EVpedia: a community web portal for extracellular vesicles research

Motivation: Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging. Results: We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research.X1110478Ysciescopu

Кераміка «terra sigillata» з с. Зимне на Волині

Стаття присвячена публікації чотирьох керамічних посудин типу «terra sigillata», знайдених на дні р. Луги у с. Зимне Володимир-Волинського району Волинської області. Попередній аналіз цих знахідок дозволяє віднести їх до Понтійського центру виробництва такого посуду. Вірогідним шляхом потрапляння цієї колекції на Волинь була готська експансія у Північне Причорномор’я

Ovarian Cancer Chemoresistance Relies on the Stem Cell Reprogramming Factor PBX1.

The evolution of chemoresistance is a fundamental characteristic of cancer that ultimately hampers its clinical management. However, it may be possible to improve patient outcomes significantly by a better understanding of resistance mechanisms, which cancers rely upon during the evolution to an untreatable state. Here we report an essential role of the stem cell reprogramming factor, PBX1, in mediating chemoresistance in ovarian carcinomas. In the clinical setting, high levels of PBX1 expression correlated with shorter survival in post-chemotherapy ovarian cancer patients. In tumor cells with low endogenous levels of PBX1, its enforced expression promoted cancer stem cell-like phenotypes, including most notably an increase in resistance to platinum-based therapy used most commonly for treating this disease. Conversely, silencing PBX1 in platinum-resistant cells that overexpressed PBX1 sensitized them to platinum treatment and reduced their stem-like properties. An analysis of published genome-wide chromatin immunoprecipitation data indicated that PBX1 binds directly to promoters of genes involved in stem cell maintenance and the response to tissue injury. We confirmed direct regulation of one of these genes, STAT3, demonstrating that the PBX1 binding motif at its promoter acted to positively regulate STAT3 transcription. We further demonstrated that a STAT3/JAK2 inhibitor could potently sensitize platinum-resistant cells to carboplatin and suppress their growth in vivo Our findings offer a mechanistic rationale to target the PBX1/STAT3 axis to antagonize a key mechanism of chemoresistance in ovarian cancers and possibly other human cancers. Cancer Res; 76(21); 6351-61. ©2016 AACR

Ovarian Cancer Chemoresistance Relies on the Stem Cell Reprogramming Factor PBX1.

The evolution of chemoresistance is a fundamental characteristic of cancer that ultimately hampers its clinical management. However, it may be possible to improve patient outcomes significantly by a better understanding of resistance mechanisms, which cancers rely upon during the evolution to an untreatable state. Here we report an essential role of the stem cell reprogramming factor, PBX1, in mediating chemoresistance in ovarian carcinomas. In the clinical setting, high levels of PBX1 expression correlated with shorter survival in post-chemotherapy ovarian cancer patients. In tumor cells with low endogenous levels of PBX1, its enforced expression promoted cancer stem cell-like phenotypes, including most notably an increase in resistance to platinum-based therapy used most commonly for treating this disease. Conversely, silencing PBX1 in platinum-resistant cells that overexpressed PBX1 sensitized them to platinum treatment and reduced their stem-like properties. An analysis of published genome-wide chromatin immunoprecipitation data indicated that PBX1 binds directly to promoters of genes involved in stem cell maintenance and the response to tissue injury. We confirmed direct regulation of one of these genes, STAT3, demonstrating that the PBX1 binding motif at its promoter acted to positively regulate STAT3 transcription. We further demonstrated that a STAT3/JAK2 inhibitor could potently sensitize platinum-resistant cells to carboplatin and suppress their growth in vivo Our findings offer a mechanistic rationale to target the PBX1/STAT3 axis to antagonize a key mechanism of chemoresistance in ovarian cancers and possibly other human cancers. Cancer Res; 76(21); 6351-61. ©2016 AACR

USP19 and RPL23 as Candidate Prognostic Markers for Advanced-Stage High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the leading causes of deaths among patients with gynecological malignancies worldwide. In order to identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. In order to predict the prognosis of patients with HGSC, we created machine learning-based models and identified USP19 and RPL23 as candidate prognostic markers. Specifically, patients with lower USP19 mRNA levels and those with higher RPL23 mRNA levels had worse prognoses. This model was then used to analyze the data of patients with HGSC hosted on The Cancer Genome Atlas; this analysis validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of USP19 and RPL23 determined using a machine-learning model could serve as prognostic markers for patients with HGSC receiving conventional therapy