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Schwann Cell-Mediated M2-Like Macrophage Polarization in Rhabdomyosarcoma.
OBJECTIVE: To investigate the cellular components and immunological characteristics of the head and neck rhabdomyosarcoma (RMS) microenvironment. METHODS: We conducted single-cell transcriptomics to analyze the cellular components of the RMS microenvironment. CellChat was utilized for analyzing intercellular interactions. The cancer genome atlas database was used for validation. CIBERSORT was applied for immune infiltration profiling. Functional enrichment analyses were performed using the Kyoto Encyclopedia of Genes and Genomes. Gene set scores were calculated using single-sample gene set enrichment analysis. Subcutaneous allograft models and bulk RNA sequencing were used for validation. Flow cytometry and immunohistochemistry were used to identify M2-like macrophages. RESULTS: Our findings revealed an extremely low presence of neutrophils in RMS samples compared with normal sample. RMS sample with high Schwann cell infiltration exhibited an increase in M2-like macrophage infiltration. Receptor-ligand pairs, specifically MIF-CD74 and PTN-SDC3, were identified between Schwann cells and M2-like macrophages. In the RMS sample characterized by significant Schwann cell infiltration, M2-like macrophages demonstrate robust expression of axon guidance factors and are enriched in the axon guidance pathway. CONCLUSIONS: Our study provides valuable insights into the microenvironment and immunological characteristics of RMS, offering crucial information for further research and potential therapeutic strategies.
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Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer.
In this study, we comprehensively charted the cellular landscape of colorectal cancer (CRC) and well-matched liver metastatic CRC using single-cell and spatial transcriptome RNA sequencing. We generated 41,892 CD45- nonimmune cells and 196,473 CD45+ immune cells from 27 samples of six CRC patients, and found that CD8_CXCL13 and CD4_CXCL13 subsets increased significantly in liver metastatic samples that exhibited high proliferation ability and tumor-activating characterization, contributing to better prognosis of patients. Distinct fibroblast profiles were observed in primary and liver metastatic tumors. F3+ fibroblasts enriched in primary tumors contributed to worse overall survival by expressing protumor factors. However, MCAM+ fibroblasts enriched in liver metastatic tumors might promote generation of CD8_CXCL13 cells through Notch signaling. In summary, we extensively analyzed the transcriptional differences of cell atlas between primary and liver metastatic tumors of CRC by single-cell and spatial transcriptome RNA sequencing, providing different dimensions of the development of liver metastasis in CRC.
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Integrated single-cell and experimental validation links effector chondrocyte-associated oxidative stress to EIF6-related p47phox-NOX2 signaling in osteoarthritis.
BACKGROUND: Oxidative stress is closely involved in osteoarthritis (OA), but the cell-type-specific oxidative stress landscape and related regulatory molecules in human cartilage remain incompletely defined. This study aimed to identify effector chondrocyte (EC)-associated oxidative stress features in OA and to prioritize candidate molecules linked to p47phox-NOX2-related ROS responses. METHODS: Public human cartilage single-cell RNA sequencing datasets were integrated to construct an OA cartilage atlas. Oxidative stress activity was evaluated using multiple scoring methods, and high oxidative stress (HOS) cells were defined according to the top 10% of integrated oxidative stress scores. Cell-state and communication features were examined using CytoTRACE and CellChat. High-dimensional weighted gene co-expression network analysis, machine learning, protein-protein interaction network analysis, summary-data-based Mendelian randomization, and molecular docking were combined to prioritize candidate genes. EIF6 and YWHAB were examined in an inflammatory EC-like model, and EIF6-associated p47phox/TGF-β responses were further assessed in DMM-induced OA mouse tissues. RESULTS: Single-cell analysis identified an EC population enriched in OA cartilage. ECs showed relatively high oxidative stress activity and represented the largest component of HOS cells. The proportion of ECs among HOS cells increased in OA samples, and HOS cells were concentrated near the EC-enriched region in the UMAP space. HOS/EC-enriched populations showed broad communication with other chondrocyte populations and were associated with inflammation, stress-related signaling, and extracellular matrix remodeling pathways. Integrated computational analyses prioritized EIF6 and YWHAB as candidate molecules associated with EC-related oxidative stress. In inflammatory EC-like cells, IL-1β and TNF-α increased EIF6 and YWHAB expression. EIF6 or YWHAB knockdown attenuated IL-1β-induced p47phox and NOX2 expression and reduced ROS-related fluorescence, with EIF6 knockdown showing a stronger effect. Co-immunoprecipitation suggested altered YWHAB-p47phox interaction under inflammatory stimulation. In DMM-induced OA tissues, EIF6 knockdown reduced p47phox upregulation, whereas EIF6 overexpression increased TGF-β staining, which was partly attenuated by p47phox knockdown. CONCLUSION: This study identifies EC-associated oxidative stress features in OA cartilage and prioritizes EIF6/YWHAB-associated p47phox-NOX2 signaling as a candidate axis linked to inflammatory ROS-related responses and tissue-level OA responses.
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eRNAs Identify Immune Microenvironment Patterns and Provide a Novel Prognostic Tool in Acute Myeloid Leukemia.
Background: Enhancer RNAs (eRNAs) play an essential role in tumorigenesis as non-coding RNAs transcribed from enhancer regions. However, the landscape of eRNAs in acute myeloid leukemia (AML) and the potential roles of eRNAs in the tumor microenvironment (TME) remain unclear. Method: Gene expression data collected from The Cancer Genome Atlas (TCGA) project were combined with Histone ChIP-seq so as to reveal the comprehensive landscape of eRNAs. Single-sample gene set enrichment analysis algorithm (ssGSEA) and ESTIMATE were employed to enumerate immune cell infiltration and tumor purity. Results: Most prognostic eRNAs were enriched in immune-related pathways. Two distinct immune microenvironment patterns, the immune-active subtype and the immune-resistant subtype, were identified in AML. We further developed an eRNA-derived score (E-score) that could quantify immune microenvironment patterns and predict the response to immune checkpoint inhibitor (ICI) treatment. Finally, we established a prognostic nomogram combining E-score and other clinical features, which showed great discriminative power in both the training set [Harrell's concordance index (C index): 0.714 (0.651-0.777), p < 0.0001] and validation set [C index: 0.684 (0.614-0.755), p < 0.0001]. Calibration of the nomogram was also validated independently. Conclusion: In this study, we systematically understood the roles of eRNAs in regulating TME diversity and complexity. Moreover, our E-score model provided the first predictive model for ICI treatment in AML.
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Senescence gene expression in clear cell renal cell carcinoma: Role of tumor immune microenvironment and senescence-associated survival prediction.
Clear cell renal cell carcinoma (ccRCC) constitutes the most prevalent histopathologic subtype of renal cell carcinoma. The interplay between aging and cancer is complicated, and we provide a relatively new set of senescence genes that has not yet been used in the study of clear cell renal cell carcinoma. Our objective is to investigate the involvement of senescence in the development and diagnosis of ccRCC. RNA-seq and clinical data for ccRCC was obtained from the cancer genome atlas and gene expression omnibus databases. Consensus clustering analysis was performed to identify novel molecular subgroups. Tumor immune status was assessed using estimating stromal and immune cells in malignancy using expression data, microenvironment cell populations, and single-sample gene set enrichment analysis analyses. Functional analysis, including gene ontology, gene set variation analysis, and gene set enrichment analysis, was conducted to explore potential mechanisms. A prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Decision trees and nomograms were developed for survival prediction. SenMayo classified ccRCC patients into 2 molecular subtypes with significantly different survival rates, and significant differences in their immune status, characterized by poor prognosis with relatively high immune status. Besides, the differentially expressed genes between the 2 subgroups were mainly enriched in immune-related pathways. The burden of aging tissues and cells may lead to immune dysregulation and drug resistance, which could contribute to poor prognosis in ccRCC patients. Risk models, decision trees, and nomogram for ccRCC survival prediction have great potential applications. In conclusion, our study establishes a clear association between aging in ccRCC and the immune microenvironment, demonstrating the predictive potential of senescence genes for ccRCC prognosis.
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Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling.
BACKGROUND: Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs' detection in other cancer types. AIM: This study aims to detect potential PRBs for colon adenocarcinoma (COAD) samples through the cancer genome atlas (TCGA) by integrating muti-omics. MATERIALS AND METHODS: The multi-omics-based prognostic analysis (MPA) model was first constructed to systemically analyze the prognosis of colon cancer based on four-omics data of gene expression, exon expression, DNA methylation and somatic mutations on COAD samples. Then, the essential features related to prognosis were functionally annotated through protein-protein interaction (PPI) network and cancer-related pathways. Moreover, the significance of those essential prognostic features were further confirmed by the target regulation simulation (TRS) model. Finally, an independent testing dataset, as well as the single cell-based expression dataset were utilized to validate the generality and repeatability of PRBs detected in this study. RESULTS: By integrating the result of MPA modeling, as well the PPI network, integrated pathway and TRS modeling, essential features with gene symbols such as EPB41, PSMA1, FGFR3, MRAS, LEP, C7orf46, LOC285000, LBP, ZNF35, SLC30A3, LECT2, RNF7, and DYNC1I1 were identified as PRBs which provide high potential as drug targets for COAD treatment. Validation on the independent testing dataset demonstrated that these PRBs could be applied to distinguish the prognosis of COAD patients. Moreover, the prognosis of patients with different clinical conditions could also be distinguished by the above PRBs. CONCLUSIONS: The MPA and TRS models constructed in this paper, as well as the PPI network and integrated pathway analysis, could not only help detect PRBs as potential therapeutic targets for COAD patients but also make it a paradigm for the prognostic analysis of other cancers.
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Single-cell transcriptomic profiling reveals liver fibrosis in colorectal cancer liver metastasis.
Tumor fibrosis is recognized as a malignant hallmark in various solid tumors; however, the clinical importance and associated molecular characteristics of tumor fibrosis in liver metastases (LM) from colorectal cancer (CRLM) remain poorly understood. Here we show that patients with CRLM whose liver metastases (LM) exhibited tumor fibrosis (Fibrosis+ LM) had significantly worse progression-free survival (P = 0.025) and overall survival (P = 0.008). Single-cell RNA sequencing revealed that the tumor microenvironment of the Fibrosis+ LM was characterized by T cells with an exhausted phenotype, macrophages displaying a profibrotic and suppressive phenotype and fibrosis-promoting fibroblasts. Further investigation highlighted the pivotal role of VCAN_eCAF in remodeling the tumor fibrosis in the tumor microenvironment of Fibrosis+ LM, emphasizing potential targetable interactions such as FGF23 or FGF3-FGFR1. Validation through multiplex immunohistochemistry/immunofluorescence and spatial transcriptomics supported these findings. Here we present a comprehensive single-cell atlas of tumor fibrosis in LM, revealing the intricate multicellular environment and molecular features associated with it. These insights deepen our understanding of tumor fibrosis mechanisms and inform improved clinical diagnosis and treatment strategies.
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A single-cell atlas of adult Drosophila ovary identifies transcriptional programs and somatic cell lineage regulating oogenesis.
Oogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modeled extensively using the Drosophila ovary. Although different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic data set for the adult Drosophila ovary and connected tissues. Using this data set, we identified the transcriptional trajectory of the entire follicle-cell population over the course of their development from stem cells to the oogenesis-to-ovulation transition. We further identify expression patterns during essential developmental events that take place in somatic and germline cell types such as differentiation, cell-cycle switching, migration, symmetry breaking, nurse-cell engulfment, egg-shell formation, and corpus luteum signaling. Extensive experimental validation of unique expression patterns in both ovarian and nearby, nonovarian cells also led to the identification of many new cell type-and stage-specific markers. The inclusion of several nearby tissue types in this data set also led to our identification of functional convergence in expression between distantly related cell types such as the immune-related genes that were similarly expressed in immune cells (hemocytes) and ovarian somatic cells (stretched cells) during their brief phagocytic role in nurse-cell engulfment. Taken together, these findings provide new insight into the temporal regulation of genes in a cell-type specific manner during oogenesis and begin to reveal the relatedness in expression between cell and tissues types.
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High-Throughput Single-Cell Mass Spectrometry Reveals Abnormal Lipid Metabolism in Pancreatic Ductal Adenocarcinoma.
Even populations of clonal cells are heterogeneous, which requires high-throughput analysis methods with single-cell sensitivity. Here, we propose a rapid, label-free single-cell analytical method based on active capillary dielectric barrier discharge ionization mass spectrometry, which can analyze multiple metabolites in single cells at a rate of 38 cells/minute. Multiple cell types (HEK-293T, PANC-1, CFPAC-1, H6c7, HeLa and iBAs) were discriminated successfully. We found evidence for abnormal lipid metabolism in pancreatic cancer cells. We also analyzed gene expression in a cancer genome atlas dataset and found that the mRNA level of a critical enzyme of lipid synthesis (ATP citrate lyase, ACLY) was upregulated in human pancreatic ductal adenocarcinoma (PDAC). Moreover, both an ACLY chemical inhibitor and a siRNA approach targeting ACLY could suppress the viability of PDAC cells. A significant reduction in lipid content in treated cells indicates that ACLY could be a potential target for treating pancreatic cancer.
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Comprehensive characterization of the tumor microenvironment for assessing immunotherapy outcome in patients with head and neck squamous cell carcinoma.
The tumor microenvironment (TME) constitutes a complex milieu of cells and cytokines that maintain equilibrium between tumor progression and prognosis. However, comprehensive analysis of the TME and its clinical significance in head and neck squamous cell carcinoma (HNSCC) remains to be unreported. In this study, based on large-scale RNA sequencing data pertaining to single nucleotide variants (SNVs) and copy number variations (CNVs) in HNSCC patients from The Cancer Genome Atlas database, we analysed subpopulations of infiltrating immune cells and evaluated the role of TME infiltration pattern (TME score) in assessing immunotherapy outcome. TME signature genes involved in several inflammation and immunity signalling pathways were observed in the TME score subtype, which were considered immunosuppressive and potentially responsible for significantly worse prognosis. In comparison with SNV- and CNV-mediated tumor mutation burden, TME score can significantly differentiate between high- and low-risk HNSCC and predict immunotherapy outcome. Our data provide clarity on the comprehensive landscape of interactions between clinical characteristics of HNSCC and tumor-infiltrating immune cells. TME score seems to be a useful biomarker that can predict immunotherapy outcome in HNSCC patients.