ResDeepSurv: A Survival Model for Deep Neural Networks Based on Residual Blocks and Self-attention Mechanism.

Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling. To address these limitations, we propose a novel residual-based self-attention deep neural network for survival modeling, called ResDeepSurv, which combines the benefits of neural networks and the Cox proportional hazards regression model. The model proposed in our study simulates the distribution of survival time and the correlation between covariates and outcomes, but does not impose strict assumptions on the basic distribution of survival data. This approach effectively accounts for both linear and nonlinear risk functions in survival data analysis. The performance of our model in analyzing survival data with various risk functions is on par with or even superior to that of other existing survival analysis methods. Furthermore, we validate the superior performance of our model in comparison to currently existing methods by evaluating multiple publicly available clinical datasets. Through this study, we prove the effectiveness of our proposed model in survival analysis, providing a promising alternative to traditional approaches. The application of deep learning techniques and the ability to capture complex relationships between covariates and survival outcomes without relying on extensive feature engineering make our model a valuable tool for personalized medicine and decision-making in clinical practice.

Silencing long non-coding RNA SNHG6 restrains proliferation, migration and invasion of Wilms' tumour cell lines by regulating miR-15a.

Wilms' tumour (WT) is a frequent primary malignant tumour of urinary system in children. LncRNAs small nucleolar RNA host gene 6 (SNHG6) modulates kinds of biological procedures of cancer cells. Our research is to explore the effect and associated regulatory mechanism of SNHG6 in WT. CCK8 assays and bromodeoxyuridine were used to determine cell viability and cell proliferation, respectively. Flow cytometric analysis was performed to measure cell apoptosis rate. Cell mobility was tested through transwell and migration assays. Western blotting was employed to test the expression of proteins related to cell proliferation, cell apoptosis and signal pathways. In the results, overexpression of SNHG6 was found in WT tissues. The knockdown of SNHG6 suppressed cell proliferation, migration and incursion, but promoted apoptosis. Further study found that the knockdown of SNHG6 elevated the expression of miR-15a. Then, the combination of miR-15a inhibitor abolished the inhibiting effect of si-SNHG6 on WT progression. We also found that the TAK1/JNK and Wnt/ß-catenin signal pathways were inactivated by the knockdown of SNHG6 through elevating the expression of miR-15a. In summary, SNHG6 is an oncogene of WT development by targeting miR-15a.