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Development and validation of a gemcitabine sensitivity-related long noncoding RNA signature for predicting the prognosis and subtypes of bladder cancer.
BACKGROUND: Bladder cancer (BLCA) is a common urogenital malignancy with significant heterogeneity, and gemcitabine serves as a key chemotherapeutic agent for BLCA. This study examined gemcitabine sensitivity-related long noncoding RNA (GSRlncRNA) in BLCA and constructed a prognostic signature. METHODS: Gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gemcitabine sensitivity of BLCA samples was quantitatively calculated. Using weighted gene coexpression network analysis (WGCNA), we analyzed the role of GSRlncRNA in BLCA. We developed a prognostic signature using machine learning algorithm integration, with the TCGA-BLCA cohort (n=405) serving as the training set and GSE31684 (n=93) serving as the validation cohort. Patients were stratified into low- and high-risk groups based on risk scores. Comparative analyses included prognosis, immune infiltration, mutational profiles, and pathway enrichment. Single-cell RNA sequencing (scRNA-seq) and in vitro experiments further validated the expression and functional roles of GSRlncRNA in BLCA. RESULTS: GSRlncRNAs were identified via WGCNA. The partial least squares regression for Cox (plsRcox) was selected as the optimal modeling approach. A prognostic signature comprising 37 GSRlncRNAs effectively stratified patients into distinct risk groups, which demonstrated significant differences in survival outcomes, pathway enrichment, immune infiltration, and mutational profiles. To enhance clinical utility, a nomogram and web-based calculator were developed. scRNA-seq clarified the critical roles of LINC00930 and EMX2OS in BLCA progression. Real-time quantitative polymerase chain reaction confirmed the differential expression of GSRlncRNA in BLCA compared to that in normal tissues. Functional assays demonstrated that LINC00930 and EMX2OS suppression impaired BLCA cell proliferation, migration, invasion, apoptosis, cell cycle, and gemcitabine sensitivity. CONCLUSIONS: We identified critical prognostic associations between GSRlncRNAs and BLCA. Based on these findings, we constructed a GSRlncRNA-based predictive signature, which demonstrated considerable potential for risk stratification and personalized treatment decisions. Functional experiments further validated the involvement of GSRlncRNAs in BLCA progression.
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SOX9 protein in goblet cells as a critical mediator and potential therapeutic target in Barrett's esophagus: An integrated bioinformatics analysis.
BACKGROUND: Barrett's esophagus (BE) is the only recognized precursor to esophageal adenocarcinoma, yet current diagnostic and therapeutic strategies remain limited by sampling bias and lack of reliable molecular markers. METHODS: We conducted a multi-omics analysis using four bulk RNA-seq datasets for differential expression and WGCNA. Hub genes were identified through PPI networks and LASSO regression, validated in external cohorts. scRNA-seq profiled gene expression in epithelial and stromal subtypes, with immune profiling via CIBERSORT. SOX9 expression in single-cell resolution was validated in two BE patient cohorts. RESULTS: Five upregulated genes-SOX9, COL1A2, SPARC, EPCAM, and KRT18-were identified as robust diagnostic markers, with AUC values >0.90. scRNA-seq showed increased SOX9 expression in goblet cells in BE, which was validated by IHC. SOX9 expression in goblet cells correlated with progression to high-grade dysplasia (HGD), while expression in non-goblet cells had a lower correlation. BE tissues showed distinct immune profiles, with enriched plasma cells and CD4+ memory T cells. Molecular docking identified potential druggable interactions with these proteins. CONCLUSION: Our integrative analysis provides a comprehensive molecular and cellular atlas of BE. These findings enhance current understanding of BE pathogenesis and offer clinically relevant candidates for early detection and targeted intervention.
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Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages.
Clear cell renal carcinoma (ccRCC) is a heterogeneous disease with a variable post-surgical course. To assemble a comprehensive ccRCC tumor microenvironment (TME) atlas, we performed single-cell RNA sequencing (scRNA-seq) of hematopoietic and non-hematopoietic subpopulations from tumor and tumor-adjacent tissue of treatment-naive ccRCC resections. We leveraged the VIPER algorithm to quantitate single-cell protein activity and validated this approach by comparison to flow cytometry. The analysis identified key TME subpopulations, as well as their master regulators and candidate cell-cell interactions, revealing clinically relevant populations, undetectable by gene-expression analysis. Specifically, we uncovered a tumor-specific macrophage subpopulation characterized by upregulation of TREM2/APOE/C1Q, validated by spatially resolved, quantitative multispectral immunofluorescence. In a large clinical validation cohort, these markers were significantly enriched in tumors from patients who recurred following surgery. The study thus identifies TREM2/APOE/C1Q-positive macrophage infiltration as a potential prognostic biomarker for ccRCC recurrence, as well as a candidate therapeutic target.
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Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity.
BACKGROUND: Understanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible. METHODS: We present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools' resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls. RESULTS: We first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5' scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn's disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.
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Histone lactylation regulates PRKN-Mediated mitophagy to promote M2 Macrophage polarization in bladder cancer.
BACKGROUND: Bladder cancer (BCa), particularly muscle-invasive bladder cancer (MIBC), is associated with poor prognosis, partly because of immune evasion driven by M2 tumor-associated macrophages (TAMs). Understanding the regulatory mechanisms of M2 macrophage polarization via PRKN-mediated mitophagy and histone lactylation (H3K18la) is crucial for improving treatment strategies. METHODS: A single-cell atlas from 46 human BCa samples was constructed to identify macrophage subpopulations. Bioinformatics analysis and experimental validation, including ChIP-seq and lactylation modulation assays, were used to investigate the role of PRKN in M2 macrophage polarization and its regulation by H3K18la. RESULTS: Single-cell analysis revealed distinct macrophage subpopulations, including M1 and M2 types. PRKN was identified as a critical regulator of mitophagy in M2 macrophages, supporting their immunosuppressive function. Bulk RNA-seq and gene intersection analysis revealed a set of mitophagy-related macrophage polarization genes (Mito_Macro_RGs) enriched in mitophagy and immune pathways. Pseudotime analysis revealed that PRKN was upregulated during the M1-to-M2 transition. siRNA-mediated PRKN knockdown impaired M2 polarization, reducing the expression of CD206 and ARG1. ChIP-seq and histone lactylation modulation confirmed that H3K18la enhanced PRKN expression, promoting mitophagy and M2 polarization and thereby facilitating immune suppression and tumor progression. CONCLUSIONS: Histone lactylation regulated PRKN-mediated mitophagy, promoting M2 macrophage polarization and contributing to immune evasion in BCa.
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A Spatially Constrained Fibroblast-Myeloid Program Associates With Immune Exclusion and Poor Prognosis in Lung Adenocarcinoma.
BACKGROUND: Although immune checkpoint blockade (ICB) therapy has improved clinical outcomes for some patients with lung adenocarcinoma (LUAD), only a subset of cases can achieve durable benefits, and primary resistance is often associated with an immune-excluded tumor microenvironment (TME). Existing studies indicate that cancer-associated fibroblasts (CAFs) are increasingly recognized as important participants in extracellular matrix (ECM) remodeling and stroma-immune crosstalk. However, in LUAD, it is still unclear which CAFs-related programs are involved in immune exclusion, particularly in relation to their spatial interactions with myeloid cell states. METHODS: We integrated eight publicly available LUAD single-cell RNA-seq cohorts (164 samples; 471,501 cells) using Harmony and annotated major lineages and stromal subsets. CAFs were reclustered to resolve subtype heterogeneity, followed by pathway activity scoring, weighted gene coexpression network analysis (WGCNA), pseudotime trajectory inference (Slingshot), and regulon analysis. Bulk TCGA-LUAD data were used for immune-exclusion correlation and survival analyses. Spatial transcriptomics was applied for in situ validation, and ligand-receptor analysis together with NicheNet was used to prioritize CAFs-derived signaling interactions and downstream targets. RESULTS: The integrated atlas identified 10 major cell lineages and revealed tumor-associated expansion of stromal and myeloid compartments. We further resolved six CAFs subtypes with distinct molecular and functional features. Tumor-enriched CAFs subsets showed stronger activation of ECM remodeling, focal adhesion, TGF-β signaling, hypoxia-related pathways, and inflammatory programs. Bulk-level analyses in TCGA-LUAD demonstrated that CAFs-related signatures were associated with a T-cell exclusion index and increased expression of the immune checkpoint-related molecule CD276 (B7-H3). Spatial transcriptomic mapping further showed that selected CAFs signatures were enriched in hypoxic, nonepithelial regions and colocalized with myeloid-associated signals, supporting the presence of a spatially constrained fibro-myeloid niche. Cell-cell communication analysis revealed extensive ligand-receptor interactions between CAFs subtypes and myeloid populations, whereas survival and immunotherapy cohort analyses showed that specific CAFs-related programs were associated with worse clinical outcomes and less favorable treatment responses. CONCLUSIONS: Multicohort single-cell integration and spatial validation define a CAFs-centered, immune-excluded niche in LUAD characterized by coordinated stromal and myeloid programs. These findings improve current understanding of CAFs heterogeneity and fibroblast-myeloid coupling in LUAD and provide a framework for future strategies aimed at targeting stromal barriers and remodeling the immune microenvironment to enhance ICB responsiveness.
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A periodic table of cell types.
Single cell biology is currently revolutionizing developmental and evolutionary biology, revealing new cell types and states in an impressive range of biological systems. With the accumulation of data, however, the field is grappling with a central unanswered question: what exactly is a cell type? This question is further complicated by the inherently dynamic nature of developmental processes. In this Hypothesis article, we propose that a 'periodic table of cell types' can be used as a framework for distinguishing cell types from cell states, in which the periods and groups correspond to developmental trajectories and stages along differentiation, respectively. The different states of the same cell type are further analogous to 'isotopes'. We also highlight how the concept of a periodic table of cell types could be useful for predicting new cell types and states, and for recognizing relationships between cell types throughout development and evolution.
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HypoAD: volumetric and single-cell analysis reveals changes in the human hypothalamus in aging and Alzheimer's disease.
Alterations in metabolism, stress response, sleep, circadian rhythms, and neuroendocrine processes are key features of aging and neurodegeneration. These fundamental processes are regulated by the hypothalamus, yet how its functionally distinct subregions and cell types change during human aging and Alzheimer's Disease (AD) remains largely unexplored. Here, we present HypoAD , a comprehensive atlas of the human hypothalamus in aging and AD, integrating high-resolution MRI from 202 individuals with single-nucleus RNA-seq (snRNA-seq) of 614,403 nuclei from young, AD, and age-matched non-dementia controls. Our analysis reveals that hypothalamic subregions governing metabolism, stress, and circadian rhythms are particularly vulnerable, exhibiting significant changes in both volumes and gene expression during aging and AD. At the molecular level, machine learning models identified the inflammatory response and regulators of circadian rhythms as key cellular predictors of AD. These signatures were reflected in specific cell types: microglia transitioned to a pro-inflammatory state, while inhibitory neurons within sleep-and circadian-regulating hypothalamic subregions showed the most profound transcriptional alterations, including disruptions in ligand-receptor interactions and G-protein-coupled receptor signaling. Together, HypoAD provides a high-resolution volumetric map and a comprehensive transcriptomic atlas of the human hypothalamus in aging and AD, linking lifestyle and behavioral changes to their underlying volumetric and molecular pathways. Additionally, HypoAD provides a framework to investigate hypothalamic dysfunction and establishes a roadmap for targeted interventions aimed at mitigating physiological disruptions to potentially slow disease progression.
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A multi-omics prognostic model integrating disulfidptosis-related molecular features in colorectal cancer.
BACKGROUND: Disulfidptosis is a recently described cell-death-related process associated with intracellular disulfide stress and may be relevant to tumor progression and immune regulation. However, its prognostic significance and broader biological relevance in colorectal cancer (CRC) remain unclear. METHODS: Transcriptomic and clinical data from TCGA and GEO cohorts were systematically integrated to characterize disulfidptosis-related molecular patterns in CRC. A four-gene prognostic signature was developed in a randomly assigned training cohort and evaluated in the corresponding internal test cohort. Public datasets were further used for gene-level expression analysis, and survival associations were assessed in the GSE39582 cohort. Immune infiltration, tumor mutation burden, microsatellite instability, and estimated drug-sensitivity analyses were performed to explore the biological correlates and therapeutic-response-related patterns associated with the model. The four model genes were further examined using single-cell RNA-seq data and public immunohistochemical images from the Human Protein Atlas, and were preliminarily assessed in 11 paired CRC tissues and matched adjacent normal tissues by RT-qPCR and western blotting. RESULTS: A four-gene signature consisting of SPINK1, GPRASP1, TIMP1, and KRT6A stratified patients into prognostically distinct risk groups. The high-risk group showed poorer survival, higher tumor mutation burden, higher TIDE scores, and elevated stromal, immune, and ESTIMATE scores, suggesting a more immune-evasive tumor microenvironment. Enrichment analyses indicated that high-risk tumors were associated with extracellular matrix remodeling and PI3K-Akt-related pathways, whereas low-risk tumors were enriched in metabolic processes. Estimated drug-sensitivity analyses indicated agent-specific differences between the two risk groups. The high-risk group showed lower estimated IC50 values for several cytotoxic and targeted agents, including cisplatin, docetaxel, doxorubicin, etoposide, vinorelbine, nilotinib, and gefitinib, whereas the low-risk group showed lower estimated IC50 values for AKT inhibitor VIII, sorafenib, lapatinib, erlotinib, and BIBW2992. CONCLUSIONS: This study identified a four-gene prognostic signature associated with disulfidptosis-related molecular heterogeneity in CRC. The signature was associated with survival differences, distinct tumor microenvironmental features, and divergent biological programs across risk groups, while KRT6A showed the most consistent pattern across public-dataset analyses and tissue-level analyses. These findings support the potential relevance of this subtype-guided signature to prognostic stratification in CRC and identify related genes for further biological investigation and independent validation.
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Single-Cell Atlas of Immune Microenvironment in Orthodontic Tooth Movement.
Orthodontic treatment depends on periodontal tissue remodeling. While the immune system has been found to regulate this process, previous studies mostly focused on the static molecular changes in conventional immune cell sets. A recent study used single-cell RNA sequencing (scRNA-seq) to analyze macrophages under orthodontic tooth movement (OTM), introducing new subclusters and functions. However, changes in other immune cells and their interactions with surrounding stromal cells are yet unclarified. Therefore, we performed scRNA-seq in mice, aiming to describe a more comprehensive immune landscape during OTM, while further exploring the dynamic changes, functions, and cellular interactions of immune cells at a higher resolution. We first confirmed the dynamic activation of osteoclasts, osteoblasts, and immune cells at various time points. The scRNA-seq analysis identified 7 cell lineages and 18 major cell types, with immune cells forming the largest proportion. Monocytic cells, granulocytes, and lymphocytes were selected for individual reclustering, followed by analysis of specific gene expression, functional enrichment, and distribution changes during OTM. Pseudotime analysis was applied to monocytic cells and granulocytes. We identified 4 developmental pathways in monocytic cells toward dendritic cells, different subsets of macrophages, and osteoclasts. Monocytic cells tended to be more differentiated during OTM. Meanwhile, in granulocytes, neutrophil subclusters were all highly differentiated. Additionally, we assessed the cellular interactions during OTM, revealing enhanced signaling from macrophages toward osteoclasts, especially in Ccl, Tnf, and Spp1 pathways. We identified that the C3ar1+ macrophage subcluster expressed these cytokines at a high level and was enriched in positive regulation of the mitogen-activated protein kinase cascade, indicating its positive regulation toward osteoclast activity. In conclusion, this study revealed the complex immune microenvironment during OTM, providing a detailed perspective on the diverse immune cell types, their specific functions, and cellular interactions.