Development of central nervous system (CNS) is regulated by both intrinsic and peripheral signals. Previous studies have suggested that environmental factors affect neurological activities under both physiological and pathological conditions. Although there is anatomical separation, emerging evidence has indicated the existence of bidirectional interaction between gut microbiota, i.e., (diverse microorganisms colonizing human intestine), and brain. The cross-talk between gut microbiota and brain may have crucial impact during basic neurogenerative processes, in neurodegenerative disorders and tumors of CNS. In this review, we discuss the biological interplay between gut-brain axis, and further explore how this communication may be dysregulated in neurological diseases. Further, we highlight new insights in modification of gut microbiota composition, which may emerge as a promising therapeutic approach to treat CNS disorders.

Glioma is one of the most common malignant tumors of the central nervous system and is characterized by extensive infiltrative growth, neovascularization, and resistance to various combined therapies. In addition to heterogenous populations of tumor cells, the glioma stem cells (GSCs) and other nontumor cells present in the glioma microenvironment serve as critical regulators of tumor progression and recurrence. In this review, we discuss the role of several resident or peripheral factors with distinct tumor-promoting features and their dynamic interactions in the development of glioma. Localized antitumor factors could be silenced or even converted to suppressive phenotypes, due to stemness-related cell reprogramming and immunosuppressive mediators in glioma-derived microenvironment. Furthermore, we summarize the latest knowledge on GSCs and key microenvironment components, and discuss the emerging immunotherapeutic strategies to cure this disease.

Background: This meta-analysis aims to evaluate the long-term efficacy of medication treatment vs. surgery treatment in patients with prolactinomas.Methods: An electronic literature search was performed using MEDLINE, EMBASE and Web of Science databases for studies dated before July in 2018. Patients with prolactinomas received primary dopamine agonists (DAs) treatment or primary surgical interventions were included in this study. A systematic review and meta-analysis were performed in pertinent studies meeting eligible criteria. The clinical outcome was measured by the long-term remission rate of prolactin (PRL) in each cohort. The pooled data was analyzed according to a random effect model.Results: Thirteen publications with total 809 patients were included in the final meta-analysis. In the overall patients with prolactinomas, long-term remission rate was achieved in 88% patients treated with surgeries and in 52% patients treated with DAs (P = 0.001). The long-term remission rates in surgery cohort were also significantly higher than medication cohort in both microprolactinomas and macroprolactinomas (91 vs. 60%, P = 0.002; 77 vs. 43%, P = 0.003).Conclusions: Patients with prolactinomas, especially microprolactinomas, can consider transsphenoidal surgery as an alternative first-line treatment strategy. After receiving primary surgical intervention, administration of DAs should be considered based on the postoperative PRL level to achieve the best long-term remission rate.

Lower-grade gliomas (LrGG), characterized by invasiveness and heterogeneity, remain incurable with current therapies. Clinicopathological features provide insufficient information to guide optimal individual treatment and cannot predict prognosis completely. Recently, an increasing amount of studies indicate that the tumor microenvironment plays a pivotal role in tumor malignancy and treatment responses. However, studies focusing on the tumor microenvironment (TME) of LrGG are still limited. In this study, taking advantage of the currently popular computational methods for estimating the infiltration of tumor-associated normal cells in tumor samples and using weighted gene co-expression network analysis, we screened the co-expressed gene modules associated with the TME and further identified the prognostic hub genes in these modules. Furthermore, eight prognostic hub genes (ARHGDIB, CLIC1, OAS3, PDIA4, PARP9, STAT1, TAP2, and TAGLN2) were selected to construct a prognostic risk score model using the least absolute shrinkage and selection operator method. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic factor for LrGG. Moreover, time-dependent ROC curves indicated that our model had favorable efficiency in predicting both short- and long-term prognosis in LrGG patients, and the stratified survival analysis demonstrated that our model had prognostic value for different subgroups of LrGG patients. Additionally, our model had potential value for predicting the sensitivity of LrGG patients to radio- and chemotherapy. Besides, differential expression analysis showed that the eight genes were aberrantly expressed in LrGG compared to normal brain tissue. Correlation analysis revealed that the expression of the eight genes was significantly associated with the infiltration levels of six types of immune cells in LrGG. In summary, the TME-related eight-gene signature was significantly associated with the prognosis of LrGG patients. They might act as potential prognostic biomarkers for LrGG patients, offer better stratification for future clinical trials, and be candidate targets for immunotherapy, thus deserving further investigation.