Браузер не даёт сохранять данные (например, приватный режим): закладки пропадут после закрытия вкладки.
Поиск статей PubMed
Ключевые слова, автор, журнал, годы — результаты из PubMed, аннотации с переводом на русский, закладки в браузере.
Найдено: 4 890
Страница 47 из 489-
Telomerase-Associated Genes as Prognostic Markers in High-Grade Serous Ovarian Cancer.
BACKGROUND High-grade serous ovarian cancer (HGSOC) is an aggressive malignancy with poor prognosis. This study investigates telomerase-associated genes (TAGs) and their cellular interactions in HGSOC to identify novel prognostic markers. MATERIAL AND METHODS Single-cell RNA sequencing and bulk RNA sequencing data were obtained from the Gene Expression Omnibus and the Cancer Genome Atlas databases. We performed cell type identification, pseudotime trajectory analysis, and CellChat analysis for intercellular communication. A prognostic model was developed using the intersection of TAGs and HGSOC differentially expressed genes (DEGs) through univariate Cox and least absolute shrinkage and selection operator analyses. Survival prognosis, immune infiltration, and drug sensitivity analyses were conducted. RESULTS Thirty cell clusters, 12 cell types, and 2089 TAGs were identified. Pseudotime trajectory analysis suggested 3 distinct cell states during HGSOC progression. CellChat analysis indicated intercellular communication via NAMPT-INSR and SPP1 (ITGAV+ITGB1) signaling pathways. In total, 1204 DEGs were identified between the transcriptomically inferred telomerase-active and -inactive cell populations, along with 1925 DEGs between HGSOC and normal tissue samples. Intersection of these gene sets yielded 768 key TAGs. The prognostic risk model categorized patients into high-risk and low-risk groups, with significant survival differences. Immune infiltration analysis displayed differential abundance of 12 immune cell types between the groups. Drug sensitivity analysis suggested a potential association between the low-risk group and increased sensitivity to certain therapeutic agents. Mutations in TP53 and TTN were frequently observed across both risk groups. CONCLUSIONS This study generates the hypothesis that TAGs are linked to HGSOC progression and yields a candidate prognostic model.
-
Cross-modal mapping of cancer stem-like cell plasticity using deep learning.
Cancer stem-like cells (CSCs) play a pivotal role in driving tumor heterogeneity, therapeutic resistance, and disease progression. Despite the power of single-cell RNA sequencing (scRNA-seq) to resolve intratumoral hierarchies, there remains a need for robust, scalable tools to consistently profile CSCs across both single-cell and bulk transcriptomic data. To address this, we developed ACSCeND-a unified, machine learning-based framework that enables high-resolution CSC state classification and tissue-level deconvolution. ACSCeND comprises (i) a supervised classifier trained on curated scRNA-seq datasets to assign cells into pluripotent-like, multipotent-like, or unipotent-like states, and (ii) an attention-guided autoencoder that deconvolves CSC subtype proportions from bulk RNA sequencing data. Compared to existing tissue deconvolution tools, ACSCeND achieves superior performance, with higher accuracy across synthetic and real-world samples. Applied to over 25 000 tumor profiles from The Cancer Genome Atlas (TCGA), PREdiction of Clinical Outcomes from Genomics (PRECOG), tumor-relapse, and checkpoint inhibitor studies, ACSCeND reveals that CSC abundance strongly correlates with poor disease-free survival and reduced immunotherapy efficacy. Moreover, it uncovers distinct CSC-state-specific molecular programs, offering insights into CSC-driven heterogeneity and tumor evolution. The model also recapitulates known developmental hierarchies in noncancerous tissues, supporting its broader biological relevance. By integrating single-cell precision with bulk-level applicability, ACSCeND offers a robust, interpretable approach to profiling CSC dynamics and establishes CSC state as a clinically meaningful, pan-cancer biomarker for guiding stemness-informed therapies. ACSCeND is available as a python package (through pip) at https://pypi.org/project/ACSCeND/.
-
Lineage-Specific Sex-Biased Transcriptional Programs in Healthy Human Truncal Skin Revealed by Single-Cell Transcriptomics.
Background/Objectives: Sex differences influence skin physiology, immune regulation, and disease susceptibility, but the cellular organization of sex-biased transcriptional programs in healthy human skin remains incompletely defined. We aimed to define sex-associated differences in cellular composition and gene expression in healthy adult truncal skin at single-cell resolution. Methods: We constructed a sex-resolved single-cell transcriptomic atlas of healthy human truncal skin by integrating scRNA-seq data from 12 donors (5 males, 7 females). After quality control, 107,967 cells were classified into 14 major cell types. Sex-associated differences were assessed using donor-level pseudo-bulk analyses at both whole-skin and cell-type-resolved levels. Results: The cellular composition was conserved between sexes, with significant differences in mast cells and regulatory T cells. Whole-skin pseudo-bulk analysis identified distinct male-biased and female-biased transcriptional programs. Male-biased signals were linked to extracellular matrix organization and immune responses, while female-biased signals involved ion transport and neuromodulation. Cell-type-resolved analysis revealed that most sex-biased genes were lineage-specific, with minimal cross-lineage sharing. Conclusions: Sexual dimorphism in healthy human truncal skin is encoded through lineage-structured transcriptional regulation rather than broad compositional changes, providing a framework for understanding sex-biased skin homeostasis and disease susceptibility.
-
Single-Cell Analysis of Late-Stage Wound-Induced Hair Follicle Neogenesis Reveals Keratinocyte Heterogeneity and Enrichment of Infundibular Basal Keratinocytes.
Scarring alopecia remains a major clinical challenge, characterized by irreversible hair follicle (HF) loss and replacement by scar tissue. Wound-induced hair follicle neogenesis (WIHN) serves as a model in which HFs can regenerate following skin wounding. In this study, we characterized the cellular landscape associated with differences in regenerative capacity between small wound (SW) and large wound (LW) tissues at a late regenerative stage (post-wounding day 22, PWD22), a time point at which newly formed hair follicles are already present. Using single-cell RNA sequencing (scRNA-seq), we profiled re-epithelialized wound tissues from SW and LW conditions and identified major cutaneous cell populations, including keratinocytes and fibroblasts. Keratinocytes were further categorized into 8 subpopulations, among which infundibular basal keratinocytes (INFU basal KCs) were increased in LW compared with SW tissues, as validated by immunofluorescence (IF) staining. Pseudotime analysis indicated differences in the temporal expression pattern of the INFU basal KC marker Fst between SW and LW conditions. Cell-cell communication between keratinocytes and fibroblasts showed that extracellular matrix (ECM)-receptor interactions were markedly enriched, while Masson's trichrome staining revealed decreased collagen content in LW. IF staining showed increased COL6A3 expression and decreased FN1 expression in LW tissues. Taken together, our study provides a descriptive single-cell atlas of late-stage wound tissues with differing regenerative outcomes, indicating differences in keratinocyte composition, transcriptional states, and intercellular communication. Collectively, our findings offer a resource for understanding cellular heterogeneity associated with WIHN and may inform future studies aimed at elucidating the mechanisms of hair follicle regeneration in cutaneous repair.
-
An lncRNA-aware single-cell framework with donor-level validation identifies reproducible MEG3 enrichment in human liver sinusoidal endothelial cells.
Long non-coding RNAs (lncRNAs) associated with metabolic liver disease are usually identified from bulk tissue, which cannot resolve the hepatic cell types that express them, and default single-cell pipelines discard most lncRNAs at feature selection. We present an lncRNA-aware single-cell analysis framework - retaining all detectable GENCODE v45 lncRNAs during highly variable gene selection - combined with donor-level validation that guards against pseudoreplication. The framework applies to already published data and needs no lncRNA-specific protocol. Applying it to the human Liver Cell Atlas (Gene Expression Omnibus accession GSE192742; 152,559 annotated cells, 16 donors), and using the source publication's own cell-type annotation, we found MEG3 enriched in liver sinusoidal endothelial cells (LSECs) in abundance as well as in detection frequency: donor-level pseudobulk expression was 6.27 counts per 10,000 versus 1.05 in the next-ranked cell type, and the LSEC pseudobulk value exceeded the pooled non-LSEC value in all nine informative donors (paired Wilcoxon signed-rank: two-sided P = 0.0039, one-sided P = 0.0020). The LSEC-enriched detection pattern reproduced in an independent five-donor atlas (all five donors concordant). KCNQ1OT1 was not LSEC-specific. Benchmarking our clustering against the published annotation showed that two marker-scored lineages contained none of their nominal cell type; separately, the candidates returned by a within-lineage pseudotime screen were driven entirely by non-endothelial cells contaminating the LSEC lineage: correlations of |ρ| > 0.31 fell below 0.10 within correctly annotated endothelial cells and reversed sign in one quarter of alternative roots. We therefore report the pseudotime screen as a negative result, and provide the framework, the annotation audit and the two-cohort analysis as a reproducible resource.
-
Integrating short-read and long-read single-cell transcriptomics of pig pituitary reveals mechanisms of high-altitude hypoxia adaptation.
Acting as the central endocrine hub, the pituitary gland is closely related to the mechanism of adaptation to high-altitude hypoxia. Here, by integratively combining long-read (Oxford Nanopore) and short-read (Illumina) single-cell sequencing approaches, we profiled pituitaries from Diqing Tibetan pigs, inhabiting high-altitude environments (3,200 m) and Diannan small-ear pigs from low-altitude regions (500 m), thereby generating a full-length single-cell atlas, which in turn enabled the identification of molecular mechanisms potentially underlying adaptation to high-altitude hypoxia stress. Systematically delineating pituitary structure and transcriptional dynamics, we profiled 27,339 single cells encompassing 28,932 expressed genes. Leveraging unsupervised clustering coupled with marker-based annotation, we identified ten major cell types. Expression profiling of 20 canonical marker genes revealed pronounced cell-type-specific expression patterns, ten of which were independently validated by immunofluorescence, thus substantiating the accuracy of cell-type annotation. Gene-ontology enrichment analysis further suggested that upregulated genes were predominantly involved in oxidative metabolism and energy production, whereas downregulated genes were significantly associated with protein biosynthesis and translation processes, indicating a functional reprogramming of metabolic pathways. Moreover, comparative analyses between breeds and cell-cell communication analyses highlighted pathway shifts, including broad upregulation of the collagen family and four ligand-receptor pairs that may mediate pituitary intercellular coordination. In parallel, transcription-factor activity analysis nominated several regulators, including EBF3, DBX2, and TCF21, which may collectively contribute to altitude-associated adaptation. Finally, integrating long-read sequencing data revealed widespread transcript isoform diversity, with ~28% novel annotations, indicating considerable isoform complexity in pigs. Our findings delineate cellular heterogeneity, inferred intercellular communication networks, and transcript-isoform diversity in the porcine pituitary, thereby providing a cell-resolved resource for understanding pituitary features associated with high-altitude environments.
-
Combined Analysis of Bulk and Single-Cell Transcriptomic Data Reveals Dormancy-Associated Genes in Colorectal Cancer.
Dormancy is an important factor influencing colorectal cancer (CRC) metastasis through diverse metabolic pathways and cell types. To elucidate its molecular mechanisms, bulk transcriptomic pathway scoring was integrated with single-cell RNA sequencing of epithelial, cancer stem, and immune cells to identify CRC dormancy-associated genes (CDAGs). Twenty-three CDAGs were identified. These genes were found to play a regulatory role in dormancy by participating in metabolic processes affecting energy supply or substance synthesis. In two independent CRC cohorts (GSE41258, GSE41568), machine learning models using these genes distinguished metastatic samples with area under the curve (AUC) of 0.79-0.87. High CDAG expression was associated with better recurrence-free survival in GSE41258 (p = 0.005), which remained significant after adjusting for age, sex, and adjuvant chemotherapy (p = 0.037). The prognostic value was validated in The Cancer Genome Atlas (TCGA) Colon and Rectal Cancer for progression-free survival (p = 0.004). Moreover, 20 CRC dormancy-associated drugs were identified, 12 of which were reported to be associated with CRC, two with experimental evidence of inhibiting CRC metastasis or recurrence. This study provided metabolic-oriented genes for characterizing CRC dormancy, which could distinguish metastatic samples and had independent prognostic value, and offered a foundation for further development of targeted therapeutic strategies.
-
Integrative analysis of bulk multiomics and single-cell transcriptomics reveals the value of neddylation in Skin Cutaneous Melanoma.
BACKGROUND: Melanoma is recognized as the most aggressive type of skin cancer, characterized by its high mortality rates and significant therapeutic challenges. OBJECTIVE: The primary aim of this research is to analyze cellular heterogeneity in SKCM through single-cell RNA sequencing (scRNA-seq) and construct a neddylation-related prognostic model. METHODS: The scRNA-seq and bulk data of Skin Cutaneous Melanoma (SKCM) samples were obtained from Gene Expression Omnibus and Cancer Genome Atlas Program databases. The nonnegative matrix factorization clustering algorithm was employed to stratify malignant cells into different neddylation cell subtypes in SKCM. RESULTS: The scRNA-seq analysis classified 31,894 high-quality cells into eight major cell types, revealing significant cellular distribution patterns within the TME. We observed that neddylation activity was significantly elevated in SKCM tissues compared to normal counterparts. Based on the expression profile of prognostic NRGs, the malignant cells were stratified into four neddylation cell subtypes in SKCM. A neddylation-related prognostic signature (NRPS) was established based on marker genes of neddylation-C3 cell subtype. The low-risk group activated immune-related signaling pathways, while the high-risk group activated various cancer-related pathways. Notably, the low-risk group exhibited better overall survival, higher immune cell infiltration, and response rates to immunotherapy compared to the high-risk group. CONCLUSION: This investigation emphasizes the crucial function of NRGs in the prognosis of SKCM, while also underscoring the importance of understanding the TME and its cellular heterogeneity. The distinct cellular composition and functional attributes within the TME highlight the potential for immunotherapy responses that can be stratified by risk.
-
Integrated single-cell and bulk transcriptomics identify autophagy-related immune-suppressive subtypes and a prognostic signature in colorectal cancer.
BACKGROUND: Colorectal cancer still causes many cancer deaths, and patient outcomes differ a lot. The tumor microenvironment can shape tumor growth and treatment response. Autophagy is a cell recycling process linked to tumor survival and immune control, but its cell-type pattern in colorectal cancer tissue and its clinical meaning are not clear. We mapped autophagy activity across cell types, defined autophagy-based subtypes, and tested their value for risk stratification. METHODS: Single-cell RNA sequencing data from colorectal cancer tumor tissues were integrated to evaluate autophagy activity across malignant, stromal, and immune cell populations. Subsequently, autophagy-related genes were curated from multiple public databases and analyzed in bulk transcriptomic cohorts obtained from The Cancer Genome Atlas and The Gene Expression Omnibus. Additionally, unsupervised consensus clustering was applied to define autophagy-based molecular subtypes. Multiple machine learning algorithms were evaluated to construct an autophagy-related prognostic model, and feature importance was assessed using explainable modeling approaches. Furthermore, immune microenvironment characteristics, genomic alterations, and stemness features were systematically analyzed. Key genes were further validated in clinical colorectal cancer specimens and through in vitro functional experiments. RESULTS: Autophagy activity varied across the tumor microenvironment. Malignant and stromal cells showed higher autophagy levels than immune cells. High-autophagy tumors showed stronger tumor-stroma interactions, weaker immune communication, and an immune-suppressive microenvironment. Using 47 prognostic autophagy-related genes, we identified four subtypes with different overall survival, genomic alteration patterns, stemness signatures, and immune landscapes. The prognostic model performed well across independent cohorts. GOLGA2 (Golgi autoantigen, golgin subfamily A member 2) was the top risk gene and was upregulated in tumor tissues. Functional assays showed that GOLGA2 promotes HCT116 cell proliferation and migration. CONCLUSION: Autophagy is closely tied to an immune-suppressive tumor microenvironment in colorectal cancer. The autophagy-based subtypes and prognostic model support patient risk stratification. Furthermore, GOLGA2 was a top-ranked gene in the signature and showed tumor-promoting effects in vitro, so it may warrant further study as a potential target.
-
scParadise: tunable, highly accurate multi-level cell type annotation, unknown cell type identification, and modality imputation.
In single-cell biology, the main limitation has shifted from data generation to converting sparse, heterogeneous single-cell RNA sequencing (scRNA-seq) datasets into accurate cell types, interpretable multi-omic states, and reproducible conclusions. To address these challenges, we present scParadise, which transforms scRNA-seq data to a new scientific knowledge. scParadise comprises three integrated tools: scAdam, a multi-level cell type annotation tool with unknown cell type identification; scEve, a cross-tissue modality imputation tool; and scNoah, a standardized benchmarking tool. Using scParadise, we corrected annotation mistakes in the Tabula Muris Senis atlas, showing that cells labeled as granulocytes are exclusively neutrophils and that presumably annotated macrophages actually represent a range of different cell types. Moreover, we identify three previously unknown natural killer T (NKT) cell subsets by imputing protein expression across tissues, which include CD56dim CD3+, CD56dim CD3+ CD4+, and CD56dim CD3+ CD8+ cell subsets in human visceral adipose tissue, which we verify by flow cytometry. These new subsets engage in obesity-related tumor necrosis factor-centric crosstalk with myeloid and adipose progenitors, thereby illuminating a new paradigm for immune-stromal interactions that contribute to chronic inflammation and impaired adipogenesis.