SEARCH · PubMed Health

Results for “Single cell transcriptomics”

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

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

At least 253 records · Page 14Linked to original sources

PubMed2026

Single-cell multi-omics dissects transcript isoform and immune repertoire dynamics in human immunosenescence.

Immunosenescence, a major hallmark of systemic aging, refers to the progressive functional decline of the immune system. This decline not only compromises host defense and immunological memory but also fuels chronic inflammation and tissue degeneration (collectively known as inflammaging). While single-cell RNA sequencing (scRNA-seq) has revealed transcriptomic alterations associated with immune aging, analyses restricted to transcript abundance fail to capture deeper regulatory layers, such as transcript isoform diversity and the remodeling of immune receptor repertoires. To address this limitation, we present a human peripheral immune single-cell multi-omics atlas that integrates gene expression, transcript isoform diversity, and immune receptor repertoires. By combining single-cell full-length transcriptome sequencing (scCycloneSEQ), short-read scRNA-seq, and single-cell immune receptor sequencing (scTCR/BCR-seq), we systematically profiled peripheral blood mononuclear cells (PBMCs) from healthy donors aged 30-40 and 60-70 years. Our analyses uncovered extensive age-related remodeling of immune cell composition, functional states, and TCR/BCR diversity. Notably, we found that CD4+ effector memory T cells exhibited widespread differential isoform usage (DIU), 3'UTR length variation, and a marked reshaping of cytotoxic T lymphocyte (CTL) clonotypes-all of which were closely associated with aging-related inflammation and cellular senescence. This multi-omics atlas delineates key molecular features of immunosenescence and provides a high-resolution resource for deciphering the regulatory architecture underlying immune aging.

TCR/BCR↗

PubMed2021

Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma.

Little is known about the transcriptomic plasticity and adaptive mechanisms of circulating tumor cells (CTCs) during hematogeneous dissemination. Here we interrogate the transcriptome of 113 single CTCs from 4 different vascular sites, including hepatic vein (HV), peripheral artery (PA), peripheral vein (PV) and portal vein (PoV) using single-cell full-length RNA sequencing in hepatocellular carcinoma (HCC) patients. We reveal that the transcriptional dynamics of CTCs were associated with stress response, cell cycle and immune-evasion signaling during hematogeneous transportation. Besides, we identify chemokine CCL5 as an important mediator for CTC immune evasion. Mechanistically, overexpression of CCL5 in CTCs is transcriptionally regulated by p38-MAX signaling, which recruites regulatory T cells (Tregs) to facilitate immune escape and metastatic seeding of CTCs. Collectively, our results reveal a previously unappreciated spatial heterogeneity and an immune-escape mechanism of CTC, which may aid in designing new anti-metastasis therapeutic strategies in HCC.

Aged↗

PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗

PubMed2025

Iterative, multimodal, and scalable single-cell profiling for discovery and characterization of signaling regulators.

Cell signaling plays a critical role in regulating cellular state, yet uncovering regulators of signaling pathways and understanding their molecular consequences remains challenging. Here, we present an iterative experimental and computational framework to identify and characterize regulators of signaling proteins, using the mTOR marker phosphorylated RPS6 (pRPS6) as a case study. We present a customized workflow that uses the 10x Flex assay to jointly profile intracellular protein levels, transcriptomes, and CRISPR perturbations in single cells. We use this to generate a "glossary" dataset of paired protein-RNA measurements across targeted perturbations, which we leverage to train a predictive model of pRPS6 levels based solely on transcriptomic data. Applying this model to a genome-wide Perturb-seq dataset enables in silico screening for pRPS6 and nominates novel regulators of mTOR signaling. Experimental validation confirms these predictions and reveals mechanistic diversity among hits, including changes in signaling output driven by anabolic activity, cellular proliferation and multiple stress pathways. Our work demonstrates how integrated experimental and computational approaches provide a scalable framework for multimodal phenotyping and discovery.

Journal Article↗

PubMed2026

Predicting cellular responses to perturbation across diverse contexts with State.

While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. State is trained using single-cell gene expression data to predict perturbation effects across sets of cells. State improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling, and chemical perturbations with significantly improved accuracy compared with baselines. Its cell embeddings trained on observational data from 167 million cells enable the identification of strong perturbations in cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that can be used to evaluate future models. Overall, the performance and flexibility of State set the stage for scaling the development of AI models of cell state.

Machine Learning↗

PubMed2026

Endothelial cell cycle inhibition enables blood vessel maturation to normalize the tumor vasculature.

Dysfunctional tumor vessels promote disease progression, whereas improved function enhances therapeutic delivery. However, current approaches to normalize tumor vasculature have limited efficacy. In vascular malformations, vessels are similarly dysfunctional, with endothelial cell (EC) hyperproliferation impairing arterial-venous specification. These defects are corrected with palbociclib, a cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) that has beneficial effects on tumor and immune cells, but the effects on tumor vasculature are not well characterized. In our studies, murine mammary tumor ECs (TECs) exhibited disrupted cell cycle and specification, and CDK4/6i promoted TEC cycle control, enabling improved tumor vascular function. To investigate transcriptomic changes, we performed single-cell RNA sequencing (scRNAseq) of treated and untreated tumors, and healthy tissues. CDK4/6i-mediated TEC cycle arrest promoted arterial-venous specification, cellular junctions, and pericyte association, and suppressed glycolytic and immunosuppressive gene expression. These effects were associated with increased vessel perfusion, decreased tumor hypoxia, and a more favorable immune landscape with immunotherapy. In scRNAseq datasets from patients treated long-term with CDK4/6i, TECs exhibited similar transcriptomic changes associated with arterial-venous specification, pericyte recruitment, and immune signaling. Thus, in contrast to current strategies, CDK4/6i-mediated vascular changes may be maintained with continued treatment, highlighting the relevance of modulating TEC cycle to improve vessel maturation/function.

Angiogenesis↗

PubMed2026

Spatial habitat radiomics predicts tertiary lymphoid structure status and identifies an IDO1+ migratory dendritic cell axis in breast cancer.

BACKGROUND: Tertiary lymphoid structures (TLS) are spatially organized immune niches associated with therapeutic response and favorable outcomes in breast cancer (BC). However, TLS assessment currently relies on invasive tissue-based analyses, and the biological mechanisms underlying imaging-based TLS prediction remain poorly understood. METHODS: We developed and validated a spatial heterogeneity-based radiomic TLS signature (shTLS) using dynamic contrast-enhanced MRI to non-invasively predict TLS status across multicenter BC cohorts. Spatial habitat radiomics were used to capture intratumoral and peritumoral immune-related heterogeneity. Integrated multi-omics analyses, including transcriptomics, pathomics, genomics, single-cell RNA sequencing, immunohistochemistry, and multiplex immunofluorescence, were performed to biologically interpret shTLS-defined subgroups. Functional drug-sensitivity assays were conducted to assess therapeutic implications. RESULTS: The shTLS model achieved robust predictive performance across independent cohorts and molecular subtypes. High shTLS scores were associated with immune-inflamed tumors characterized by spatially clustered activated T cells and dendritic cells (DCs). In contrast, shTLS-low tumors exhibited an immunosuppressive spatial niche with peripheral accumulation of CD4+ PD-1+ T cells and plasma cells, increased immune-tumor separation, and enhanced inflammatory and immunoregulatory signaling. An indoleamine 2,3-dioxygenase 1 (IDO1)-associated immunoregulatory program was observed in the shTLS-low tumors, which appeared to be preferentially expressed by LAMP3+CCR7+ migratory DCs. Pharmacologic inhibition of IDO1 enhanced chemotherapy and CDK4/6 inhibitor sensitivity in vitro. CONCLUSION: This study establishes spatial radiomics as a non-invasive approach to decode TLS-associated immune ecosystems and supports the presence of an IDO1-associated immunosuppressive phenotype, providing biological insight and translational rationale for patient stratification and future combination strategies.

Humans↗

PubMed2026

Urine-Derived Cells in Kidney Transplantation: Linking Cellular Phenotypes, Secretome Signatures and Multi-Omic Technologies.

Kidney transplant monitoring and early identification of graft dysfunction are central needs for long-term graft survival. Although kidney biopsy represents the gold standard in diagnostic procedures, its invasiveness may limit frequent longitudinal evaluation, highlighting the necessity of non-invasive diagnostic tools. Urine has emerged over the years as an easily obtainable source of cellular and molecular components derived from transplanted kidneys. Particularly, exfoliated cells and extracellular vesicles (EVs) represent complementary and interconnected markers able to reflect tissue injury, inflammatory signals, immune cell infiltration, and regenerative processes occurring in the graft. Also, recent advances in transcriptomics, proteomics, metabolomics, and single-cell technologies have expanded the diagnostic and mechanistic value of urinary liquid biopsy, enabling the identification of disease-specific molecular signatures associated with rejection, delayed graft function, fibrosis, and graft loss. This review discusses the emerging concept of an integrated urinary cell-EV ecosystem and highlights how integrated multi-omic approaches may transform non-invasive graft surveillance and advance precision medicine in kidney transplantation.

Humans↗

PubMed2025

Next-generation sequencing in breast cancer: current clinical applications and future directions.

INTRODUCTION: Breast cancer is a heterogeneous disease that claims 670,000 lives by 2022. Omic technologies, particularly next generation sequencing (NGS) offers promising avenues for precision medicine. American Society of Clinical Oncology (ASCO) outlines genomic testing's utility, emphasizing prognostic and diagnostic potential. OBJECTIVES: This review succinctly explores NGS's evolution and clinical applications of NGS in breast cancer, thereby guiding future research to enhance patient care. METHODS: Comprehensive literature searches were conducted using databases such as PubMed, Google Scholar, and ResearchGate, focusing on keywords including breast cancer, HER-2 low breast cancer, circulating tumour DNA, single-cell RNA sequencing, and next-generation sequencing. Peer-reviewed, high-quality articles published in English were selected for inclusion. RESULTS: Previous studies have explored the evolution of NGS technology and its clinical applications in breast cancer, including genomic and transcriptomic characterization, treatment guidance, and resistance prediction. Molecular profiling of challenging entities such as early-onset breast cancer and HER-2 low tumours was summarized, with key findings highlighted. This review also discusses emerging technologies, including circulating DNA and single-cell sequencing, as promising avenues for discovery. CONCLUSION: NGS has revealed the genomic and transcriptomic diversity of breast cancer, identifying actionable alterations associated with chemotherapy response and resistance to therapies such as trastuzumab, TKIs, and CDK4/6 inhibitors. Circulating tumour DNA (ctDNA) shows potential for diagnosis, prediction, prognosis, and monitoring, despite tumour heterogeneity. Single-cell analysis enables exploration of individual cell transcriptomes, though high costs and low throughput remain barriers to widespread adoption. HER2-low tumours continue to pose significant research challenges.

Humans↗

PubMed2025

Blood phenylalanine lowering partially reverses white matter changes in a mouse model of phenylketonuria.

Phenylketonuria (PKU) is a genetic defect caused by lack of the liver enzyme phenylalanine hydroxylase (PAH). This deficiency results in elevated blood phenylalanine (Phe) levels and neurotoxicity, which is manifested by reduced brain size, lower neurotransmitter levels, and reduced myelination. The goal of this study was to investigate brain myelination defects and their reversibility upon blood Phe lowering by analyzing the corpus callosum (CC) of adult Pahenu2 (PAH-deficient) mice. MRI and immunostaining demonstrated a significant reduction in CC volume in Pahenu2 mice. Treatment with an adeno-associated vector (AAV) encoding mouse PAH for 3.5 months improved but did not completely normalize CC volume. Total cholesterol, a major component of myelin, was unchanged in the CC of Pahenu2 mouse, while some sterol intermediates were significantly reduced by treatment. Single-nuclei transcriptomics showed an upregulation of oxidative stress-related pathways and increased expression of transthyretin, ApoE, Cst3, and Cd81 in CC in Pahenu2 mice. Normalization of blood Phe restored gene expression to levels comparable to those of heterozygous mice and was associated with the generation of differentiated myelin-producing oligodendrocyte subtypes and neuroprotective astrocytes. In summary, Pahenu2 mice showed white matter abnormalities and changes in transcriptome and sterol profiles, which were partially corrected by the normalization of blood Phe.

Animals↗

PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

PubMed2026

Spatially resolved single-cell atlas reveals the macroevolutionary trajectory of animal hearts.

Animal hearts display diverse anatomical structures during adaptive evolution. Here, we present a multiomics atlas of adult hearts from 27 species across chordates, arthropods, and mollusks. Joint analysis indicates that Bilateria hearts share a core gene repertoire, taking a stepwise "add-on" approach as a universal evolutionary strategy. The "proto-heart" is populated by key cell types, including cardiomyocytes, fibroblasts, endothelial cells, and neural cells, which maintained core signatures while evolving with shifts in living environments and corresponding adaptations in the cardiovascular system. Additionally, we reveal an evolutionarily conserved cardiomyocyte state dynamic potentially linked to cardiac development and stress responses. Finally, we identify a common molecular program underpinning chamber evolution from a ventricular foundation. This work establishes a resource for understanding the intrinsic mechanisms of heart evolution.

Animals↗

PubMed2026

Beyond the gene: isoform diversity as a key contributor to human brain disorders.

The human brain exhibits exceptional transcriptomic complexity, with alternative splicing, promoter usage, and polyadenylation generating extensive transcript-isoform diversity. Isoform dysregulation is increasingly implicated in neurodevelopmental and psychiatric disorders (NPDs), yet the landscape, function, and genetic regulation of brain isoforms remain poorly understood due to limitations of short-read RNA sequencing. Advances in long-read sequencing (LR-seq) enable scalable full-length transcriptome profiling with single-cell and spatial resolution across developmental stages. Here, we review recent progress in isoform discovery, quantification, functional annotation, and genetic regulation, highlighting emerging links to human neurodevelopment and disease. LR-seq studies have uncovered tens of thousands of previously unannotated brain isoforms, with neuronal maturation characterized by increased exon inclusion and progressive 3' untranslated region (3' UTR) lengthening. Isoform-resolved genetic mapping outperforms gene-level analyses for NPD gene discovery and mechanistic interpretation. We argue that a shift from gene-centric to isoform-centric frameworks is essential to fully capture regulatory complexity in human neurogenetics. Together, these advances establish isoform diversity as a fundamental yet underappreciated axis of brain gene regulation and a key entry point for dissecting NPD biology.

Humans↗

PubMed2026

The DLX/Notch axis is necessary for spatiotemporal regulation of neural cell fate.

Neuronal-glial cell fate switch during forebrain development is highly regulated. DLX transcription factors are necessary for promoting GABAergic interneuron differentiation and migration but the mechanisms for concomitant repression of glial fate in neural progenitors remain elusive. Here, the DLX2 regulatory network dynamic in the developing ventral telencephalon is characterised using a multi-omic approach at single-cell resolution, including single-cell whole genome spatial transcriptomics. We identify a secondary proliferative zone in the ventral subventricular zone and spatiotemporal-context dependent Notch pathway repression by DLX2 in maintaining progenitor populations and facilitating neural differentiation. We find that DLX2 controls cell fate determination by directly repressing Notch signalling genes as well as glial fate-promoting transcription factors, thereby inhibiting early adoption of oligodendroglial differentiation during neurogenesis. Here, we show that temporal cell fate switch is mediated by DLX2 via a multilayer gene regulatory network, redefining current understanding of neuronal-glial cell specification mechanisms in the developing telencephalon.

Animals↗

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

EBV reactivation priming of the peripheral immune system in multiple sclerosis relapse.

Despite decades of research, the cellular and molecular events preceding multiple sclerosis (MS) relapse remain incompletely understood. Here, in this observational study of longitudinal blood samples from patients with relapsing-remitting MS, we used single-cell RNA sequencing, bulk transcriptomics, multiparameter flow cytometry and targeted viral reverse transcription quantitative polymerase chain reaction (RT-qPCR) to construct a time-resolved atlas of immune perturbations surrounding relapse. A reproducible pre-relapse signature in monocytes and B cells, emerging up to 3 months before clinical onset, was enriched for host genes responsive to Epstein-Barr virus (EBV) lytic reactivation factors. RT-qPCR confirmed elevated EBV LMP-1 transcripts in pre-relapse B cells, and flow cytometry demonstrated expansion of CD11c+ atypical B cell populations displaying EBV surface protein gp350. Pre-relapse transcriptional modules overlapped with MS genome-wide association study (GWAS) risk loci and EBNA-2-bound enhancers, suggesting that inherited MS susceptibility and EBV-responsive programs operate through shared regulatory elements. How this peripheral activation relates to central nervous system lesion formation remains to be established. These findings nonetheless suggest that EBV reactivation, when occurring within a genetically predisposed peripheral immune environment, is a proximal precursor of MS relapse.

Journal Article↗

PubMed2026

Novel association of NAV3 with dilated cardiomyopathy and its role in cardiac fibrosis.

A genome-wide association study (GWAS) identified neuron navigator 3 (NAV3) as a potential genetic determinant of myocardial recovery in dilated cardiomyopathy (DCM). This study aimed to understand its functional role in cardiac pathophysiology by leveraging omics approaches. Single-cell RNA-seq transcriptomic data from previously published adult human hearts indicate that NAV3 expression is highest in cardiac fibroblasts, suggesting its functional role in these cells. In vitro, stimulation of primary human ventricular cardiac fibroblasts with transforming growth factor β1 (TGF-β1) induced NAV3 expression in a dose and time-dependent manner. Small-interfering-RNA-mediated knockdown of NAV3 significantly attenuated TGF-β1-induced fibroblast activation, reducing the expression of α-smooth muscle actin (α-SMA), collagens, and fibronectin. RNA sequencing of NAV3-silenced fibroblasts, confirmed by Western blot, revealed upregulation of cell cycle regulators and downregulation of profibrotic markers, suggesting that NAV3 facilitates TGF-β1-induced cell cycle arrest and fibroblast-to-myofibroblast transition. Notably, NAV3 silencing did not alter canonical SMAD2/3 phosphorylation, implying a role for NAV3 in modulating fibrotic signaling through other pathways. Our findings provide functional and mechanistic insights into NAV3's novel role in cardiac fibrosis, showing that reduced NAV3 expression attenuates TGF-β1-mediated fibroblast activation by regulating cell cycle signaling. These results support further investigation of NAV3 as a potential modulator of cardiac fibrosis and myocardial recovery in DCM.NEW & NOTEWORTHY This study uncovers a previously unrecognized role for NAV3 in TGF-β1-driven cardiac fibroblast activation. We show that NAV3 facilitates profibrotic remodeling through noncanonical signaling and cell cycle arrest, independently of SMAD2/3. These findings position NAV3 as a novel regulator of fibroblast phenotype and a potential modulator of cardiac fibrosis.

Humans↗

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↗