The causal effect of air pollution on the risk of essential hypertension: a Mendelian randomization study.

Background: Air pollution poses a major threat to human health by causing various illnesses, such as cardiovascular diseases. While plenty of research indicates a correlation between air pollution and hypertension, a definitive answer has yet to be found. Methods: Our analyses were performed using the Genome-wide association study (GWAS) of exposure to air pollutants from UKB (PM2.5, PM10, NO2, and NOX; n = 423,796 to 456,380), essential hypertension from FinnGen (42,857 cases and 162,837 controls) and from UKB (54,358 cases and 408,652 controls) as a validated cohort. Univariable and multivariable Mendelian randomization (MR) were conducted to investigate the causal relationship between air pollutants and essential hypertension. Body mass index (BMI), alcohol intake frequency, and the number of cigarettes previously smoked daily were included in multivariable MRs (MVMRs) as potential mediators/confounders. Results: Our findings suggested that higher levels of both PM2.5 (OR [95%CI] per 1 SD increase in predicted exposure = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) and PM10 (OR [95%CI] = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) were linked to an increased risk for essential hypertension. Even though we used MVMR to adjust for the impacts of smoking and drinking on the relationship between PM2.5 exposure and essential hypertension risks, our findings suggested that although there was a direct positive connection between them, it is not present after adjusting BMI (OR [95%CI] = 1.05 [0.87-1.27], p = 6.17E-01). Based on the study, higher exposure to PM2.5 and PM10 increases the chances of developing essential hypertension, and this influence could occur through mediation by BMI. Conclusion: Exposure to both PM2.5 and PM10 is thought to have a causal relationship with essential hypertension. Those impacted by substantial levels of air pollution require more significant consideration for their cardiovascular health.

A TGF-ß signaling-related lncRNA signature for prediction of glioma prognosis, immune microenvironment, and immunotherapy response.

AIMS: The dysregulation of TGF-ß signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-ß signaling in glioma is currently unclear. METHODS: Data on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. In vitro and in vivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA. RESULTS: In glioma, a TGF-ß signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in in vitro and in vivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-ß signaling pathway. CONCLUSIONS: A prognostic lncRNA signature of TGF-ß signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.

PD-L1-related IncRNAs are associated with malignant characteristics and immune microenvironment in glioma.

BACKGROUND: The expression of long non-coding RNA (lncRNA) can function as diagnostic and therapeutic biomarker for tumors. This research explores the role of PD-L1-related lncRNAs in affecting malignant characteristics and the immune microenvironment of glioma. METHODS: Downloading gene expression profiles and clinicopathological information of glioma from TCGA and CGGA databases, 6 PD-L1-related lncRNAs were identified through correlation analysis, Cox and LASSO regression analysis, establishing the risk score model based on them. Bioinformatics analysis and cell experiments in vitro were adopted to verify the effects of LINC01271 on glioma. RESULTS: Risk scores based on 6 PD-L1-related lncRNAs (AL355974.3, LINC01271, AC011899.3, MIR4500HG, LINC02594, AL357055.3) can reflect malignant characteristics and immunotherapy response of glioma. Patients with high LINC01271 expression had a worse prognosis, a higher abundance of M1 subtype macrophages in the immune microenvironment, and a higher degree of tumor malignancy. Experiments in vitro confirmed its positive regulatory effect on the proliferation and migration of glioma cells. CONCLUSIONS: The risk score model based on 6 PD-L1-related lncRNAs can reflect the malignant characteristics and prognosis of glioma. LINC01271 can independently be used as a new target for prognosis evaluation and therapy.

Association between depression and HIV infection vulnerable populations in United States adults: a cross-sectional analysis of NHANES from 1999 to 2018.

Background: Although the government has made a commitment to advance education on HIV disclosure, depression continues to play a significant role in whether people living with HIV (PLWH) choose to disclose their HIV status to families or friends. Vulnerable populations who are at risk of contracting HIV may also be more susceptible to mental illness. However, there is a limited understanding of the association between depression and vulnerable populations affects by HIV among United States adults. We aimed to explore the incidence of depression in the HIV infection vulnerable populations and assessed the association between the HIV infection vulnerable populations and depression. Methods: We analyzed the most current statistics from the National Health and Nutrition Examination Survey (NHANES) that included 16,584 participants aged 18 years or older between 1999 and 2018. The Patient Health Questionnaire-9 (PHQ-9) was used to evaluate symptoms of depressive disorder. Demographic characteristics were compared between the HIV infection vulnerable groups and HIV infection low-risk groups. Multivariable logistic regression analysis was also carried out to evaluate the odds rate and association between the HIV infection vulnerable populations and depression. Results: Based on the most recent statistics from NHANES, HIV infection vulnerable populations were male, younger, less married or living together, non-Hispanic White people, lower income, and lower body mass index (BMI), with higher levels of cigarette smoking, alcohol drinking, a higher prevalence of depression, lower prevalence of hypertension and diabetes mellitus (DM; p < 0.05). Additionally, individuals with severe depression had a higher prevalence of cardiovascular disease (CVD), hypertension, DM, chronic kidney disease (CKD), and a higher proportion of HIV infection vulnerable populations and less married or living together (p < 0.01). Finally, the odds of depression from the logistic regression were significantly increased in HIV infection vulnerable groups (p < 0.01). Conclusion: Depression might be associated with HIV infection vulnerable populations in the United States adults. More research is needed to evaluate the association between HIV infection vulnerable populations and depression and explore their causal associations. In addition, prevention efforts focusing on HIV disclosure and HIV infection vulnerable populations in the United States should address common co-prevalent depression to reduce new HIV infections.

A novel prognostic related lncRNA signature associated with amino acid metabolism in glioma.

Background: Glioma is one of the deadliest malignant brain tumors in adults, which is highly invasive and has a poor prognosis, and long non-coding RNAs (lncRNAs) have key roles in the progression of glioma. Amino acid metabolism reprogramming is an emerging hallmark in cancer. However, the diverse amino acid metabolism programs and prognostic value remain unclear during glioma progression. Thus, we aim to find potential amino-related prognostic glioma hub genes, elaborate and verify their functions, and explore further their impact on glioma. Methods: Glioblastoma (GBM) and low-grade glioma (LGG) patients' data were downloaded from TCGA and CCGA datasets. LncRNAs associated with amino acid metabolism were discriminated against via correlation analysis. LASSO analysis and Cox regression analysis were conducted to identify lncRNAs related to prognosis. GSVA and GSEA were performed to predict the potential biological functions of lncRNA. Somatic mutation data and CNV data were further built to demonstrate genomic alterations and the correlation between risk scores. Human glioma cell lines U251 and U87-MG were used for further validation in vitro experiments. Results: There were eight amino-related lncRNAs in total with a high prognostic value that were identified via Cox regression and LASSO regression analyses. The high risk-score group presented a significantly poorer prognosis compared with the low risk-score group, with more clinicopathological features and characteristic genomic aberrations. Our results provided new insights into biological functions in the above signature lncRNAs, which participate in the amino acid metabolism of glioma. LINC01561 is one of the eight identified lncRNAs, which was adopted for further verification. In in vitro experiments, siRNA-mediated LINC01561 silencing suppresses glioma cells' viability, migration, and proliferation. Conclusion: Novel amino-related lncRNAs associated with the survival of glioma patients were identified, and a lncRNA signature can predict glioma prognosis and therapy response, which possibly has vital roles in glioma. Meanwhile, it emphasized the importance of amino acid metabolism in glioma, particularly in providing deeper research at the molecular level.

A novel inflammation-related lncRNAs prognostic signature identifies LINC00346 in promoting proliferation, migration, and immune infiltration of glioma.

In this study, a total of 13 inflammation-related lncRNAs with a high prognostic value were identified with univariate, multivariate Cox regression analysis, and LASSO analysis. LINC00346, which is one of the 13 lncRNAs identified, was positively associated with type 2 macrophage activation and the malignant degree of glioma. Fluorescence in situ hybridization (FISH) and immunohistochemical staining showed that LINC00346 was highly expressed in high-grade glioma, while type 2 macrophages key transcription factor STAT3 and surface marker CD204 were also highly expressed simultaneously. LINC00346 high-expression gliomas were more sensitive to the anti-PD-1 and anti-CTLA-4 therapy. LINC00346 was also associated with tumor proliferation and tumor migration validated by EdU, cell colony, formation CCK8, and transwell assays. These findings reveal novel biomarkers for predicting glioma prognosis and outline relationships between lncRNAs inflammation, and glioma, as well as possible immune checkpoint targets for glioma.

Corrigendum: Antigen Presentation Machinery Signature-Derived CALR Mediates Migration, Polarization of Macrophages in Glioma and Predicts Immunotherapy Response.

[This corrects the article DOI: 10.3389/fimmu.2022.833792.].

Antigen Presentation Machinery Signature-Derived CALR Mediates Migration, Polarization of Macrophages in Glioma and Predicts Immunotherapy Response.

Immunogenicity, influenced by tumor antigenicity and antigen presenting efficiency, critically determines the effectiveness of immune checkpoint inhibitors. The role of immunogenicity has not been fully elucidated in gliomas. In this study, a large-scale bioinformatics analysis was performed to analyze the prognostic value and predictive value of antigen presentation machinery (APM) signature in gliomas. ssGSEA algorithm was used for development of APM signature and LASSO regression analysis was used for construction of APM signature-based risk score. APM signature and risk score showed favorable performance in stratifying survival and predicting tumorigenic factors of glioma patients. APM signature and risk score were also associated with different genomic features in both training cohort TCGA and validating cohort CGGA. Furthermore, APM signature-based risk score was independently validated in three external cohorts and managed to predict immunotherapy response. A prognostic nomogram was constructed based on risk score. Risk score-derived CALR was found to mediate the invasion and polarization of macrophages based on the coculture of HMC3 and U251 cells. CALR could significantly predict immunotherapy response. In conclusion, APM signature and APM signature-based risk score could help promote the clinical management of gliomas.

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy.

BACKGROUND: The tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy. METHODS: The data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The Cox and LASSO regression analysis was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient's response to immunotherapy and chemotherapy, respectively. RESULTS: The prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment. CONCLUSION: An immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.

A comprehensive prognostic signature for glioblastoma patients based on transcriptomics and single cell sequencing.

PURPOSE: Glioblastoma (GBM) is the most common and deadly brain tumor. We aimed to reveal potential prognostic GBM marker genes, elaborate their functions, and build an effective a prognostic model for GBM patients. METHODS: Through data mining of The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we screened for significantly differentially expressed genes (DEGs) to calculate risk scores for individual patients. Published data of somatic mutation and copy number variation profiles were analyzed for distinct genomic alterations associated with risk scores. In addition, single-cell sequencing was used to explore the biological functions of the identified prognostic marker genes. By combining risk scores and other clinical features, we built a comprehensive prognostic GBM model. RESULTS: Seven DEGs (CLEC5A, HOXC6, HOXA5, CCL2, GPRASP1, BSCL2 and PTX3) were identified as being prognostic for GBM. Expression of these genes was confirmed in different GBM cell lines using real-time PCR. Risk scores calculated from the seven DEGs revealed prognostic value irrespective of other clinical factors, including IDH mutation status, and were negatively correlated with TP53 expression. The prognostic genes were found to be associated with tumor proliferation and progression based on pseudo-time analysis in neoplastic cells. A final prognostic model was developed and validated with a good performance, especially in geriatric GBM patients. CONCLUSIONS: Using genetic profiles, age, IDH mutation status, and chemotherapy and radiotherapy, we constructed a comprehensive prognostic model for GBM patients. The model has a good performance, especially in geriatric GBM patients.

Aging-related genes are potential prognostic biomarkers for patients with gliomas.

Aging has a significant role in the proliferation and development of cancers. This study explored the expression profiles, prognostic value, and potential roles of aging-related genes in gliomas. We designed risk score and cluster models based on aging-related genes and glioma cases using LASSO Cox regression analysis, consensus clustering analysis and univariate cox regression analyses. High risk score was related to malignant clinical features and poor prognosis based on 10 datasets, 2953 cases altogether. Genetic alterations analysis revealed that high risk scores were associated with genomic aberrations of aging-related oncogenes. GSVA analysis exhibited the potential function of the aging-related genes. More immune cell infiltration was found in high-risk group cases, and glioma patients in high-risk group may be more responsive to immunotherapy. Knock-down of CTSC, an aging-related gene, can inhibit cell cycle progression, colony formation, cell proliferation and increase cell senescence in glioma cell lines in vitro. Indeed, high expression of CTSC was associated with poor prognosis in glioma cases. In conclusion, this study revealed that aging-related genes have prognostic potential for glioma patients and further identified potential mechanisms for aging-related genes in tumorigenesis and progression in gliomas.

[Retracted] Methylation­induced downregulation and tumor-suppressive role of microRNA­98 in glioma through targeting Sal­like protein 4.

Following the publication of this paper, it was drawn to the Editors' attention by a concerned reader that certain of the cell Transwell assay data in the article (featured in Figs. 4D and 6D) were strikingly similar to data appearing in different form in other articles by different authors at different research institutions, which were already under consideration for publication or had already been published elsewhere at the time of the present article's submission. Owing to the fact that the contentious data in the above article had already appeared in different form in other articles prior to its submission to International Journal of Molecular Medicine, the Editor has decided that this paper should be retracted from the Journal. After having been in contact with the authors, they agreed with the decision to retract the paper. The Editor apologizes to the readership for any inconvenience caused. [the original article was published in International Journal of Molecular Medicine 41: 2651-2659, 2018; DOI: 10.3892/ijmm.2018.3464].

PDIA5 is Correlated With Immune Infiltration and Predicts Poor Prognosis in Gliomas.

Gliomas are the most common and lethal primary malignant tumor of the brain. Routine treatment including surgical resection, chemotherapy, and radiotherapy produced limited therapeutic effect, while immunotherapy targeting the glioma microenvironment has offered a novel therapeutic option. PDIA5 protein is the member of PDI family, which is highly expressed in glioma and participates in glioma progression. Based on large-scale bioinformatics analysis, we discovered that PDIA5 expression level is upregulated in aggressive gliomas, with high PDIA5 expression predicting poor clinical outcomes. We also observed positive correlation between PDIA5 and immune infiltrating cells, immune related pathways, inflammatory activities, and other immune checkpoint members. Patients with high PDIA5 high-expression benefited from immunotherapies. Additionally, immunohistochemistry revealed that PDIA5 and macrophage biomarker CD68 were upregulated in high-grade gliomas, and patients with low PDIA5 level experienced favorable outcomes among 33 glioma patients. Single cell RNA sequencing exhibited that PDIA5 was in high level presenting in neoplastic cells and macrophages. Cell transfection and co-culture of glioma cells and macrophages revealed that PDIA5 in tumor cells mediated macrophages exhausting. Altogether, our findings indicate that PDIA5 overexpression is associated with immune infiltration in gliomas, and may be a promising therapeutic target for glioma immunotherapy.

Identified lung adenocarcinoma metabolic phenotypes and their association with tumor immune microenvironment.

BACKGROUND: Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), causes high mortality around the world. Previous studies have suggested that the metabolic pattern of tumor is associated with tumor response to immunotherapy and patient's survival outcome. Yet, this relationship in LUAD is still unknown. METHODS: Therefore, in this study, we identified the immune landscape in different tumor subtypes classified by metabolism-related genes expression with a large-scale dataset (tumor samples, n = 2181; normal samples, n = 419). We comprehensively correlated metabolism-related phenotypes with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in LUAD patients. RESULTS: And we confirmed tumors with activated lipid metabolism tend to have higher immunocytes infiltration and better response to checkpoint immunotherapy. This work highlights the connection between the metabolic pattern of tumor and tumor immune infiltration in LUAD. A scoring system based on metabolism-related gene expression is not only able to predict prognosis of patient with LUAD but also applied to pan-cancer. LUAD response to checkpoint immunotherapy can also be predicted by this scoring system. CONCLUSIONS: This work revealed the significant connection between metabolic pattern of tumor and tumor immune infiltration, regulating LUAD patients' response to immunotherapy.

Genetic Profiles Playing Opposite Roles of Pathogenesis in Schizophrenia and Glioma.

BACKGROUND: Patients diagnosed with schizophrenia were found having lower risks to develop cancers, including glioma. Based on this epidemiology, we hypothesized that there were gene profiles playing opposite roles in pathogenesis of schizophrenia and glioma. METHODS: Based on GEO datasets and TCGA, key genes of schizophrenia genes on the opposite development of glioma were screened by different expressed genes (DEGs) screening, weighted gene coexpression network analysis (WGCNA), disease-specific survival (DSS), and glioma grading and verified by gene set enrichment analysis (GSEA). RESULTS: First, 612 DEGs were screened from schizophrenia and control brain samples. Second, 134 key genes more specific to schizophrenia were left by WGCNA, with 93 key genes having annotations in TCGA. Third, DSS of glioma helped to find 42 key gene expressions of schizophrenia oppositely associated with survival of glioma. Finally, 24 key genes showed opposite expression trends in schizophrenia and different glioma grading, i.e., the upregulated key genes in schizophrenia expressed increasingly in higher grade glioma, and vice versa. CAMK2D and MPC2 were taken as the examples and evaluated by GSEA, which indeed showed opposite trends in the same pathways of schizophrenia and glioma. CONCLUSION: This workflow of selecting novel targeted genes which may have opposite roles in pathogenesis of two diseases was firstly and innovatively generated by our team. Some filtered key genes were indeed found by their potential effects in several mechanism studies, indicating our process could be effective to generate novel targeted genes. These 24 key genes may provide potential directions for future biochemical and pharmacotherapeutic research studies.

Protein disulfide isomerases are promising targets for predicting the survival and tumor progression in glioma patients.

The present study focused on the expression patterns, prognostic values and potential mechanism of the PDI family in gliomas. Most PDI family members' mRNA expressions were observed significantly different between gliomas classified by clinical features. Construction of the PDI signature, cluster and risk score models of glioma was done using GSVA, consensus clustering analysis, and LASSO Cox regression analysis respectively. High values of PDI signature/ risk score and cluster 1 in gliomas were associated with malignant clinicopathological characteristics and poor prognosis. Analysis of the distinctive genomic alterations in gliomas revealed that many cases having high PDI signature and risk score were associated with genomic aberrations of driver oncogenes. GSVA analysis showed that PDI family was involved in many signaling pathways in ERAD, apoptosis, and MHC class I among many more. Prognostic nomogram revealed that the risk score was a good prognosis indicator for gliomas. The qRT-PCR and immunohistochemistry confirmed that P4HB, PDIA4 and PDIA5 were overexpressed in gliomas. In summary, this research highlighted the clinical importance of PDI family in tumorigenesis and progression in gliomas.

Clinical characteristics and disease-specific prognostic nomogram for primary gliosarcoma: a SEER population-based analysis.

Because the study population with gliosarcoma (GSM) is limited, the understanding of this disease is insufficient. In this study, the authors aimed to determine the clinical characteristics and independent prognostic factors influencing the prognosis of GSM patients and to develop a nomogram to predict the prognosis of GSM patients after craniotomy. A total of 498 patients diagnosed with primary GSM between 2004 and 2015 were extracted from the 18 Registries Research Data of the Surveillance, Epidemiology, and End Results (SEER) database. The median disease-specific survival (DSS) was 12.0 months, and the postoperative 0.5-, 1-, and 3-year DSS rates were 71.4%, 46.4% and 9.8%, respectively. We applied both the Cox proportional hazards model and the decision tree model to determine the prognostic factors of primary GSM. The Cox proportional hazards model demonstrated that age at presentation, tumour size, metastasis state and adjuvant chemotherapy (CT) were independent prognostic factors for DSS. The decision tree model suggested that age <71 years and adjuvant CT were associated with a better prognosis for GSM patients. The nomogram generated via the Cox proportional hazards model was developed by applying the rms package in R version 3.5.0. The C-index of internal validation for DSS prediction was 0.67 (95% confidence interval (CI), 0.63 to 0.70). The calibration curve at one year suggested that there was good consistency between the predicted DSS and the actual DSS probability. This study was the first to develop a disease-specific nomogram for predicting the prognosis of primary GSM patients after craniotomy, which can help clinicians immediately and accurately predict patient prognosis and conduct further treatment.

Minimally Invasive Surgery is Superior to Conventional Craniotomy in Patients with Spontaneous Supratentorial Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.

BACKGROUND: Outcomes of minimally invasive surgery (MIS) versus conventional craniotomy (CC) for patients with spontaneous supratentorial intracerebral hemorrhage (SICH) have not been compared previously. We reviewed the current evidence regarding the safety and efficacy of MIS compared with CC in patients with SICH. METHODS: We conducted a meta-analysis of studies comparing MIS and CC in patients with computed tomography-confirmed SICH published between January 2000 and April 2018 in MEDLINE, Embase, and the Cochrane Controlled Trials Register based on PRISMA inclusion and exclusion criteria. Binary outcomes comparisons between MIS and CC were described using odds ratios (ORs). RESULTS: Five randomized controlled trials (RCTs) and 9 prospective controlled studies (non-RCTs), involving a total of 2466 patients, met our inclusion criteria. There was a statistically significant difference in mortality rate between MIS and CC (OR, 0.76; 95% confidence interval [CI], 0.60-0.97). MIS was associated with a lower rate of rebleeding (OR, 0.42; 95% CI, 0.28-0.64) and a higher rate of good recovery compared with CC (OR, 2.27; 95% CI, 1.34-3.83). CONCLUSIONS: Patients with SICH may benefit more from MIS than CC. Our study could help clinicians optimize treatment strategies in SICH.

Methylation-induced downregulation and tumor-suppressive role of microRNA-98 in glioma through targeting Sal-like protein 4.

MicroRNAs (miRs) have been found to play key roles in various human cancers, but the detailed regulatory mechanism of miR-98 in glioma remains largely unknown. The findings of the present study demonstrated that miR-98 was frequently downregulated in glioma tissues and cell lines (U87, U251, U373 and SHG44), and the decreased miR-98 levels were associated with DNA methylation. Treatment with 5-Aza-20-deoxycytidine, a DNA methyltransferase inhibitor, significantly increased the expression of miR-98 in glioma cells. Moreover, both miR-98 downregulation and methylation were significantly associated with a more aggressive tumor phenotype in glioma, as well as shorter survival time of glioma patients. Restoration of miR-98 expression caused a marked decrease in the migration and invasion of U87 cells, but did not affect cell proliferation. Sal-like protein 4 (SALL4) was further identified as a novel target gene of miR-98, and its protein expression was negatively regulated by miR-98 in U87 cells. Restoration of SALL4 expression reversed the suppressive effects of miR-98 on the migration and invasion of U87 cells. Furthermore, SALL4 was significantly upregulated in glioma tissues and cell lines, and an inverse correlation between miR-98 and SALL4 expression in glioma tissues was identified. In addition, the increased expression of SALL4 was significantly associated with glioma progression. Taken together, these data demonstrated that downregulation of miR-98, induced by methylation, promotes glioma cell migration and invasion via targeting SALL4. Therefore, miR-98 may become a potential therapeutic candidate for glioma.

Similar Fecal Microbiota Signatures in Patients With Diarrhea-Predominant Irritable Bowel Syndrome and Patients With Depression.

BACKGROUND & AIMS: Patients with irritable bowel syndrome (IBS) often have psychiatric comorbidities. Alterations in the intestinal microbiota have been associated with IBS and depression, but it is not clear if there is a microbial relationship between these disorders. We studied the profiles of fecal microbiota samples from patients with IBS, depression, or comorbidities of IBS and depression; we determined the relationships among these profiles and clinical and pathophysiological features of these disorders. METHODS: We used 454 pyrosequencing to analyze fecal microbiota samples from 100 subjects (40 with diarrhea-predominant IBS [IBS-D], 15 with depression, 25 with comorbidities of IBS and depression, and 20 healthy individuals [controls]), recruited at Peking University. Abdominal and psychological symptoms were evaluated with validated questionnaires. Visceral sensitivity was evaluated using a barostat. Colonic mucosal inflammation was assayed by immunohistochemical analyses of sigmoid tissue biopsy specimens. RESULTS: Fecal microbiota signatures were similar between patients with IBS-D and depression in that they were less diverse than samples from controls and had similar abundances of alterations. They were characterized by high proportions of Bacteroides (type I), Prevotella (type II), or nondominant microbiota (type III). Most patients with IBS-D or depression had type I or type II profiles (IBS-D had 85% type I and type II profiles, depression had 80% type I and type II profiles). Colon tissues from patients with type I or type II profiles had higher levels of inflammatory markers than colon tissues from patients with type III profiles. The level of colon inflammation correlated with the severity of IBS symptoms. CONCLUSIONS: Patients with IBS-D and depression have similar alterations in fecal microbiota; these might be related to the pathogenesis of these disorders. We identified 3 microbial profiles in patients that could indicate different subtypes of IBS and depression or be used as diagnostic biomarkers.