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Distinct immunophenotypic profiles and neutrophil heterogeneity in colorectal cancer.
Colorectal cancer (CRC) exhibits significant molecular and immunological heterogeneity. Neutrophil infiltration patterns play a crucial yet poorly understood role in tumor progression and patient outcomes. This study presents a comprehensive single-cell atlas of the CRC tumor microenvironment (TME), integrating transcriptomic data from 388,511 cells across 98 samples from 63 patients. Employing advanced computational methods, we stratified patients based on their immune cell infiltration profiles, revealing distinct immunophenotypes with potential therapeutic implications. Our analysis focused on tissue-resident neutrophils (TRNs) and uncovered previously uncharacterized subpopulations with diverse functional states. Trajectory inference analysis revealed a dynamic differentiation path from normal-associated neutrophils to tumor-associated neutrophils, highlighting the remarkable plasticity of these cells within the tumor environment. By integrating single-cell data with bulk transcriptomic and clinical information, we identified specific neutrophil-derived gene signatures associated with poor prognosis in CRC, suggesting their potential as novel prognostic biomarkers. This study not only provides unprecedented insights into neutrophil heterogeneity in CRC but also identifies potential targets for immunomodulatory therapies. Our findings lay the groundwork for developing more nuanced, personalized immunotherapeutic strategies for CRC, potentially improving treatment efficacy for patients who currently show a limited response to existing immunotherapies.
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A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.
INTRODUCTION: Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers. METHODS: To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms-such as LASSO, CoxBoost, and StepCox-based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. RESULTS: Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. Multi-omics validation confirmed PLXNA3's prognostic relevance. Spatial and single-cell transcriptomics demonstrated their enrichment in malignant epithelial regions and negative association with immune cell infiltration, particularly CD8+ T cells and plasma cells. Transcription factor (TF) and microRNA (miRNA) correlation analyses revealed potential upstream regulators of PLXNA3 linked to tumor stemness and immune suppression. Functional enrichment indicated its association with cell cycle, DNA damage repair, and interferon signaling pathways. Immunohistochemistry (IHC) confirmed PLXNA3 overexpression in tumor tissues and its correlation with nodal metastasis. Moreover, drug sensitivity profiling and Connectivity Map (CMap) analysis identified potential compounds, including imatinib, MS-275 and fasudil, capable of reversing PLXNA3-driven transcriptional programs. DISCUSSION: This study identifies PLXNA3 as a novel immune-related biomarker in colorectal cancer and elucidates its multifaceted role in tumor progression, immune evasion, and therapeutic resistance. These findings provide a foundation for incorporating PLXNA3 into precision oncology frameworks for gastrointestinal malignancies.
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Multi-omics analysis of kinesin family member 2C in human tumors: novel prognostic biomarker and tumor microenvironment regulator.
Kinesin family member 2C (KIF2C) is the best-characterized member of the kinesin-13 family and is involved in accurately fine-tuned dynamics of mitotic spindles. As KIF2C is involved in both spindle formation and regulation of DNA double-strand breaks, precise regulation of KIF2C is essential to prevent malignant transformation associated with gains and losses of DNA content. In the present study, we initially reviewed The Cancer Genome Atlas database and observed that KIF2C is abundantly expressed in most tumor types. We then analyzed the gene alteration profile, protein expression, prognosis, and immune reactivities of KIF2C in more than 10,000 samples from several well-established databases. In addition, we conducted a gene enrichment set analysis to investigate the potential mechanisms underlying the role of KIF2C in tumorigenesis. Multi-omics analysis of KIF2C demonstrated significant statistical correlations between KIF2C expression and clinical prognosis, oncogenic signature gene sets, myeloid-derived suppressor cell infiltration, ImmunoScore, immune checkpoints, microsatellite instability, and tumor mutational burden across multiple tumors. Single-cell data showed that KIF2C is abundantly expressed in malignant cells. The experimental validation demonstrated that KIF2C is highly expressed in gastric cancer cell lines, gastric adenocarcinoma, and hepatocelluar carcinoma. The findings of this study provide important insight for understanding the role and mechanisms of KIF2C in tumorigenesis and immunotherapy in a variety of cancers.
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Integrative network toxicology and transcriptomic analyses identify DCP1B and HAS2 as key prognostic biomarkers in formaldehyde-associated Wilms tumor.
Background and objectivesWilms tumor, a common childhood malignancy, has been hypothesized to be associated with environmental factors such as formaldehyde exposure. As a potential endocrine disruptor with reported pro-proliferative effects, the molecular mechanism underlying the role of formaldehyde in Wilms tumor remains unclear. This study systematically investigated the mechanisms linking formaldehyde exposure to Wilms tumor using network toxicology and transcriptomic analysis.MethodsTranscriptomic data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, whereas formaldehyde-related targets were predicted using the Search Tool for Interactions of Chemicals and Comparative Toxicogenomics databases. Candidate genes were screened through differential expression (|log2FC| > 2.5, p < 0.05) and weighted gene coexpression network analyses. Prognostic key genes were identified using univariate and Lasso-Cox regression, with nomogram construction and survival analysis. Single-cell RNA sequencing data (GSE223373) were further analyzed to characterize cellular heterogeneity and the cell-type-specific expression patterns of the identified key genes.ResultsSeventy-nine candidate genes associated with Wilms tumor were identified using multidimensional intersection analysis. These genes were significantly involved in pathways such as microRNAs in cancer and the cell cycle. Univariate Cox and Lasso-Cox regression analyses identified DCP1B and HAS2 as key genes closely associated with the prognosis of patients with Wilms tumor. Kaplan-Meier survival analysis demonstrated that higher expressions of DCP1B and HAS2 were significantly associated with better overall survival in these patients. The nomogram model constructed based on the two key genes demonstrated excellent calibration (area under the curve > 0.8) and clinical applicability. Network analysis identified NEAT1, the hsa-let-7 microRNA, and transcription factors such as PDX1 and E2F1 were as core regulatory nodes. Single-cell analysis demonstrated pronounced stromal expansion within Wilms tumor specimens and further revealed that DCP1B was preferentially enriched in tumor epithelial cells, whereas HAS2 exhibited a stromal-skewed distribution, predominantly within mesangial cells.ConclusionsDCP1B and HAS2 were identified as key prognostic genes for Wilms tumor. These findings suggest that formaldehyde exposure is associated with Wilms tumor pathogenesis through the potential involvement of cancer-associated microRNA pathways and cell cycle signaling, which could contribute to dysregulated cell proliferation and apoptosis.
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A pan-cancer analysis of the FAT1 in human tumors.
FAT atypical cadherin 1 (FAT1) is one of the most mutagenic genes in tumors, and several critical studies have revealed its role in tumors, although no pan-cancer studies are currently available. Therefore, we explored the potential oncogenic role of FAT1 in 33 tumors based on The Cancer Genome Atlas and Gene Expression Omibus datasets. We found that FAT1 was strongly expressed in most tumors and significantly correlated with their prognosis. Additionally, we analyzed the association of FAT1 with tumors from multiple perspectives, including single-cell sequencing, mutations, high tumor mutational burden, microsatellite instability, immune cell infiltration, and immune microenvironment. Our first pan-cancer study provided a relatively comprehensive understanding of the oncogenic role of FAT1 in tumors.
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Cross-tissue single-cell atlas reveals Id2 promotes sepsis-induced tissue damage by mediating macrophage metabolic reprogramming via Pkm2.
As important immune cells, macrophage polarization is directly related to tissue damage in sepsis, and the polarization of macrophages is associated with their metabolic patterns. Previous studies exploring macrophage function in sepsis mainly focused on specific tissues, lacking comprehensive comparisons between tissues. Herein, we performed single-cell RNA sequencing (scRNA-seq) to systematically profile macrophages derived from the brain, heart, intestine, lung, spleen, and peripheral blood mononuclear cells (PBMCs) under homeostatic and septic conditions. Under steady-state, we detected the markers of macrophages in different tissues, classified the macrophages into 10 functional subtypes and compared the differences in their distribution among tissues. In sepsis, we found that Pkm2 and Id2 played important roles in macrophage glycolysis. Mechanistically, Id2 mediated the metabolic reprogramming of macrophages by regulating the chromatin accessibility of Pkm2. Helichrysetin, an inhibitor of Id2, could significantly alleviate tissue damage and increase the survival of septic mice. In summary, our research depicted a cross-tissue macrophage landscape at the single-cell level that encompasses both homeostasis and sepsis. We also provided a new target and a potential drug for the treatment of sepsis by inhibiting macrophage metabolic reprogramming.
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Assessment of intra-tumoural colorectal cancer prognostic biomarkers using RNA in situ hybridisation.
Genome-wide expression studies using microarrays and RNAseq have increased our understanding of colorectal cancer development. Translating potential gene biomarkers from these studies for clinical utility has typically relied on PCR-based technology and immunohistochemistry. Results from these techniques are limited by tumour sample heterogeneity and the lack of correlation between mRNA transcript abundance and corresponding protein levels. The aim of this research was to investigate the clinical utility of the RNA in situ hybridisation technique, RNAscope®, for measuring intra-tumoural gene expression of potential prognostic markers in a colorectal cancer cohort. Two candidate gene markers (GFI1 and TNFRSF11A) assessed in this study were identified from a previous study led by the The Cancer Genome Atlas (TCGA) Network, and analysis was performed on 112 consecutively collected, archival FFPE colorectal cancer tumour samples. Consistent with the TCGA Network study, we found reduced GFI1 expression was associated with high-grade and left-sided tumours, and reduced TNFRSF11A expression was associated with metastasis and high nodal involvement. RNAscope® combined with image analysis also enabled quantification of GFI1 and TNFRSF11A mRNA expression levels at the single cell level, allowing cell-type determination. These data showed that reduced mRNA transcript abundance measured in patients with poorer prognosis occurred in carcinoma cells, and not lymphocytes, stromal cells or normal epithelial cells. To our knowledge, this is the first study to assess the intra-tumoural expression patterns of GFI1 and TNFRSF11A and to validate their microarray expression profiles using RNAscope. We also demonstrate the utility of RNAscope® technology to show that expression differences are derived from carcinoma cells rather than from cells located in the tumour microenvironment.
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Single-cell full-length transcriptome of human lung reveals genetic effects on isoform regulation beyond gene-level expression.
Genetic regulation of splicing uniquely contributes to trait-associated genome-wide association studies (GWAS) signals. However, quantitative trait loci (QTL) analysis using short-read sequencing of bulk tissues fails to capture full-length and cell-type-specific isoforms. Here, we present an isoform-level lung cell atlas from 129 never-smoking Korean women using single-cell long-read RNA-sequencing, identifying abundant unannotated and cell-type-specific isoforms. Isoform-level signatures of 37 lung cell types display a larger difference and therefore improve cell-type classification compared to gene-level expression. Notably, isoform-QTLs (isoQTLs) detect unannotated and/or cell-type-specific isoforms with independent genetic regulation from expression-QTL (eQTL), supported by enriched splicing functional elements. IsoQTLs nominate susceptibility isoforms from previously unexplained lung function and cancer GWAS loci, via eQTL-independent signals. We highlight a potentially functional novel variant of PPIL6 in multiciliated cells underlying lung cancer risk through alternative splicing. This isoform-level resource advances our understanding of cell-type-specific isoform regulation and its contribution to lung traits and diseases.
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Landscape of Epstein-Barr virus gene expression and perturbations in cancer.
Epstein-Barr virus (EBV) is the causative agent for multiple neoplastic diseases of epithelial and lymphocytic origin1-3. The heterogeneity of the viral elements expressed and the mechanisms by which these coding and non-coding genes maintain cancer cell properties in vivo remain elusive4,5. Here we conducted a multi-modal transcriptomic analysis of EBV-associated neoplasms and identified that the ubiquitously expressed RPMS1 non-coding RNAs support cancer cell properties by disruption of the interferon response. Our map of EBV expression shows a variable, but pervasive expression of BNLF2 discerned from the overlapping LMP1 RNA in bulk sequencing data. Using long-read single-molecule sequencing, we identified three new viral elements within the RPMS1 gene. Furthermore, single-cell sequencing datasets allowed for the separation of cancer cells and healthy cells from the same tissue biopsy and the characterization of a microenvironment containing interferon gamma excreted by EBV-stimulated T-lymphocytes. In comparison with healthy epithelium, EBV-transformed cancer cells exhibited increased proliferation and inhibited immune response induced by the RPMS1-encoded microRNAs. Our atlas of EBV expression shows that the EBV-transformed cancer cells express high levels of non-coding RNAs originating from RPMS1 and that the oncogenic properties are maintained by RPMS1 microRNAs. Through bioinformatic disentanglement of single cells from cancer tissues we identified a positive feedback loop where EBV-activated immune cells stimulate cancer cells to proliferate, which in turn undergo viral reactivation and trigger an immune response.
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Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain.
Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1 technologies to generate 301,626 methylomes and 176,003 chromatin conformation-methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular-spatial and regulatory genome diversity of the mouse brain.