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PubMed2026

Spatial proximity sequencing maps developmental dynamics in the germinal center.

Spatial profiling of proteins and protein interactions facilitates understanding of cell functions within tissues and is essential for studies in signaling, immunity, and cancer. We present spatial proximity sequencing (Sprox-seq) for simultaneous profiling of surface proteins, protein complexes, and mRNAs, recording the tissue location of each molecule. Sprox-seq profiled 32 proteins, 528 pairwise interactions, and thousands of mRNAs with spatial resolution across human tonsils and germinal centers. Mapping tissue-wide protein interactions recapitulated RNA-defined tissue architecture but also revealed higher interaction complexity in the light zone. Protein-interaction trajectories uncovered a B cell state transition distinct from that inferred by RNA. Integrated protein-complex and mRNA analysis related spatially enriched complexes with mitotic pathways. Sprox-seq captured cell-cell interactions, such as B cell-follicular dendritic cell interactions mediated by the receptor complex VLA-4-VCAM1. Sprox-seq provides a spatially resolved multi-modal view of cell states and an integrated study of protein and cellular interactions across tissues.

Humans↗

PubMed2026

Spatial isoform sequencing at single-cell resolution reveals cell-type-specific spatial isoform variability in multiple brain cell types.

Spatial long-read technologies are increasingly common but usually lack single-cell resolution. This leaves unanswered whether spatially variable isoforms reflect variability within one cell type or differences in region-specific cell-type composition. Here, we developed Spl-ISO-Seq2 (500-nm resolution) and accompanying software, Spl-IsoQuant-2 and Spl-IsoFind, enabling long-read sequencing of >450 million barcodes versus 80,000 previously. Applying this to the adult mouse brain, we compared differential isoform abundance between known regions and spatial isoform patterns independent of predefined regions. Both identified overlapping hits, for example, Rps24 in oligodendrocytes. For known Snap25 spatial isoform variation, we show that it occurs in excitatory neurons. The region-agnostic approach also uncovered patterns missed by region-based comparisons, for example, for Ighm. Notably, many spatial isoform signals are not driven by cell-type composition alone. Finally, our software is applicable to many spatial and single-cell protocols, demonstrating reproducibility between platforms (for example, Visium HD/Stereo-seq). Overall, our experimental/analytical methods enable a submicron-resolution-isoform view and open avenues for spatial isoform disease research.

Animals↗

PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗

PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Journal Article↗

PubMed2026

SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

MOTIVATION: Understanding cellular states and regulatory programs requires integrative analysis of multiple omics layers. Although recent spatial sequencing technologies allow molecular profiling of cells within their tissue context, paired spatial multi-omics assays are still limited by technical complexity and cost. This creates a pressing need for diagonal integration methods that enable joint analysis of unpaired spatial omics datasets. RESULTS: We propose SIVA, a deep generative framework based on Spatially-Informed Variational Autoencoders with Anchor Guidance, for diagonal integration of spatial multi-modal data. SIVA employs modality-specific variational autoencoders (VAEs) with a hybrid latent embedding that integrates Gaussian process and standard Gaussian priors, enabling joint modeling of spatially structured variation and dominant underlying data distributions across modalities. To facilitate cross-modal alignment in the absence of one-to-one cell correspondence, SIVA adopts a dual integration strategy combining global distribution alignment via Maximum Mean Discrepancy and local correspondence guidance using mutual nearest neighbor anchors. Extensive experiments across multiple cross-slice integration scenarios demonstrate that SIVA achieves robust and accurate integration of unpaired spatial omics datasets, consistently outperforming existing methods. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/PelenJiang/SIVA.

Autoencoder↗

PubMed2026

Spatial Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Tissue by spRandom-seq.

The molecular pathogenesis of infectious diseases and cancer is orchestrated by nanoscale of host and microbial RNA transcripts within the tissue microenvironment. Nevertheless, spatially resolving the comprehensive transcriptional landscape within complex clinical tissues, like formalin-fixed paraffin-embedded (FFPE) specimens, still poses a formidable challenge. Here, we present spRandom-seq, a random primer-based spatial total RNA sequencing technology designed to spatially resolve complete transcriptomes from host, bacteria, and even nanoscale viruses in FFPE tissues. Capitalizing on the random primer design, our technology not only facilitated the discovery of specific lncRNAs and alternative splicing events in mouse brain and olfactory bulb, but also delineated pronounced spatial heterogeneity in clinical FFPE sections-across distinct tumor regions in breast cancer and microbial infection sites in Klebsiella pneumoniae-infected tissues. Importantly, integrated analysis of host and viral RNAs in FFPE samples from hepatitis B virus (HBV)‑positive hepatocellular carcinoma (HCC) demonstrated that complement and coagulation pathways were specifically activated across expansive HBV‑infected tumor areas, which also exhibited an increased burden of copy number variations (CNVs). Owing to its compatibility with existing spatial transcriptomics platforms and minimal operational complexity, spRandom-seq represents a practical and scalable approach for clinical pathology applications and infection diagnostics.

Paraffin Embedding↗

PubMed2026

A spatially coordinated keratinocyte-fibroblast circuit recruits MMP9+ myeloid cells to drive type I interferon-driven inflammation in photosensitive autoimmunity.

Photosensitivity is central to cutaneous lupus erythematosus and dermatomyositis (DM), but the mechanisms linking UVB exposure to tissue-specific autoimmunity are poorly defined. Using single-cell RNA sequencing, spatial transcriptomics, proteomics, UVB provocation and in vitro modeling, we identify MMP9+CD14+ myeloid cells as critical mediators of photosensitivity. These cells expand significantly in lesional skin, produce interferon-β (IFNβ) and colocalize with cytotoxic CD4+ T cells at the dermal-epidermal junction. Keratinocytes activate fibroblasts in the superficial dermis, prompting them to release chemokines (CCL2, CCL19, CCL7, CCL8) that recruit MMP9+CD14+ cells. In vitro, type I interferon-primed keratinocytes exposed to UVB release cytokines activating dendritic cells, mirroring in vivo responses. UVB irradiation of non-lesional skin of patients with DM rapidly recruits these myeloid cells. In a clinical proof-of-concept study, anti-type I interferon treatment with anifrolumab prevented UVB-induced myeloid infiltration and reduced photosensitivity. Therefore, targeting MMP9+CD14+ cells may offer therapeutic potential for managing photosensitive autoimmune skin conditions.

Humans↗

PubMed2026

Omics in optic neuropathies: From molecular landscapes to personalized therapeutics.

Optic neuropathies comprise a heterogeneous group of disorders involving transient or permanent injury to retinal ganglion cells (RGCs) and their axons. Clinically, these neurodegenerative conditions manifest as dyschromatopsia, decreased visual acuity, and visual field defects, and in severe cases may ultimately lead to blindness and disability. The marked heterogeneity across disease subtypes, incompletely understood etiologies, and complex pathogenic mechanisms pose substantial challenges to precise diagnosis and effective treatment. Recent advances in omics technologies - including genomics, transcriptomics, proteomics, metabolomics, lipidomics, single-cell and spatial sequencing, and integrative multi-omics approaches - have ushered optic nerve degenerative disease research into an era of high-resolution comprehensive investigation. In this review, we summarize representative applications of omics approaches to elucidate genetic alterations, signaling dysregulation, metabolic reprogramming, and immune responses in optic neuropathies. We further discuss the emerging potential of multi-omics in identifying early diagnostic biomarkers and informing individualized therapeutic strategies. Finally, we provide a forward-looking perspective on the future trajectory of omics technologies and their prospects in both fundamental research and clinical translation, with the overarching aim of accelerating the bench-to-bedside transition in this critical eye disease field.

biomarkers↗

PubMed2025

Balancing LncRNA H19 and miR-675 Bioconversion as a Key Regulator of Embryonic Myogenesis Under Maternal Obesity.

BACKGROUND: Maternal obesity (MO) impairs fetal skeletal muscle development, but the underlying mechanisms remain poorly defined. The regulatory roles of lncRNA H19 and its first exon derived microRNA675 (miR675) in prenatal muscle development remain to be examined. H19/Igf2 are in the same imprinting cluster with H19 expressed from the maternal allele while Igf2 expresses paternally. H19 contains a G-rich loop, and KH-type splicing regulatory protein (KHSRP) mediates the biogenesis of pre-miRNAs containing G-rich loops, which depends on its phosphorylation by AKT, a key mediator of IGF2 signalling. This study aims to depict the elusive function of these regulators that are affected by MO during embryonic myogenesis. METHODS: Single-cell transcriptomic sequencing and GeoMx spatial RNA sequencing were performed to identify the differentially expressed genes between embryos from MO and control (CT) mice. Both E11.5 and E13.5 embryos were collected and analysed to validate the sequencing data. The roles of H19 and miR657 in myogenesis were further analysed in P19 embryonic cells via CRISPR/dCas9-mediated H19 activation and inhibition. The epigenetic changes of H19 were analysed by methylated DNA immunoprecipitation, and allele-targeted analysis of H19 was performed by crossing C57BL/6J and CAST/EiJ mice. RESULTS: Transcriptomic analysis showed that MO embryos contained less differentiated myocytes (1.34%) than CT embryos (2.86%). Myogenesis-related GO biological processes were down-regulated in the MO embryonic myotome region. MO embryos showed lower expression of myogenic transcription factors such as Myf5, Myod1, Myog, Mef2c and Myh3 (p < 0.05). MO altered epigenetic modifications of the H19 genomic cluster, showing a decreased methylation level in H19 imprinting control region (p < 0.05) and a diallelic expression pattern of H19, which elevated its expression in MO embryos. Overexpression of H19 inhibited myogenesis in P19 cells, but miR675 promoted myogenesis, suggesting the critical regulatory roles of bioconversion of H19 to miR675. A KHSRP mediates the biogenesis of miR675, a process that relies on its phosphorylation by IGF2/AKT signalling. Knocking-down of KHSRP and inhibition of AKT abolished miR675 biogenesis. MO suppressed IGF2/AKT signalling and blocked KHSRP-dependent miR675 biogenesis in embryos. CONCLUSIONS: We found differential effects of H19 and miR675 on embryonic myogenesis. MO up-regulates H19 but blocks its miR675 bioconversion via suppressing IGF2/AKT/KHSRP signalling axis. Myogenesis in MO embryos was impeded due to the highly accumulated H19 and blocked miR675 biogenesis.

RNA, Long Noncoding↗

PubMed2026

Mapping antibody sequences and effector functions across spatial niches.

Antibodies are fundamental to human health but can also drive pathology. Each antibody has a molecular specificity, encoded by their clonally heritable B cell receptor (BCR). Recent advances in spatial transcriptomics coupled with repertoire sequencing have enabled capturing antibody-secreting cells (ASCs) and their clonal BCR within their tissue microenvironment. However, our understanding of antibody production niches remains limited. Furthermore, where antibodies are produced can be distinct from where antibodies exert their effector function. Here, we propose a conceptual spatial framework to distinguish between 'antibody production niches', defined by the ASC, BCR, and niche composition, versus 'antibody functional niches', composed of the antibody, antigen, and effector landscape. We then examine the possibilities and challenges to map and link antibody-encoding sequences and antibody effector functions using current and emerging technologies. Combined, we argue that integrating spatial sequence data with the antibody functional context is essential to decode the architecture of antibody-mediated immunity.

Humans↗

PubMed2025

Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.

The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.

Humans↗

PubMed2026

Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization. METHODS: We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses. RESULTS: SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response. CONCLUSION: Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.

Humans↗

PubMed2025

BISON: bi-clustering of spatial omics data with feature selection.

MOTIVATION: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes (DGs) that elucidate the clustering result, referred to as spatial domain-specific DGs. Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as double-dipping. RESULTS: To address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. AVAILABILITY AND IMPLEMENTATION: The R/C++ implementation of BISON is available at https://github.com/new-zbc/BISON.

Software↗

PubMed2025

Integrating genomic and spatial analyses to describe tuberculosis transmission: a scoping review.

Tuberculosis remains a leading cause of infection-related mortality, and efforts to reduce its incidence have been hindered by an incomplete understanding of local Mycobacterium tuberculosis transmission dynamics. Advances in pathogen sequencing and spatial analysis have created new opportunities to map M tuberculosis transmission patterns more precisely. In this scoping review, we searched for studies combining pathogen genetics and location data to analyse the spatial patterns of M tuberculosis transmission and identified 142 studies published between 1994 and 2024. Secular changes in genetic methods were observed, with genome sequencing approaches largely replacing lower-resolution genotyping methods since 2020. The included studies addressed four primary research questions: how are tuberculosis cases and M tuberculosis transmission clusters geographically distributed; do spatially concentrated M tuberculosis clusters exist, and where are these areas located; when spatial concentration occurs, what host, pathogen, or environmental factors contribute to these patterns; and do identifiable relationships exist between the spatial proximity of tuberculosis cases and the genetic similarity of the M tuberculosis isolates infecting these individuals? Collectively, in this Review, we examined the available study data, evaluated the analytical requirements for addressing these questions, and discussed opportunities and challenges for future research. We found that the integration of spatial and genomic data can inform a detailed understanding of local M tuberculosis transmission patterns, but improved study designs and new analytical methods to address gaps in sampling completeness and to integrate additional movement data are needed to fully realise the potential of these tools.

Humans↗

PubMed2026

Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.

Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.

Humans↗

PubMed2026

Multiomics analysis reveals that senescent CXCL16+ macrophages promote lung adenocarcinoma progression through TGF-β signalling.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer and remains a leading cause of cancer-related mortality worldwide. Although, immunotherapy has become a cornerstone of first-line treatment, only 20-30% of patients achieve a durable clinical benefit, largely because of the complexity and heterogeneity of the tumour immune microenvironment. Emerging evidence indicates that cellular senescence, particularly within immune cells, contributes to tumour progression by impairing antitumour immunity; however, its mechanistic role in LUAD remains incompletely understood. METHODS: We performed an integrative multiomics analysis incorporating genome-wide association studies (GWASs), bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics to characterize immune heterogeneity in LUAD. Cellular senescence was validated by performing staining for senescence-associated β-galactosidase and the canonical markers p16 and p21. SHAP analysis was applied to evaluate the contribution of CXCL16+ macrophages. Functional roles were assessed using coculture assays, in vitro and in vivo tumour models, orthotopic tumour implantation, and multiplex immunofluorescence staining of clinical specimens. RESULTS: A summary data-based on Mendelian randomization analysis integrating GWAS and TCGA data identified CXCL16 as a senescence-associated gene that is causally linked to the LUAD risk. Single-cell RNA sequencing revealed that CXCL16 is predominantly expressed in macrophages, and the pseudotime analysis together with β-galactosidase staining confirmed its association with macrophage senescence. Spatial transcriptomics and immunofluorescence staining showed the marked enrichment of CXCL16+ macrophages in LUAD tissues. The cell-cell communication analysis further revealed a strong association between the number of CXCL16+ macrophages and the activation of the TGF-β signalling pathway within the tumour microenvironment. Functionally, CXCL16+ macrophages promoted LUAD progression via TGF-β signalling, as validated in vitro and in subcutaneous and orthotopic tumour models. Molecular dynamics simulations additionally suggested that LUAD patients with high levels of CXCL16+ macrophage infiltration may exhibit increased sensitivity to bosutinib. CONCLUSIONS: CXCL16 promotes macrophage senescence, and senescent CXCL16+ macrophages drive LUAD progression through TGF-β signalling. These findings identify CXCL16+ macrophages as a biologically and therapeutically relevant immune cell population, highlighting a potential target for precision intervention in LUAD.

Humans↗

PubMed2026

Integrated Pan-Cancer, Single-Cell, and Spatial Transcriptomic Analyses Identify ZDHHC12 as a Biomarker Associated with Macrophage Infiltration and the Immune Landscape in Glioma.

BACKGROUND: The tumor immune microenvironment (TME) critically influences cancer progression and therapeutic response. However, the pan-cancer expression landscape, prognostic relevance, and spatial distribution of ZDHHC12 remain incompletely characterized. This study investigated the prognostic value of ZDHHC12 and its associations with immune microenvironmental features and drug sensitivity. METHODS: Data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets were used to evaluate ZDHHC12 expression and prognosis across cancer types. Immune infiltration analyses, single-cell RNA sequencing, and spatial transcriptomics were integrated to characterize the associations of ZDHHC12 with the cancer immunity cycle and the spatial architecture of glioma. Drug sensitivity and immunotherapy-related metrics were assessed using pharmacogenomic databases and computational prediction models. RESULTS: ZDHHC12 was aberrantly expressed across multiple tumors and was associated with patient prognosis. Its expression was broadly correlated with immune cell recruitment- and activation-related signatures. In glioma, single-cell and spatial transcriptomic analyses showed enrichment of ZDHHC12 in monocyte/macrophage populations and spatial co-localization with BAK1, CD68, and CD163. ZDHHC12 expression was also associated with predicted drug sensitivity and immunotherapy-related metrics. CONCLUSION: ZDHHC12 may serve as a candidate pan-cancer prognostic biomarker. In glioma, its expression is associated with macrophage-enriched and immunosuppressive microenvironmental features. Functional studies are required to establish causality and determine its therapeutic relevance.

GBM↗

PubMed2025

CCDC137 knockdown suppresses bladder cancer progression by downregulating SCD.

BACKGROUND: The Coiled-coil domain-containing (CCDC) family, due to its unique protein structural domain and broad involvement in diverse biological processes, has emerged as a focus in oncology research. Nevertheless, its clinical significance and function in bladder cancer (BLCA) remain poorly defined. METHODS: Machine learning algorithms were employed to identify pivotal CCDC genes in the cancer genome atlas (TCGA), and a prognostic model was subsequently constructed. Multi-omics data encompassing pan-cancer cohorts, single-cell sequencing, and spatial transcriptomics were integrated to characterize the expression patterns and prognostic significance of Coiled-coil domain-containing 137 (CCDC137), a previously uncharacterized CCDC family member in BLCA. Tissue microarray confirmed CCDC137 abnormal expression in bladder carcinoma specimens. The effect of CCDC137 knockdown on BLCA progression was evaluated through CCK8 assay, clonogenic formation, wound healing, Transwell, and subcutaneous xenograft models. RNA sequencing, quantitative RT-PCR, and western blot were utilized to delineate its regulatory network. RESULTS: A prognostic model incorporating 10 CCDC genes was successfully established in the TCGA-BLCA cohort. Then, we found that CCDC137 exhibited pan-cancer overexpression and usually correlation with poor clinical outcomes. Immunohistochemistry further substantiated its dysregulation in bladder carcinoma. Integrated multi-omics analyses suggested associations between CCDC137 expression and a tumor immunosuppressive microenvironment. CCDC137 knockdown significantly suppressed bladder cancer cell proliferation and migratory capacity in vitro. Correspondingly, subcutaneous xenograft tumor growth was inhibited in vivo. Moreover, decreased expression of stearoyl-CoA desaturase (SCD), a key lipid metabolic enzyme, accompanied CCDC137 depletion. These findings collectively suggest a cancer-promoting role for CCDC137 in bladder carcinoma. CONCLUSIONS: This systematic investigation combining multi-omics bioinformatics analyses and experimental validation demonstrates the role of CCDC137 in bladder carcinoma progression, providing novel mechanistic insights into the pathogenesis of BLCA and offering a theoretical foundation for therapeutic targeting of CCDC137 in urothelial malignancies.

Urinary Bladder Neoplasms↗