CIC::NUTM1 sarcomas occurred in soft tissues of upper limbs : a rare case report and literature review.

BACKGROUND: CIC-rearranged sarcomas (CRS) represent a new entity of undifferentiated small round cell sarcoma belonging to the Ewing-like sarcomas family. CRS are the most common type. Fusion partners for the CIC gene include DUX4, FOXO4, and the recently recognizedNUTM1. Rare cases of CIC::NUTM1 sarcoma in pediatric patients have recently been reported in brain, kidney, bone, and soft tissues. However, such cases have not been identified in the soft tissues of the limbs. CASE PRESENTATION: We reported a case of CIC::NUTM1 sarcoma located in the right upper limb of an 18-year-old man. The tumor displayed morphologic features typical of CIC::DUX4 sarcomas, with small- to medium-sized round cells, a lobular pattern, focal spindling, myxoid stroma, and patchy necrosis. The tumor diffusely expressed NUTM1, was positive for WT1cter at weak to moderate intensity, and was focally positive for CD99, while it was negative for keratins, EMA, P40, MyoD1, myogenin, NKX2.2, BCOR, and pan-TRK. Fluorescence in situ hybridization analyses revealed cleavage of the CIC and NUTM1 genes. CONCLUSION: CIC::NUTM1 sarcomas represent a novel molecular variant of CRS with a preference for the central nervous system and younger pediatric persons. Its morphology and phenotype may be mistaken for NUT carcinomas, and the behavior is more progressive than other forms of CRS. For this rare and newly discovered gene fusion variant, it is necessary to integrate molecular and immunohistochemical findings with morphologic features in the diagnosis of undifferentiated neoplasms.

Graph-Regularized Non-Negative Matrix Factorization for Single-Cell Clustering in scRNA-Seq Data.

The advent of single-cell RNA sequencing (scRNA-seq) has brought forth fresh perspectives on intricate biological processes, revealing the nuances and divergences present among distinct cells. Accurate single-cell analysis is a crucial prerequisite for in-depth investigation into the underlying mechanisms of heterogeneity. Due to various technical noises, like the impact of dropout values, scRNA-seq data remains challenging to interpret. In this work, we propose an unsupervised learning framework for scRNA-seq data analysis (aka Sc-GNNMF). Based on the non-negativity and sparsity of scRNA-seq data, we propose employing graph-regularized non-negative matrix factorization (GNNMF) algorithm for the analysis of scRNA-seq data, which involves estimating cell-cell similarity and gene-gene similarity through Laplacian kernels and p-nearest neighbor graphs ( p-NNG). By assuming intrinsic geometric local invariance, we use a weighted p-nearest known neighbors ( p-NKN) of cell-cell interactions to guide the matrix decomposition process, promoting the closeness of cells with similar types in cell-gene data space and determining a more suitable embedding space for clustering. Sc-GNNMF demonstrates superior performance compared to other methods and maintains satisfactory compatibility and robustness, as evidenced by experiments on 11 real scRNA-seq datasets. Furthermore, Sc-GNNMF yields excellent results in clustering tasks, extracting useful gene markers, and pseudo-temporal analysis.

ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering.

MOTIVATION: Categorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes. RESULT: To achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.

ARPC1B is a novel prognostic biomarker for kidney renal clear cell carcinoma and correlates with immune infiltration.

Background: Actin-related protein 2/3 complex subunit 1B (ARPC1B) is reported to be involved in tumorigenesis and progression. However, its role in kidney renal clear cell carcinoma (KIRC), correlation with tumor-infiltrating immune cells, and prognostic significance remain unclear. Methods: Data sets from the TCGA, GTEx, GEPIA, GEO, UALCAN, and CPTAC databases were extracted and analyzed to investigate the expression difference, prognosis, and clinicopathological features of ARPC1B. Single-sample Gene Set Enrichment Analysis (ssGSEA), CIBERSORT, and TISCH2 analysis were used to examine the relationship between ARPC1B expression and tumor immune infiltration in KIRC. The potential function of ARPC1B in KIRC was explored by GO functional annotation and KEGG pathway analysis. The TIDE algorithm was used to predict and analyze the relationship between ARPC1B expression and response to immune checkpoint blockade (ICB). The expression of ARPC1B was further validated by using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Results: The study showed that ARPC1B expression was an independent prognostic factor of KIRC, with high ARPC1B expression being associated with poor overall survival (OS). Enrichment of GO annotation and pathway analysis showed multiple immune-related functional pathways affected by ARPC1B such as regulation of immune effector process, inflammatory response regulation, antigen processing and presentation, asthma, autoimmune thyroid disease, graft versus host disease, intestinal immune network for IgA production, and type I diabetic mellitus. Moreover, ARPC1B expression positively correlated with infiltrating levels of myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs) in KIRC. Importantly, high ARPC1B expression predicted a low response to ICB in KIRC. Conclusion: This study indicates that ARPC1B expression is an independent prognostic biomarker for OS in KIRC patients. High ARPC1B expression is closely associated with MDSCs and Tregs infiltration. These findings suggest that ARPC1B may serve as a biomarker for prognosis and immune infiltration in KIRC, potentially aiding in the development of novel treatment strategies to improve the survival outcomes for KIRC patients.

Exploring the glycolytic cross-talk genes between inflammatory bowel disease and colorectal cancer.

Patients with inflammatory bowel disease (IBD) have a higher risk of developing colorectal cancer (CRC). Glycolysis is involved in the development of both IBD and CRC. However, the mechanisms and outcomes of glycolysis shared between IBD and CRC remain unclear. This study aimed to explore the glycolytic cross-talk genes between IBD and CRC integrating bioinformatics and machine learning. With WGCNA, LASSO, COX, and SVM-RFE algorithms, P4HA1 and PMM2 were identified as glycolytic cross-talk genes. The independent risk signature of P4HA1 and PMM2 was constructed to predict the overall survival rate of patients with CRC. The risk signature correlated with clinical characteristics, prognosis, tumor microenvironment, immune checkpoint, mutants, cancer stemness, and chemotherapeutic drug sensitivity. CRC patients with high risk have increased microsatellite instability, tumor mutation burden. The nomogram integrating risk score, tumor stage, and age showed high accuracy for predicting overall survival rate. In addition, the diagnostic model for IBD based on P4HA1 and PMM2 showed excellent accuracy. Finally, immunohistochemistry results showed that P4HA1 and PMM2 were significantly upregulated in IBD and CRC. Our study reveals the presence of glycolytic cross-talk genes P4HA1 and PMM2 between IBD and CRC. This may prove to be beneficial in advancing research on the mechanism of development of IBD-associated CRC.

Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn's disease.

Crohn's disease (CD), a subtype of inflammatory bowel disease (IBD), causes chronic gastrointestinal tract inflammation. Thirty percent of patients do not respond to anti-tumor necrosis factor (TNF) therapy. Sialylation is involved in the pathogenesis of IBD. We aimed to identify potential biomarkers for diagnosing CD and predicting anti-TNF medication outcomes in CD. Three potential biomarkers (SERPINB2, TFPI2, and SLC9B2) were screened using bioinformatics analysis and machine learning based on sialylation-related genes. Moreover, the combined model of SERPINB2, TFPI2, and SLC9B2 showed excellent diagnostic value in both the training and validation cohorts. Importantly, a Sial-score was constructed based on the expression of SERPINB2, TFPI2, and SLC9B2. The Sial-low group showed a lower level of immune infiltration than the Sial-high group. Anti-TNF therapy was effective for 94.4% of patients in the Sial-low group but only 15.8% in the Sial-high group. The Sial-score had an outstanding ability to predict and distinguish between responders and non-responders. Our comprehensive analysis indicates that SERPINB2, TFPI2, and SLC9B2 play essential roles in pathogenesis and anti-TNF therapy resistance in CD. Furthermore, it may provide novel concepts for customizing treatment for individual patients with CD.

Active Interdiction Defence Scheme Against False Data-Injection Attacks: A Stackelberg Game Perspective.

The security issue is of the fundamental importance for cyber-physical systems (CPSs) due to the vulnerability to cyber attacks. In this article, an active interdiction defence scheme is proposed as an extra safeguard against the strategic false data-injection (FDI) attacker. To tackle the coupling between the decision-making processes of the FDI attacker and the active interdiction defence scheme, a framework of dynamic Stackelberg game is formulated to characterize the hierarchical interaction due to their asymmetric information structures. Subsequently, the Stackelberg strategies of both players are constructed. Furthermore, a sufficient condition is derived to guarantee asymptotic stability of the closed-loop system with the FDI attack and the active interdiction defence scheme. Finally, two simulation examples are provided to illustrate the correctness and effectiveness of the proposed active interdiction defence scheme.

Invariance Principles for Nonlinear Discrete-Time Switched Systems and Its Application to Output Synchronization of Dynamical Networks.

In this article, we develop two invariance principles for nonlinear discrete-time switched systems based on multiple Lyapunov functions and multiple weak Lyapunov functions, respectively, which allow the first differences of multiple weak Lyapunov functions to be positive on certain sets. It is shown that the solution of the system is attracted to the largest weakly invariant set in a certain specific region. Then, based on the invariance principle developed and geometrical dissipativity, we obtain the generalized output synchronization for discrete-time dynamical networks with nonidentical nodes by an appropriate switching among several communication topologies. Finally, two examples are provided to demonstrate the effectiveness of the main results.

Conversion therapy from unresectable stage IIIC non-small-cell lung cancer to radical surgery via anti-PD-1 immunotherapy combined with chemotherapy and anti-angiogenesis: A case report and literature review.

The prognosis of patients with stage IIIC non-small-cell lung cancer (NSCLC) is poor due to the loss of surgical treatment opportunities. Improving the prognosis of these patients with IIIC NSCLC urgently needs to be addressed. Here, we report a stage IIIC (T4N3M0 IIIC (AJCC 8th)) NSCLC patient treated with 2 cycles of anti-PD-1 immunotherapy combined with chemotherapy and anti-angiogenesis therapy; after two cycles of treatment, the patient achieved a partial response and obtained the opportunity for surgical treatment. After the operation, the patient achieved a pathological complete response and successfully transformed from unresectable stage IIIC lung cancer to radical surgery (ypT0N0M0). Our study is expected to provide new ideas for treating patients with unresectable stage IIIC NSCLC in the future.

Identification of a Ferroptosis-Related Prognostic Signature in Sepsis via Bioinformatics Analyses and Experiment Validation.

Ferroptosis is a new type of programmed cell death that is different from apoptosis, cell necrosis, and autophagy, which might be involved in development of sepsis. However, the potential role of ferroptosis-related genes (FRGs) in sepsis remained unclear. We identified 41 ferroptosis-related differential expression genes by weighted correlation network and differential expression analysis. The hub module of 41 ferroptosis-related differential expression genes in the protein-protein interaction network was identified. Next, we estimated diagnostic values of genes in hub modules. TLR4, WIPI1, and GABARAPL2 with high diagnostic value were selected for construction of risk prognostic model. The high risk-scored patients had significantly higher mortality than the patients with low risk scores in discovery dataset. Furthermore, the risk scores of nonsurvivor were higher than those of survivor in validation dataset. It suggested that risk score was significantly correlated to prognosis in sepsis. Then, we constructed a nomogram for improving the clinical applicability of risk signature. Moreover, the risk score was significantly associated with immune infiltration in sepsis. Our comprehensive analysis of FRGs in sepsis demonstrated the potential roles in diagnosis, prognosis, and immune infiltration. This work may benefit in understanding FRGs in sepsis and pave a new path for diagnosis and assessment of prognosis.

Differential expression of insulin-like growth factor type 1 receptor identifies heterogeneous intrahepatic regulatory T subsets in mouse hepatocellular carcinoma.

Understanding regulatory T-cell (Treg)-mediated tumor tolerance is critical for designing immunotherapy against hepatocellular carcinoma (HCC). In this study, we characterized the expression of insulin-like growth factor type 1 receptor (IGF1R) in intrahepatic Tregs in a chemical-induced mouse HCC model. We found two intrahepatic Treg subsets with differential IGF1R expression: IGF1Rhi Tregs and IGF1Rlo/- Tregs. Functional assays indicated that compared with IGF1Rlo/- Tregs, IGF1Rhi Tregs produced more TGF-ß and IL-10 and were more proliferative in vivo. Furthermore, IGF1Rhi Tregs exhibited higher phosphorylation of the mammalian target of the rapamycin complex 1 (mTORC1) in vivo. However, in vitro stimulation and immunosuppression assay revealed that the immunosuppressive capacity of the two Treg subsets was equivalent, as evidenced by comparable cytokine production and immunosuppressive effect over conventional T cells. The transcriptome sequencing analysis revealed up-regulation of genes that encode proteins essential for glycolysis, oxidative phosphorylation, and electron transport chain in IGF1Rhi Tregs. Consistently, IGF1Rhi Tregs produces more adenosine triphosphate (ATP), lactate, and reactive oxygen species (ROS). Furthermore, malignant cells in the tumor nodules induced IGF1R down-regulation in Tregs at the mRNA level. In summary, we identified the heterogeneity of intrahepatic Tregs in HCC which might play significant roles in tumor immunity.

Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix.

Limitations of bulk sequencing techniques on cell heterogeneity and diversity analysis have been pushed with the development of single-cell RNA-sequencing (scRNA-seq). To detect clusters of cells is a key step in the analysis of scRNA-seq. However, the high-dimensionality of scRNA-seq data and the imbalances in the number of different subcellular types are ubiquitous in real scRNA-seq data sets, which poses a huge challenge to the single-cell-type detection.We propose a meta-learning-based model, SiaClust, which is the combination of Siamese Convolutional Neural Network (CNN) and improved spectral clustering, to achieve scRNA-seq cell type detection. To be specific, with the help of the constrained Sigmoid kernel, the raw high-dimensionality data is mapped to a low-dimensional space, and the Siamese CNN learns the differences between the cell types in the low-dimensional feature space. The similarity matrix learned by Siamese CNN is used in combination with improved spectral clustering and t-distribution Stochastic Neighbor Embedding (t-SNE) for visualization. SiaClust highlights the differences between cell types by comparing the similarity of the samples, whereas blurring the differences within the cell types is better in processing high-dimensional and imbalanced data. SiaClust significantly improves clustering accuracy by using data generated by nine different species and tissues through different scNA-seq protocols for extensive evaluation, as well as analogies to state-of-the-art single-cell clustering models. More importantly, SiaClust accurately locates the exact site of dropout gene, and is more flexible with data size and cell type.

An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network.

BACKGROUND: Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. RESULTS: In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. CONCLUSIONS: The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.

Association of Renal Arteriosclerosis With Atherosclerotic Cardiovascular Disease Risk in Lupus Nephritis.

OBJECTIVE: Lupus nephritis (LN) predicts a 9-fold higher atherosclerosis cardiovascular disease (ASCVD) risk, highlighting the urgent need to target ASCVD prevention. Studies in IgA nephropathy reported that severe renal arteriosclerosis (r-ASCL) in diagnostic biopsies strongly predicted ASCVD risk. We recently found that 50% of LN pathology reports overlooked r-ASCL reporting, which could explain prior negative LN ASCVD risk studies. The present study was undertaken to examine associations between a composite of reported and overread r-ASCL and ASCVD events in LN. METHODS: Data were abstracted from all LN patients who underwent diagnostic biopsy between 1994 and 2017, including demographic information, ASCVD risk factors, and pathology reports at the time of LN diagnosis. We manually validated all incident ASCVD events. We overread 25% of the biopsies to grade r-ASCL using the Banff criteria. We supplemented the overread r-ASCL grade, when available, to determine the composite of reported and overread r-ASCL grade. RESULTS: Among 189 incident LN patients, 78% were female, 73% White, and the median age was 25 years. Overall, 31% had any reported r-ASCL, and 7% had moderate-to-severe r-ASCL. After incorporating systematically re-examined r-ASCL grade, the prevalence of any and moderate-to-severe r-ASCL increased to 39% and 12%, respectively. We found 22 incident ASCVD events over 11 years of follow-up. Using a composite of reported and overread r-ASCL grade, we found that severe r-ASCL in diagnostic LN biopsies was associated with 9-fold higher odds of ASCVD. CONCLUSION: Severe r-ASCL can predict ASCVD in LN; therefore, larger studies are required to systematically report r-ASCL and examine ASCVD associations.

High Burden of Premature Arteriosclerosis on Renal Biopsy Results in Incident Lupus Nephritis.

OBJECTIVE: Cardiovascular disease (CVD) is accelerated in patients with systemic lupus erythematosus and lupus nephritis (LN). Despite the literature suggesting that renal arteriosclerosis predicts CVD in other glomerulonephritis diseases, arteriosclerosis grading and reporting might be particularly overlooked in LN biopsies. Our objective was to examine the burden of renal arteriosclerosis in LN and to assess whether arteriosclerosis is underreported in LN biopsies. METHODS: We identified all patients with LN undergoing kidney biopsy between 1994 and 2017 at an academic center. We interpreted LN biopsy reports to classify the Banff categories of absent, mild, moderate, or severe renal arteriosclerosis. The prevalence of renal arteriosclerosis was compared with the prevalence published for age-matched healthy peers, and predictors of arteriosclerosis were examined. We overread biopsies for Banff renal arteriosclerosis grading and compared to pathology reports. RESULTS: Among 189 incident patients with LN, renal arteriosclerosis prevalence was 2 decades earlier compared to their healthy peers, affecting 40% of patients ages 31-39 years with LN compared to 44% of healthy peers ages 50-59 years. A multivariable analysis showed a 3-fold higher odds of renal arteriosclerosis in patients ages ≥30 years with LN. LN chronicity on biopsy results predicted a 4-fold higher odds of renal arteriosclerosis. The overreads determined that 50% of standard LN biopsy reports missed reporting the presence or absence of renal arteriosclerosis. CONCLUSION: Renal arteriosclerosis is accelerated by 2 decades in patients with LN compared to their healthy peers and is overlooked by pathologists in half of the routine biopsy reports. We propose incorporating Banff renal arteriosclerosis grading in all LN biopsy reports.

B-cell Deficiency Attenuates Transplant Glomerulopathy in a Rat Model of Chronic Active Antibody-mediated Rejection.

BACKGROUND: Transplant glomerulopathy (TG) is a pathological feature of chronic active antibody-mediated rejection (cAMR) and is associated with renal allograft failure. The specific role of B cells in the pathogenesis of TG is unclear. METHODS: We used a minor mismatched rat kidney transplant model with B cell-deficient recipients, generated by clustered regularly interspaced short palindromic repeats/Cas9 technology, to investigate the impact of B-cell depletion on the pathogenesis of TG. We hypothesized that B-cell deficiency would prevent TG in the rat kidney transplant model of cAMR. Treatment groups included syngeneic, allogeneic, sensitized allogeneic, and B cell-deficient allogeneic transplant recipients. RESULTS: B cell-deficient recipients demonstrated reduced TG lesions, decreased microvascular inflammation, reduced allograft infiltrating macrophages, and reduced interferon gamma transcripts within the allograft. Allograft transcript levels of interferon gamma, monocyte chemoattractant protein-1, and interleukin-1ß correlated with numbers of intragraft macrophages. B cell-deficient recipients lacked circulating donor-specific antibodies and had an increased splenic regulatory T-cell population. CONCLUSIONS: In this model of cAMR, B-cell depletion attenuated the development of TG with effects on T cell and innate immunity.

Leucine rich repeats and calponin homology domain containing 1 inhibits NK-92 cell cytotoxicity through attenuating Src signaling.

NK-92 cell line has been used as anti-tumor cytotoxic effector cells in immunotherapy. Leucine-rich repeats and calponin homology domain containing 1 (LRCH1) is a novel gene of which the function is unclear. In the present study, we investigated the role of LRCH1 in NK-92 cell cytotoxicity. LRCH1 was ablated in NK-92 cells through CRISP-Cas9-mediated knockout. LRCH1 knockout did not influence the basal behavior of NK-92 cells such as cell survival, expression of natural cytotoxicity receptors, and proliferation. However, upon the contact with tumor cells, LRCH1 knockout promoted NK-92 cell cytotoxicity to tumor cells. Besides, LRCH1 knockout increased the production of cytotoxic mediators such as IFN-γ, TNF-α, IL-2, and granzyme B in NK-92 cells after tumor cell contact. Similarly, LRCH1 knockout increased the production of cytokines and granzyme B upon NKp30 engagement. Further experiments revealed that LRCH1 knockout enhanced the activation of Src and Lck kinase which are important for natural killer cell cytotoxicity. The in vivo assay confirmed the up-regulation of the tumoricidal activity of LRCH1-/- NK-92 cells, as demonstrated by more robust tumor cell killing. Importantly, human primary natural killer cells exhibited a similar increase in the production of IFN-γ and TNF-α when LRCH1 was knocked out. In conclusion, our study revealed the role of LRCH1 as a negative regulator of NK-92 cell cytotoxicity.

Eszopiclone for the treatment of primary insomnia: a systematic review and meta-analysis of double-blind, randomized, placebo-controlled trials.

STUDY OBJECTIVE: In this study, we performed a systematic review and meta-analysis of double-blind, randomized, placebo-controlled trials to evaluate the efficacy and safety of eszopiclone for the treatment of primary insomnia. METHODS: We searched MEDLINE, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials and PubMed from inception to June 2018. Additionally, we searched the ClinicalTrials.gov trials register for other relevant trials. According to participants, intervention, comparison, outcome (PICO) criteria, studies were included that focused on: adults diagnosed with primary insomnia, aged 18-65 and > 65 years; eszopiclone for the treatment of primary insomnia; comparison were made between eszopiclone and placebo; as well as primary outcomes, secondary outcomes, and adverse effects. RESULTS: A total of six randomized trials involving 2809 patients with primary insomnia were included in our analysis. Our analysis suggested that eszopiclone was associated with significant improvements in subjective sleep latency, wake after sleep onset, number of awakenings, total sleep time at one week, two weeks, one month, three months and six months. Meanwhile, eszopiclone was associated with increased quality of sleep, ability to function, daytime alertness and sense of physical well-being at one week, one month, three months and six months. Dizziness and unpleasant taste were the most common adverse effects in elderly subgroup. Alternately, non-elderly patients may be more prone to adverse effects such as infection, pharyngitis, somnolence, unpleasant taste and dry mouth. CONCLUSION: This meta-analysis showed that eszopiclone is an effective and safe therapy option for patients with primary insomnia, especially in elderly patients. However, due to the high clinical heterogeneity in some outcomes, further standardized preparation, large-scale and rigorously designed trials are needed.

Association and clinical implication of the USP10 and MSH2 proteins in non-small cell lung cancer.

Ubiquitin-specific protease 10 (USP10) is involved in a number of biological processes by stabilizing several proteins, which have been implicated in multiple stages of tumorigenesis and progression. Previous studies have indicated that USP10 stabilizes and deubiquitinates MutS homolog 2 (MSH2) in in vitro and in vivo models. The level of MSH2 protein has been positively correlated with that of the USP10 protein in a panel of lung cancer cell lines. Furthermore, depletion of USP10 in lung cancer cells causes decreased apoptosis and increased cell survival upon treatment with DNA-damaging agents. However, the expression and clinical implication of USP10 protein in lung cancer tissues is not clear. Additionally, whether the level of MSH2 protein is positively correlated with that of the USP10 protein in lung cancer tissues also remains unresolved. Therefore, USP10 protein expression was detected in 148 human non-small cell lung cancer (NSCLC) and 139 non-cancerous lung tissues using immunohistochemistry, whereas mRNA was investigated by Gene Expression Omnibus dataset and The Cancer Genome Atlas database analyses. It was identified that USP10 protein expression was significantly downregulated in NSCLC tissues compared with in normal lung tissues (P<0.05). However, no significant difference in USP10 mRNA expression between the two tissues was identified. In addition, a positive correlation was observed between the USP10 and MSH2 proteins in NSCLC tissues (P<0.05). However, the clinicopathological features and survival analysis indicated that the USP10 and MSH2 proteins were not associated with clinical features, including age, sex, tumor size, Tumor-Node-Metastasis stage and tumor cell differentiation, along with the prognosis of NSCLC. Collectively, these results suggest that downregulation of USP10 protein serves an important function in the tumorigenesis of NSCLC, and the level of USP10 protein is positively correlated with that of MSH2 protein in NSCLC tissues, which may indicate that USP10 also stabilizes the MSH2 protein in patients with lung cancer.

Kbtbd2 inhibits the cytotoxic activity of immortalized NK cells through down-regulating mTOR signaling in a mouse hepatocellular carcinoma model.

Natural killer cell (NK cell)-based immunotherapy is a promising therapeutic strategy for hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying the regulation of NK cell function in the tumor sites are not completely elucidated. In this study, we identified the enhanced expression of kelch repeat and BTB (POZ) domain containing 2 (Kbtbd2) in intratumoral NK cells in a mouse HCC implantation model as a negative regulator of NK cells. To investigate this interaction, we used a Tet-on inducible expression system to control Kbtbd2 expression in an immortalized mouse NK cell line KIL C.2. With this approach, we found that overexpression of Kbtbd2 reduced KIL C.2 cell proliferation, decreased expression certain of Ly49 receptor family members, and substantially impaired cytotoxic activity of KIL C.2 cells in vitro. Moreover, phosphorylation of mTOR and its target 4E-binding protein 1 was reduced in Kbtbd2-expressing KIL C.2 cells, along with down-regulated phosphorylation of Erk1/2. Adoptively transferred Kbtbd2-expressing KIL C.2 cells exhibited weaker tumoricidal effect on hepatocellular carcinoma cells in the HCC implantation model, in comparison with transferred control KIL C.2 cells. Taken together, our investigation indicates that Kbtbd2 is an inhibitory molecule for the tumoricidal activity of KIL C.2 cells and perhaps intratumoral NK cells.