The role of ncRNA regulatory mechanisms in diseases-case on gestational diabetes.

Non-coding RNAs (ncRNAs) are a class of RNA molecules that do not have the potential to encode proteins. Meanwhile, they can occupy a significant portion of the human genome and participate in gene expression regulation through various mechanisms. Gestational diabetes mellitus (GDM) is a pathologic condition of carbohydrate intolerance that begins or is first detected during pregnancy, making it one of the most common pregnancy complications. Although the exact pathogenesis of GDM remains unclear, several recent studies have shown that ncRNAs play a crucial regulatory role in GDM. Herein, we present a comprehensive review on the multiple mechanisms of ncRNAs in GDM along with their potential role as biomarkers. In addition, we investigate the contribution of deep learning-based models in discovering disease-specific ncRNA biomarkers and elucidate the underlying mechanisms of ncRNA. This might assist community-wide efforts to obtain insights into the regulatory mechanisms of ncRNAs in disease and guide a novel approach for early diagnosis and treatment of disease.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.

BACKGROUND: The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS: In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS: This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

Variable importance evaluation with personalized odds ratio for machine learning model interpretability with applications to electronic health records-based mortality prediction.

The interpretability of machine learning models, even though with an excellent prediction performance, remains a challenge in practical applications. The model interpretability and variable importance for well-performed supervised machine learning models are investigated in this study. With the commonly accepted concept of odds ratio (OR), we propose a novel and computationally efficient Variable Importance evaluation framework based on the Personalized Odds Ratio (VIPOR). It is a model-agnostic interpretation method that can be used to evaluate variable importance both locally and globally. Locally, the variable importance is quantified by the personalized odds ratio (POR), which can account for subject heterogeneity in machine learning. Globally, we utilize a hierarchical tree to group the predictors into five groups: completely positive, completely negative, positive dominated, negative dominated, and neutral groups. The relative importance of predictors within each group is ranked based on different statistics of PORs across subjects for different application purposes. For illustration, we apply the proposed VIPOR method to interpreting a multilayer perceptron (MLP) model, which aims to predict the mortality of subarachnoid hemorrhage (SAH) patients using real-world electronic health records (EHR) data. We compare the important variables derived from MLP with other machine learning models, including tree-based models and the L1-regularized logistic regression model. The top importance variables are consistently identified by VIPOR across different prediction models. Comparisons with existing interpretation methods are also conducted and discussed based on publicly available data sets.

MiR-10b-5p Impairs TET2-Mediated Inhibition of PD-L1 Transcription Thus Promoting Immune Evasion and Tumor Progression in Glioblastoma.

Glioblastoma (GBM) is a highly aggressive primary brain tumor that shows intratumoral heterogeneity at the cellular and molecular level. Activation of programmed death receptor 1 (PD-1) interaction with its ligand PD-L1 is a well-known mechanism requisite for immune evasion deployed by malignant tumors including GBM. Herein, we set out to dissect the mechanism explaining the regulation of PD-L1 gene expression in GBM. The clinical samples consisted of 37 GBM tissues and 18 normal brain tissues. GBM cell model was treated by microRNA (miRNA) inhibitor, DNA constructs, and siRNAs. Assays of CCK-8 and Transwell insert were employed to assess the survival, migratory and invasive ability of GBM cell model. The immunosuppressive factor production, T cell apoptosis, and T cell cytotoxicity to GBM cells were evaluated in the co-culture system. GBM exhibited more miR-10b-5p abundance than normal at both tissue and cellular level. Suppression of miR-10b-5p weakened the ability of GBM cell model to survive, migrate, and invade, decreased the release of immunosuppressive factors, reduced T cell apoptosis, and strengthened the T cell cytotoxicity to GBM cell model. MiR-10b-5p conferred a negative control of Ten-eleven translocation 2 (TET2) that was downregulated in GBM. The functions of miR-10b-5p on GBM cell aggressiveness and immune evasion were mediated by TET2. TET2 recruited histone deacetylases HDAC1 and HDAC2 into the PD-L1 promoter region thus inhibiting its transcription. The study demonstrated the importance of miR-10b-5p-mediated repression of TET2 in PD-L1-driven immune evasion and their potential for immunotherapeutic targeting in GBM.

Review of the Application of Dual Drug Delivery Nanotheranostic Agents in the Diagnosis and Treatment of Liver Cancer.

Liver cancer has high incidence and mortality rates and its treatment generally requires the use of a combination treatment strategy. Therefore, the early detection and diagnosis of liver cancer is crucial to achieving the best treatment effect. In addition, it is imperative to explore multimodal combination therapy for liver cancer treatment and the synergistic effect of two liver cancer treatment drugs while preventing drug resistance and drug side effects to maximize the achievable therapeutic effect. Gold nanoparticles are used widely in applications related to optical imaging, CT imaging, MRI imaging, biomarkers, targeted drug therapy, etc., and serve as an advanced platform for integrated application in the nano-diagnosis and treatment of diseases. Dual-drug-delivery nano-diagnostic and therapeutic agents have drawn great interest in current times. Therefore, the present report aims to review the effectiveness of dual-drug-delivery nano-diagnostic and therapeutic agents in the field of anti-tumor therapy from the particular perspective of liver cancer diagnosis and treatment.

Recent Advances in Cancer Therapeutic Copper-Based Nanomaterials for Antitumor Therapy.

Copper serves as a vital microelement which is widely present in the biosystem, functioning as multi-enzyme active site, including oxidative stress, lipid oxidation and energy metabolism, where oxidation and reduction characteristics are both beneficial and lethal to cells. Since tumor tissue has a higher demand for copper and is more susceptible to copper homeostasis, copper may modulate cancer cell survival through reactive oxygen species (ROS) excessive accumulation, proteasome inhibition and anti-angiogenesis. Therefore, intracellular copper has attracted great interest that multifunctional copper-based nanomaterials can be exploited in cancer diagnostics and antitumor therapy. Therefore, this review explains the potential mechanisms of copper-associated cell death and investigates the effectiveness of multifunctional copper-based biomaterials in the field of antitumor therapy.

Erratum: Overexpression of lncRNA H19/miR-675 promotes tumorigenesis in head and neck squamous cell carcinoma: Erratum.

[This corrects the article DOI: 10.7150/ijms.16571.].

LncRNA PCED1B-AS1 activates the proliferation and restricts the apoptosis of glioma through cooperating with miR-194-5p/PCED1B axis.

Glioma with poor prognosis is accepted as a lethal, malignant intracranial tumor among central nervous system diseases. It has been frequently exhibited that long noncoding RNAs (lncRNAs) exert indispensable functions in glioma through regulating gene expression through various molecular mechanisms. To unveil a novel lncRNA functioning in glioma, we browsed the cancer genome atlas dataset and chose the lncRNA PC-esterase domain containing 1B antisense RNA 1 (PCED1B-AS1) for further investigations. Loss-of-function experiments depicted that the proliferation ability was restrained and apoptosis ability was induced in glioma cells by PCED1B-AS1 silencing and this phenomenon was also observed when PCED1B was knocked down. In view of the position of PCED1B-AS1 in the cytoplasm, we produced the Venn diagram and discovered one shared microRNA of PCED1B-AS1 and PCED1B. The competing endogenous RNA network formed by PCED1B-AS1, miR-194-5p, and PCED1B was attested by mechanism assays. Rescue experiments reconfirmed that miR-194-5p suppression or PCED1B overexpression neutralized the obstructive impacts of PCED1B-AS1 silence on proliferation and the promoting effects of PCED1B-AS1 silence on apoptosis. The modulation mechanism of the PCED1B-AS1/miR-194-5p/PCED1B axis in glioma was investigated and affirmed, which supports researchers with a new insight into the therapy of patients with glioma.

Vasopressor treatment and mortality following nontraumatic subarachnoid hemorrhage: a nationwide electronic health record analysis.

OBJECTIVE: Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular condition, not only due to the effect of initial hemorrhage, but also due to the complication of delayed cerebral ischemia (DCI). While hypertension facilitated by vasopressors is often initiated to prevent DCI, which vasopressor is most effective in improving outcomes is not known. The objective of this study was to determine associations between initial vasopressor choice and mortality in patients with nontraumatic SAH. METHODS: The authors conducted a retrospective cohort study using a large, national electronic medical record data set from 2000-2014 to identify patients with a new diagnosis of nontraumatic SAH (based on ICD-9 codes) who were treated with the vasopressors dopamine, phenylephrine, or norepinephrine. The relationship between the initial choice of vasopressor therapy and the primary outcome, which was defined as in-hospital death or discharge to hospice care, was examined. RESULTS: In total, 2634 patients were identified with nontraumatic SAH who were treated with a vasopressor. In this cohort, the average age was 56.5 years, 63.9% were female, and 36.5% of patients developed the primary outcome. The incidence of the primary outcome was higher in those initially treated with either norepinephrine (47.6%) or dopamine (50.6%) than with phenylephrine (24.5%). After adjusting for possible confounders using propensity score methods, the adjusted OR of the primary outcome was higher with dopamine (OR 2.19, 95% CI 1.70-2.81) and norepinephrine (OR 2.24, 95% CI 1.80-2.80) compared with phenylephrine. Sensitivity analyses using different variable selection procedures, causal inference models, and machine-learning methods confirmed the main findings. CONCLUSIONS: In patients with nontraumatic SAH, phenylephrine was significantly associated with reduced mortality in SAH patients compared to dopamine or norepinephrine. Prospective randomized clinical studies are warranted to confirm this finding.

The effect of technical details of percutaneous catheter drainage on the clinical outcomes of infected necrotizing pancreatitis patients

Background/aim: This study aimed to investigate the effect of technical details of percutaneous catheter drainage (PCD) on the clinical outcomes of patients with infected necrotizing pancreatitis (INP). Materials and methods: A total of 44 INP patients treated in our hospital from October 2013 to October 2015 were included. The correlations of the first PCD treatment data and the clinical outcomes were analyzed. Results: The number of catheters was positively correlated with hospital readmission (r = 0.335, P = 0.032). Receiver operating characteristic curve analysis showed that patients with ≥ 3 catheters were more likely to have hospital readmission. Patients with pleural effusion undergoing thoracentesis were more likely to have new intensive care unit admission (P = 0.025) and bleeding in need of intervention (P = 0.032). Patients with more effusion regions had higher incidences of mortality (P = 0.012) and new intensive care unit admissions (2.44 ± 1.03 vs. 1.88 ± 0.80; P = 0.059). Patients with PCD only were less likely to have new intensive care unit admissions (22.22% vs. 54.55%; P = 0.038) than those with PCD + small incision or/and videoscopic assisted retroperitoneal debridement. Conclusion: Number of catheters greater than three was associated with unfavorable outcomes of PCD treatment in INP patients. Patients that received PCD treatment only had better outcomes.

Sappanone A Attenuates Allergic Airway Inflammation in Ovalbumin-Induced Asthma.

BACKGROUND: Sappanone A (SA) is isolated from the heartwood of Caesalpinia sappan and exerts a wide range of pharmacological activities. In the present study, we investigated the protective effects of SA on allergic asthma in a murine model of ovalbumin (OVA)-induced asthma. METHODS: BALB/c mice were sensitized and challenged. Then, the mice were intraperitoneally injected with SA (12.5, 25 and 50 mg/kg) 1 h before OVA challenge; 24 h after the last challenge, the mice were sacrificed, and data were collected by different experimental methods. RESULTS: The results showed that SA dose-dependently reduced inflammatory cell counts, levels of cytokines IL-4, IL-5 and IL-13, and OVA-specific IgE in bronchoalveolar lavage fluid. The level of IFN-γ decreased by OVA was upregulated by the treatment with SA. Furthermore, SA was found to attenuate the airway inflammation and mucus hypersecretion induced by the OVA challenge. In addition, SA dose-dependently upregulated the expression of Nrf2 and HO-1. SA inhibited OVA-induced asthma by activating the Nrf2 signaling pathway. CONCLUSIONS: These data suggest that SA may have a potential use as a therapeutic agent for asthma.

Overexpression of lncRNA H19/miR-675 promotes tumorigenesis in head and neck squamous cell carcinoma.

There is accumulating evidence indicating that long non-coding RNA H19 and its mature product miR-675 play essential roles for tumor growth and progression. However, their prognostic value in human head and neck squamous cell carcinoma (HNSCC), particular in laryngeal carcinoma, remains to be elucidated. In this study, we observed that both H19 and miR-675 were significantly overexpressed in a cohort of 65 primary tumor samples and two HNSCC cell lines. Importantly, when paired with patient follow-up data, higher expression of either H19 or miR-675 was significantly correlated with higher risk of patient relapse, and associated with worse overall survival and poor disease-free survival. Knockdown miR-675 caused significant reduction of cell viability, migratory and invasive capabilities. Taken together, these results suggest that the strong correlation of H19 overexpression together with higher miR-675 and lymph node metastases could be useful predictive markers, indicating a potentially therapeutic strategy for HNSCC patients.

Constitutive secretion of Interleukin-4 dictates CD133+ side population cells to resist drug treatment and cell death.

PURPOSE: In the present study, we made an attempt to elucidate the role of oversecretion of interleukin-4 (IL-4) in cancer stem cells (CSCs) of head and neck squamous cell carcinoma (HNSCC). METHODS: HNSCC samples were analyzed for the presence of CSCs by flow cytometry. In addition, we have performed drug and apoptosis resistance assays to determine the role of IL-4 in CSCs. RESULTS: HNSCC samples contained 3.3% of CD133+ cancer stem like side population (SP) cells in HNSCC which displayed infinite cell proliferation and they had high self-renewal capacity. These CD133+ cells displayed enhanced expression of IL-4, which promoted multidrug and apoptosis resistance. After neutralizing IL-4, the CD133+ SP cells became more sensitive to drug treatment and apoptosis. CONCLUSIONS: Our data suggest that the autocrine secretion of IL-4 is a potential target for the development of novel anticancer drugs to prevent the CSCs-mediated therapy failure and tumorigenesis.

Effects of polystyrene microplastics on the metabolic level of Pseudomonas aeruginosa.

Given the widespread presence of Pseudomonas aeruginosa in water and its threat to human health, the metabolic changes in Pseudomonas aeruginosa when exposed to polystyrene microplastics (PS-MPs) exposure were studied, focusing on molecular level. Through non-targeted metabolomics, a total of 64 differential metabolites were screened out under positive ion mode and 44 under negative ion mode. The content of bacterial metabolites changed significantly, primarily involving lipids, nucleotides, amino acids, and organic acids. Heightened intracellular oxidative damage led to a decrease in lipid molecules and nucleotide-related metabolites. The down-regulation of amino acid metabolites, such as L-Glutamic and L-Proline, highlighted disruptions in cellular energy metabolism and the impaired ability to synthesize proteins as a defense against oxidation. The impact of PS-MPs on organic acid metabolism was evident in the inhibition of pyruvate and citrate, thereby disrupting the cells' normal participation in energy cycles. The integration of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that PS-MPs mainly caused changes in metabolic pathways, including ABC transporters, Aminoacyl-tRNA biosynthesis, Purine metabolism, Glycerophospholipid metabolism and TCA cycle in Pseudomonas aeruginosa. Most of the differential metabolites enriched in these pathways were down-regulated, demonstrating that PS-MPs hindered the expression of metabolic pathways, ultimately impairing the ability of cells to synthesize proteins, DNA, and RNA. This disruption affected cell proliferation and information transduction, thus hampering energy circulation and inhibiting cell growth. Findings of this study supplemented the toxic effects of microplastics and the defense mechanisms of microorganisms, in turn safeguarding drinking water safety and human health.

Engineering Nanozymes for Tumor Therapy via Ferroptosis Self-Amplification.

Ferroptosis induction is an emerging strategy for tumor therapy. Reactive oxygen species (ROS) can induce ferroptosis but are easily consumed by overexpressed glutathione (GSH) in tumor cells. Therefore, achieving a large amount of ROS production in tumor cells without being consumed is key to efficiently inducing ferroptosis. In this study, a self-amplifying ferroptosis-inducing therapeutic agent, Pd@CeO2-Fe-Co-WZB117-DSPE-PEG-FA (PCDWD), is designed for tumor therapy. PCDWD exhibits excellent multi-enzyme activities due to the loading of Fe-Co dual atoms with abundant active sites, including peroxidase-like enzymes, catalase-like enzymes, and glutathione oxidases (GSHOx), which undergo catalytic reactions in the tumor microenvironment to produce ROS, thereby inducing ferroptosis. Furthermore, PCDWD can also deplete GSH in tumor cells, thus reducing the consumption of ROS by GSH and inhibiting the expression of GSH peroxidase 4. Moreover, the photothermal effect of PCDWD can not only directly kill tumor cells but also further enhance its own enzyme activities, consequently promoting ferroptosis in tumor cells. In addition, WZB117 can reduce the expression of heat shock protein 90 by inhibiting glucose transport, thereby reducing the thermal resistance of tumor cells and further improving the therapeutic effect. Finally, X-ray computed tomography imaging of PCDWD guides it to achieve efficient tumor therapy.

[Expression of MiR-144/451 in Different Types of Anemia].

OBJECTIVE: To investigate the expression level and clinical correlation of microRNA-144/451 gene cluster (miR-144/451) in different types of anemia. METHODS: The peripheral blood of patients with aplastic anemia (AA), myelodysplastic syndrome (MDS) and diffuse large B-cell lymphoma (DLBCL) who had been diagnosed with anemia for the first time and after chemotherapy were collected. The expression levels of miR-144 and miR-451 were measured by RT-qPCR, and the correlation between the expression levels of miR-144 and miR-451 and routine laboratory indexes was analyzed by Spearman correlation analysis. RESULTS: The expression levels of miR-144 and miR-451 in the peripheral blood of AA and MDS patients were significantly lower than those in normal controls (all P < 0.01). No statistical differences were observed in the expression level of miR-144 in three subgroups of DLBCL patients (P >0.05), while the expression level of miR-451 in peripheral blood of three subgroups of DLBCL patients were significantly higher than those in normal controls (all P < 0.05). Correlation analysis showed that the expression levels of miR-144 and miR-451 in AA patients were positively correlated with red blood cell distribution width-coefficient of variation (RDW-CV) (r =0.629, 0.574). There were no significant correlations between the expression levels of miR-144 and miR-451 and laboratory parameters in MDS and DLBCL patients. CONCLUSION: Different types of anemia disorders have varying levels of miR-144 and miR-451 expression, which is anticipated to develop into a secondary diagnostic and differential diagnostic indicator for clinical anemia diseases.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

The transcription factor GATA3 positively regulates NRP1 to promote radiation-induced pulmonary fibrosis.

Radiation-Induced Pulmonary Fibrosis (RIPF) frequently arises as a delayed complication following radiation therapy for thoracic cancers, encompassing lung, breast, and esophageal malignancies. Characterized by a relentless and irreversible accumulation of extracellular matrix (ECM) proteins within the lung parenchyma, RIPF presents a significant clinical challenge. While the modulation of gene expression by transcription factors is a recognized aspect in various pathologies, their specific role in the context of RIPF has been less clear. This study elucidates that ionizing radiation prompts the translocation of the transcription factor GATA3 into the nucleus. This translocation facilitates GATA3's binding to the NRP1 promoter, thereby enhancing the transcription and subsequent translation of NRP1. Further investigations demonstrate that the TGF-ß pathway agonist, SRI-011381, can mitigate the effects of NRP1 knockdown on epithelial-mesenchymal transition (EMT) and ECM deposition, suggesting a pivotal role of the GATA3/NRP1/TGF-ß axis in the pathogenesis of RIPF. In conclusion, our findings not only underscore the critical involvement of GATA3 in RIPF but also highlight the GATA3/NRP1/TGF-ß signaling pathway as a promising target for therapeutic intervention in RIPF management.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.