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Background  The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown.    Methods  Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi.    Results  We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or ‘periods’ during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where immune-associated genes were up-regulated after middle EMT stages. The presence of EMT-dynamic gene expression patterns supports the presence of differential activation and repression timings at the transcriptional level for various pathways and functions during EMT that are not detected in pure E or M cells. Importantly, the cell line identified EMT-dynamic genes were found to be present in lung cancer patient tissues and associated with patient outcomes.    Conclusions  Our study suggests that in vitro identified EMT-dynamic genes capture elements of gene EMT expression dynamics at the patient level. Measurement of EMT dynamic genes, as opposed to E or M only, is potentially useful in future efforts aimed at classifying patient’s responses to treatments based on the EMT dynamics in the tissue.    Electronic supplementary material  The online version of this article (10.1186/s12885-017-3832-1) contains supplementary material, which is available to authorized users.

Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages