Abstract
The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
Highlights
The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning
Each readout gene was perturbed in the human squamous carcinoma cell line A431 via transfection with short interfering RNAs
The 40 top ranked genes were perturbed by short interfering RNAs (siRNAs) in the well characterized human A431 squamous carcinoma cell line (Table S2)
Summary
The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. Cancer subtype-specific gene regulatory networks (GRN) encode intracellular d ynamics[1], and offer understanding into the functional changes driving disease development. Inference of such models generally exploits certain aspects of the experimental setup, such as pooling among replicates to amplify signal, or makes use of prior k nowledge[2,3]. While Genie[3] is the best performing method in DREAM5, it performed relatively poorly in the other two Such disparities may be caused by differences in the specific conditions under which the benchmarks were run or in parameters of the synthetic data creation such as size, noise, and network properties. Even methods such as neural networks, which in other research settings have performed exceedingly w ell[14], performed poorly here
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