Search Results (1,206)

Background: We aimed to identify key molecular drivers of gastric cancer progression and poor prognosis by integrating multi-omics analyses with experimental validation. Methods: Single-cell RNA-seq data were clustered to delineate major cell types. InferCNV identified tumor epithelial cells, and reclustering revealed a malignant subset with poor prognosis. The overlap between subset markers and The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) upregulated differentially expressed genes (DEGs) was modeled with univariate, LASSO-, and multivariate Cox to derive a prognostic signature. Patients were stratified according to signature scores, and group differences in survival and immunologic features were compared. Spatial transcriptomics defined the localization patterns of key signature genes. In vitro functional assays (CCK-8, colony formation, EdU incorporation, flow cytometry, Transwell migration and invasion, and wound healing) confirmed the pivotal role of SRI. Results: Reclustering of tumor epithelial cells yielded seven subsets (C0–C6), with C5 displaying marked malignant features and correlating with poor prognosis in multiple cohorts. Intersecting 208 genes yielded a five-gene signature (ASCL2, REPIN1, CXCL3, TMEM176A, SRI). The signature stratified patients into high- and low-risk groups. The high-risk cohort exhibited significantly poorer survival, distinct immune-infiltration patterns, elevated immune-evasion scores, and a reduced predicted response to immunotherapy. Single-cell and spatial transcriptomics localized TMEM176A to fibroblasts and SRI to the tumor epithelium. Finally, in vitro knockdown of SRI inhibited tumor cell proliferation, migration and invasion. Conclusions: Our multi-omics approach identified a malignant epithelial subset, C5, and a five-gene signature that stratifies gastric cancer prognosis and immune response. Functional assays showed that SRI knockdown impairs tumor cell growth, migration and invasion. Full article

In animal models, elimination of the senescent cells in the hematopoietic stem cells (HSCs) compartment leads to the rejuvenation of hematopoiesis. Whether this treatment principle can be applied to the human system remains controversial. The identification of senescent cells in human bone marrow poses another major challenge. To address these questions, we have studied hematopoietic stem and progenitor cells (HSPCs, CD34⁺) from the bone marrow of 15 healthy human subjects (age range: 19–74 years). Single-cell RNA sequencing, functional transcriptome analysis, and development trajectory studies were performed. In a previous report, we demonstrated the accumulation of a senescent population in the aging HSC compartment. The present study focuses on the differences with age downstream in the lymphoid trajectory. While a reduction in B progenitors in the early lymphoid compartment can be confirmed, the accumulation of a lymphoid cluster downstream upon aging is novel and remarkable. This cluster comprises cells with a significant deficiency in B differentiation markers, as well as 9.4% cells with transcriptome signatures of memory-like natural killer (NK) progenitors. Applying our analysis algorithm to other human bone marrow datasets from the literature, we are able to validate the presence of this unique cluster in aged lymphoid progenitors. The accumulation of a population comprising cells defective in B differentiation potential, as well as cells with transcriptome features of memory-like NK progenitors represents a novel hallmark for senescence in the late development trajectory of human lymphoid compartment. Full article

With rapid industrial expansion, air pollution’s adverse neurological effects have gained increasing attention. Children face a greater risk of neurological damage because of their higher breathing rates, developing brains, and limited ability to detoxify harmful substances. Fine particulate matter has been identified as a primary neurotoxic contributor affecting developing brains. Strong evidence connects environmental pollutant exposure to the prevalence of Autism Spectrum Disorder (ASD), a neurodevelopmental condition marked by lasting difficulties with social communication and interaction. This study explores the association between long-term PM_2.5 exposure and ASD symptom exacerbation, investigating underlying mechanisms. We hypothesize that long-term PM_2.5 exposure exacerbates ASD symptoms through neuroinflammatory activation, leading to neuronal damage and impaired synaptic plasticity. Our investigation employs three complementary approaches: First, integrated analysis combining Global Burden of Disease data with Mendelian randomization demonstrates a significant association between PM_2.5 exposure and increased ASD severity risk. Second, utilizing the China High-Resolution Air Pollution Database in conjunction with cohort studies, we provide evidence that ambient air pollution substantially influences autism severity, with PM_2.5 identified as the predominant environmental determinant. Third, through network toxicology, single-cell transcriptomics, and animal experimentation, we demonstrate that chronic PM_2.5 exposure exacerbates valproic acid-induced autism-like behaviors in murine models, identifying CTNNB1, PTEN, CCR2, AKT1, and mTOR as potential core mediating genes. Importantly, these findings represent preliminary results. Several potential confounding factors such as co-exposure to other pollutants and socioeconomic variables have not been fully addressed. While our multi-modal approach provides converging lines of evidence, further validation in larger, more diverse populations with refined control of confounders will be essential to establish causality and elucidate mechanisms. Nonetheless, these early insights advance our understanding of PM_2.5-induced neurotoxicity in the context of ASD and offer timely, albeit preliminary, evidence to inform public health policy. Full article

Megakaryocytes (MKs) have traditionally been viewed as terminal hematopoietic cells responsible solely for platelet production. However, recent advances in imaging and single-cell transcriptomics have revealed substantial heterogeneity among MK populations and diverse functions beyond thrombopoiesis. MKs actively participate in innate and adaptive immunity, modulate the hematopoietic stem cell (HSC) niche, and adapt to physiological and pathological stimuli. Located in distinct anatomical sites such as bone marrow and lung, MKs exhibit compartment-specific specializations that enable them to serve as critical integrators of hemostatic, immune, and regenerative processes. Experimental models using human pluripotent stem cells and inducible MKs have enhanced mechanistic insights, while innovative bioreactor platforms and xenotransplantation strategies advance translational applications in platelet production and therapy. Furthermore, immune MK subsets derived from pluripotent stem cells show promising therapeutic potential for modulating inflammation and autoimmune diseases. Continued exploration of MK diversity, tissue-specific roles, and intercellular communication will unlock new opportunities for leveraging MK plasticity in regenerative medicine, immunotherapy, and hematologic disorders, repositioning these versatile cells as central players in systemic homeostasis and defense. Full article

Cutaneous T-Cell Lymphomas (CTCL) are a heterogenous group of T-cell malignancies in the skin and have poor treatment outcomes in advanced stages. CD5, a surface glycoprotein expressed on most mature T cells, has emerged as a promising target for chimeric antigen receptor (CAR) T-cell therapy in systemic T-cell lymphomas. However, its expression profile in CTCL and relevance for targeted therapy remain unclear. Notably, in CTCL, the cell surface expression of receptors, such as CD7 and CD26, tends to become downregulated on the surfaces of malignant T cells In this study, we analyzed single-cell RNA sequencing (scRNA-seq) data from patients at two institutions with mycosis fungoides (MF), the most common subtype of CTCL with a predominantly CD4 phenotype. We utilized 5 patch/plaque MF skin biopsies (majority from early-stage patients), 8 MF tumor biopsies (all from advanced-stage patients), and 8 healthy control biopsies to evaluate lesion-specific CD5 gene expression on CD4 T cells. We found that CD5 was significantly increased in malignant MF CD4 T cells compared to healthy control CD4 T cells (21.1% of MF CD4 T cells expressed CD5 vs. 5.2% of healthy control CD4 T cells, respectively). In subgroup analysis, patch/plaque stage MF biopsies showed higher expression of CD5 in CD4 T cells than tumor stage MF biopsies. Notably, 94.3% of malignant CD4⁺ T cells in tumor stage MF lesions exhibited complete CD5 loss compared to only 76.6% in patch-plaque MF lesions, suggesting antigen escape in tumor stage disease. These findings demonstrate that CD5 expression in CTCL is dynamic and varies based on lesion type. Our work suggests CD5 may be a viable therapeutic target in MF with patch/plaque presentations but may not be as effective in advanced stages of MF with tumor presentations. This work informs CD5 gene expression in MF based on clinical lesion type and further information is needed to clarify clinical implications as a future therapeutic target. Full article

Genistein has anti-cancer effects, but its molecular targets in gastric adenocarcinoma (GA) are unclear. This study used single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore genistein’s “drug-gene-cell” interactions in GA. GA- and genistein-related target genes were retrieved and intersected with differentially expressed genes identified from bulk transcriptomic data. Machine learning screened candidates, and survival analysis assessed prognosis. Molecular docking with genistein validated key genes, with molecular dynamics assessing binding stability. HSD17B1, EZH2, CCNB1, CCNB2, CDKN2A, and IGFBP6 were identified as key candidate genes with prognostic value for GA. Specifically, samples in the IGFBP6 high-expression group were associated with higher survival probability, whereas the opposite trend was observed for the other five genes. In addition, HSD17B1 was genistein’s main target in GA treatment, showing a strong binding affinity with genistein (binding energy of −8.1 kcal/mol). scRNA-seq analysis indicated that HSD17B1 was predominantly expressed in epithelial cells and was significantly involved during their malignant transformation (confirmed by ST). This study identified HSD17B1 as a critical target gene for genistein in GA treatment, emphasizing its roles in the malignant transformation of epithelial cells, thus providing a theoretical foundation for understanding the therapeutic mechanism of genistein in GA. Full article

Gastric diseases represent a significant challenge to global health. A comprehensive understanding of their complex molecular mechanisms, particularly the pathways of molecular progression in precancerous lesions, is essential for enhancing diagnosis and treatment. StomachDB, the first comprehensive multi-omics database dedicated to gastric diseases, has been developed to address these research needs. This database integrates 6 types of biological data: genomics, transcriptomics, emerging single-cell and spatial transcriptomics, proteomics, metabolomics, and therapeutic-related information. It encompasses 44 gastric-related pathologies, including various forms of gastric cancer, gastric ulcers, and gastritis, primarily involving humans and mice as model organisms. The database compiles approximately 2.5 million curated and standardized profiles, along with 268,394 disease-gene associations. The user-friendly analytics platform provides tools for browsing, querying, visualizing, and downloading data, facilitating systematic exploration of multi-omics features. This integrative approach addresses the limitations of single-omics analyses, such as data heterogeneity and insufficient analytical dimensions. Researchers can investigate the clinical significance of target genes (e.g., CDH1) across different omics levels and explore potential regulatory mechanisms. Furthermore, StomachDB emphasizes the discovery of therapeutic targets by cataloging interactions among chemical drugs, traditional herbal medicines, and probiotics. As an open-access resource, it serves as a powerful tool for studying complex biological interactions and regulatory mechanisms. Full article

Background/Objectives: Immune checkpoint blockade (ICB) has emerged as a promising therapeutic option for hepatocellular carcinoma (HCC), yet reliable biomarkers to predict clinical outcomes remain limited. Tumor-associated macrophages (TAMs) are increasingly recognized as key regulators of the tumor immune microenvironment. Methods: We interrogated a publicly available HCC single-cell RNA sequencing (scRNA-seq) dataset to characterize intratumoral immune cell subpopulations. Through unsupervised clustering and gene signature analysis, we identified a distinct subset of SPP1 (secreted phosphoprotein 1, also known as osteopontin) and BCL2A1 (Bcl-2-related protein A1) double-positive TAMs. Their abundance was quantified and associated with patient outcomes. Further independent HCC transcriptomic datasets with annotated PD1-based ICB response status were used for examination. Results: Across the discovery (GSE149614; n = 10) cohort, elevated expression of intratumoral SPP1⁺BCL2A1⁺ TAMs was identified in HCC. In the ICB datasets (GSE151530; n = 4), patients with high SPP1⁺BCL2A1⁺ TAM expression further exhibited significantly poorer responses to ICB therapy. Further, the validation cohort (GSE206325; n = 18) confirmed these findings accordingly. Notably, these TAMs were expressed thoroughly within the immunosuppressive T-cell microenvironment in non-responders but were distinctly expressed among the cytotoxic T-cell responses in responders. Conclusions: Our findings identify SPP1⁺BCL2A1⁺ TAMs as a poor prognostic biomarker in HCC patients undergoing ICB therapy. By promoting an immunosuppressive microenvironment, SPP1⁺BCL2A1⁺ TAMs, which are survival-advantaged, may represent both a predictive marker and a potential therapeutic target to enhance the efficacy of immunotherapy. Full article

The emergence of single-cell sequencing and computational analysis has dramatically improved our understanding of cellular diversity and gene expression dynamics. The rapid advancement of high-throughput omics technologies has led to an exponential growth in biological data. However, many gene regulatory processes at the single-cell level remain underexplored, especially those regulated by post-transcriptional mechanisms involving microRNAs (miRNAs). miRNAs are essential regulators of gene expression, affecting cellular functions in both normal and disease states. Recent innovations, such as single-cell gene expression profiling and bioinformatic analysis, have enabled comprehensive studies that uncover previously hidden miRNA profiles. In this context, we present experimental tools and computational methods for analysing cell-specific miRNA abundance and investigating their mechanisms. These approaches are expected to reveal the complex nature of miRNA biology and, more broadly, enhance our understanding of life sciences and diseases. Full article

Pluripotent stem cells (PSCs) exhibit remarkable self-renewal capacity and differentiation potential, necessitating tight regulation of gene expression at both transcriptional and post-transcriptional levels. Among post-transcriptional mechanisms, RNA turnover and degradation together play pivotal roles in maintaining transcriptome homeostasis and controlling RNA stability. RNA degradation plays a pivotal role in determining transcript stability for both messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs), thereby influencing cell identity and fate transitions. The core RNA decay machinery, which includes exonucleases, decapping complexes, RNA helicases, and the RNA exosome, ensures timely and selective decay of transcripts. In addition, RNA modifications such as 5′ capping and N6-methyladenosine (m⁶A) further modulate RNA stability, contributing to the fine-tuning of gene regulatory networks essential for maintaining PSC states. Recent single-cell and multi-omics studies have revealed that RNA degradation exhibits heterogeneous and dynamic kinetics during cell fate transitions, highlighting its role in preserving transcriptome homeostasis. Conversely, disruption of RNA decay pathways has been implicated in developmental defects and disease, underscoring their potential as therapeutic targets. Collectively, RNA degradation emerges as a central regulator of PSC biology, integrating the decay of both mRNAs and ncRNAs to orchestrate pluripotency maintenance, lineage commitment, and disease susceptibility. Full article

Spermatogonial stem cells (SSCs) are unipotent germline cells with emerging pluripotent potential under specific in vitro conditions. Understanding their capacity for reprogramming and the molecular mechanisms involved offers valuable insights into regenerative medicine and fertility preservation. SSCs were isolated from Oct4-GFP C57BL/6 transgenic mice using enzymatic digestion and cultured in defined media. Under these conditions, ES-like colonies emerged expressing pluripotency markers. These cells were characterized by immunocytochemistry, teratoma assays, and transcriptomic analyses using bulk and single-cell RNA sequencing datasets. Gene expression profiles were compared with ESCs and SSCs using datasets from GEO (GSE43850, GSE38776, GSE149512). Protein–protein interaction (PPI) networks and co-expression modules were explored through STRING, Cytoscape, and WGCNA. ES-like cells derived from SSCs exhibited strong expression of OCT4, DAZL, and VASA. Transcriptomic analysis revealed key differentially expressed genes and shared regulatory networks with ESCs. WGCNA identified key co-expression modules and hub regulatory RNA binding genes (Ctdsp1, Rest, and Stra8) potentially responsible for the reprogramming process. Teratoma assays confirmed pluripotency, and single-cell RNA-seq validated expression of critical markers in cultured SSCs. This study demonstrates that SSCs can acquire pluripotency features and be reprogrammed into ES-like cells. The integration of transcriptomic and network-based analyses reveals novel insights into the molecular drivers of SSC reprogramming, highlighting their potential utility in stem cell-based therapies and male fertility preservation. Full article

Background/Objectives: Thyroid cancer (TC) is the most common type of endocrine neoplasm and is increasing in incidence, particularly papillary thyroid carcinoma (PTC). Early-stage disease has a favorable prognosis; however, advanced forms, such as anaplastic thyroid carcinoma, complicate treatment. Long non-coding RNAs (lncRNAs), longer than 200 nucleotides and non-coding, together with microRNAs, have emerged as major regulators of TC pathogenesis. This review summarizes data on how dysregulated lncRNAs influence the hallmarks of cancer in thyroid malignancies. Methods: We reviewed the literature on the role of lncRNAs and microRNAs in TC, focusing on their functions as competing endogenous RNAs (ceRNAs), regulators of PI3K/AKT and Wnt/β-catenin pathways, and controllers of epigenetic alterations. Results: Dysregulated lncRNAs contribute to hallmarks including sustained growth, evading suppressors, resisting death, replicative immortality, angiogenesis, invasion, metabolic reprogramming, immune evasion, genomic instability, and tumor-promoting inflammation. ceRNA mechanisms amplify immune evasion by regulating checkpoint proteins and cytokines, altering immune cell activity. Altered lncRNA profiles correlate with aggressiveness, metastasis, and prognosis. Notable lncRNAs, such as H19, MALAT1, and DOCK9-AS2, dysregulate oncogenic pathways and represent potential biomarkers. Conclusions: Advances in therapeutics suggest inhibiting oncogenic lncRNAs or restoring tumor-suppressive lncRNAs via RNA interference, antisense oligonucleotides, or CRISPR/Cas9 editing. New technologies, including single-cell RNA sequencing and spatial transcriptomics, will improve understanding of heterogeneous lncRNA–microRNA networks in TC and support precision medicine. LncRNAs signify both molecular drivers and clinical targets for thyroid cancer. Full article

IgA nephropathy (IgAN) is the most prevalent type of primary glomerulonephritis, with heterogeneous clinical outcomes. Conventional prognostic factors, such as proteinuria, eGFR, and Oxford histologic classification, have poor sensitivity and specificity. Recently, transcriptomic profiling has been employed to provide insights into the molecular definition of IgAN and facilitate patient stratification in those at risk of disease progression. In this review, we summarize our current understanding of IgAN derived from bulk RNA sequencing, single-cell transcriptomics, spatial transcriptomics, and gene expression profiling to elucidate the molecular characteristics of IgAN. Bulk transcriptomics of glomerular and tubulointerstitial compartments highlighted consistently upregulated genes (e.g., CCL2, CXCL10, LCN2, HAVCR1, COL1A1) and altered pathways (e.g., NF-κB, TGF-β, JAK/STAT, and complement) that are associated with clinical decline. Single-cell and single-nucleus RNA-sequencing has also identified the value of pathogenic cell types and regulatory networks in mesangial cells, tubular epithelium, and immune infiltrates. Furthermore, noninvasive transcriptomic signatures developed from urine and blood may represent useful real-time surrogates of tissue activity. With the advent of integrated analyses and machine learning approaches, personalized risk models that outperform traditional metrics are now available. While challenges remain, particularly related to standardization, cohort size, and clinical deployment, transcriptomics is likely to revolutionize IgAN by providing early risk predictions and precision therapeutics. Unlike prior reviews, our work provides an integrative synthesis across bulk, single-cell, spatial, and noninvasive transcriptomics, linking molecular signatures directly to clinical translation in risk stratification and precision therapeutics. Full article

Soft tissue manipulation (STM) is widely used by physical therapists, massage therapists, athletic trainers, and osteopathic physicians to manage musculoskeletal pain, yet its biological mechanisms remain poorly understood. Preclinical studies indicate that STM can alter immune cell behavior in animal models, increasing anti-inflammatory cytokines (IL-4, IL-10) and reducing chemokines such as RANTES/CCL5. Single-cell transcriptomic analyses suggest mechanical treatment may reprogram stromal cells and shift immune cell recruitment in aged or inflamed tissues. However, many of these mechanistic findings have yet to be confirmed in human studies. Early clinical trials show massage therapy can modify circulating leukocytes and reduce cytokine responses, but direct tissue-level evidence in human subjects remains limited. This narrative review summarizes existing insights and emphasizes the need for future clinical investigations targeting populations with chronic inflammation, repetitive-use injuries, post-surgical fibrosis, or age-related muscle decline. We advocate for studies incorporating tissue or fluid sampling, cytokine profiling, and molecular assays such as flow cytometry or transcriptomics to characterize STM’s immunological effects in people. Rather than simply easing symptoms, STM may act as a precision mechanical stimulus that recalibrates immune tone and promotes tissue repair. Bridging basic science with clinical research will be essential to establish STM as a biologically informed, mechanobiology-based therapeutic strategy. Full article

Classifying brain tumour transcriptomic data is crucial for precision medicine but remains challenging due to high dimensionality and limited interpretability of conventional models. This study benchmarks three image-based deep learning approaches, DeepInsight, Fotomics, and a novel saliency-guided convolutional neural network (CNN), for transcriptomic classification. DeepInsight utilises dimensionality reduction to spatially arrange gene features, while Fotomics applies Fourier transforms to encode expression patterns into structured images. The proposed method transforms each single-cell gene expression profile into an RGB image using PCA, UMAP, or t-SNE, enabling CNNs such as ResNet to learn spatially organised molecular features. Gradient-based saliency maps are employed to highlight gene regions most influential in model predictions. Evaluation is conducted on two biologically and technologically different datasets: single-cell RNA-seq from glioblastoma GSM3828672 and bulk microarray data from medulloblastoma GSE85217. Outcomes demonstrate that image-based deep learning methods, particularly those incorporating saliency guidance, provide a robust and interpretable framework for uncovering biologically meaningful patterns in complex high-dimensional omics data. For instance, ResNet-18 achieved the highest accuracy of 97.25% on the GSE85217 dataset and 91.02% on GSM3828672, respectively, outperforming other baseline models across multiple metrics. Full article