GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.

BACKGROUND: A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs. METHOD: In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations. RESULTS: The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer. CONCLUSIONS: The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.

Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks.

BACKGROUND: Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the large number of lncRNAs and the complex and time-consuming nature of biological experiments, applying computer techniques to predict potential lncRNA-disease associations is very effective. To explore information between complex network structures, existing methods rely mainly on lncRNA and disease information. Metapaths have been applied to network models as an effective method for exploring information in heterogeneous graphs. However, existing methods are dominated by lncRNAs or disease nodes and tend to ignore the paths provided by intermediate nodes. METHODS: We propose a deep learning model based on hierarchical graphical attention networks to predict unknown lncRNA-disease associations using multiple types of metapaths to extract features. We have named this model the MMHGAN. First, the model constructs a lncRNA-disease-miRNA heterogeneous graph based on known associations and two homogeneous graphs of lncRNAs and diseases. Second, for homogeneous graphs, the features of neighboring nodes are aggregated using a multihead attention mechanism. Third, for the heterogeneous graph, metapaths of different intermediate nodes are selected to construct subgraphs, and the importance of different types of metapaths is calculated and aggregated to obtain the final embedded features. Finally, the features are reconstructed using a fully connected layer to obtain the prediction results. RESULTS: We used a fivefold cross-validation method and obtained an average AUC value of 96.07% and an average AUPR value of 93.23%. Additionally, ablation experiments demonstrated the role of homogeneous graphs and different intermediate node path weights. In addition, we studied lung cancer, esophageal carcinoma, and breast cancer. Among the 15 lncRNAs associated with these diseases, 15, 12, and 14 lncRNAs were validated by the lncRNA Disease Database and the Lnc2Cancer Database, respectively. CONCLUSION: We compared the MMHGAN model with six existing models with better performance, and the case study demonstrated that the model was effective in predicting the correlation between potential lncRNAs and diseases.

Effects of vitamin D supplementation on glucose and lipid metabolism in patients with type 2 diabetes mellitus and risk factors for insulin resistance.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease featured by insulin resistance (IR) and decreased insulin secretion. Currently, vitamin D deficiency is found in most patients with T2DM, but the relationship between vitamin D and IR in T2DM patients requires further investigation. AIM: To explore the risk factors of IR and the effects of vitamin D supplementation on glucose and lipid metabolism in patients with T2DM. METHODS: Clinical data of 162 T2DM patients treated in First Affiliated Hospital of Harbin Medical University between January 2019 and February 2022 were retrospectively analyzed. Based on the diagnostic criteria of IR, the patients were divided into a resistance group (n = 100) and a non-resistance group (n = 62). Subsequently, patients in the resistance group were subdivided to a conventional group (n = 44) or a joint group (n = 56) according to the treatment regimens. Logistic regression was carried out to analyze the risk factors of IR in T2DM patients. The changes in glucose and lipid metabolism indexes in T2DM patients with vitamin D deficiency were evaluated after the treatment. RESULTS: Notable differences were observed in age and body mass index (BMI) between the resistance group and the non-resistance group (both P < 0.05). The resistance group exhibited a lower 25-hydroxyvitamin D3 (25(OH)D3) level, as well as notably higher levels of 2-h postprandial blood glucose (2hPG), fasting blood glucose (FBG), and glycosylated hemoglobin (HbA1c) than the non-resistance group (all P < 0.0001). Additionally, the resistance group demonstrated a higher triglyceride (TG) level but a lower high-density lipoprotein-cholesterol (HDL-C) level than the non-resistance group (all P < 0.0001). The BMI, TG, HDL-C, 25(OH)D3, 2hPG, and HbA1c were found to be risk factors of IR. Moreover, the post-treatment changes in levels of 25(OH)D3, 2hPG, FBG and HbA1c, as well as TG, total cholesterol, and HDL-C in the joint group were more significant than those in the conventional group (all P < 0.05). CONCLUSION: Patients with IR exhibit significant abnormalities in glucose and lipid metabolism parameters compared to the non-insulin resistant group. Logistic regression analysis revealed that 25(OH)D3 is an independent risk factor influencing IR. Supplementation of vitamin D has been shown to improve glucose and lipid metabolism in patients with IR and T2DM.

Effect of DHCR7 for the co-occurrence of hypercholesterolemia and vitamin D deficiency in type 2 diabetes: Perspective of health prevention.

Type 2 diabetes mellitus (T2DM) is a complex disease caused by multiple factors, which are often accompanied by the disorder of glucose and lipid metabolism and the lack of vitamin D.Over the years, researchers have conducted numerous studies into the pathogenesis and prevention strategies of diabetes. In this study, diabetic SD rats were randomly divided into type 2 diabetes group, vitamin D intervention group, 7-dehydrocholesterole reductase (DHCR7) inhibitor intervention group, simvastatin intervention group, and naive control group. Before and 12 weeks after intervention, liver tissue was extracted to isolate hepatocytes. Compared with naive control group, in the type 2 diabetic group without interference, the expression of DHCR7 increased, the level of 25(OH)D3 decreased, the level of cholesterol increased. In the primary cultured naive and type 2 diabetic hepatocytes, the expression of genes related to lipid metabolism and vitamin D metabolism were differently regulated in each of the 5 treatment groups. Overall, DHCR7 is an indicator for type 2 diabetic glycolipid metabolism disorder and vitamin D deficiency. Targeting DHCR7 will help with T2DM therapy.The management model of comprehensive health intervention can timely discover the disease problems of diabetes patients and high-risk groups and reduce the incidence of diabetes.

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction.

Increasing evidence indicates that mutations and dysregulation of long non-coding RNA (lncRNA) play a crucial role in the pathogenesis and prognosis of complex human diseases. Computational methods for predicting the association between lncRNAs and diseases have gained increasing attention. However, these methods face two key challenges: obtaining reliable negative samples and incorporating lncRNA-disease association (LDA) information from multiple perspectives. This paper proposes a method called NDMLDA, which combines multi-view feature extraction, unsupervised negative sample denoising, and stacking ensemble classifier. Firstly, an unsupervised method (K-means) is used to design a negative sample denoising module to alleviate the imbalance of samples and the impact of potential noise in the negative samples on model performance. Secondly, graph attention networks are employed to extract multi-view features of both lncRNAs and diseases, thereby enhancing the learning of association information between them. Finally, lncRNA-disease association prediction is implemented through a stacking ensemble classifier. Existing research datasets are integrated to evaluate performance, and 5-fold cross-validation is conducted on this dataset. Experimental results demonstrate that NDMLDA achieves an AUC of 0.9907and an AUPR of 0.9927, with a 5-fold cross-validation variance of less than 0.1%. These results outperform the baseline methods. Additionally, case studies further illustrate the model's potential in cancer diagnosis and precision medicine implementation.

Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.

More and more evidences have showed that the unnatural expression of long non-coding RNA (lncRNA) is relevant to varieties of human diseases. Therefore, accurate identification of disease-related lncRNAs can help to understand lncRNA expression at the molecular level and to explore more effective treatments for diseases. Plenty of lncRNA-disease association prediction models have been raised but it is still a challenge to recognize unknown lncRNA-disease associations. In this work, we have proposed a computational model for predicting lncRNA-disease associations based on geometric complement heterogeneous information and random forest. Firstly, geometric complement heterogeneous information was used to integrate lncRNA-miRNA interactions and miRNA-disease associations verified by experiments. Secondly, lncRNA and disease features consisted of their respective similarity coefficients were fused into input feature space. Thirdly, an autoencoder was adopted to project raw high-dimensional features into low-dimension space to learn representation for lncRNAs and diseases. Finally, the low-dimensional lncRNA and disease features were fused into input feature space to train a random forest classifier for lncRNA-disease association prediction. Under five-fold cross-validation, the AUC (area under the receiver operating characteristic curve) is 0.9897 and the AUPR (area under the precision-recall curve) is 0.7040, indicating that the performance of our model is better than several state-of-the-art lncRNA-disease association prediction models. In addition, case studies on colon and stomach cancer indicate that our model has a good ability to predict disease-related lncRNAs.

PRDX6: A protein bridging S-palmitoylation and diabetic neuropathy.

Diabetic neuropathy is regarded as one of the most debilitating outcomes of diabetes. It can affect both the peripheral and central nervous systems, leading to pain, decreased motility, cognitive decline, and dementia. S-palmitoylation is a reversible posttranslational lipid modification, and its dysregulation has been implicated in metabolic syndrome, cancers, neurological disorders, and infections. However, the role of S-palmitoylation in diabetic neuropathy remains unclear. Here we demonstrate a potential association between activating protein palmitoylation and diabetic neuropathy. We compared the proteomic data of lumbar dorsal root ganglia (DRG) of diabetes mice and palmitoylome profiling data of the HUVEC cell line. The mapping results identified peroxiredoxin-6 (PRDX6) as a novel target in diabetic neuropathy, whose biological mechanism was associated with S-palmitoylation. Bioinformatic prediction revealed that PRDX6 had two palmitoylation sites, Cys47 and Cys91. Immunofluorescence results indicated PRDX6 translocating between the cytoplasm and cell membrane. Protein function analysis proposed that increased palmitoylation could competitively inhibit the formation of disulfide-bond between Cys47 and Cys91 and change the spatial topology of PRDX6 protein. Cl-HCO3- anion exchanger 3 (AE3) was one of the AE family members, which was proved to express in DRG. AE3 activity evoked Cl- influx in neurons which was generally associated with increased excitability and susceptibility to pain. We demonstrated that the S-palmitoylation status of Cys47 could affect the interaction between PRDX6 and the C-terminal domain of AE3, thereby regulating the activity of AE3 anion exchanger enzyme in the nervous system. The results highlight a central role for PRDX6 palmitoylation in protection against diabetic neuropathy.

Prolactin-regulated Pbk is involved in pregnancy-induced ß-cell proliferation in mice.

Gestational diabetes mellitus (GDM) is a condition of diabetes with onset or first recognition in pregnancy. Its incidence is increasing, and GDM deleteriously affects both mother and the fetus during and even after pregnancy. Previous studies in mice have shown that during pregnancy, ß-cell proliferation increases in the middle and late stages of pregnancy and returns to normal levels after delivery. Hormones, such as prolactin, estradiol, and progesterone as well as protein kinases, play important roles in regulating gestation-mediated ß-cell proliferation; however, the regulatory relationship between them is uncertain. We previously found that protein kinase Pbk was crucial for basal proliferation of mouse islet cells. Herein we show that Pbk is upregulated during pregnancy in mice and Pbk kinase activity is required for enhanced ß- cell proliferation during pregnancy. Notably, knock-in (KI) of a kinase-inactivating Pbk mutation leads to impaired glucose tolerance and reduction of ß-cell proliferation and islet mass in mice during pregnancy. Prolactin upregulates the expression of Pbk, but the upregulation is diminished by knockdown of the prolactin receptor and by the inhibitors of JAK and STAT5, which mediate prolactin receptor signaling, in ß-cells. Treatment of ß-cells with prolactin increases STAT5 binding to the Pbk locus, as well as the recruitment of RNA polymerase II, resulting in increased Pbk transcription. These results demonstrate that Pbk is upregulated during pregnancy, at least partly by prolactin-induced and STAT5-mediated enhancement of gene transcription, and Pbk is essential for pregnancy-induced ß-cell proliferation, increase in islet mass, and maintenance of normal blood glucose during pregnancy in preclinical models. These findings provide new insights into the interplay between hormones and protein kinases that ultimately prevent the development of GDM.

Circular RNA Circ_0067934 Attenuates Ferroptosis of Thyroid Cancer Cells by miR-545-3p/SLC7A11 Signaling.

Ferroptosis is an emerging programmed cell death distinguished from apoptosis and autophagy and plays essential roles in tumorigenesis. Thyroid cancer is a prevalent endocrine tumor, but the molecular mechanism of ferroptosis during thyroid cancer development remains unclear. Here, we identified the critical function of circular RNA circ_0067934 in repressing ferroptosis of thyroid cancer cells. Our data showed that the ferroptosis activator erastin decreased thyroid cancer cell viabilities, while the circ_0067934 shRNA further attenuated erastin-inhibited cell viabilities. The silencing of circ_0067934 enhanced the levels of ferroptosis-related markers, including Fe2+, iron, and ROS in the cells. The knockdown of circ_0067934 induced thyroid cancer cell apoptosis and repressed thyroid cancer cell proliferation in vitro and in vivo. Circ_0067934 upregulated the expression of the ferroptosis-negative regulator SLC7A11 by sponging and inhibiting miR-545-3p in thyroid cancer cells. The overexpression of SLC7A11 or the inhibitor of miR-545-3p reversed circ_0067934 silencing-regulated thyroid cancer cell proliferation. Therefore, we concluded that Circ_0067934 attenuated ferroptosis of thyroid cancer cells by miR-545-3p/SLC7A11 signaling. Circ_0067934 may serve as a potential therapeutic target by regulating ferroptosis for the treatment of thyroid cancer.

CircHIPK3 Promotes Thyroid Cancer Tumorigenesis and Invasion through the Mirna-338-3p/RAB23 Axis.

Objective：Thyroid cancer is a common type of endocrine malignancy, and its incidence has been steadily increasing in many regions of the world. Numerous studies have found that the circRNAs in various cancer types are aberrantly expressed, which could be potential biological diagnostic markers and therapeutic targets. The purpose of this study was to investigate the role of circHIPK3 in the development and progression of thyroid cancer and its mechanism. Subject and Methods：qRT-PCR was used to detect the relative expression levels of circHIPK3 in thyroid cancer cell lines (K1, CAL-62, TPC1), human thyroid normal cells (Nthy-ori 3-1), 10 pairs of thyroid cancer tissues and corresponding adjacent normal tissues. CCK-8 and Transwell assays were used to detect the proliferation and metastasis ability of cells. The targeted relationships between circHIPK3-miR-338-3p and miR-338-3p-RAB23 were predicted by bioinformatics analysis and verified by dual-luciferase reporter assays. Results and Conclusion: The downregulation of circHIPK3 significantly reduced the migration, invasion and proliferation of thyroid carcinoma. Then, we demonstrated that circHIPK3 up-regulated the expression of its target gene RAB23 by sponging miR-338-3p to promote the tumorigenesis and invasiveness of thyroid cancer. This study is the first to find that circHIPK3 plays the role of oncogenetic circRNA in thyroid cancer, which may provide new insights into how circRNA affects the progression of thyroid cancer. Our study also showed that circHIPK3 could be a novel biomarker for thyroid cancer.

Identifying potential functional lncRNAs in metabolic syndrome by constructing a lncRNA-miRNA-mRNA network.

The metabolic syndrome (MS) is a cluster of interrelated risk factors including diabetes mellitus, abdominal obesity, high cholesterol, and hypertension, which can significantly increase mortality and disability. Accumulating evidence suggest that long non-coding RNAs (lncRNAs) are involved in the pathogenesis of human metabolic diseases. However, little is known about the regulatory role of lncRNAs in MS. In this work, we proposed a method for identifying potential MS-associated lncRNAs by constructing an lncRNA-miRNA-mRNA network (LMMN). Firstly, we constructed LMMN by integrating MS-associated genes, miRNA-mRNA interactions, miRNA-lncRNA interactions and mRNA/miRNA expression profiles in patients with MS. Then, we predicted potential MS-associated lncRNAs based on the topological properties of LMMN. As a result, we identified XIST as the most important lncRNA in LMMN. Furthermore, we focused on XIST/miR-214-3p and mir-181a-5p/PTEN axis and validated their expression in MS using real-time quantitative polymerase chain reaction (RT-qPCR). The RT-qPCR results showed that the expression of XIST and PTEN was significantly decreased (P < 0.05) while the expression of miR-214-3p was significantly increased (P < 0.05) in peripheral blood mononuclear cells (PBMCs) of patients with MS, compared with healthy controls. In addition, correlation analysis showed that XIST was negatively correlated with serum C peptide and PTEN was positively correlated with BMI of MS patients. Our findings provided new evidence for further exploring the regulatory role of XIST and other lncRNAs in MS.

A random forest based computational model for predicting novel lncRNA-disease associations.

BACKGROUND: Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. RESULTS: To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. CONCLUSIONS: Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

LncRNA MALAT1 regulates diabetic cardiac fibroblasts through the Hippo-YAP signaling pathway.

Diabetic cardiomyopathy (DCM) is a major diabetes-related microvascular disease. LncRNA MALAT1 is widely expressed in cardiomyocytes responding to hypoxia and high levels of glucose (high glucose). In this study, cardiac fibroblasts (CFs) were transfected with si-MALAT1 and exposed to high glucose. CFs in the high glucose groups were treated with 30 mmol/L glucose, and the control CFs were treated with 5.5 mmol/L glucose. The expression of MALAT1 in the nucleus and cytoplasm of CFs was detected. The biological behavior of CFs, as well as collagen production, activity of the Hippo-YAP pathway, and nuclear localization of YAP were measured. Mouse models of DCM were established to observe the pathological changes to myocardium and determine the levels of collagen I, Bax, and Bcl-2. The interaction between MALAT1 and YAP was analyzed, and CREB expression in the high-glucose treated CFs was detected. MALAT1 was upregulated in high-glucose CFs and located in the nucleus. High-glucose increased collagen production, inflammation, cell proliferation, cell invasiveness, and phosphorylation of MST1 and LATS1, and also promoted nuclear translocation of YAP. These trends in high-glucose treated CFs and the DCM mice were reversed by transfection with si-MALAT1. MALAT1 positively regulated the nuclear translocation of YAP by binding to CREB. CREB levels were increased in the high-glucose CFs, but decreased after silencing MALAT1. These results indicate that si-MALAT1 reduces inflammation and collagen accumulation in high-glucose CFs and DCM mice via the Hippo-YAP pathway and CREB.

The function of SNHG7/miR-449a/ACSL1 axis in thyroid cancer.

Thyroid cancer (TC) has been characterized as the most common malignant malady of the endocrine system. Small nucleolar RNA host gene 7 (SNHG7) has been reported to serve as a key regulator in a large number of human cancer types, but its role in TC and the underlying regulatory mechanism have never been evaluated yet. The present study indicated that the expression of SNHG7 was markedly higher in TC cell lines. Knockdown of SNHG7 led to a suppression of TC cell progression and migration. Acyl-CoA synthetase long-chain family member 1 (ACSL1) has also been demonstrated as an oncogene in many cancers. Herein an inhibition of ACSL1 after SNHG7 knockdown was captured. Further, the suppressing effects of SNHG7 knockdown on TC cell processes were counteracted by ACSL1 overexpression. Data from online bioinformatics analysis, RNA immunoprecipitation, and luciferase reporter assays validated the interaction between microRNA-449a (miR-449a) and SNHG7 or ACSL1. It was also verified that SNHG7 sequestered miR-449a and therefore elevated ACSL1 expression levels. To conclude, the current study indicated that SNHG7 promoted proliferation and migration of TC cells by sponging miR-449a and therefore upregulating ACSL1. The present study may provide more explorations about the molecular regulation mechanism of long noncoding RNAs in TC progression.

Adipocyte-derived extracellular vesicles modulate appetite and weight through mTOR signalling in the hypothalamus.

AIM: Type 2 diabetes and obesity are diseases related to surplus energy in the body. Abnormal interaction between the hypothalamus and adipose tissues is a key trigger of energy metabolism dysfunction. Extracellular vesicles (EVs) regulate intercellular communication by transporting intracellular cargo to recipient cells thereby altering the function of recipient cells. This study aimed to evaluate whether adipocyte-derived EVs can act on hypothalamic neurons to modulate energy intake and to identify the EV-associated non-coding RNAs. METHODS: Confocal imaging was used to trace the uptake of labelled adipocyte-derived exosomes by hypothalamic anorexigenic POMC neurons. The effects of adipocyte-derived EVs on the mammalian target of rapamycin (mTOR) signalling pathway in POMC neurons were evaluated based on mRNA and protein expression in vitro using quantitative real-time PCR and western blotting. In addition, adipocyte-derived EVs were injected into recipient mice, and changes in mice body weight and daily food intake were monitored. The biological effects of the EV-associated MALAT1 on POMC neurons were explored. RESULTS: Adipocyte-derived EVs were successfully transferred into POMC neurons in vitro. Results showed that adipocytes of obese mice secreted MALAT1-containing EVs, which increased appetite and weight when administered to lean mice. Conversely, adipocyte-derived EVs from lean mice decreased food intake and weight when administered to obese mice. CONCLUSION: Adipocyte-derived EVs play important roles in mediating the interaction between adipocytes and hypothalamic neurons. Adipocyte-derived EVs can regulate POMC expression through the hypothalamic mTOR signalling in vivo and in vitro, thereby affecting body energy intake.

ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome.

Metabolic syndrome is a cluster of the most dangerous heart attack risk factors (diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure), and has become a major global threat to human health. A number of studies have demonstrated that hundreds of non-coding RNAs, including miRNAs and lncRNAs, are involved in metabolic syndrome-related diseases such as obesity, type 2 diabetes mellitus, hypertension, etc. However, these research results are distributed in a large number of literature, which is not conducive to analysis and use. There is an urgent need to integrate these relationship data between metabolic syndrome and non-coding RNA into a specialized database. To address this need, we developed a metabolic syndrome-associated non-coding RNA database (ncRNA2MetS) to curate the associations between metabolic syndrome and non-coding RNA. Currently, ncRNA2MetS contains 1,068 associations between five metabolic syndrome traits and 627 non-coding RNAs (543 miRNAs and 84 lncRNAs) in four species. Each record in ncRNA2MetS database represents a pair of disease-miRNA (lncRNA) association consisting of non-coding RNA category, miRNA (lncRNA) name, name of metabolic syndrome trait, expressive patterns of non-coding RNA, method for validation, specie involved, a brief introduction to the association, the article referenced, etc. We also developed a user-friendly website so that users can easily access and download all data. In short, ncRNA2MetS is a complete and high-quality data resource for exploring the role of non-coding RNA in the pathogenesis of metabolic syndrome and seeking new treatment options. The website is freely available at http://www.biomed-bigdata.com:50020/index.html.

CircRNA circ_0067934 Overexpression Correlates with Poor Prognosis and Promotes Thyroid Carcinoma Progression.

BACKGROUND Circular RNAs are important regulators in human cancers, including thyroid carcinoma. The circ_0067934 RNA is reported to participate in hepatocellular carcinoma, esophageal squamous cell carcinoma, and lung cancer. Whether it regulates thyroid carcinoma remains unclear. The purpose of this study was to research potential mechanisms of circ_0067934 in thyroid tumors to provide potential new diagnostic and treatment targets. MATERIAL AND METHODS The expression level of circ_0067934 in thyroid tumors, adjacent tissues, and cell lines was measured by qRT-PCR. The Kaplan-Meier survival curve analysis was used to explore the relationship between circ_0067934 level and survival time of patients. Circ_0067934 was knocked down to research its functional role in thyroid tumors. Cell proliferation was detected by CCK-8 (cell counting kit-8) assay. Migration and invasion were analyzed by Transwell assay. Western blot was applied to analyze the expression of epithelial-mesenchymal-transition (EMT) and PI3K/AKT related proteins. RESULTS Compared with adjacent tissue, circ_0067934 was highly expressed in thyroid tumors. Circ_0067934 expression level was highly expressed in thyroid tumor cell lines. Patients with high expression of circ_0067934 showed lower survival rates. Knockdown of circ_0067934 inhibited cell proliferation, migration, and invasion and also promoted apoptosis. In addition, circ_0067934 knockdown inhibited EMT and PI3K/AKT signaling pathways. CONCLUSIONS circ_0067934 could improve the development of thyroid carcinoma by promoting EMT and PI3K/AKT signaling pathways.

The relationship between vitronectin and hepatic insulin resistance in type 2 diabetes mellitus.

The World Health Organization (WHO) estimates that approximately 300 million people will suffer from diabetes mellitus by 2025. Type 2 diabetes mellitus (T2DM) is much more prevalent. T2DM comprises approximately 90% of diabetes mellitus cases, and it is caused by a combination of insulin resistance and inadequate compensatory insulin secretory response. In this study, we aimed to compare the plasma vitronectin (VN) levels between patients with T2DM and insulin resistance (IR) and healthy controls. Seventy patients with IR and 70 age- and body mass index (BMI)-matched healthy controls were included in the study. The insulin, Waist-to-Hip Ratio (WHR), C-peptide (CP) and VN levels of all participants were examined. The homeostasis model of assessment for insulin resistence index (HOMA-IR (CP)) formula was used to calculate insulin resistance. The levels of BMI, fasting plasma gluose (FPG), 2-hour postprandial glucose (2hPG), glycated hemoglobins (HbA1c), and HOMA-IR (CP) were significantly elevated in case group compared with controls. VN was found to be significantly decreased in case group. (VN Mean (Std): 8.55 (2.92) versus 12.88 (1.26) ng/mL p < 0.001). Multiple linear regression analysis was performed. This model explained 43.42% of the total variability of VN. Multiple linear regression analysis showed that HOMA-IR (CP) and age independently predicted VN levels. The VN may be a candidate target for the appraisal of hepatic insulin resistance in patients with T2DM.

Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis.

BACKGROUND: Diabetic macroangiopathy, atherosclerosis secondary to diabetes mellitus, causes cerebro-cardiovascular diseases (CVD), which increase the risk of death in patients with DM and significantly reduce their quality of life. Therefore, mechanisms underlying the common shared pathologies between type 2 diabetes (T2D) and atherosclerosis are key to prevention and treatment of diabetic macroangiopathy. However, the common shared pathological links between T2D and atherosclerosis are not fully understood. METHODS: We constructed a T2D and atherosclerosis associated protein interaction sub-network (TAN) to investigate common shared mechanisms between T2D and atherosclerosis. In addition, MCODE plugin of Cytoscape was applied to TAN to identify most significant functional modules. The network modules were further mapped to KEGG pathway enrichment analysis. Finally, we established a miRNA-gene regulatory network by searching disease associated miRNAs and integrated them into miRNA-gene interaction network for each module. RESULTS: TAN contains 1230 nodes which represent the union of T2D and atherosclerosis related genes and 3683 edges which represent the interactions of gene pairs. MCODE plugin was applied and five most significant modular clusters were identified. KEGG analysis of functional modules showed these genes were involved in several pathways including type 2 diabetes mellitus, ErbB and neurotrophin signaling pathway. miRNAgene interaction network was established and these miRNA-gene interactions mediated common shared pathologies between T2D and atherosclerosis. CONCLUSIONS: Analysis of TAN demonstrated that modular organization of the interaction network elucidates shared pathologies of T2D and atherosclerosis. Furthermore, the disease associated miRNA-gene interaction network enriched our insight into role of miRNAs in mediating common shared pathologies between T2D and atherosclerosis. Thus, miRNAs constitute attractive targets for the development of novel therapies for treating both T2D and atherosclerosis.

RNA-seq analysis of the transcriptome of the liver of cynomolgus monkeys with type 2 diabetes.

Genetic and environmental factors such as high-fat diet are involved in the development of type 2 diabetes mellitus (T2DM). Cynomolgus monkey shares similar genetic makeup, tissue structures, physiology and metabolic function to human. This study aimed to establish T2DM model in cynomolgus monkey and compare expression profiles of hepatic genes and their associated pathways in normal cynomolgus monkeys and those with T2DM. We employed RNA-seq technique and identified 1451 differentially expressed genes (DEGs) with a false discovery rate (FDR) of 0.1% between normal and T2DM animals. KEGG pathway analysis revealed that DEGs were associated with 12 KEGG pathways (P < 0.05). Two of these pathways were associated with metabolism and five were related to immunity. Unexpected, we found ECM-receptor interaction pathway. In conclusion, our data suggest that three major pathways may be implicated in the development of T2DM, including steroid biosynthesis, immune response and ECM. Further characterization of these pathways may provide new targets for the prevention and therapy of T2DM.