Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12–15 months, and the 5 years survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival-related genes were found among these DEGs ( p < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Moreover, with the assistance of GEPIA http://gepia.cancer-pku.cn/ , all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.

Tumor mutation burden (TMB) is a useful biomarker to predict prognosis and the efficacy of immune checkpoint inhibitors (ICIs). In this study, we aimed to explore the prognostic value of TMB and the potential association between TMB and immune infiltration in lower-grade gliomas (LGGs). Somatic mutation and RNA-sequencing (RNA-seq) data were downloaded from the Cancer Genome Atlas (TCGA) database. TMB was calculated and patients were divided into high- and low-TMB groups. After performing differential analysis between high- and low-risk groups, we identified six hub TMB and immune-related genes that were correlated with overall survival in LGGs. Then, Gene Set Enrichment Analysis was performed to screen significantly enriched GO terms between the two groups. Moreover, an immune-related risk score system was developed by LASSO Cox analysis based on the six hub genes and was validated with the Chinese Glioma Genome Atlas dataset. Using the TIMER database, we further systematically analyzed the relationships between mutants of the six hub genes and immune infiltration levels, as well as the relationships between the immune-related risk score system and the immune microenvironment in LGGs. The results showed that TMB was negatively correlated with OS and high TMB might inhibit immune infiltration in LGGs. Furthermore, the risk score system could effectively stratify patients into low- and high-risk groups in both the training and validation datasets. Multivariate Cox analysis demonstrated that TMB was not an independent prognostic factor, but the risk score was. Higher infiltration of immune cells (B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells) and higher levels of immune checkpoints (PD-1, CTLA-4, LAG-3, and TIM-3) were found in patients in the high-risk group. Finally, a novel nomogram model was constructed and evaluated to estimate the overall survival of LGG patients. In summary, our study provided new insights into immune infiltration in the tumor microenvironment and immunotherapies for LGGs.

Background Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression. Methods We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers. Results We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells. Conclusions In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.

Background Streptococcus suis meningoencephalitis is a zoonotic disease that mostly infects slaughterhouse workers. Rapid diagnosis of Streptococcus suis meningoencephalitis is critical for effective clinical management of this condition. However, the current diagnostic techniques are not effective for early diagnosis of this condition. To the best of our knowledge, the use of cerebrospinal fluid metagenomic next generation sequencing in the diagnosis of Streptococcus suis meningoencephalitis has been rarely reported. Case presentation Here, we report a case of Streptococcus suis meningoencephalitis in a 51-year-old female patient. The patient had a history of long-term contact with pork and had a three-centimeter-long wound on her left leg prior to disease onset. Conventional tests, including blood culture, gram staining and cerebrospinal fluid culture, did not reveal bacterial infection. However, Streptococcus suis was detected in cerebrospinal fluid using metagenomic next generation sequencing. Conclusions Metagenomic next generation sequencing is a promising approach for early diagnosis of central nervous system infections. This case report indicates that cases of clinical meningeal encephalitis of unknown cause can be diagnosed through this method.

Methyltransferase-like 7B (METTL7B) is a member of the methyltransferase-like protein family that plays an important role in the development and progression of tumors. However, its prognostic value and the correlation of METTL7B expression and tumor immunity in some cancers remain unclear. By analyzing online data, we found that METTL7B is abnormally overexpressed in multiple human tumors and plays an important role in the overall survival (OS) of patients with 8 cancer types and disease-free survival (DFS) of patients with 5 cancer types. Remarkably, METTL7B expression was positively correlated with the OS and DFS of patients with lower-grade glioma (LGG). In addition, a positive correlation between METTL7B expression and immune cell infiltration in LGG was observed. Moreover, we identified a strong correlation between METTL7B expression and immune checkpoint gene expression in kidney chromophobe (KICH), LGG and pheochromocytoma and paraganglioma (PCPG). Furthermore, METTL7B was involved in the extracellular matrix (ECM) and immune-related pathways in LGGs. Finally, in vitro experiments showed that knockdown of METTL7B inhibited the growth, migration, invasion and the epithelial–mesenchymal transition (EMT) of LGG cells. METTL7B expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in LGG.

BackgroundThe relationship between smoking and depression remains controversial. This study aimed to investigate the association between smoking and depression from three aspects: smoking status, smoking volume, and smoking cessation.MethodsData from adults aged ≥20 who participated in the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018 were collected. The study gathered information about the participants' smoking status (never smokers, previous smokers, occasional smokers, daily smokers), smoking quantity per day, and smoking cessation. Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9), with a score ≥10 indicating the presence of clinically relevant symptoms. Multivariable logistic regression was conducted to evaluate the association of smoking status, daily smoking volume, and smoking cessation duration with depression.ResultsPrevious smokers [odds ratio (OR) = 1.25, 95% confidence interval (CI): 1.05–1.48] and occasional smokers (OR = 1.84, 95% CI: 1.39–2.45) were associated with a higher risk of depression compared with never smokers. Daily smokers had the highest risk of depression (OR = 2.37, 95% CI: 2.05–2.75). In addition, a tendency toward a positive correlation was observed between daily smoking volume and depression (OR = 1.65, 95% CI: 1.24–2.19) (P for trend &lt; 0.05). Furthermore, the longer the smoking cessation duration, the lower the risk of depression (OR = 0.55, 95% CI: 0.39–0.79) (P for trend &lt; 0.05).ConclusionsSmoking is a behavior that increases the risk of depression. The higher the smoking frequency and smoking volume, the higher the risk of depression, whereas smoking cessation is associated with decreased risk of depression, and the longer the smoking cessation duration, the lower the risk of depression.

As a novel member of the Ras family, ERas, found in murine embryonic stem (ES) cells in 2003, was considered a pseudogene. To date, there are a few reports on the relationship between ERas and tumors. It was recently suggested that ERas could affect gastric carcinoma (GC) metastasis, but no significant relationship was found with tumor proliferation. Since ERas plays an important role in tumor-like growth of ES cells subcutaneously injected into nude mice, we hypothesized that ERas plays a role in tumor proliferation. In this experiment, we selected 7 GC strains from different sources with different differentiation degrees, we detected the expression of full length ERas transcript, and selected two ERas highly expressing GC strains, MKN-28 and BGC-823. After knocking down the ERas gene by siRNA, we observed that there was a significant decrease in proliferation, metastasis as well as clonality. Therefore, ERas is confirmed to be an important gene in affecting tumor proliferation and metastasis. Furthermore, the significance of the ERas mechanism and signaling pathway is shown.

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p &lt; 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.