CircRNA hsa_circ_0014130 function as a miR-132-3p sponge for playing oncogenic roles in bladder cancer via upregulating KCNJ12 expression.

The modern categories of endogenous non-coding RNAs, namely circular RNAs (circRNAs), involved within the carcinogenesis and progression of various human cancers. The fundamental aim of the current investigation was the evaluation of the hsa_circ_0014130 expressions, their biological functions, and potential regulatory network in bladder cancer. The level of expression for hsa_circ_0014130 was evaluated by qRT-PCR, and its relationships to clinicopathological features and survival outcomes of cases experiencing cancer of the bladder were scrutinized. The impact of hsa_circ_0014130 expressions on biological attitudes of bladder cancer cells in vitro was investigated. The interactions between hsa_circ_0014130 and microRNA (miRNA) sponge, miRNA, and its direct targets were determined by RNA pull-down as well as luciferase reporter gene assay. The correlations of their expression were determined by Pearson's correlation analysis. Rescue experiments were carried out to identify the biological roles of the regulation network. The expressions of hsa_circ_0014130 were markedly ameliorated in bladder cancer samples and linked with aggressive characteristics and unfavorable survival. Ectopic expression of hsa_circ_0014130 clearly enhanced the differentiation, proliferative, migratory, invasive potential of the cell in bladder cancer, and the development of tumor xenograft in vivo, while malignant biological behaviors were inhibited by hsa_circ_0014130 knockdown. The expression of hsa_circ_0014130 was tied to miR-132-3p in a negative manner with the cells and tissues of bladder cancer. hsa_circ_0014130 function as a competitive endogenous RNA for miR-132-3p to play oncogenic roles in bladder cancer cells. On the other hand, KCNJ12 was a straightforward target of miR-132-3p at the downstream, and the expressions of KCNJ12 were inversely related to that of miR-132-3p. Furthermore, a significantly positive correlation was found between hsa_circ_0014130 and KCNJ12 mRNA expression. More importantly, the oncogenic impact of hsa_circ_0014130 on bladder cancer cells was partly suppressed by ectopic expression of miR-132-3p or KCNJ12 knockdown. The underlined data revealed that hsa_circ_0014130 exerted its biological roles by regulating miR-132-3p/KCNJ12 expression. Further research revealed hsa_circ_0014130/miR-132-3p/KCNJ12 axis has participated in the Epithelial-mesenchymal transition (EMT) progress and GSK3ß/AKT signaling pathway. hsa_circ_0014130 works as a sponge of miR-132-3p to advance the oncogenesis and metastasis of bladder cancer by regulation of the KCNJ12 expression. These achievements might ameliorate the comprehension of tumor pathogenesis and provide novel therapeutic targets for cancer of the bladder.

Impact of Long Non-coding RNAs Associated With Microenvironment on Survival for Bladder Cancer Patients.

BACKGROUND: Cumulative evidence from several tumor studies, including bladder cancer, emphasizes the importance of the tumor microenvironment (TME) in tumorigenesis, development, and metastasis, which can be regulated by long non-coding RNAs (lncRNAs). This study aims to identify bladder cancer (BC) microenvironment-associated lncRNAs for their prognostic value predicting the survival of BC patients. METHODS: The data of BC patients regarding lncRNA expression and corresponding clinical characteristics were obtained from The Cancer Genome Atlas (TCGA). The Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis were performed to screen lncRNAs following the calculation of the immune score for each sample. For the screened lncRNAs, a risk score model was constructed to predict the survival, and 3- and 5-year overall survival (OS) rates were assessed using a nomogram. The calibration curve and concordance index (C-index) validated the performance of the nomogram. Finally, to explore the potential function related to the screened lncRNAs, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. RESULTS: The multivariate Cox regression analysis screened five TME-associated lncRNAs regarded as independent factors influencing the tumor progression. The corresponding risk score model was established as follows: (-0.15816 AC064805.1) + (0.10015 AC084033.3) + (-0.17977 AC092112.1) + (-0.05673AC103691.1) + (0.17789 AL391704.1) + (-0.16258 LINC00892). The C-index for the nomogram was 0.63 (95% CI: 0.625-0.635). Also, the calibration curve verified the predictive effectiveness by showing a good concordance between the nomogram prediction and the actual observation. GO and KEGG analysis demonstrated that six TME-associated lncRNAs were most likely linked to tumor metastasis and progression. CONCLUSION: The present study determined six lncRNAs as independent immuno-biomarkers in the TME, constructed a nomogram to predict their prognostic value, and investigated the potential biological processes to understand their regulatory roles in the progression of BC.

Arrhythmias Classification Using Short-Time Fourier Transform and GAN Based Data Augmentation.

Lacking sufficient training samples of different heart rhythms is a common bottleneck to obtain arrhythmias classification models with high accuracy using artificial neural networks. To solve this problem, we propose a novel data augmentation method based on short-time Fourier transform (STFT) and generative adversarial network (GAN) to obtain evenly distributed samples in the training dataset. Firstly, the one-dimensional electrocardiogram (ECG) signals with a fixed length of 6 s are subjected to STFT to obtain the coefficient matrices, and then the matrices of different heart rhythm samples are used to train GAN models respectively. The generated matrices are later employed to augment the training dataset of classification models based on four convolutional neural networks (CNNs). The result shows that the performances of the classification networks are all improved after we adopt the data enhancement strategy. The proposed method is helpful in augmentation and classification of biomedical signals, especially in detecting multiple arrhythmias, since adequate training samples are usually inaccessible in these studies.

[Prediction of recurrence of paroxysmal atrial fibrillation based on RR interval].

Atrial fibrillation (AF) is one of the most common arrhythmias, which does great harm to patients. Effective methods were urgently required to prevent the recurrence of AF. Four methods were used to analyze RR sequence in this paper, and differences between Pre-AF (preceding an episode of AF) and Normal period (far away from episodes of AF) were analyzed to find discriminative criterion. These methods are: power spectral analysis, approximate entropy (ApEn) and sample entropy (SpEn) analysis, recurrence analysis and time series symbolization. The RR sequence data used in this research were downloaded from the Paroxysmal Atrial Fibrillation Prediction Database. Supporting vector machine (SVM) classification was used to evaluate the methods by calculating sensitivity, specificity and accuracy rate. The results showed that the comprehensive utilization of recurrence analysis parameters reached the highest accuracy rate (95%); power spectrum analysis took second place (90%); while the results of entropy analyses and time sequence symbolization were not satisfactory, whose accuracy were both only 70%. In conclusion, the recurrence analysis and power spectrum could be adopted to evaluate the atrial chaotic state effectively, thus having certain reference value for prediction of AF recurrence.