AN ANALYSIS OF EXPORT PARADOX: THE TURKISH CASE IN THE AFTERMATH OF APRIL 5TH 1994 AUSTERITY MEASURES PACKAGE

The combined impact of the fragility of Turkish economy, variable currency rates and global scale crises have greatly contributed to the formation of probable shocks. Alongside with the financial deregulation of post-1980s economic setting in Turkey, a favorite hub of short-term foreign direct investment in the mentioned time period, in particular, the post 1990s multitude economic crises have led to the shortening of the time interval of foreign direct investments stay and affected the infrastructure of Turkish economics. In this regard, this study aims to elucidate and elaborate the changes in foreign trade in the light of economic austerity measures by means of VAR analysi

An integrative study on the impact of highly differentially methylated genes on expression and cancer etiology

<div><p>DNA methylation is an important epigenetic phenomenon that plays a key role in the regulation of expression. Most of the studies on the topic of methylation’s role in cancer mechanisms include analyses based on differential methylation, with the integration of expression information as supporting evidence. In the present study, we sought to identify methylation-driven patterns by also integrating protein-protein interaction information. We performed integrative analyses of DNA methylation, expression, SNP and copy number data on paired samples from six different cancer types. As a result, we found that genes that show a methylation change larger than 32.2% may influence cancer-related genes via fewer interaction steps and with much higher percentages compared with genes showing a methylation change less than 32.2%. Additionally, we investigated whether there were shared cancer mechanisms among different cancer types. Specifically, five cancer types shared a change in AGTR1 and IGF1 genes, which implies that there may be similar underlying disease mechanisms among these cancers. Additionally, when the focus was placed on distinctly altered genes within each cancer type, we identified various cancer-specific genes that are also supported in the literature and may play crucial roles as therapeutic targets. Overall, our novel graph-based approach for identifying methylation-driven patterns will improve our understanding of the effects of methylation on cancer progression and lead to improved knowledge of cancer etiology.</p></div

Numbers of genes with large methylation changes (32.2%) and normal methylation change (15%) that reached the driver genes and the average distances between them.

<p>Genes with large methylation changes tend to reach to driver genes in higher proportion and in fewer steps compared to the genes with normal methylation change.</p

Diagram illustrating the genes shared by different cancers and the cancers among which they are shared.

<p>Diagram showing the overlaps of affected oncogenes/tumor suppressors between different cancer types. Black bars are representing the number of genes that are overlapping between different cancers and that are unique to a specific cancer. Gene names for the genes observed in more than single cancer is also shown. Blue bars indicate the total amount of affected oncogenes/tumor suppressors in that specific type of cancer. AGTR1 and IGF1 are observed as affected in 5 different cancer types, improving their significance compared to other oncogenes/tumor suppressors.</p

Procedure of calculating the number of methylation-driven interaction steps necessary to reach cancer-related genes.

<p>This figure was created for oncogenes, and numerical values can be inversed for tumor suppressors. In order to decide on whether large methylation change is the causative reason behind expression change in driver genes, we have looked for all pairwise relationships from large methylation deregulation to “driver” gene, as all of the intermediate steps between the driver gene and 32.2% methylation change should obey the rules forced by the previous gene. The example scenarios, which pass the defined rules, are shown on this figure. Shortest path from large methylation change to driver gene is only considered at further analysis.</p

A general picture showing affected genes in KEGG: Pathways in Cancer.

<p>The genes are color-coded according to the number or cancer types among which they are shared. Red indicates sharing by 5 cancer types; orchid indicates sharing by 4 cancer types; coral indicates sharing by 3 cancer types; and light pink indicates sharing by 2 cancer types. In contrast, the genes that were affected only in a single cancer type are represented with the following colors: only THCA, cornflower blue; only CHOL, light sea green; only COAD, cyan; only KIRP, gold; and only LUSC, magenta. Unfortunately, there were no genes that were specific to LIHC.</p

Numbers of differentially methylated and differentially expressed genes for each cancer type.

<p>The rightmost column provides information about the numbers of genes that were both differentially expressed and differentially methylated.</p

Flow chart of calculating the distance of cancer driver genes from large methylation change.

<p>This figure was created for oncogenes, whereas for tumor suppressors we have searched for decrease in expression at the first step instead of an increase. In this procedure, if the fold-change in an oncogene’s expression was >2 or that of a tumor suppressor was <-2, then that gene was added to the short list of cancer driver genes. For each gene on the short list, we searched for a path until we reached a gene showing a change in methylation of >32.2% that caused a corresponding expression fold-change >|2| in inverse order. Moreover, we have also considered activation, inactivation relationships and corresponding expression changes between the genes. If satisfying all these constraints, then that pattern is added to the final list for further analysis.</p