Antiapoptotic and Prometastatic Roles of Cytokine FAM3B in Triple-Negative Breast Cancer.

Background: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. FAM3B, a secreted protein, has been extensively studied in various types of tumors. However, its function in breast cancer remains poorly understood.

Insights from a Computational-Based Approach for Analyzing Autophagy Genes across Human Cancers.

In the last decade, there has been a boost in autophagy reports due to its role in cancer progression and its association with tumor resistance to treatment. Despite this, many questions remain to be elucidated and explored among the different tumors. Here, we used omics-based cancer datasets to identify autophagy genes as prognostic markers in cancer.

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy.

In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells.

Binary Expression Enhances Reliability of Messaging in Gene Networks.

The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the ) is communicated by the amounts of transcription factors that it expresses (the ) to modulate the state of the promoter and expression levels of another gene (the ). The reliability of the gene network dynamics can be quantified by Shannon's entropy of the message and the mutual information between the message and the promoter state.

A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene.

This manuscript presents a comparison of noise properties exhibited by two stochastic binary models for: (i) a self-repressing gene; (ii) a repressed or activated externally regulating one. The stochastic models describe the dynamics of probability distributions governing two random variables, namely, protein numbers and the gene state as ON or OFF. In a previous work, we quantify noise in protein numbers by means of its Fano factor and write this quantity as a function of the covariance between the two random variables.