Integrated genome-wide gene expression map and high-resolution analysis of aberrant chromosomal regions in squamous cell lung cancer

Positive Caricature Transcriptomic Effects Associated with Broad Genomic Aberrations in Colorectal Cancer

We re-examined the correlation between Broad Genomic Aberrations (BGAs) and transcriptomic profiles in Colorectal Cancer (CRC). Two types of BGAs have been examined: Broad Copy-Number Abnormal regions (BCNAs), distinguished in gain- and loss-type, and Copy-Neutral Loss of Heterozygosities (CNLOHs). Transcripts are classified as “OverT” or “UnderT” if overexpressed or underexpressed comparing CRCs bearing a specific BGA to CRCs not bearing it and as “UpT” or “DownT” if upregulated or downregulated in cancer compared to normal tissue. BGA-associated effects were evaluated by changes in the “Chromosomal Distribution Index” (CDI) of different transcript classes. Data show that UpT are more sensitive than DownT to BCNA-associated gene dosage effects. “Over-UpT” genes are upregulated in cancer and further overexpressed by gene dosage, defining the so called “positive caricature transcriptomic effect”. When Over-UpT genes are ranked according to overexpression, top positions are occupied by genes implicated at the functional and therapeutic level in CRC. We show that cancer-upregulated transcripts are sensitive markers of BCNA-induced effects and suggest that analysis of positive caricature transcriptomic effects can provide clues toward the identification of BCNA-associated cancer driver genes.

Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks

Background

Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results

Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions

Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.