Protein Reference Database; the topological properties of PPI network overlaps were additionally obtained. Hub genes in the PPI network were further confirmed via reverse transcription­quantitative polymerase chain reaction (RT­qPCR). The results of the present study revealed that the majority of DEGs and DMSs were upregulated and hypomethylated in patients with OA, respectively. DEGs and DMSs were primarily involved in inflammatory, immune and gene expression regulation­associated processes and pathways. Cross analysis revealed 30 genes that exhibited differential expression and methylation in OA simultaneously. Topological analysis of the PPI network revealed that numerous genes, including G protein subunit α1 (GNAI1), runt related transcription factor 2 (RUNX2) and integrin subunit ß2 (ITGB2), may be involved in the development of OA. Additionally, RT­qPCR analysis of GNAI1, RUNX2 and ITGB2 provided further confirmation. Numerous known and novel therapeutic targets were obtained via network analysis. The results of the present study may be beneficial for the diagnosis and treatment of OA.

Combined analysis of ChIP-seq and gene microarray datasets identify the E2-mediated genes in ERα-dependent manner in osteosarcoma.

Osteosarcoma is a common bone tumor which is affected by E2, the most representative estrogen. Gene regulation function of E2 is highly dependent on estrogen receptor. The purpose of this study was to explore the gene regulation patterns of E2 through estrogen receptor α (ESR1) in osteosarcoma based on the combined analysis of ChIP-seq and gene microarray. All of the datasets were downloaded from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) in E2 treated U2OS cells expressing ESR1 (U2OS-ERα) compared with those treated with vehicle were obtained based on R programming software. ESR1-specific binding sites (peaks) in E2 treated U2OS cells were identified through MACS. Overlaps between DEGs and ESR1 target genes which contained peaks in promoters were considered as reliable E2-mediated genes through ESR1 in osteosarcoma. Moreover, we conducted miRNA-Gene regulation analysis for those genes through miRWalk database to identify potential therapeutic targets for the genes. Functional enrichment analysis of DEGs indicated their potential involvement in cancer, and cell activity-related processes. Fifteen overlaps were identified between DEGs and target genes of ESR1, of which 12 were found to be regulated by miRNA. Several known estrogen response genes and novel genes were obtained in this study and they might provide potential therapeutic targets for osteosarcoma.