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A pathomics-based pyroptosis signature predicts survival in clear cell renal cell carcinoma.
Better prognostic tools are needed to improve the clinical management of clear cell renal cell carcinoma (ccRCC). To address this, we developed a novel prognostic model by integrating pathomics features with pyroptosis-related signaling, a strategy not previously explored in ccRCC. Analysis of The Cancer Genome Atlas (TCGA) whole-slide images identified 59 quantitative image features significantly correlated with a pyroptosis gene set. Based on these features, a prognostic risk score was developed using the StepCox[forward]+Lasso algorithm and validated as an independent predictor of patient survival. This model demonstrated robust predictive performance, with time-dependent AUCs of 0.744, 0.729, and 0.716 for 1-, 3-, and 5-year survival and C-indexes of 0.71 and 0.64 in the training and validation sets. This model implicates key pyroptosis-related genes (e.g., GSDMD, GSDME, CASP5, and several CHMP family genes), whose expression links pathological phenotypes to patient outcomes. Single-cell sequencing revealed their specific expression patterns in the ccRCC microenvironment, and functional exploration highlighted GSDMD's potential role. By providing a novel, biologically integrated signature, this model offers a refined tool for prognostic assessment that complements conventional clinical parameters. Ultimately, this pyroptosis-based pathomics model could help guide personalized treatment strategies for ccRCC patients in the future.
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Single-Cell Atlas of Renal Cell Carcinoma Brain Metastasis Uncovers Mechanisms of Immune Dysfunction and Resistance.
Brain metastasis (BM) in renal cell carcinoma (RCC) remains poorly understood and often resistant to immune checkpoint inhibitors. We generated a large single-nucleus RNA-seq data of RCC BM, profiling 14 BM samples alongside matched extracranial metastases and primary tumors. Tumor cells in BM displayed neuronal infiltration, neural-like adaptation, and marked remodeling of the microenvironment, including expansion of immunosuppressive myeloid cells and depletion of antigen-presenting dendritic cells. Tumor, immune, and stromal cells exhibited metabolic rewiring characterized by fatty-acid metabolism, oxidative phosphorylation, and MYC-driven programs. CD8 T cells showed terminal exhaustion and impaired proliferative capacity, and tertiary lymphoid structures were absent. Spatial profiling of 12 BM samples (13,128 cells) validated key cellular interactions, while ligand-receptor analysis revealed immunoregulatory circuits between tumor, stromal, and immune cells. These findings define BM-specific adaptations that promote immune evasion and resistance, revealing therapeutic vulnerabilities in RCC BM.
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Identification of therapeutic targets for giant cell arteritis through integrated analysis of multi-omics datasets.
BACKGROUND: Giant cell arteritis (GCA), the most common systemic vasculitis affecting elderly individuals, currently lacks specific therapies. This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets. METHODS: We constructed a multi-stage analytical framework encompassing 32 proteomic datasets (covering 2914 unique plasma proteins) and 6 transcriptomic datasets. Multi-omics integration strategies, including two-sample Mendelian randomization, colocalization analysis, and functional enrichment analysis, were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts. Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships. RESULTS: We identified 43 plasma proteins causally associated with GCA [false discovery rate (FDR) < 0.05], with 17 representing novel therapeutic targets. Through dual validation using proteome-wide association studies and transcriptome-wide association studies, we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns. Unc-51 like kinase 3 (ULK3) emerged as the strongest protective factor (odds ratio = 0.47, 95% confidence interval: 0.37-0.71) through autophagy regulation, while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab. Five targets have existing approved drugs (SLAMF7, ICAM1, IL18, IL6ST, CTSS). Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients, with cell-type-specific alterations in inflammatory gene expression, and TYMP as the most critical hub gene. CONCLUSIONS: This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA, identifying novel mechanisms including autophagy modulation and metabolic reprogramming, with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns. These findings require experimental validation before clinical translation.
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Multiomics profiling of mouse polycystic kidney disease progression at a single-cell resolution.
Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to kidney failure. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single-nucleus multimodal atlas of an orthologous mouse PKD model at early, mid, and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalog differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single-nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution.
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Barcoding Notch signaling in the developing brain.
Developmental signaling inputs are fundamental for shaping cell fates and behavior. However, traditional fluorescent-based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER-seq, a CRISPR-Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single-cell transcriptomics. We applied SABER-seq to record Notch signaling in developing zebrafish brains. SABER-seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch-dependent manner with inducible control, while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER-seq with an expanded juvenile brain atlas to identify cell types derived from Notch-active founders. Our data reveal rare examples where differential Notch activities in ancestral progenitors are detected in terminally differentiated neuronal subtypes. SABER-seq is a novel platform for rapid, scalable and high-resolution mapping of signaling activity during development.
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Integrated mendelian randomization and bioinformatics approach unveiling the immunological and prognostic significance of FBXO2 in breast cancer.
BACKGROUND: Breast cancer (BRCA) is a typical females' malignant tumors. Ubiquitin-proteasome system (UPS) is a critical pathway for pathogenesis of BRCA. Activation of UPS is determined by SKP1-cullin 1-F-box protein (SCF) E3 ligase complexes. F-Box Protein 2 (FBXO2), a pivotal member of F-box proteins family, is associated with UPS. FBXO2 has attracted increasing attention in various cancers, yet its relationship with many cancer types is still unclear. Therefore, systematic research on FBXO2 and pan-cancer is very important for understanding BRCA progression and drug resistance. METHOD: Transcriptome expression data of 33 cancer types were acquired from the Cancer Genome Atlas (TCGA) database. Wilcoxon test was performed to estimate the molecular characteristic of FBOX2 across human pan-cancer analyses. To confirmed the relationship between FBXO2 and BRCA, mendelian randomization (MR) was conducted. Kaplan-Meier (KM) analyses was used to evaluated the survival features of FBXO2. Additionally, protein and methylation level of FBXO2 were examined using Wilcoxon test. Single-cell sequence analysis was further applied to confirm the importance of FBXO2. The underlying mechanism of FBXO2 was explored from various perspectives, including gene function, immune checkpoint and tumor microenvironment. Finally, stable FBXO2-knockdown and overexpression models in MDA-MB-231 and MCF-7 were established, and functional assays including CCK-8, Transwell, and wound healing experiments were conducted to validate the role of FBXO2 in vitro. RESULT: Our pan-cancer analysis revealed that FBXO2 was up-regulated in multiple cancer types, but was significantly down-regulated in BRCA. MR analysis confirmed a caused relationship between reduced FBXO2 expression and increased BRCA risk. Consistent with transcriptional findings, FBXO2 protein levels were also decreased in BRCA. KM analysis indicated that low FXBO2 expression was associated with poor overall survival (OS) and recurrence free survival (RFS) in BRCA patients. Single-cell sequence analysis further revealed that FBXO2 obviously enriched in malignant epithelial cells. In vitro functional experiments demonstrated that knockdown of FBXO2 significantly promoted cell proliferation, migration and invasion, whereas its overexpression suppressed these malignant phenotypes in BRCA. CONCLUSION: In conclusion, our comprehensive analysis revealed that FBXO2 plays a critical tumor-suppression role in BRCA, with its down-regulation contributing to poor prognosis. Furthermore, FBXO2 was implicated in immune regulation and tumor microenvironment remodeling. Our findings provided novel insights into the pathogenesis of BRCA.
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Integrative profiling of diverse post-translational modifications for prognostic stratification and personalized therapy in papillary thyroid cancer.
Post-translational modifications (PTMs) are pivotal in tumor biology, yet their role in papillary thyroid cancer (PTC) remains unclear. We integrated bulk and single-cell transcriptomes with clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to analyze 17 PTMs and construct a prognostic model using 12 machine learning algorithms for predicting the thyroid cancer-free interval (TCFi). Enrichment analysis, single-cell analysis, and immune-related analysis were performed to elucidate the biological role of PTMs. Therapeutic responses of PTC patients were predicted based on the model. We validated the expression of model genes and identified the key signature associated with the malignant phenotypes of PTC. We filtered out 12 genes to construct a post-translational modification index (PTMI) and identified three molecular clusters of PTC. Shapley additive explanations (SHAP) and nomogram models confirmed the predictive efficacy of PTMI. Integrated analyses revealed significant associations between PTMI and immune features. High-PTMI patients showed sensitivity to FDA-approved drugs and chemotherapeutics but resistance to radioactive iodine therapy. Notably, we identified TYMS as a key functional PTMI signature. The PTMI proposed in this study holds strong potential as a prognostic biomarker and therapeutic predictor, offering valuable insights for personalized management of PTC patients.
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A comprehensive pan-cancer examination of transcription factor MAFF: Oncogenic potential, prognostic relevance, and immune landscape dynamics.
AIMS: Previous studies indicate that MAF BZIP Transcription Factor F (MAFF) facilitates ectopic metastasis and tumor cell migration. While its role in neoplasm progression is recognized, a thorough pan-cancer analysis of MAFF's impact remains pending. MAIN METHODS: MAFF expression across normal and tumor tissues was analyzed using transcriptomic data from Genomic Data Commons (GDC) and UCSC XENA, with protein details from Human Protein Atlas (HPA) and GeneMANIA. Tumor Immune Single-cell Hub (TISCH) and Spatial Transcriptomics Omics DataBase (STOmics DB) identified MAFF expression in the tumor microenvironment (TME). MAFF's prognostic significance and immune-related gene associations were evaluated through univariate Cox regression, TIMER2.0 immune cell infiltration analysis, and Spearman correlation. Critical pathways were identified using Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), while molecular docking explored anticancer agent interactions. KEY FINDINGS: MAFF expression varies across cancers, affecting tumor prognosis, notably in monocytes/macrophages and endothelial cells. Copy number variation (CNV) positively correlates with MAFF expression, while methylation shows inverse correlation. MAFF mutations significantly affect LGG patient prognosis and correlate with immune therapy responses. ESTIMATE and immune profiling linked MAFF to immunosuppression pathways. Molecular docking identified MAFF-targeted drugs, with validated effects on breast cancer and endometrial cancer cell survival and migration in vitro. SIGNIFICANCE: Multi-omics analysis identified MAFF as a potential prognostic marker correlating with tumor immunity and microenvironment, suggesting its value for personalized cancer immunotherapy, particularly in BRCA and UCEC.
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Mapping the developing human immune system across organs.
Single-cell genomics studies have decoded the immune cell composition of several human prenatal organs but were limited in describing the developing immune system as a distributed network across tissues. We profiled nine prenatal tissues combining single-cell RNA sequencing, antigen-receptor sequencing, and spatial transcriptomics to reconstruct the developing human immune system. This revealed the late acquisition of immune-effector functions by myeloid and lymphoid cell subsets and the maturation of monocytes and T cells before peripheral tissue seeding. Moreover, we uncovered system-wide blood and immune cell development beyond primary hematopoietic organs, characterized human prenatal B1 cells, and shed light on the origin of unconventional T cells. Our atlas provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine, and disease understanding.
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High SIGLEC10-Expressing Tumor-Associated Macrophages Participate in Remodeling the Tumor Immune Microenvironment and Predict Poor Prognosis in Colorectal Cancer.
BACKGROUND: Sialic acid-binding immunoglobulin-like lectin 10 (SIGLEC10), a member of the SIGLEC family, is selectively expressed on multiple immune cell subsets and plays a key role in immune regulation through the recognition of sialylated ligands. Recent studies suggest that SIGLEC10 functions as an emerging immune checkpoint that contributes to the regulation of tumor-associated macrophages (TAMs) within the tumor microenvironment (TME). METHODS: Immunohistochemistry was performed to assess SIGLEC10 expression in a CRC cohort comprising 202 patients, and the association between SIGLEC10 expression and patient survival and prognosis was evaluated. Immunofluorescence staining and flow cytometry were used to characterize the localization and phenotypic features of SIGLEC10-expressing cells within tumor tissues. The Cancer Genome Atlas (TCGA) and RNA sequencing (RNA-seq) datasets were analyzed to investigate SIGLEC10-associated tumor immune signaling pathways. Additionally, single-cell datasets from Tumor Immune Single-cell Hub 2 (TISCH2) were used to further investigate the regulatory mechanisms of SIGLEC10high TAMs within the TME. Finally, the therapeutic potential of SIGLEC10 as an immunotherapeutic target in CRC was evaluated. RESULTS: High SIGLEC10 expression predicts poor prognosis in CRC patients and serves as an independent prognostic factor. SIGLEC10 is predominantly expressed in CD68+ macrophages and is associated with TAMs phenotypes. Functional enrichment analyses indicate that SIGLEC10high TAMs are involved in the regulation of multiple inflammatory and tumor immune signaling pathways. Immune infiltration and differential analyses suggest that SIGLEC10high TAMs may facilitate tumor immune evasion by promoting increased infiltration of immune cells and recruiting various immune chemokines. Single-cell analyses reveal that SIGLEC10high TAMs may remodel the TME through TNFSF13B-mediated regulation of multiple immune cell populations, including B cells. Finally, SIGLEC10 exhibits considerable potential as an immunotherapeutic target for CRC. CONCLUSION: This study highlights SIGLEC10 as an independent prognostic factor in patients with CRC and its potential as a promising target for immunotherapy in CRC.