Breast cancer is the most common cancer type in women. Long non-coding RNAs (lncRNAs) have been reported as potential new diagnostic markers, prognostic factors, and therapeutic targets in cancer. However, the specific roles and mechanisms of lncRNAs in breast cancer remain to be elucidated. Here we demonstrated the downregulation of lncRNA SNORD3A in breast cancer cells and tissues and verified its non-protein-coding property. SNORD3A overexpression had no effect on cell proliferation but specifically sensitized breast cancer cells to 5-fluorouracil (5-FU) in vitro and in vivo. Mechanistically, SNORD3A exerts its effect via enhancing uridine monophosphate synthetase (UMPS) protein expression. SNORD3A acts as a competing endogenous RNA for miR-185-5p, leading to UMPS protein upregulation. miR-185-5p overexpression disrupted the effect of SNORD3A on chemosensitization to 5-FU in vitro and in vivo. Moreover, Meis1 overexpression transcriptionally promotes SNORD3A expression, and Meis1 is downregulated in breast cancer cells and tissues. In breast cancer tissues, SNORD3A level positively correlates with Meis1 and UMPS protein levels, whereas miR-185-5p level negatively correlates with UMPS protein level. High SNORD3A transcript and Meis1 and UMPS protein levels predicts a better outcome, but high miR-185-5p level predicts a worse outcome in breast cancer patients receiving 5-FU-based chemotherapy. Our findings indicate that Meis1-regulated SNORD3A specifically sensitizes breast cancer cells to 5-FU via enhancing UMPS expression. The SNORD3A-UMPS axis may serve as a potential biomarker and therapeutic target to improve the efficacy of 5-FU-based chemotherapy for breast cancer patients.
Background: Lung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.
Methods:The genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.Results: Several statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low-and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients' overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies. Conclusions: We constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.
Glioma is the most common primary brain tumor with poor prognosis and high mortality. The purpose of this study was to use the epigenetic signature to predict prognosis and evaluate the degree of immune infiltration in gliomas. We integrated gene expression profiles and DNA methylation data of lower-grade glioma and glioblastoma to explore epigenetic differences and associated differences in biological function. Cox regression and lasso analysis were used to develop an epigenetic signature based on eight DNA methylation sites to predict prognosis of glioma patients. Kaplan–Meier analysis showed that the overall survival time of high- and low-risk groups was significantly separated, and ROC analysis verified that the model had great predictive ability. In addition, we constructed a nomogram based on age, sex, 1p/19q status, glioma type, and risk score. The epigenetic signature was obviously associated with tumor purity, immune checkpoints, and tumor-immune infiltrating cells (CD8+ T cells, gamma delta T cells, M0 macrophages, M1 macrophages, M2 macrophages, activated NK cells, monocytes, and activated mast cells) and thus, it may find application as a guide for the evaluation of immune infiltration or in treatment decisions in immunotherapy.
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