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PubMed2026

Increased IL4I1 expression predicts poor survival and modulates the immune microenvironment in acute myeloid leukemia.

BACKGROUND: The immunometabolic enzyme Interleukin-4-induced-1 (IL4I1) is implicated in cancer pathogenesis, yet its specific function and clinical relevance in acute myeloid leukemia (AML) remain unclear. METHODS: Comparative analysis of IL4I1 mRNA levels between AML patients and normal controls was performed using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The Kaplan–Meier survival analysis was conducted to evaluate the prognostic value of IL4I1. Functional insights were derived from analyses of differentially expressed genes (DEGs), Gene Set Enrichment Analysis (GSEA), and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Immune infiltration was evaluated using the ssGSEA, ESTIMATE, quanTIseq and single-cell RNA sequencing (scRNA-seq) analysis. Finally, in vitro and in vivo functional experiments were perfromed to explore the impact of IL4I1 on AML progression and immunoregulation. RESULTS: IL4I1 expression was significantly elevated in AML compared to normal controls (p = 0.0004) and associated with poorer overall survival (p = 0.003). Bioinformatic analysis revealed that IL4I1 was linked to immune-related pathways—including humoral immune response, leukocyte interactions, and chemokine signaling—and to cellular amino acid metabolism. Its expression correlated with immune cell infiltration and checkpoint molecule expression. Experimentally, IL4I1 promoted leukemia cell proliferation in vitro and in vivo (p < 0.05). Furthermore, silencing IL4I1 suppressed M2 macrophage polarization and reduced secretion of inflammatory factors (p < 0.05). CONCLUSIONS: IL4I1 may serve as a potential biomarker for poor prognosis and an attractive target for immune-based therapeutic interventions in AML.

Humans↗

PubMed2026

Analysis and validation of abnormal signaling pathways and immune cell infiltration characteristics in digestive system cancers based on peroxisome-related genes.

BACKGROUND: Although emerging evidence suggests a role for peroxisomes in tumorigenesis, their functions in digestive cancers remain unclear. This study aims to investigate the association between peroxisomes and digestive tract tumors. METHODS: To systematically investigate peroxisomal functions in digestive cancers, we first constructed and validated tumor-specific prognostic signatures based on peroxisome-related genes (PRGs) through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. We then characterized the tumor immune microenvironment (TIME) with CIBERSORT, X-CELL, and EPIC algorithms, and identified tumor-specific and common signalings via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Focusing on hepatocellular carcinoma (HCC), we experimentally validated peroxisome-related therapeutic responses by profiling signature genes in radioresistant cells and an orthotopic transarterial chemoembolization (TACE) rat model. PEX13 knockdown further assessed peroxisomal role in radiosensitivity and targeted therapy response. Clinical relevance of PEX13 was evaluated in HCC cohort. Single-cell RNA sequencing dataset and lipidomics further revealed peroxisomal mechanisms in HCC progression. Finally, peroxisomal function in colorectal cancer (CRC) was validated in vitro. RESULTS: Novel peroxisome-related prognostic signatures demonstrated strong predictive power in HCC, colon adenocarcinoma, rectal adenocarcinoma, pancreatic adenocarcinoma, gastric adenocarcinoma, esophageal adenocarcinoma, esophageal squamous cell carcinoma, and cholangiocarcinoma. High-risk patients displayed an immunosuppressive microenvironment, characterized by increased infiltration of regulatory T cells, M2 macrophages, Th2 cells, or cancer-associated fibroblasts, or Th1 cells' reduction. Peroxisomes engaged in several distinct yet convergent pathways, most notably "positive regulation of response to stimuli". HCC prognostic genes were dynamically regulated in response to therapeutic stimuli, including radiotherapy, targeted therapy, and TACE. Clinically, the expression of PEX13 was markedly upregulated in tumor tissues from therapy-resistant HCC patients. Mechanistically, peroxisomal dysfunction induced by silencing PEX13 in HCC or UBE2D2 in CRC may overcome therapeutic resistance (radiotherapy/ lenvatinib resistance in HCC, radioresistance in CRC) through reprogramming lipid metabolism. CONCLUSIONS: Peroxisomes act as pivotal regulators of digestive cancer progression by modulating signaling pathways, the TIME, therapeutic resistance, and lipid metabolism. Targeting peroxisomal function, particularly in high-risk subgroups of HCC and CRC, warrants further exploration as a promising therapeutic strategy.

Peroxisomes↗

PubMed2025

Understanding disease-associated metabolic changes in human colonic epithelial cells using the iColonEpithelium metabolic reconstruction.

The colonic epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colonic epithelium, our present study focuses on the generation of the first cell-type-specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at the systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. iColonEpithelium is also capable of performing metabolic tasks specific to the colonic epithelium. A unique transport reaction compartment has been included to allow for the simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We used single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples to build disease-specific iColonEpithelium metabolic networks in order to predict metabolic signatures of colonocytes in both healthy and disease states. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, relative to healthy control, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and we show an initial proof-of-concept for how our tool can be leveraged to explore the metabolic interactions between host and gut microbiota.

Humans↗

PubMed2026

Regnase-1-mediated regulation of neutrophils modulates SARS-CoV-2 pneumonia.

The innate immune response to viral infection needs to be tightly regulated to ensure effective pathogen clearance while avoiding excessive immune activation. During SARS-CoV-2 infection, however, the immune system often fails to elicit appropriate responses, resulting in cytokine-release syndrome in patients with COVID-19. In this study, we show that reduced expression of Regnase-1, an RNase that negatively regulates immune cell activation, confers resistance to infection with the mouse-adapted SARS-CoV-2 MA10 strain. In Regnase-1+/- mice, altered neutrophil function contributed to the amelioration of MA10-induced pneumonia. Single-cell RNA sequencing of lung tissue during MA10 infection revealed four distinct neutrophil subsets, and among these, a subset characterized by an interferon-stimulated gene (ISG) signature was decreased in Regnase-1+/- mice. Furthermore, Regnase-1+/- neutrophils exhibited reduced ISG expression without corresponding changes in proinflammatory gene expression. Regnase-1 was found to repress the expression of Tsc22d3, a gene involved in the negative regulation of interferon responses, through its 3' untranslated region. Collectively, these findings suggest that Regnase-1 attenuates resistance to SARS-CoV-2 MA10 infection by promoting excessive interferon responses in neutrophils.

Animals↗

PubMed2025

Divergent PTEN-p53 interaction upon DNA damage in a human thyroid organoid model with germline PTEN mutations.

Germline mutations in the tumor suppressor phosphatase and tensin homolog (PTEN) cause PTEN hamartoma tumor syndrome (PHTS). PHTS is characterized by an elevated lifetime risk of differentiated thyroid cancer (DTC), 30 times higher than the general population. However, only 1 in 3 PHTS patients develop DTC, and it remains unknown whether specific PTEN variants are associated with an increased risk of DTC. PTEN antagonizes the phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway, a frequently affected pathway in sporadic DTC. PTEN also acts as a guardian of the genome by interacting with other tumor suppressors. Here, we report how ionizing radiation, an environmental tumorigenic contributor, modifies the DNA damage response based on the type of germline PTEN variants. We hypothesized that certain PTEN variants associated with DTC create a pro-oncogenic molecular signature upon radiation-induced DNA damage. DTC-associated (PTEN M134R ) or DTC-non-associated (PTEN G132D ) germline PTEN mutant alleles were introduced into a human induced pluripotent cell (hiPSC) line derived from a healthy donor utilizing CRISPR-Cas9 gene editing technology. We determined radiation-induced transcriptomic changes in functional thyroid organoids induced from wild-type and both heterozygous PTEN mutant hiPSCs. Both bulk and single-cell RNA sequencing data indicated that radiation upregulated the p53 network more potently in the thyroid organoids with PTEN WT/G132D than those with PTEN WT/M134R , which could be mediated by AKT-dependent MDM2 inactivation and PTEN-p53 physical interaction. Our data suggest that the lack of p53 pathway activation through PTEN-p53 network interactions explains why PTEN M134R is a DTC-susceptible variant.

Humans↗

PubMed2026

Non-structural maintenance of chromosome condensin I complex subunit H knockdown suppresses malignant progression of esophageal squamous cell carcinoma via the Wnt/β-catenin signaling pathway.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) remains a major cause of cancer-related mortality, and effective therapeutic targets are still limited. Non-structural maintenance of chromosome condensin I complex subunit H (NCAPH) has been implicated in tumorigenesis; however, its clinical relevance, functional roles, and underlying mechanisms in ESCC are not fully defined. We aimed to characterize the expression pattern, prognostic value, biological functions, and mechanistic basis of NCAPH in ESCC. METHODS: Public datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to evaluate NCAPH expression and clinical associations. Single-cell RNA sequencing (scRNA-seq) data were used to map cell-type-specific distribution of NCAPH in tumor and adjacent tissues. NCAPH was silenced in KYSE150 and KYSE510 cells using lentiviral short hairpin RNAs (shRNAs), followed by Cell Counting Kit-8 (CCK-8), colony formation, wound-healing, and Transwell migration/invasion assays. A nude mouse xenograft model was established to assess the effect of NCAPH knockdown in vivo. RNA sequencing (RNA-seq), quantitative polymerase chain reaction (qPCR), western blotting, and enzyme-linked immunosorbent assay (ELISA) were performed to explore potential mechanisms. RESULTS: NCAPH was consistently upregulated in ESCC across multiple cohorts and was associated with unfavorable clinicopathological features and poorer survival. Functional assays demonstrated that NCAPH knockdown significantly inhibited ESCC cell proliferation, migration, invasion, and clonogenic growth. In vivo, NCAPH silencing suppressed xenograft tumor growth. Mechanistically, transcriptomic profiling and molecular validation indicated attenuation of Wnt/β-catenin signaling following NCAPH depletion, accompanied by reduced β-catenin and downstream targets. CONCLUSIONS: NCAPH promotes malignant progression of ESCC, at least in part through activation of the Wnt/β-catenin pathway, and may serve as a potential biomarker and therapeutic target.

Esophageal squamous cell carcinoma (ESCC)↗

PubMed2026

Pan-cancer characterization of HMGA1 reveals its oncogenic role in tumor microenvironment and stemness: functional validation in pancreatic cancer migration and invasion.

BACKGROUND: HMGA1 is a chromatin-associated oncogenic factor implicated in tumor progression, epithelial-mesenchymal transition (EMT), stemness, and metastasis. However, its pan-cancer expression and prognostic patterns, epigenetic activation, and relationship with malignant-cell stemness/plasticity and tumor microenvironment (TME) remodeling in pancreatic adenocarcinoma (PAAD) remain incompletely defined. This study aimed to systematically characterize HMGA1 across cancers and clarify its clinical and biological relevance in PAAD. METHODS: Pan-cancer transcriptomic, clinical, genetic, methylation, immune, and stemness data were integrated from multiple public databases. PAAD single-cell RNA sequencing data were analyzed to localize HMGA1 expression, infer malignant-cell pseudotime, calculate a stemness module score, and assess ligand-receptor communication using CellChat. Public HMGA1-knockdown RNA sequencing data were reanalyzed to evaluate transcriptional remodeling. The Cancer Genome Atlas (TCGA)-PAAD expression and methylation data were used to assess TME-remodeling, immune-suppression, stemness/plasticity, cytokine/chemokine, checkpoint, and promoter-methylation features. HMGA1 expression and function were further examined using immunohistochemistry (IHC), quantitative real-time polymerase chain reaction, Western blotting, wound-healing assays, and Transwell migration and invasion assays. RESULTS: HMGA1 was upregulated in most tumor types, and high expression was associated with unfavorable survival in multiple cancers, including PAAD. In PAAD, HMGA1 was enriched in malignant epithelial cells and positively correlated with pseudotime (Spearman's rho =0.594), while the stemness module score increased along pseudotime (rho =0.748). HMGA1-high malignant cells showed markedly stronger CellChat-inferred outgoing communication, predominantly involving extracellular matrix (ECM)-receptor, adhesion-related, and selected immunomodulatory ligand-receptor axes. HMGA1 knockdown was associated with broad remodeling of EMT, TGF-β, Hedgehog, IL6/JAK/STAT3, KRAS, and cancer stem cell/stemness-related programs rather than uniform suppression of these programs. HMGA1 promoter methylation was inversely correlated with HMGA1 expression (rho =-0.633) and the TME-remodeling score (rho =-0.347). HMGA1 was associated with selected mediators, including PPIA, PLAU, ANXA1, LGALS9, TGFB1, CD276, and CD47, but not with a generalized checkpoint-high phenotype. Functionally, HMGA1 knockdown significantly reduced pancreatic cancer (PC) cell migration and invasion. CONCLUSIONS: These findings support an association-based model in which promoter hypomethylation-associated HMGA1 activation is linked to malignant epithelial stemness/plasticity, ECM/adhesion-dominant TME remodeling, selected immunomodulatory programs, and aggressive PAAD phenotypes. Further mechanistic and clinical validation is required before HMGA1 can be used for therapeutic stratification or immunotherapy-response prediction.

HMGA1↗

PubMed2026

Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.

BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.

Golgi apparatus↗

PubMed2026

Microplastics and nanoplastics-related genes signature predicts prognosis in pancreatic ductal adenocarcinoma and functional validation of interleukin 1 alpha.

BACKGROUND: Microplastics and nanoplastics (MNPs), as emerging environmental pollutants, have garnered significant attention from the global scientific community due to their potential threats to human health, particularly their association with the occurrence and development of cancer. The goal of our study is to create a predictive marker for pancreatic ductal adenocarcinoma (PAAD) based on MNPs-related genes, with the purposes of predicting survival outcomes and assessing the tumor immune microenvironment. METHODS: Using multi-cohort data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC), we assessed the association between MNPs and PAAD prognosis through the Xiantao Academic (https://www.xiantao.love/). The development of a prognostic signature was followed by an assessment of its significance through the Kaplan-Meier method, time-dependent receiver operating characteristic (ROC), and decision curve analysis (DCA). The validity of the risk model was confirmed through the ICGC and GSE71729 cohorts. The model was then assessed for levels of tumor immune infiltration. To explore MNPs-related genes expression characteristics within immune cells in PAAD, we performed single-cell RNA sequencing and spatial transcriptomics analysis through the Sparkle Platform (https://grswsci.top/). Finally, in vitro experiments were conducted to investigate the biological function of interleukin 1 alpha (IL1A). RESULTS: A four-gene signature comprising XDH, IL1A, KIF20A, and ASPM, based on MNPs, was developed to stratify PAAD patients into two distinct risk groups. The high-risk group showed a significantly poorer prognosis. A similar trend was verified in the external cohorts ICGC and GSE71729. The signature risk score affected immune cell infiltration in the PAAD microenvironment. The infiltration of B cells, CD8+ T cells, cytotoxic cells, immature dendritic cells (iDCs), mast cells, plasmacytoid dendritic cell (pDC), T cells, Tem cells, T follicular helper (TFH) cells, and T helper 17 (Th17) cells had a positive correlation with the low-risk group. In contrast, high-risk patients tended to have increased number of T helper (Th2) cells and higher expression of SIGLEC15, CD274, IGSF8. Knockdown of IL1A in PAAD cells inhibited their tumor proliferation ability in vitro. CONCLUSIONS: Using MNPs-related genes, we built a prognostic model for PAAD, revealing that patients with high-risk scores are likely to have a worse prognosis. This model is designed to develop personalized treatment strategies tailored to the specific needs of each patient, thereby improving clinical outcomes for PAAD patients. Furthermore, IL1A could be a promising therapeutic candidate for PAAD.

Microplastics↗

PubMed2026

A murine model of sepsis induces age- and sex-specific chromatin remodeling in myeloid-derived suppressor cells.

INTRODUCTION: Sepsis survivors frequently develop long-term immune dysfunction, but the epigenetic mechanisms underlying persistent myeloid suppression remain unclear. Myeloid-derived suppressor cells (MDSCs), whose function is shaped by host age and sex, are key contributors to post-sepsis immune dysregulation. METHODS: Here, we present a high-resolution epigenetic map targeting gene promoters of MDSCs after sepsis and daily chronic stress using MAPit-FENGC, a single-molecule assay that simultaneously profiles DNA methylation and chromatin accessibility. In a clinically relevant murine model, including young and older adult male and female mice, splenic MDSCs were isolated for MAPit-FENGC and single-cell RNA sequencing. RESULTS: Unsupervised clustering identified nine promoter classes reflecting chromatin dynamics: age- and sex-dependent sepsis-induced opening (Classes 1-4), persistent closure with varying levels of DNA methylation (Classes 5-7), and constitutive openness post-sepsis (Classes 8, 9). Transcriptomic profiling corroborated these promoter states, linking accessibility with gene expression. CONCLUSIONS: These findings define promoter-level epigenetic classes across a targeted locus panel in splenic CD11b+Gr1+ cells within this murine sepsis model and generate mechanistic hypotheses regarding age- and sex-associated chromatin states.

Animals↗

PubMed2026

Placenta-derived Exosomes Mitigate Hypoxia-Induced Trophoblast Apoptosis and Inflammatory Progression via SASH1.

SASH1 is a signal adaptor protein involved in cell growth, apoptosis, and immune regulation, and has been increasingly studied in tumor and immune cells. Emerging evidence suggests that SASH1 plays an important role in inflammatory responses and cellular homeostasis, processes that are closely associated with the development of PE. This study aimed to determine whether SASH1 contributes to trophoblast apoptosis and inflammatory responses in PE and whether P-EXOS exerts protective effects through SASH1 regulation. In this study, three PE-related transcriptomic datasets (GSE75010, GSE10588, and GSE60438) were analyzed to identify shared differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning algorithms were further applied to screen key candidate genes, and single-cell RNA sequencing data were used to characterize cellular heterogeneity in placental tissue and to determine cell type-specific expression patterns. SASH1 was identified as a consensus candidate gene and was significantly upregulated in trophoblast cells from PE samples. In vitro, a hypoxia-treated HTR-8/SVneo trophoblast cell model was established, combined with SASH1 knockdown, SASH1 overexpression, and co-culture with P-EXOS. Functional experiments showed that knockdown of SASH1 significantly suppressed hypoxia-induced trophoblast apoptosis and reduced the secretion of pro-inflammatory cytokines, including IL-6, IL-1β, and TNF-α, whereas SASH1 overexpression promoted apoptosis and inflammatory responses. In addition, P-EXOS treatment markedly reduced SASH1 expression at both mRNA and protein levels and attenuated hypoxia-induced trophoblast injury, while SASH1 overexpression largely abolished these protective effects. Taken together, these findings indicate that SASH1 plays a critical role in trophoblast apoptosis and inflammatory responses in PE. P-EXOS may alleviate hypoxia-induced trophoblastic injury by suppressing SASH1 expression, providing new insights into the molecular mechanisms and potential therapeutic targets for PE.

Trophoblasts↗

PubMed2025

Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.

Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality of the spatial resolution or gene coverage is still much lower than the quality of the single-cell transcriptomics data. To this end, we develop a ST-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods. When applied to the human brain and squamous cell carcinoma data, STALocator could robustly reconstruct the relative spatial organization of critical cell populations. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for fluorescence in situ hybridization (FISH) and Xenium data, leading to the identification of more spatially variable genes and more biologically relevant Gene Ontology (GO) terms compared with the raw data. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗

PubMed2026

Construction and validation of a β-hydroxybutyrylation-related molecular model for predicting prognosis of papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) usually has a favorable prognosis, yet a subset of patients develops persistent, recurrent, or biologically aggressive disease. The clinical relevance of lysine β-hydroxybutyrylation (Kbhb)-related transcriptional programs in PTC remains unclear. Accordingly, this study aimed to characterize Kbhb-related molecular heterogeneity in PTC, construct a prognostic signature, and explore its association with the tumor microenvironment (TME). METHODS: Transcriptomic and clinical data from PTC samples within The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort were analyzed to identify Kbhb-related differentially expressed genes (DEGs), define molecular subtypes, construct a prognostic signature, and characterize tumor microenvironmental features. Single-cell RNA-sequencing data from PTC were further used to explore the cellular distribution of representative genes. RESULTS: We identified 51 Kbhb-related DEGs in PTC and defined two Kbhb molecular subtypes. The Kbhb_C2 subtype showed shorter progression-free interval (PFI) and a more immune- and stroma-enriched microenvironment. A six-gene prognostic signature comprising TARID, CDSN, PIMREG, KLRC1, SYT13, and NPR3 was then established. High-risk patients had significantly worse PFI in the full, training, and testing cohorts, with 1-, 3-, and 5-year areas under the curve (AUCs) of 0.715, 0.793, and 0.771, respectively, in the full cohort. High-risk tumors also exhibited higher stromal, immune, and ESTIMATE scores, altered immune infiltration, and increased expression of multiple immune checkpoint molecules. Single-cell analysis confirmed distinct cell-type-specific expression patterns of representative genes. CONCLUSIONS: Kbhb-related transcriptional programs define clinically relevant molecular heterogeneity in PTC and are closely associated with prognosis and TME remodeling. The identified six-gene signature provides a biologically interpretable framework for risk stratification in PTC.

Papillary thyroid carcinoma (PTC)↗

PubMed2025

Scalable single-cell total RNA-seq reveals non-coding programs in immunity, infection, and brain development.

Non-coding RNAs represent a widespread and diverse layer of post-transcriptional regulation across cell types and states, yet much of their diversity remains uncharted at single-cell resolution. This gap stems from the limitations of widely used single-cell RNA-sequencing protocols, which focus on polyadenylated transcripts and miss many short or non-polyadenylated RNAs. Here, we adapted single-cell RNA-sequencing on the 10x Genomics platform to capture a broad complement of coding and non-coding RNAs-including miRNAs, tRNAs, lncRNAs, histone RNAs, and non-adenylated viral transcripts. This approach enabled the discovery of rich, dynamic non-coding RNA programs across immune cells, virally infected hepatocytes, and the developing human brain. In dengue virus-infected hepatocytes, we detect non-adenylated viral transcripts and distinguish active from transcriptionally quiescent infected states, each with distinct host regulatory signatures. In brain tissue, we identify biotype-specific, cell-type-restricted non-coding RNAs, including miRNAs whose expression anticorrelates with predicted targets, consistent with post-transcriptional regulatory relationships. We show that MIR137, one of the strongest GWAS loci associated with schizophrenia and intellectual disability, is expressed specifically in Cajal-Retzius cells, an early-born but transient population that guides subsequent cortical neuron migration. These findings demonstrate the importance of non-coding RNAs in defining cell identity and state, and show how expanded transcriptome coverage can reveal additional layers of gene control-now accessible through practical and scalable single-cell profiling.

Journal Article↗

PubMed2026

Multiomics approaches reveal direct NF-κB p65 target genes in pancreatic islets during cytokine exposure and in type 1 diabetes.

Autoimmune diseases, including Type 1 diabetes (T1D), are often characterized by overactive inflammatory signaling pathways. The proinflammatory cytokine interleukin-1β (IL-1β) elicits global gene expression changes in islet β-cells which overlap with islets obtained from human donors with T1D. The direct transcriptional link between NF-κB subunit p65 and target genes involved with autoimmune events was investigated. We used a multiomics approach including bulk RNA-sequencing (RNA-Seq), single-cell RNA-sequencing (scRNA-Seq), and chromatin immunoprecipitation coupled to deep sequencing (ChIP-Seq), alongside molecular docking simulations, and transcriptional assays. Through the various experimental modalities, we identified early response genes driven by IL-1β that were differentially expressed in pancreatic islets from human T1D donors and also conserved across mouse, rat, and human tissues. ChIP-Seq revealed genes that are direct genomic targets of the NF-κB p65 transcription factor. Moreover, regions that gained RNA polymerase II binding following cellular exposure to IL-1β were identified, complementing the early response gene profile induced by β-cell exposure to IL-1β. Molecular docking simulations predicted that mutations reducing p65 transcriptional capacity do not alter DNA binding ability. These findings clearly show that IL-1β signaling in pancreatic β-cells directs p65 to specific genomic regions congruent with increased gene expression relevant to T1D in β-cell lines as well as mouse and human islets exposed to cytokines. Islets from human donors with T1D express genes identified as direct p65 targets using unbiased approaches, implicating heightened NF-κB activity as a critical component of autoimmune disease etiology.NEW & NOTEWORTHY Using multiple Seq-based approaches, this study identified genes expressed in human pancreatic tissue from donors with Type 1 diabetes that are regulated acutely by exposure to the cytokine interleukin-1beta. The NF-kB transcription factor p65 (RelA) was determined via ChIP-Seq to be a major control node regulating this immediate early response. These collective datasets are consistent with a paradigm of overactive NF-kB signaling as a critical component of autoimmunity in both rodents and humans.

Humans↗

PubMed2026

Lactylation-related immune-metabolic dysregulation defines prognostic and therapeutic stratification in lung adenocarcinoma.

BACKGROUND: Lactylation links lactate metabolism with inflammatory signaling and immune regulation in tumors. However, its cellular distribution and translational value in lung adenocarcinoma (LUAD) remain unclear. METHODS: Single-cell RNA-sequencing datasets GSE189357 and GSE171145 were integrated to characterize lactylation-related activity, intercellular communication, and malignant epithelial cell states in LUAD. Single-cell-derived lactylation-related differentially expressed genes were mapped to TCGA-LUAD and multiple GEO cohorts. Univariate Cox regression and machine learning algorithms were used to construct a lactylation-related prognostic signature (LRPS). The associations of LRPS with prognosis, immunotherapy response, drug sensitivity, genomic alterations, immune infiltration, and inflammation- and metabolism-related pathways were evaluated. KRT7 was further validated using virtual knockout analysis, spatial transcriptomics, and in vitro and in vivo experiments. RESULTS: lactylation-related transcriptional activity showed heterogeneous distribution across LUAD cell populations and was associated with altered cell-cell communication. In malignant epithelial cells, LRTS-high and LRTS-low states exhibited distinct metabolic, inflammatory, and tumor-related pathway activities. LRPS showed stable prognostic performance in TCGA-LUAD and multiple GEO cohorts and remained an independent prognostic factor. Low LRPS was associated with greater potential benefit from immunotherapy, whereas different LRPS groups displayed distinct drug sensitivity, genomic alteration, and immune microenvironment patterns. KRT7 was highly expressed in LUAD and associated with poor prognosis. KRT7 knockdown suppressed LUAD cell proliferation, migration, invasion, colony formation, and tumor growth in vivo. CONCLUSIONS: This study identifies lactylation-related immune-metabolic dysregulation as a clinically relevant feature of LUAD and develops a single-cell-guided LRPS for prognosis and therapeutic stratification. KRT7 emerged as an LRPS-related functional candidate with experimentally supported roles in malignant LUAD phenotypes.

Immunotherapy↗

PubMed2025

LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Single-Cell Analysis↗

PubMed2026

transfactor: transcription factor activity estimation via probabilistic gene expression deconvolution.

Gene expression is a primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential gene expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies. Single-cell RNA-sequencing; TF activity; bioinformatics; GRN.

Transcription Factors↗