Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “gene features”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 37 records · Page 2Linked to original sources
PubMed2025

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.

Genome-wide association studies (GWAS) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over two million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipids GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic QTL regulatory mechanism datasets whilst considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals, while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.

Journal Article↗
PubMed2026

Loss of function of the chromatin remodeling gene INO80D leads to neurogenic features of schizophrenia.

Schizophrenia has been linked to severely damaging de novo mutations in synaptic junction proteins, neurotransmitter receptors, transcription factors, and chromatin remodeling proteins. In a patient with schizophrenia in the absence of a family history of severe mental illness, we identified de novo nonsense mutation, INO80D p.Q568X, associated with both a truncated protein and partial nonsense-mediated decay. Three experiments were undertaken to evaluate the consequences of the mutation. 1) In neural stem cells (iNSCs) differentiated from WTC11 iPSCs, CRISPRi knockdown of INO80D led to downregulation of three subunits of the AMPA-glutamate receptor, of multiple genes mutant in schizophrenia, and of genes of synaptic function. 2) INO80D p.Q568X iNSCs and neurons differentiated from patient-derived induced pluripotent stem cells (iPSCs) had significantly lower expression of neurogenesis genes compared to patient-derived cells with the mutation corrected by CRISPR-Cas9 gene editing. Patient-derived INO80D p.Q568X neurons had significantly higher expression of cell division genes compared to lines with the mutation corrected, consistent with the possibility that some of these cells may be undergoing mitosis, which is not normal for neurons. 3) Finally, on microelectrode array (MEA) plates, WTC11-derived glutamatergic neurons with reduced expression of INO80D had more rapid firing rate and increased average network burst duration, both features of neurons derived from patients with neurodevelopmental disorders. Overall, these findings suggest that partial loss of INO80D function due to de novo mutation may have disrupted normal neurodevelopment and contributed to the schizophrenia of this patient.

Humans↗
PubMed2026

Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer.

Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential for guiding the use of postoperative adjuvant chemotherapy (ACT). In this study, we identified novel prognostic gene expression biomarkers in patients with pStage II CRC and developed a new risk stratification framework for ACT decision-making. First, genome-wide biomarker discovery was conducted to identify prognostic gene expression biomarkers associated with recurrence risk in pStage II CRC. This analysis identified 10 differentially expressed genes as potential biomarkers for recurrence. The efficacy of these biomarkers was then tested using 188 clinical surgical specimens obtained from patients with pStage II CRC. A predictive panel was developed using qRT-PCR and used to assess 93 clinical specimens with an area under the curve (AUC) of 0.82, and its performance was further validated in an independent cohort (n = 95). By incorporating key clinicopathological features, a Gene expression-based Prediction of Recurrence in pStage II CRC (GPRSC) signature was developed, which robustly predicted postoperative recurrence (AUC: 0.80). Finally, combining the GPRSC signature, microsatellite instability status, and conventional criteria, we developed a novel risk stratification system for postoperative ACT decision-making in pStage II CRC. Overall, we identified novel gene expression biomarkers and developed a prognostic signature that informs clinical decision-making regarding postoperative ACT in patients with pStage II CRC.

Humans↗
PubMed2026

The halophilic archaeon Halogranum roseipondis sp. nov. is susceptible to a virus carrying an exceptionally high number of viral tRNA genes.

UNLABELLED: Archaea constitute a diverse group of organisms, many of which inhabit extreme environments, such as haloarchaea that dominate hypersaline ecosystems, like solar salterns. Sampling of solar salterns and other hypersaline environments has resulted in numerous haloarchaeal isolates, including 3 classified and 27 uncharacterized Halogranum species. However, no complete genome has so far been reported for any member of this genus. Here, we present the first comprehensive study of Halogranum sp. SS5-1 isolated from a solar saltern in Samut Sakhon, Thailand. Hgn. SS5-1 is a pleomorphic, aerobic heterotroph that thrives in high salinity and moderate temperature and is capable of hydrolyzing starch. Its genome consists of a 3.6 Mbp chromosome and seven additional plasmids. Based on our phylogenetic analyses, which establish Hgn. SS5-1 as a distinct species, we propose that it will be classified as the novel species Halogranum roseipondis sp. nov. SS5-1T. Additionally, we report that Hgn. roseipondis sp. nov. SS5-1T is infected by Hagravirus capitaneum (HGTV-1), the only virus known to infect a Halogranum host. HGTV-1 exhibits a unique head-tailed morphology and encodes the largest archaeal virus double-stranded DNA genome known to date, including 34 tRNA-encoding genes. Codon usage analysis of the viral genome suggests partial alignment with host preferences, yet the abundance of viral tRNA genes hints at broader roles, potentially including roles in translation and host regulation. This study establishes Hgn. roseipondis and HGTV-1 as a novel virus-host system, opening avenues to explore infection dynamics and the roles of virus-encoded tRNA in archaea. IMPORTANCE: Archaea that thrive in high-salinity environments are key players in geochemical cycles and important contributors to ecosystem productivity. Despite their ecological significance and importance for the development of novel methodologies in synthetic biology, haloarchaea remain poorly studied. Further exploration of haloarchaea is required to obtain valuable information on the evolution of cellular complexity and the molecular mechanisms that allow cells to thrive in harsh environmental conditions. Here, we present the characterization of a novel archaeon, Halogranum roseipondis sp. SS5-1T, alongside the infection cycle of its associated virus, Hagravirus capitaneum. This tailed myovirus carries an extraordinary set of 34 viral tRNA genes, a feature that opens intriguing questions about virus-host interactions and translational control. Our findings lay the groundwork for future investigations into the expression and function of viral tRNAs in an archaeal model system, thereby opening a new frontier for studying archaeal translation and virus-driven modulation of host cellular processes.

Halobacteriaceae↗
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

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

UNLABELLED: Breast and prostate cancers are both hormone-driven adenocarcinomas that undergo analogous invasion programs. Using lightsheet microscopy on intact tumors, we identified transitional junctions between precancerous and invasive regions. We then developed a multimodal serial-section workflow integrating volumetric reconstruction with spatial transcriptomics. Analysis of 319 spatial assays from 51 cases revealed gene expression features and novel structural insights defining the shift from precancer to invasive disease. In breast cancer, loss of MGP and PLAT was associated with invasive transition and promoted tumorigenesis in functional assays. In prostate cancer, GDF15, ALDH1A3, ANPEP, and FASN were upregulated along invasive progression, and their knockdown in PC-3 cells suppressed proliferation and migration. Enrichment of tumor-associated macrophages (SPP1+ and MS4A6A+) along non-triple-negative breast cancer breast cancer transitions highlights immune involvement as a potential driver of invasiveness. SIGNIFICANCE: Our method of defining precise spatial locations of invasive transition allows for the direct interrogation of transition drivers, presenting new therapeutic targets for the two most prevalent cancers and providing a framework for studying spatially defined mechanisms of tumor progression. See related commentary by Jing and Li, p. 1720.

Humans↗
PubMed2026

Pretraining improves prediction of genomic datasets across species.

MOTIVATION: Recent studies suggest that deep neural network models trained on thousands of human genomic datasets can accurately predict genomic features, including gene expression and chromatin accessibility. However, training these models is computation- and time-intensive, and datasets of comparable size do not exist for most other organisms. RESULTS: Here, we identify modifications to an existing state-of-the-art model that improve model accuracy while reducing training time and computational cost. Using this streamlined model architecture, we investigate the ability of models pretrained on human genomic datasets to transfer performance to a variety of different tasks. Models pretrained on human data but fine-tuned on genomic datasets from diverse tissues and species achieved significantly higher prediction accuracy while significantly reducing training time compared to models trained from scratch, with Pearson correlation coefficients between experimental results and predictions as high as 0.8. Further, we found that including excessive training tasks decreased model performance and that this decrease could be partially but not completely rescued by fine-tuning. Thus, simplifying model architecture, applying pretrained models, and carefully considering the number of training tasks may be effective and economical techniques for building new models across data types, tissues, and species. AVAILABILITY AND IMPLEMENTATION: Code is available on GitHub and Figshare: https://github.com/optimizedlearning/genomicsML, https://doi.org/10.6084/m9.figshare.31796116.

Genomics↗
PubMed2026

BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.

SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine. AVAILABILITY AND IMPLEMENTATION: The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https://bioneuralnet.readthedocs.io. Code archived at https://doi.org/10.5281/zenodo.17503083.

Graph Neural Networks↗
PubMed2025

DeepWheat: predicting the effects of genomic variants on gene expression and regulatory activities across tissues and varieties in wheat using deep learning.

Spatiotemporal gene expression shapes key agronomic traits, yet tissue-specific prediction remains challenging in complex crops. We present DeepWheat, a broadly applicable deep learning framework comprising DeepEXP and DeepEPI, for accurate, tissue-specific gene expression prediction. DeepEXP integrates sequence and epigenomic features to predict gene expression (PCC 0.82-0.88), while DeepEPI predicts epigenomic maps from DNA sequence to support model transfer across varieties. Validations in five wheat cultivars confirm robustness and accuracy. DeepWheat also identifies regulatory variants with strong expression effects, enabling targeted cis-regulatory elements editing and offering a powerful tool for crop functional genomics and breeding.

Triticum↗
PubMed2026

Comparative analyses of olfactory receptor repertoires in Schizothorax fish based on the chromosome-level genomes: Implications for regulatory roles of dietary differentiation and ploidy variation.

The olfactory receptor (OR) genes constitute the molecular basis of fish olfaction, mediating survival behaviors and environmental adaptation while coevolving with habitat-driven evolution. Schizothorax, a cyprinid genus endemic to the Qinghai-Tibetan Plateau, exhibits remarkable dietary divergence and ploidy variation in response to plateau environmental changes, which presumably facilitates the adaptive evolution of OR genes. However, the evolutionary patterns of OR genes associated with trophic divergence and ploidy variation in this genus remain unclear. In this study, three species were selected: the herbivorous diploid S. macropogon, the carnivorous diploid S. lantsangensis, and the herbivorous tetraploid S. curvilabiatus. S. macropogon possessed 142 OR genes (92.25% functional), primarily located on chromosomes 14 and 24, with the fewest sequence clusters. Such compact gene repertoire and highly overlapping chromosomal clusters indicated specialization for a herbivorous olfactory niche. S. lantsangensis contained 127 OR genes (93.70% functional), concentrated on chromosomes 4 and 5, with fewer sequence clusters and a scattered distribution, reflecting evolution of OR genes under carnivorous feeding habits. The herbivorous tetraploid S. curvilabiatus exhibited striking features: 316 OR genes (94.30% functional), the most subfamilies, unique ε and κ OR subfamilies, and species-specific motifs. These characteristics revealed that ploidy, rather than herbivory, dominated OR gene evolution. In conclusion, dietary differentiation and ploidy variation together drove olfactory adaptive evolution in Schizothorax, providing new insights into vertebrate OR gene ecological adaptation.

Animals↗
PubMed2025

Neuroimaging PheWAS and molecular phenotyping implicate PSMC3 in Alzheimer's Disease.

INTRODUCTION: Neuroimaging genetics have advanced Alzheimer's disease (AD) research, yet frameworks mechanistically connecting genes to neurological outcomes via functional genomics are needed to elucidate genetic associations. To address this challenge, we assessed relationships between AD-associated variants and disease via their impact on gene expression and neuroimaging phenotypes. METHODS: We mapped established AD genes to neuroimaging traits using NeuroimaGene atlas and predicted transcript-driven AD neurological features by comparing gene-derived neuroimaging features to clinical neuroimaging data. Genetic correlation and covariance analyses characterized shared genetic architecture between AD endophenotypes and neuroimaging features and identified neuroimaging features associated with dementia family history. RESULTS: Our analyses implicate PSMC3 expression as a strong contributor to AD pathophysiology and indicate AD endophenotypes, including dementia family history, linked to frontal cortex thickness, volume, and cerebrospinal fluid volume changes. DISCUSSION: Our findings prioritize AD genes whose regulation is associated with vulnerable brain regions, offering a potential mechanistic framework for downstream functional validation.

Alzheimer’s Disease↗
PubMed2023

Identification of a common secondary mutation in the Neurospora crassa knockout collection conferring a cell fusion-defective phenotype.

Gene-deletion mutants represent a powerful tool to study gene function. The filamentous fungus Neurospora crassa is a well-established model organism, and features a comprehensive gene knockout strain collection. While these mutant strains have been used in numerous studies, resulting in the functional annotation of many Neurospora genes, direct confirmation of gene-phenotype relationships is often lacking, which is particularly relevant given the possibility of background mutations, sample contamination, and/or strain mislabeling. Indeed, spontaneous mutations resulting in phenotypes resembling many cell fusion mutants have long been known to occur at relatively high frequency in N. crassa, and these secondary mutations are common in the Neurospora deletion collection. The identity of these mutations, however, is largely unknown. Here, we report that the Δada-3 strain from the N. crassa knockout collection, which exhibits a cell fusion defect, harbors a secondary mutation responsible for this phenotype. Through whole-genome sequencing and genetic analyses, we found a ~30-Kb deletion in this strain affecting a known cell fusion-related gene, so/ham-1, and show that it is the absence of this gene—and not of ada-3—that underlies its cell fusion defect. We additionally found three other knockout strains harboring the same deletion, suggesting that this mutation may be common in the collection and could have impacted previous studies. Our findings provide a cautionary note and highlight the importance of proper functional validation of strains from mutant collections. We discuss our results in the context of the spread of cell fusion-defective cheater variants in N. crassa cultures.

Neurospora crassa↗
PubMed2025

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.

Glioblastoma↗
PubMed2026

Genomic signatures associated with epidemiologically defined high-risk pathogenic Escherichia coli isolates identified by interpretable machine learning.

Pathogenic Escherichia coli is a major cause of foodborne illness worldwide and includes strains capable of causing severe disease. To establish a genome-informed framework for foodborne outbreak surveillance, we analyzed 1,029 E. coli isolates from clinical, food, livestock, and environmental sources using whole-genome sequencing. Pathogenic isolates obtained from human clinical cases or linked to documented outbreaks were classified as epidemiologically defined high-risk (EpiHR), whereas the remaining pathogenic isolates were classified as non-EpiHR. Virulence-associated genomic features were extracted using a bioinformatics pipeline, and four machine learning (ML) algorithms, including gradient boosting machine, random forest (RF), and support vector machines with linear and radial basis function kernels, were evaluated. Among them, the RF model showed the best performance, achieving an area under the curve (AUC) of 0.98 and accuracy of 0.93 in 10-fold cross-validation. Additional leave-one-group-out validation showed retained discrimination across held-out sequence types and serotypes, although performance was reduced when isolates were grouped by isolation source. Evaluation using an independent test dataset of 1,908 publicly available pathogenic E. coli genomes showed an AUC of 0.97 and a sensitivity of 0.98. Feature importance analysis using Shapley additive explanations identified influential predictive features, including traT, etpB, and enterotoxin-associated genes. A reduced 10-feature model achieved an AUC of 0.79 in the independent test dataset, supporting its exploratory use for future simplified screening approaches. These results indicate that genome-based ML provides a sensitive framework for surveillance-oriented prioritization of EpiHR pathogenic E. coli isolates, with model predictions interpreted together with epidemiological information.

Escherichia coli↗
PubMed2025

MNMO: discover driver genes from a multi-omics data based-multi-layer network.

MOTIVATION: Cancer as a public health problem is driven by genomic variations in "cancer driver" genes. The identification of driver genes is critical for the discovery of key biomarkers and the development of personalized therapy. RESULTS: We propose a prediction method MNMO: a multi-layer network model based on multi-omics data. MNMO firstly constructs a dynamically adjusted four-layer network composed of miRNAs and three kinds of genes with different features. Then three kinds of scores, i.e. control capacity, mutation score, and network score, are devised and calculated by harmonic mean to produce the integrated gene score. Experiments were performed on three kinds of real cancer data to compare the identification performance of method MNMO with that of six state-of-the-art ones. The results indicate that method MNMO presents the best identification performance under most circumstances. The genes prioritized by method MNMO not only have a better match to the benchmark ones than those identified by the other methods, but also are all associated with the development and progression of cancers. In addition, some extended versions of method MNMO can further achieve better performance on most evaluation metrics for some specific datasets. They may be more conducive to identifying tissue-specific genes, which has been verified through a number of experiments. AVAILABILITY AND IMPLEMENTATION: The source code and the R package "MNMO" are available at https://github.com/Zheng-D/MNMO. The dataset and code are archived at https://doi.org/10.5281/zenodo.14969986.

Humans↗
PubMed2026

Spindle Cell Predominant Anaplastic Pleomorphic Xanthoastrocytoma (WHO Grade 3) With Focal Piloid Features: A Rare Case Study With Comprehensive Molecular Profiling.

Pleomorphic xanthoastrocytoma (PXA) is a rare astrocytic tumor of the central nervous system. The typical form demonstrates relatively low-grade histologic features, whereas an anaplastic variant shows more aggressive behavior, including increased mitotic activity and necrotic changes. These tumors are often associated with alterations involving key growth signaling pathways and cell cycle regulatory genes, with molecular features that may resemble those seen in other high-grade astrocytic neoplasms. We describe an unusual example of an anaplastic pleomorphic xanthoastrocytoma showing focal piloid differentiation. The patient presented with acute neurologic symptoms, and imaging demonstrated a large, enhancing, well-circumscribed cerebral lesion with limited surrounding edema. Histologic evaluation revealed a highly cellular astrocytic neoplasm composed of spindle-shaped cells with marked pleomorphism, including scattered multinucleated forms, brisk mitotic activity, and necrotic areas. At the periphery, regions with elongated bipolar glial cells and occasional cytoplasmic inclusions suggestive of piloid morphology were identified. Molecular analysis demonstrated an activating alteration in the mitogen-activated protein kinase (MAPK) pathway along with additional genomic abnormalities, while mutations commonly associated with diffuse gliomas were not detected. The presence of piloid features within an otherwise anaplastic tumor is rare and may be relevant to the relatively favorable outcome observed during extended follow-up.

anaplastic↗
PubMed2025

Determinants of odorant receptor transcription and gene choice.

The mammalian olfactory system enables the detection of a wide variety of chemical compounds via the expression of a repertoire of olfactory receptors comprising the largest gene family in the mammalian genome. Olfactory sensory neurons (OSNs) each express only 1 odorant receptor (OR) gene. In mice, this requires activation of 1 OR gene and repression of over 1,400 other OR genes. In this review, we describe the mechanisms that support the transcription of OR genes and how these mechanisms impact which OR is expressed in each neuron. First, we discuss what is currently known about the role of transcription in OR choice. We then describe the role of specific features of OR genes and enhancers in the regulation of OR transcription. Finally, we discuss characteristics of OSNs which specify transcription of some OR genes while restricting the transcription of others.

Receptors, Odorant↗
PubMed2026

Complete telomere-to-telomere genome assembly of Guazuma ulmifolia uncovers evolutionary mechanisms, drought adaptation, and flavonoid biosynthesis.

The first T2T reference genome of Guazuma ulmifolia is reported, which serves as a core genomic resource for stress adaptation research and stress-tolerant breeding in cacao wild relatives. Climate change, particularly increased incidence of drought, poses a major threat to food security. Understanding the genomic basis of environmental adaptation in crop wild relatives can provide valuable resources for improving stress resilience. Guazuma ulmifolia, a wild relative of Theobroma cacao with important ecological and medicinal value, lacks high-quality reference genomic resources. Here, we report the first telomere-to-telomere (T2T) chromosome-level genome assembly of G. ulmifolia, with a genome size of 311.31 Mb, contig N50 of 35.19 Mb, and 98.70% BUSCO completeness. Repetitive sequences constitute 27.43% of the G. ulmifolia genome, with LTR retrotransposons as the predominant class. Comparative genomic analyses revealed that genome-size variation among Malvaceae species is associated with differences in polyploidization history and TE dynamics. Ancestral karyotype reconstruction identified five lineage-specific chromosome fusion events distinguishing G. ulmifolia from T. cacao. Comparative analyses further identified tandem duplication-associated expansion of stress-related LEA and GST gene families, suggesting potential genomic features associated with stress responses. Flavonoid biosynthesis genes were largely conserved in copy number but showed tissue-specific expression patterns, providing candidate genes for investigating secondary metabolism. Together, this study establishes a high-quality T2T genome resource for exploring genome evolution, chromosome organization, and stress-related genomic features in Malvaceae.

Genome, Plant↗