Comprehensive analyses of brain cell communications based on multiple scRNA-seq and snRNA-seq datasets for revealing novel mechanism in neurodegenerative diseases.

AIMS: Complex cellular communications between glial cells and neurons are critical for brain normal function and disorders, and single-cell level RNA-sequencing datasets display more advantages for analyzing cell communications. Therefore, it is necessary to systematically explore brain cell communications when considering factors such as sex and brain region. METHODS: We extracted a total of 1,039,459 cells derived from 28 brain single-cell RNA-sequencing (scRNA-seq) or single-nucleus RNA-sequencing (snRNA-seq) datasets from the GEO database, including 12 human and 16 mouse datasets. These datasets were further divided into 71 new sub-datasets when considering disease, sex, and region conditions. In the meanwhile, we integrated four methods to evaluate ligand-receptor interaction score among six major brain cell types (microglia, neuron, astrocyte, oligodendrocyte, OPC, and endothelial cell). RESULTS: For Alzheimer's disease (AD), disease-specific ligand-receptor pairs when compared with normal sub-datasets, such as SEMA4A-NRP1, were identified. Furthermore, we explored the sex- and region-specific cell communications and identified that WNT5A-ROR1 among microglia cells displayed close communications in male, and SPP1-ITGAV displayed close communications in the meninges region from microglia to neurons. Furthermore, based on the AD-specific cell communications, we constructed a model for AD early prediction and confirmed the predictive performance using multiple independent datasets. Finally, we developed an online platform for researchers to explore brain condition-specific cell communications. CONCLUSION: This research provided a comprehensive study to explore brain cell communications, which could reveal novel biological mechanisms involved in normal brain function and neurodegenerative diseases such as AD.

Vitamin D supplementation for patients with alopecia areata: A protocol for systematic review and meta-analysis.

BACKGROUND: Several studies have reported an association between low Vitamin D (VD) levels and Alopecia areata (AA), and suggested VD preparations as a potential therapy. VD has immunomodulatory function closely associated with a variety of skin diseases, but there is no conclusive evidence to support VD supplementation for patients with AA. Therefore, we will conduct a meta-analysis to collect and evaluate the efficacy and safety of VD supplementation in the treatment of AA. METHODS: We will search the following databases according to the developed strategy: Cochrane Library, PubMed, Web of Science, EMBASE, Scopus, Google scholar for Randomized controlled trials related to VD supplementation for AA. The retrieval time is from the establishment of each database to July 2022. Two reviewers will independently complete the literature search and screening, risk of bias assessment and data extraction. Severity of Alopecia Tool, Alopecia Density and Extent, Lesional area and senstivity (score) will be the primary results. RevMan V.5.3 will be used for data analysis and synthesis. For dichotomous outcomes and the continuous outcomes, we will calculate risk ratio with 95% Confidence intervals and mean differences or standardized mean differences with 95% Confidence intervals respectively. The reporting bias will be investigated using funnel plots, and the asymmetry of the funnel plots will be explained using the Harbord modified test or the Egger's regression test. RESULTS: The results of the study expect to provide a high-quality, evidence-based recommendation on VD supplementation in the treatment of AA for clinicians. CONCLUSIONS: The study will provide scientific and useful evidence for better use of VD supplementation in treating AA.

Comprehensive analysis of ncRNA involvement in brain microglia immunology.

Microglia is a major class of brain-resident myeloid cells and non-coding RNAs (ncRNAs) serves as key regulators in microglia homeostasis and inflammatory process. Here, we constructed the systematical association between microglia and ncRNAs including miRNAs, lncRNAs and circRNAs from two aspects, manual retrieval and computational detection. A total of 648 experimental verified ncRNA-microglia associations were obtained from published studies, including ncRNA regulatory patterns within different experimental models. Furthermore, we extracted 9 miRNA and 1 lncRNA expression profiles from the GEO database. Also, we obtained 31 sample-match miRNA and mRNA expression profiles, containing a total of 2335 normal or disordered brain samples. Finally, we developed a platform named MG-ncRexplorer (http://bio-bigdata.hrbmu.edu.cn/MG-ncRexplorer/), exploring the associations between ncRNAs and microglia among experimental validated and computational detection. To demonstrate the usage of MG-ncRexplorer, we constructed regulatory target networks based on manual retrieval associations and identified risk glioma miRNAs among multiple high-throughput expression profiles.

Comprehensive analysis of microglia gene and subpathway signatures for glioma prognosis and drug screening: linking microglia to glioma.

Glioma is the most common malignant tumors in the brain. Previous studies have revealed that, as the innate immune cells in nervous system, microglia cells were involved in glioma pathology. And, the resident microglia displayed its specific biological roles which distinguished with peripheral macrophages. In this study, an integrated analysis was performed based on public resource database to explore specific biological of microglia within glioma. Through comprehensive analysis, the biological characterization underlying two conditions, glioma microglia compared to glioma macrophage (MicT/MacT) as well as glioma microglia compared to normal microglia (MicT/MicN), were revealed. Notably, nine core MicT/MicN genes displayed closely associations with glioma recurrence and prognosis, such as P2RY2, which was analyzed in more than 2800 glioma samples from 25 studies. Furthermore, we applied a random walk based strategy to identify microglia specific subpathways and developed SubP28 signature for glioma prognostic analysis. Multiple validation data sets confirmed the predictive performance of SubP28 and involvement in molecular subtypes. The associations between SuP28 score and microglia M1/M2 polarization were also explored for both GBM and LGG types. Finally, a comprehensive drug-subpathway network was established for screening candidate medicable molecules (drugs) and identifying therapeutic subpathway targets. In conclusions, the comprehensive analysis of microglia related gene and functional signatures in glioma pathobiologic events by large-scale data sets displayed a framework to dissect inner connection between microglia and glioma, and identify robust signature for glioma clinical implications.

Prognostic Features of the Tumor Immune Microenvironment in Glioma and Their Clinical Applications: Analysis of Multiple Cohorts.

Glioma is the most common malignant tumor of the central nervous system. Tumor purity is a source of important prognostic factor for glioma patients, showing the key roles of the microenvironment in glioma prognosis. In this study, we systematically screened functional characterization related to the tumor immune microenvironment and constructed a risk model named Glioma MicroEnvironment Functional Signature (GMEFS) based on eight cohorts. The prognostic value of the GMEFS model was also verified in another two glioma cohorts, glioblastoma (GBM) and low-grade glioma (LGG) cohorts, from The Cancer Genome Atlas (TCGA). Nomograms were established in the training and testing cohorts to validate the clinical use of this model. Furthermore, the relationships between the risk score, intrinsic molecular subtypes, tumor purity, and tumor-infiltrating immune cell abundance were also evaluated. Meanwhile, the performance of the GMEFS model in glioma formation and glioma recurrence was systematically analyzed based on 16 glioma cohorts from the Gene Expression Omnibus (GEO) database. Based on multiple-cohort integrated analysis, risk subpathway signatures were identified, and a drug-subpathway association network was further constructed to explore candidate therapy target regions. Three subpathways derived from Focal adhesion (path: 04510) were identified and contained known targets including platelet derived growth factor receptor alpha (PDGFRA), epidermal growth factor receptor (EGFR), and erb-b2 receptor tyrosine kinase 2 (ERBB2). In conclusion, the novel functional signatures identified in this study could serve as a robust prognostic biomarker, and this study provided a framework to identify candidate therapeutic target regions, which further guide glioma patients' clinical decision.

Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization.

Gastric Cancer (GC) is a common cancer worldwide with a high morbidity and mortality rate in Asia. Many prognostic signatures from genes and non-coding RNA (ncRNA) levels have been identified by high-throughput expression profiling for GC. To date, there have been no reports on integrated optimization analysis based on the GC global lncRNA-miRNA-mRNA network and the prognostic mechanism has not been studied. In the present work, a Gastric Cancer specific lncRNA-miRNA-mRNA regulatory network (GCsLMM) was constructed based on the ceRNA hypothesis by combining miRNA-target interactions and data on the expression of GC. To mine for novel prognostic signatures associated with GC, we performed topological analysis, a random walk with restart algorithm, in the GCsLMM from three levels, miRNA-, mRNA-, and lncRNA-levels. We further obtained candidate prognostic signatures by calculating the integrated score and analyzed the robustness of these signatures by combination strategy. The biological roles of key candidate signatures were also explored. Finally, we targeted the PHF10 gene and analyzed the expression patterns of PHF10 in independent datasets. The findings of this study will improve our understanding of the competing endogenous RNA (ceRNA) regulatory mechanisms and further facilitate the discovery of novel prognostic biomarkers for GC clinical guidelines.