Cross talk between energy cost and expression of Methyl Jasmonate-regulated genes: from DNA to protein

Journal of Plant Biochemistry and Biotechnology - Plant cell balances energy consumption for its different biological processes under oxidative circumstances. The aim of the present study is to...     

Ovarian cancer stem cell: A potential therapeutic target for overcoming multidrug resistance

The cancer stem cell (CSC) model encompasses an advantageous paradigm that in recent decades provides a better elucidation for many important biological aspects of cancer initiation, progression, metastasis, and, more important, development of multidrug resistance (MDR). Such several other hematological malignancies and solid tumors and the identification and isolation of ovarian cancer stem cells (OV-CSCs) show that ovarian cancer also follows this hierarchical model. Gaining a better insight into CSC-mediated resistance holds promise for improving current ovarian cancer therapies and prolonging the survival of recurrent ovarian cancer patients in the future. Therefore, in this review, we will discuss some important mechanisms by which CSCs can escape chemotherapy, and then review the recent and growing body of evidence that supports the contribution of CSCs to MDR in ovarian cancer.     

Adipocyte lineage differentiation potential of MSCs isolated from reaming material

Mesenchymal stem cells (MSCs) obtained from various sources have been used for different therapeutic applications including tissue regeneration. Reamer/irrigator/aspirator (RIA) has been increasingly used in recent years for the derivation of MSCs. Here in this investigation we have comparatively analyzed MSCs obtained from iliac crest bone marrow (ICBM) and RIA for their morphology, cluster determinant (CD) markers, and adipogenic differentiation capacity. MSCs were isolated, cultured, and purified from both sources and then flow cytometric studies were performed to study their characteristics. The differentiation potential of RIA and ICBM was examined by an Oil Red O staining protocol. Moreover, the tissue-specific markers related to adipogenesis were analyzed by real-time polymerase chain reaction (RT-PCR). The cells were cultured in the relevant induction medium and then adipogenic lineage differentiation was tested and confirmed for all MSC preparations. Additionally, analysis by flow cytometer was indicative of RIA derived MSCs (RIA-MSCs) having a more homogenous population than ICBM derived MSCs. The RIA-MSCs differentiation toward adipogenic lineage was more efficient compared with ICBM-MSCs. Direct comparative analysis of RIA to ICBM-MSCs indicated that the RIA-MSCs had a higher potential toward adipocyte lineage differentiation compared with ICBM-MSCs.     

Competing endogenous RNA (ceRNA) cross talk and language in ceRNA regulatory networks: A new look at hallmarks of breast cancer

Breast cancer (BC) is the most frequently occurring malignancy in women worldwide. Despite the substantial advancement in understanding the molecular mechanisms and management of BC, it remains the leading cause of cancer death in women. One of the main reasons for this obstacle is that we have not been able to find the Achilles heel for the BC as a highly heterogeneous disease. Accumulating evidence has revealed that noncoding RNAs (ncRNAs), play key roles in the development of BC; however, the involving of complex regulatory interactions between the different varieties of ncRNAs in the development of this cancer has been poorly understood. In the recent years, the newly discovered mechanism in the RNA world is “competing endogenous RNA (ceRNA)” which proposes regulatory dialogues between different RNAs, including long ncRNAs (lncRNAs), microRNAs (miRNAs), transcribed pseudogenes, and circular RNAs (circRNAs). In the latest BC research, various studies have revealed that dysregulation of several ceRNA networks (ceRNETs) between these ncRNAs has fundamental roles in establishing the hallmarks of BC development. And it is thought that such a discovery could open a new window for a better understanding of the hidden aspects of breast tumors. Besides, it probably can provide new biomarkers and potential efficient therapeutic targets for BC. This review will discuss the existing body of knowledge regarding the key functions of ceRNETs and then highlights the emerging roles of some recently discovered ceRNETs in several hallmarks of BC. Moreover, we propose for the first time the “ceRnome” as a new term in the present article for RNA research.     

Global estimate of gastric cancer in Helicobacter pylori–infected population: A systematic review and meta-analysis

There is information regarding the rates of gastric cancer (GC) in different populations and the important role of Helicobacter pylori in GC development; however, no comprehensive study has yet been performed to investigate the prevalence of GC in H. pylori–infected patients. PubMed, Embase, and Cochrane Library through January 1, 2000 were searched without language restrictions. Quality of included studies was assessed with a critical appraisal checklist recommended by the Joanna Briggs Institute. All of the analyses were conducted using Comprehensive Meta-Analysis Software Version 2.0 and Stata 14.0. Forty-four studies from 17 countries were included. The pooled frequency of GC was 17.4% (95% confidence interval: 16.4–18.5) in H. pylori–infected population. The frequency of GC among H. pylori–infected population varied markedly across countries. The highest rate of GC was observed in H. pylori–infected individuals from Asian countries. The frequency of GC was relatively high in H. pylori–infected population in the world. However, the eradication of H. pylori might be a promising strategy for GC prevention, especially in high-risk populations such as Asian countries.     

Clinicopathological and prognostic value of lncRNA PANDAR expression in solid tumors: Evidence from a systematic review and meta-analysis

PANDAR (promoter of CDKN1A antisense DNA damage activated RNA) has been shown to be aberrantly expressed in many types of cancer. Considering conflicting data, the current study was aimed to assess its potential role as a prognostic marker in malignant tumors. A comprehensive literature search of PubMed, Medline, and Web of Science was performed to identify all eligible studies describing the use of PANDAR as a prognostic factor for different types of cancer. Data related to overall survival (OS) and clinicopathologic features were collected and analyzed. The pooled hazard ratio (HR) and odds radio (OR) with a 95% confidence interval (CI) were used to estimate associations. Ten original studies containing 1,231 patients were included. The results showed that in patients with cancer, high PANDAR expression is correlated with lymph node metastasis (LNM; OR = 2.57; 95% CI, 1.76–3.81; p < 0.001), tumor stage (OR = 2.90; 95% CI, 1.25–6.75; p = 0.013), and tumor size (OR = 1.79; 95% CI, 1.11–2.91; p = 0.018). However, sensitivity analysis further demonstrated a significant association between high PANDAR expression and OS, both in multivariate and univariate analysis models (pooled HR 2.01; 95% CI, 1.17–3.44 and pooled HR 2.62; 95% CI, 1.98–3.47, respectively), after omitting one study. These results suggested that PANDAR expression might be indicative of advanced disease and poor prognosis in patients with cancer. Further studies are necessary to determine the value of this risk stratification biomarker in clinical management of patients with cancer.     

Performance evaluation measures for protein complex prediction

Protein complexes play a dominant role in cellular organization and function. Prediction of protein complexes from the network of physical interactions between proteins (PPI networks) has thus become one of the important research areas. Recently, many computational approaches have been developed to identify these complexes. Various performance assessment measures have been proposed for evaluating the efficiency of these methods. However, there are many inconsistencies in the definitions and usage of the measures across the literature. To address this issue, we have gathered and presented the most important performance evaluation measures and developed a tool, named CompEvaluator, to critically assess the protein complex prediction methods. The tool and documentation are publicly available at https://sourceforge.net/projects/compevaluator/files/.