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Integrative Multi-Omics and Functional Characterization Reveal MCM4 as a Key Oncogenic Regulator in Hepatocellular Carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) is a highly lethal malignancy with limited therapeutic options and poor prognosis. Identifying robust prognostic biomarkers and therapeutic targets is essential for improving patient outcomes. Minichromosome maintenance complex component 4 (MCM4), a DNA replication licensing factor, has been associated various malignancies, yet its involvement in HCC remains underexplored. METHODS: We performed integrative bioinformatics analyses on three public HCC datasets (GSE14520, GSE56545, and GSE84402) to identify consistently dysregulated genes. Functional enrichment analyses were conducted using GO, KEGG, and Reactome databases. PPI networks were constructed via STRING. The expression and prognostic value of MCM4 were evaluated using GEPIA, Human Protein Atlas, and KM-Plotter. Single-cell and spatial transcriptomics from the HCCDB were analyzed to explore MCM4 localization. Functional roles of MCM4 were validated in vitro using siRNA-mediated knockdown and plasmid-based overexpression in HepG2 and Huh7 cells. RESULTS: MCM4 was identified as a consistently upregulated gene in HCC and was associated with poor overall, disease-free, recurrence-free, and disease-specific survival. Single-cell and spatial transcriptomic analyses revealed MCM4 enrichment in proliferative tumor regions. Functional assays demonstrated that MCM4 promotes HCC cell growth, motility, invasiveness, and enhances EMT and stemness. Conversely, MCM4 knockdown attenuated these malignant phenotypes. CONCLUSION: Our study establishes MCM4 as a key regulator of HCC progression and a potential prognostic biomarker. These findings suggest that MCM4 may serve as a potential target and underscore integrative and spatial transcriptomic approaches in cancer biomarker discovery.
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A single-nucleus chromatin accessibility atlas reveals epigenetic dynamics in aging mouse skeletal muscle.
Skeletal muscle undergoes a progressive decline in mass and function with aging, a condition that in its extreme form is known as sarcopenia. This is driven by complex cellular and molecular alterations, such as shifts in myonucleus composition, increased fibrosis, and fat or immune cell infiltration. Despite extensive research, effective therapeutic interventions for sarcopenia remain limited. Recent advances in single-cell omics technologies have begun to unravel the cellular and molecular heterogeneity of mouse and human skeletal muscle across the lifespan, identifying age-enriched cell states and dynamic transcriptional changes. However, epigenetic regulation during skeletal muscle aging is less well characterized. To help address this gap, we performed single-nucleus Assay for Transposase-Accessible Chromatin using sequencing (snATAC-seq) on skeletal muscle from young adult and aged male mice, generating chromatin accessibility profiles from over 43,000 nuclei. Among other findings, our analyses reveal an age-enriched pro-atrophy subpopulation of type IIb myonuclei marked by increased chromatin accessibility at the Ampd3 locus. Furthermore, we delineate the epigenetic mechanisms underlying the transition of healthy type IIb myonuclei into Ampd3+ myonuclei, revealing key chromatin remodeling events that drive this phenotypic shift. Moreover, by integrating with an existing single-nucleus RNA sequencing dataset of the same anatomical origin, we identified thousands of cell-type-specific cis-regulatory elements related to aging programs. Within these elements, we observed a broad depletion of binding motifs for transcription factors with roles in cellular identity and muscle regeneration, concomitant with the gain of stress-responsive transcription factors. Our work helps understand the epigenetic events underlying mammalian skeletal muscle aging.
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Single-cell RNA sequencing reveals the CRTAC1+ population actively contributes to the pathogenesis of spinal ligament degeneration by SPP1+ macrophage.
Degenerative spinal stenosis is a chronic disease that affects the spinal ligaments and associated bones, resulting in back pain and disorders of the limbs among the elderly population. There are few preventive strategies for such ligament degeneration. We here aimed to establish a comprehensive transcriptomic atlas of ligament tissues to identify high-priority targets for pharmaceutical treatment of ligament degeneration. Here, single-cell RNA sequencing was performed on six degenerative ligaments and three traumatic ligaments to understand tissue heterogeneity. After stringent quality control, high-quality data were obtained from 32,014 cells. Distinct cell clusters comprising stromal and immune cells were identified in ligament tissues. Among them, we noted that collagen degradation associated with CTHRC1+ fibroblast-like cells and calcification linked to CRTAC1+ chondrocyte-like cells were key features of ligament degeneration. SCENIC analysis and further experiments identified ATF3 as a key transcription factor regulating the pathogenesis of CRTAC1+ chondrocyte-like cells. Typically, immune cells infiltrate localized organs, causing tissue damage. In our study, myeloid cells were found to be inflammatory-activated, and SPP1+ macrophages were notably enriched in degenerative ligaments. Further exploration via CellChat analysis demonstrated a robust interaction between SPP1+ macrophages and CRTAC1+ chondrocyte-like cells. Activated by SPP1, ATF3 propels the CRTAC1/MGP/CLU axis, fostering ligament calcification. Our unique resource provides novel insights into possible mechanisms underlying ligament degeneration, the target cell types, and molecules that are expected to mitigate degenerative spinal ligament. We also highlight the role of immune regulation in ligament degeneration and calcification, enhancing our understanding of this disease.
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Bioinformatics Analysis and Experimental Validation of Lactylation Related Genes in Lung Adenocarcinoma.
PURPOSE: Lactylation, a novel post-translational modification, is dysregulated in various tumors and influences lung cancer progression. However, its role in lung adenocarcinoma (LUAD) remains unclear. Based on multi-omics analysis results, this study investigated lactylation levels in LUAD tissues and explored the dual research positioning of lactylation as a prognostic marker and therapeutic target. METHODS: Lactylation levels in LUAD tissue microarrays were assessed using immunohistochemistry and immunofluorescence. Western blot analysis validated these findings. Differential expression analysis of lactylation-related genes was conducted using The Cancer Genome Atlas (TCGA, n=365), based on |log2 fold-change (FC)|≥2. KEGG pathway analysis identified key biological pathways, and COX regression analysis pinpointed prognostic genes. Single-cell RNA sequencing data from the GEO database validated these genes, with mitochondrial gene threshold <20%. RESULTS: Lactylation levels were significantly elevated in LUAD tissues compared to adjacent non-cancerous tissues, as shown by immunohistochemistry and confirmed by Western blot analysis. Differential analysis identified 17 lactylation-related genes enriched in pathways such as AMPK signaling and cellular senescence. COX regression analysis identified five risk genes: KIF2C, MKI67, HMGA1, PFKP, and CCNA2. Validation with single-cell RNA sequencing data revealed high expression levels of these genes in LUAD tissues and the LUAD cell line H1299. Functional validation revealed that the 5 genes panel significantly regulates global lactylation modification in vitro. CONCLUSION: LUAD tissues exhibit elevated lactylation levels, suggesting their potential as prognostic biomarkers. The identified genes-KIF2C, MKI67, HMGA1, PFKP, and CCNA2-are highly expressed in cancerous tissues and correlate with LUAD prognosis. These findings highlight their value as tumor biomarkers and therapeutic targets, offering new opportunities for targeted LUAD treatments.
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Systematic identification of clinically relevant miRNAs for potential miRNA-based therapy in lung adenocarcinoma.
Lung adenocarcinoma (LUAD), the most common histological type of non-small cell lung cancer, is one of the most malignant and deadly diseases. Current treatments for advanced LUAD patients are far from ideal and require further improvements. Here, we utilized a systematic integrative analysis of LUAD microRNA sequencing (miRNA-seq) and RNA-seq data from The Cancer Genome Atlas (TCGA) to identify clinically relevant tumor suppressor miRNAs. Three miRNA candidates (miR-195-5p, miR-101-3p, and miR-338-5p) were identified based on their differential expressions, survival significance levels, correlations with targets, and an additive effect on survival among them. We further evaluated mimics of the three miRNAs to determine their therapeutic potential in inhibiting cancer progression. The results showed not only that each of the miRNA mimics alone but also the three miRNA mimics in combination were efficient at inhibiting tumor growth and progression with equal final concentrations, meaning that the three miRNA mimics in combination were more effective than the single miRNA mimics. Moreover, the combined miRNA mimics provided significant therapeutic effects in terms of reduced tumor volume and metastasis nodules in lung tumor animal models. Hence, our findings show the potential of using the three miRNAs in combination to treat LUAD patients with poor survival outcomes.
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Identification of key ferroptosis genes and subtypes in kidney renal clear cell carcinoma.
Tumour immunity is highly important for the occurrence and development of tumours, and many cancers are resistant to ferroptosis. This study aims to explore the relationship between ferroptosis-related genes (FRGs) and the immunological characteristics of kidney renal clear cell carcinoma (KIRC). We obtained RNA-seq profiles and clinical data of KIRC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and identified CD44 and GLRX5 as the key FRGs involved in KIRC immune infiltration through Spearman's correlation analysis. Based on the expression of CD44 and GLRX5, the consensus clustering algorithm was used to classify the TCGA-KIRC samples into two clusters. A nomogram was constructed to evaluate the prognosis of KIRC patients. ESTIMATE, CIBERSORT, and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate immune infiltration between the two clusters. A weighted gene co-expression network analysis (WGCNA) was used to identify the most relevant genes to the clusters and immunity. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. The external dataset GSE53757 was used to validate the immunological features between the two clusters. Cluster 2 patients had more active immune infiltration and might be more sensitive to immunotherapy; Cluster 2 patients also had a worse prognosis and might be at a more advanced stage of KIRC. We identified key ferroptosis-related genes and subgroups involved in the immune infiltration of KIRC, which is highly important for exploring the molecular mechanisms and treatments of KIRC.
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q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics.
Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.
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Key molecular alterations in endothelial cells in human glioblastoma uncovered through single-cell RNA sequencing.
Passage of systemically delivered pharmacological agents into the brain is largely blocked by the blood-brain-barrier (BBB), an organotypic specialization of brain endothelial cells (ECs). Tumor vessels in glioblastoma (GBM), the most common malignant brain tumor in humans, are abnormally permeable, but this phenotype is heterogeneous and may differ between the tumor's center and invasive front. Here, through single-cell RNA sequencing (scRNA-seq) of freshly isolated ECs from human glioblastoma and paired tumor peripheral tissues, we have constructed a molecular atlas of human brain ECs providing unprecedented molecular insight into the heterogeneity of the human BBB and its molecular alteration in glioblastoma. We identified 5 distinct EC phenotypes representing different states of EC activation and BBB impairment, and associated with different anatomical locations within and around the tumor. This unique data resource provides key information for designing rational therapeutic regimens and optimizing drug delivery.
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Network-based single-cell analysis identifies a proliferative GZMB+ plasma cell subset linked to Hedgehog signaling and prognostic risk in lung squamous cell carcinoma.
BACKGROUND: Lung squamous cell carcinoma (LUSC) is a heterogeneous cancer characterized by complex cellular interactions. Plasma cells, a critical immune population, exhibit functional diversity in LUSC. The aim of this study was to characterize plasma cell heterogeneity and their interactions with tumor cells using an integrative single-cell framework. METHODS: We constructed a high-resolution single-cell atlas of LUSC using single-cell RNA-sequencing data to identify plasma cell subsets. Plasma cell heterogeneity was assessed through clustering and differential expression analysis, and differentiation trajectories were plotted. Cell-cell communication was analyzed using the "CellChat" package. A prognostic nine-gene signature was derived using LASSO-Cox regression and further validated across independent GEO cohorts. Upstream regulatory mechanisms of prognostic genes were investigated through integrative transcriptomic analysis. The role of ZBTB20 in tumor progression was validated through functional assays. RESULTS: Three plasma cell subsets were identified, including a proliferative GZMB+ (C2) subset characterized by higher G2M/S activity and stemness-associated features. Trajectory analysis revealed a differentiation axis from IGLC2+ to IGKC+ to GZMB+ plasma cells. Network-based communication analysis demonstrated that C2 plasma cells interact with LUSC tumor cells via the SHH-PTCH1 signaling axis. A nine-gene prognostic signature robustly stratified patients, and was associated with immune signaling, cytokine interactions, apoptosis regulation, and tumor microenvironment remodeling. Integrative regulatory analysis identified UBB as a central prognostic gene, with ZBTB20 and PPARG as potential upstream transcriptional regulators. The prognostic significance of the C2-related gene signature was validated in independent GEO cohorts. ZBTB20 was further identified as a key regulator of tumor-related signaling and cellular proliferation, and its oncogenic role was verified through functional experiments. CONCLUSIONS: The proliferative C2 plasma cell subset has prognostic and functional significance in LUSC. ZBTB20 regulates key tumor-related pathways in LUSC cells through UBB-centered transcriptional control. These findings provide a systems-level framework for understanding plasma cell dynamics and tumor-immune interactions in LUSC progression for future development of targeted therapies.
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Single-nucleus and spatial transcriptomics reveal the cell populations of intercalary meristems in bamboo.
Intercalary meristems (IcMs), specialized developmental zones that drive rapid stem elongation in monocots, exhibit distinct spatiotemporal dynamics; however, their genetic basis remains poorly understood due to their transient activity and cellular heterogeneity. Moso bamboo (Phyllostachys edulis)-with its exceptional daily growth rates of up to 114.5 cm, prolonged IcM activity spanning 45 to 60 d, and large, accessible culm structure-provides an ideal system for building a comprehensive IcM cell atlas. Here, we integrated a chromosome-level genome assembly of Moso bamboo with a high-resolution anatomical atlas to delineate three critical developmental stages of IcM activity: the initial cell division phase (ID), the rapid cell division phase (RD), and the rapid cell elongation phase (RE). By combining single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics, we reconstructed a dynamic single-nucleus transcriptomic continuum spanning from proliferative to elongation states. Multiomics integration, along with in situ hybridization and ultrastructural imaging, identified IcM cells as short-columnar cells adjacent to elongating ground tissue parenchyma (Gp) cells, with the IcM1 subpopulation functioning as stem-like cells essential for maintaining the proliferative capacity. Pseudotemporal trajectory analysis revealed transcriptional transitions from stem-like IcM states to differentiated Gp cells. Functional experiments revealed that clrGene008562, a WOX2 homolog, enhances callus regeneration, indicating its potential role in promoting cell division and differentiation processes relevant to IcM development. These findings provide a comprehensive molecular and cellular framework for understanding IcM function and offer valuable multiomics resources for advancing meristem research in bamboo and other Poaceae species.