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This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

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Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature’s prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

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BackgroundOsteosarcoma is a frequent bone malignancy in children and young adults. Despite the availability of some prognostic biomarkers, most of them fail to accurately predict prognosis in osteosarcoma patients. In this study, we used bioinformatics tools and machine learning algorithms to establish an autophagy-related long non-coding RNA (lncRNA) signature to predict the prognosis of osteosarcoma patients.MethodsWe obtained expression and clinical data from osteosarcoma patients in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We acquired an autophagy gene list from the Human Autophagy Database (HADb) and identified autophagy-related lncRNAs by co-expression analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the autophagy-related lncRNAs were conducted. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of the autophagy-related lncRNA signature and validate the relationship between the signature and osteosarcoma patient survival in an independent cohort. We also investigated the relationship between the signature and immune cell infiltration.ResultsWe initially identified 69 autophagy-related lncRNAs, 13 of which were significant predictors of overall survival in osteosarcoma patients. Kaplan-Meier analyses revealed that the 13 autophagy-related lncRNAs could stratify patients based on their outcomes. Receiver operating characteristic curve analyses confirmed the superior prognostic value of the lncRNA signature compared to clinically used prognostic biomarkers. Importantly, the autophagy-related lncRNA signature predicted patient prognosis independently of clinicopathological characteristics. Furthermore, we found that the expression levels of the autophagy-related lncRNA signature were significantly associated with the infiltration levels of different immune cell subsets, including T cells, NK cells, and dendritic cells.ConclusionThe autophagy-related lncRNA signature established here is an independent and robust predictor of osteosarcoma patient survival. Our findings also suggest that the expression of these 13 autophagy-related lncRNAs may promote osteosarcoma progression by regulating immune cell infiltration in the tumor microenvironment.

Background: Spinal cord injury (SCI) is a severe central nervous system injury for which few efficacious drugs are available. Rosmarinic acid (RA), a water-soluble polyphenolic phytochemical, has antioxidant, anti-inflammatory, and anti-apoptotic properties. However, the effect of RA on SCI is unclear. We investigated the therapeutic effect and underlying mechanism of RA on SCI in vivo and in vitro. Methods: In vivo experiment, The BBB locomotion scale, the inclined plane test, Nissl staining, and spinal cord edema were employed to determine the neuroprotective effects of RA treatment after SCI. Inflammatory and oxidative stress markers were detected by commercial kits and cell apoptosis status was measured by TUNEL staining. A proteomics and bioinformatics approach, together with Western blotting, was used to investigate the effect of RA on the proteome of SCI rats. In vitro experiment, oxidative stress and inflammatory injury were induced by H2O2 and LPS stimulation. Effects of RA on cell viability, apoptosis, inflammatory, and oxidative stress were evaluated. Results: Using a rat model of SCI, we showed that RA improved locomotor recovery after SCI and significantly mitigated neurological deficit, increased neuronal preservation, and reduced apoptosis. Also, RA inhibited activation of microglia and the release of TNF-α, IL-6, and IL-1β and MDA. Moreover, proteomics analyses identified the Nrf2 and NF-κB pathways as targets of RA. Pretreatment with RA increased levels of Nrf2 and HO-1 and reduced those of TLR4 and MyD88 as well as phosphorylation of IkB and subsequent nuclear translocation of NF-κB-p65. Using H2O2- and LPS-induced PC12 cells, we found that RA ameliorated the H2O2-induced decrease in viability and increase in apoptosis and oxidative injury by activating the Nrf2/HO-1 pathway. Also, LPS-induced cytotoxicity and increased apoptosis and inflammatory injury in PC-12 cells were mitigated by RA by inhibiting the TLR4/NF-κB pathway. The Nrf2 inhibitor ML385 weakened the effect of RA on oxidant stress, inflammation and apoptosis in SCI rats, and significantly increased the nuclear translocation of NF-κB. Conclusions: Therefore, the neuroprotective effect on SCI of RA may be due to its antioxidant and anti-inflammatory properties, which are mediated by modulation of the Nrf2/HO-1 and TLR4/NF-κB pathways. Moreover, RA activated Nrf2/HO-1, which amplified its inhibition of the NF-κB pathway.