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Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer.
Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed. Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package "limma," and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package "clusterProfiler." Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves. Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations. Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.
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Cross-species single-cell and spatial transcriptomic mapping reveals EFNA1-EPHA4-mediated stem-like epithelial-macrophage crosstalk driving colorectal cancer progression.
Colorectal cancer (CRC) typically follows the "normal-adenoma-carcinoma" (NAC) progression, with approximately 70-90% of cases driven by an adenomatous polyposis coli (APC) mutation-dependent pathway. The Apc-mutant (Min) mouse, valuable for dissecting gene function and mechanisms in CRC, provides an important basis for cross-species analyses with human data. Here, we performed a cross-species analysis of single-cell and spatial transcriptomic data across multiple stages of colorectal tissues in both humans and Min mice, constructing a spatiotemporal atlas. Our study identified key microenvironmental regulatory networks involved in CRC progression and highlighted the central role of epithelial-macrophage interactions within the tumor microenvironment. We further validated the suitability of the Min mouse as a model for the intrinsic Consensus Molecular Subtypes 2(iCMS2) microsatellite-stable (MSS) subtype of CRC. Focusing on the crosstalk between tumor-associated macrophages (TAMs) and epithelial cells, we identified the EFNA1-EPHA4 axis as a critical regulator promoting the immunosuppressive polarization of TAMs and enhancing tumor cell stemness. In addition, inhibition of EFNA1 was found to slow tumor growth. This study not only provides a systematic framework for mapping CRC correspondence between humans and mice, but also uncovers key molecular mechanisms underlying CRC progression and proposes promising therapeutic targets.
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Expansion of Single Cell Transcriptomics Data of SARS-CoV Infection in Human Bronchial Epithelial Cells to COVID-19.
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 19 (COVID-19) that was emerged as a new member of coronaviruses since December 2019 in Wuhan, China and then after was spread in all continentals. Since SARS-CoV-2 has shown about 77.5% similarity to SARS-CoV, the transcriptome and immunological regulations of SARS-CoV-2 was expected to have high percentage of overlap with SARS-CoV. RESULTS: In this study, we applied the single cell transcriptomics data of human bronchial epithelial cells (2B4 cell line) infected with SARS-CoV, which was annotated in the Expression Atlas database to expand this data to COVID-19. In addition, we employed system biology methods including gene ontology (GO) and Reactome pathway analyses to define functional genes and pathways in the infected cells with SARS-CoV. The transcriptomics analysis on the Expression Atlas database revealed that most genes from infected 2B4 cell line with SARS-CoV were downregulated leading to immune system hyperactivation, induction of signaling pathways, and consequently a cytokine storm. In addition, GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) were shown as top three GOs in the ontology network of infected cells with SARS-CoV. Meanwhile, R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected cells with SARS-CoV. PTEN plays a critical role in the activation of dendritic cells, B- and T-cells, and secretion of proinflammatory cytokines, which cooperates with downregulated genes in the promotion of cytokine storm in the COVID-19 patients. CONCLUSIONS: Based on the high similarity percentage of the transcriptome of SARS-CoV with SARS-CoV-2, the data of immunological regulations, signaling pathways, and proinflammatory cytokines in SARS-CoV infection can be expanded to COVID-19 to have a valid platform for future pharmaceutical and vaccine studies.
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Single-cell RNA sequencing revealed potential targets for immunotherapy studies in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is a solid tumor prone to chemotherapy resistance, and combined immunotherapy is expected to bring a breakthrough in HCC treatment. However, the tumor and tumor microenvironment (TME) of HCC is highly complex and heterogeneous, and there are still many unknowns regarding tumor cell stemness and metabolic reprogramming in HCC. In this study, we combined single-cell RNA sequencing data from 27 HCC tumor tissues and 4 adjacent non-tumor tissues, and bulk RNA sequencing data from 374 of the Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples to construct a global single-cell landscape atlas of HCC. We analyzed the enrichment of signaling pathways of different cells in HCC, and identified the developmental trajectories of cell subpopulations in the TME using pseudotime analysis. Subsequently, we performed transcription factors regulating different subpopulations and gene regulatory network analysis, respectively. In addition, we estimated the stemness index of tumor cells and analyzed the intercellular communication between tumors and key TME cell clusters. We identified novel HCC cell clusters that specifically express HP (HCC_HP), which may lead to higher tumor differentiation and tumor heterogeneity. In addition, we found that the HP gene expression-positive neutrophil cluster (Neu_AIF1) had extensive and strong intercellular communication with HCC cells, tumor endothelial cells (TEC) and cancer-associated fibroblasts (CAF), suggesting that clearance of this new cluster may inhibit HCC progression. Furthermore, ErbB signaling pathway and GnRH signaling pathway were found to be upregulated in almost all HCC tumor-associated stromal cells and immune cells, except NKT cells. Moreover, the high intercellular communication between HCC and HSPA1-positive TME cells suggests that the immune microenvironment may be reprogrammed. In summary, our present study depicted the single-cell landscape heterogeneity of human HCC, identified new cell clusters in tumor cells and neutrophils with potential implications for immunotherapy research, discovered complex intercellular communication between tumor cells and TME cells.
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Comprehensive bioinformatics and experimental analysis of PPOX reveals its carcinogenic effect in clear cell renal cell carcinoma.
BACKGROUND: Dysregulated heme biosynthesis is a hallmark of metabolic reprogramming in cancer; however, the role of protoporphyrinogen oxidase (PPOX), the penultimate enzyme in the heme synthesis pathway, remains poorly characterized in clear cell renal cell carcinoma (ccRCC). This study aims to elucidate the regulatory mechanism of PPOX on the malignant phenotype of ccRCC to evaluate its potential as a precision biomarker. METHODS: To investigate the role of PPOX in ccRCC, we integrated multi-omics bioinformatic analysis with experimental validation. First, PPOX expression and subcellular localization were assessed across multiple databases, including Tumor IMmune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), and the Human Protein Atlas (HPA). These findings were subsequently validated via immunohistochemistry (IHC) on ccRCC tissues and confirmed by RT-qPCR/Western blot across a panel of cell models (HK2, 786-O, 769-P, and A498). Utilizing the TCGA-KIRC cohort, Kaplan-Meier curves and univariate/multivariate Cox proportional hazards models were employed to determine the independent prognostic value of PPOX. Second, Mendelian Randomization (MR) using the inverse-variance weighted (IVW) method was performed to explore potential causal links between PPOX and ccRCC. Third, single-cell RNA sequencing (scRNA-seq) analysis mapped PPOX distribution within the tumor microenvironment (TME), while the CIBERSORT algorithm explored associations between PPOX expression, immune cell infiltration, and immune checkpoints. Fourth, potential therapeutic agents targeting PPOX were identified via drug sensitivity analysis and molecular docking. Finally, in vitro functional assays in ccRCC cell lines demonstrated the impact of PPOX on malignant phenotypes and the Wnt/β-catenin signaling pathway. RESULTS: PPOX was significantly upregulated in ccRCC and served as an independent risk factor for poor patient prognosis. A nomogram was initially constructed to illustrate its potential prognostic utility. MR analysis supported a potential causal relationship between PPOX levels and ccRCC risk. ScRNA-seq results revealed significant PPOX enrichment in malignant cell clusters, and its expression correlated with various immune cells and checkpoints, suggesting predictive value for immunotherapy. Furthermore, PPOX expression was associated with increased sensitivity to therapeutic agents such as 5-fluorouracil and doxorubicin. Mechanistic studies confirmed that PPOX expression positively correlated with cell proliferation, migration, and invasion, and regulated the Wnt/β-catenin signaling pathway. CONCLUSIONS: This study demonstrates that PPOX is a critical regulatory factor driving the malignant progression of ccRCC. The discovery of the PPOX-Wnt/β-catenin axis not only explains the biological basis of PPOX as a prognostic biomarker but also provides a potential metabolic therapeutic target for patients with advanced ccRCC.
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Analyzing the cellular and molecular atlas of ovarian mesenchymal cells provides a strategy against female reproductive aging.
Ovarian mesenchymal cells (oMCs) constitute a distinct microenvironment that supports folliculogenesis under physiological conditions. Supplementation of exogenous non-ovarian mesenchymal-related cells has been reported to be an efficient approach to improve ovarian functions. However, the development and cellular and molecular characteristics of endogenous oMCs remain largely unexplored. In this study, we surveyed the single-cell transcriptomic landscape to dissect the cellular and molecular changes associated with the aging of oMCs in mice. Our results showed that the oMCs were composed of five ovarian differentiated MC (odMC) populations and one ovarian mesenchymal progenitor (oMP) cell population. These cells could differentiate into various odMCs via an oMP-derived route to construct the ovarian stroma structures. Comparative analysis revealed that ovarian aging was associated with decreased quantity of oMP cells and reduced quality of odMCs. Based on the findings of bioinformatics analysis, we designed different strategies involving supplementation with young oMCs to examine their effects on female fertility and health. Our functional investigations revealed that oMCs supplementation prior to ovarian senescence was the optimal method to improve female fertility and extend the reproductive lifespan of aged females in the long-term.
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Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin.
The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5-12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.
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CCR7 immune cell receptor expression in inflammatory breast cancer.
Inflammatory breast cancer (IBC) is characterized by congestion of dermal lymphovascular spaces by tumor emboli. We characterized expression of CCR7, a lymphocyte homing chemokine receptor, in IBC cell lines and patient tissues. CCR7 gene expression was quantified using World IBC Consortium Database and correlated with protein expression in cell lines and IBC mastectomy samples post-neoadjuvant therapy. CCR7 expression on tissue microarray (TMA) was scored by staining pattern (complete vs. incomplete membranous), percent tumor stained, and staining intensity. CCR7 was highly expressed in IBC cell lines and a previously validated preclinical mouse model. Among 137 IBC and 252 non-IBC patient samples, CCR7 gene expression was significantly higher in IBC compared to non-IBC (p = 0.0007). Within IBC samples, gene expression was higher in HER2+ (p = 0.0002), basal (p = 0.0161), and ER- IBC patients (p = 0.010). Of the 24 IBC TMAs, almost all were CCR7 positive (23, 95.8%), with 15 (62.5%) demonstrating completely membranous expression. CCR7 is highly expressed in IBC cell lines and patient tumor samples, with preferential expression in HER2-positive and basal-like IBC subtypes. Given its high prevalence, CCR7 may serve as a potential target for antibody-based drug design in future IBC studies.
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Single-cell RNA sequencing and multi-omics analysis of prognosis-related staging in papillary thyroid cancer.
BACKGROUND: Papillary thyroid cancer (PTC) is the most common thyroid cancer, but current molecular features inadequately stratify its risk. Whether distinct underlying mechanisms can further classify PTC and improve prognostic precision remains unclear. METHODS: We integrated single-cell RNA sequencing data (158,577 cells from 11 PTC patients; GEO: GSE184362) with bulk-RNA sequencing data from The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort (501 patients). Multi-omics analyses were employed to elucidate PTC heterogeneity, identify malignant cell differentiation and prognosis-related genes (MCD&PRGs), and construct a novel molecular classification, the Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC). A prognostic risk score was developed, and the classification's prognostic relevance was further explored in an independent institutional cohort using qRT-PCR. RESULTS: Single-cell analysis revealed three malignant cell differentiation states (PTC1-3) and a 34-gene signature (MCD&PRGs). This formed the basis of our Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC), defining three subtypes: Inflammation-associated (IPTCC), BRAF/autophagy-related (BAPTCC), and lipid metabolism-related (LPTCC). These subtypes showed distinct molecular profiles and significantly different progression-free survival (IPTCC poorest, P = 0.044). A 7-gene risk score derived from MCD&PRGs independently predicted prognosis (multivariate HR = 21.511, P < 0.001). qRT-PCR validation in an independent cohort (n = 48) using key markers (DEPTOR, APOE, APOC1) confirmed that OSPTCC-based risk stratification correlated with adverse clinical features, including higher recurrence rates in the high-risk group (P = 0.007). CONCLUSIONS: This study introduces OSPTCC, a prognostically significant molecular classification for PTC based on tumor cell differentiation states. The identified subtypes, characterized by distinct biological mechanisms, provide deeper insights into PTC's molecular pathology and offer a framework for improved risk stratification and potential precision therapies.
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A single-cell atlas of bladder cancer unveils dynamic cellular composition and endothelial functional shifts during progression.
PURPOSE: Bladder cancer (BC) is characterized by high heterogeneity, with non-muscle-invasive (NMIBC) and muscle-invasive (MIBC) stages differing significantly in clinical behavior and outcomes. The transition from NMIBC to MIBC involves extensive tumor microenvironment (TME) remodeling, particularly in endothelial cells (ECs), which drive angiogenesis and modulate immune and extracellular matrix (ECM) interactions. However, the precise roles of ECs in this progression remain poorly defined. METHODS: Public single-cell RNA sequencing (scRNA-seq) datasets from 47 BC patients were analyzed to characterize endothelial cell heterogeneity and functional states across NMIBC and MIBC. Computational tools such as CellChat were applied to reconstruct cell-cell communication networks, focusing on pathways related to angiogenesis, immune crosstalk, and ECM remodeling. RESULTS: Twelve major cell types were identified, with endothelial cells exhibiting distinct transcriptional profiles between NMIBC and MIBC. NMIBC-associated ECs promoted adhesion and migration through HMGB1 and CXCL12 signaling. In contrast, MIBC was enriched in an ADAM10+ endothelial subset associated with vascular remodeling and activation of Wnt signaling via CTNNB1. Key ligand-receptor interactions highlighted the dynamic roles of ECs in TME modulation during BC progression. CONCLUSIONS: This study reveals stage-specific endothelial cell phenotypes and signaling networks in BC. The identification of an MIBC-specific ADAM10+ endothelial subset underscores its potential role in driving tumor progression and highlights opportunities for stage-adapted vascular-targeted therapies. These findings advance our understanding of BC pathogenesis and provide the foundation for novel therapeutic strategies.