Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference.

The visual question generation (VQG) task aims to generate human-like questions from an image and potentially other side information (e.g. answer type). Previous works on VQG fall in two aspects: i) They suffer from one image to many questions mapping problem, which leads to the failure of generating referential and meaningful questions from an image. ii) They fail to model complex implicit relations among the visual objects in an image and also overlook potential interactions between the side information and image. To address these limitations, we first propose a novel learning paradigm to generate visual questions with answer-awareness and region-reference. Concretely, we aim to ask the right visual questions with Double Hints - textual answers and visual regions of interests, which could effectively mitigate the existing one-to-many mapping issue. Particularly, we develop a simple methodology to self-learn the visual hints without introducing any additional human annotations. Furthermore, to capture these sophisticated relationships, we propose a new double-hints guided Graph-to-Sequence learning framework, which first models them as a dynamic graph and learns the implicit topology end-to-end, and then utilizes a graph-to-sequence model to generate the questions with double hints. Experimental results demonstrate the priority of our proposed method.

KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs.

Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE. Unlike previous approaches, KG-TREAT constructs dual-focus KGs and integrates a deep bi-level attention synergy method for in-depth information fusion, enabling distinct encoding of treatment-covariate and outcome-covariate relationships. KG-TREAT also incorporates two pre-training tasks to ensure a thorough grounding and contextualization of patient data and KGs. Evaluation on four downstream TEE tasks shows KG-TREAT's superiority over existing methods, with an average improvement of 7% in Area under the ROC Curve (AUC) and 9% in Influence Function-based Precision of Estimating Heterogeneous Effects (IF-PEHE). The effectiveness of our estimated treatment effects is further affirmed by alignment with established randomized clinical trial findings.

Remote collaboration fuses fewer breakthrough ideas.

Theories of innovation emphasize the role of social networks and teams as facilitators of breakthrough discoveries1-4. Around the world, scientists and inventors are more plentiful and interconnected today than ever before4. However, although there are more people making discoveries, and more ideas that can be reconfigured in new ways, research suggests that new ideas are getting harder to find5,6-contradicting recombinant growth theory7,8. Here we shed light on this apparent puzzle. Analysing 20 million research articles and 4 million patent applications from across the globe over the past half-century, we begin by documenting the rise of remote collaboration across cities, underlining the growing interconnectedness of scientists and inventors globally. We further show that across all fields, periods and team sizes, researchers in these remote teams are consistently less likely to make breakthrough discoveries relative to their on-site counterparts. Creating a dataset that allows us to explore the division of labour in knowledge production within teams and across space, we find that among distributed team members, collaboration centres on late-stage, technical tasks involving more codified knowledge. Yet they are less likely to join forces in conceptual tasks-such as conceiving new ideas and designing research-when knowledge is tacit9. We conclude that despite striking improvements in digital technology in recent years, remote teams are less likely to integrate the knowledge of their members to produce new, disruptive ideas.

TeKo: Text-Rich Graph Neural Networks With External Knowledge.

Graph neural networks (GNNs) have gained great prevalence in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants adopt a message-passing principle that obtains network representations by the attribute propagates along network topology, which however ignores the rich textual semantics (e.g., local word-sequence) that exist in numerous real-world networks. Existing methods for text-rich networks integrate textual semantics by mainly using internal information such as topics or phrases/words, which often suffer from an inability to comprehensively mine the textual semantics, limiting the reciprocal guidance between network structure and textual semantics. To address these problems, we present a novel text-rich GNN with external knowledge (TeKo), in order to make full use of both structural and textual information within text-rich networks. Specifically, we first present a flexible heterogeneous semantic network that integrates high-quality entities as well as interactions among documents and entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity descriptions, to gain a deeper insight into textual semantics. Furthermore, we devise a reciprocal convolutional mechanism for the constructed heterogeneous semantic network, enabling network structure and textual semantics to collaboratively enhance each other and learn high-level network representations. Extensive experiments illustrate that TeKo achieves state-of-the-art performance on a variety of text-rich networks as well as a large-scale e-commerce searching dataset.

TrustGNN: Graph Neural Network-Based Trust Evaluation via Learnable Propagative and Composable Nature.

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Toward Subgraph-Guided Knowledge Graph Question Generation With Graph Neural Networks.

Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting that is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers. In addition, most previous works built on either RNN-or Transformer-based models to encode a linearized KG subgraph, which totally discards the explicit structure information of a KG subgraph. To address this issue, we propose to apply a bidirectional Graph2Seq model to encode the KG subgraph. Furthermore, we enhance our RNN decoder with a node-level copying mechanism to allow direct copying of node attributes from the KG subgraph to the output question. Both automatic and human evaluation results demonstrate that our model achieves new state-of-the-art scores, outperforming existing methods by a significant margin on two QG benchmarks. Experimental results also show that our QG model can consistently benefit the question-answering (QA) task as a means of data augmentation.

Multilevel Graph Matching Networks for Deep Graph Similarity Learning.

While the celebrated graph neural networks (GNNs) yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however, ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this article, we propose a multilevel graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network (NGMN) for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese GNN to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, multilevel graph matching network (MGMN) also exhibits stronger robustness as the sizes of the two input graphs increase.

Deep Graph Translation.

Deep generative models for graphs have recently achieved great successes in modeling and generating graphs for studying networks in biology, engineering, and social sciences. However, they are typically unconditioned generative models that have no control over the target graphs given a source graph. In this article, we propose a novel graph-translation-generative-adversarial-nets (GT-GAN) model that transforms the source graphs into their target output graphs. GT-GAN consists of a graph translator equipped with innovative graph convolution and deconvolution layers to learn the translation mapping considering both global and local features. A new conditional graph discriminator is proposed to classify the target graphs by conditioning on source graphs while training. Extensive experiments on multiple synthetic and real-world datasets in the domain of cybernetworks, the Internet of Things, and neuroscience demonstrate that the proposed GT-GAN model significantly outperforms other baseline methods in terms of both effectiveness and scalability. For instance, GT-GAN outperforms the classical state-of-the-art (SOTA) methods in functional connectivity (FC) prediction of brain networks by at least 32.5%.

Flat teams drive scientific innovation.

With teams growing in all areas of scientific and scholarly research, we explore the relationship between team structure and the character of knowledge they produce. Drawing on 89,575 self-reports of team member research activity underlying scientific publications, we show how individual activities cohere into broad roles of 1) leadership through the direction and presentation of research and 2) support through data collection, analysis, and discussion. The hidden hierarchy of a scientific team is characterized by its lead (or L) ratio of members playing leadership roles to total team size. The L ratio is validated through correlation with imputed contributions to the specific paper and to science as a whole, which we use to effectively extrapolate the L ratio for 16,397,750 papers where roles are not explicit. We find that, relative to flat, egalitarian teams, tall, hierarchical teams produce less novelty and more often develop existing ideas, increase productivity for those on top and decrease it for those beneath, and increase short-term citations but decrease long-term influence. These effects hold within person-the same person on the same-sized team produces science much more likely to disruptively innovate if they work on a flat, high-L-ratio team. These results suggest the critical role flat teams play for sustainable scientific advance and the training and advancement of scientists.

Long noncoding RNA Gas5 induces cell apoptosis and inhibits tumor growth via activating the CHOP-dependent endoplasmic reticulum stress pathway in human hepatoblastoma HepG2 cells.

In recent years, long noncoding RNAs (lncRNAs) have been demonstrated to be important tumor-associated regulatory factors. LncRNA growth arrest-specific transcript 5 (Gas5) acts as an anti-oncogene in most cancers. Whether Gas5 acts as an oncogene or anti-oncogene in hepatocellular carcinoma (HCC) remains unclear. In the present study, the expression and role of Gas5 in HCC were investigated in vitro and in vivo. Lower expression levels of Gas5 were determined in HCC tissues and cells by quantitative reverse transcription-polymerase chain reaction. Overexpressed Gas 5 lentiviral vectors were constructed to analyze their influence on cell viability, migration, invasion, and apoptosis. Fluorescence in situ hybridization was used to identify the subcellular localization of Gas5. Protein complexes that bound to Gas5 were isolated from HepG2 cells through pull-down experiments and analyzed by mass spectrometry. A series of novel Gas5-interacting proteins were identified and bioinformatics analysis was carried out. These included ribosomal proteins, proteins involved in protein folding, sorting, and transportation in the ER, some nucleases and protein enzymes involved in gene transcription, translation, and other proteins with various functions.78 kDa glucose-regulated protein (GRP78) was identified as a direct target of Gas5 by Rip-qPCR and Western blot analysis assay. Gas5 inhibited HepG2 cell growth and induced cell apoptosis via upregulating CHOP to activate the ER stress signaling pathway. Further studies indicated that the knockdown of CHOP by shRNA partially reversed Gas5-mediated apoptosis in HepG2 cells. Magnetic resonance imaging showed that the ectopic expression of Gas5 inhibited the growth of HCC in nude mice. These findings suggest that Gas5 functions as a tumor suppressor and induces apoptosis through activation of ER stress by targeting the CHOP signal pathway in HCC.

Long non­coding RNA MEG3 suppresses epithelial­to­mesenchymal transition by inhibiting the PSAT1­dependent GSK­3ß/Snail signaling pathway in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is the main subtype of esophageal cancer in China, and the prognosis of patients remains poor mainly due to the occurrence of lymph node and distant metastasis. The long non­coding RNA (lncRNA) maternally expressed gene 3 (MEG3) has been shown to have tumor­suppressive properties and to play an important role in epithelial­to­mesenchymal transition (EMT) in some solid tumors. However, whether MEG3 is involved in EMT in ESCC remains unclear. In the present study, the MEG3 expression level and its association with tumorigenesis were determined in 43 tumor tissues of patients with ESCC and in ESCC cells using reverse transcription­quantitative PCR analysis. Gene microarray analysis was performed to detect differentially expressed genes (DEGs). Based on the functional annotation results, the effects of ectopic expression of MEG3 on cell growth, migration, invasion and EMT were assessed. MEG3 expression level was found to be markedly lower in tumor tissues and cells. Statistical analysis revealed that MEG3 expression was significantly negatively associated with lymph node metastasis and TNM stage in ESCC. Fluorescence in situ hybridization assay demonstrated that MEG3 was expressed mainly in the nucleus. Ectopic expression of MEG3 inhibited cell proliferation, migration, invasion and cell cycle progression in EC109 cells. Gene microarray results demonstrated that 177 genes were differentially expressed ≥2.0 fold in MEG3­overexpressing cells, including 23 upregulated and 154 downregulated genes. Functional annotation revealed that the DEGs were mainly involved in amino acid biosynthetic process, mitogen­activated protein kinase signaling, and serine and glycine metabolism. Further experiments indicated that the ectopic expression of MEG3 significantly suppressed cell proliferation, migration, invasion and EMT by downregulating phosphoserine aminotransferase 1 (PSAT1). In pathological tissues, PSAT1 and MEG3 were significantly negatively correlated, and high expression of PSAT1 predicted poor survival. Taken together, these results suggest that MEG3 may be a useful prognostic biomarker and may suppress EMT by inhibiting the PSAT1­dependent glycogen synthase kinase­3ß/Snail signaling pathway in ESCC.

[Prenatal diagnosis and genetic analysis of a fetus with Xp22.12 microduplication].

OBJECTIVE: To provide prenatal diagnosis for a pregnant women carrying a chromosome translocations using single nucleotide polymorphism array (SNP-array). METHODS: The fetus and its parents were subjected to chromosome karyotyping and SNP array analysis. RESULTS: A Xp22.12 microduplication was identified in the fetus with a size of 496.3 kb. Search of literature and database indicated the microduplication to be variant of unclear significance. The phenotypically normal mother has carried a 505.8 kb duplication at the same position. The father was normal for the testing. The couple decided to continue with the pregnancy and gave birth to a healthy girl at full-term. No abnormality was found during the follow-up. CONCLUSION: The Xp22.12 microduplication encompassed part of RPS6KA3 gene, which shows various features of Coffin-Lowry syndrome. Female with Xp22.12 microduplications may be asymptomatic carriers due to X chromosome inactivation. Our case may provide data for delineating the phenotype-genotype correlation of Xp22.12 microduplication.

Estrogen induces IDO expression via TGF­ß in chorionic villi and decidua during early stages of pregnancy.

Indoleamine 2,3­dioxygenase (IDO) is one of the most important proteins protecting the embryos from the mother's immune system during pregnancy; however, little is known about the regulation of expression of this protein at the maternal­fetal interface. In the current study, chorionic villi and decidua were collected from women at early stages of pregnancy. Samples of chorionic villi and decidua were cultured in medium containing different concentrations of 17ß­estradiol and estriol respectively, with or without fulvestrant. Western blot analysis and/or immunofluorescent staining were used to detect the expression of transforming growth factor ß (TGF­ß) and IDO in chorionic villi and decidua tissues. Both TGF­ß and IDO were expressed in chorionic villi and decidua. The expression levels of these two proteins increased the most in samples of chorionic villi and decidua cultured in medium containing 17ß­estradiol at the concentration of 10 ng/ml, or estriol at the concentration of 1 µg/ml. This increase could be reversed when fulvestrant was added in the medium at the concentration of 10 µg/ml. IDO expression increased in a dose­dependent manner in tissue samples cultured in medium containing TGF­ß. The results of the current study revealed that administration of estrogen at doses similar to those observed in healthy pregnant women may upregulate the expression of IDO by TGF­ß, suggesting that estrogen may prevent allogeneic fetal rejection and may be used as an immunomodulator.

The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books.

Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through storytelling. Using the word embedding techniques, we reveal the constructed emotional dependency of female characters on male characters in stories. We call this narrative structure "Cinderella complex", which assumes that women depend on men in the pursuit of a happy, fulfilling life. Our analysis covers a substantial portion of narratives that shape the modern collective memory, including 7,226 books, 6,087 movie synopses, and 1,109 movie scripts. The "Cinderella complex" is observed to exist widely across periods and contexts, reminding how gender stereotypes are deeply rooted in our society. Our analysis of the words surrounding female and male characters shows that the lives of males are adventure-oriented, whereas the lives of females are romantic-relationship oriented. Finally, we demonstrate the social endorsement of gender stereotypes by showing that gender-stereotypical movies are voted more frequently and rated higher.

LncRNA MEG3 contributes to adenosine-induced cytotoxicity in hepatoma HepG2 cells by downregulated ILF3 and autophagy inhibition via regulation PI3K-AKT-mTOR and beclin-1 signaling pathway.

Adenosine is a promising cytotoxic reagent for tumors, long noncoding RNA (lncRNA) maternally expressed gene 3 (MEG3) has been indicated to play critical roles in tumorigenesis, ILF3 has been recognized as a MEG3-binding protein, however, the roles of adenosine and MEG3 on hepatoma are still ambiguous. To clarify the effects of MEG3 on the adenosine-induced cytotoxicity in hepatoma, MEG3 and ILF3 lentivirus were transduced into human hepatoma HepG2 cells to stimulate overexpression of MEG3 (OE MEG3) and overexpression of ILF3 (OE ILF3), furthermore, ILF3 small interfering RNA (siRNA) was also applied to downregulate the expression of ILF3. In this study, autophagy was markedly inhibited by low concentration of adenosine, which present by not only inhibited transformation from LC3-I to LC3-II and autophagosomes formation, but also the elevation of mTOR and reduction of beclin-1 proteins. Furthermore, low concentration of adenosine also exerted marked cytotoxicity representing induced cell apoptosis together with reductions of cell viability and migration, which were also markedly enhanced by OE MEG3. Novelly and excitingly, adenosine markedly stimulated MEG3 expression, OE MEG3 markedly decreased the ILF3 expression in HepG2 cells, and the adenosine-induced autophagy inhibition, together with the ratio of p-PI3K/PI3K, p-AKT/AKT, and p-mTOR/mTOR were also boosted by OE MEG3. More interestingly, OE ILF3 increased autophagy, whereas downregulated ILF3, especially in the case of adenosine, led to marked autophagy inhibition by decreasing beclin-1. The present study demonstrates autophagy inhibition is involved in the adenosine-induced cytotoxicity in HepG2 cells, the cytotoxicity can be synergized by OE MEG3 via downregulated ILF3 to activate PI3K/Akt/mTOR and inactivate the beclin-1 signaling pathway. In conclusion, MEG3 and inhibition of autophagy might be potential targets for augmenting adenosine-induced cytotoxicity in hepatoma.

Large teams develop and small teams disrupt science and technology.

One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence1-3. Increases in team size have been attributed to the specialization of scientific activities3, improvements in communication technology4,5, or the complexity of modern problems that require interdisciplinary solutions6-8. This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954-2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future-if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes.

Inhibition of autophagy enhances adenosine­induced apoptosis in human hepatoblastoma HepG2 cells.

In cancer research, autophagy acts as a double­edged sword: it increases cell viability or induces cell apoptosis depending upon the cell context and functional status. Recent studies have shown that adenosine (Ado) has cytotoxic effects in many tumors. However, the role of autophagy in Ado­induced apoptosis is still poorly understood. In the present study, Ado­induced apoptotic death and autophagy in hepatoblastoma HepG2 cells was investigated and the relationship between autophagy and apoptosis was identified. In the present study, it was demonstrated that Ado inhibited HepG2 cell growth in a time­ and concentration­dependent manner and activated endoplasmic reticulum (ER) stress, as indicated by G0/G1 cell cycle arrest, the increased mRNA and protein levels of GRP78/BiP, PERK, ATF4, CHOP, cleaved caspase­3, cytochrome c and the loss of mitochon-drial membrane potential (ΔΨm). Ado also induced autophagic flux, revealed by the increased expression of the autophagy marker microtubule­associated protein 1 light chain 3­II (LC3­II), Beclin­1, autophagosomes, and the degradation of p62, as revealed by western blot analysis and macrophage­derived chemokine (MDC) staining. Blocking autophagy using LY294002 notably entrenched Ado­induced growth inhibition and cell apoptosis, as demonstrated with the increased expression of cytochrome c and p62, and the decreased expression of LC3­II. Conversely, the autophagy inducer rapamycin alleviated Ado­induced apoptosis and markedly increased the ΔΨm. Moreover, knockdown of AMPK with si­AMPK partially abolished Ado­induced ULK1 activation and mTOR inhibition, and thus reinforced CHOP expression and Ado­induced apoptosis. These results indicated that Ado­induced ER stress resulted in apoptosis and autophagy concurrently. The AMPK/mTOR/ULK1 signaling pathway played a protective role in the apoptotic procession. Inhibition of autophagy may effectively enhance the anticancer effect of Ado in human hepatoblastoma HepG2 cells.

Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy.

Rapid research progress in science and technology (S&T) and continuously shifting workforce needs exert pressure on each other and on the educational and training systems that link them. Higher education institutions aim to equip new generations of students with skills and expertise relevant to workforce participation for decades to come, but their offerings sometimes misalign with commercial needs and new techniques forged at the frontiers of research. Here, we analyze and visualize the dynamic skill (mis-)alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE). The visualizations and computational models presented here can help key decision makers understand the evolving structure of skills so that they can craft educational programs that serve workforce needs. Our study uses millions of publications, course syllabi, and job advertisements published between 2010 and 2016. We show how courses mediate between research and jobs. We also discover responsiveness in the academic, educational, and industrial system in how skill demands from industry are as likely to drive skill attention in research as the converse. Finally, we reveal the increasing importance of uniquely human skills, such as communication, negotiation, and persuasion. These skills are currently underexamined in research and undersupplied through education for the labor market. In an increasingly data-driven economy, the demand for "soft" social skills, like teamwork and communication, increase with greater demand for "hard" technical skills and tools.

Relationship of SOCS3 and TGF-ß with IDO expression in early pregnancy chorionic villi and decidua.

We aimed to investigate the expression of suppressors cytokine signaling (SOCS)-3, transforming growth factor (TGF)-ß and indoleamine 2,3-dioxygense (IDO) and to analyse the relationship of SOCS3 and TGF-ß with IDO expression in early pregnancy chorionic villi and decidua in the maternal-fetal interface. Western blot analysis and immunohistochemical method were used to detect the expression of TGF-ß, SOCS3 and IDO in chorionic villi and decidua tissues of normal pregnant women. SOCS3, TGF-ß and IDO protein was identified in chorionic villi and decidua tissues of normal pregnant women and there was a negative correlation between the expression of IDO and SOCS3, but TGF-ß expression was positively correlated with IDO expression. The levels of IDO expression in the decidua from normal pregnancies were significantly higher than those in chorionic villi, while the expression of SOCS3 was no significant difference between decidua and chorionic villi. In normal physiological state of pregnancy, SOCS3 and TGF-ß may be involved in the regulation of immune tolerance by positive or negative regulation of IDO expression at maternal fetal interface.

Long non-coding RNA MEG3 induces cell apoptosis in esophageal cancer through endoplasmic reticulum stress.

Long non-coding RNAs (lncRNAs) play important roles in diverse biological processes, such as cell growth, apoptosis and migration. Although downregulation of lncRNA MEG3 has been identified in several cancers, little is known about its role in esophageal squamous cell carcinoma (ESCC). The aim of the present study was to detect MEG3 expression in clinical ESCC tissues, investigate its biological functions and the endoplasmic reticulum (ER) stress-relative mechanism. MEG3 expression levels were detected by qRT-PCR in both tumor tissues and adjacent non-tumor tissues from 28 ESCC patients. PcDNA3.1-MEG3 recombinant plasmids were constructed and transfected to EC109 cells. Cell growth was analyzed by CCK-8 assay. Cell apoptosis was analyzed by fluorescence microscope and Annexin V/PI assay. The protein expression was determined by western blot analysis. The results showed that MEG3 decreased significantly in ESCC tissues relative to adjacent normal tissues. PcDNA3.1-MEG3 plasmids were successfully constructed and the expression level of MEG3 significantly increased after MEG3 transfection to EC109 cells. Ectopic expression of MEG3 inhibited EC109 cell proliferation and induced apoptosis in vitro. MEG3 overexpression increased the expression of ER stress­related proteins (GRP78, IRE1, PERK, ATF6, CHOP and cleaved­caspase-3). Our results first demonstrate that MEG3 is downregulated in ESCC tissues. MEG3 was able to inhibit cell growth and induced apoptosis in EC109 cells, most probably via activation of the ER stress pathway.