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.

Identification of key metabolites for complex diseases is a challenging task in today's medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer's disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at http://bioinfo.hrbmu.edu.cn/PROFANCY.

Background: Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority. Methods: We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model. Results: Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset. Conclusion: Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non‐small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 ('low infiltration' cluster), cluster 2 ('heterogeneous infiltration' cluster) and cluster 3 ('high infiltration' cluster). The immune cells were responsible for a significantly favourable prognosis for the 'high infiltration' community. Cluster 1 had the lowest cytotoxic activity, tumour‐infiltrating lymphocytes and interferon‐gamma (IFN‐γ), as well as immune checkpoint molecules expressions. In addition, MHC‐I and immune co‐stimulator were also found to have lower cluster 1 expressions, indicating a possible immune escape mechanism. A total of 43 differentially expressed genes (DEGs) that overlapped among the groups were determined based on three clusters. Finally, based on a univariate Cox regression model, prognostic immune‐related genes were identified and combined to construct a risk score model able to predict overall survival (OS) rates in the validation datasets.

The transcriptional heterogeneity of tumor microenvironment (TME) cells is a crucial factor driving the diversity of cellular response to drug treatment and resistance. Therefore, characterizing the cells associated with drug treatment and resistance will help us understand therapeutic mechanisms, discover new therapeutic targets and facilitate precision medicine. Here, we describe a database, scDrugAct (http://bio-bigdata.hrbmu.edu.cn/scDrugAct/), which aims to establish connections among drugs, genes and cells and dissect the impact of TME cellular heterogeneity on drug action and resistance at single-cell resolution. ScDrugAct is curated with drug–cell connections between 3838 223 cells across 34 cancer types and 13 857 drugs and identifies 17 274 drug perturbation/resistance-related genes and 276 559 associations between >10 000 drugs and 53 cell types. ScDrugAct also provides multiple flexible tools to retrieve and analyze connections among drugs, genes and cells; the distribution and developmental trajectories of drug-associated cells within the TME; functional features affecting the heterogeneity of cellular responses to drug perturbation and drug resistance; the cell-specific drug-related gene network; and drug–drug similarities. ScDrugAct serves as an important resource for investigating the impact of the cellular heterogeneity of the TME on drug therapies and can help researchers understand the mechanisms of action and resistance of drugs, as well as discover therapeutic targets.