Correlation between plasma long non-coding RNA MEG-3 and inflammatory cytokines in patients with diabetic nephropathy.

To identify the correlation between the plasma long non-coding RNA maternally expressed gene 3 (lncRNA MEG-3) and inflammatory cytokines in patients with diabetic nephropathy (DN) and search for a potential index for the diagnosis of DN. Quantitative real-time PCR (qPCR) was used to assess lncRNA MEG-3 expression. The levels of plasma cytokines were detected via enzyme-linked immunosorbent assay (-ELISA). 20 patients with type 2 diabetes (T2DM) and DN (DM+DN+ group), 19 patients with T2DM (DM+DN- group), and 17 healthy subjects (DM-DN- group) were finally enrolled. The expression of lncRNA MEG-3 was significantly upregulated in the DM+DN+ group compared to the DM+DN- group (p < 0.05) and the DM-DN- group (p < 0.001). Pearson's correlation analysis showed a positive correlation of lncRNA MEG-3 levels with cystatin C (Cys-C) (r = 0.468, p < 0.05), albumin-creatinine ratio (ACR) (r = 0.532, p < 0.05), and creatinine (Cr) (r = 0.468, p < 0.05), and a negative correlation with estimated glomerular filtration rate (eGFR) (r = -0.674, p < 0.01). Furthermore, the expression level of plasma lncRNA MEG-3 had a significantly positive correlation with the level of interleukin 1ß (IL-1ß) (r = 0.524, p < 0.05) and interleukin 18 (IL-18) (r = 0.230, p < 0.05). Binary regression analysis showed that lncRNA MEG-3 was a risk factor for DN with odds ration (OR) value of 1.71 (p < 0.05). The area under receiver operation characteristic (ROC) curve (AUC) of DN identified by lncRNA MEG-3 was 0.724. LncRNA MEG-3 was highly expressed in DN patients and showed a positive correlation with IL-1ß, IL-18, ACR, Cys-C, and Cr.

Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients.

Background: Growing evidence has revealed that m6A modification of long noncoding RNAs (lncRNAs) dynamically controls tumor stemness and tumorigenesis-related processes. However, the prognostic significance of m6A-related lncRNAs and their associations with stemness in low-grade glioma (LGG) remain to be clarified. Methods: A multicenter transcriptome analysis of lncRNA expression in 1,247 LGG samples was performed in this study. The stemness landscape of LGG tumors was presented and associations with clinical features were revealed. The m6A-related lncRNAs were identified between stemness groups and were further prioritized via least absolute shrinkage and selection operator Cox regression analysis. A risk score model based on m6A-related lncRNAs was constructed and validated in external LGG datasets. Results: Based on the expression of LINC02984, PFKP-DT, and CRNDE, a risk model and nomogram were constructed; they successfully predicted the survival of patients and were extended to external datasets. Significant correlations were observed between the risk score and tumor stemness. Moreover, patients in different risk groups exhibited distinct tumor immune microenvironments and immune signatures. We finally provided several potential compounds suitable for specific risk groups, which may aid in LGG treatment. Conclusions: This novel signature presents noteworthy value in the prediction of prognosis and stemness status for LGG patients and will foster future research on the development of clinical regimens.