Oxidative stress promotes oral carcinogenesis via Thbs1-mediated M1-like tumor-associated macrophages polarization.

Although oxidative stress is closely associated with tumor invasion and metastasis, its' exact role and mechanism in the initial stage of oral cancer remain ambiguous. Glutamine uptake mediated by alanine-serine-cysteine transporter 2 (ASCT2) participates in glutathione synthesis to resolve oxidative stress. Currently, we firstly found that ASCT2 deletion caused oxidative stress in oral mucosa and promoted oral carcinogenesis induced by 4-Nitroquinoline-1-oxide (4-NQO) using transgenic mice of ASCT2 knockout in oral epithelium. Subsequently, we identified an upregulated gene Thbs1 linked to macrophage infiltration by mRNA sequencing and immunohistochemistry. Importantly, multiplex immunohistochemistry showed M1-like tumor-associated macrophages (TAMs) were enriched in cancerous area. Mechanically, targeted ASCT2 effectively curbed glutamine uptake and caused intracellular reactive oxygen species (ROS) accumulation, which upregulated Thbs1 in oral keratinocytes and then activated p38, Akt and SAPK/JNK signaling to polarize M1-like TAMs via exosome-transferred pathway. Moreover, we demonstrated M1-like TAMs promoted malignant progression of oral squamous cell carcinoma (OSCC) both in vitro and in vivo by a DOK transformed cell line induced by 4-NQO. All these results establish that oxidative stress triggered by ASCT2 deletion promotes oral carcinogenesis through Thbs1-mediated M1 polarization, and indicate that restore redox homeostasis is a new approach to prevent malignant progression of oral potentially malignant disorders.

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.

BACKGROUND: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. METHODS: Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. RESULTS: We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. CONCLUSIONS: The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

Comparative assessment of the capability of machine learning-based radiomic models for predicting omental metastasis in locally advanced gastric cancer.

The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospective collection of 478 pathologically confirmed LAGC patients was undertaken, encompassing both clinical features and arterial phase computed tomography images. Radiomic features were extracted using 3D Slicer software. Clinical and radiomic features were further filtered through lasso regression. Selected clinical and radiomic features were used to construct omental metastasis predictive models using support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and logistic regression (LR). The models' performance metrics included accuracy, area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the RF predictive model surpassed LR, SVM, DT, and KNN in terms of accuracy, AUC, sensitivity, specificity, PPV, and NPV. Compared to the other four predictive models, the RF model significantly improved PPV. In the test cohort, all five machine learning predictive models exhibited lower PPVs. The DT model demonstrated the most significant variation in performance metrics relative to the other models, with a sensitivity of 0.231 and specificity of 0.990. The LR-based predictive model had the lowest PPV at 0.210, compared to the other four models. In the external validation cohort, the performance metrics of the predictive models were generally consistent with those in the test cohort. The LR-based model for predicting omental metastasis exhibited a lower PPV. Among the machine learning algorithms, the RF predictive model demonstrated higher accuracy and improved PPV relative to LR, SVM, KNN, and DT models.

Metal-organic cages for gas adsorption and separation.

The unique high surface area and tunable cavity size endow metal-organic cages (MOCs) with superior performance and broad application in gas adsorption and separation. Over the past three decades, for instance, numerous MOCs have been widely explored in adsorbing diverse types of gas including energy gases, greenhouse gases, toxic gases, noble gases, etc. To gain a better understanding of the structure-performance relationships, great endeavors have been devoted to ligand design, metal node regulation, active metal site construction, cavity size adjustment, and function-oriented ligand modification, thus opening up routes toward rationally designed MOCs with enhanced capabilities. Focusing on the unveiled structure-performance relationships of MOCs towards target gas molecules, this review consists of two parts, gas adsorption and gas separation, which are discussed separately. Each part discusses the cage assembly process, gas adsorption strategies, host-guest chemistry, and adsorption properties. Finally, we briefly overviewed the challenges and future directions in the rational development of MOC-based sorbents for application in challenging gas adsorption and separation, including the development of high adsorption capacity MOCs oriented by adsorbability and the development of highly selective adsorption MOCs oriented by separation performance.

Hub gene identification and molecular subtype construction for Helicobacter pylori in gastric cancer via machine learning methods and NMF algorithm.

Helicobacter pylori (HP) is a gram-negative and spiral-shaped bacterium colonizing the human stomach and has been recognized as the risk factor of gastritis, peptic ulcer disease, and gastric cancer (GC). Moreover, it was recently identified as a class I carcinogen, which affects the occurrence and progression of GC via inducing various oncogenic pathways. Therefore, identifying the HP-related key genes is crucial for understanding the oncogenic mechanisms and improving the outcomes of GC patients. We retrieved the list of HP-related gene sets from the Molecular Signatures Database. Based on the HP-related genes, unsupervised non-negative matrix factorization (NMF) clustering method was conducted to stratify TCGA-STAD, GSE15459, GSE84433 samples into two clusters with distinct clinical outcomes and immune infiltration characterization. Subsequently, two machine learning (ML) strategies, including support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), were employed to determine twelve hub HP-related genes. Beyond that, receiver operating characteristic and Kaplan-Meier curves further confirmed the diagnostic value and prognostic significance of hub genes. Finally, expression of HP-related hub genes was tested by qRT-PCR array and immunohistochemical images. Additionally, functional pathway enrichment analysis indicated that these hub genes were implicated in the genesis and progression of GC by activating or inhibiting the classical cancer-associated pathways, such as epithelial-mesenchymal transition, cell cycle, apoptosis, RAS/MAPK, etc. In the present study, we constructed a novel HP-related tumor classification in different datasets, and screened out twelve hub genes via performing the ML algorithms, which may contribute to the molecular diagnosis and personalized therapy of GC.

Exploring Cancer Dependency Map genes and immune subtypes in colon cancer, in which TIGD1 contributes to colon cancer progression.

BACKGROUND: Tumour-dependent genes identified in CRISPR-Cas9 screens have been widely reported in Cancer Dependency Maps (CDMs). CDM-derived tumour-dependent genes play an important role in tumorigenesis and progression; however, they have not been investigated in colon cancer (CC). METHODS: CDM genes overexpressed in CC were identified from the TCGA-COAD dataset and CDM platform. A CDM signature and prognostic nomogram were constructed by Lasso Cox regression and multivariate Cox analyses. A weighted correlation network analysis (WGCNA) and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. A comprehensive investigation was performed between the two subtypes and immune regulation, the immune microenvironment and the impact of immunotherapy. RESULTS: First, 1304 overexpressed CDM genes were identified. Then, a CDM signature with five cancer-dependent genes (MMS19, NOP14, POLRMT, SNAPC5 and TIGD1) and a prognostic nomogram were constructed, and they demonstrated robust predictive performance and a close relationship with clinical characteristics in different CC datasets. Patients with high CDM risk scores showed worse survival outcome and weaker response to chemotherapy. Additionally, TIGD1 genes were oncogenes that affected the CC cell cycle, according to cell functional experiments that involved the suppression of the TIGD1 gene. Furthermore, WGCNA and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. Finally, systematic investigations were conducted with the relationship between the CDM subtypes and immune regulation. CONCLUSIONS: This study constructed a CDM signature consisting of five risk genes that predict survival in CC patients. In addition, the immune subtypes provided valuable insights into immunotherapy for CC patients. TIGD1, as an oncogene, is independent prognostic factors for CC, and contributes to CC progression.

Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer.

""We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798-0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710-0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730-0.879), had the better predictive ability. The Hosmer-Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726-0.945) and 0.779 (95% CI 0.634-0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.

Multiomics characteristics and immunotherapeutic potential of EZH2 in pan-cancer.

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.1.0). Furthermore, numerous bioinformatics analysis databases also reapplied to comprehensively explore and elucidate the oncogenic mechanism and therapeutic potential of EZH2 from pan-cancer insight. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemical assays were performed to verify the differential expression of EZH2 gene in various cancers at the mRNA and protein levels. EZH2 was widely expressed in multiple normal and tumor tissues, predominantly located in the nucleoplasm. Compared with matched normal tissues, EZH2 was aberrantly expressed in most cancers either at the mRNA or protein level, which might be caused by genetic mutations, DNA methylation, and protein phosphorylation. Additionally, EZH2 expression was correlated with clinical prognosis, and its up-regulation usually indicated poor survival outcomes in cancer patients. Subsequent analysis revealed that EZH2 could promote tumor immune evasion through T-cell dysfunction and T-cell exclusion. Furthermore, expression of EZH2 exhibited a strong correlation with several immunotherapy-associated responses (i.e., immune checkpoint molecules, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) status, and neoantigens), suggesting that EZH2 appeared to be a novel target for evaluating the therapeutic efficacy of immunotherapy.

Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images.

Background: Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. Methods: We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. Results: The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. Conclusions: These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.

CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.

Background: DNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC). Methods: In this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts. Results: The radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values <0.001). A radiomic nomogram incorporating a radiomic signature and two clinical characteristics (age and CT-reported N stage) represented good discrimination in the training cohort with an AUC of 0.902 (95% CI: 0.853-0.951), in the internal validation cohort with an AUC of 0.972 (95% CI: 0.945-1.000) and in the external validation cohort with an AUC of 0.891 (95% CI: 0.825-0.958). Conclusion: The CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.

Background: This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC). Materials and methods: This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared. Results: We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively. Conclusion: We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma.

Background: As a caspase-independent type of cell death, necroptosis plays a significant role in the initiation, and progression of gastric cancer (GC). Numerous studies have confirmed that long non-coding RNAs (lncRNAs) are closely related to the prognosis of patients with GC. However, the relationship between necroptosis and lncRNAs in GC remains unclear. Methods: The molecular profiling data (RNA-sequencing and somatic mutation data) and clinical information of patients with stomach adenocarcinoma (STAD) were retrieved from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was conducted to identify the necroptosis-related lncRNAs (NRLs). Subsequently, univariate Cox regression and LASSO-Cox regression were conducted to establish a 12-NRLs signature in the training set and validate it in the testing set. Finally, the prognostic power of the 12-NRLs signature was appraised via survival analysis, nomogram, Cox regression, clinicopathological characteristics correlation analysis, and the receiver operating characteristic (ROC) curve. Furthermore, correlations between the signature risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analyzed. Results: In the present study, a 12-NRLs signature comprising REPIN1-AS1, UBL7-AS1, LINC00460, LINC02773, CHROMR, LINC01094, FLNB-AS1, ITFG1-AS1, LASTR, PINK1-AS, LINC01638, and PVT1 was developed to improve the prognosis prediction of STAD patients. Unsupervised methods, including principal component analysis and t-distributed stochastic neighbor embedding, confirmed the capability of the present signature to separate samples with RS. Kaplan-Meier and ROC curves revealed that the signature had an acceptable predictive potency in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 12-NRLs signature were risk factors independent of various clinical parameters. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and half-inhibitory concentration differed significantly among different risk subtypes, which implied that the signature could assess the clinical efficacy of chemotherapy and immunotherapy. Conclusion: This 12-NRLs risk signature may help assess the prognosis and molecular features of patients with STAD and improve treatment modalities, thus can be further applied clinically.

RNF114 Silencing Inhibits the Proliferation and Metastasis of Gastric Cancer.

RNF114 (E3 ubiquitin ligase RING finger protein 114) was first identified as a zinc-binding protein that promotes psoriasis development; however, its role in gastric cancer is still unclear. We explored the relationship between RNF114 and gastric cancer using bioinformatics and molecular biology techniques. The results showed that RNF114 was highly expressed in gastric cancer and negatively correlated with the patient's prognosis. Functional assays suggested that RNF114 silencing suppressed the proliferation and metastasis of gastric cancer cells to a certain extent. Further studies showed that RNF114 expression was potentially targeted by miR-218-5p and methylation modification, and mediated downstream EGR1 (early growth response 1) degradation by the ubiquitylation approach. Together, the present results highlight the detrimental effects of RNF114 overexpression in gastric cancer and contribute to a better understanding of the mechanisms underlying RNF114 functionality.

Identification of Circular RNA-Based Immunomodulatory Networks in Colorectal Cancer.

BACKGROUND: Circular RNAs (circRNAs) have been recently proposed as hub molecules in various diseases, especially in tumours. We found that circRNAs derived from ribonuclease P RNA component H1 (RPPH1) were highly expressed in colorectal cancer (CRC) samples from Gene Expression Omnibus (GEO) datasets. OBJECTIVE: We sought to identify new circRNAs derived from RPPH1 and investigate their regulation of the competing endogenous RNA (ceRNA) and RNA binding protein (RBP) networks of CRC immune infiltration. METHODS: The circRNA expression profiles miRNA and mRNA data were extracted from the GEO and The Cancer Genome Atlas (TCGA) datasets, respectively. The differentially expressed (DE) RNAs were identified using R software and online server tools, and the circRNA-miRNA-mRNA and circRNA-protein networks were constructed using Cytoscape. The relationship between targeted genes and immune infiltration was identified using the GEPIA2 and TIMER2 online server tools. RESULTS: A ceRNA network, including eight circRNAs, five miRNAs, and six mRNAs, was revealed. Moreover, a circRNA-protein network, including eight circRNAs and 49 proteins, was established. The targeted genes, ENOX1, NCAM1, SAMD4A, and ZC3H10, are closely related to CRC tumour-infiltrating macrophages. CONCLUSIONS: We analysed the characteristics of circRNA from RPPH1 as competing for endogenous RNA binding miRNA or protein in CRC macrophage infiltration. The results point towards the development of a new diagnostic and therapeutic paradigm for CRC.

LCP1 is a prognostic biomarker correlated with immune infiltrates in gastric cancer.

BACKGROUND: Previous studies have identified LCP1 as a diagnostic and prognostic marker in several cancers. However, the role of LCP1 in gastric cancer (GC) and its effect on tumor immune infiltration remain unclear. OBJECTIVE: The aim was to explore the role of LCP1 in GC and its effect on tumor immune infiltration. METHODS: We explored the expression of LCP1 relative to clinicopathology in GC patients by bioinformatics analysis and immunohistochemistry. Using cBioportal database, we analyzed the characteristic genetic variations of LCP1 in GC. In addition, we evaluated the correlation between LCP1 expression and tumor-infiltrating lymphocytes (TILs) using R software, TIMER and TISIDB databases. Finally, we analyzed the biological functions in which LCP1 may participate and the signaling pathways it may regulate. RESULTS: Here, we showed that LCP1 expression is significantly correlated with tumor aggressiveness and poor prognosis in GC patients. Additionally, the results indicated that LCP1 was associated with TILs, including both immunosuppressive and immunosupportive cells, and was strongly correlated with various immune marker sets in GC. GSEA analysis demonstrated that LCP1 expression played an important role in lymphocyte formation and immune reaction. CONCLUSIONS: LCP1 may be a potential prognostic biomarker for GC patients and a marker for tumor immunotherapy.

The therapeutic effect of artesunate on rosacea through the inhibition of the JAK/STAT signaling pathway.

Acne rosacea is a type of chronic dermatosis with the characteristics of erubescence, angiotelectasis and pustule formation. However, current treatment methods are limited due to the side effects. Artesunate demonstrated a promising therapeutic efficacy with a high safety margin. HaCaT cells were treated with antibacterial peptide LL­37 to simulate rosacea caused by Demodex folliculorum (D. folliculorum) infection. Cell Counting kit 8 and flow cytometry assays were performed to measure cellular proliferation, apoptosis, the stage of the cell cycle and reactive oxygen species generation in order to determine the level of cell damage. Then the damaged cells were treated with different concentrations of artesunate and doxycycline to determine the therapeutic effect of artesunate. Pro­inflammatory cytokines tumor necrosis factor­α (TNF­α), interleukin (IL)­6, IL­8 and C­C motif chemokine 2 (MCP­1) were measured using an ELISA, while western blotting was used to detect the expression of Janus kinase 2 (JAK2) and signal transducer and transcription activator (STAT3). As a result, LL­37 treated HaCaT cells decreased in cell viability, had an increased apoptotic rate and cell cycle arrest, indicating that cell damage caused by rosacea was simulated. In addition, upregulated concentrations of the pro­inflammatory cytokines TNF­α, IL­6, IL­8 and MCP­1 were attenuated in the artesunate group in a dose­dependent fashion, indicating the therapeutic effect of artesunate. Furthermore, higher concentrations of artesunate exhibited an improved effect compared with the doxycycline group. In addition, increased expression levels of JAK2 and STAT3 following treatment with LL­37 suggested that rosacea caused by D. folliculorum infection may lead to inflammation through the JAK/STAT signaling pathway. In conclusion, the potential mechanism by which damage occurs in rosacea was revealed and a promising therapeutic method against rosacea was demonstrated.

Molecular defects identified by whole exome sequencing in a child with atypical mucopolysaccharidosis IIIB.

BACKGROUND: Mucopolysaccharidosis IIIB (MPS IIIB) is a genetic disease characterized by mutations in the NAGLU gene, deficiency of α-N-acetylglucosaminidase, multiple congenital malformations and an increased susceptibility to malignancy. Because of the slow progressive nature of this disease and its atypical symptoms, the misdiagnosis of MPS IIIB is not rare in clinical practice. This misdiagnosis could be avoided by using next-generation sequencing (NGS) techniques, which have been shown to have superior performance for detecting mutations underlying rare inherited disorders in previous studies. CASE PRESENTATION: Whole exome sequencing (WES) was conducted and the putative pathogenic variants were validated by Sanger sequencing. The activity of MPS IIIB related enzyme in the patient's blood serum was assayed. A heterozygous, non-synonymous mutation (c.1562C>T, p.P521L) as well as a novel mutation (c.1705C>A, p.Q569K) were found in the NAGLU gene of the patient. The two mutations were validated by Sanger sequencing. Our data showed that this patient's c.1562C>T, p.P521L mutation in the NAGLU gene was inherited from his father and c.1705C>A, p.Q569K was from his mother. The diagnosis was further confirmed by an enzymatic activity assay after patient recall and follow-up. CONCLUSIONS: Our results describe an atypical form of MPS IIIB and illustrate the diagnostic potential of targeted WES in Mendelian disease with unknown etiology. WES could become a powerful tool for molecular diagnosis of MPS IIIB in clinical setting.

Ghrelin inhibits proinflammatory responses and prevents cognitive impairment in septic rats.

OBJECTIVES: A novel stomach-derived peptide, ghrelin, is down-regulated in sepsis and its IV administration decreases proinflammatory cytokines and mitigates organ injury. In this study, we wanted to investigate the effects of ghrelin on proinflammatory responses and cognitive impairment in septic rats. DESIGN: Prospective, randomized, controlled experiment. SETTING: Animal basic science laboratory. SUBJECTS: Sprague-Dawley rats, weighing 250-300 g. INTERVENTIONS: Sepsis was induced by cecal ligation and puncture. Animals were randomly divided into four groups: sham, sham + ghrelin, cecal ligation and puncture, and cecal ligation and puncture + ghrelin. Saline was given subcutaneously (30 mL/kg) at 4 and 16 hours after surgery for all rats. Septic rats were treated with ceftriaxone (30 mg/kg) and clindamycin (25 mg/kg) subcutaneously at 4 and 16 hours after surgery. Ghrelin (80 µg/kg) was administrated intraperitoneally 4 and 16 hours after surgery in sham + ghrelin group and cecal ligation and puncture + ghrelin group. MEASUREMENTS AND MAIN RESULTS: The levels of proinflammatory cytokines in hippocampus were measured by enzyme-linked immunosorbent assay, and cleaved caspase-3 was detected by Western blot 24 hours after surgery. Neuronal apoptosis was determined by terminal deoxynucleotidyl transferase dUTP nick-end labeling staining 48 hours after surgery. Additional animals were monitored to record survival and body weight changes for 10 days after surgery. Survival animals underwent behavioral tasks 10 days after surgery: open-field, novel object recognition, and continuous multiple-trial step-down inhibitory avoidance task. Ghrelin significantly decreased the levels of proinflammatory cytokines and inhibited the activation of caspase-3 in the hippocampus after cecal ligation and puncture. The density of terminal deoxynucleotidyl transferase dUTP nick-end labeling-positive apoptotic neurons was significantly lowered by ghrelin. In addition, ghrelin improved the survival rates after cecal ligation and puncture. There were no differences in the distance and move time between groups in open-field task. However, the survivors after cecal ligation and puncture were unable to recognize the novel object and required more training trials to reach the acquisition criterion. All these long-term impairments were prevented by ghrelin. CONCLUSIONS: Ghrelin inhibited proinflammatory responses, improved the survival rate, and prevented cognitive impairment in septic rats.