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PubMed2026

Single-cell and spatial transcriptomics define a progenitor subpopulation and fibroinflammatory niche at the leading edge of parathyroid carcinoma.

Parathyroid carcinoma (PC) is a rare but clinically aggressive endocrine malignancy with limited treatment options and a poorly defined tumor microenvironment (TME). To elucidate its cellular heterogeneity and spatial architecture, we integrated single-cell and spatial transcriptomic profiling with whole-exome sequencing and multiplex immunohistochemistry on eight parathyroid neoplasm specimens, including PC, parathyroid adenoma, and atypical parathyroid tumor. We identified a distinct progenitor-like endocrine subpopulation (Ca-1) enriched in CDC73-mutant PC, exhibiting stem-like properties, elevated cell cycle activity, and pronounced genomic instability. Spatial mapping revealed that Ca-1 cells preferentially localize at the leading edge, forming a fibroinflammatory niche characterized by the enrichment of inflammatory cancer-associated fibroblasts (iCAFs) and SPP1+ macrophages. Within this niche, the dipeptidyl peptidase 4 (DPP4) is selectively expressed in Ca-1 cells and iCAFs, implicating a potential paracrine axis driving stromal remodeling and immunosuppression. These findings suggest that a spatially organized ecosystem may promote PC progression through TME remodeling and highlight the DPP4-CXCL2 axis as a candidate pathway for future investigation in aggressive parathyroid neoplasms.

Humans↗

PubMed2026

Zea mays Drought-Overly Sensitive1/TUBA4 Is Wilty3, and Transcriptome Co-Expression Analysis of Shoot Meristem Mutant Tissues Reveals Wilty2/TUB6:Wi3 Interactions Associated With Stem Vascular Bundle Development.

Plant vasculature is essential for the transport of water, nutrients, and signaling molecules across organs, while also providing critical mechanical support for growth and development. Disruptions in vascular bundle formation can therefore lead to severe physiological and developmental defects. In maize, ethyl methanesulfonate (EMS)-induced dominant nonallelic Wilty mutants exhibit a pronounced wilting phenotype even under well-watered conditions, indicating underlying defects in vascular function. In this study, we characterized the Wi3 mutant, identified as ZmDrought-Overly-Sensitive1/DOS1, and compared it with the previously described Wi2 mutant to uncover shared mechanisms underlying their phenotypes. We provide evidence, by bulk segregant resequencing linkage disequilibrium of SNPs adjacent to the causal Wilty SNPs in respective ß- and α-tubulin genes, for the personal communication from Gerry Neuffer that Wi2/ß-tub6 provenance is from ACR-related stock, whereas Wi3/α-tub4 allele is from Mo17, not B73 as claimed by the authors who cloned Dos1. Histochemical staining and Fourier-transform infrared (FTIR) spectroscopy of vascular bundles in Wi3 indicated apparent alterations in cellulose and lignin content consistent with those observed in Wi2. Transcriptome analysis of shoot meristems further indicated that similar sets of genes and pathways are differentially expressed in both mutants, suggesting convergence on common biological pathways. Using bulk-segregant whole-genome resequencing, we identified alpha-tubulin4 (TUA4) as the causal gene in Wi3 (ZmDOS1), harboring a C-to-T substitution within the N-terminal GTPase-binding domain. This mutation results in a glutamic acid196-to-lysine substitution. Given that α- and β-tubulin subunits heterodimerize, and in many plants and animal mutant alleles are dominant-negative gains-of-function, we infer Wi2, Wi3, and likely Wi4, based on very similar FTIR biophysical difference spectra, may act as effectors of vascular bundle cell wall deposition, potentially involving vesicle trafficking as recently shown for asymmetric cell divisions in maize stomatal development. Together, these findings highlight the functional interdependence of tubulin subunits and provide a plausible mechanistic framework for the striking biophysical, transcriptomic, and phenotypic similarities observed between Wi2, Wi3/ZmDOS1, and Wi4 mutants.

bulk segregant analysis↗

PubMed2025

Transcriptomic landscape of NK cell-related genes in hepatocellular carcinoma: associations with prognosis and therapeutic response.

Hepatocellular carcinoma (HCC) is a highly aggressive and heterogeneous malignancy, in which natural killer (NK) cells play a crucial role in tumor progression and immune surveillance. This study aimed to characterize the transcriptomic landscape of NK cell-associated genes (NAGs) and explore their associations with clinical outcomes and therapeutic responses in HCC. Using transcriptomic data from The Cancer Genome Atlas (TCGA), we identified key NAGs through comprehensive statistical analyses. Patients were stratified into distinct risk groups based on NAG expression profiles. Low-risk patients demonstrated better survival, higher immune infiltration, and greater predicted sensitivity to immunotherapy, whereas high-risk patients were associated with reduced chemotherapy responsiveness. These findings contribute to a deeper understanding of the immunogenomic features of HCC and provide a basis for developing personalized therapeutic approaches centered on NK cell-related mechanisms.

Hepatocellular carcinoma↗

PubMed2026

Integrated transcriptomic analysis of mRNA and miRNA in Brown adipose tissue of the greater horseshoe bats during hibernation.

Hibernation enables animals survive harsh environments by conserving energy through reduced metabolism and body temperature. Brown adipose tissue (BAT) plays a critical role in non-shivering thermogenesis, crucial for warming up during arousal phase. The greater horseshoe bats (Rhinolophus nippon) are typical hibernators and non-shivering thermogenesis in BAT tissue may persist throughout the arousal process in bats. This study examines gene expression and regulatory changes in BAT of these bats across active, hibernation, and arousal phases using transcriptome and miRNA sequencing. A total of 2721 differentially expressed mRNAs and 268 differentially expressed miRNAs were identified. The results reveal that the BAT transcriptome undergoes state-dependent remodeling throughout the hibernation process. The most pronounced divergence occurs between the active phase and torpor, involving cell cycle arrest, immunosuppression, thermogenic signal desensitization, and upregulation of lipid metabolism and autophagy pathways, reflecting the coordinated adaptation of energy conservation and thermogenic reserve. In contrast, transcriptional alterations between torpor and arousal are extremely limited, indicating that torpid BAT is already pre-primed for thermogenesis and requires only modest transcriptional adjustments to activate heat production. Notably, although body temperature recovers to active-phase levels during arousal, the molecular signature of BAT remains highly similar to that of the torpid state. Furthermore, the core thermogenic gene UCP1 showed no significant expression differences across the three groups. In conclusion, this study systematically delineates the miRNA-mRNA regulatory landscape of bat BAT across the hibernation process, and deepens our understanding of the thermoregulatory mechanisms underlying mammalian hibernation.

BAT↗

PubMed2026

Linking MRI radiomics to transcriptomics-based radiosensitivity in lower-grade glioma: A radiogenomic framework.

BACKGROUND: RSI is a transcriptomics-based biomarker associated with radiotherapy outcomes, but its clinical application is constrained by the requirement for tumor tissue and RNA sequencing. This study investigates whether MRI-derived radiomic features can reflect RSI-defined intrinsic radiosensitivity in lower-grade glioma.This addresses a critical gap arising from the limited availability of matched imaging and genomic data in routine clinical practice. METHODS: MRI-derived radiomic features were extracted from FLAIR images of lower-grade glioma patients obtained from TCIA and matched with transcriptomic data from TCGA. A total of 107 patients with both MRI and RNA sequencing data were included in the radiogenomic analysis. Radiomic features were ranked using a Borda-based ensemble feature selection strategy. Five supervised machine-learning classifiers were trained to predict RSI-based radiosensitivity classification, and model interpretability was assessed using SHAP within radiogenomic framework. RESULTS: Classification performance increased with feature number and stabilized at compact subset of 13 radiomic features. Logistic regression showed stable performance with an AUC of 0.82 (95 % CI: 0.71-0.93). SHAP analysis indicated that heterogeneity-related texture features were dominant contributors to model predictions, with many associated with the RR phenotype, while others were linked to the RS phenotype. CONCLUSION: An MRI-based radiomic signature enables non-invasive prediction of RSI-defined radiosensitivity in lower-grade glioma. Rather than offering an immediately deployable clinical tool, this study establishes a proof-of-concept radiogenomic framework demonstrating that intrinsic radiosensitivity, traditionally assessed through invasive molecular assays, can be approximated using quantitative imaging features. These findings highlight the potential of imaging-based radiosensitivity assessment and provide a foundation for future radiogenomic investigations.

Lower-grade glioma↗

PubMed2026

Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

Arachidonic acid metabolism↗

PubMed2026

Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia.

Temporal lobe epilepsy (TLE) is the most common acquired epilepsy, causing refractory seizures and cognitive deficits. We performed single-nucleus RNA sequencing on hippocampal tissue from mice 3 and 6 weeks following pilocarpine-induced status epilepticus, a robust model of TLE. Epilepsy samples showed reductions in Cck and Lamp5-Lhx6 interneuron subclusters, alongside increases in Cajal-Retzius cells, dentate granule (DG) cell precursors, and a mature DG cell subcluster. Among glia, an astrocyte subcluster and a markedly expanded microglia sublcuster were increased. We term this microglia population epilepsy-associated microglia (EAM). The transcriptomic profile of EAM overlaps with microglia described in models of Alzheimer's disease and traumatic brain injury, including enrichment of Myo1e and Igf1. EAM display amoeboid morphology, can be found in clumps around pyramidal and granule cell body layers, and exhibit enlarged vesicles and mitochondria. Cell-cell interaction analysis predicts DG cells as their primary interaction partners. This dataset defines transcriptomic programs underlying key cellular alterations in TLE, enabling mechanistic dissection of epileptogenesis.

TLE↗

PubMed2026

Genomic and transcriptomic features of relapsed small cell lung cancer.

BACKGROUND: Relapsed small cell lung cancer is characterized by treatment resistance and poor outcomes. Genomic and transcriptomic alterations in relapsed SCLC have not been characterized well. We comprehensively profiled relapsed SCLC samples along with patient-matched treatment-naive samples, when available, using whole-exome (WES), whole-genome (WGS), and RNA-sequencing (RNA-seq) to describe the molecular landscape of relapsed SCLC. Our goal is to identify potential novel pathways for additional functional validation and eventually novel therapeutic options. METHODS: We analyzed 54 relapsed and 27 treatment-naive SCLC samples using WES (with 26 patient-matched paired samples). A subset of the samples was also analyzed by WGS (n=28) and RNA-seq (n=31). Differences in mutational signatures, gene expression, structural variants, splicing, and neoantigen profiles at diagnosis and relapse were investigated. RESULTS: Relapsed SCLC samples demonstrated mutation signatures characteristic of platinum and APOBEC mutagenesis. Furthermore, these samples were characterized by MYC, MYCL and MYCN amplifications. Both treatment-naive and relapsed SCLC samples showed high prevalence of mutation-associated neoantigens (median= 86 in treatment-naive and 90 in relapsed SCLC; p=0.8) and TP53 was the most frequently altered gene to result in a neoantigen (48% of analyzed samples). Potential mechanisms of immune evasion, including amplification of CD24, overexpression of IDO1, increased M2 macrophage presence, and upregulation of HLA-E were also observed in relapse samples. Differences in alternative splicing patterns were observed between treatment-naive and relapsed small cell samples. Retained intron events were significantly enriched in treatment-naive samples and affected genes involved in DNA repair, metabolism, and WNT and MYC pathways. CONCLUSIONS: This study highlights the genomic and transcriptomic features of relapsed SCLC. These samples were characterized by genomic instability, WNT and MYC dysregulation, and splicing aberrations. Additional studies targeting the splicing machinery, WNT signaling, and immune evasion pathways could identify novel therapeutic vulnerabilities in SCLC.

Journal Article↗

PubMed2026

jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics technologies record genome-wide measurements of gene expression with high spatial resolution. These technologies generate large and high-dimensional datasets requiring efficient automated methods for their analysis. We introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data. RESULTS: jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation. The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, spatial domains are derived by simple clustering and spatially variable genes are identified directly from the principal component coefficients. A joint representation of multiple slices and samples without spatial alignment is obtained by computing common principal components via joint diagonalization. By leveraging data sparsity and non-convex manifold optimization, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA against 10 state-of-the-art methods on two reference databases. Our approach demonstrated excellent performance, comparable or better than state-of-the-art methods, while being much faster, interpretable, and scalable to very large datasets.

Journal Article↗

PubMed2026

Admission whole-blood transcriptomic characterization of a neutrophil-predominant systemic immune response in patients with acute traumatic brain injury.

BACKGROUND: Acute traumatic brain injury (TBI) is accompanied by systemic immune responses, but their whole-blood transcriptomic features at hospital arrival remain incompletely characterized. We aimed to characterize these features in patients with acute TBI compared with healthy controls. METHODS: In this single-center prospective observational study, we performed whole-blood RNA sequencing on hospital-arrival samples from 42 patients with acute TBI and 21 healthy controls. Analyses included differential expression (limma-voom; FDR < 0.05, |log2FC| > 0.7), functional enrichment, Ingenuity Pathway Analysis, CIBERSORTx LM22 deconvolution, and per-sample neutrophil degranulation signature scoring. RESULTS: Differential expression analysis identified 996 upregulated and 863 downregulated genes, with marked upregulation of inflammation-, innate immunity-, and neutrophil-related genes including DUSP1, HMGB2, MMP9, and S100A8. Canonical pathways with positive IPA z-scores included Neutrophil degranulation, Neutrophil Extracellular Trap Signaling Pathway, and Toll-like Receptor Signaling; upstream regulators included TNF, IL1B, IFNG, and STAT3. Deconvolution identified 7 of 22 differing subsets (q < 0.05), with relatively higher myeloid and lower lymphoid fractions in TBI. The Neutrophil degranulation signature score correlated with Injury Severity Score within TBI (Spearman ρ = +0.55; q < 0.001). CONCLUSIONS: Admission whole-blood transcriptomics characterized a neutrophil-predominant systemic transcriptional response in patients with acute TBI. This response was also evident among patients without major extracranial injury and was associated with total ISS. However, because the study lacked an appropriately matched non-TBI trauma comparator, the findings should be interpreted as a descriptive characterization of a systemic injury response accompanying TBI and do not establish a TBI-specific molecular signature or mechanism.

gene expression↗

PubMed2025

SpaceBar enables clone tracing in spatial transcriptomic data.

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

Journal Article↗

PubMed2025

Single-cell-scale spatial transcriptome of the developing and adult mouse ovary.

Mammalian ovary development is essential for female fertility, involving the complex spatial patterning of diverse cell types to establish the finite reserve of ovarian follicles. While single-cell transcriptome analyses have provided important insights into the mechanisms driving specification and developmental trajectories of ovarian cells, they disrupt this crucial spatial context. To overcome this limitation, we used 10X Genomics Visium HD spatial transcriptomics to analyze the developing mouse ovary while maintaining its native cellular architecture. We captured all ovarian cell types at eight key fetal and postnatal timepoints, generating a near single cell resolution library of spatial gene expression across ovarian development. This comprehensive dataset allows analysis of dynamic transcriptional signatures associated with unique spatial patterning throughout development, including the establishment of cortex and medulla and assembly of ovarian follicles in each region. This dataset represents a fundamental resource for the investigation of regulatory mechanisms driving spatial patterning of the ovary and opens new avenues to explore the spatial determinants of female fertility and reproductive longevity.

Journal Article↗

PubMed2025

Comprehensive Transcriptome Annotation of Thousands of HIV-1 Genomes.

Alternative splicing in HIV-1 has been a central focus of decades of research, uncovering key mechanisms of viral gene regulation, immune evasion, and therapeutic response - yet, no reference resource has existed to support transcriptome-wide analysis, limiting adoption of modern computational methods. We present HIV Atlas (https://ccb.jhu.edu/HIV_Atlas), the first reference-quality annotation of HIV-1 and SIV transcriptional diversity. We manually curated transcriptomes for HIV-1HXB2 and SIVmac239 and developed Vira, an automated annotation-transfer method specifically designed to address unique challenges of viral genome biology, to generate high-quality annotations for 2,077 complete HIV-1 genomes. Using the resources presented in our work, we evaluated conservation of splice sites, revealing near-perfect preservation of major donors and acceptors. Furthermore, using several public datasets, we demonstrate how HIV Atlas enhances methodology, improves the quality and novelty of results, and opens novel avenues for research, supporting more accurate and comprehensive analyses of bulk, single-cell, and spatial RNA-seq in HIV-1 studies.

Journal Article↗

PubMed2026

Integrated transcriptome analysis and machine learning to construct a homeostatic model of acetylation for bladder cancer and validate the key gene CES1.

BACKGROUND: Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Protein acetylation (PA) plays a critical role in regulating multiple biological processes (BPs), cellular homeostasis, and cancer-related signaling pathways. This study aimed to construct a homeostatic model of acetylation for BLCA using integrated transcriptome analysis and machine learning and to validate the key gene CES1. METHODS: RNA sequencing (RNA-seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Acetylation-related differentially expressed genes (DEGs) in BLCA were screened using differential expression analysis (DEA). An acetylation homeostatic model was constructed via univariate, machine learning-based least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, followed by validation in multiple cohorts. Single-cell RNA-seq analysis was used to explore gene expression patterns in diverse cell types. Enrichment analysis (EA), immune infiltration, and drug sensitivity analysis (DSA) were performed to characterize molecular features of different risk groups. Finally, the biological function of CES1 as the key gene was verified by in vitro knockdown experiments. RESULTS: We established a robust acetylation homeostatic model consisting of five genes, which effectively predicted overall survival (OS) and served as an independent prognostic factor in BLCA. High-risk patients showed significantly poorer prognosis, distinct immune infiltration profiles, and differential drug sensitivity. CES1 was identified and validated as the key gene in this model, which was highly expressed in BLCA and associated with poor prognosis. Knockdown of CES1 markedly suppressed cell proliferation, invasion, and migration, and reduced intracellular coenzyme A (CoA) levels, thereby regulating PA homeostasis. CONCLUSIONS: We developed and validated a novel acetylation homeostatic model for survival stratification and personalized treatment guidance in BLCA, based on integrated transcriptome analysis and machine learning. CES1 is closely associated with intracellular CoA levels and the malignant progression of BLCA. Its potential association with PA homeostasis requires further mechanistic validation, and it may act as a candidate therapeutic biomarker for BLCA.

Bladder cancer (BLCA)↗

PubMed2026

Transcriptome Analysis and Experimental Validation of Palmitoylation- Related Biomarkers in Atherosclerosis.

INTRODUCTION: Protein palmitoylation contributes to membrane localisation, signal transduction, and cell-fate regulation. It is closely associated with lipid metabolic dysfunction, immune inflammation, and vascular remodelling in atherosclerosis (AS). However, key palmitoylation-related transcriptomic markers and their potential causal associations with AS remain incompletely defined. METHODS: The Gene Expression Omnibus (GEO) dataset GSE100927 was used as the training cohort, and GSE43292 was used as an external validation cohort. Differentially expressed genes were identified using limma and intersected with palmitoylation-related genes to obtain palmitoylation-related differentially expressed genes (PRDEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed using clusterProfiler. Two-sample Mendelian randomisation was used to evaluate potential causal relationships between characteristic genes and AS. Feature selection was conducted using random forest and support vector machine recursive feature elimination (SVM-RFE), and the overlapping genes selected by both methods were retained. Receiver operating characteristic (ROC) curves were used to assess diagnostic performance. A five-gene nomogram was constructed, and its clinical utility was evaluated using calibration curves and decision curve analysis (DCA). Gene set variation analysis (GSVA) was applied to compare pathway activity between high- and low-expression groups for each core gene. Single-cell analysis using Seurat and expression-based cell-cell communication analysis using CellChat were conducted with GSE159677, and upstream transcription factors were predicted using NetworkAnalyst. For in vivo validation, an AS model was established in ApoE⁸/⁸ mice fed a high-fat diet, and aortic gene and protein expression were assessed by RT-qPCR and western blotting. RESULTS: In GSE100927, 51 PRDEGs were identified. GO and KEGG enrichment analyses highlighted pathways associated with regulation of monoatomic ion transport, sarcomere and myofibril organisation, and immune inflammation. Mendelian randomisation suggested a potential protective causal association between SLC7A7 and AS. By integrating MR with random forest and SVM-RFE feature selection, we prioritised five core genes: PLCB2, GMIP, NEXN, PLN, and SLC7A7. These genes showed good diagnostic performance in GSE43292. The resulting nomogram was well calibrated and demonstrated stable net benefit in decision curve and clinical impact curve analyses. Single-gene GSVA identified consistently activated pathways across multiple genes, including innate and adaptive immune recognition, calcium signalling and myocardial contraction/cardiomyopathy, extracellular matrix-receptor interaction, cell junction pathways, autophagy-lysosome pathways, and several metabolic programmes. At the single-cell level, PLCB2 and GMIP were predominantly expressed in T cells and macrophages, NEXN and PLN were enriched in vascular smooth muscle cells, and SLC7A7 was mainly expressed in macrophages. CellChat analysis indicated increased signals for immune-related ligand-receptor interactions. In ApoE⁸/⁸ mice fed a high-fat diet, PLCB2, GMIP, and SLC7A7 were upregulated, whereas NEXN and PLN were downregulated; protein-level changes were concordant with the transcriptomic trends. DISCUSSION: These findings indicate that palmitoylation-related dysregulation in AS converges on immune inflammation, calcium signalling/contractile programmes, ECM remodelling, and autophagy-linked metabolism. The five-gene panel is supported by external validation, single-cell localisation to immune and vascular compartments, and concordant results in ApoE⁸/⁸ mice. CONCLUSION: This study identified and validated five palmitoylation-related genes associated with AS. SLC7A7 showed a potential protective causal signal in MR analysis. The enriched pathway patterns linked these genes to immune inflammation, calcium signalling-contraction coupling, ECM remodelling, cell adhesion, and autophagy- associated metabolic reprogramming. The five-gene nomogram showed potential utility for diagnostic classification and decision support, nominating candidate biomarkers and pathway targets for AS molecular subtyping, diagnosis, and mechanistic investigation.

Atherosclerosis (AS)↗

PubMed2026

Transcriptomic Association Between Poliovirus Receptor (PVR/CD155) and Claudin Signaling Pathways in Colorectal Cancer.

BACKGROUND/AIM: Enterotoxigenic Bacteroides fragilis promotes colorectal carcinogenesis through toxin-mediated cleavage of E-cadherin, a process facilitated by membrane-associated Claudin-4 (CLDN4). Separately, the poliovirus receptor (PVR/CD155) modulates tumor epithelial and immune dynamics. This study explored potential transcriptomic interactions and co-expression frameworks between PVR and claudin signaling pathways in colorectal cancer. MATERIALS AND METHODS: Transcriptomic and proteomic data from the The Cancer Genome Atlas-colon adenocarcinoma cohort (TCGA-COAD) were evaluated. An exploratory E-cadherin Cleavage Index was modeled to capture transcript-protein discordance. To control for tissue composition heterogeneity without mathematical circularity, a de-circularized, non-parametric partial rank residual model adjusted for independent CLDN4 expression was deployed within the stable microsatellite-stable (MSS) sub-cohort (N=473). RESULTS: Multivariable survival models showed no independent associations between overall survival and continuous PVR (p=0.79) or CLDN3 (p=0.56) expression. Robust linear modeling revealed no significant baseline interaction between PVR and CLDN4 regarding the exploratory Cleavage Index (p=0.82). However, de-circularized partial correlation analysis revealed a highly stable, positive co-expression between PVR and CLDN3 (rho=0.2459, p=3.23×10-7). Both epithelial markers retained modest inverse correlations with the infiltrating lymphocytic axis (TIGIT and CD96). CONCLUSION: Baseline PVR expression is coordinated with CLDN3 tissue programs independent of general epithelial cellularity but does not interact with the CLDN4 axis or impact overall survival in an unexposed cohort. Because TCGA lacks virome or active microbial exposure tracking, these findings serve as baseline benchmarks for future context-dependent mechanistic studies.

Bacteroides fragilis toxin↗

PubMed2025

African Swine Fever Virus MGF 360-2L Disrupts Host Antiviral Immunity Based on Transcriptomic Analysis.

Background/Objectives: The African swine fever virus (ASFV) multi-gene family (MGF) 360 proteins play critical roles in immune evasion, replication regulation, and virulence determination. Despite substantial advances in this field, the functional roles of many members within this gene family remain to be fully characterized. Methods: In this study, Transcriptional kinetics analysis indicated that the expression profile of MGF 360-2L was consistent with that of the late marker gene B646L (p72). Transcriptomic profiling identified 13 and 171 differentially expressed genes (DEGs) at 12 and 24 h post-infection (hpi) with ΔMGF 360-2L, respectively. Results: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these DEGs were predominantly enriched in Type I interferon (IFN-I) signaling pathways. It is noteworthy that transcriptome analysis further demonstrates that the absence of MGF 360-2L specifically results in the dysregulation of expression of the replication-essential genes E199L and E301R. These findings indicate that MG F360-2L is essential for maintaining the stable expression of these proteins. Conclusions:MGF 360-2L is a late gene that contributes to the precise regulation of viral protein expression and modulates the host immune response during infection.

African swine fever virus↗

PubMed2026

S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis.

Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.

Humans↗