SEARCH · PubMed Health

Results for “ATAC-seq”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources

PubMed2026

In vivo genome-wide CRISPR screens identify FOXR1 as a suppressor of CD8+ T cell antitumor immunity.

T cell dysfunction critically limits the efficacy of T cell-based immunotherapies in solid tumors, yet the intrinsic regulators of T cell dysfunction remain incompletely understood. Through an in vivo genome-wide CRISPR screen in tumor-infiltrating CD8+ T cells, we identified Forkhead Box R1 (FOXR1) as a potent transcriptional suppressor of CD8+ T cell effector functions. Genetic ablation of FOXR1 significantly enhanced cytokine production and cytotoxic capacity in both murine and human CD8+ T cells, whereas its overexpression impaired T cell activation and effector molecule expression. Mechanistically, multiomics integration of RNA-seq, CUT&Tag-seq, and ATAC-seq revealed that FOXR1 binds directly to promoter regions of key effector genes, including IL2, GZMB, and PRF1, and represses their expression. Importantly, FOXR1 deletion in human anti-CD19 CAR T cells improved their efficacy against solid tumors, demonstrating that FOXR1 is a checkpoint of T cell effector function and targeting FOXR1 is a promising strategy to enhance CAR T cell efficacy against solid tumors.

Animals↗

PubMed2025

scMGCL: accurate and efficient integration representation of single-cell multi-omics data.

MOTIVATION: Single-cell multi-omics data integration is essential for understanding cellular states and disease mechanisms, yet integrating heterogeneous data modalities remains a challenge. We present scMGCL, a graph contrastive learning framework for robust integration of single-cell ATAC-seq and RNA-seq data. Our approach leverages self-supervised learning on cell-cell similarity graphs, in which each modality's graph structure serves as an augmentation for the other. This cross-modality contrastive paradigm enables the learning of biologically meaningful, shared representations while preserving modality-specific features. RESULTS: Benchmarking against state-of-the-art methods demonstrates that scMGCL outperforms others in cell-type clustering, label transfer accuracy, and preservation of marker-gene correlations. Additionally, scMGCL significantly improves computational efficiency, reducing runtime and memory usage. The method's effectiveness is further validated through extensive analyses of cell-type similarity and functional consistency, providing a powerful tool for multi-omics data exploration. AVAILABILITY AND IMPLEMENTATION: Code and datasets are released at https://github.com/zlCreator/scMGCL.

Single-Cell Analysis↗

PubMed2026

Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.

MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. RESULTS: We present a new approach, MetaFR, to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. AVAILABILITY AND IMPLEMENTATION: MetaFR is available under https://github.com/SchulzLab/MetaFR.

Single-Cell Analysis↗

PubMed2026

DeepGeSeq: deep learning library for genomic sequence modeling and analysis.

MOTIVATION: Deep learning methods have demonstrated significant potential in genomics, enabling broad applications such as sequence activity prediction, regulatory rule identification, and variant effect quantification. However, their widespread adoption is often hindered by the steep computational learning curve required for model construction, training, and downstream biological interpretation. Here, we introduce DeepGeSeq, a user-friendly Deep-learning library tailored for Genomic Sequence modeling and analysis. RESULTS: By integrating state-of-the-art architectural modules, DeepGeSeq streamlines the entire deep learning workflow, requiring minimal user input via a simple configuration file and an intuitive agentic skill. We comprehensively validate the efficacy of DeepGeSeq through diverse case studies, encompassing pipeline verification using synthetic datasets, the reproduction and application of established models, and model fine-tuning coupled with biological interpretation on user-defined data. Furthermore, we demonstrate DeepGeSeq's versatility in domain-specific applications, including single-cell ATAC-seq modeling for cell-type clustering, and MPRA data modeling coupled with in silico saturation mutagenesis to dissect cis-regulatory elements. Ultimately, DeepGeSeq bridges the gap between computational complexity and biological discovery, providing an accessible resource that facilitates the development and broad application of deep learning methods in genomics research. AVAILABILITY AND IMPLEMENTATION: https://github.com/JiaqiLi1024/DeepGeSeq.

Deep Learning↗

PubMed2025

The Fire Ant Social Chromosome Exerts a Major Influence on Genome Regulation.

Supergenes underlying complex trait polymorphisms ensure that sets of coadapted alleles remain genetically linked. Despite their prevalence in nature, the mechanisms of supergene effects on genome regulation are poorly understood. In the fire ant Solenopsis invicta, a supergene containing over 500 individual genes influences trait variation in multiple castes to collectively underpin a colony level social polymorphism. Here, we present results of an integrative investigation of supergene effects on gene regulation. We present analyses of ATAC-seq data to investigate variation in chromatin accessibility by supergene genotype and STARR-seq data to characterize enhancer activity by supergene haplotype. Integration with gene co-expression analyses, newly mapped intact transposable elements (TEs), and previously identified copy number variants (CNVs) collectively reveals widespread effects of the supergene on chromatin structure, gene transcription, and regulatory element activity, with a genome-wide bias for open chromatin and increased expression in the presence of the derived supergene haplotype, particularly in regions that harbor intact TEs. Integrated consideration of CNVs and regulatory element divergence suggests each evolved in concert to shape the expression of supergene encoded factors, including several transcription factors that may directly contribute to the trans-regulatory footprint of a heteromorphic social chromosome. Overall, we show how genome structure in the form of a supergene has wide-reaching effects on gene regulation and gene expression.

Animals↗

PubMed2025

Integrative Genomic, Transcriptomic and Epigenomic Analysis Reveals cis-regulatory Contributions to High-altitude Adaptation in Tibetan Pigs.

The Qinghai-Tibet Plateau, characterized by its extreme environmental conditions, presents significant challenges to life, making it an ideal region for studying adaptation and evolution. Tibetan pigs, known for their high genetic diversity and exceptional adaptability to high altitudes, serve as excellent models for investigating high-altitude adaptation. While previous studies have extensively identified genetic determinants associated with high-altitude adaptation, the molecular mechanisms, particularly cis-regulatory patterns, remain poorly understood. Here, we conducted a selective sweep analysis using 484 genomes from Chinese and Western pig breeds across various altitudes, revealing 38.56 Mb of genomic regions under selection in Tibetan pigs. Enrichment analysis identified the lung as the primary functional tissue involved in high-altitude adaptation, supported by tissue-specific transcriptional and regulatory patterns observed between Tibetan and Meishan pigs (low altitude). By integrating genomic, RNA-seq, ATAC-seq, and H3K27ac HiChIP data, we constructed comprehensive enhancer-promoter regulatory maps of candidate genes and pinpointed promising genetic determinants associated with high-altitude adaptation, including SNPs in EPAS1, KLF13, SPRED1, and CFD. These loci were predicted to influence chromatin accessibility and the interactions of regulatory elements, with altered binding strength of relevant transcription factors. Further in vitro experiments confirmed that these loci function as allele-specific enhancers, modulating the expression of target genes. Our findings elucidate the regulatory basis of high-altitude adaptation in Tibetan pigs and provide valuable insights for exploring hypoxia-related diseases in livestock and humans.

Animals↗

PubMed2024

Transcriptional landscape and dynamics involved in sugar and acid accumulation during apple fruit development.

In fleshy fruit, sugars and acids are central components of fruit flavor and quality. To date, the mechanisms underlying transcriptional regulation of sugar and acid during fruit development remain largely unknown. Here, we combined ATAC-seq with RNA-seq to investigate the genome-wide chromatin accessibility and to identify putative transcription factors related to sugar and acid accumulation during apple (Malus domestica) fruit development. By integrating the differentially accessible regions and differentially expressed genes, we generated a global data set of promoter-accessibility and expression-increased genes. Using this strategy, we constructed a transcriptional regulatory network enabling screening for key transcription factors and target genes involved in sugar and acid accumulation. Among these transcription factors, 5 fruit-specific DNA binding with one finger genes were selected to confirm their regulatory effects, and our results showed that they could affect sugar or acid concentration by regulating the expression of sugar or acid metabolism-related genes in apple fruits. Our transcriptional regulatory network provides a suitable platform to identify candidate genes that control sugar and acid accumulation. Meanwhile, our data set will aid in analyzing other characteristics of apple fruit that have not been illuminated previously. Overall, these findings support a better understanding of the regulatory dynamics during apple fruit development and lay a foundation for quality improvement of apple.

Malus↗

PubMed2025

Chromatin accessibility analysis reveals functional cis-regulatory regions related to fruit development and domestication in tomato.

Non-coding DNA sequences harbor vast regulatory programs that ensure the precise spatiotemporal control of gene expression, which is essential for proper plant development and trait formation. Chromatin accessibility analysis could identify functional DNA regions within the extensive non-coding sequences and infer regulatory elements, serving as a crucial approach to unravel the mysteries of non-coding DNA sequences. Tomato fruit, a fleshy organ, provides a special system for studying fruit development and trait formation. However, the role of cis-accessible chromatin regions (cis-ACRs) during tomato fruit development, particularly in comparison with protein-coding DNA sequences, remains poorly understood. Here, we used ATAC-seq to define the landscape of cis-ACRs during fruit development and domestication in tomato. Temporal differential analysis revealed the dynamic opening and closing of cis-ACRs during fruit development. Comparative analysis of cis-ACRs between cultivated and wild tomatoes highlighted their significant contributions to fruit domestication. Combining analysis with genomic structural variations (SVs) suggested that SVs are likely a key factor in the formation of specific accessible cis-ACRs in cultivated tomatoes. Moreover, using gene editing, we identified a functional cis-ACR within the intron of the MBP3 gene that regulates fruit development and size traits. Overall, our findings provide a comprehensive perspective on the roles of cis-ACRs in tomato fruit development and domestication.

Solanum lycopersicum↗

PubMed2025

Genetic effects on chromatin accessibility reveal the molecular mechanisms of complex traits in maize.

Cis-regulatory elements (CREs) are critical for modulating gene expression and phenotypic diversity in maize. While genome-wide association study (GWAS) hits and expression quantitative trait loci (eQTLs) are often enriched in CREs, their molecular mechanisms remain poorly understood. Characterizing CREs within accessible chromatin regions (ACRs) offers a powerful approach to link noncoding variants to chromatin structure alterations and phenotypic variation. Here, we generated ATAC-seq profiles from seedling leaves of 214 maize inbred lines, identifying 82 174 consensus ACRs. Notably, 39.55% of these ACRs exhibited significant population-wide chromatin accessibility variation. By mapping chromatin accessibility quantitative trait loci (caQTLs), we discovered 27 004 loci, including 1398 predicted to disrupt transcription factor (TF)-binding sites. Integration with multi-omics data revealed 7405 caACR-target gene pairs and linked 56 caACRs to GWAS signals for 51 agronomic traits, with significant enrichment in flowering-related pathways. Functional candidates such as ZmZIM30 - putatively regulated by caACRs - emerged as key regulators of flowering time. At the fad7 locus associated with linolenic acid content, allelic variants overlapping a caQTL showed differential chromatin accessibility. Our study provides a high-resolution cis-elements of maize leaves, deciphers the genetic basis of chromatin accessibility variation, and bridges noncoding caQTLs to molecular mechanisms underlying GWAS hits.

Zea mays↗

PubMed2026

Multi-omics analysis of glucocorticoid receptor crosstalk with Type I and Type II inflammatory signaling in human airway smooth muscle cells.

Airway smooth muscle (ASM) dysfunction in obstructive airway disease is treated with glucocorticoids. Through RNA-seq analysis of cultured human ASM, we identified repressive effects of dexamethasone, a glucocorticoid, on the baseline expression of a subset of genes that are induced by either IL1B or IL13, which model Type I and Type II inflammation, respectively. ChIP-seq analysis of glucocorticoid receptor (GR) and the p65 subunit of NFkB occupancy indicated canonical motifs for both factors occur at sites of p65 occupancy but did not provide biochemical support for significant repressive tethering between GR and p65. Instead, ATAC-seq revealed significant chromatin remodeling and increased accessibility at binding motifs for the NFkB complex in association with dex + IL1B co-treatment in comparison to IL1B treatment alone. Our data support a competition-based primary repressive effect of glucocorticoids on both IL1B and IL13 signaling and provide evidence for transcriptional cooperation between GR and NFkB on a genome-wide basis in ASM, including at regulatory elements that control expression of anti-inflammatorygenes.

chromatin↗

PubMed2026

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗

PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗

PubMed2025

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.

Glioblastoma↗

PubMed2025

OmnibusX: A unified platform for accessible multi-omics analysis.

OmnibusX is an integrated, privacy-centric platform that enables code-free multi-omics data analysis by bridging computational methodologies with user-friendly interfaces. Designed to overcome challenges posed by fragmented analytical tools and high computational barriers, OmnibusX consolidates workflows for diverse technologies - including bulk RNA-seq, single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics - into a single, cohesive application. The application integrates established open-source tools such as Scanpy, DESeq2, SciPy, and scikit-learn into transparent, reproducible pipelines, offering users control over analytical parameters. Additionally, OmnibusX features proprietary modules, including a highly accurate cell-type prediction engine and an interactive plotting editor for generating publication-quality visualizations. Available as a standalone desktop application and an enterprise edition for centralized server deployment, OmnibusX ensures all data processing is conducted locally, eliminating external data transfer and usage tracking. By lowering technical barriers and enhancing reproducibility, OmnibusX aims to accelerate biological discovery and foster robust, data-driven collaborations. A fully documented trial version is accessible at: https://omnibusx.com/apps.

Computational Biology↗

PubMed2026

SWI/SNF Alterations Define a Chromatin-Dependent Subtype of Urothelial Carcinoma.

PURPOSE: SWI/SNF (BAF) chromatin remodeling complex alterations are common in urothelial carcinoma, yet no biomarker-directed therapeutic strategies have been established for this population. We investigated whether BAF alterations delineate a biologically distinct, therapeutically actionable urothelial carcinoma subtype. EXPERIMENTAL DESIGN: We performed integrative genomic and transcriptomic analyses of 792 urothelial carcinoma tumors from the Oncology Research Information Exchange Network (ORIEN) and validated findings in the TCGA-BLCA cohort. Mechanistic studies incorporated RNA sequencing and ATAC-seq following histone deacetylase (HDAC) inhibition. Functional dependencies were assessed using patient-derived xenograft organoids and cell line models. Clinical relevance was explored in a biomarker-enriched investigator-initiated trial. RESULTS: Approximately half of urothelial carcinoma tumors exhibited BAF alterations, defining a previously unrecognized chromatin-altered molecular subtype characterized by activation of proliferative programs, loss of lineage identity, and altered metabolic signaling. This subtype was enriched for transcriptomic programs associated with HDAC inhibitor sensitivity and depleted of HDAC inhibitor resistance signatures. Mechanistically, HDAC inhibition induced widespread chromatin remodeling with reduced accessibility at AP-1 and TEAD-associated regions, and downregulation of E2F- and MYC-driven transcriptional networks. Functional studies confirmed enhanced HDAC inhibition sensitivity in ARID1A -mutated cell lines and a patient-derived organoid model. Early clinical observations demonstrated a durable responder treated with HDAC inhibitors and immunotherapy. CONCLUSIONS: BAF alterations define a chromatin-dependent tumor state in urothelial carcinoma that is selectively vulnerable to HDAC inhibition. Integrating genomic, epigenomic, functional, and early clinical evidence, these findings provide a rationale for biomarker-enriched clinical trials and HDAC inhibitor-based combination strategies in urothelial carcinoma.

Journal Article↗

PubMed2027

A Standardized Protocol for Generating iPSC-Derived Human Microglia for Functional Genomic Assays.

Human induced pluripotent stem cell (iPSC)-derived microglia (iMG) provide an in vitro experimental system for studying human microglial biology, neuroinflammation, and genetic risk mechanisms associated with neurological disease. This chapter describes a standardized, scalable, and reproducible protocol for the differentiation of human iPSCs into functional microglia-like cells, with particular emphasis on applications in transcriptional and epigenomic network analysis. The protocol supports high-viability floating iMG production, compatibility with pooled CRISPR perturbation approaches, and downstream multiomic profiling, including single-cell RNA sequencing, chromatin accessibility assays, and proteomics. Detailed procedures are provided for iPSC maintenance, hematopoietic progenitor cell generation, microglial maturation, functional genomics integration, and quality control.

Humans↗

PubMed2026

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify transcriptional and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around silent genes prior to their expression in response to injury. These results provide evidence for a latent activated stem cell state in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Animals↗

PubMed2026

A stem-like chromatin program in small-cell lung cancer is associated with poor outcomes after chemoimmunotherapy.

Small-cell lung cancer (SCLC) is an aggressive malignancy with substantial tumor heterogeneity and limited clinically actionable biomarkers beyond established features such as liver metastases. We profile tumor-intrinsic chromatin accessibility in a patient-derived xenograft biobank and identify three recurrent chromatin programs: neuroendocrine, marked by ASCL1/NEUROD1 activity; immunogenic, marked by IRF-associated activity; and stem-like, marked by TEAD/OCT activity. These programs are reproduced at the cohort level across bulk and single-cell transcriptomic datasets comprising more than 800 tumors, including 300 extensive-stage samples. In patients treated with chemoimmunotherapy, the stem-like program is associated with inferior survival, including a median overall survival of 7.41 months versus 15.9 and 12.6 months for immunogenic and neuroendocrine groups, respectively. This association remains significant after adjustment for liver metastases, brain metastases, and elevated lactate dehydrogenase. These findings support a high-risk stem-like SCLC chromatin program for prospective biomarker refinement and therapeutic investigation.

ATAC-seq↗