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Multi‑omics identification of a signature based on malignant cell-associated ligand-receptor genes for lung adenocarcinoma.
PURPOSE: Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. METHODS: We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. RESULTS: We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. CONCLUSION: We have identified malignant cell-associated ligand-receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.
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A multi-omics atlas reveals spatially resolved sphingolipid metabolic reprogramming after spinal cord injury.
Secondary injury cascades following spinal cord injury (SCI) are closely associated with local metabolic dysregulation and persistent neuroinflammation. However, the spatiotemporal patterns of alterations centered on sphingolipid metabolism within the injury microenvironment, as well as their specific regulatory mechanisms in relation to neuroimmune inflammation, remain poorly understood at a systematic level. In this study, we integrated high-throughput lipidomics, spatial transcriptomics, and single-cell RNA sequencing (scRNA-seq) of multiple SCI models, to systematically construct a multidimensional spatiotemporal atlas of sphingolipid metabolism following injury. Lipidomics analysis revealed remodeling of sphingolipid metabolism, characterized by the progressive accumulation of neurotoxic sphingolipid metabolites and the depletion of myelin-associated lipids. Spatial transcriptomics demonstrated that regions of sphingolipid metabolism were strikingly confined to the 'inflammatory infiltration core' and the 'scar interface'. At the single-cell level, microglia were identified as the central cellular mediators of this metabolic reprogramming. Notably, we discovered a distinct microglial subpopulation characterized by high expression of the peroxisomal enzyme Hsd17b4 and the disease-associated marker Spp1, termed the 'high-sphingolipid metabolism' subtype. Pseudotime trajectory analysis indicated that sustained elevation of sphingolipid metabolic activity constitutes a potential determinant that prevents microglia from returning to homeostasis and drives their maladaptive polarization toward a chronic pathological phenotype. Immunofluorescence staining confirmed the specific enrichment of Hsd17b4+/Spp1+ microglia within the injury core. Our findings demonstrate that sphingolipid metabolism reprogramming in microglia represents a key mechanistic step in the propagation of secondary injury following SCI. These results provide novel theoretical foundations and potential therapeutic targets for modulating the immuno-metabolic microenvironment to promote functional recovery after spinal cord injury.
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NSUN2-Mediated m5C Methylation Impairs Endometrial Receptivity.
Impaired endometrial decidualization is the primary cause of recurrent implantation failure (RIF). RNA methylation modification, especially NSUN family mediated m5C, is crucial for various physiological events, such as maternal-to-zygotic transition, gametogenesis, embryonic development, organismal lifespan, and cell cycle. However, the regulatory mechanisms between NSUN family mediated m5C modification and RIF remain unknown. We acquired NSUN2 expression data of 15 human endometrium samples at proliferative and secretory stages from reproductive cell atlas. The overall pattern of m5C sites and genes was elucidated through m5C-BS-seq, whereas the overall m5C levels in different groups were revealed by dot blot assay. BrdU and western blotting assays were carried out to evaluate the role of NSUN2 in proliferation and autophagy. The effects of NSUN2-mediated m5C modification on embryo attachment were evaluated by an in vitro model of a confluent monolayer of Ishikawa cells cocultured with BeWo spheroids, and its downstream targets were evaluated by real-time reverse-transcription PCR and western blotting in Ishikawa cells. The molecular mechanism for NSUN2 regulating its downstream targets' expression was determined by Cut&Tag and coimmunoprecipitation assays. NSUN2 was increased in SOX9+ cells and widespread in epithelial cell type at the proliferative stage by previous single-cell RNA sequencing data. NSUN2 overexpression (NSUN2OE) in the Ishikawa cell line elevated m5C levels and promoted cell proliferation and autophagy. NSUN2OE reduced attachment efficiency of BeWo cell spheres. Overexpressed NSUN2 was found to increase STAT1 and MMP14 mRNA expressions by inducing exon skipping. NSUN2 interacted with CLDN4 through m5C modification, and NSUN2OE or NSUN2 knockdown resulted in a similar variation tendency of CLDN4. Overexpression of NSUN2 increased CLDN4 H3K9ac modification by downregulating SIRT4 expression at the protein level, leading to the upregulation of CLDN4 mRNA expression. Our results uncovered a novel intricate regulatory mechanism between NSUN2-mediated m5C and RIF and suggested a potential new therapeutic strategy for RIF.
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Integrative Mendelian Randomization and Single-Cell Pseudotime Analysis Reveal DKK3 as a PI3K-AKT-Modulated Driver of Esophageal Squamous Cell Carcinoma.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) remains a highly lethal malignancy, and the molecular drivers of its progression are not fully defined. Dickkopf-3 (DKK3), a context-dependent modulator of oncogenic signaling, has been implicated in several solid tumors, but its role in ESCC is unclear. METHODS: We integrated Mendelian randomization based on cis-expression quantitative trait loci (cis-eQTLs) from 31,684 individuals with The Cancer Genome Atlas (TCGA) ESCC transcriptomic data (|log2FC| > 1 and p < 0.05) , single-cell RNA sequencing, and in vitro/in vivo functional assays to identify causal ESCC-associated genes. DKK3 expression was validated by Western blotting, its effects on ESCC cell proliferation, migration, and clonogenicity were tested experimentally, and DKK3-related pathways were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. RESULTS: Integrative differential expression and MR analyses identified 22 ESCC-associated genes. Single-cell epithelial profiling further prioritized three candidates (DKK3, NINJ2, and SAPCD2), among which only DKK3 showed high and progressively increasing expression along the malignant epithelial trajectory and was therefore selected for mechanistic validation. DKK3 knockdown significantly inhibited ESCC cell proliferation, migration, and clonogenic capacity in vitro and suppressed tumor growth in nude mice. DKK3 silencing reduced p-PI3K and p-AKT levels, and enrichment analyses supported that DKK3 promotes ESCC progression at least partly through activation of the PI3K-AKT signaling pathway. CONCLUSION: By linking germline regulatory variation with single-cell tumor profiling and functional validation, this study identifies DKK3 as a causally relevant oncogenic regulator in ESCC that drives epithelial tumor growth and migration via PI3K-AKT pathway activation, supporting DKK3 as a potential biomarker and therapeutic targe.
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Spatiotemporal multiomics reveal a default CD4 fate and a stem-like CD8 T cell subset in the thymus.
BACKGROUND: T cell development in the thymus proceeds through coordinated transcriptional and spatial transitions. While key stages have been described, the precise timing and regulatory logic of lineage bifurcation-such as the separation between αβ and γδ T cells, and the commitment to CD4 or CD8 fate remains incompletely understood. A high-resolution, integrated view is needed to clarify how distinct T cell identities are established. OBJECTIVES: The goal is to construct a comprehensive, multimodal map of thymocyte development that defines early lineage decisions, intermediate states, and spatial checkpoints underlying T cell maturation. METHODS: We generated an integrated single-cell atlas by combining single-cell transcriptomics, protein profiling (CITE-seq), chromatin accessibility (scATAC-seq), and spatial transcriptomics of murine thymus. Data integration was used to resolve developmental trajectories, identify transcription factor programs, and map the anatomical localization of thymocyte subpopulations. RESULTS: Our analysis revealed that αβ and γδ lineages diverge as early as the DN1 stage. Within the αβ lineage, we identified a continuum of double-positive thymocyte substages distinguished by shifts in cell surface markers, cell-cycle activity, and recombination gene expression. CD4 lineage commitment emerged as a transcriptional default program initiated by Gata3. It was maintained independently of class II MHC signaling, whereas CD8 specification required Runx3 activation and class I MHC signaling, consistent with a sequential reprogramming model. We also identified a stem-like CD8-positive population enriched for self-renewal and developmental potential genes. Spatial mapping showed medullary enrichment of CD4 cells and localization of thymocyte exit cues to the cortico-medullary junction. CONCLUSIONS: This multimodal atlas provides a detailed framework for the molecular and spatial logic of T cell development. The findings uncover early lineage bifurcation events, refine and mechanistically characterize intermediate developmental states, and highlight anatomical checkpoints critical for thymic output and immune competence.
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Identification of CAV1 and CDH5 as potential diagnostic and prognostic biomarkers in lung adenocarcinoma.
Lung adenocarcinoma (LUAD) is the most prevalent and lethal subtype of lung cancer worldwide and despite advances in diagnostic and therapeutic strategies, its prognosis remains poor. The present study aimed to identify key genes in LUAD through bioinformatics approaches. Transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas databases were analyzed using differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction network construction and machine learning algorithms, and were validated using reverse transcription-quantitative PCR. Gene set enrichment analysis (GSEA) was performed to explore potential mechanisms associated with the involvement of key genes in LUAD, and single-cell transcriptomic data, collected from the Tumor Immune Single-cell Hub database, were used to validate cell-specific gene expression patterns. The results demonstrated that caveolin-1 (CAV1) and cadherin 5 (CDH5) are potential key genes in LUAD, both of which were significantly downregulated in tumor tissues compared with normal lung tissues. GSEA suggested that these genes are involved in the MAPK, Wnt and TGF-β signaling pathways, which are implicated in tumor progression. Furthermore, single-cell analysis revealed that CAV1 and CDH5 are predominantly expressed in endothelial cells, indicating a possible role in angiogenesis and tumor microenvironment regulation. In conclusion, CAV1 and CDH5 were systematically identified as potential tumor suppressor genes in LUAD, exhibiting robust diagnostic value confirmed by ROC analyses (GSE31210: CAV1 AUC=0.979; CDH5 AUC=0.969; GSE68465: 0.963 and 0.999; TCGA: 0.994 and 0.984). Therefore, CAV1 and CDH5 may serve as promising molecular targets for future therapeutic interventions, warranting further functional and clinical investigations.
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A Prognostic Riskscore Model Related to Helicobacter pylori Infection in Stomach Adenocarcinoma.
Background: Helicobacter pylori (HP) is associated with the development of various stomach diseases, one of the major risk factors for stomach adenocarcinoma (STAD). Methods: The HP infection score between tumor and normal groups was compared by single-sample gene set enrichment analysis (ssGSEA). The key modules related to HP infection were identified by weighted gene coexpression network analysis (WGCNA), and functional enrichment analysis was conducted on these module genes. Further, the limma package was used to screen the differentially expressed genes (DEGs) between HP-positive and HP-negative STAD. The prognostic genes were obtained to construct the riskscore model, and the performance of the model was validated. The correlation between riskscore and tumor immune microenvironment (TIME) was analyzed by Spearman's method. The single-cell atlas of HP-positive STAD was delineated. The mRNA expression levels of the prognostic genes were verified using STAD cells, and the migration and invasion capacities of STAD cells were evaluated by using the wound healing assay and transwell assay. Results: The HP infection score in the tumor group was significantly higher than that in the normal group. The purple and royal blue modules showed higher correlation with HP infection in STAD, and these module genes were enriched in the immune-related pathway. Further, five prognostic genes (CTLA4, CPVL, EMB, CXCR4, and FAM241A) were screened from the HP infection-related DEGs, which were utilized for establishing the riskscore model, with good robustness. Riskscore exhibited strong correlation with TIME in STAD. Single-cell atlas of HP-positive STAD revealed that CXCR4 is highly expressed in Epithelial Cell 1, Epithelial Cell 2, and parietal cells of the tumor group. CPVL, EMB, CTLA4, FAM241A, and CXCR4 showed high expression in STAD cells, and the silencing of CPVL could suppress the migration and invasion of STAD cells. Conclusion: This study established a riskscore model based on HP infection-related genes, which could provide reference for prognostic prediction and treatment targets of STAD.
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Revealing the function and mechanism of piRNA-related genes in bladder cancer through single-cell sequencing and methylation analyses and construction of prognostic features based on consensus clustering.
BACKGROUND: This study aimed to explore the functions and potential mechanisms of PIWI-interacting RNA-related genes (piRPGs) in bladder cancer (BC) development and to identify potential prognostic genes. METHODS: This study used differential analysis and machine learning techniques to identify the differentially expressed piRPGs in BC. Consensus clustering was performed on The Cancer Genome Atlas-Urothelial Bladder Carcinoma dataset, and univariate and multivariate analyses were conducted to construct a BC prognostic model consisting of 3 piRPGs. Kaplan-Meier survival curves were used for survival analysis. Quantitative polymerase chain reaction was performed to validate the expression levels of piRPGs in BC cells and tissues. The functional roles and potential mechanisms of piRPGs in BC were investigated via single-cell sequencing and differentially methylated position sequencing. The DSigDB and CellMiner databases were used to screen for small-molecule drugs associated with piRPGs. RESULTS: This study identified 6 piRPGs that were significantly associated with BC: MAPK13, INHBA, LAMB2, DDX3X, TARBP2, and CDK2. A prognostic model comprising MAPK13, INHBA, and LAMB2 was constructed using consensus clustering technology. Kaplan-Meier curves demonstrated significantly prolonged survival in cluster 2 compared with cluster 1 (p < 0.01), validating the effectiveness of the prognostic model. Single-cell sequencing confirmed that MAPK13 expression was significantly upregulated in bladder tissues (p < 0.001). Methylation site sequencing and methylation-specific polymerase chain reaction revealed significantly decreased methylation levels of INHBA and MAPK13 in BC tissues, which were inversely correlated with their expression levels. CONCLUSIONS: This study effectively developed a 3-gene prognostic signature comprising MAPK13, INHBA, and LAMB2 using consensus clustering and multifactorial logistic regression. In addition, the functional roles and intrinsic mechanisms of piRPGs in bladder carcinogenesis were comprehensively explored using single-cell sequencing, methylation sequencing, and functional enrichment analysis.
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Integrating multi-omics and machine learning to decipher the molecular pathways of bisphenol a-associated lactylation-related genes driving bladder cancer.
In this study, we systematically investigated bladder cancer-related gene signatures using a toxicogenomics-informed framework, with particular attention to genes associated with lactylation-related pathways. Multi-omics data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were integrated, and Weighted Gene Co-expression Network Analysis (WGCNA), a toxicology database, and lactylation-related gene sets were combined for intersection screening. Machine learning algorithms, including LASSO, SVM, and random forest, were then applied to identify key genes. Four prioritized BPA-lactylation-associated candidate genes-ENO1, WBP11, GTF2F1, and SPR-were ultimately identified and showed consistent associations with metabolic, immune, and transcription-related features. Multi-level validation, including immune infiltration analysis, single-cell transcriptome localization, proteomic validation, and molecular docking and kinetic simulation, supported the structural plausibility of BPA-protein interactions at the molecular level. This study proposes a toxicogenomics-informed, hypothesis-generating framework that prioritizes candidate genes and pathways potentially linking BPA-related signatures with lactylation-associated processes in bladder cancer.
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Identification of novel diagnostic biomarkers and host-directed drug screening for Mycobacterium avium infection: A multi-omics and artificial intelligence study.
OBJECTIVE: The rising incidence of Mycobacterium avium (MAV) infection poses significant challenges due to diagnostic delays and refractory treatment. Understanding host immune remodeling-particularly myeloid heterogeneity-is critical for identifying precise therapeutic targets. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) with machine learning to profile peripheral blood from MAV patients. CellChat analysis mapped intercellular communication, while an ensemble of 113 machine learning algorithms screened for diagnostic biomarkers. Network pharmacology and molecular docking were employed to identify host-directed therapies, validated via RT-qPCR. RESULTS: The single-cell atlas revealed a pathological expansion of hyper-inflammatory activated macrophages and neutrophils in progressive disease. Analysis identified a monocyte-derived MIF signaling axis that recruits and "locks" APP-expressing macrophages, driving immune dysregulation. A diagnostic model (Stepglm+GBM) based on five core genes (IL1B, STAT3, TNF, STAT1, TLR4) achieved high accuracy (AUC > 0.88). Furthermore, molecular docking confirmed that Wogonin forms high-affinity bonds with STAT3 and TNF, suggesting its potential as an immunomodulator. CONCLUSION: This study delineates the high-resolution immune landscape of MAV infection, elucidating the MIF-APP axis-driven myeloid reprogramming. We established a robust diagnostic model and identified Wogonin as a potential therapeutic agent, providing novel strategies to break the "immune stalemate" in MAV infection.