Saitohin Q7R polymorphism is associated with late-onset Alzheimer's disease susceptibility among caucasian populations: a meta-analysis

An improved clear cell renal cell carcinoma stage prediction model based on gene sets

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment.

RESULTS

In this study, we developed new strategies to extract important gene features and trained machine learning-based classifiers to predict stages of ccRCC samples. The novelty of our approach is that (i) We improved the feature preprocessing procedure by binning and coding, and increased the stability of data and robustness of the classification model. (ii) We proposed a joint gene selection algorithm by combining the Fast-Correlation-Based Filter (FCBF) search with the information value, the linear correlation coefficient, and variance inflation factor, and removed irrelevant/redundant features. Then the logistic regression-based feature selection method was used to determine influencing factors. (iii) Classification models were developed using machine learning algorithms. This method is evaluated on RNA expression value of clear cell renal cell carcinoma derived from The Cancer Genome Atlas (TCGA). The results showed that the result on the testing set (accuracy of 81.15% and AUC 0.86) outperformed state-of-the-art models (accuracy of 72.64% and AUC 0.81) and a gene set FJL-set was developed, which contained 23 genes, far less than 64. Furthermore, a gene function analysis was used to explore molecular mechanisms that might affect cancer development.

CONCLUSIONS

The results suggested that our model can extract more prognostic information, and is worthy of further investigation and validation in order to understand the progression mechanism.

It's all about tau

Tau is a protein that is highly enriched in neurons and was originally defined by its ability to bind and stabilize microtubules. However, it is now becoming evident that the functions of tau extend beyond its ability to modulate microtubule dynamics. Tau plays a role in mediating axonal transport, synaptic structure and function, and neuronal signaling pathways. Although tau plays important physiological roles in neurons, its involvement in neurodegenerative diseases, and most prominently in the pathogenesis of Alzheimer disease (AD), has directed the majority of tau studies. However, a thorough knowledge of the physiological functions of tau and its post-translational modifications under normal conditions are necessary to provide the foundation for understanding its role in pathological settings. In this review, we will focus on human tau, summarizing tau structure and organization, as well as its posttranslational modifications associated with physiological processes. We will highlight possible mechanisms involved in mediating the turnover of tau and finally discuss newly elucidated tau functions in a physiological context.