Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients

In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result, we obtain a small set of seven of the most-useful biomarkers for diagnostics. They can also be used to build tests that avoid the costly and time-consuming existing methods. We summarize the current biological knowledge of the chosen biomarkers and contrast it with our findings

The T-box transcription factor Eomesodermin is essential for AVE induction in the mouse embryo.

Reciprocal inductive interactions between the embryonic and extraembryonic tissues establish the anterior-posterior (AP) axis of the early mouse embryo. The anterior visceral endoderm (AVE) signaling center emerges at the distal tip of the embryo at embryonic day 5.5 and translocates to the prospective anterior side of the embryo. The process of AVE induction and migration are poorly understood. Here we demonstrate that the T-box gene Eomesodermin (Eomes) plays an essential role in AVE recruitment, in part by directly activating the homeobox transcription factor Lhx1. Thus, Eomes function in the visceral endoderm (VE) initiates an instructive transcriptional program controlling AP identity