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Single-Cell Transcriptome Analysis Reveals Inter-Tumor Heterogeneity in Bilateral Papillary Thyroid Carcinoma.
BACKGROUND: The tumor microenvironment (TME) plays a pivotal role in cancer progression in papillary thyroid carcinoma (PTC), yet the composition and the phenotype of cells within the TME in bilateral PTC are poorly understood. METHODS: We performed unbiased transcriptome-wide single-cell RNA sequencing (scRNA-seq) analysis on 29,561 cells from 3 pairs of bilateral PTC and 1 non-tumor thyroid sample. The results of the analysis were validated by a large-scale bulk transcriptomic dataset deposited in The Cancer Genome Atlas (TCGA) database. RESULTS: Our integrative analysis of thyroid follicular cells revealed 42 signaling pathways enriched in malignant follicular cells, including cytokine-cytokine receptor interaction, PI3K/Akt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and tumor necrosis factor (TNF) signaling pathway. A 6-gene signature (CXCL3, CXCL1, IL1A, CCL5, TNFRSF12A, and IL18) in the cytokine-cytokine receptor interaction pathway was constructed to predict the prognosis of patients with PTC, with high risk scores being associated with decreased overall survival [hazard ratio (HR) = 3.863, 95% CI = 2.233-6.682, p < 0.001]. Gene set variation analysis (GSVA) indicated that the pathways enriched in bilateral PTC were significantly different, indicating great heterogeneity in bilateral PTC, even with the same BRAF V600E mutation. Comprehensive analysis of T cells revealed that the proportion of CD8+ tissue-resident memory T cells expressing IFNG decreased in tumor samples with advanced N stage. Within the myeloid compartment, the ratio of suppressive M2-like to pro-inflammatory M1-like macrophages increased with advanced disease stage, which was confirmed in the bulk dataset using transcriptomic profiles. In addition, we also identified numerous biologically critical interactions among myeloid cells, T cells, and follicular cells, which were related to T-cell recruitment, M2-like macrophage polarization, malignant follicular cell progression, and T-cell inhibitory signaling. CONCLUSION: Our integrative analyses revealed great inter-tumor heterogeneity within the TME in bilateral PTC, which will offer assistance for precise diagnosis and treatment.
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Novel prognostic biomarker TBC1D1 is associated with immunotherapy resistance in gliomas.
BACKGROUND: Glioma, an aggressive brain tumor, poses a challenge in understanding the mechanisms of treatment resistance, despite promising results from immunotherapy. METHODS: We identified genes associated with immunotherapy resistance through an analysis of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases. Subsequently, qRT-PCR and western blot analyses were conducted to measure the mRNA and protein levels of TBC1 Domain Family Member 1 (TBC1D1), respectively. Additionally, Gene Set Enrichment Analysis (GSEA) was employed to reveal relevant signaling pathways, and the expression of TBC1D1 in immune cells was analyzed using single-cell RNA sequencing (scRNA-seq) data from GEO database. Tumor Immune Dysfunction and Exclusion (TIDE) database was utilized to assess T-cell function, while Tumor Immunotherapy Gene Expression Resource (TIGER) database was employed to evaluate immunotherapy resistance in relation to TBC1D1. Furthermore, the predictive performance of molecules on prognosis was assessed using Kaplan-Meier plots, nomograms, and ROC curves. RESULTS: The levels of TBC1D1 were significantly elevated in tumor tissue from glioma patients. Furthermore, high TBC1D1 expression was observed in macrophages compared to other cells, which negatively impacted T cell function, impaired immunotherapy response, promoted treatment tolerance, and led to poor prognosis. Inhibition of TBC1D1 was found to potentially synergistically enhance the efficacy of immunotherapy and prolong the survival of cancer patients with gliomas. CONCLUSION: Heightened expression of TBC1D1 may facilitate an immunosuppressive microenvironment and predict a poor prognosis. Blocking TBC1D1 could minimize immunotherapy resistance in cancer patients with gliomas.
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Non-coding Regulatory Variants in ASD (Autism Spectrum Disorders) Disrupt CTCF Domains and Shape Cell-Type-Specific Neurodevelopmental Landscapes Revealed by Single-Cell Analyses and Cortical Organoids.
BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with substantial genetic heterogeneity. While coding variants have been extensively studied, non-coding regulatory variants-which constitute approximately 99% of the human genome-remain largely underexplored. Their functional impact, particularly at the single-cell level and in organoid models, requires further characterization to better understand how these variants contribute to ASD pathogenesis. METHODS: We performed targeted sequencing of 85,394 active cis-regulatory elements (cCREs) from ENCODE v2 in 600 samples (200 trios) previously negative for pathogenic coding variants. Using the Sei deep learning framework, we predicted regulatory impact scores for de novo and ultra-rare inherited variants. Gene mapping of regulatory variants was done using the T Gene tool which assigns target genes by integrating expression data. Differential gene expression was assessed using single-nucleus RNA-seq data from ASD and control brains (n = 64 individuals, ~ 600,000) from Wembley et al, 2023 and temporal gene expression dynamics was characterized in human cortical organoids scRNA seq data across four developmental stages (23 days to 6 months) from the Cortical Organoid Atlas (Broad Institute). RESULTS: We identified 164 de novo variants in 103 probands and 53,023 ultra-rare inherited variants in 199 probands. Following Sei-based prioritization, 47 de novo variants in 44 probands and 13,258 inherited variants in 198 probands showed significant regulatory impact score > 1.1. De novo variants were enriched in promoter-like sequences (PLS) (p = 0.02), with 53.19% affecting CTCF-mediated chromatin boundaries. Inherited variants showed significant enrichment for CTCF-binding loss (p = 1.55×10-8) and demonstrated maternal transmission bias (50.85% vs 49.15%, p = 9.97×10-5). Single-cell analysis of the target predicted genes revealed distinct cellular architectures for both gene groups. De novo genes ( POGZ, ROCK2, NFIB ) showed focused dysregulation in oligodendrocytes and specific neuronal populations, while inherited genes ( SYNJ2, UBC, CLU, DLC1 ) exhibited broader effects across multiple cell types. Cortical organoid profiling demonstrated that de novo genes display progressive developmental activation with precise spatiotemporal specificity- POGZ and ROCK2 exhibited bimodal expression peaks at 1 and 6 months in outer radial glia while NFIB showed linear upregulation across progenitor populations. In contrast, inherited genes maintained constitutive expression throughout development, with the exception of CLU, which showed regulated upregulation at later stages. Remarkably, de novo regulatory genes (POGZ, NFIB) are previously identified autism risk genes in large scale sequencing studies, providing validation of dosage sensitivity mechanisms. CONCLUSIONS: Our analysis of regulatory variants in ASD indicates that non-coding mutations significantly contribute to ASD risk by disrupting CTCF domains, leading to alterations in cell type-specific gene regulation. Most de novo regulatory mutations predominantly target genes whose expression is dynamic, varying across different cell types and neurodevelopmental stages, whereas inherited regulatory variants affect constitutively expressed genes across multiple cell types, with stable expression throughout development with the exception of CLU. We also found that genes affected by de novo regulatory variants substantially overlap with those harboring pathogenic coding mutations identified in large-scale sequencing studies. These.
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TGF-β1 induces autophagy and mediates the effect on macrophages differentiation in primary liver cancer.
BACKGROUND: Tumor-associated macrophages (TAMs) are closely associated with tumor development and patient outcomes due to their plasticity and polarization capacity. Several distinct TAMs have been proposed, but a complete understanding of heterogeneity and differentiation spectrum of macrophage in human primary liver cancer remains elusive. METHODS: Deep single-cell RNA sequencing (scRNA-seq) data from 19 primary liver cancer patients were used to profile the transcriptomes of TAMs in liver cancer. Ingenuity pathway analysis (IPA) and in vitro experiments were used to explore possible mechanisms responsible for related signaling pathways altered at the transcriptional level. Finally, we analyzed the relationship between the abundance of the TAMs and the survival outcomes of the 428 patients in the Cancer Genome Atlas (TCGA). RESULTS: Transcriptional profiles allowed us to identify four distinct TAMs cell subsets based on molecular and functional properties and to reconstruct their developmental trajectory. Specifically, TAM_c4 was preferentially enriched and potentially expanded in the advanced-stage patients or those receiving immune checkpoint blockade therapy (ICT). Gene pathway analysis revealed aberrant TGFB1 activation in TAM_c4, which was experimentally confirmed to drive TAM phenotypic transitions via autophagy signaling. High abundance of TAM_c4 is found to be related to a short survival time and low abundance of CD8+ T cells in primary liver cancers. CONCLUSIONS: This integrated transcriptome compendium and experimental validation offer both mechanistic insights and a resource for understanding TAM heterogeneity in primary liver cancers.
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Analyzing the cytotoxicity and pro-tumorigenic role of acetyl tributyl citrate in prostate through network toxicology and the immune microenvironment.
BACKGROUND: Acetyl tributyl citrate (ATBC) is an alternative plasticizer to Di(2-ethylhexyl) phthalate (DEHP) and bisphenol A (BPA) that may contribute to the development of various cancers. The cytotoxic activity and pro-tumorigenic role of ATBC in prostate cancer (PCa) progression remains poorly understood, highlighting the need for a systematic evaluation of its molecular toxicity. METHODS: This study identified potential targets of ATBC in PCa by integrating multiple online databases, including GEO, ChEMBL, STITCH, and Swiss Target Prediction. Protein-protein interaction (PPI) analysis was performed on the intersecting targets, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, and immune microenvironment analyses. Subsequent these crucial targets were identified through the application of machine learning algorithms, specifically the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). The expression of these targets was validated using external datasets from The Cancer Genome Atlas (TCGA) and in vitro experiments. Molecular docking analysis was used to explore the interactions between the key genes and ATBC. Finally, virtual knockout and pathway enrichment analysis simulations on single-cell RNA-seq data were conducted to investigate the biological functions of these genes. RESULTS: Through the utilization of multiple online databases, we identified 45 potential targets associated with ATBC exposure-related PCa. Further refinement using STRING and Cytoscape software elucidated the PPI network. GO and KEGG pathway analyses revealed that these targets, associated with ATBC and PCa, were predominantly enriched in several signal pathways. Subsequent analysis employing machine learning algorithms identified three core targets: EZH2, DNMT1, and PRMT5, which influence the immune infiltration of PCa. Additionally, molecular docking analysis and in vitro experiments further confirmed these potential therapeutic targets. Virtual knockdown of DNMT1, EZH2, and PRMT5 reveals their essential role in preserving collagen-rich ECM integrity and cell-matrix adhesion to maintain prostatic stromal homeostasis, whose disruption mediates ATBC-induced cytotoxicity and potential pro-tumorigenic effects. CONCLUSION: Overall, the findings provide new insights into linking the effects of ATBC to PCa, and they provide a preventive and therapeutic strategy for PCa that exposure to the environments with excessive ATBC levels.
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Spatiotemporal dynamics of early oogenesis in pigs.
BACKGROUND: In humans and other mammals, the process of oogenesis initiates asynchronously in specific ovarian regions, leading to the localization of dormant and growing follicles in the cortex and medulla, respectively; however, the current understanding of this process remains insufficient. RESULTS: Here, we integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to comprehend spatial-temporal gene expression profiles and explore the spatial organization of ovarian microenvironments during early oogenesis in pigs. Projection of the germ cell clusters at different stages of oogenesis into the spatial atlas unveils a "cortical to medullary (C-M)" distribution of germ cells in the developing porcine ovaries. Cross-species analysis between pigs and humans unveils a conserved C-M distribution pattern of germ cells during oogenesis, highlighting the utility of pigs as valuable models for studying human oogenesis in a spatial context. RNA velocity analysis with ST identifies the molecular characteristics and spatial dynamics of granulosa cell lineages originating from the cortical and medullary regions in pig ovaries. Spatial co-occurrence analysis and intercellular communication analysis unveils a distinct cell-cell communication pattern between germ cells and somatic cells in the cortex and medulla regions. Notably, in vitro culture of ovarian tissues verifies that intercellular NOTCH signaling and extracellular matrix (ECM) proteins played crucial roles in initiating meiotic and oogenic programs, highlighting an underappreciated role of ovarian microenvironments in orchestrating germ cell fates. CONCLUSIONS: Overall, our work provides insight into the spatial characteristics of early oogenesis and the regulatory role of ovarian microenvironments in germ cell fate within a spatial context.
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Single-cell transcriptomics reveals the prognostic and immune infiltration significance of FOLFOX-bevacizumab treatment and lysine crotonylation characteristics in colon cancer.
BACKGROUND: Colon cancer (CC), a malignant tumor originating from the colonic mucosal epithelium, frequently exhibits limited efficacy in advanced patients undergoing first-line FOLFOX (oxaliplatin, leucovorin, and 5-fluorouracil)-bevacizumab (FOLFOX-Bev) therapy due to resistance. Lysine crotonylation (Kcr), a novel histone post-translational modification, regulates gene transcription and participates in processes such as cell proliferation, metabolic reprogramming, and epithelial-mesenchymal transition, presenting potential research value in CC. However, its impact on FOLFOX-Bev treatment response and immune microenvironment remodeling at the single-cell level remains unclear. Therefore, this study aimed to integrate single-cell transcriptomics with The Cancer Genome Atlas (TCGA) data to systematically characterize the role of Kcr modification in FOLFOX-Bev treatment response and immune microenvironment remodeling in CC, and to construct a Kcr-based prognostic risk model for guiding precision therapy. METHODS: This study integrated single-cell transcriptome with TCGA CC data [colon adenocarcinoma (COAD)]. Malignant cells were identified using Inference of Copy Number Variations and were stratified into high/low groups based on area under the curve cell-level enrichment analysis scores calculated for a Kcr-related gene set. Differential analysis and cell communication studies were performed. Sensitive/resistant malignant subpopulations were distinguished based on pre-/post-treatment changes, and their Kcr scores were compared to screen differentially expressed genes (DEGs) between "sensitive & high- Kcr" vs. "drug-resistant & low-Kcr" cells. In TCGA-COAD, Kcr single-sample Gene Set Enrichment Analysis (ssGSEA) scores, weighted gene co-expression network analysis modules, and single-cell DEGs were intersected. Utilizing the intersecting genes, a prognostic risk model was constructed via univariate-Least Absolute Shrinkage and Selection Operator (LASSO)-multivariate Cox regression. Immune infiltration, immunotherapy response, mutational landscape, and pathway enrichment features were assessed between high-/low-risk groups. RESULTS: Focusing on "sensitive & high-Kcr" vs. "drug-resistant & low-Kcr" cells, 387 key genes were identified. Univariate-LASSO-multivariate Cox regression identified four core biomarkers (USP53, UACA, C7orf50, and IDH2) to form the risk prognostic model, which demonstrated good predictive performance in two independent datasets. The two risk groups exhibited remarkably different immune characteristics: the high-risk group showed poorer predicted immunotherapy response and lower mutation rates. Six categories of differential candidate drugs were screened (e.g., Erlotinib_1168, AZD3759_1915, Dasatinib_1079, and Sepantronium.bromide_1941). CONCLUSIONS: This study is the first to reveal, at single-cell resolution, that Kcr modification drives FOLFOX-Bev resistance in CC by reshaping the immune microenvironment. The prognostic model, based on key genes (USP53/UACA/C7orf50/IDH2), accurately stratifies patient risk. This model provides novel insights for targeted therapy and immune-combination strategies in CC.
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SPOCK1 promotes the progression of breast cancer by modulating cancer-associated fibroblasts and exerts a synergistic effect with ANXA2.
BACKGROUND: SPOCK1, a matricellular glycoprotein, has been implicated in tumor progression, metastasis, and the tumor immune microenvironment, yet its specific roles in breast cancer (BRCA) remain unclear. This study aimed to systematically explore the expression pattern, prognostic significance, mutation landscape, immune association, and spatial localization of SPOCK1 in breast cancer through integrated multi-omics analyses. METHODS: Transcriptomic, genomic, and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were utilized. Bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) analyses were conducted, including functional enrichment, immune infiltration assessments, mutation profiling, and transcription factor activity analysis. Multiplex immunohistochemistry (mIHC) was performed to validate the spatial distribution of SPOCK1+ cancer-associated fibroblasts (CAFs) within the tumor microenvironment. Statistical analyses were performed using R and GraphPad Prism. RESULTS: SPOCK1 was broadly overexpressed in multiple cancer types and significantly associated with poor prognosis in BRCA. High SPOCK1 expression correlated with immune checkpoint activation, enhanced immune infiltration, and enriched metastasis-related pathways such as epithelial-mesenchymal transition (EMT) and TGF-β signaling. Single-cell analysis identified CAFs as the primary cell population expressing SPOCK1, with spatial mIHC confirming their close proximity to tumor cells. Furthermore, SPOCK1-high CAFs exhibited stronger intercellular communications with malignant cells via collagen, fibronectin, and IGFBP signaling pathways, alongside distinct transcription factor and metabolic profiles. In breast cancer CAF cell lines with knockdown of ANXA2 we found that the expression of both SPOCK1 and IGF1 was reduced. CONCLUSION: SPOCK1 serves as a critical regulator of breast cancer progression, influencing tumor metastasis and reshaping the immune microenvironment via CAF-mediated mechanisms. These findings suggest that targeting SPOCK1+ CAFs could offer new therapeutic opportunities for breast cancer treatment.
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Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references.
The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell populations. However, achieving accurate cell-type annotation using public references presents challenges due to inconsistent annotations, batch effects, and rare cell types. Here, we introduce SELINA (single-cell identity navigator), an integrative and automatic cell-type annotation framework based on a pre-curated reference atlas spanning various tissues. SELINA employs a multiple-adversarial domain adaptation network to remove batch effects within the reference dataset. Additionally, it enhances the annotation of less frequent cell types by synthetic minority oversampling and fits query data with the reference data using an autoencoder. SELINA culminates in the creation of a comprehensive and uniform reference atlas, encompassing 1.7 million cells covering 230 distinct human cell types. We substantiate its robustness and superiority across a multitude of human tissues. Notably, SELINA could accurately annotate cells within diverse disease contexts. SELINA provides a complete solution for human single-cell RNA-seq data annotation with both python and R packages.
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Transcriptomic and genomic profiling of multiple primary colorectal cancers reveals intratumor heterogeneity and a distinct immune microenvironment.
This study reported on the intratumor genomic and immunological heterogeneity of different tumor lesions from a single patient with multiple primary colorectal cancer (MPCC). The goal of this study was to explore the molecular and microenvironment characteristics of tumor lesions from different primary sites in a patient with MPCC. A total of three tumor lesions located in the hepatic flexure of the transverse colon, sigmoid colon, and rectum were collected from a 72-year-old male patient with MPCC. All three tumor samples were examined by using whole-exome sequencing (WES) and single-cell RNA sequencing (scRNA-seq). The transcriptome data of The Cancer Genome Atlas (TCGA) colon cancer (COAD) dataset were explored to characterize the biological impacts of certain immune cells. Only three nonsynonymous mutations were shared by all of the tumor lesions, whereas a number of single nucleotide variant (SNV) and copy number variation (CNV) mutations were shared by tumor samples from the sigmoid colon and rectum. Transcriptomic analysis showed that tumor lesions derived from the transverse colon had decreased levels of RTK, ERK, and AKT pathway activity, thus suggesting lower oncogenic properties in the transverse lesion compared to the other two samples. Further immune landscape evaluation by using single-cell transcriptomic analysis displayed significant intratumor heterogeneity in MPCC. Specifically, more abundant mucosal-associated invariant T (MAIT) cell infiltration was found in transverse colon tumor lesions. Afterwards, we found that higher MAIT cell infiltration may correlate with a better prognosis of patients with colon cancer (immunohistochemical status was MSI-L/pMMR) by using a publicly available TCGA dataset.