Meta-Analysis of EMT Datasets Reveals Different Types of EMT

<div><p>As a critical process during embryonic development, cancer progression and cell fate conversions, epithelial-mesenchymal transition (EMT) has been extensively studied over the last several decades. To further understand the nature of EMT, we performed meta-analysis of multiple microarray datasets to identify the related generic signature. In this study, 24 human and 17 mouse microarray datasets were integrated to identify conserved gene expression changes in different types of EMT. Our integrative analysis revealed that there is low agreement among the list of the identified signature genes and three other lists in previous studies. Since removing the datasets with weakly-induced EMT from the analysis did not significantly improve the overlapping in the signature-gene lists, we hypothesized the existence of different types of EMT. This hypothesis was further supported by the grouping of 74 human EMT-induction samples into five distinct clusters, and the identification of distinct pathways in these different clusters of EMT samples. The five clusters of EMT-induction samples also improves the understanding of the characteristics of different EMT types. Therefore, we concluded the existence of different types of EMT was the possible reason for its complex role in multiple biological processes.</p></div

Low overlapping among samples with different EMT induction.

<p>74 human and 31 mouse EMT-induction samples were evaluated with four scoring systems and classified into three groups, samples with strong EMT, medium EMT or weak EMT. Significant gene expression changes were identified in these three groups. The overlapping of genes in these three lists, Strong-list, Medium-list and Weak-list, were indicated.</p

Removing weakly-induced samples does not improve overlapping.

<p>Removing weakly-induced samples does not improve overlapping.</p

GO terms enriched in one or two clusters.

<p>Five clusters of human EMT-induction samples were used to identify five gene lists, which were than subjected for GO analysis. The GO terms enriched in Cluster I, II or V were listed in (A), while those enriched in Cluster III or IV were in (B). The GO terms enriched in two of the five clusters were listed in (C). The enrichment score for one group of GO terms were calculated by averaging the scores of included GO terms, which were provided by DAVID. Enrichment scores over 1.30 were considered as significantly enriched.</p

The overlapping among EMT generic signature lists.

<p>The overlapping among EMT generic signature lists.</p

GO analysis with identified gene signatures.

<p>579 genes were identified after analyzing 74 human and 31 mouse EMT-induction samples as described in Materials and methods. 378 up-regulated and 201 down-regulated genes were subjected to GO analysis, and enriched GO terms were listed in (A) and (B). The average–log₁₀ (p-Value) is shown next to the bar plots.</p

EMT during neuron trans-differentiation.

<p>(A) The 773-gene list in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156839#pone.0156839.s005" target="_blank">S5 Table</a> was used to determine the correlation between PO-lists, EM-list, SC-list and gene lists identified in five clusters. Identification of the 773 genes in all the eight lists were remarked as “1” in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156839#pone.0156839.s005" target="_blank">S5 Table</a>. The samples were merged in close proximity using the farthest neighbor clustering method in linkage criteria, i.e. complete linkage. (B) Four scoring systems mentioned above were used to evaluate the expression change during neuron trans-differentiation (GSE68902). (C) The expression changes on Day 10 during neuron trans-differentiation (GSE68902) were evaluated by gene listed from five clusters as described in Materials and Methods. Overlapping score was calculated by subtracting the absolute log2 values of genes with opposite expression changes from the summary of genes with consistent expression changes.</p

Clustering EMT-induction samples.

<p>(A-B) 74 human and 31 mouse EMT-induction samples were grouped into clusters by the expression profiles. The correlations among pairs of samples were shown in (A) for human and (B) for mouse. (C-D) 74 human EMT-induction samples were grouped into five clusters. The numbers of samples with strong EMT, medium EMT or weak EMT were listed in (C). The average scores of samples in these five clusters were listed in (D).</p

Five clusters of human EMT-induction samples overlap differently with other lists.

<p>Five clusters of human EMT-induction samples overlap differently with other lists.</p

Additional file 1: of Opioid doses required for pain management in lung cancer patients with different cholesterol levels: negative correlation between opioid doses and cholesterol levels

A list of current patients’ information. The records of 282 patients were listed after removing primary information. ID was the reference number provided by the authors for easier accession. “Chol level” represented the serum cholesterol level measured and recorded. “Year” represented when the initial diagnosis of cancer was made. “Initial dose”, “stable dose” and “converted dose” represented the initial dose opioid administration, the final dose of opioids used for analysis and the dose when converted to equivalent oxycodone hydrochloride, respectively. The “increased” column, “1” or “0” were used to represented that “stable dose” was higher than “initial dose”. If the patient has one or multiple additional measurement of cholesterol during the first month after opioid administration, the cholesterol level with largest difference from initial cholesterol level was selected, recorded in “Chol level with largest difference” column, and normalized to the initial cholesterol level. (XLSX 43 kb