Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism.

Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence of bond information on atom node representation, leading to insufficient drug representation. Moreover, key molecular substructures have significant contribution to the DDIs prediction results. Therefore, in this work, we propose a novel Graph learning framework of Mutual Interaction Attention mechanism (called GMIA) to predict DDIs by effectively representing the drug molecules. Specifically, we build the node-edge message communication encoder to aggregate atom node and the incoming edge information for atom node representation and design the mutual interaction attention decoder to capture the mutual interaction context between molecular graphs of drug pairs. GMIA can bridge the gap between two encoders for the single drug molecules by attention mechanism. We also design a co-attention matrix to analyze the significance of different-size substructures obtained from the encoder-decoder layer and provide interpretability. In comparison with other recent state-of-the-art methods, our GMIA achieves the best results in terms of area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), and F1 score on two different scale datasets. The case study indicates that our GMIA can detect the key substructure for potential DDIs, demonstrating the enhanced performance and interpretation ability of GMIA.

Exo-miR-144-3p as a promising diagnostic biomarker for depressive symptoms in heart failure.

AIMS: The prevalence of depression is higher in heart failure (HF) patients. Early screening of depressive symptoms in HF patients and timely intervention can help to improve patients' quality of life and prognosis. This study aims to explore diagnostic biomarkers by examining the expression profile of serum exosomal miRNAs in HF patients with depressive symptoms. METHODS: Serum exosomal RNA was isolated and extracted from 6 HF patients with depressive symptoms (HF-DS) and 6 HF patients without depressive symptoms (HF-NDS). High-throughput sequencing was performed to obtain miRNA expression profiles and target genes were predicted for the screened differentially expressed miRNAs. Biological functions of the target genes were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, we collected serum exosomal RNAs from HF-DS (n = 20) and HF-NDS (n = 20). The differentially expressed miRNAs selected from the sequencing results were validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Finally, the diagnostic efficacy of the differentially expressed exosomal miRNAs for HF-DS was evaluated by using receiver operating characteristic (ROC) curves. RESULTS: A total of 19 significantly differentially expressed exosomal miRNAs were screened by high-throughput sequencing, consisting of 12 up-regulated and 7 down-regulated exosomal miRNAs. RT-qPCR validation demonstrated that the expression level of exo-miR-144-3p was significantly down-regulated in the HF-DS group, and the expression levels of exo-miR-625-3p and exo-miR-7856-5p were significantly up-regulated. In addition, the expression level of exo-miR-144-3p was negatively correlated with the severity of depressive symptoms in HF patients, and that the area under the curve (AUC) of exo-miR-144-3p for diagnosing HF-DS was 0.763. CONCLUSIONS: In this study, we examined the serum exosomal miRNA expression profiles of HF patients with depressive symptoms and found that lower level of exo-miR-144-3p was associated with more severe depressive symptoms. Exo-miR-144-3p is a potential biomarker for the diagnosis of HF-DS.

MiR-183-5p promotes migration and invasion of prostate cancer by targeting TET1.

BACKGROUND: Prostate cancer (PCa) is one of the common malignant tumors worldwide. MiR-183-5p has been reported involved in the initiation of human PCa, this study aimed to investigate whether miR-183-5p affects the development of prostate cancer. METHODS: In this study, we analyzed the expression of miR-183-5p in PCa patients and its correlation with clinicopathological parameters based on TCGA data portal. CCK-8, migration assay and invasion and wound-healing assay were performed to detect proliferation, migration and invasion in PCa cells. RESULTS: We found the expression of miR-183-5p was significantly increased in PCa tissues, and high expression of miR-183 was positively associated with poor prognosis of PCa patients. Over-expression of miR-183-5p promoted the migration, invasion capacities of PCa cells, whereas knockdown of miR-183-5p showed reversed function. Furthermore, luciferase reporter assay showed TET1 was identified as a direct target of miR-183-5p, which was negatively correlation with miR-183-5p expression level. Importantly, rescue experiments demonstrated TET1 over-expression could reverse miR-183-5p mimic induced-acceleration of PCa malignant progression. CONCLUSION: Our results indicated that miR-183-5p could act as a tumor promoter in PCa and it accelerated the malignant progression of PCa by directly targeting and down-regulating TET1.

Few-Shot Drug Synergy Prediction With a Prior-Guided Hypernetwork Architecture.

Predicting drug synergy is critical to tailoring feasible drug combination treatment regimens for cancer patients. However, most of the existing computational methods only focus on data-rich cell lines, and hardly work on data-poor cell lines. To this end, here we proposed a novel few-shot drug synergy prediction method (called HyperSynergy) for data-poor cell lines by designing a prior-guided Hypernetwork architecture, in which the meta-generative network based on the task embedding of each cell line generates cell line dependent parameters for the drug synergy prediction network. In HyperSynergy model, we designed a deep Bayesian variational inference model to infer the prior distribution over the task embedding to quickly update the task embedding with a few labeled drug synergy samples, and presented a three-stage learning strategy to train HyperSynergy for quickly updating the prior distribution by a few labeled drug synergy samples of each data-poor cell line. Moreover, we proved theoretically that HyperSynergy aims to maximize the lower bound of log-likelihood of the marginal distribution over each data-poor cell line. The experimental results show that our HyperSynergy outperforms other state-of-the-art methods not only on data-poor cell lines with a few samples (e.g., 10, 5, 0), but also on data-rich cell lines.

A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions.

Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug-drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs. To address the above issues, we first leverage Balance theory and Status theory in social networks to reveal the topological patterns among directed pharmacological DDIs, which are modeled as a signed and directed network. Then, we design a novel graph representation learning model named SGRL-DDI (social theory-enhanced graph representation learning for DDI) to realize the multitask prediction of DDIs. SGRL-DDI model can capture the task-joint information by integrating relation graph convolutional networks with Balance and Status patterns. Moreover, we utilize task-specific deep neural networks to perform two tasks, including the prediction of enhancive/depressive DDIs and the prediction of directed DDIs. Based on DDI entries collected from DrugBank, the superiority of our model is demonstrated by the comparison with other state-of-the-art methods. Furthermore, the ablation study verifies that Balance and Status patterns help characterize directed pharmacological DDIs, and that the joint of two tasks provides better DDI representations than individual tasks. Last, we demonstrate the practical effectiveness of our model by a version-dependent test, where 88.47 and 81.38% DDI out of newly added entries provided by the latest release of DrugBank are validated in two predicting tasks respectively.

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs.

The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between drugs within the DDI network and new drugs from outside the DDI network. In addition, they are not designed to explore the underlying mechanisms of DDIs and lack interpretative capacity. Thus, here we propose a novel method of GNN-DDI to predict potential DDIs by constructing a five-layer graph attention network to identify k-hops low-dimensional feature representations for each drug from its chemical molecular graph, concatenating all identified features of each drug pair, and inputting them into a MLP predictor to obtain the final DDI prediction score. The experimental results demonstrate that our GNN-DDI is suitable for each of two DDI predicting scenarios, namely the potential DDIs among known drugs in the DDI network and those between drugs within the DDI network and new drugs from outside DDI network. The case study indicates that our method can explore the specific drug substructures that lead to the potential DDIs, which helps to improve interpretability and discover the underlying interaction mechanisms of drug pairs.

Directed graph attention networks for predicting asymmetric drug-drug interactions.

It is tough to detect unexpected drug-drug interactions (DDIs) in poly-drug treatments because of high costs and clinical limitations. Computational approaches, such as deep learning-based approaches, are promising to screen potential DDIs among numerous drug pairs. Nevertheless, existing approaches neglect the asymmetric roles of two drugs in interaction. Such an asymmetry is crucial to poly-drug treatments since it determines drug priority in co-prescription. This paper designs a directed graph attention network (DGAT-DDI) to predict asymmetric DDIs. First, its encoder learns the embeddings of the source role, the target role and the self-roles of a drug. The source role embedding represents how a drug influences other drugs in DDIs. In contrast, the target role embedding represents how it is influenced by others. The self-role embedding encodes its chemical structure in a role-specific manner. Besides, two role-specific items, aggressiveness and impressionability, capture how the number of interaction partners of a drug affects its interaction tendency. Furthermore, the predictor of DGAT-DDI discriminates direction-specific interactions by the combination between two proximities and the above two role-specific items. The proximities measure the similarity between source/target embeddings and self-role embeddings. In the designated experiments, the comparison with state-of-the-art deep learning models demonstrates the superiority of DGAT-DDI across a direction-specific predicting task and a direction-blinded predicting task. An ablation study reveals how well each component of DGAT-DDI contributes to its ability. Moreover, a case study of finding novel DDIs confirms its practical ability, where 7 out of the top 10 candidates are validated in DrugBank.

deepMDDI: A deep graph convolutional network framework for multi-label prediction of drug-drug interactions.

It is crucial to identify DDIs and explore their underlying mechanism (e.g., DDIs types) for polypharmacy safety. However, the detection of DDIs in assays is still time-consuming and costly, due to the need for experimental search over a large space of drug combinations. Thus, many computational methods have been developed to predict DDIs, most of them focusing on whether a drug interacts with another or not. And a few deep learning-based methods address a more realistic screening task for identifying various DDI types, but they assume a DDI only triggers one pharmacological effect, while a DDI can trigger more types of pharmacological effects. Thus, here we proposed a novel end-to-end deep learning-based method (called deepMDDI) for the Multi-label prediction of Drug-Drug Interactions. deepMDDI contains an encoder derived from relational graph convolutional networks and a tensor-like decoder to uniformly model interactions. deepMDDI is not only efficient for DDI transductive prediction, but also inductive prediction. The experimental results show that our model is superior to other state-of-the-art deep learning-based methods. We also validated the power of deepMDDI in the DDIs multi-label prediction and found several new valid DDIs in the case study. In conclusion, deepMDDI is beneficial to uncover the mechanism and regularity of DDIs.

Effects of nitrogen application rate on the photosynthetic pigment, leaf fluorescence characteristics, and yield of indica hybrid rice and their interrelations.

A field experiment employing the rice cultivars Qyou6 and Yixiangyou2115 as materials and different nitrogen application rates was conducted in Huangping County, Guizhou Province in 2019 to determine the effects of nitrogen application rate on photosynthetic pigments, leaf fluorescence characteristics, yield, and their interrelations in indica hybrid rice. The results showed that photosynthetic pigment contents generally increased with increasing nitrogen application rate. As the nitrogen rate increased, the maximal quantum yield of PSII (Fv/Fm), actual quantum yield of PSII (ΦPSII), and relative electron transfer rate at PSII (ETR) first decreased and then increased at the booting stage; Fv/Fm and ΦPSII decreased while ETR first increased and then decreased at the heading stage; nevertheless, Fv/Fm and ΦPSII first decreased and then increased but ETR was just the opposite at the maturity stage. Non-photochemical quenching coefficient (qN) and quantum yield of regulatory energy dissipation at PSII (Y(NPQ)) first increased and then decreased whereas quantum yield of non-regulatory energy dissipation at PSII (Y(NO)) first decreased and then increased at the booting, heading, and maturity stages with increasing nitrogen application rate. Photochemical quenching coefficient (qP) showed an increasing trend as the nitrogen rate increased in the range of 150-300 kg/ha at the heading and maturity stages. Photosynthetic pigments, leaf fluorescence characteristics, and yield and its components were significantly correlated. First, chlorophylls a and b were significantly negatively correlated with Fv/Fm while significantly positively correlated with qP at the heading stage. Secondly, Carotenoids were significantly positively correlated with the effective panicle number (EPN) at the booting stage while significantly negatively correlated with the spikelets per panicle (SPP) at the heading stage. Chlorophyll a and carotenoids were significantly positively correlated with EPN but significantly negatively correlated with spikelet filling (SF) at the maturity stage. In addition, qP was significantly negatively correlated with EPN at the booting stage. At the heading stage, Fv/Fm and Y(NO) were significantly negatively correlated with EPN and SPP, respectively, and Fv/Fm and ΦPSII were significantly positively related to SF. Moreover, qP was extremely significantly positively related to EPN whereas Fv/Fm was extremely significantly negatively correlated with grain yield at the maturity stage. Appropriate nitrogen application rates can enhance photosynthetic pigment contents, improve the photochemical efficiency and proportion of the open part of the reaction center of PSII, and promote the quantum efficiency and self-protection ability of PSII, thereby increasing photosynthetic efficiency and yield. Under the conditions adopted in this experiment, a parabolic relationship was observed between the nitrogen application rate and grain yield. The regression analysis results showed that the best nitrogen application rate of indica hybrid rice is 168.16 kg ha-1 and the highest yield is 11,804.87 kg ha-1.

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

DPDDI: a deep predictor for drug-drug interactions.

BACKGROUND: The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases. RESULTS: In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs. CONCLUSION: We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.

Stability and applicability of the leaf value model for variable nitrogen application based on SPAD value in rice.

Many fertilization models have been created to scientifically determine the amount of fertilization. With the same purpose, we constructed a nitrogen (N) application model, the leaf value model, which can make N fertilizer decisions in a timely, fast and nondestructive manner during rice planting. However, only one area (A1, Jiuzhou Town, Xixiu District, Guizhou Province) and one cultivar (Qyou6) were involved in the construction of the leaf value model. Its stability and applicability could not be well evaluated. Thus, we chose another area (A2, Jiuzhou Town, Huangping County, Guizhou Province) in Guizhou Province and carried out the experiment by using four cultivars (Nie5you5399, Qyou6, Yixiangyou2115 and Zhongzheyou8) for the leaf value model construction. Compared with the average value of apparent total N uptake (Nz) obtained in 2 years in the A1 area, that in the Qyou6 leaf value model in the A2 area increased by 12%, reaching 635.72 kg ha-1, whereas the corresponding target yield changed slightly, reaching 10,999.90 kg ha-1. Simultaneously, the linear relationship between several good SPAD value-derived indexes (Ys) and apparent N supply of the field (Nx) was still significant or extremely significant in the Qyou6 leaf value model. Compared with the A1 area, it slightly differed, and the R2 of SPADL1 was higher than that of SPADL3×L4/mean. In the leaf value model of the other three cultivars, the relationship between yield and Nx and that between Ys and Nx were significant or extremely significant. The Nz of Yixiangyou2115 and Zhongzheyou8 (618.33 and 617.76 kg ha-1) were close to that of Qyou6 and the corresponding target yields were 10313.36 and 10301.99 kg ha-1, respectively. The Nz and target yield of Nie5you5399 were lowest at 546.63 and 10680.24 kg ha-1, respectively. In general, this study showed that relationships used in the construction of leaf value model had certain stability and applicability to difference areas and cultivars. The leaf value model can be considered in N fertilizer decision-making of rice planting management.

Resveratrol inhibits the tumor migration and invasion by upregulating TET1 and reducing TIMP2/3 methylation in prostate carcinoma cells.

BACKGROUND: Recently, resveratrol (Res) has been suggested to suppress the migration and invasion of prostate cancer (PCa). In the present study, we aimed to investigate the effects of Res on genomic DNA methylation, as well as the migration and invasion of PCa cells. METHODS: The suppression by Res of the growth of PCa cells was verified through a cytotoxicity assay. In addition, the effects of Res on 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), and ten-eleven translocation 1 (TET1) levels were assessed, and the cell migration and invasion were also determined. The expressions of TET1, tissue inhibitor of metalloproteinases (TIMP) 2, TIMP3, MMP2, and MMP9 were detected through Western blot analysis. Afterward, TET1 was silenced using lentiviral short hairpin RNA to examine the effect of TET1 on the Res-triggered inhibition of migration and invasion of PCa cells. RESULTS: Our results showed that Res upregulated the 5hmC and TET1 levels and downregulated the 5mC level. Moreover, Res also inhibited the migration and invasion of PCa cells, promoted the demethylation of TIMP2 and TIMP3 to upregulate their expressions, and suppressed the expressions of MMP2 and MMP9. The silencing of TET1 in the presence of Res showed that Res could exert its effect through TET1. CONCLUSIONS: Our findings indicated that Res inhibited the migration and invasion of PCa cells via the TET1/TIMP2/TIMP3 pathway, which might potentially serve as a target for the treatment of PCa.

Association between interleukin-36γ and tumor progression in non-small cell lung cancer.

Immunotherapy is effective in improving the survival and prognosis of patients with non-small cell lung cancer (NSCLC), and identifying effective immunomarkers is important for immunotherapy. Interleukin (IL)-36γ is a novel immunomarker that has an important function in the antitumor immune response. The present study investigated the association between IL-36γ and NSCLC to provide novel insight into immunotherapy for patients with NSCLC. Tissue microarrays of lung adenocarcinoma and squamous cell carcinoma were purchased for immunohistochemical analysis of IL-36γ expression levels and clinical parameters. In addition, fresh clinical NSCLC and adjacent normal tissue samples were collected to analyze IL-36γ mRNA expression levels using quantitative PCR. IL-36γ protein was primarily located in the cytoplasm, with a small quantity in the nucleus, and IL-36γ mRNA and protein expression levels in lung cancer tissues were significantly higher compared with those in adjacent normal tissues. Elevated IL-36γ protein expression levels were significantly associated with a higher tumor grade of lung adenocarcinoma; however, IL-36γ mRNA expression levels were inversely associated with the clinical Tumor-Node-Metastasis stage in patients with lung squamous cell carcinoma. In addition, patients with adenocarcinoma with high IL-36γ protein expression levels tended to longer post-operative survival times. These findings indicate that IL-36γ may have potential as an immunomarker for prediction of tumor progression and survival in patients with NSCLC.

Clinical characteristics of diabetic nephropathy in patients with type 2 diabetic mellitus manifesting heavy proteinuria: A retrospective analysis of 220 cases.

AIMS: To determine the predictability of diagnosing diabetic nephropathy (DN) versus non-diabetic renal disease (NDRD) from clinical and laboratory data in Chinese patients with type 2 diabetes mellitus (T2DM) manifesting heavy proteinuria. METHODS: We retrospectively analyzed the clinical and laboratory data of patients with T2DM manifesting heavy proteinuria who underwent renal biopsy from January 2014 to December 2017. RESULTS: According to renal biopsy, 220 patients were finally enrolled, including 109 cases diagnosed with DN alone (49.55%), 94 with NDRD alone (42.73%) and 17 with DN plus superimposed NDRD (7.73%). Multivariate analysis showed the significant risk factors for DN alone were age, duration of diabetes, presence of retinopathy, 24-h proteinuria, serum albumin and SBP. Presence of retinopathy achieved the highest overall diagnostic efficiency with the area under the curve of 0.852, sensitivity of 78.9% and specificity of 91.5%. The combined diagnosis with four indicators (duration of diabetes, retinopathy, SBP, and serum albumin) showed the area under the curve of 0.938, sensitivity of 88.1% and specificity of 87.2%. CONCLUSIONS: The prevalence of DN is high in patients with T2DM manifesting heavy proteinuria. Renal biopsy should be performed in diabetics in the atypical clinical scenario.

Short hairpin RNA-mediated knockdown of nuclear factor erythroid 2-like 3 exhibits tumor-suppressing effects in hepatocellular carcinoma cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with high mortality-to-incidence ratios. Nuclear factor erythroid 2-like 3 (NFE2L3), also known as NRF3, is a member of the cap 'n' collar basic-region leucine zipper family of transcription factors. NFE2L3 is involved in the regulation of various biological processes, whereas its role in HCC has not been elucidated. AIM: To explore the expression and biological function of NFE2L3 in HCC. METHODS: We analyzed the expression of NFE2L3 in HCC tissues and its correlation with clinicopathological parameters based on The Cancer Genome Atlas (TCGA) data portal. Short hairpin RNA (shRNA) interference technology was utilized to knock down NFE2L3 in vitro. Cell apoptosis, clone formation, proliferation, migration, and invasion assays were used to identify the biological effects of NFE2L3 in BEL-7404 and SMMC-7721 cells. The expression of epithelial-mesenchymal transition (EMT) markers was examined by Western blot analysis. RESULTS: TCGA analysis showed that NFE2L3 expression was significantly positively correlated with tumor grade, T stage, and pathologic stage. The qPCR and Western blot results showed that both the mRNA and protein levels of NFE2L3 were significantly decreased after shRNA-mediated knockdown in BEL-7404 and SMMC-7721 cells. The shRNA-mediated knockdown of NFE2L3 could induce apoptosis and inhibit the clone formation and cell proliferation of SMMC-7721 and BEL-7404 cells. NFE2L3 knockdown also significantly suppressed the migration, invasion, and EMT of the two cell lines. CONCLUSION: Our study showed that shRNA-mediated knockdown of NFE2L3 exhibited tumor-suppressing effects in HCC cells.

miR-10b Downregulated by DNA Methylation Acts as a Tumor Suppressor in HPV-Positive Cervical Cancer via Targeting Tiam1.

BACKGROUND/AIMS: microRNAs (miRNAs) are known to act as oncogenes or tumor suppressors in diverse cancers. Although miR-10b is an oncogene implicated in many tumors, its role in cervical cancer (CC) remains largely unclear. Here, we investigated the function and underlying mechanisms of miR-10b in human CC. METHODS: Quantitative RT-PCR was used to measure miR-10b expression in CC and normal tissues, and its association with clinicopathologic features was analyzed. Methylation of CpG sites in the miR-10b promoter was analyzed by methylation sequencing. Cell proliferation, apoptosis, migration, and invasion assays were used to elucidate the biological effects of miR-10b and expression of the target gene was assayed with Western blot. RESULTS: miR-10b was downregulated in CC tissues compared with normal tissues, and less miR-10b expression was associated with larger tumors, vascular invasion and HPV-type 16 positivity. miR-10b expression decreased in HeLa (HPV18-positive) and SiHa (HPV16-positive) cells compared with C-33A (HPV-negative), but increased after treatment with 5-Aza-CdR. Methylation ratio of site -797 in the miR-10b promoter in C-33A was lower than that in HeLa and SiHa. Further analysis indicates that site -797 is located within a transcription factor AP-2A (TFAP2A) binding element. Functionally, overexpression of miR-10b in HeLa and SiHa suppressed cell proliferation, migration and invasion, and induced apoptosis and miR-10b downregulation had opposite effects. Mechanistically, T-cell lymphoma invasion and metastasis 1 (Tiam1) was identified as a direct and functional target of miR-10b. CONCLUSION: miR-10b acts as a tumor suppressor in CC by suppressing oncogenic Tiam1, and its expression may be downregulated through methylation of TFAP2A binding element by HPV.

Interleukin-33, a Potential Cytokine Expressed in Tumor Microenvironment Involves in Antitumor Immunotherapy Through Facilitates CD8+ T Cells.

Interleukin-33 (IL-33) is a cytokine with pleiotropic functions in various diseases; however, its role in the antitumor immune response is still unclear. We found the expression of IL-33/ST2 in nonsmall cell lung tumor microenvironment. Furthermore, we found that IL-33 promoted effector functions of CD8+ T cells that play a critical role in antitumor immune response. In addition, we found that IL-33 enhanced tumor vaccine effector functions in mice. Altogether, these findings suggest that IL-33, through facilitates CD8+ T cells in microenvironment to provide a profound effect in antitumor immunotherapy.

Increased apolipoprotein M induced by lack of scavenger receptor BI is not activated via HDL-mediated cholesterol uptake in hepatocytes.

BACKGROUND: Scavenger receptor BI (SR-BI) is a classic high-density lipoprotein (HDL) receptor, which mediates selective lipid uptake from HDL cholesterol esters (HDL-C). Apolipoprotein M (ApoM), as a component of HDL particles, could influence preß-HDL formation and cholesterol efflux. The aim of this study was to determine whether SR-BI deficiency influenced the expression of ApoM. METHODS: Blood samples and liver tissues were collected from SR-BI gene knockout mice, and serum lipid parameters, including total cholesterol (TC), triglyceride (TG), high and low-density lipoprotein cholesterol (HDL-C and LDL-C) and ApoM were measured. Hepatic ApoM and ApoAI mRNA levels were also determined. In addition, BLT-1, an inhibitor of SR-BI, was added to HepG2 cells cultured with cholesterol and HDL, under serum or serum-free conditions. The mRNA and protein expression levels of ApoM were detected by RT-PCR and western blot. RESULTS: We found that increased serum ApoM protein levels corresponded with high hepatic ApoM mRNA levels in both male and female SR-BI-/- mice. Besides, serum TC and HDL-C were also significantly increased. Treatment of HepG2 hepatoma cells with SR-BI specific inhibitor, BLT-1, could up-regulate ApoM expression in serum-containing medium but not in serum-free medium, even in the presence of HDL-C and cholesterol. CONCLUSIONS: Results suggested that SR-BI deficiency promoted ApoM expression, but the increased ApoM might be independent from HDL-mediated cholesterol uptake in hepatocytes.

Apolipoprotein M promotes proliferation and invasion in non-small cell lung cancers via upregulating S1PR1 and activating the ERK1/2 and PI3K/AKT signaling pathways.

Apolipoprotein M (ApoM) is a sphingosine 1-phosphate (S1P) carrier involved in the regulation of S1P. Signaling pathways involving sphingosine kinases (SphKs) and S1P-S1P receptors (S1PRs) play important roles in the oncogenesis of multiple cancers including non-small cell lung cancer (NSCLC). In the present study we have clarified the potential roles of ApoM on the oncogenesis process of NSCLC cells. We detected the ApoM expression in NSCLC tissues and further analyzed its clinical significance. Moreover, we determined effects of ApoM overexpression on tumor cellular behaviours of NSCLC in vitro and in vivo. Our results demonstrated that ApoM protein mass were clearly higher in the NSCLC tissues than in non-NSCLS tissues. Overexpression of ApoM could promote NSCLC cell proliferation and invasion in vitro and tumor growth in vivo, which might be via upregulating S1PR1 and activating the ERK1/2 and PI3K/AKT signaling pathways. It is concluded that up-regulation of ApoM in NSCLC might be associated with the tumor induced inflammation and tumor microenvironment as well as promoting oncogenesis of NSCLC. Further study needs to elucidate the underlying mechanisms.