A computational strategy to select optimized protein targets for drug development toward the control of cancer diseases.

ABSTRACT: In this report, we describe a strategy for the optimized selection of protein targets suitable for drug development against neoplastic diseases taking the particular case of breast cancer as an example. We combined human interactome and transcriptome data from malignant and control cell lines because highly connected proteins that are up-regulated in malignant cell lines are expected to be suitable protein targets for chemotherapy with a lower rate of undesirable side effects. We normalized transcriptome data and applied a statistic treatment to objectively extract the sub-networks of down- and up-regulated genes whose proteins effectively interact. We chose the most connected ones that act as protein hubs, most being in the signaling network. We show that the protein targets effectively i

SUBMITTER: Carels N 

PROVIDER: S-EPMC4308075 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature