Intensive Ambulance-Delivered Blood-Pressure Reduction in Hyperacute Stroke.

BACKGROUND: Treatment of acute stroke, before a distinction can be made between ischemic and hemorrhagic types, is challenging. Whether very early blood-pressure control in the ambulance improves outcomes among patients with undifferentiated acute stroke is uncertain. METHODS: We randomly assigned patients with suspected acute stroke that caused a motor deficit and with elevated systolic blood pressure (≥150 mm Hg), who were assessed in the ambulance within 2 hours after the onset of symptoms, to receive immediate treatment to lower the systolic blood pressure (target range, 130 to 140 mm Hg) (intervention group) or usual blood-pressure management (usual-care group). The primary efficacy outcome was functional status as assessed by the score on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days after randomization. The primary safety outcome was any serious adverse event. RESULTS: A total of 2404 patients (mean age, 70 years) in China underwent randomization and provided consent for the trial: 1205 in the intervention group and 1199 in the usual-care group. The median time between symptom onset and randomization was 61 minutes (interquartile range, 41 to 93), and the mean blood pressure at randomization was 178/98 mm Hg. Stroke was subsequently confirmed by imaging in 2240 patients, of whom 1041 (46.5%) had a hemorrhagic stroke. At the time of patients' arrival at the hospital, the mean systolic blood pressure in the intervention group was 159 mm Hg, as compared with 170 mm Hg in the usual-care group. Overall, there was no difference in functional outcome between the two groups (common odds ratio, 1.00; 95% confidence interval [CI], 0.87 to 1.15), and the incidence of serious adverse events was similar in the two groups. Prehospital reduction of blood pressure was associated with a decrease in the odds of a poor functional outcome among patients with hemorrhagic stroke (common odds ratio, 0.75; 95% CI, 0.60 to 0.92) but an increase among patients with cerebral ischemia (common odds ratio, 1.30; 95% CI, 1.06 to 1.60). CONCLUSIONS: In this trial, prehospital blood-pressure reduction did not improve functional outcomes in a cohort of patients with undifferentiated acute stroke, of whom 46.5% subsequently received a diagnosis of hemorrhagic stroke. (Funded by the National Health and Medical Research Council of Australia and others; INTERACT4 ClinicalTrials.gov number, NCT03790800; Chinese Trial Registry number, ChiCTR1900020534.).

GADRP: graph convolutional networks and autoencoders for cancer drug response prediction.

Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations， and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.

TOXRIC: a comprehensive database of toxicological data and benchmarks.

The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.

Identification and characterization of extrachromosomal circular DNA in large-artery atherosclerotic stroke.

Extrachromosomal circular DNA (eccDNA) is a new biomarker and regulator of diseases. However, the role of eccDNAs in large-artery atherosclerotic (LAA) stroke remains unclear. Through high-throughput circle-sequencing technique, the length distribution, genomic characteristic and motifs feature of plasma eccDNA from healthy controls (CON) and patients with LAA stroke were analysed. Then, the potential functions of the annotated eccDNAs were investigated using GO and KEGG pathway analyses. EccDNAs mapped to the reference genome showed SHN3 and BCL6 were LAA stroke unique transcription factors. The genes of differentially expressed eccDNAs between LAA stroke patients and CON were mainly involved in axon/dendrite/neuron projection development and maintenance of cellular structure via Wnt, Rap1 and MAPK pathways. Moreover, LAA stroke unique eccDNA genes played a role in regulation of coagulation and fibrinolysis, and there were five LAA stroke unique eccDNAs (Chr2:12724406-12724784, Chr4:1867120-186272046, Chr4:186271494-186271696, Chr7:116560296-116560685 and Chr11:57611780-5761192). Additionally, POLR2C and AURKA carried by ecDNAs (eccDNA size >100 kb) of LAA stroke patients were significantly associated with development of LAA stroke. Our data firstly revealed the characteristics of eccDNA in LAA stroke and the functions of LAA stroke unique eccDNAs and eccDNA genes, suggesting eccDNA is a novel biomarker and mechanism of LAA stroke.

PCSK9 Inhibitor Added to High-Intensity Statin Therapy to Prevent Cardiovascular Events in Patients with Acute Coronary Syndrome after Percutaneous Coronary Intervention: A Randomized, Double- Blind, Placebo-Controlled, Multicenter SHAWN Study.

BACKGROUND: It is currently uncertain whether the combination of a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor and high-intensity statin treatment can effectively reduce cardiovascular events in patients with acute coronary syndrome (ACS) who have undergone percutaneous coronary intervention (PCI) for culprit lesions. METHODS: This study protocol describes a double-blind, randomized, placebo-controlled, multicenter study aiming to investigate the efficacy and safety of combining a PCSK9 inhibitor with high-intensity statin therapy in patients with ACS following PCI. A total of 1212 patients with ACS and multiple lesions will be enrolled and randomly assigned to receive either PCSK9 inhibitor plus high-intensity statin therapy or high-intensity statin monotherapy. The randomization process will be stratified by sites, diabetes, initial presentation and use of stable (≥4 weeks) statin treatment at presentation. PCSK 9 inhibitor or its placebo is injected within 4 hours after PCI for the culprit lesion. The primary endpoint is the composite of cardiovascular death, myocardial infarction, stroke, re-hospitalization due to ACS or heart failure, or any ischemia-driven coronary revascularization at one-year follow-up between two groups. Safety endpoints mean PCSK 9 inhibitor and statin intolerance. CONCLUSION: The SHAWN study has been specifically designed to evaluate the effectiveness and safety of adding a PCSK9 inhibitor to high-intensity statin therapy in patients who have experienced ACS following PCI. The primary objective of this study is to generate new evidence regarding the potential benefits of combining a PCSK9 inhibitor with high-intensity statin treatment in reducing cardiovascular events among these patients.

DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.

Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experimental identification of DTIs is costly and time-consuming. In recent years, the emerging graph neural network (GNN) has been applied to DTI prediction because DTIs can be represented effectively using graphs. However, some of these methods are only based on homogeneous graphs, and some consist of two decoupled steps that cannot be trained jointly. To further explore GNN-based DTI prediction by integrating heterogeneous graph information, this study regards DTI prediction as a link prediction problem and proposes an end-to-end model based on HETerogeneous graph with Attention mechanism (DTI-HETA). In this model, a heterogeneous graph is first constructed based on the drug-drug and target-target similarity matrices and the DTI matrix. Then, the graph convolutional neural network is utilized to obtain the embedded representation of the drugs and targets. To highlight the contribution of different neighborhood nodes to the central node in aggregating the graph convolution information, a graph attention mechanism is introduced into the node embedding process. Afterward, an inner product decoder is applied to predict DTIs. To evaluate the performance of DTI-HETA, experiments are conducted on two datasets. The experimental results show that our model is superior to the state-of-the-art methods. Also, the identification of novel DTIs indicates that DTI-HETA can serve as a powerful tool for integrating heterogeneous graph information to predict DTIs.

An enhanced cascade-based deep forest model for drug combination prediction.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs.

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

Machine learning methods, databases and tools for drug combination prediction.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods.

SUMMARY: A variety of computational methods have been developed to identify functionally related gene modules from genome-wide gene expression profiles. Integrating the results of these methods to identify consensus modules is a promising approach to produce more accurate and robust results. In this application note, we introduce COMMO, the first web server to identify and analyze consensus gene functionally related gene modules from different module detection methods. First, COMMO implements eight state-of-the-art module detection methods and two consensus clustering algorithms. Second, COMMO provides users with mRNA and protein expression data for 33 cancer types from three public databases. Users can also upload their own data for module detection. Third, users can perform functional enrichment and two types of survival analyses on the observed gene modules. Finally, COMMO provides interactive, customizable visualizations and exportable results. With its extensive analysis and interactive capabilities, COMMO offers a user-friendly solution for conducting module-based precision medicine research. AVAILABILITY AND IMPLEMENTATION: COMMO web is available at https://commo.ncpsb.org.cn/, with the source code available on GitHub: https://github.com/Song-xinyu/COMMO/tree/master.

CircRNA and Stroke: New Insight of Potential Biomarkers and Therapeutic Targets.

Stroke, the second-largest cause of death and the leading cause of disability globally, presents significant challenges in terms of prognosis and treatment. Identifying reliable prognosis biomarkers and treatment targets is crucial to address these challenges. Circular RNA (circRNA) has emerged as a promising research biomarkers and therapeutic targets because of its tissue specificity and conservation. However, the potential role of circRNA in stroke prognosis and treatment remains largely unexplored. This review briefly elucidate the mechanism underlying circRNA's involvement in stroke pathophysiology. Additionally, this review summarizes the impact of circRNA on different forms of strokes, including ischemic stroke and hemorrhagic stroke. And, this article discusses the positive effects of circRNA on promoting cerebrovascular repair and regeneration, maintaining the integrity of the blood-brain barrier (BBB), and reducing neuronal injury and immune inflammatory response. In conclusion, the significance of circRNA as a potential prognostic biomarker and a viable therapeutic target was underscored.

Curative Effect Analysis of Endoscopic Submucosal Dissection in Giant Laterally Spreading Rectal Tumors.

GOAL: The objective of this study was to investigate the clinical efficacy of endoscopic submucosal dissection (ESD) in the treatment of giant lateral developing rectal-type tumors (laterally spreading tumors, LSTs). BACKGROUND: There are no specialized studies on the efficacy of ESD in the treatment of LSTs measuring >5 cm in diameter, surgery was often used in the past, but it has the disadvantages of large trauma, many complications, and high cost. METHODS: The data of 185 patients with rectal LSTs who had undergone ESD in the digestive endoscopy center of our hospital from January 2012 to June 2020 were retrospectively analyzed. Based on the size of the lesions, the patients were divided into 2 groups: diameter ≤5 cm (110 cases) and diameter >5 cm (75 cases), and we summarized and analyzed the en bloc resection rate, curative resection rate, procedure time, muscle injury, bleeding, perforation, postoperative stricture, and recurrence. RESULTS: There was no difference in the en bloc resection rate and R0 resection rate between the 2 groups ( P =0.531). Moreover, there was no difference in the incidence of delayed perforation, postoperative stenosis, and recurrence, but the incidence of delayed bleeding was significantly higher in the giant LST group than the small LST group ( P =0.001). Moreover, for giant rectal LSTs, the growth pattern of the lesion, JNET classification, and the extent of postoperative mucosal defect do not significantly affect the efficacy of ESD. It is worth mentioning that the operation time was longer in the group with a diameter >5 cm, in which perforation was more frequent and the muscle layer was more likely to be injured during ESD ( P <0.001). The muscle injury during ESD was mainly related to the diameter of the lesion, the crossing the rectal pouch, and the operation time. CONCLUSIONS: The use of ESD to treat giant rectal LSTs (>5 cm) is relatively difficult and can easily lead to intraoperative muscle injury, perforation, and late postoperative bleeding. However, if active intervention is performed, patients can still achieve good efficacy and prognosis, which can be applied in hospitals with certain conditions.

Characterization of genital chlamydia trachomatis infection among women attending infertility and gynecology clinics in Hunan, China.

BACKGROUND: Genital infection with Chlamydia trachomatis (C. trachomatis) is a major public health issue worldwide. It can lead to cervicitis, urethritis, and infertility. This study was conducted to determine the characteristics of genital C. trachomatis infection among women attending to the infertility and gynecology clinics. METHODS: Endocervical swabs were collected from 8,221 women for C. trachomatis nucleotide screening and genotyping, while serum samples were collected for C. trachomatis pgp3 antibody determination using luciferase immunosorbent assays. RESULTS: High C. trachomatis DNA prevalence (3.76%) and seroprevalence (47.46%) rates were found, with genotype E (27.5%) being the most prevalent. C. trachomatis omp1 sense mutation was associated with cervical intraepithelial neoplasia (CIN) (odds ratio [OR] = 6.033, 95% confidence interval [CI] = 1.219-39.185, p = 0.045). No significant differences in C. trachomatis seroprevalence rates were observed between women with detectable C. trachomatis DNA in the infertility and routine physical examination groups (86.67% vs. 95%, p > 0.05); however, among women with negative C. trachomatis DNA, the former group had a markedly higher seroprevalence than the latter group (56.74% vs. 20.17%, p < 0.001). C. trachomatis DNA, but not pgp3 antibody, was significantly associated with CIN (OR = 4.087, 95% CI = 2.284-7.315, p < 0.001). CONCLUSION: Our results revealed a high prevalence, particularly seroprevalence, of C. trachomatis among women with infertility. Furthermore, we found an association between C. trachomatis omp1 sense mutations and CIN. Therefore, C. trachomatis serves as a risk factor for CIN.

Seasonal nitrate variations, risks, and sources in groundwater under different land use types in a thousand-year-cultivated region, northwestern China.

The global public health concern of nitrate (NO3-) contamination in groundwater is particularly pronounced in irrigated agricultural regions. This paper aims to analyze the spatial distribution of groundwater NO3-, assess potential health risks for local residents, and quantitatively identify nitrate sources during different seasons and land use types in the Jinghuiqu Irrigation District, a region in northwestern China with a longstanding agricultural history. The investigation utilizes hydrochemical parameters, dual isotopic data, and the Bayesian stable isotope mixing model (MixSIAR). The findings underscore significant seasonal variations in the average concentrations of NO3-, with values of 87.72 mg/L and 101.87 mg/L during the wet and dry seasons, respectively. Furthermore, distinct fluctuations in nitrate concentration were observed across different land use types, whereby vegetable lands manifested the maximum concentration. Prolonged exposure to elevated nitrate concentrations may pose potential health risks to residents, especially in the dry season when the non-carcinogenic groundwater nitrate risk surges past its wet season counterpart. The MixSIAR analysis revealed that chemical fertilizers accounted for the majority of nitrate pollution in vegetable lands, both during the dry season (49.6%) and wet season (41.2%). In contrast, manure and sewage contributed significantly to NO3-concentrations in residential land during the wet (74.9%) and dry seasons (67.6%). For croplands, soil nitrogen emerged as a dominant source during the wet season (42.2%), while chemical fertilizers prevailed in the dry season (38.7%). In addition to source variations, the nitrate concentration of groundwater is further affected by hydrogeological conditions, with more permeable aquifers tending to display higher nitrate concentrations. Thus, targeted measures were proposed to modify or impede the nitrogen migration pathway, taking into consideration hydrogeological conditions and incorporating domestic sewage, organic fertilizer, and agricultural management practices.

Forensic Identification and Evaluation of 25 Obstetric Brachial Plexus Palsy Medical Damage Cases.

OBJECTIVES: To analyze the high risk factors of obstetric brachial plexus palsy (OBPP), and to explore how to evaluate the relationship between fault medical behavior and OBPP in the process of medical damage forensic identification. METHODS: A retrospective analysis was carried out on 25 cases of medical damage liability disputes related to OBPP from 2017 to 2021 in Beijing Fayuan Judicial Science Evidence Appraisal Center. The shortcomings of hospitals in birth weight assessment, delivery mode selection, labor process observation and shoulder dystocia management, and the causal relationship between them and the damage consequences of the children were summarized. RESULTS: Fault medical behavior was assessed as the primary cause in 2 cases, equal cause in 10 cases, secondary cause in 8 cases, minor cause in 1 case, no causal relationship in 1 case, and unclear causal force in 3 cases. CONCLUSIONS: In the process of forensic identification of OBPP, whether medical behaviors fulfill diagnosis and treatment obligations should be objectively analyzed from the aspects of prenatal evaluation, delivery mode notification, standardized use of oxytocin, standard operation of shoulder dystocia, etc. Meanwhile, it is necessary to fully consider the objective risk of different risk factors and the difficulty of injury prevention, and comprehensively evaluate the causal force of fault medical behavior in the damage consequences.

EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction.

INTRODUCTION: There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process. RELATED WORK: Decision trees and their ensemble algorithms are considered to be suitable methods for pharmaceutical applications due to their excellent performance and good interpretability. We review existing decision trees or decision tree ensemble algorithms in the medical field and point out their shortcomings. METHOD: This study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST). EXPERIMENT: The two-drug combination data sets are collected from two cell lines with three classes (additive, antagonistic and synergistic effects) to test our method. Experimental results show that both our DST and EDST perform better than other methods. Besides, the rules generated by our methods are more compact and more accurate than other rule-based algorithms. Finally, we also analyze the extracted knowledge by the model in the field of bioinformatics. CONCLUSION: The novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.

Lysyl hydroxylase LH1 promotes confined migration and metastasis of cancer cells by stabilizing Septin2 to enhance actin network.

BACKGROUND: Excessive extracellular matrix deposition and increased stiffness are typical features of solid tumors such as hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC). These conditions create confined spaces for tumor cell migration and metastasis. The regulatory mechanism of confined migration remains unclear. METHODS: LC-MS was applied to determine the differentially expressed proteins between HCC tissues and corresponding adjacent tissue. Collective migration and single cell migration microfluidic devices with 6 µm-high confined channels were designed and fabricated to mimic the in vivo confined space. 3D invasion assay was created by Matrigel and Collagen I mixture treat to adherent cells. 3D spheroid formation under various stiffness environment was developed by different substitution percentage GelMA. Immunoprecipitation was performed to pull down the LH1-binding proteins, which were identified by LC-MS. Immunofluorescent staining, FRET, RT-PCR, Western blotting, FRAP, CCK-8, transwell cell migration, wound healing, orthotopic liver injection mouse model and in vivo imaging were used to evaluate the target expression and cellular phenotype. RESULTS: Lysyl hydroxylase 1 (LH1) promoted the confined migration of cancer cells at both collective and single cell levels. In addition, LH1 enhanced cell invasion in a 3D biomimetic model and spheroid formation in stiffer environments. High LH1 expression correlated with poor prognosis of both HCC and PDAC patients, while it also promoted in vivo metastasis. Mechanistically, LH1 bound and stabilized Septin2 (SEPT2) to enhance actin polymerization, depending on the hydroxylase domain. Finally, the subpopulation with high expression of both LH1 and SEPT2 had the poorest prognosis. CONCLUSIONS: LH1 promotes the confined migration and metastasis of cancer cells by stabilizing SEPT2 and thus facilitating actin polymerization.

Fusobacterium nucleatum promotes the early occurrence of esophageal cancer through upregulation of IL-32/PRTN3 expression.

Previous studies have shown that gastrointestinal microbiome is associated with the development of esophageal cancer, but the relationship and molecular mechanism between esophageal microbiota and the early development of esophageal cancer remain unclear. Here, we found that Lactobacillus, Escherichia-Shigella, Rikenellaceae-RC9-gut-group, Morganella, and Fusobacterium were more abundant in early-stage esophageal cancer (EEC) tissues compared with normal esophageal tissues. The abundance of bacteria such as Prevotella, Fusobacterium, Porphyromonas, Actinobacillus, and Neisseria in advanced esophageal cancer (AEC) was higher than that in EEC. Then, we further verified that Fusobacterium nucleatum (Fn) was enriched in EEC tissues and that its abundance increased with the progression of esophageal cancer by FISH and RT-PCR. Next, we demonstrated that Fn promoted the proliferation of esophageal squamous cell carcinoma (ESCC) in vitro and in vivo. Finally, we confirmed that Fn promoted ESCC proliferation by upregulating the expression of interleukin (IL)-32/proteinase 3 (PRTN3) and then activating the PI3K/AKT signaling pathway. In conclusion, Fn promoted the early development of ESCC by upregulating the expression of IL-32/PRTN3 and thereby activating the PI3K/AKT signaling pathway. A better understanding of the molecular mechanism of Fn in early esophageal cancer may contribute to the development of early screening markers to diagnose ESCC and provide new targets for treatment.

Transcriptional regulation of NDUFA4L2 by NFIB induces sorafenib resistance by decreasing reactive oxygen species in hepatocellular carcinoma.

Sorafenib is one a first-line therapeutic drugs for advanced hepatocellular carcinoma (HCC). However, only 30% of patients benefit from sorafenib due to drug resistance. We and other groups have revealed that nuclear factor I B (NFIB) regulates liver regeneration and carcinogenesis, but its role in drug resistance is poorly known. We found that NFIB was more upregulated in sorafenib-resistant SMMC-7721 cells compared to parental cells. NFIB knockdown not only sensitized drug-resistant cells to sorafenib but also inhibited the proliferation and invasion of these cells. Meanwhile, NFIB promoted the proliferation and invasion of HCC cells in vitro and facilitated tumor growth and metastasis in vivo. Knocking down NFIB synergetically inhibited tumor growth with sorafenib. Mechanically, gene expression profiling and subsequent verification experiments proved that NFIB could bind with the promoter region of a complex I inhibitor NDUFA4L2 and promote its transcription. Transcriptional upregulation of NDUFA4L2 by NFIB could thus inhibit the sorafenib-induced reactive oxygen species accumulation. Finally, we found that NFIB was highly expressed in HCC tissues, and high NFIB expression level was associated with macrovascular invasion, advanced tumor stage, and poor prognosis of HCC patients (n = 156). In summary, we demonstrated that NFIB could transcriptionally upregulate NDUFA4L2 to enhance both intrinsic and acquired sorafenib resistance of HCC cells by reducing reactive oxygen species induction.