Search

Ehud Zigmond,1,* Oshrat Tayer-Shifman,1,* Gadi Lalazar,1 Ami Ben Ya'acov,1 Sarah Weksler-Zangen,2 David Shasha,1 Miriam Sklair-Levy,3 Lidya Zolotarov,1 Zvi Shalev,1 Rony Kalman,2 Ehud Ziv,2 Itamar Raz,2 Yaron Ilan1 1Liver Unit, 2Diabetes Unit, 3Department of Radiology, Hebrew University-Hadassah Medical Center, Jerusalem, Israel *These authors contributed equally to this workAbstract: Liver steatosis is a common characteristic of obesity and type 2 diabetes, and fatty liver disease is increasingly recognized as a major health burden. Accumulating evidence suggests that β-glycosphingolipids play an important role in insulin sensitivity and thus could affect hepatic steatosis. To determine the effect associated with β-glycosphingolipid-mediated amelioration of liver injury, seven groups of Psammomys obesus on a high-energy diet were studied. Animals were treated with daily injections of β-glucosylceramide, β-lactosylceramide, or a combination of both. β-glycosphingolipids ameliorated the hepatic injury manifested by decreased liver enzymes, liver weight, and hepatic fat, and improved liver histology. Administration of both β-glucosylceramide and β-lactosylceramide also decreased interferon (IFN)-γ serum levels. These effects were associated with improved serum cholesterol and triglyceride levels. These data suggest that β-glycosphingolipids ameliorate liver injury in an animal model of nonalcoholic steatohepatitis.Keywords: NAFLD, glycolipids, STAT, NASH, insulin resistance, diabetes

IntroductionDespite popular speculation that nostalgic social media use skyrocketed during the COVID-19 pandemic, this has yet to be formally investigated in the scientific literature.MethodsInterrupted time series analysis (ITSA) using a segmented regression model was performed to examine the changes in the weekly volume of searches for nostalgic songs on Douyin (the Chinese version of TikTok), as a proxy for nostalgic social media use, before and after the lockdown of Wuhan (signaled the start of the pandemic on a national scale in China).ResultsAcross the study period (January 1, 2019–February 28, 2021), an immediate and significant increase in nostalgic social media use was observed when the pandemic initially started (95% CI = [47314.30, 154969.60], p

Cytotoxic CD8+ T lymphocytes (CTLs) have been implicated in the severity of COVID-19. The TCR-pMHC ternary complex, formed by the T cell receptor (TCR) and peptide-MHC (major histocompatibility complex), constitutes the molecular basis of CTL responses against SARS-CoV-2. While numerous studies have been conducted on T cell immunity, the molecular mechanisms underlying CTL-mediated immunity against SARS-CoV-2 infection have not been well elaborated. In this review, we described the association between HLA variants and different immune responses to SARS-CoV-2 infection, which may lead to varying COVID-19 outcomes. We also summarized the specific TCR repertoires triggered by certain SARS-CoV-2 CTL epitopes, which might explain the variations in disease outcomes among different patients. Importantly, we have highlighted the primary strategies used by SARS-CoV-2 variants to evade T-cell killing: disrupting peptide-MHC binding, TCR recognition, and antigen processing. This review provides valuable insights into the molecule mechanism of CTL responses during SARS-CoV-2 infection, aiding efforts to control the pandemic and prepare for future challenges.

Walnut (Juglans regia L.) is a widely grown nut plant worldwide, including in Guizhou Province, located in southwest China. The high quality and special taste make Guizhou walnuts, particularly those produced in Hezhang County, a “Chinese National Geographical Indication Product” that substantially contributes to the local economy and grower’s income. In July 2022, a serious occurrence of leaf spot disease was observed in a walnut plantation area, Shuitang Town, Hezhang County, Guizhou Province, China (27°07′67″N, 104°64′61″E). The causal agent was identified as Didymella segeticola through morphological characterization and amplification and sequencing of the internal transcribed spacer (ITS) region, beta-tubulin (TUB) gene, and glyceraldehyde-3-phosphate dehydrogenase (G3PD) gene. Koch’s postulates, including re-isolation and identification, were performed to confirm its pathogenicity on healthy leaves. To our knowledge, this is the first report of D. segeticola causing leaf spot on walnuts worldwide. Further, to determine its biological characteristics, which could be utilized for future disease management, the effects of temperature, light, and carbon and nitrogen resources on mycelial growth, conidia production, and conidia germination and the effects of humidity on conidia germination were studied. The optimum temperature for mycelial growth of representative strain D. segeticola C27 was 20°C. Increasing the light period significantly decreased conidia production and conidia germination. Maltose and beef extract were the best carbon and nitrogen sources, respectively, for the pathogen. Conidia germination was enhanced at 90% humidity. In vitro screening of effective fungicides was conducted. Among the 20 screened fungicides, difenoconazole showed the best inhibition rate, with an EC50 (concentration for 50% of the maximal effect) of 0.0007 μg/mL. Tetramycin also showed sufficient inhibitory effects against D. segeticola, with an EC50 value of 0.0009 μg/mL. Our study provides new insights into the causal agent of walnut leaf spot in Guizhou, China, as well as the first pathogen characteristics and promising candidate fungicides for its management.

ObjectiveThe long-term glucose monitoring is essential to the risk assessment of diabetic retinopathy (DR), the aim of this study was to investigate the predictive ability of visit-to-visit fasting blood glucose (FBG) indices on the risk of DR.MethodsThis was a community-based, cohort study conducted from 2013 to 2021. DR was diagnosed by digital fundus photography. The FPG indices included FBG, var. Associations of each FBG indices and DR were estimated using multinomial logistic regression models adjusting for confounders, and discrimination was determined by area under the curve (AUC). Predictive utility of different models was compared by changes in AUC, integrated discrimination improvement (IDI), and net reclassification index (NRI).ResultsThis study analyzed 5054 participants, the mean age was 46.26 ± 11.44 years, and 2620 (51.84%) were women. After adjustment for confounders, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for FBG, SD, CV, VIM, ARV, M-FBG, and cumulative FBG load were 1.62 (1.52—1.73), 2.74 (2.38—3.16), 1.78 (1.62—1.95), 1.11 (0.95—1.29), 1.72 (1.56—1.91), 2.15 (1.96—2.36), and 2.57 (2.31—2.85), respectively. The AUC of the model with separate cumulative FBG load and classical risk factors was 0.9135 (95%CI 0.8890—0.9380), and no substantive improvement in discrimination was achieved with the addition of other FBG indices once cumulative FBG load was in the model.ConclusionsCumulative FBG load is adequate for capturing the glucose-related DR risk, and the predictive utility of cumulative FBG load is not significantly improved by adding or replacing other FBG indices in the assessment of DR risk.

Abstract NLRP inflammasomes are a group of cytosolic multiprotein oligomer pattern recognition receptors (PRRs) involved in the recognition of pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs) produced by infected cells. They regulate innate immunity by triggering a protective inflammatory response. However, despite their protective role, aberrant NLPR inflammasome activation and gain-of-function mutations in NLRP sensor proteins are involved in occurrence and enhancement of non-communicating autoimmune, auto-inflammatory, and neurodegenerative diseases. In the last few years, significant advances have been achieved in the understanding of the NLRP inflammasome physiological functions and their molecular mechanisms of activation, as well as therapeutics that target NLRP inflammasome activity in inflammatory diseases. Here, we provide the latest research progress on NLRP inflammasomes, including NLRP1, CARD8, NLRP3, NLRP6, NLRP7, NLRP2, NLRP9, NLRP10, and NLRP12 regarding their structural and assembling features, signaling transduction and molecular activation mechanisms. Importantly, we highlight the mechanisms associated with NLRP inflammasome dysregulation involved in numerous human auto-inflammatory, autoimmune, and neurodegenerative diseases. Overall, we summarize the latest discoveries in NLRP biology, their forming inflammasomes, and their role in health and diseases, and provide therapeutic strategies and perspectives for future studies about NLRP inflammasomes.

The survey highlights that most enterprise digital transformation (DT) projects failed to reach the expected outcomes. This is often due to a limited understanding of DT’s value and its creation mechanism (VM). The purpose of this article is to compile a list of variables that affect DT’s VM and assess the VM factors according to causal links, thereby further understanding of the intricate connections among VM factors. This article adopts a mixed approach called “CS: GT-DEMATEL-ISM” which incorporates the concepts of case study (CS), grounded theory (GT), decision experimentation and evaluation laboratory (DEMATEL), and interpretative structural modeling (ISM). Through GT, 32 concepts related to the DT’s VM system were extracted and summarized into 15 VM elements, which further converged into 7 categories: IT, data, process, organization, business, business mode, and values. Using DEMATEL and ISM, DT’s VM system model is established, it shows digital technology enables the reconstruction and evolution of data, processes, organizations, and business, thereby establish, or optimize business models, ultimately improve enterprise capabilities and performance. Three main aspects make up research contributions. In terms of methodology, this study suggests that using a mixed approach known as “CS: GT-DEMATEL-ISM” to identify and examine the factors of complex systems. In terms of theoretical and practical contribution, this article is the first to systematically identify DT’s values and build the framework of DT’s VM system, which fills the gap in DT-related theory and would help enterprise management for DT planning and DT project implementation in DT practice.

A growing number of clinical and animal studies suggest that the nucleus accumbens (NAc), especially the shell, is involved in the pathogenesis of temporal lobe epilepsy (TLE). However, the role of parvalbumin (PV) GABAergic neurons in the NAc shell involved in TLE is still unclear. In this study, we induced a spontaneous TLE model by intrahippocampal administration of kainic acid (KA), which generally induce acute seizures in first 2 h (acute phase) and then lead to spontaneous recurrent seizures after two months (chronic phase). We found that chemogenetic activation of NAc shell PV neurons could alleviate TLE seizures by reducing the number and period of focal seizures (FSs) and secondary generalized seizures (sGSs), while selective inhibition of PV exacerbated seizure activity. Ruby-virus mapping results identified that the hippocampus (ventral and dorsal) is one of the projection targets of NAc shell PV neurons. Chemogenetic activation of the NAc-Hip PV projection fibers can mitigate seizures while inhibition has no effect on seizure ictogenesis. In summary, our findings reveal that PV neurons in the NAc shell could modulate the seizures in TLE via a long-range NAc-Hip circuit. All of these results enriched the investigation between NAc and epilepsy, offering new targets for future epileptogenesis research and precision therapy.

Epithelial–mesenchymal transition (EMT) refers to the transformation of polar epithelial cells into motile mesenchymal cells under specific physiological or pathological conditions, thus promoting the metastasis of cancer cells. Epithelial cadherin (E-cadherin) is a protein that plays an important role in the acquisition of tumor cell motility and serves as a key EMT epithelial marker. In the present study, AW01178, a small-molecule compound with potential therapeutic efficacy, was identified via in-cell Western high-throughput screening technology using E-cadherin as the target. The compound induced the upregulation of E-cadherin at both mRNA and protein levels and inhibited the EMT of breast cancer cells in vitro as well as metastasis in vivo. Mechanistically, AW01178 is a novel benzacetamide histone deacetylase inhibitor (HDACi) mainly targeting class I histone deacetylases. AW01178 promoted the transcription and expression of E-cadherin through enhancing the acetylation level of histone H3 in the E-cadherin promoter region, thereby inhibiting the metastasis of breast cancer cells. The collective findings support the potential utility of the novel HDACi compound identified in this study, AW01178, as a therapeutic drug for breast cancer and highlight its value for the future development of HDACi structures as anticancer drugs.

Objective: This study aims to identify the periodontitis factor that activates excessive autophagy in pancreatic β cells, resulting in organic lesions of pancreatic islet tissues and diminished insulin secretion, thereby accelerating the progression of diabetes mellitus (DM). Methods: Sprague-Dawley (SD) rats were induced with periodontitis (PD), type 2 diabetes mellitus (T2DM), or the combination of T2DM and PD (DP) through a high-sugar/high-fat diet and ligation of the tooth neck with silk thread. Alveolar bone resorption was assessed using Micro-CT, blood glucose levels were measured with a blood glucose meter, pancreatic tissue pathology was examined through HE staining, and the expression of autophagy-related proteins Beclin1 and LC3II/LC3I was analyzed using Western blotting. Results: Micro-CT results revealed more pronounced alveolar bone resorption and root bifurcation exposure in the PD and DP groups compared to the control group, with the DP group exhibiting the most severe condition. HE staining demonstrated the formation of periodontal pockets, severe alveolar bone destruction, and abnormal pancreatic islet tissue morphology in the PD and DP groups. The serum levels of IL-6, TNF-α, and IL-1β increased sequentially in the control, DM, PD, and DP groups (P 0.05), while the mRNA expressions in the DP and DM groups increased (P

Since China entered the aging society, the surging demand for elderly care and the industrial upgrading of “silver economy” has forced the domestic service industry to face endogenous challenges. Among them, the formalization of the domestic service industry can effectively reduce the transaction costs and risks of actors, innovate the endogenous vitality of the industry, and promote the improvement of elderly care quality through a triangular employment relationship. By constructing a tripartite asymmetric evolutionary game model of clients, domestic enterprises and governmental departments, this study uses the stability theorem of differential equations to explore the influencing factors and action paths of the system's evolutionary stable strategies (ESS), and uses the research data collected from China to assign values to models for simulation analysis. This study finds that the ratio of the initial ideal strategy, the difference between profits and costs, subsidies to clients, and subsidies or punishments for breach of contract to domestic enterprises are the key factors affecting the formalization of the domestic service industry. Subsidy policy programs can be divided into long-term and periodic programs, and there are differences in the influence paths and effects of the key factors in different situations. Increasing domestic enterprises' market share with employee management systems, formulating subsidy programs for clients, and setting up evaluation and supervision mechanisms are efficient ways through which to promote the formalization of the domestic service industry in China. Subsidy policy of governmental departments should focus on improving the professional skills and quality of elderly care domestic workers, and also encourage domestic enterprises with employee management systems at the same time, to expand the scope of service beneficiaries by running nutrition restaurants in communities, cooperating with elderly care institutions, etc.

In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.

Data loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss. In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology. In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations. Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms.

The consistency problem of multiagent systems with the output feedback and state delay was considered in this paper. First, the reduced-order observer based on the consensus protocol of state delay is designed, and the consensus protocol is proposed by the output information of neighboring agents. Then, by constructing the Lyapunov–Krasovskii functional and combining matrix inequality and the Schur complement lemma, the asymptotic stability of the system can be obtained, and the consistency of state-delay multiagent systems is derived. Finally, the theoretical results are verified by a simulation example.

For the problem that the conventional oil pipeline detection system cannot effectively detect the leakage of the two-phase flow pipelines, a leakage identification method based on the acoustic emission signal was proposed for the gasliquid two-phase flow pipelines. According to the basic principles of acoustic emission detection technology, the leakage detection tests were carried out for the three flow patterns of laminar flow, slug flow and annular flow at a gas pressure of 0.1 to 0.4 MPa in three different orientations of leak holes with two different diameters. The main signal components extracted by wavelet packet decomposition combined with local mean decomposition were taken as the input of pattern recognition, and the BP artificial neural network pattern recognition was also performed. The test results show that the average accuracy of the leakage identification of the three two-phase flow pipelines of laminar flow, slug flow and annular flow is 83.5%, which is high in accuracy. Further, the proposed method has a reference value for the leakage detection of gas-liquid twophase flow pipelines.

The NLRP1 inflammasome functions as canonical cytosolic sensor in response to intracellular infections and is implicated in auto-inflammatory diseases. But the regulation and signal transduction mechanisms of NLRP1 are incompletely understood. Here, we show that the T60 variant of CARD8, but not the canonical T48 isoform, negatively regulates the NLRP1 inflammasome activation by directly interacting with the receptor molecule NLRP1 and inhibiting inflammasome assembly. Furthermore, our results suggest that different ASC preference in three types of inflammasomes, namely the ASC-indispensable NLRP1 inflammasome, ASC-dispensable mNLRP1b inflammasome and ASC-independent CARD8 inflammasome, is mainly caused by the CARD domain, not the UPA subdomain. Based on the systematic site-directed mutagenesis and structural analysis, we find that signal transduction of the NLRP1 inflammasome relies on multiple interaction surfaces at its CARD domain. Finally, our results partly explain how mutations in NLRP1 lead to its constitutive activation in auto-inflammatory diseases. In conclusion, our study not only reveals how CARD8 downregulates the NLRP1 inflammasome activation, but also provides insights into the assembly mechanisms of CARD-containing inflammasomes.

Slow lorises are small arboreal and nocturnal primates. Due to the illegal trade, a large number of slow lorises were confiscated into wildlife sanctuaries or rescue centers. The re-release has been considered a preferable approach for alleviating the captive pressure, but inappropriate and long-term confinement make it difficult to achieve this goal. In this study, we investigated and compared the fecal and oral microbiome of Bengal slow lorises (Nycticebus bengalensis) under long-term captivity (LC) and short-term captivity (SC) groups based on 16s rRNA high-throughput gene sequencing. The oral microbiome displayed higher Chao1 richness but lower Shannon and Simpson indices than the fecal microbiome. The Bengal slow lorises under long-term captivity had abundant pathogenic genera in both gut and oral microbiomes, such as Desulfovibrio, Actinomyces, Capnocytophaga, Neisseria, and Fusobacterium, while some specific bacterial taxa associated with intestinal balance were more enriched in the SC group. Due to the plant gum scarcity in the diet, both groups had a low abundance of Bifidobacterium. Function profile prediction indicated that the LC group was enriched with genetic information processing and metabolism pathways due to the stable food intake. The increased membrane transport and xenobiotic metabolism and degradation functions in the SC group could be explained by the function of the host microbiome in facilitating adaptation to changing environments and diets. The results demonstrated that the oral microbiome had the potential to be used as a regular surveillance tool. Also, current captive management should be improved to ensure reintroduction success.

The classical Kalman filter is a very important state estimation approach, which has been widely used in many engineering applications. The Kalman filter is optimal for linear dynamic systems with independent Gaussian noises. However, the independence and Gaussian assumptions may not be satisfied in practice. On the one hand, modeling physical systems usually results in discrete-time state-space models with correlated process and measurement noises. On the other hand, the noise is non-Gaussian when the system is disturbed by heavy-tailed noise. In this case, the performance of the Kalman filter will deteriorate, or even diverge. This paper is devoted to addressing the state estimation problem of linear dynamic systems with high-order autoregressive moving average (ARMA) non-Gaussian noise. First, a triplet Markov model is introduced to model the system with high-order ARMA noise, since this model relaxes the independence assumption of the hidden Markov model. Then, a new filter is derived based on correntropy, instead of the commonly used minimum mean square error (MMSE), to deal with non-Gaussian noise. Unlike the MMSE, which uses only second-order statistics of error, correntropy can capture second-order and higher-order statistics. Finally, simulation results verify the effectiveness of the proposed algorithm.

Plant spacing usually refers to distances between plants within and between rows in the field. Different spacing in crop planting would generally influence the size, plant architecture, economic productivity, etc. The present research provided a time course monitoring of stem development in tobacco with different plant spacing. The result showed that cambium activity, vascular bundle thickness, lignin, cellulose, and hemicellulose content, as well as the macronutrient deposition in the stem varied because of the different plant spacing. Furthermore, the genes (NtHB8s and NtNST3s) coding the homologs of HB8 and NST3 transcription factors, which are involved in plant secondary growth, were cloned in tobacco. In the time course, they also indicated diverse expression patterns among altered plant-spacing treatments. Their transcriptomic activities were validated, and the motifs that might bind transcription factors in their promoter regions were predicted. Promoters of NtHB8s and NtNST3s genes were rich in light-response elements; as a result, light might be the main environmental factor in plant spacing to regulate stem secondary growth.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.

Mastitis results in great economic loss to the dairy goat industry. Many approaches have attempted to decrease the morbidity associated with this disease, and among these, transgenic strategy have been recognized as a potential approach. A previous mammalian study reports that interferon-gamma (IFN-γ) has potential anti-bacterial bioactivity against infection in vitro; however, its capacity in vivo is ambiguous. In this study, we initially constructed targeting and homologous recombination vectors (containing the IFN-γ gene) and then transferred the vectors into goat mammary gland epithelial cells (GMECs). Enzyme digestion and sequencing analysis indicated that the vectors used in this study were built correctly. Subsequently, monoclonal cells were selected using puromycin and the polymerase chain reaction (PCR) test indicated that IFN-γ was correctly inserted downstream of the casein promoter. Monoclonal cells were then assessed for reducible expression, and reverse transcriptase-PCR (RT-PCR) and Western blot tests confirmed that monoclonal cells could express IFN-γ. Finally, anti-bacterial capacity was evaluated using bacterial counts and flow cytometry analysis. Decreased bacterial counts and cell apoptosis rates in transgenic GMECs demonstrated that the secretion of IFN-γ could inhibit bacterial proliferation. Therefore, IFN-γ gene transfection in goat mammary epithelial cells could inhibit bacterial proliferation and reduce the risk of mammary gland infection in goats.

Glioma is the most common malignancy of the nervous system with high mortality rates. The MS4A family members have been reported as potential prognostic biomarkers in several cancers; however, the relationship between the MS4A family and glioma has not been clearly confirmed. In our study, we explored the prognostic value of MS4As as well as their potential pro-cancer mechanisms of glioma. Using bioinformatics analysis methods based on the data from public databases, we found that the expression of MS4A4A, MS4A4E, MS4A6A, MS4A7, TMEM176A, and TMEM176B was significantly overexpressed in glioma tissues compared with that of normal tissues. The Kaplan–Meier method and Cox proportional hazards models revealed that high levels of MS4As can be associated with a poorer prognosis; TMEM176A, TMEM176B, age, WHO grade, and IDH status were identified as independent prognostic factors. Enrichment analysis predicted that MS4As were related to tumor-related pathways and immune response, which might regulate the process of MS4As promoting tumorigenesis. Additionally, we analyzed the correlations of MS4A expression with immune cells and immune inhibitory molecules. Finally, data from the cell culture suggested that knockdown of the TMEM176B gene contributes to the decreased proliferation and migration of glioma cells. In conclusion, MS4A4A, MS4A4E, MS4A6A, MS4A7, TMEM176A, and TMEM176B may act as potential diagnostic or prognostic biomarkers in glioma and play a role in forming the immune microenvironment in gliomas.