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Single-cell analyses unravel ecosystem dynamics and intercellular crosstalk during gallbladder cancer malignant transformation.
BACKGROUND: Gallbladder cancer (GBC) is a rare but aggressive malignancy, often detected late due to early asymptomatic stages. Understanding cellular and molecular changes from normal tissue to high-grade intraepithelial neoplasia (HGIN) and invasive GBC is vital for identifying early biomarkers and therapeutic targets. METHODS: We performed single-cell RNA sequencing on 98,113 cells derived from 2 normal adjacent tissues (NAT), 2 HGIN, and 6 GBC samples. The cellular diversity and heterogeneity, particularly within epithelial and immune cell populations in NAT-HGIN-GBC, were investigated utilizing single-cell RNA sequencing, bulk RNA sequencing (bulk RNA-seq), and 10 machine learning methodologies. Furthermore, the intercellular crosstalk between epithelial cells and tumor immune microenvironment cells was examined and validated through multiplex immunofluorescence staining. RESULTS: The constructed cell atlas elucidated alterations in the immune landscape across various states of NAT-HGIN-GBC, highlighting a more pronounced inhibitory immune microenvironment in GBC. The epithelial subtype TOP2A+ Epi is markedly elevated in GBC and is correlated with a poor prognosis. Key genes associated with this subtype may include GMNN, CYTOR, KLK6, and BIRC5. Similarly, immunosuppressive macrophages, identified as TOP2A+ Macro, also increase along the NAT-HGIN-GBC sequence and are linked to reduced patient survival. Furthermore, TOP2A+ Macro and CD8+ exhausted T cells (CD8+ Tex) engage in intercellular communication with epithelial TOP2A+Epi cells via the TWEAK/FN14 signaling pathway, thereby promoting tumor progression and immune evasion in GBC. The findings were further corroborated through multiplex immunofluorescence staining conducted on specimens from patients. CONCLUSIONS: This study elucidates significant alteration in the cellular ecosystems and intercellular signaling within the tumor immune microenvironment across the NAT-HGIN-GBC sequence. It identifies TOP2A, TWEAK, and FN14 as potential biomarkers and therapeutic targets for GBC.
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Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.
PURPOSE: Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicators. Metabolic reprogramming, a cancer hallmark, is linked to immune microenvironment remodeling, yet its role in LGG prognostic modeling remains underexplored. This study aims to develop a robust metabolism-related prognostic signature and elucidate its interaction with the immune microenvironment. MATERIALS AND METHODS: Multi-omics data from 1322 LGG patients were obtained from public databases, including the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and others. Metabolism-related genes were identified using three strategies: (1) differential expression analysis; (2) univariate Cox regression; and (3) weighted gene co-expression network analysis (WGCNA). Overlapping genes were further refined using protein-protein interaction (PPI) network analysis and four algorithms. We systematically compared 101 machine learning algorithms and selected the Cox model with likelihood-based boosting (CoxBoost) and Ridge regression (Ridge) to construct the hub metabolism-related gene risk score (HMRG-RS). RESULT: A total of 7 hub metabolic genes were identified (TYMS, PLA2G5, GPX7, GLRX, CYP17A1, ALOX15B, ACACB). HMRG-RS demonstrated good prognostic predictive performance across multiple external validation cohorts, with an average concordance index (C-index) of 0.723 and 1/3/5-year area under the receiver operating characteristic curve (AUC) of 0.778/0.797/0.745. Patients in the high-risk group exhibited significantly shorter survival and an immunosuppressive microenvironment characterized by M2 macrophage enrichment and increased tumor mutational burden(TMB). Notably, the prognostic value of HMRG-RS and the metabolic subtypes it characterizes were significantly dependent on Isocitrate dehydrogenase 1 (IDH1) mutation status. Drug sensitivity analysis revealed differential responsiveness to specific chemotherapeutic/targeted agents (e.g., AZD6482, fluvastatin) across risk groups. Molecular docking further predicted multiple therapeutic compounds (e.g., prunellin, mometasone, isoliquiritigenin) with high affinity for pivotal metabolic genes. Single-cell analysis confirmed high expression of hub metabolism-related genes (HMRGs) in myeloid cells (particularly metabolically active protumor M2 macrophages), implicating them in lipid metabolism reprogramming and immune evasion. CONCLUSION: This study constructed and validated a metabolism-driven prognostic model. The model enables prognostic stratification of LGG patients and links high-risk scores to metabolic dysregulation and an immunosuppressive microenvironment characterized by M2 macrophage enrichment, based on multi-omics data. Mechanistic exploration indicates this association is particularly pronounced in myeloid cells, predominantly within metabolism-related M2 macrophage subpopulations. Furthermore, computational analysis suggests differences in drug sensitivity between risk groups and identifies potential therapeutic compounds, providing clues for future exploration of therapeutic strategies targeting metabolic-immune interactions.
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Identification of Smmhc-expressing mesenchymal cells in orofacial bone at single-cell resolution.
Craniofacial bone regeneration remains a major clinical challenge, yet the identity of orofacial mesenchymal stem/stromal cells (OMSCs) has not been fully elucidated. Here, we performed single-cell RNA sequencing (scRNA-seq) on mouse orofacial bone and identified multiple stromal cell clusters. Cell-cell communication mapping and trajectory inference uncovered the heterogeneity of OMSCs and functional divergence among subpopulations. We identified a previously unrecognized population, Smmhc-expressing mesenchymal stem/stromal cells (MSCs), at the earliest stage of the progenitor lineage trajectory. In vivo lineage tracing demonstrated that Smmhc+ MSCs are multipotent, giving rise to osteoblasts, osteocytes, periodontal ligament (PDL) cells, and dental pulp cells. Targeted ablation of Smmhc+ MSCs using SmmhcCreER;iDTR mouse model led to impaired orofacial bone development and disrupted orofacial tissue homeostasis, characterized by reduced osteogenic differentiation and non-cell autonomous reduction of bone resorption. Collectively, this study establishes a cellular atlas of OMSCs and identifies Smmhc+ MSCs as a functionally indispensable subset for craniofacial bone homeostasis, orchestrating the dynamic balance between osteogenesis and bone resorption within the orofacial skeletal niche.
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Identification and validation of transferrin receptor protein 1 for predicting prognosis and immune infiltration in lower grade glioma.
INTRODUCTION: Transferrin receptor protein 1 (TFRC), an ananda molecule associated with ferroptosis, has been identified as affecting a wide spectrum of pathological processes in various cancers, but the prognostic value correlates with the tumor microenvironment of TFRC in lower-grade glioma (LGG) is still unclear. MATERIALS AND METHODS: Clinical pathological information and gene expression data of patients with LGG come from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GTEx, Oncomine, UCSC Xena, and GEO databases. We then used various bioinformatics methods and mathematical models to analyze those data, aiming to investigate the clinical significance of TFRC in LGG and illustrate its association with tumor immunity. In addition, the molecular function and mechanisms of TFRC were revealed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Immunohistochemical experiments and single-cell analysis have been performed. RESULTS: TFRC expression was highly expressed in many tumors and showed a poor prognosis. Including gliomas, it was significantly associated with several poor clinical prognostic variables, tumor immune microenvironment, tumor mutational burden (TMB), m6a modification, and ferroptosis in LGG. TFRC as a key factor was further used to build a prediction nomogram. The C-index, calibration curve, and decision curve analysis showed the nomogram was clinically useful and calibration was accurate. At the same time, we also demonstrated that promoter hypomethylation of DNA upstream of TFRC could lead to high TFRC expression and poor overall survival. There is a significant correlation between TFRC and CD8 + T cell, macrophage cell infiltration, and several immune checkpoints, such as PD-L1(cd274), CTLA4, and PD1, suggesting a novel direction for future clinical application. Functional and molecular mechanism analysis showed an association of TFRC expression with immune-related pathways through GSEA, GO, and KEGG analysis. Finally, immunohistochemical experiments and single-cell analysis confirmed the expression of TFRC in glioma. CONCLUSION: TFRC may be a potential prognostic biomarker and an immunotherapeutic target for glioma.
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Single-nucleus and spatial transcriptomics of paediatric ovary: Molecular insights into the dysregulated signalling pathways underlying premature ovarian insufficiency in classic galactosemia.
BACKGROUND: Classic galactosemia (CG) is an inborn error of galactose metabolism caused by mutations in the GALT gene. Premature ovarian insufficiency (POI) is a later complication that affects 80% of women with CG due to a significant decline in ovarian reserve (primordial follicle pool). The definite mechanisms underlying the early onset of POI in CG patients are not fully understood. METHODS: In this study, we performed single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics on ovary tissue biopsies from prepubertal girls diagnosed with CG to investigate dynamic changes in gene expression and altered signalling pathways in granulosa cells, oocytes, and stromal cells. RESULTS: We generated single-nucleus and spatial transcriptomics atlas of human ovaries from prepubertal girls diagnosed with and without CG. snRNA-seq profiling of the paediatric ovary revealed a diverse ovarian microenvironment with seven distinct major cell types. Our transcriptomic analysis revealed an increase in the expression of several endoplasmic reticulum stress and oxidative stress associated genes, which can promote apoptosis of granulosa cells in CG. PTEN/PI3K/AKT signalling, which is crucial for primordial follicle activation and survival was dysregulated as supported by upregulated PTEN transcripts and a significant reduction in phospho-AKT levels in the granulosa cells and oocytes. We also found a marked increase in expression of phospho-H2A.X, LC3A/B and CASP9 in the primordial follicles of CG ovaries suggesting DNA damage, autophagy, and accelerated follicular atresia. Furthermore, we noticed genes participating in extracellular matrix organisation, integrin and gap junction signalling, essential for structural support of the ovarian stroma were profoundly altered. CONCLUSIONS: Our findings provide molecular insights into the dysregulated cellular signalling pathways essential for primordial follicle growth and survival that can explain the etiology of POI in CG patients. This study has implications in the development of future therapeutic interventions to preserve ovarian function and promote female reproductive health. HIGHLIGHTS: Created a comprehensive single-nucleus transcriptomic atlas and spatial landscape of paediatric ovary tissue from prepubertal girls diagnosed with classic galactosemia (CG). Our transcriptomic analysis revealed activation of genes associated with ER-stress signalling, oxidative stress response and ATM signalling/DNA damage response as shown by significant increase in expression of p-EIF2A, p-H2A.X and LC3A/B in the primordial follicles of CG ovary. PTEN/PI3K/AKT signalling pathways was dysregulated evidenced by a significant reduction in phospho-AKT expression in the primordial follicles of CG ovary, suggesting impaired follicle activation and survival.
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SCAFE: a software suite for analysis of transcribed cis-regulatory elements in single cells.
MOTIVATION: Cell type-specific activities of cis-regulatory elements (CRE) are central to understanding gene regulation and disease predisposition. Single-cell RNA 5'end sequencing (sc-end5-seq) captures the transcription start sites (TSS) which can be used as a proxy to measure the activity of transcribed CREs (tCREs). However, a substantial fraction of TSS identified from sc-end5-seq data may not be genuine due to various artifacts, hindering the use of sc-end5-seq for de novo discovery of tCREs. RESULTS: We developed SCAFE-Single-Cell Analysis of Five-prime Ends-a software suite that processes sc-end5-seq data to de novo identify TSS clusters based on multiple logistic regression. It annotates tCREs based on the identified TSS clusters and generates a tCRE-by-cell count matrix for downstream analyses. The software suite consists of a set of flexible tools that could either be run independently or as pre-configured workflows. AVAILABILITY AND IMPLEMENTATION: SCAFE is implemented in Perl and R. The source code and documentation are freely available for download under the MIT License from https://github.com/chung-lab/SCAFE. Docker images are available from https://hub.docker.com/r/cchon/scafe. The submitted software version and test data are archived at https://doi.org/10.5281/zenodo.7023163 and https://doi.org/10.5281/zenodo.7024060, respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Annexin A's Life in Pan-Cancer: Especially in Glioma Immune Cells.
The Annexin A (ANXA) family plays a critical role in cancer, with particular emphasis on their prognostic significance in pan-cancer analyses and gliomas. By integrating multi-omics data from The Cancer Genome Atlas (TCGA) and single-cell sequencing analysis, we conducted a comprehensive evaluation of ANXA2 and ANXA4 to investigate their expression patterns and functional impacts across various cancers, with a focus on glioblastoma (GBM). Our analysis encompassed several key components, including literature review, identification of differentially expressed genes (DEGs) in cancer, survival analysis, co-expression studies, competing endogenous RNA networks, cellular functional analysis, tumor microenvironment response to chemotherapy, and tumor stemness. Special attention was given to glioblastoma and low-grade glioma. Notably, our findings highlighted discrepancies among the analytical tools used, underscoring the necessity of employing multiple methods for accurate identification of DEGs. Additionally, we determined that ANXA2 and ANXA4 are predominantly expressed by M2 macrophages in GBM, based on our characterization of human glioma macrophages. These results suggest a strong correlation between ANXA2 and ANXA4 expression levels and the presence of macrophages and CD4 + resting memory T cells in gliomas, offering valuable insights into the complex interplay between the ANXA family and cancer progression.
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Single-Cell Atlas Reveals the Hemocyte Subpopulations and Stress Responses in Asian Giant Softshell Turtle during Hibernation.
Hibernation in turtle species is an adaptive survival strategy to colder winter conditions or food restrictions. However, the mechanisms underlying seasonal adaptions remain unclear. In the present study, we collected hemocytes from Pelochelys cantorii and compared the molecular signature of these cells between the active state and hibernation period based on single-cell RNA sequencing (scRNA-seq) analysis. We found six cell types and identified a list of new marker genes for each cell subpopulation. Moreover, several heat shock genes, including the Hsp40 family chaperone gene (DNAJ) and HSP temperature-responsive genes (HSPs), were upregulated during the hibernation period, which predicted these genes may play crucial roles in the stress response during hibernation. Additionally, compared to hemocytes in the active state, several upregulated differentially expressed immune-related genes, such as stat1, traf3, and socs6, were identified in hemocytes during the hibernation period, thus indicating the important immune function of hemocytes. Therefore, our findings provide a unified classification of P. cantorii hemocytes and identify the genes related to the stress response, thereby providing a better understanding of the adaptive mechanisms of hibernation.
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Potential molecular metabolic mechanisms underlying the effects of cimifugin in gastric cancer through single-cell and bulk RNA sequencing combined with network pharmacology.
BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, posing a significant clinical challenge due to its complex tumor microenvironment (TME) and metabolic heterogeneity. Despite continuous improvements in treatment strategies including surgery, chemotherapy, and targeted therapies, the metabolic reprogramming in GC continues to impede treatment efficacy, highlighting an urgent need for the development of novel therapeutic strategies. This persistent issue underscores the urgent need for novel therapeutic approaches that can effectively address the diverse and dynamic characteristics of GC. Cimifugin, a traditional Chinese medicine (TCM), has garnered attention for its potential role in alleviating inflammation, neurological disorders, pain, and metabolic disorders. Its multi-targeting properties and minimal side effects suggest a broad potential for cancer management, which is currently being explored. This study aims to delineate the molecular mechanisms that cimifugin may impact within the TME and metabolic pathways of GC, with the expectation of contributing to a deeper understanding of GC and the development of innovative treatment strategies. METHODS: We identified the GC-related TME cell types and metabolic profiles and pathways by using relevant data from the single-cell RNA sequencing (scRNA-seq) database GSE134520 and the stomach adenocarcinoma (STAD) data set from The Cancer Genome Atlas (TCGA). We also assessed the effects of cimifugin on MKN28 cell proliferation, invasion, and migration. By using six public platforms, we comprehensively predicted the potential biological targets of cimifugin. Clinical prognosis and immunohistochemistry (IHC), molecular docking, and dynamics simulations were used to confirm the clinical relevance and stability of the aforementioned targets. RESULTS: Cimifugin inhibited MKN28 cell proliferation, migration, and invasion. Cimifugin may potentially act on various metabolic pathways in GC, including folate biosynthesis, xenobiotic metabolism via cytochrome P450 (CYP), glutathione metabolism, steroid hormone biosynthesis, and tryptophan metabolism. Cimifugin was noted to stably bind to three significant core targets associated with metabolic reprogramming in GC: AKR1C2, MAOB, and PDE2A; all three targets were strongly expressed in endocrince cells, pit mucous cells (PMCs), and common myeloid progenitors (CMPs). CONCLUSIONS: We verified the pharmacological effects of cimifugin on GC cell proliferation, invasion, and migration. AKR1C2, MAOB, and PDE2A were identified as the key targets of cimifugin in GC-related metabolic reprogramming and pathogenesis. Our research provides preliminary insights into the potential therapeutic effects of cimifugin, which could be considered for future exploration in the context of GC treatment.
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SMG9 is a novel prognostic-related biomarker in glioma correlating with ferroptosis and immune infiltrates.
BACKGROUND: Glioma is the most frequent type of malignancy that may damage the brain with high morbidity and mortality rates and patients' prognoses are still dismal. Ferroptosis, a newly uncovered mode of programmed cell death, may be triggered to destroy glioma cells. Nevertheless, the significance of ferroptosis-related genes (FRGs) in predicting prognosis in glioma individuals is still a mystery. METHODS: The CGGA (The Chinese Glioma Atlas), GEO (Gene Expression Omnibus), and TCGA (The Cancer Genome Atlas) databases were all searched to obtain the glioma expression dataset. First, TCGA was searched to identify differentially expressed genes (DEGs). This was followed by a machine learning algorithm-based screening of the glioma's most relevant genes. Additionally, these genes were subjected to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. The chosen biological markers were then submitted to single-cell, immune function, and gene set enrichment analysis (GSEA). In addition, we performed functional enrichment and Mfuzz expression profile clustering on the most promising biological markers to delve deeper into their regulatory mechanisms and assess their clinical diagnostic capacities. RESULTS: We identified 4444 DEGs via differential analysis and 564 FRGs from the FerrDb database. The two were subjected to intersection analysis, which led to the discovery of 143 overlapping genes. After that, glioma biological markers were identified in fourteen genes by the use of machine learning methods. In terms of its use for clinical diagnosis, SMG9 stands out as the most significant among these biomarkers. CONCLUSION: In light of these findings, the identification of SMG9 as a new biological marker has the potential to provide information on the mechanism of action and the effect of the immune milieu in glioma. The promise of SMG9 in glioma prognosis prediction warrants more study.