Differential microRNA expression signatures and cell type-specific association with Taxol resistance in ovarian cancer cells

Paclitaxel (Taxol) resistance remains a major obstacle for the successful treatment of ovarian cancer. MicroRNAs (miRNAs) have oncogenic and tumor suppressor activity and are associated with poor prognosis phenotypes. miRNA screenings for this drug resistance are needed to estimate the prognosis of the disease and find better drug targets. miRNAs that were differentially expressed in Taxol-resistant ovarian cancer cells, compared with Taxol-sensitive cells, were screened by Illumina Human MicroRNA Expression BeadChips. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to identify target genes of selected miRNAs. Kaplan–Meier survival analysis was applied to identify dysregulated miRNAs in ovarian cancer patients using data from The Cancer Genome Atlas. A total of 82 miRNAs were identified in ovarian carcinoma cells compared to normal ovarian cells. miR-141, miR-106a, miR-200c, miR-96, and miR-378 were overexpressed, and miR-411, miR-432, miR-494, miR-409-3p, and miR-655 were underexpressed in ovarian cancer cells. Seventeen miRNAs were overexpressed in Taxol-resistant cells, including miR-663, miR-622, and HS_188. Underexpressed miRNAs in Taxol-sensitive cells included miR-497, miR-187, miR-195, and miR-107. We further showed miR-663 and miR-622 as significant prognosis markers of the chemo-resistant patient group. In particular, the downregulation of the two miRNAs was associated with better survival, perhaps increasing the sensitivity of cancer cells to Taxol. In the chemo-sensitive patient group, only miR-647 could be a prognosis marker. These miRNAs inhibit several interacting genes of p53 networks, especially in TUOS-3 and TUOS-4, and showed cell line-specific inhibition effects. Taken together, the data indicate that the three miRNAs are closely associated with Taxol resistance and potentially better prognosis factors. Our results suggest that these miRNAs were successfully and reliably identified and would be used in the development of miRNA therapies in treating ovarian cancer

Cancer association study of aminoacyl-tRNA synthetase signaling network in glioblastoma.

Aminoacyl-tRNA synthetases (ARSs) and ARS-interacting multifunctional proteins (AIMPs) exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM), the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN), 124 cancer-associated druggable target genes (DTGs) and 404 protein-protein interactors (PPIs) of ARSs using NCI's cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA) were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS), and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc). Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM

Nuclear PTEN-Mediated Growth Suppression Is Independent of Akt Down-Regulation

The tumor suppressor gene PTEN is a phosphoinositide phosphatase that is inactivated by deletion and/or mutation in diverse human tumors. Wild-type PTEN is expressed both in the cytoplasm and nucleus in normal cells, with a preferential nuclear localization in differentiated or resting cells. To elucidate the relationship between PTEN′s subcellular localization and its biologic activities, we constructed different PTEN mutants that targeted PTEN protein into different subcellular compartments. Our data show that the subcellular localization patterns of a PTEN (ΔPDZB) mutant versus a G129R phosphatase mutant were indistinguishable from those of wild-type PTEN. In contrast, the Myr-PTEN mutant demonstrated an enhanced association with the cell membrane. We found that nuclear PTEN alone is capable of suppressing anchorage-independent growth and facilitating G(1) arrest in U251MG cells without inhibiting Akt activity. Nuclear compartment-specific PTEN-induced growth suppression is dependent on possessing a functional lipid phosphatase domain. In addition, the down-regulation of p70S6K could be mediated, at least in part, through activation of AMP-activated protein kinase in an Akt-independent fashion. Introduction of a constitutively active mutant of Akt, Akt-DD, only partially rescues nuclear PTEN-mediated growth suppression. Our collective results provide the first direct evidence that PTEN can contribute to G(1) growth arrest through an Akt-independent signaling pathway

ARSN biology-dominant groups in patients with GBM.

<p>(<b>a</b>) We identified probe sets whose expression most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). This analysis identified that 122 resulting probe sets of ARSN, DTGs, and PPIs that were correlated with survival in patients with GBM. Then, we performed a supervised clustering with the probesets and GBM subtypes such as proneural (PN), proliferative (Prolif) and mesenchymal (Mes). This analysis showed that 61 probeset as signature genes were differentially expressed in the three discrete subgroups. The 61 probe sets are presented in matrix format, where rows represent individual genes and columns represent each tissue. Each cell in the matrix represents the expression level of a gene in an individual tissue. Red and green cells reflect high and low expression levels, respectively. (<b>b</b>) Tumor subgroups are distinguished by CARS and FARS. Horizontal bars denote mean values. CARS is enriched in Mes and Prolif subgroups, while FARS in PN subgroup. Each Kaplan-Meier plot of overall survival in 130 GBM patients grouped on the basis of expression of CARS and FARS. The difference between two groups was significant when the P value was less than 0.05. (<b>c</b>) Hierarchical clustering of the GSE4290 dataset of 81 GBM samples from patients with GBM and 23 non-tumor tissues based on the 61 probe sets. Each gene with an expression status were shown in Supplementary <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040960#pone.0040960.s021" target="_blank">Figure S21</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040960#pone.0040960.s023" target="_blank">S23</a>. Nine probes were significantly overexpressed in the non-tumor samples, with 2 probes not showing in this analysis.</p

Correlation patterns of 23 ARSs and AIMPs to three different genesets.

<p>(<b>a</b>) We identified 846 resulting probe sets including 168 DTGs and 678 PPIs that can directly interact with ARSN using 254 GBM affymetrix U133plus2 microarray dataset in TCGA. For the comparison, we also selected 978 probe sets among 1874 nonCAGs. To understand ARSN-DTGs/PPIs/nonCAGs interactions and visualize the relationship between genesets, a correlation map was made on the basis of their correlation levels with each set. The probe sets are presented in matrix format, where rows represent individual genes of DTGs, PPIs, and nonCAGs, respectively, and columns represent each gene of ARSN. Each cell in the matrix represents the correlation level of a gene in an ARSN. Red color indicates that the gene tends to be up or down-regulated together; Blue color indicates the opposite tendency (The darker, the stronger the association between two genes). (<b>b</b>) Hierarchical clustering analysis showed that ARSN were shared by three groups with 31 DTGs (FDR <0.005). 31 DTGs were generated on a supervised hierarchical clustering analysis. (<b>c</b>) Hierarchical clustering of ARSN based on the 16 DTGs based on nonlinear association between two gene expression sets. 16 DTGs were correlated with three subgroups of ARSN.</p

Cancer-associated interactions between 23 ARSs and AIMPs, and three genesets.

<p>3501 genes were selected by manual curation, clinical examination and causal relationship to cancer. Using 11 public database showing the curated interactions of human proteins (HPRD, BioGRID, KEGG, Reactome, BIND, MINT, IntAct, InnateDB, DIP, STRING, and PharmDB), we further selected 124 DTGs and 404 genes as PPIs of ARSs. Using a cancer-associated interactions analysis, a cancer-association map was established to display how much ARSs and AIMPs could be differently interacted to ten different cancers. Each brown node indicates each gene of respective cancer and each node size indicates the degree of cancer-dependent co-association of a gene. Line indicates the co-association between ten cancers and seven ARSN. The cancer node size indicates the number of interactions with the brown node gene. Seven components of ARSN (green nodes) show relatively higher cancer-associated network.</p