Background Circular RNAs (circRNAs) are involved in diverse processes that drive cancer development. However, the expression landscape and mechanistic function of circRNAs in osteosarcoma (OS) remain to be studied. Methods Bioinformatic analysis and high-throughput RNA sequencing tools were employed to identify differentially expressed circRNAs between OS and adjacent noncancerous tissues. The expression level of circ_001422 in clinical specimens and cell lines was measured using qRT-PCR. The association of circ_001422 expression with the clinicopathologic features of 55 recruited patients with OS was analyzed. Loss- and gain-of-function experiments were conducted to explore the role of circ_001422 in OS cells. RNA immunoprecipitation, fluorescence in situ hybridization, bioinformatics database analysis, RNA pulldown assays, dual-luciferase reporter assays, mRNA sequencing, and rescue experiments were conducted to decipher the competitive endogenous RNA regulatory network controlled by circ_001422. Results We characterized a novel and abundant circRNA, circ_001422, that promoted OS progression. Circ_001422 expression was dramatically increased in OS cell lines and tissues compared with noncancerous samples. Higher circ_001422 expression correlated with more advanced clinical stage, larger tumor size, higher incidence of distant metastases and poorer overall survival in OS patients. Circ_001422 knockdown markedly repressed the proliferation and metastasis and promoted the apoptosis of OS cells in vivo and in vitro, whereas circ_001422 overexpression exerted the opposite effects. Mechanistically, competitive interactions between circ_001422 and miR-195-5p elevated FGF2 expression while also initiating PI3K/Akt signaling. These events enhanced the malignant characteristics of OS cells. Conclusions Circ_001422 accelerates OS tumorigenesis and metastasis by modulating the miR-195-5p/FGF2/PI3K/Akt axis, implying that circ_001422 can be therapeutically targeted to treat OS.

Osteosarcoma is the most common primary malignancy of the bones, and is associated with a high rate of metastasis and a poor prognosis. A tight association between the tumor microenvironment (TME) and osteosarcoma metastasis has been established. In the present study, the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to calculate the immune and stromal scores of patients with osteosarcoma based on data from The Cancer Genome Atlas database. A metagene approach and deconvolution method were used to reveal distinct TME landscapes in patients with osteosarcoma. Bioinformatics analysis was used to identify differentially expressed genes (DEGs) associated with metastasis and immune infiltration in osteosarcoma, and a risk model was constructed using the DEGs with potential prognostic significance. Subsequently, gene set enrichment and Spearman's correlation analyses were used to delineate the biological processes associated with these prognostic biomarkers. Finally, immunohistochemical (IHC) analysis was performed to evaluate the expression levels of immune infiltrates and prognostic biomarkers in clinical osteosarcoma tissues. The results of the ESTIMATE demonstrated that patients with non-metastatic osteosarcoma presented with higher immune/stromal scores and a more favorable prognosis compared with those with metastatic osteosarcoma. The TME landscapes in patients with osteosarcoma suggested that high levels of tumor-infiltrating immune cells (TIICs) may suppress metastasis. Increased numbers of CD56 bright natural killer cells, immature B cells, M1 macrophages and neutrophils, and lower levels of M2 macrophages were observed in the non-metastatic tissues compared with those in the metastatic tissues. A total of 69 DEGs were identified to be associated with metastasis and immune infiltration in osteosarcoma. Of these, GATA3, LPAR5, EVI2B, RIAM and CFH exhibited prognostic potential and were highly expressed in non-metastatic osteosarcoma tissues based on the IHC analysis results. These biomarkers were involved in various immune-related biological processes and were positively associated with multiple TIICs and immune signatures. The risk model constructed using these prognostic biomarkers demonstrated high predictive accuracy for the prognosis of osteosarcoma. In conclusion, the present study proposed a five-biomarker prognostic signature for the prediction of metastasis and immune infiltration in patients with osteosarcoma.

BackgroundCircular RNAs (circRNAs) are involved in diverse processes that drive cancer development. Nevertheless, the expression landscape and mechanistic function of circRNAs in osteosarcoma (OS) remain to be studied.MethodsBioinformatics analysis and high-throughput RNA sequencing tools were employed to determine differentially expressed circRNAs between OS and adjacent healthy tissues. The expression levels of circ_001422 in clinical specimens and cell lines were measured using qRT-PCR. A total of 55 OS patients were recruited to analyze the association of circ_001422 expression with clinicopathologic features. Loss- and gain-of-function tests were conducted to explore the role of circ_001422 in OS cells. RNA immunoprecipitation, fluorescent in situ hybridization, bioinformatics databases, RNA pull-down assay, dual-luciferase reporter assay, mRNA sequencing, and rescue experiments were conducted to decipher the competitive endogenous RNAs regulatory network dominated by circ_001422.ResultsWe characterized a novel and abundant circRNA, circ_001422, which promoted the progression of OS. Circ_001422 expression was dramatically higher in OS cell lines and tissues relative to normal samples. Increased circ_001422 correlated with more advanced clinical stage, larger tumor size, more distant metastases and poorer overall survival of OS patients. Knockdown of circ_001422 markedly repressed proliferation and metastasis and promoted apoptosis of OS cells in vivo and in vitro, whereas overexpression of circ_001422 exerted an opposite effect. Mechanistically, competitive interactions between circ_001422 and miR-195-5p elevated the expression of FGF2 while also initiating the PI3K/Akt signaling pathway. These events enhanced the malignant characteristics of OS cells.ConclusionsCirc_001422 accelerates OS tumorigenesis and metastasis through modulating the miR-195-5p/FGF2/PI3K/Akt axis, implying that circ_001422 could be therapeutically targeted to treat OS patients.

Osteosarcoma (OS) often occurs in children and often undergoes metastasis, resulting in lower survival rates. Information on the complexity and pathogenic mechanism of OS is limited, and thus, the development of treatments involving alternative molecular and genetic targets is hampered. We categorized transcriptome data into metastasis and nonmetastasis groups, and 400 differential RNAs (230 messenger RNAs (mRNAs) and 170 long noncoding RNAs (lncRNAs)) were obtained by the edgeR package. Prognostic genes were identified by performing univariate Cox regression analysis and the Kaplan–Meier (KM) survival analysis. We then examined the correlation between the expression level of prognostic lncRNAs and mRNAs. Furthermore, microRNAs (miRNAs) corresponding to the coexpression of lncRNA-mRNA was predicted, which was used to construct a competitive endogenous RNA (ceRNA) regulatory network. Finally, multivariate Cox proportional risk regression analysis was used to identify hub prognostic genes. Three hub prognostic genes (ABCG8, LOXL4, and PDE1B) were identified as potential prognostic biomarkers and therapeutic targets for OS. Furthermore, transcriptions factors (TFs) (DBP, ESX1, FOS, FOXI1, MEF2C, NFE2, and OTX2) and lncRNAs (RP11-357H14.16, RP11-284N8.3, and RP11-629G13.1) that were able to affect the expression levels of genes before and after transcription were found to regulate the prognostic hub genes. In addition, we identified drugs related to the prognostic hub genes, which may have potential clinical applications. Immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) confirmed that the expression levels of ABCG8, LOXL4, and PDE1B coincided with the results of bioinformatics analysis. Moreover, the relationship between the hub prognostic gene expression and patient prognosis was also validated. Our study elucidated the roles of three novel prognostic biomarkers in the pathogenesis of OS as well as presenting a potential clinical treatment for OS.

Objectives: We aimed to examine whether metformin (MET) use is associated with a reduced risk of total knee arthroplasty (TKA) and low severity of knee pain in patients with knee osteoarthritis (OA) and diabetes and/or obesity. Methods: Participants diagnosed with knee OA and diabetes and/or obesity from June 2000 to July 2019 were selected from the information system of a local hospital. Regular MET users were defined as those with recorded prescriptions of MET or self-reported regular MET use for at least 6 months. TKA information was extracted from patients’ surgical records. Knee pain was assessed using the numeric rating scale. Log-binomial regression, linear regression, and propensity score weighting (PSW) were performed for statistical analyses. Results: A total of 862 participants were included in the analyses. After excluding missing data, there were 346 MET non-users and 362 MET users. MET use was significantly associated with a reduced risk of TKA (prevalence ratio: 0.26, 95% CI: 0.15 to 0.45, p < 0.001), after adjustment for age, gender, body mass index, various analgesics, and insurance status. MET use was significantly associated with a reduced degree of knee pain after being adjusted for the above covariates (β: −0.48, 95% CI: −0.91 to −0.05, p = 0.029). There was a significantly accumulative effect of MET use on the reduced risk of TKA. Conclusion: MET can be a potential therapeutic option for OA. Further clinical trials are needed to determine if MET can reduce the risk of TKA and the severity of knee pain in metabolic-associated OA patients.

Spinal cord injury (SCI) is a serious disorder of the central nervous system with a high disability rate. Long noncoding RNAs (lncRNAs) are reported to mediate many biological processes. The aim of this study was to explore lncRNA and mRNA expression profiles and functional networks after SCI. Differentially expressed genes between SCI model rats and sham controls were identified by microarray assays and analyzed by functional enrichment. Key lncRNAs were identified using a support vector machine- (SVM-) recursive feature elimination (RFE) algorithm. A trans and cis regulation model was used to analyze the regulatory relationships between lncRNAs and their targets. An lncRNA-related ceRNA network was established. We identified 5465 differentially expressed lncRNAs (DE lncRNAs) and 8366 differentially expressed mRNAs (DE mRNAs) in the SCI group compared with the sham group ( fold   change > 2.0 , p < 0.05 ). Four genes were confirmed by qRT-PCR which were consistent with the microarray data. GSEA analysis showed that most marked changes occurred in pathways related to immune inflammation and nerve cell function, including cytokine-cytokine receptor interaction, neuroactive ligand-receptor interaction, and GABAergic synapse. Enrichment analysis identified 30 signaling pathways, including those associated with immune inflammation response. A total of 40 key lncRNAs were identified using the SVM-RFE algorithm. A key lncRNA-mRNAs coexpression network was generated for 230 951 lncRNA-mRNA pairs with half showing positive correlations. Several key DE lncRNAs were predicted to have “cis”- or “trans”-regulated target genes. The transcription factors, Sp1, JUN, and SOX10, may regulate the interaction between XR_001837123.1 and ETS 1. In addition, five pairs of ceRNA regulatory sequences were constructed. Many mRNAs and lncRNAs were found to be dysregulated after SCI. Bioinformatic analysis showed that DE lncRNAs may play crucial roles in SCI. It is anticipated that these findings will provide new insights into the underlying mechanisms and potential therapeutic targets for SCI.

ObjectivesOsteosarcoma is a malignant bone tumor with poor outcomes affecting the adolescents and elderly. In this study, we comprehensively assessed the metabolic characteristics of osteosarcoma patients and constructed a hexosamine biosynthesis pathway (HBP)-based risk score model to predict the prognosis and tumor immune infiltration in patients with osteosarcoma.MethodsGene expression matrices of osteosarcoma were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. GSVA and univariate Cox regression analysis were performed to screen the metabolic features associated with prognoses. LASSO regression analysis was conducted to construct the metabolism-related risk model. Differentially expressed genes (DEGs) were identified and enrichment analysis was performed based on the risk model. CIBERSORT and ESTIMATE algorithms were executed to evaluate the characteristics of tumor immune infiltration. Comparative analyses for immune checkpoints were performed and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapeutic response. Finally, hub genes with good prognostic value were comprehensive analyzed including drug sensitivity screening and immunohistochemistry (IHC) experiments.ResultsThrough GSVA and survival analysis, the HBP pathway was identified as the significant prognostic related metabolism feature. Five genes in the HBP pathway including GPI, PGM3, UAP1, OGT and MGEA5 were used to construct the HBP-related risk model. Subsequent DEGs and enrichment analyses showed a strong correlation with immunity. Further, CIBERSORT and ESTIMATE algorithms showed differential immune infiltration characteristics correlated with the HBP-related risk model. TIDE algorithms and immune checkpoint analyses suggested poor immunotherapeutic responses with low expression of immune checkpoints in the high-risk group. Further analysis revealed that the UAP1 gene can predict metastasis. IHC experiments suggested that UAP1 expression correlated significantly with the prognosis and metastasis of osteosarcoma patients. When screening for drug sensitivity, high UAP1 expression was suggestive of great sensitivity to antineoplastic drugs including cobimetinib and selumetinib.ConclusionWe constructed an HBP-related gene signature containing five key genes (GPI, PGM3, UAP1, OGT, MGEA5) which showed a remarkable prognostic value for predicting prognosis and can guide immunotherapy and targeted therapy for osteosarcoma.

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