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PubMed2026

Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.

INTRODUCTION: The availability of a large number of cancer expression profiles presents an excellent opportunity to re-investigate various biological and clinical questions. While several expression profiles have been established for different cancers, merging them may provide a more powerful platform for extensively extrapolating molecular and clinical features across multiple cohorts. MATERIALS AND METHODS: In this study, five gastric tumor expression profiles from the Gene Expression Omnibus [GEO] and one in-house cohort comprising a total of 1,060 samples were merged. The batch effect was removed using non-parametric ComBat analysis, and the seamless merging of datasets was confirmed through various parameters. RESULTS: Extrapolation of ACRG [Asian Cancer Research Group] and TCGA [The Cancer Genome Atlas] molecular subtypes in the merged cohort of 1,060 gastric tumors revealed nine distinct clusters. Notably, the following patterns were observed: [i] mutual exclusivity between Epithelial to Mesenchymal Transition [EMT] and Microsatellite Instability [MSI] subtypes in 90% of tumors; [ii] overlapping occurrence of EMT and MSI subtypes in the remaining tumors; [iii] overlap between MSI and Epstein-Barr Virus [EBV] subtype tumors; [iv] both commonalities and differences between EMT and Genomically Stable [GS] subtypes; and [v] an association between EBV positivity and PI3K mutation. CONCLUSION: The current study demonstrates that compiling a larger expression profile is valuable for revisiting the molecular features and epidemiology associated with molecular subtypes, thereby aiding in the development of novel diagnostics and targeted therapeutics.

Humans↗

PubMed2026

Comparative Transcriptomic Analyses Identify Candidate Genes for Convergent Reproductive Shifts in a Bimodal Viviparous Amphibian.

Shifts in reproductive mode represent key evolutionary innovations that shape species' life histories and evolutionary trajectories. Species showing bimodal reproductive strategies with multiple independent origins offer a rare opportunity to gain insights into the adaptive processes and mechanisms underlying convergent traits. The fire salamander, Salamandra salamandra, is the only amphibian exhibiting intraspecific variation in reproductive mode across multiple independent reproductive shifts, enabling investigation of the transition between larviparity (females give birth to aquatic larvae) and pueriparity (females give birth to fully developed terrestrial juveniles) within a single species and across different timescales. Pueriparity is an adaptive innovation that skips the aquatic larval stage, allowing individuals to exploit habitats with no available water bodies. The fire salamander is larviparous across most of its range, but pueriparity has evolved independently at least three times: once in the early Pleistocene within S. s. bernardezi in the mountains of northern Spain, and more recently on two land-bridge islands (NW Spain) inhabited by S. s. gallaica. To identify candidate genes associated with these distinct reproductive modes, we compared gene expression profiles of the uterus and oviduct of pregnant females across two independent evolutionary transitions using RNA-sequencing. We detected shared changes in maternal gene expression among pueriparous S. s. bernardezi and S. s. gallaica relative to their larviparous counterparts, in addition to differences unique to each independent evolutionary transition. Functional enrichment analyses indicated that differentially expressed genes were associated with reproductive timing, angiogenesis, and maternal signalling, consistent with the phenotypic differences observed in the uterine environment and embryonic development between the two reproductive modes. This study represents an important first step towards understanding the genomic basis of the evolution of pueriparity in a remarkable bimodal reproductive system, and provides transcriptomic resources and candidate genes for future research into the genomic architecture underlying this poorly understood adaptive trait.

Animals↗

PubMed2025

GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data.

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest (GOI). The co-expression between the GOI and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the GOI is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. We furthermore include a differential GeneCOCOA mode, thus presenting the first implementation of a gene-focused approach to experiment-specific gene set enrichment analysis. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA.

Gene Expression Profiling↗

PubMed2026

An Integrated Proteomics and Genomics Approach to Identify Essential Protein Kinases During Human Trophoblast Development.

In the developing human placenta, three subtypes of trophoblast cells, cytotrophoblasts (CTBs), extravillous trophoblasts (EVTs), and syncytiotrophoblasts (STBs), mediate critical functions essential for a successful pregnancy. CTBs constitute the stem/progenitor compartment and differentiate into STBs and EVTs within the floating and anchoring villi, respectively. STBs establish the maternal-fetal exchange interface and secrete human chorionic gonadotropin (hCG), a hormone vital for the maintenance of early pregnancy. EVTs anchor the maternal endometrium and invade the uterine tissue to remodel maternal cells, supporting implantation and progression of pregnancy. In this study, we used human trophoblast stem cells (hTSCs) as a model system and performed quantitative, label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) to profile the proteome and phosphoproteome in TSC stem state (analogous to undifferentiated CTBs) and following their differentiation to STBs and EVTs. Through a multiomics approach, we integrated our proteomics data with global gene expression profiles to correlate cell-type specific gene and protein expression during human trophoblast development. We also identified global phosphoproteome and analyzed kinases that are specifically active in hTSC stem state, as well as in differentiated STBs and EVTs. We experimentally validated specific kinases, such as BUB1B, PAK6, PKYMT1, and TNIK, that are essential for maintaining the hTSC stem-state. Additionally, atypical protein kinase C isoforms PKCζ are essential for STB development, whereas PTK2B, SRC, TRIO, and LYN are important for EVT development. Our findings highlight key kinases uniquely required for specific stages of trophoblast development during human placentation and suggest that pharmacological inhibition of these kinases could negatively impact the placentation process during pregnancy.

Humans↗

PubMed2026

Genome-wide Identification and Expression Profiling Reveal the Galectin Gene Family Diversity and their Possible Role in Antibacterial Mucosal Immunity in Japanese Flounder (Paralichthys olivaceus).

Galectins are a family of proteins that bind specifically to β-galactosides. Their importance in innate immunity of mammals has been well-documented. However, the systematic identification and characterization of galectin gene family remain limited in teleost. In this study, we identified 13 galectin genes (lgals2, lgals2a, lgals2b, lgals3, lgals3a, lgals3b, lgals4, lgals8, lgals8a, lgals9, grp, grp-b, grp-c) from Paralichthys olivaceus genome and analyzed their tissue expressions and expressions in response to Gram-negative and Gram-positive bacterial infections in mucosal tissues (gills, intestine and skin). The P. olivaceus galections were classified into three distinct types based on carbohydrate recognition domains (CRDs). Phylogenetic and syntenic analyses revealed that these galectins are closely related to their counterparts in turbot and zebrafish. Moreover, the transcripts of the 13 galectins were widespread across all tested tissues of healthy fish and regulated following challenge with Vibrio anguillarum or Streptococcus iniae in mucosal tissues, indicating their involvement in P. olivaceus immune response to bacterial infections. The lgals2a was significantly upregulated in the three mucosal tissues by either bacterial infection, whereas lgals9 and grp were basically downregulated in these tissues by either infection. On the other hand, the lgals3b and lgals4 exhibited a bacteria-specific responsive expression as they were upregulated by V. anguillarum whereas remained stable upon S. iniae infection in the gills. We also observed a positive correlation between expression level and bacterial load for the upregulated galectin genes and a negative correlation for the downregulated galectin genes. These results suggest a functional divergence among galectin members in mucosal immunity against bacterial infection in P. olivaceus.

Animals↗

PubMed2026

Stage-Independent Real-Time Subtype Classification and Comprehensive Biopsy Profiling of Urothelial Carcinomas by the Lund Taxonomy System.

Bladder cancer is a heterogeneous malignancy with diverse clinical outcomes, and conventional pathological assessment alone is insufficient to capture its underlying biology. Gene expression profiling can stratify tumors into molecular subtypes with prognostic and predictive potential, but the reliability of transcriptomic classification and its clinical utility remains to be established. The translational/observational UROSCANSEQ study (ISRCTN15459149) prospectively evaluates RNA-based Lund Taxonomy (LundTax) molecular subtype classification in a clinical setting. Among 784 consecutive biopsies collected between 2018 and 2022, RNA sequencing was successful for 90% of all biopsies, encompassing 662 bladder cancer patients with a stage distribution of 48% Ta, 27% T1, 24% ≥T2, and 1% CIS. We demonstrate that the LundTax subtype classification algorithm, applied to individual samples, accurately identifies cancer cell phenotypes with characteristic gene and protein expression patterns in a manner robust to RNA quality, data preprocessing strategies, and batch effects, supporting its clinical feasibility across both non-muscle-invasive and muscle-invasive disease. We further extend the LundTax framework by incorporating single-sample molecular risk scores reflecting tumor grade, proliferation, and progression risk, as well as tumor microenvironment signatures. Both risk scores and overall immune and stromal content in biopsies were significantly associated with an increased risk of clinical progression in noninvasive disease. In a separate analysis of the relative cellular composition of the tumor microenvironment, however, only the fraction of natural killer cells remained significant. Together, the expanded LundTax system provides a comprehensive molecular portrait of individual tumor biopsies. By explicitly separating cancer cell-intrinsic phenotypes, prognostic indexes, and microenvironmental signals, the framework minimizes biological confounding and establishes a strong foundation for future studies evaluating clinical outcomes and treatment responses.

Humans↗

PubMed2025

Structural properties of short-chain carboxylic acids and alcohols relate to the molecular and physiological response of Salmonella enterica in an acidic environment.

Short-chain carboxylic acids (SCCA) and short-chain alcohols (SCALC) are naturally occurring antimicrobials that contribute to the biopreservation of food fermentations. This study investigated the effect of structurally different SCCA/SCALC with two-carbon (acetic acid; phenylacetic acid; 2-phenylethanol), three-carbon (propionic acid; 3-phenylpropionic acid; 3-phenylpropanol), and three-carbon chain with an additional hydroxyl group (lactic acid; 3-phenyllactic acid; 1-phenylpropanol) on the fitness, metabolic activity and gene expression of the pathogen Salmonella enterica at pH 4.5. SCCA inhibited Salmonella at lower concentrations than SCALC with the exception of lactic acid, which was partly consumed. The presence of a phenyl group enhanced antimicrobial activity. SCCA but not SCALC increased the lag phase of S. enterica, and in general, acetate was formed when cell growth was reduced by 20% suggesting a negative impact on bacteria fitness. Principal component analysis and hierarchical clustering indicated distinct gene expression profiles of S. enterica in response to SCCA or SCALC. In the presence of certain SCCA/SCALC, Salmonella activated pathways related to cellular pH control, and 1,2-propanediol, propionic acid and ethanolamine metabolism that involved the formation of metabolosomes. Genes related to flagellar assembly were less expressed and mobility was lower in the presence of lactic and 3-phenyllactic acid compared to controls suggesting a compound-specific response. KEY POINTS: • Differences in response among structurally different SCCA/SCALC at acidic condition. • SCCA/SCALC stress interfered with cell growth and metabolism of acetic and propionic acid. • Lactic acid prolonged the lag phase and reduced motility of Salmonella.

Salmonella enterica↗

PubMed2024

Analysis of different expression RNA binding protein genes in mouse microglia cell from the brains of mice 72 h after subarachnoid hemorrhage or sham operation.

BACKGROUND: The prognosis of brain injury caused by subarachnoid hemorrhage (SAH) is poor. Previous studies showed that abnormal function of RBPs might be involved in brain injury, neuroinflammation and further affect microglia homeostasis. However, no studies have systematically analyzed the genome-wide abnormal expression of RBPs genes in microglia during SAH. METHODS: RNA-seq data of microglia from the SAH mouse group (SAH) and control sham-operated mouse group (sham) were downloaded from the GEO database in GSE167957, including four samples from the sham group and four samples from the SAH group for subsequent analysis.Utilizing GO and KEGG functional enrichment analyses, we conducted a comprehensive study of differentially expressed genes (DEGs), alternative splicing patterns, and co-expression networks to gain deeper insights into the differential expression of RNA-binding proteins (RBPs) and differential alternative splicing events (ASEs) between the SAH (subarachnoid hemorrhage) and sham groups. This analysis aimed to elucidate the potential mechanisms underlying the aberrant expression of RBPs in microglia during brain injury caused by SAH. RESULTS: ASEs and co-expression analyses of differentially expressed RBPs and differential ASEs were carried out in microglia in terms of gene expression. GO and KEGG functional enrichment analysis showed that aberrantly expressed RBPs such as Mcm7, Mtdh, SRSF3, and Hnrnpa2b1 may affect and regulate downstream Csnk1d, Uckl1 and other protein phosphorylation-related genes by alterative splicing. CONCLUSION: RBPs were aberrantly expressed in microglia during the development of brain injury secondary to SAH, regulating alterative splicing of downstream genes and influencing the progression of SAH brain injury in this study. This implies that RBPs are important for the identification of new therapeutic targets for brain injury after SAH.

Animals↗

PubMed2026

The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases.

The increasing availability of genomic and transcriptomic sequencing has uncovered diverse genomic alterations and distinct gene expression profiles driving hematologic diseases, yet a data integration and sharing platform dedicated to hematology remains lacking. We developed the American Society of Hematology (ASH) HematOmics Program (ASHOP; ashop.hematology.org), a resource for exploring somatic alterations and gene fusions, transcriptomic results, and clinical data from 5960 patients spanning B-cell precursor and T-cell acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndromes, and chronic lymphocytic leukemia. Users can explore genomic alteration landscapes and comutation patterns via lollipop and matrix plots and analyze significantly altered genes in user-defined subcohorts. Transcriptomes can be explored through interactive uniform manifold approximation and projections, clustering, differential expression, and pathway enrichment. Genomic, transcriptomic features, and clinical outcomes can be correlated in a user-driven manner or combined to precisely define study cohorts. We illustrate the following 4 use cases of ASHOP: (1) stratification of DUX4-rearranged B-cell leukemias into Early/Multipotent and Committed subgroups with distinct outcomes, (2) characterization of HOXA/HOXB expression patterns in acute myeloid leukemias, (3) correlating mutational burden with mismatch repair deficiency and mutational signatures, and (4) investigation of TP53 alteration landscape. ASHOP is an open-access resource to inform genomic and transcriptomic data interpretation for hematologic malignancies and will expand to support additional diseases and data modalities from the ASH community.

Humans↗

PubMed2026

IL-33 Drives Inflammatory Changes and Extracellular Trap Formation in Eosinophils Involving Oxidised LDL and Complement Pathways.

BACKGROUND: IL-33 levels are elevated in the airways of patients with eosinophilic diseases, and IL-33 receptor expression on eosinophils is upregulated in type 2-high environments. However, the role of IL-33 in the regulation of human eosinophils remains unclear. OBJECTIVE: To elucidate the inflammatory effects of IL-33 on the cellular function of human eosinophils. METHODS: Blood eosinophils were stimulated with IL-33, TNF-α, oxidised low-density lipoprotein (oxLDL) and complement fragments (C3a and C5a). Multi-omics analyses, including transcriptomics and proteomics, were performed. Extracellular trap formation (ETosis) was assessed by SYTOX nucleic acid staining and was visualised by immunofluorescence and transmission electron microscopy. RESULTS: Multi-omics analyses revealed an IL-33- and TNF-α-induced inflammatory gene signature characterised by the upregulation of cell surface markers (oxLDL receptor 1, CD22, CD4 and ICAM-1) and inflammatory mediators (C3, CCL3/4 and IL1A/B). CD22 upregulation was specific to IL-33 stimulation. Eosinophils derived from nasal polyps exhibited a gene expression profile similar to that of IL-33-stimulated eosinophils. Functional assays demonstrated that oxLDL and complement fragments differentially prolonged eosinophil survival and altered the expression of adhesion molecules. OxLDL- and complement fragment-induced gene signatures were partly detected in eosinophils derived from nasal polyps. Furthermore, IL-33 triggered ETosis via NADPH oxidase, mitogen-activated protein kinase and phosphoinositide 3-kinase pathways. CONCLUSIONS: IL-33, in conjunction with oxLDL and the complement cascade, induces inflammatory changes in eosinophils, promoting an ETosis-prone phenotype. These pathways represent potential therapeutic targets in refractory eosinophilic diseases.

Humans↗

PubMed2025

Unravelling the transcriptomic characteristics of bronchoalveolar lavage in post-covid pulmonary fibrosis.

BACKGROUND: Post-Covid Pulmonary Fibrosis (PCPF) has emerged as a significant global issue associated with a poor quality of life and significant morbidity. Currently, our understanding of the molecular pathways of PCPF is limited. Hence, in this study, we performed whole transcriptome sequencing of the RNA isolated from the bronchoalveolar lavage (BAL) samples of PCPF and compared it with idiopathic pulmonary fibrosis (IPF) and non-ILD (Interstitial Lung Disease) control to understand the gene expression profile and associated pathways. METHODS: BAL samples from PCPF (n = 3), IPF (n = 3), and non-ILD Control (n = 3) (individuals with apparent healthy lung without interstitial lung disease) groups were obtained and RNA were isolated for whole transcriptomic sequencing. Differentially Expressed Genes (DEGs) were determined followed by functional enrichment analysis and qPCR validation. RESULTS: A panel of differentially expressed genes were identified in bronchoalveolar lavage fluid cells (BALF) of PCPF as compare to control and IPF. Our analysis revealed dysregulated pathways associated with cell cycle regulation, immune responses, and neuroinflammatory processes. Real-time validation further supported these findings. The PPI network and module analysis shed light on potential biomarkers and underscore the complex interplay of molecular mechanisms in PCPF. The comparison of PCPF and IPF identified a significant downregulation of pathways that were more prominent in IPF. CONCLUSION: This investigation provides crucial insights into the molecular mechanism of PCPF and also outlines avenues for prospective research and the development of therapeutic approaches.

Humans↗

PubMed2025

Genome-wide DNA methylation and transcriptome sequencing analyses of lens tissue in an age-related mouse cataract model.

DNA methylation is known to be associated with cataracts. In this study, we used a mouse model and performed DNA methylation and transcriptome sequencing analyses to find epigenetic indicators for age-related cataracts (ARC). Anterior lens capsule membrane tissues from young and aged mice were analyzed by MethylRAD-seq to detect the genome-wide methylation of extracted DNA. The young and aged mice had 76,524 and 15,608 differentially methylated CCGG and CCWGG sites, respectively. The Pearson correlation analysis detected 109 and 33 differentially expressed genes (DEGs) with negative methylation at CCGG and CCWGG sites, respectively, in their promoter regions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses showed that DEGs with abnormal methylation at CCGG sites were primarily associated with protein kinase C signaling (Akap12, Capzb), protein threonine kinase activity (Dmpk, Mapkapk3), and calcium signaling pathway (Slc25a4, Cacna1f), whereas DEGs with abnormal methylation at CCWGG sites were associated with ribosomal protein S6 kinase activity (Rps6ka3). These genes were validated by pyrosequencing methylation analysis. The results showed that the ARC group (aged mice) had lower Dmpk and Slc25a4 methylation levels and a higher Rps6ka3 methylation than the control group (young mice), which is consistent with the results of the joint analysis of differentially methylated and differentially expressed genes. In conclusion, we confirmed the genome-wide DNA methylation pattern and gene expression profile of ARC based on the mouse cataract model with aged mice. The identified methylation molecular markers have great potential for application in the future diagnosis and treatment of ARC.

Animals↗

PubMed2026

CeLLTra: aligning cell names with gene expression via a pathway-informed transformer.

MOTIVATION: Single-cell RNA sequencing (scRNA-Seq) technology enables detailed exploration of gene expression at the individual cell level, crucial for annotating cell types and understanding cellular diversity. Traditional methods for cell type annotation often rely on marker genes and manual labeling, posing challenges due to low data quality and incomplete reference datasets. RESULTS: We developed CeLLTra, a novel contrastive learning framework that leverages a Transformer-based model integrating biological pathway information to group genes into super tokens, effectively capturing comprehensive gene expression from scRNA-Seq data. By combining this pathway-informed Transformer with a pretrained domain-specific language model, CeLLTra accurately aligns cell-type annotations with gene expression profiles. Evaluations on a large-scale human scRNA-Seq dataset showed that CeLLTra significantly outperformed state-of-the-art methods in supervised and zero-shot cell-type prediction. Additionally, CeLLTra generalized well to external datasets, improving clustering performance and enabling better characterization of cancerous cell states in tumor-infiltrating myeloid cells from non-small cell lung cancer patients. AVAILABILITY AND IMPLEMENTATION: CeLLTra is freely available on GitHub (https://github.com/WJZheng-group/CeLLTra) and Zenodo (https://doi.org/10.5281/zenodo.17666735). The datasets underlying this article are the following: GSE201333 and GSE127465. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.

Humans↗

PubMed2026

Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.

BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.

Golgi apparatus↗

PubMed2024

A Strong Dysregulated Myeloid Component in the Epigenetic Landscape of Systemic Sclerosis: An Integrated DNA Methylome and Transcriptome Analysis.

OBJECTIVE: Nongenetic factors influence systemic sclerosis (SSc) pathogenesis, underscoring epigenetics as a relevant contributor to the disease. We aimed to unravel DNA methylation abnormalities associated with SSc through an epigenome-wide association study. METHODS: We analyzed DNA methylation data from whole-blood samples in 179 patients with SSc and 241 unaffected individuals to identify differentially methylated positions (DMPs) with a false discovery rate (FDR) <0.05. These results were further integrated with RNA sequencing data from the same patients to assess their functional consequence. Additionally, we examined the impact of DNA methylation changes on transcription factors and analyzed the relationship between alterations of the methylation and gene expression profile and serum proteins levels. RESULTS: This analysis yielded 525 DMPs enriched in immune-related pathways, with leukocyte cell-cell adhesion being the most significant (FDR = 4.91 × 10-9), prioritizing integrins as they were exposed by integrating methylome and transcriptome data. Furthermore, through this integrative approach, we observed an enrichment of neutrophil-related pathways, highlighting this myeloid cell type as a relevant contributor in SSc pathogenesis. In addition, we uncovered novel profibrotic and proinflammatory mechanisms involved in the disease. Finally, the altered epigenetic and transcriptomic signature revealed an increased activity of CCAAT/enhancer-binding protein transcription factor family in SSc, which is crucial in the myeloid lineage development. CONCLUSION: Our findings uncover the impaired epigenetic regulation of the disease and its impact on gene expression, identifying new molecules for potential clinical applications and improving our understanding of SSc pathogenesis.

Humans↗

PubMed2025

The skeletal muscle of aged male mice exhibits sustained growth regulatory transcriptional profile following glucocorticoid exposure compared with young males.

Excess glucocorticoids induce skeletal muscle myopathy by changing gene expression. Advanced age augments glucocorticoid-mediated muscle phenotypes, yet the transcriptional responses underlying those augmented phenotypes are unclear. The purpose of this study was to define the glucocorticoid-responsive transcriptome in young and aged muscle following both acute and more prolonged glucocorticoid treatment. Young (4-mo-old) or aged (24-mo-old) male mice were administered either an acute injection of dexamethasone (DEX) or vehicle or daily DEX or vehicle injections for 7 days. Muscles were harvested 6.5 h after the final or only injection. The tibialis anterior (TA) was selected for RNA sequencing analysis as DEX treatment lowered TA mass specifically in aged males. In silico analyses identified enriched pathways and transcription factors predicted to regulate DEX-sensitive genes. Acute DEX altered similar numbers of genes in young (950) versus aged males (913), although aged males had greater magnitudes of fold change. After 7 days of DEX treatment, aged muscle exhibited more DEGs compared with acute exposure (1,196 vs. 913), whereas young muscle exhibited fewer DEGs than after acute exposure (599 vs. 950). In aged males, glucocorticoid-sensitive genes were consistently enriched for growth regulatory processes across both time points, a pattern that was not evident in young males. Despite those age-associated transcriptional differences, the transcription factors predicted to regulate the glucocorticoid-sensitive genes were similar in young and aged males. These data expand our understanding into how aging modifies the transcriptional response to excess glucocorticoids in skeletal muscle.NEW & NOTEWORTHY Glucocorticoids promote mass loss in certain muscles with advanced age but not at younger ages. In a muscle whose mass is lost in response to elevated glucocorticoids only in advanced age in males, we show that glucocorticoids initiate a unique and exaggerated transcriptional profile after both acute exposure to the hormone and after prolonged treatment that is consistent with muscle atrophy. These findings expand our understanding of the effect primary aging has on glucocorticoid-induced atrophy in males.

Animals↗

PubMed2026

Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines.

Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.

Humans↗

PubMed2026

Integrating machine learning and GWAS for variant prioritization in the INCIPE cohort highlights ABC transporter genes in chronic kidney disease.

INTRODUCTION: Chronic kidney disease (CKD) is a major public health challenge, affecting approximately 674 million people worldwide and representing one of the fastest-growing causes of mortality. Since CKD is frequently asymptomatic in its early stages, the identification of novel genetic biomarkers may improve early detection and risk stratification. Genome-Wide Association Studies (GWAS) have identified numerous genetic loci associated with CKD and related traits; however, their performance is often limited in small and imbalanced cohorts, where reduced statistical power increases both false-positive and false-negative findings. Machine learning (ML) approaches can complement conventional GWAS by prioritizing biologically relevant genetic signals from high-dimensional genomic data. METHODS: In this study, we implemented a nested ensemble (NCBC) model composed of an undersampler and a CatBoostClassifier (CBC) to prioritize candidate genetic variants associated with CKD in the INCIPE cohort. Prioritized variants were functionally annotated and evaluated through enrichment analyses, GTEx gene expression profiling, and protein-protein interaction network analyses. Genes identified by the CKDGen Consortium were analysed as an external reference set and used to validate the biological relevance of the prioritized results. RESULTS: The NCBC model outperformed conventional ML classifiers, achieving a ROC AUC score of 87.77%, compared to 50%-53% for the other evaluated models. Among the prioritized genes, 56.25% showed protein-protein interactions with genes previously reported by the CKDGen Consortium, whereas only 1.9% of randomly generated gene sets showed interactions. DISCUSSION: Our study demonstrates that the NCBC model improves the prioritization of biologically plausible candidate variants in a small and imbalanced CKD cohort. Functional analyses suggested ABC transporter-related genes, including ABCA13, ABCA4, and ABCC4 genes, as promising candidate for future validation, with ABCA4 showing substantial expression in kidney tissues. Overall, these findings support the integration of ML with GWAS to prioritize candidate genes and investigate the genetic architecture of complex diseases.

SNP prioritization↗