Time-course gene profiling and networks in demethylated retinoblastoma cell line

Abstract

Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.

Immuno-regulated common markers but different network signatures in two associated cancers: evidences from epigenetic treatment

“…We briefly summarize the data generation aspects relevant to the novel developments, and refer the readers for further details on HOS and RB treatments to our previous work (10,11). Here, we embrace networks for inference purposes, and following known techniques (12)(13)(14).…”

“…In particular, the objective if the study is to analyze the marker role of genes that were detected as differentially expressed (DEG) in both cell types, and assess the network signatures induced by these common genes by targeting both co-expression associative dynamics and interactions between their products. In earlier work (10,11), we focused on the osteosarcoma-derived HosDXR150 cell line and found that its proliferation was effectively reduced by treatment with the DAC 5-Aza-dC (decitabine) alone, among other types of inhibitors. We also obtained a DEG profile from time course microarray experiments on the RB-derived WERI-RB1 cell line treated with 5-Aza-dC only.…”

Biological Networks for Cancer Candidate Biomarkers Discovery

“…A series of GCNs were constructed for genes identified by a guilt-by-association algorithm, and the conserved gene interactions that impact cancer outcomes were detected. 17 Malusa et al 18 built coexpression networks based on time-course gene profiling data to find key modules related to the demethylated retinoblastoma cell line, and Xing and Zeng 19 also constructed a GCN for biomarker discovery in glioma. In weighted GCNs, the correlations can be weighted with an adjacency function.…”

Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints after Treatment of Cell Lines with Demethylation

“…Network-driven identification of these epigenetic signatures is an inference strategy revealing synergisms of regulation and control for target genes, as seen already at a smaller scale. 21 Modules and communities reflect the idea of genes acting contextually in signaling pathways and regulatory networks. Thus, the detection of significantly perturbed subnetworks can offer insight on the mechanism of action of drugs or treatment effects.…”