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A nine-gene nicotine-metabolism signature predicts prognosis and characterizes the immune landscape in colon adenocarcinoma.
BACKGROUND: Smoking is a preventable cause of colorectal cancer (CRC), and nicotine metabolism may promote tumorigenesis. We aimed to investigate the prognostic significance of nicotine metabolism‑related genes (NRGs) in colon adenocarcinoma (COAD) and to develop a gene signature for patient stratification. METHODS: Using the Cancer Genome Atlas COAD cohort, we performed differential expression analysis and weighted gene co-expression network analysis to identify NRGs. Functional enrichment, Cox regression, and least absolute shrinkage and selection operator (LASSO) modelling were applied to build a multigene signature, which was validated in external cohorts. Single-cell transcriptomic data and immune infiltration analyses were used to evaluate gene expression patterns and tumor microenvironmental features. RESULTS: We identified 767 differentially expressed NRGs enriched in extracellular matrix (ECM) organization and signaling pathways. A nine‑gene signature (FOXC1, TRIP6, NRCAM, TIMP1, TSPAN11, STC2, CST2, SIX2, GPRASP1) was derived; this risk model independently predicted overall survival and showed robust performance across multiple datasets. Single‑cell analyses confirmed cell‑type‑specific expression of these genes. High‑risk scores were associated with altered immune cell infiltration, distinct mutation profiles, and differential drug sensitivity patterns. CONCLUSIONS: We propose a novel NRG-based prognostic signature that accurately predicts outcomes in COAD. The model highlights the interplay between nicotine metabolism, ECM remodeling, and immune responses, highlighting genes and pathways that may inform future therapeutic stratification.
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Overexpression of PLXDC2 in Stromal Cell-Associated M2 Macrophages Is Related to EMT and the Progression of Gastric Cancer.
The tumor microenvironment (TME) comprises distinct cell types, including stromal types such as fibroblast cells and macrophage cells, which have recently become a critical factor in tumor development and progression. Here, we identified the TME-related gene, plexin domain containing 2 (PLXDC2), in a high-stromal-score population. And we revealed that this gene was related to poor survival and advanced (tumor-node-metastasis) stage in gastric cancer (GC) patients from The Cancer Genome Atlas database. An integrated gene profile and functional analysis of the proportions of tumor-infiltrating immune cells revealed that the expression of the M2 macrophages cell marker CD163 was positively correlated with PLXDC2 expression. In addition, the M2 macrophages gene signature and high PLXDC2 expression were associated with the inflammatory signaling pathway and the epithelial-to-mesenchymal transition (EMT)-related gene signature. Single-cell study of GC identified PLXDC2 was enriched specifically in fibroblasts and monocytes/macrophages populations, which supported its important role in the stroma. Furthermore, according to a tissue microarray immunohistochemistry analysis, the expression of PLXDC2 elevated in human GC stromal specimens compared to tumor tissue specimens. Moreover, PLXDC2 overexpression in the stromal compartment was associated with CD163-positive regulatory M2 macrophages, and its functions were related to the pathogenesis of GC. Multiplexed immunohistochemistry verified PLXDC2's correlation with EMT markers. Our data suggested that PLXDC2 was expressed in stromal cells and that its crosstalk with tumor-associated macrophages could contribute to cancer biology by inducing the EMT process.
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Pan-cancer analysis of AIM2 inflammasomes with potential implications for immunotherapy in human cancer: A bulk omics research and single cell sequencing validation.
BACKGROUND: The absent in melanoma 2 (AIM2) inflammasome is a multi-protein platform that recognizes aberrant cytoplasmic double-stranded DNA(dsDNA) and induces cytokine maturation, release, and pyroptosis. Some studies found that the AIM2 inflammasome was a double-edged sword in many cancers. However, there have been fewer studies on AIM2 inflammasomes in pan-cancer. METHODS: Gene expression was analyzed using The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) database. Immunohistochemistry (IHC) was used to validate the expression of the AIM2. We used the survival curve to explore the prognostic significance of the AIM2 inflammasomes in pan-cancer. Mutations and methylation of AIM2 inflammasome-related genes (AIM2i-RGs) were also comprehensively analyzed. Single sample gene set enrichment analysis was used to calculate the AIM2 inflammasomes score and explore the correlation of the AIM2 inflammasomes score with immune-related genes and immune infiltrations. The function of AIM2 inflammasomes in pan-cancer was analyzed at the single-cell level. Single-cell transcriptome sequencing (scRNA-seq) data was used to assess the activation state of the AIM2 inflammasomes in the tumor microenvironment. RESULTS: We found that AIM2i-RGs were aberrantly expressed in tumors and were strongly associated with prognosis. In pan-cancer, the expression of AIM2i-RGs was positively associated with copy number variation and negatively associated with methylation. In AIM2i-RGs, missense mutations were the predominant type of single nucleotide polymorphism. Moreover, we found that the drugs dimethyloxallyl glycine (DMOG) and Z-LNle-CHO may be sensitive to the AIM2 inflammasomes. The AIM2 inflammasomes score was significantly and positively correlated with the tumor immunity score and the stroma score. In most tumors, the AIM2 inflammasomes score was significantly and positively correlated with CD8+ T cell abundance in the tumor microenvironment. Additionally, the AIM2 inflammasomes score was significantly correlated with immune checkpoint genes in pan-cancer as well as immune checkpoint therapy-related markers including tumor mutational burden (TMB), microsatellite instability(MSI), and tumor immune dysfunction and exclusion(TIDE). scRNA-seq analysis suggested that AIM2 inflammasomes differ significantly among different cells in the tumor microenvironment. IHC confirmed low expression of AIM2 in colorectal cancer. DISCUSSION: AIM2 inflammasomes may be a new target for future tumor therapy It is likely involved in tumor development, and its high expression may serve as a predictor of tumor immunotherapy efficacy.
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Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer.
Breast cancer (BRCA) remains the most common malignancy and the leading cause of cancer-related death among women worldwide. Recent studies have highlighted dysregulated copper homeostasis as a contributor to tumorigenesis, with cuproptosis-a copper-dependent form of regulated cell death-emerging as a potential therapeutic target. In this study, we systematically evaluated the prognostic significance of cuproptosis-related genes (CRGs) in BRCA by integrating multi-omics data from TCGA and GEO cohorts. Through weighted gene co-expression network analysis (WGCNA), we identified four key CRGs (CCDC24, TMEM65, XPOT, and NUDCD1) to construct a prognostic signature. The resulting risk score effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly worse overall survival, higher TP53 mutation frequency, and features of an immunosuppressive tumor microenvironment (TME). Functional enrichment analyses further implicated these genes in immune evasion and metabolic reprogramming pathways. Single-cell RNA sequencing (scRNAseq) confirmed heterogeneous expression of the signature genes across distinct cell populations, supporting their involvement in both tumor biology and immune modulation. Clinically, this CRG-based model provides a promising tool for individualized risk assessment and treatment planning. High-risk patients may benefit from intensified therapies or immunomodulatory strategies, while low-risk individuals could be considered for treatment de-escalation. In summary, our findings suggest that cuproptosis may play a regulatory role in BRCA progression and offer a novel, clinically actionable framework for prognostic stratification.
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Decoding gene networks controlling hypothalamic and prethalamic neuron development.
The hypothalamus and prethalamus regulate diverse physiological and behavioral processes, yet the gene regulatory networks guiding their development remain poorly defined. Using single-cell RNA and ATAC sequencing, we profile over 660,000 cells in the developing mouse hypothalamus and prethalamus between embryonic day 11 and postnatal day 8. This resource maps key transcriptional and chromatin dynamics underlying regionalization, neurogenesis, and neuronal subtype differentiation. We identify distinct neurogenic progenitor populations and uncover gene regulatory networks controlling their spatial and temporal identity. Integration with genome-wide association study data reveals that transcription factors active in supramammillary and prethalamic lineages are associated with metabolic and neuropsychiatric traits. Cross-repressive interactions among regional transcription factors reinforce hypothalamic boundaries. Functional analysis of Dlx1/2 shows that their loss disrupts GABAergic neuron specification, leading to impaired thalamic inhibition and hyperactivity. This study provides a foundational atlas of hypothalamic and prethalamic development and highlights the importance of early gene regulatory programs in health and disease.
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Identification of lactylation-associated immune and metabolic regulators in bladder cancer via integrated bulk and single-cell transcriptomics.
BACKGROUND: Lactate-driven metabolic reprogramming and histone lactylation play pivotal roles in bladder cancer (BLCA) progression, yet their underlying mechanisms and regulatory genes remain poorly understood. METHODS: Using transcriptomic data from The Cancer Genome Atlas (TCGA), we identified lactylation-associated genes and constructed a prognostic signature. Comprehensive bioinformatics analyses were conducted to assess immune infiltration, tumor microenvironment characteristics, and the lactylation landscape at the single-cell level. Furthermore, we performed in vitro experiments to evaluate the biological functions of key lactylation-related genes in BLCA cells. RESULTS: Six lactylation-related hub genes were identified, among which FASN and RUNX2 were significantly upregulated in BLCA and associated with poor prognosis. Single-cell analyses revealed elevated lactylation signatures in tumor epithelial and immune cells. Knockdown of FASN or RUNX2 in BLCA cell lines significantly suppressed cell proliferation, induced apoptosis, and reduced intracellular lactate levels. Correspondingly, global protein lactylation was diminished, with dominant modification signals observed around 40 kDa, indicating a potential set of non-histone proteins as key functional targets. CONCLUSIONS: Our study highlights a metabolic-enzymatic axis wherein FASN and RUNX2 regulate lactate-driven protein lactylation in BLCA. These findings provide new insights into the non-histone functions of lactylation and suggest potential therapeutic targets at the intersection of metabolism and tumor immunity.
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KLF4 orchestrates a Ccl2+ fibroblast-mediated inflammatory network driving preterm birth.
BACKGROUND: Preterm birth (PTB) remains the leading cause of neonatal mortality and long-term complications, yet its molecular mechanisms are incompletely understood. Fibroblasts, as critical components of the gestational microenvironment, have an undefined role in PTB pathogenesis. METHODS: This study employed single-cell RNA sequencing (scRNA-seq) datasets from murine preterm birth models (GSE200289 and E-MTAB-11491), processed using Seurat and Harmony for clustering and integration, to analyze cellular heterogeneity. This was integrated with Mendelian randomization (MR) using eQTLs from FinnGen and IEU GWAS databases as instrumental variables, and validated through functional experiments including LPS-induced placental explant cultures, qRT-PCR, western blotting, immunohistochemistry, and an in vivo rat intrauterine infection model with AAV-shKLF4 knockdown. RESULTS: We first established a single-cell transcriptomic atlas of preterm mice, identifying nine cell clusters, among which the Ccl2+ fibroblast subpopulation (Ccl2+fib) exhibited significant expansion under pathological conditions. MR analysis identified KLF4 as a genetic risk factor for PTB (OR = 1.32, 95% CI: 1.12-1.56), with reverse MR confirming the absence of reverse causality. Clinical validation revealed KLF4-specific upregulation in peripheral blood and placental tissues of PTB patients. Functional studies demonstrated dose-dependent KLF4 upregulation in LPS-induced placental inflammation, while KLF4 knockdown suppressed TNF-α and IL-1β secretion and reduced apoptosis. In a rat intrauterine infection model, KLF4 knockdown decreased LPS-induced preterm birth rates from 65% to 20%, accompanied by significantly reduced fetal stillbirth rates. CONCLUSION: This study reveals that the fibroblast subpopulation Ccl2+fib contributes to preterm birth through a KLF4-dependent inflammatory regulatory network. KLF4 downregulation significantly reduces infection-induced preterm birth rates and adverse pregnancy outcomes, suggesting that KLF4 is a critical therapeutic target for preterm birth.
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CanCellVar: A database for single-cell variants map in human cancer.
Numerous variants, including both single-nucleotide variants (SNVs) in DNA and A>G RNA edits in mRNA as essential drivers of cellular proliferation and tumorigenesis, are commonly associated with cancer progression and growth. Thus, mining and summarizing single-cell variants will provide a refined and higher-resolution view of cancer and further contribute to precision medicine. Here, we established a database, CanCellVar, which aims to provide and visualize the comprehensive atlas of single-cell variants in tumor microenvironment. The current CanCellVar identified ∼3 million variants (∼1.4 million SNVs and ∼1.4 million A>G RNA edits) involved in 2,754,531 cells of 5 major cell types across 37 cancer types. CanCellVar provides the basic annotation information as well as cellular and molecular function properties of variants. In addition, the clinical relevance of variants can be obtained including tumor grade, treatment, metastasis, and others. Several flexible tools were also developed to aid retrieval and to analyze cell-cell interactions, gene expression, cell-development trajectories, regulation, and molecular structure affected by variants. Collectively, CanCellVar will serve as a valuable resource for investigating the functions and characteristics of single-cell variations and their roles in human tumor evolution and treatment.
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Single-cell and spatially resolved transcriptomics for liver biology.
Single-cell transcriptomics enables the identification of rare cell types and the inference of state transitions, whereas spatially resolved transcriptomics allows the quantification of cells and genes in the context of tissues. The recent progress in these new technologies is improving our understanding of the cell landscape and its roles in diseases. Here, we review key biological insights into liver homeostasis, development, regeneration, chronic liver disease, and cancer obtained from single-cell and spatially resolved transcriptomics. We highlight recent progress in the liver cell atlas that characterizes the comprehensive cellular composition; diversity and function; the spatial architecture such as liver zonation, cell communication, and proximity; the cell identity conversion and cell-specific alterations that are associated with liver pathology; and new therapeutic targets. We further discuss outstanding challenges, advanced experimental technologies, and computational methods that help to address these challenges.
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Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human injured brain.
Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain. With the advances of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of spatial organization and functional orientation. Here, we generate spatial transcriptomes and metabolites from six patients with brain trauma with surgical samples. The resulting spatial marker gene, which is highly replicable across analysis methods, sequencing technologies, and modalities, is a comprehensive molecular marker of the diverse metabolic changes in human injured brains. The atlas includes an area of lipid peroxidation that resembles injured neurons in the brain. We further discover imbalanced myo-inositol and myo-inositol phosphate and related spatial markers. Our results highlight the complex transcriptomic regulation and metabolic alterations in the injured brain and will directly enable the design of reagents to target specific genes in the human brain for functional analysis.