Gene Biomarkers Derived from Clinical Data of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common cancer of high mortality, mainly due to the difficulty in diagnosis during its clinical stage. Here we aim to find the gene biomarkers, which are of important significance for diagnosis and treatment. In this work, 3682 differentially expressed genes on HCC were firstly differentiated based on the Cancer Genome Atlas database (TCGA).

Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection.

Background: Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened.

Gene transcription in bursting: a unified mode for realizing accuracy and stochasticity.

There is accumulating evidence that, from bacteria to mammalian cells, messenger RNAs (mRNAs) are produced in intermittent bursts - a much 'noisier' process than traditionally thought. Based on quantitative measurements at individual promoters, diverse phenomenological models have been proposed for transcriptional bursting. Nevertheless, the underlying molecular mechanisms and significance for cellular signalling remain elusive.

Kinetics of transcription initiation directed by multiple cis-regulatory elements on the glnAp2 promoter.

Transcription initiation is orchestrated by dynamic molecular interactions, with kinetic steps difficult to detect. Utilizing a hybrid method, we aim to unravel essential kinetic steps of transcriptional regulation on the glnAp2 promoter, whose regulatory region includes two enhancers (sites I and II) and three low-affinity sequences (sites III-V), to which the transcriptional activator NtrC binds. By structure reconstruction, we analyze all possible organization architectures of the transcription apparatus (TA).

Reconciling the concurrent fast and slow cycling of proteins on gene promoters.

During gene transcription, proteins appear to cycle on and off some gene promoters with both long (tens of minutes) and short periods (no more than several minutes). The essence of these phenomena still remains unclear. Here, we propose a stochastic model for the state evolution of promoters in terms of DNA-protein interactions.

Dynamic mechanism for the transcription apparatus orchestrating reliable responses to activators.

The transcription apparatus (TA) is a huge molecular machine. It detects the time-varying concentrations of transcriptional activators and initiates mRNA transcripts at appropriate rates. Based on the general structural organizations of the TA, we propose how the TA dynamically orchestrates transcriptional responses.