Introduction: This study aimed to investigate the differences between pregnant women with chronic hepatitis B virus (HBV) infection and intrafamilial infection and those without intrafamilial infection. Methods: HBV-DNA was extracted from the sera of 16 pregnant women with chronic hepatitis B (CHB) and their family members for gene sequencing and phylogenetic analyses. A total of 74 pregnant women with CHB were followed up from the second trimester to 3 months postpartum. Viral markers and other laboratory indicators were compared between pregnant women with CHB with and without intrafamilial infection. Results: The phylogenetic tree showed that HBV lines in the mother-spread pedigree shared a node, whereas there was an unrelated genetic background for HBV lines in individuals without intrafamilial infection. From delivery to 3 months postpartum, compared with those without intrafamilial infection, pregnant women with intrafamilial infection were related negatively to HBV-DNA (β = −0.43, 95% confidence interval [CI]: −0.76 to −0.12, p = 0.009), HBeAg (β = −195.15, 95% CI: −366.35 to −23.96, p = 0.027), and hemoglobin changes (β = −8.09, 95% CI: −15.54 to −0.64, p = 0.035) and positively to changes in the levels of alanine aminotransferase (β = 73.9, 95% CI: 38.92–108.95, p < 0.001) and albumin (β = 2.73, 95% CI: 0.23–5.23, p = 0.033). Conclusion: The mother-spread pedigree spread model differs from that of non-intrafamilial infections. Pregnant women with intrafamilial HBV infection have less hepatitis flares and liver damage, but their HBV-DNA and HBeAg levels rebound faster after delivery, than those without intrafamilial infection by the virus.

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1987 ovarian cancer patients spanning 26 independent Gene Expression Omnibus cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust ten-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

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.

There is a growing body of evidence that innate immunity also plays an important role in the progression of hepatitis B virus (HBV) infection. However, there is less study on systematically elucidating the characteristics of innate immunity in HBV-infected pregnant women. We compared the features of peripheral blood mononuclear cells in three healthy pregnant women and three HBV-infected pregnant women by single-cell RNA sequencing. 10 DEGs were detected between groups and monocytes were the main expression source of most of the DEGs, which involved in the inflammatory response, apoptosis and immune regulation. Meanwhile, qPCR and ELISA were performed to verify above genes. Monocytes displayed immune response defect, reflecting poor ability of response to IFN. In addition, eight clusters were identified in monocytes. We identified molecular drivers in monocytes subpopulations.TNFSF10+ monocytes, MT1G+ monocytes and TUBB1+ monocytes were featured with different gene expression pattern and biological function.TNFSF10+ monocytes and MT1G+ monocytes were characterized by high levels of inflammation response.TNFSF10+ monocytes, MT1G+ monocytes and TUBB1+ monocytes showed decreased response to IFN. Our results dissects alterations in monocytes related to the immune response of HBV-infected pregnant women and provides a rich resource for fully understanding immunopathogenesis and developing effective preventing HBV intrauterine infection strategies.

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.

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.