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BMDB: An integrated database and web platform for single-cell transcriptomic profiling of bone marrow microenvironment.
The bone marrow microenvironment orchestrates hematopoiesis and disease processes, yet its profound cellular and molecular complexity remains incompletely resolved. While single-cell RNA sequencing offers unprecedented resolution, the lack of a unified resource integrating niche-specific data across health and disease states, and developmental stages hinders systematic exploration. Here, we present BMDB (https://bmdb.jflab.ac.cn:8084/), an integrated database and web platform aggregating 435,682 single cells from 142 healthy and 82 pathological human and mouse samples, establishing species- and stage-specific reference atlases that harmonize niche cell annotation, improve cross-study comparability, and enable translational analyses. Its interactive web platform provides modules for functional exploration, reference-guided analysis, dynamic comparison across states, and knowledge graphs linking genes, variants, pathways, and pathologies. Additionally, users can upload their own scRNA-seq datasets to map onto the reference atlas for cell type annotation and downstream analyses. Collectively, BMDB provides high-quality reference atlases and interactive tools, serving as a valuable resource for investigating bone marrow microenvironment heterogeneity and hematopoietic regulation.
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Gene regulatory networks analysis for the discovery of prognostic genes in gliomas.
Gliomas are the most common and aggressive primary tumors of the central nervous system. Dysregulated transcription factors (TFs) and genes have been implicated in glioma progression, yet these tumors' overall structure of gene regulatory networks (GRNs) remains undefined. We analyzed transcriptional data from 989 primary gliomas in The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to address this. GRNs were reconstructed using the RTN package which identifies regulons-sets of genes regulated by a common TF based on co-expression and mutual information. Regulon activity was evaluated through Gene Set Enrichment Analysis. Elastic net regularization and Cox regression identified 31 and 32 prognostic genes in the TCGA and CGGA datasets, respectively, with 11 genes overlapping, many of which are associated with neural development and synaptic processes. GAS2L3, HOXD13, and OTP demonstrated the strongest correlations with survival outcomes among these. Single-cell RNA-seq analysis of 201,986 cells revealed distinct expression patterns for these genes in glioma subpopulations, particularly oligoprogenitor cells. This study uncovers key GRNs and prognostic genes in gliomas, offering new insights into tumor biology and potential therapeutic targets.
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A mouse tissue atlas of small noncoding RNA.
Small noncoding RNAs (ncRNAs) play a vital role in a broad range of biological processes both in health and disease. A comprehensive quantitative reference of small ncRNA expression would significantly advance our understanding of ncRNA roles in shaping tissue functions. Here, we systematically profiled the levels of five ncRNA classes (microRNA [miRNA], small nucleolar RNA [snoRNA], small nuclear RNA [snRNA], small Cajal body-specific RNA [scaRNA], and transfer RNA [tRNA] fragments) across 11 mouse tissues by deep sequencing. Using 14 biological replicates spanning both sexes, we identified that ∼30% of small ncRNAs are distributed across the body in a tissue-specific manner with some also being sexually dimorphic. We found that some miRNAs are subject to "arm switching" between healthy tissues and that tRNA fragments are retained within tissues in both a gene- and a tissue-specific manner. Out of 11 profiled tissues, we confirmed that brain contains the largest number of unique small ncRNA transcripts, some of which were previously annotated while others are identified in this study. Furthermore, by combining these findings with single-cell chromatin accessibility (scATAC-seq) data, we were able to connect identified brain-specific ncRNAs with their cell types of origin. These results yield the most comprehensive characterization of specific and ubiquitous small RNAs in individual murine tissues to date, and we expect that these data will be a resource for the further identification of ncRNAs involved in tissue function in health and dysfunction in disease.
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Integrated analysis of single-cell and bulk RNA-sequencing reveals a novel signature based on NK cell marker genes to predict prognosis and immunotherapy response in gastric cancer.
Natural killer (NK) cells play essential roles in the tumor development, diagnosis, and prognosis of tumors. In this study, we aimed to establish a reliable signature based on marker genes in NK cells, thus providing a new perspective for assessing immunotherapy and the prognosis of patients with gastric cancer (GC). We analyzed a total of 1560 samples retrieved from the public database. We performed a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of gastric cancer and identified 377 marker genes for NK cells. By performing Cox regression analysis, we established a 12-gene NK cell-associated signature (NKCAS) for the Cancer Genome Atlas (TCGA) cohort, that assigned GC patients into a low-risk group (LRG) or a high-risk group (HRG). In the TCGA cohort, the areas under curve (AUC) value were 0.73, 0.81, and 0.80 at 1, 3, and 5 years. External validation of the predictive ability for the signature was then validated in the Gene Expression Omnibus (GEO) cohorts (GSE84437). The expression levels of signature genes were measured and validated in GC cell lines by real-time PCR. Moreover, NKCAS was identified as an independent prognostic factor by multivariate analysis. We combined this with a variety of clinicopathological characteristics (age, M stage, and tumor grade) to construct a nomogram to predict the survival outcomes of patients. Moreover, the LRG showed higher immune cell infiltration, especially CD8+ T cells and NK cells. The risk score was negatively associated with inflammatory activities. Importantly, analysis of the independent immunotherapy cohort showed that the LRG had a better prognosis and immunotherapy response when compared with the HRG. The identification of NK cell marker genes in this study suggests potential therapeutic targets. Additionally, the developed predictive signatures and nomograms may aid in the clinical management of GC.
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Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing.
OBJECTIVE: Tumour heterogeneity represents a major obstacle to accurate diagnosis and treatment in gastric adenocarcinoma (GA). Here, we report a systematic transcriptional atlas to delineate molecular and cellular heterogeneity in GA using single-cell RNA sequencing (scRNA-seq). DESIGN: We performed unbiased transcriptome-wide scRNA-seq analysis on 27 677 cells from 9 tumour and 3 non-tumour samples. Analysis results were validated using large-scale histological assays and bulk transcriptomic datasets. RESULTS: Our integrative analysis of tumour cells identified five cell subgroups with distinct expression profiles. A panel of differentiation-related genes reveals a high diversity of differentiation degrees within and between tumours. Low differentiation degrees can predict poor prognosis in GA. Among them, three subgroups exhibited different differentiation grade which corresponded well to histopathological features of Lauren's subtypes. Interestingly, the other two subgroups displayed unique transcriptome features. One subgroup expressing chief-cell markers (eg, LIPF and PGC) and RNF43 with Wnt/β-catenin signalling pathway activated is consistent with the previously described entity fundic gland-type GA (chief cell-predominant, GA-FG-CCP). We further confirmed the presence of GA-FG-CCP in two public bulk datasets using transcriptomic profiles and histological images. The other subgroup specifically expressed immune-related signature genes (eg, LY6K and major histocompatibility complex class II) with the infection of Epstein-Barr virus. In addition, we also analysed non-malignant epithelium and provided molecular evidences for potential transition from gastric chief cells into MUC6+TFF2+ spasmolytic polypeptide expressing metaplasia. CONCLUSION: Altogether, our study offers valuable resource for deciphering gastric tumour heterogeneity, which will provide assistance for precision diagnosis and prognosis.
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Inference and analysis of cell-cell communication using CellChat.
Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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A stromal-derived five-gene signature predicts gastric cancer recurrence through integrated bioinformatics and single-cell analysis.
BACKGROUND: Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide, with recurrence rates exceeding 40% following curative resection. While molecular profiling has revealed GC heterogeneity, computational approaches integrating tumor microenvironment (TME) characteristics for prognostic assessment remain underexplored. Therefore, this study aimed to develop and validate a stromal-derived gene signature for predicting gastric cancer recurrence and to characterize its tumor-microenvironmental and cell-type specificity through integrated bulk transcriptomic, machine-learning, and single-cell analyses. METHODS: Using publicly available datasets, we developed a prognostic signature through bootstrap-based feature selection (1,000 iterations) using 279 GC patients from GSE62254, divided into training (n=196) and validation (n=83) cohorts. Weighted gene co-expression network analysis (WGCNA) identified recurrence-associated modules. Single-cell RNA sequencing (scRNA-seq) of 43,560 cells determined cell-type specificity. Six machine learning algorithms assessed predictive performance. External validation used The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), including 419 patients with complete risk-score data, and pan-cancer cohorts (n=9,328 across 33 cancer types). RESULTS: The five-gene signature (FBXL2, MLLT11, NES, HEYL, AKAP12) achieved significant risk stratification [training: hazard ratio (HR) =3.074, 95% confidence interval (CI): 2.283-4.140, P=1.44×10-13; validation: HR =3.07, 95% CI: 1.56-6.05, P=7.93×10-4] with area under the curve (AUC) of 0.792 (95% CI: 0.721-0.863) at 12 months. Single-cell analysis revealed cell-type-enriched expression patterns: AKAP12 in fibroblasts (18.3%), NES in myeloid cells (11.3%), HEYL in smooth muscle cells (7.2%), and MLLT11 in secretory cells (4.6%). WGCNA localized 80% of signature genes to extracellular matrix (ECM) remodeling pathways. High-risk patients exhibited elevated predicted chemotherapy resistance across eight agents (all P<0.001) and distinct immune correlation patterns, with MLLT11 correlating with M2 macrophages (r=0.224, P=1.73×10-4). The signature remained prognostic independent of tumor mutational burden (TMB) (P=0.78). Pan-cancer analysis across 33 TCGA cohorts (n=9,328) demonstrated tissue-specific prognostic value in 4 cancer types after false discovery rate (FDR) correction (FDR <0.05), including the primary GC cohort (STAD, HR =1.6, FDR =0.041), indicating limited cross-cancer applicability. CONCLUSIONS: This stromal-derived five-gene signature suggests that microenvironment composition is strongly associated with GC recurrence, independent of tumor-intrinsic mutational burden. The predominant stromal expression and independence from TMB indicate that prognostic value derives from stromal-tumor interactions rather than tumor cell-autonomous features. While experimental validation is required to establish causality, these findings identify the stromal compartment as a potential therapeutic target and highlight the importance of integrating microenvironment characteristics into prognostic assessment.
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Identification and Validation of Prognostic Model for Tumor Microenvironment-Associated Genes in Bladder Cancer Based on Single-Cell RNA Sequencing Data Sets.
PURPOSE: The purpose of this study was to elucidate the relationship between the tumor microenvironment (TME) and cellular diversity in bladder cancer (BLCA) progression, leveraging single-cell RNA sequencing (scRNA-seq) data to identify potential prognostic biomarkers and construct a prognostic model for BLCA. METHODS: We analyzed scRNA-seq data of normal and tumor bladder cells from the Gene Expression Omnibus (GEO) database to uncover crucial markers within the bladder TME. The study compared gene expression in normal versus tumor bladder cells, identifying differentially expressed genes. These genes were subsequently assessed for their prognostic significance using patient follow-up data from The Cancer Genome Atlas. Prognostic models were constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses, focusing on eight genes of interest. The predictive performance of the model was also tested against additional GEO data sets (GSE31684, GSE13507, and GSE32894). RESULTS: The prognostic model demonstrated reliable prediction of patient outcomes. Validation through gene set enrichment analysis and immune cell infiltration assessment supported the model's efficacy. The results from both the univariate and multivariate analyses suggest that the risk score is an independent prognostic factor with a hazard ratio of 2.97 (95% CI, 2.28 to 3.9, P < .001). In the validation cohort, the AUC at 1, 2, and 3 years is 0.74, 0.74, and 0.72, respectively. CONCLUSION: Our findings proposed biomarkers with prognostic potential, laying the groundwork for future in vitro validation and therapeutic exploration. This contributes to a deeper understanding of the genes associated with bladder TME and may improve prognostic precision in BLCA management.
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Single-Cell transcriptomic analysis reveals distinct cellular and molecular signatures of human oral mucosa and skin.
Oral mucosal wounds heal faster and with minimal scarring compared to skin injuries, yet the underlying mechanisms remain poorly understood. To explore the cellular and molecular basis of this difference, we performed single-cell RNA sequencing (scRNA-seq) on paired, uninjured human oral mucosa and skin tissues from the same donors. This approach enabled the construction of a comprehensive single-cell transcriptomic atlas, facilitating direct comparison of cellular composition, gene expression profiles, and intercellular communication between the two tissues. Our analysis revealed distinct tissue-specific heterogeneity among keratinocytes, fibroblasts, immune cells, and endothelial cells. Oral keratinocytes exhibited signatures associated with proliferation and metabolic activity, while oral fibroblasts and immune cells expressed gene profiles suggestive of pro-regenerative and anti-fibrotic functions. Cell-cell communication analysis indicated that endothelial cells in oral mucosa participate in interactions that may promote rapid tissue remodeling. Although our data were derived from uninjured tissues, the identified differentially expressed genes and enriched pathways suggest potential regulatory networks that may underlie the distinct wound-healing behaviors of oral mucosa and skin. A subset of these genes was validated by RT-PCR in both autologous and allogeneic samples across different age and sex groups, confirming the robustness and reproducibility of our findings. This study provides the first single-cell transcriptomic comparison of intact human oral mucosa and skin under steady-state conditions, establishing a foundational atlas that reveals intrinsic tissue-specific features and identifies candidate targets for promoting scarless healing.
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Integrating transcriptomics, single-cell omics, and deep learning-based histopathological features to identify OLFML3 in bladder cancer.
BACKGROUND: Bladder cancer (BCa) represents the most common malignancy of the urinary system, characterized by a high recurrence rate, with approximately 61% of patients experiencing recurrence within 1 year post-surgery. Current monitoring methods, such as cystoscopy and urine cytology, are constrained by low sensitivity and patient discomfort. This study employed multi-omics data to investigate the role of OLFML3 in BCa recurrence, with the aim of enhancing the accuracy of recurrence prediction and improving clinical management. METHODS: This study utilized RNA sequencing (RNA-seq) data and clinical information from The Cancer Genome Atlas (TCGA) to analyze patients with BCa, stratifying them into relapse and non-relapse groups. Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with 1-year BCa recurrence. Subsequently, univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were conducted to select eight prognostic genes, and a risk model was developed and validated in both TCGA and Gene Expression Omnibus (GEO) datasets. Additionally, single-cell RNA sequencing (scRNA-seq) data from Guangdong Provincial Second People's Hospital were analyzed to evaluate gene expression across high and low tumor stromal BCa subtypes and explore the relationship between the expression of these eight genes and clinical features. A deep learning model based on the ResNet50 architecture was developed to predict OLFML3 expression in hematoxylin and eosin-stained images. Statistical analysis was performed using R software (version 4.4.2), with significance set at P<0.05. RESULTS: WGCNA identified gene modules associated with BCa recurrence, with the red module exhibiting a significantly positive correlation with recurrence status. Through univariate and LASSO Cox regression analyses, we selected eight prognosis-related genes. Kaplan-Meier survival analysis demonstrated that these genes effectively differentiated between high- and low-risk groups, with statistically significant survival differences observed in both TCGA and GEO datasets. Further Kaplan-Meier survival analysis of each of the eight genes indicated that high OLFML3 expression was associated with lower survival rates. scRNA-seq revealed differential expression of OLFML3 between high and low tumor stromal subtypes, suggesting that OLFML3 may be a key gene associated with 1-year BCa recurrence. High OLFML3 expression also correlated with BCa invasiveness, grade, and stage. Using a deep learning model based on BCa pathological features, we constructed a random forest (RF) model that successfully predicted high and low OLFML3 expression levels, providing novel insights for the clinical prognosis of BCa recurrence within 1 year. CONCLUSIONS: Multi-omics approaches effectively identified the OLFML3 gene as a critical factor potentially associated with 1-year BCa recurrence. Furthermore, a deep learning model based on pathological features was developed to predict OLFML3 expression. Further research is warranted to elucidate its role in BCa recurrence within 1 year.