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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Prognostic role of long non-coding RNA XIST expression in patients with solid tumors: a meta-analysis.

BACKGROUND: The aberrant expression of long non-coding RNA (lncRNA) X inactivate-specific transcript (XIST) has been demonstrated to be involved in the tumourigenesis and the development of various cancers. Therefore, we conducted a meta-analysis to assess the prognostic role of lncRNA XIST expression in solid tumors. METHODS: The databases of PubMed, EMBase, Web of Science, Cochrane library (up to Dec 31, 2017) were searched for the related studies and identified 15 eligible studies containing 1209 patients to include in the meta-analysis. Hazards ratios (HRs) with corresponding 95% confidence intervals (CIs) were pooled to estimate the association between lncRNA XIST expression and survival of cancer patients from Asian. RESULTS: The result showed that higher lncRNA XIST expression in cancer tissue was related to a worse overall survival (OS) (HR = 1.54, 95% CI 1.07-2.23). In subgroup analysis, it revealed that lncRNA XIST overexpression was significantly associated with worse OS in digestive system tumors (HR = 1.67, 95% CI 1.11-2.51, p = 0.031). In addition, the association between high lncRNA XIST expression and poor OS was also statistically significant in other subgroups, including multivariate analysis (HR = 2.39, 95% CI 1.28-4.46, p = 0.006, random-effect), patients' number was greater than 65 (HR = 1.75, 95% CI 1.24-2.47, p = 0.001, random-effect), and reported in text (HR = 2.50, 95% CI 1.49-4.18, p = 0.000, random-effect). CONCLUSIONS: The expression of lncRNA XIST could be regarded as a poor prognostic biomarker for solid tumors, which might shed new light on epigenetic diagnostics and therapeutics in tumors.

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.

17ß-estradiol regulates the expression of apolipoprotein M through estrogen receptor α-specific binding motif in its promoter.

BACKGROUND: We have previously demonstrated that estrogen could significantly enhance expression of apolipoprotein M (apoM), whereas the molecular basis of its mechanism is not fully elucidated yet. To further investigate the mechanism behind the estrogen induced up-regulation of apoM expression. RESULTS: Our results demonstrated either free 17ß-estradiol (E2) or membrane-impermeable bovine serum albumin-conjugated E2 (E2-BSA) could modulate human apoM gene expression via the estrogen receptor alpha (ER-α) pathway in the HepG2 cells. Moreover, experiments with the luciferase activity analysis of truncated apoM promoters could demonstrate that a regulatory region (from-1580 to -1575 bp (-GGTCA-)) upstream of the transcriptional start site of apoM gene was essential for the basal transcriptional activity that regulated by the ER-α. With the applications of an electrophoresis mobility shift assay and a chromatin immunoprecipitation assay, we could successfully identify a specific ER-α binding element in the apoM promoter region. CONCULSION: In summary, the present study indicates that 17ß-estradiol induced up-regulation of apoM in HepG2 cells is through an ER-α-dependent pathway involving ER-α binding element in the promoter of the apoM gene.

Personalized discovery of altered pathways in clear cell renal cell carcinoma using accumulated normal sample data.

PURPOSE: To identify altered pathways in an individual with clear cell renal cell carcinoma (ccRCC) using accumulated normal sample data. METHODS: Gene expression data of E-GEOD-40435 was downloaded from the ArrayExpress database. Gene-level statistics of genes in tumor and normal samples were computed. Then, the Average Z method was applied to calculate the individual pathway aberrance score (iPAS). Subsequently, the significantly altered pathways in a ccRCC sample were identified using T-test based on the pathway statistics values of normal and ccRCC samples. Moreover, the identified altered pathways were verified through two methods: one was assessing classification capability for microarray data samples, and the other was computing the changed percentage of each pathway in ccRCC samples. RESULTS: Based on the threshold, 886 altered pathways were identified in all samples. The most significant pathways were potassium transport channels, proton-coupled monocarboxylate transport, beta oxidation of octanoyl-CoA to hexanoyl-CoA, antigen presentation: folding, assembly and peptide loading of class I MHC, and so on. Additionally, iPAS separated ccRCC from normal controls with an accuracy of 0.980. Moreover, a total of 5 significant pathways with change in 100% ccRCC samples were extracted including proton-coupled monocarboxylate transport, antigen presentation: folding, assembly and peptide loading of class I MHC, and so on. CONCLUSIONS: iPAS is useful to predict marker pathways for ccRCC with a high accuracy. Pathways of proton-coupled monocarboxylate transport, and antigen presentation: folding, assembly and peptide loading of class I MHC might play crucial roles in ccRCC progression.

Involvement of NEDD9 in the invasion and migration of gastric cancer.

Recent studies have demonstrated that neural precursor cell expressed, developmentally downregulated 9 (NEDD9) is highly expressed in various tumor tissues and cell lines. However, research on the role of NEDD9 in gastric cancer (GC) is rare, and the potential mechanism in tumor progression has not yet been explored. In this study, we investigated the role and mechanism of NEDD9 in GC. The expression of NEDD9 in GC tissues and cell lines was measured by immunohistochemistry, qRT-PCR, and Western blot, respectively. Inhibiting NEDD9 expression was carried out by siRNA transfection, and upregulating of NEDD9 was via NEDD9 overexpression plasmid. The ability of proliferation, migration, and invasion was detected by MTT assay, scratch wound assay, and transwell assay, respectively. The expression of vimentin, E-cadherin, Zeb1, and Zeb2 was measured by Western blot and qRT-PCR. We found that NEDD9 expression was dramatically increased both in GC tissues and cell lines, and the expression was significantly related to GC development. Knockdown of NEDD9 in SGC-7901 strongly inhibited its malignant capacity in vitro. Meanwhile, upregulation of NEDD9 in GES-1 increased the malignant capacity. In addition, the expression of vimentin, Zeb1, and Zeb2 was positively correlated with NEDD9, while E-cadherin was opposite. Collectively, our findings suggest that NEDD9 acts as an oncogene and promotes GC metastasis via EMT.

Palmitic acid suppresses apolipoprotein M gene expression via the pathway of PPARß/δ in HepG2 cells.

It has been demonstrated that apolipoprotein M (APOM) is a vasculoprotective constituent of high density lipoprotein (HDL), which could be related to the anti-atherosclerotic property of HDL. Investigation of regulation of APOM expression is of important for further exploring its pathophysiological function in vivo. Our previous studies indicated that expression of APOM could be regulated by platelet activating factor (PAF), transforming growth factors (TGF), insulin-like growth factor (IGF), leptin, hyperglycemia and etc., in vivo and/or in vitro. In the present study, we demonstrated that palmitic acid could significantly inhibit APOM gene expression in HepG2 cells. Further study indicated neither PI-3 kinase (PI3K) inhibitor LY294002 nor protein kinase C (PKC) inhibitor GFX could abolish palmitic acid induced down-regulation of APOM expression. In contrast, the peroxisome proliferator-activated receptor beta/delta (PPARß/δ) antagonist GSK3787 could totally reverse the palmitic acid-induced down-regulation of APOM expression, which clearly demonstrates that down-regulation of APOM expression induced by palmitic acid is mediated via the PPARß/δ pathway.

Rosiglitazone enhances apolipoprotein M (Apom) expression in rat's liver.

Apolipoprotein M (APOM) has been suggested as a vasculoprotective constituent of high density lipoprotein (HDL), which plays a crucial role behind the mechanism of HDL-mediated anti-atherosclerosis. Previous studies demonstrated that insulin resistance could associate with decreased APOM expressions. In agreement with our previous reports, here, we further confirmed that the insulin sensitivity was also reduced in rats treated with high concentrations of glucose; such effect could be reversed by administration of rosiglitazone, a peroxisome proliferator-activated receptor-γ (PPARγ). The present study shows that Apom expression is significantly affected by either rosiglitazone or hyperglycemia alone without cross interaction with each other, which indicates that the pathway of Apom expression regulating by hyperglycemia might be differed from that by rosiglitazone. Further study indicated that hyperglycemia could significantly inhibit mRNA levels of Lxrb (P=0.0002), small heterodimer partner 1 (Shp1) (P<0.0001), liver receptor homologue-1 (Lrh1) (P=0.0012), ATP-binding cassette transporter 1 (Abca1) (P=0.0012) and Pparb/d (P=0.0043). Two-way ANOVA analysis demonstrated that the interactions between rosiglitazone and infusion of 25% glucose solution on Shp1 (P=0.0054) and Abca1 (4E, P=0.0004) mRNA expression was statistically significant. It is concluded that rosiglitazone could increase Apom expression, of which the detailed mechanism needs to be further investigated. The downregulation of Apom by hyperglycemia might be mainly through decreasing expression of Pparg and followed by inhibiting Lxrb in rats.

Exploring the prognostic implications of cuproptosis-associated alterations in clear cell renal cell carcinoma via in vitro experiments.

This study investigated the impact of novel copper ionophores on the prognosis of clear cell renal cell carcinoma (ccRCC) and the tumor microenvironment (TME). The differential expression of 10 cuproptosis and 40 TME-pathway-related genes were measured in 531 tumor samples and 71 adjacent kidney samples in The Cancer Genome Atlas database. A risk score model was constructed with LASSO cox to predict the prognosis of ccRCC patients. Forest plot and function enrichment were used to study the biological function of the key genes in depth. The study found that the risk score model accurately predicted the prognosis of ccRCC patients. Patients with high scores had higher immune responses with a higher proportion of anti-tumor lymphocytes and a lower proportion of immunosuppressive M2-like macrophages. However, the high-score group also exhibited a higher proportion of T follicular helper cells and regulatory T cells. These results suggest that cuproptosis-based therapy may be worth further investigation for the treatment of ccRCC and TME. Subsequently, by using RNAi, we established the stable depletion models of FDX1 and PDHB in ccRCC cell lines 786-O and ACHN. Through CCK8, colony formation, and Transwell assays, we observed that the knockdown of FDX1 and PDHB could significantly reduce the capabilities of proliferation and migration in ccRCC cells. In conclusion, this study illuminates the potential effectiveness of copper ionophores in the treatment of ccRCC, with higher risk scores correlating with better TME immune responses. It sets the stage for future cuproptosis-based therapy research in ccRCC and other cancers, focusing on copper's role in TME.