Correction : Control of PD-L1 expression by miR-140/142/340/383 and oncogenic activation of the OCT4-miR-18a pathway in cervical cancer.

This research was supported by a grant from the Department of Women’s Health Educational System, JSPS Grant-in-Aid for Scientific Research (C) (15K10697 and 16K11123) and the Science and Technology Planning Project of Guangdong Province, China (2014A020212124). We thank Dr. Zhujie Xu for experimental assistance. The authors declare that they have no conflict of interest.Peer reviewedPublisher PD

Knowledge-Enhanced Dual-Channel GCN for Aspect-Based Sentiment Analysis

As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks

Knowledge-Enhanced Dual-Channel GCN for Aspect-Based Sentiment Analysis

As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks

Control of PD-L1 expression by miR-140/142/340/383 and oncogenic activation of the OCT4–miR-18a pathway in cervical cancer

PD-L1, a key inhibitory immune receptor, has crucial functions in cancer immune evasion, but whether PD-L1 promotes the malignant properties of cervical cancer (CC) cells and the mechanism by which PD-L1 is regulated in CC remains unclear. We report that PD-L1 is overexpressed in CC, and shRNA-mediated PD-L1 depletion suppresses the proliferation, invasion, and tumorigenesis of CC cells. Loss of miR-140/142/340/383 contributes to PD-L1 upregulation. miR-18a enhances PD-L1 levels by targeting PTEN, WNK2 (ERK1/2 pathway inhibitor), and SOX6 (Wnt/β-catenin pathway inhibitor and p53 pathway activator) to activate the PI3K/AKT, MEK/ERK, and Wnt/β-catenin pathways and inhibit the p53 pathway, and miR-18a also directly suppresses the expression of the tumor suppressors BTG3 and RBSP3 (CTDSPL). miR-18a overexpression in CC cells is triggered by OCT4 overexpression. Our data implicate PD-L1 as a novel oncoprotein and indicate that miR-140/142/340/383 and miR-18a are key upstream regulators of PD-L1 and potential targets for CC treatment

Table_1_Causal association of lipoprotein-associated phospholipids on the risk of sepsis: a Mendelian randomization study.xlsx

BackgroundMany previous studies have revealed a close relationship between lipoprotein metabolism and sepsis, but their causal relationship has, until now, remained unclear. Therefore, we performed a two-sample Mendelian randomization analysis to estimate the causal relationship of lipoprotein-associated phospholipids with the risk of sepsis.Materials and methodsA two-sample Mendelian randomization (MR) analysis was performed to investigate the causal relationship between lipoprotein-associated phospholipids and sepsis based on large-scale genome-wide association study (GWAS) summary statistics. MR analysis was performed using a variety of methods, including inverse variance weighted as the primary method, MR Egger, weighted median, simple mode, and weighted mode as complementary methods. Further sensitivity analyses were used to test the robustness of the data.ResultsAfter Bonferroni correction, the results of the MR analysis showed that phospholipids in medium high-density lipoprotein (HDL; ORIVW = 0.82, 95% CI 0.71-0.95, P = 0.0075), large HDL (ORIVW = 0.92, 95% CI 0.85-0.98, P = 0.0148), and very large HDL (ORMR Egger = 0.83, 95% CI 0.72-0.95, P = 0.0134) had suggestive causal relationship associations with sepsis. Sensitivity testing confirmed the accuracy of these findings. There was no clear association between other lipoprotein-associated phospholipids and sepsis risk.ConclusionsOur MR analysis data suggestively showed a correlation between higher levels of HDL-associated phospholipids and reduced risk of sepsis. Further studies are required to determine the underlying mechanisms behind this relationship.</p

DataSheet_1_Causal association of lipoprotein-associated phospholipids on the risk of sepsis: a Mendelian randomization study.docx

BackgroundMany previous studies have revealed a close relationship between lipoprotein metabolism and sepsis, but their causal relationship has, until now, remained unclear. Therefore, we performed a two-sample Mendelian randomization analysis to estimate the causal relationship of lipoprotein-associated phospholipids with the risk of sepsis.Materials and methodsA two-sample Mendelian randomization (MR) analysis was performed to investigate the causal relationship between lipoprotein-associated phospholipids and sepsis based on large-scale genome-wide association study (GWAS) summary statistics. MR analysis was performed using a variety of methods, including inverse variance weighted as the primary method, MR Egger, weighted median, simple mode, and weighted mode as complementary methods. Further sensitivity analyses were used to test the robustness of the data.ResultsAfter Bonferroni correction, the results of the MR analysis showed that phospholipids in medium high-density lipoprotein (HDL; ORIVW = 0.82, 95% CI 0.71-0.95, P = 0.0075), large HDL (ORIVW = 0.92, 95% CI 0.85-0.98, P = 0.0148), and very large HDL (ORMR Egger = 0.83, 95% CI 0.72-0.95, P = 0.0134) had suggestive causal relationship associations with sepsis. Sensitivity testing confirmed the accuracy of these findings. There was no clear association between other lipoprotein-associated phospholipids and sepsis risk.ConclusionsOur MR analysis data suggestively showed a correlation between higher levels of HDL-associated phospholipids and reduced risk of sepsis. Further studies are required to determine the underlying mechanisms behind this relationship.</p