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PubMed2026

DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.

Paired spatial multi-omics provides a supervised basis for learning RNA-protein correspondence in situ, but predicting protein abundance from spatial transcriptomic data alone remains challenging across tissue contexts and protein panels. Here, we present DPAS-Graph, an adaptive relation-learning framework for spatial RNA-to-protein prediction. Rather than directly merging spatial proximity and transcriptomic similarity as fixed graph priors, DPAS-Graph represents them as two relation channels on a shared edge support and updates their contributions during representation learning for protein prediction. Its Niche-Coupled Field Encoder combines layer-wise edge-relation modeling, intra-branch relation refinement, and cross-branch residual correction to learn spot representations for protein abundance prediction. In a leave-one-dataset-out benchmark across seven paired spatial multi-omics datasets, DPAS-Graph achieved lower aggregate prediction errors and improved spot-level agreement of protein expression profiles, with gains mainly reflected in error-based metrics and PCC-Spot. Spatial autocorrelation and protein-derived domain agreement analyses were further used to characterize the spatial behavior of the predicted protein maps. When applied to external RNA-only spatial sections, DPAS-Graph generated qualitatively interpretable marker-level virtual protein maps, illustrating its use as a complementary tool for protein-level interpretation of transcriptomics-only spatial data.

RNA↗
PubMed2026

NextLongIso: a comprehensive Nextflow pipeline for multi-dimensional long-read RNA-seq analysis.

SUMMARY: Long-read RNA sequencing technologies, including Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), enable direct characterization of full-length transcripts and transcriptome complexity. However, analysis of long-read RNA-seq data remains fragmented across multiple tools, limiting the ability to obtain a unified view of transcript structure, expression, and regulatory variation in long-read transcriptomes. We present NextLongIso, a scalable and reproducible Nextflow pipeline that enables coordinated analysis of multiple layers of transcript regulation. Rather than focusing solely on transcript reconstruction, NextLongIso integrates transcript discovery with downstream regulatory analyses to jointly characterize alternative splicing, isoform switching, transcript boundary dynamics (including alternative promoters and polyadenylation), and transposable element-associated transcription from both PacBio and ONT datasets. By eliminating complex cross-tool data harmonization, this unified framework facilitates the transition from transcript identification to functional interpretation of transcriptomic variation. AVAILABILITY AND IMPLEMENTATION: NextLongIso is implemented in Nextflow and is freely available at github: https://github.com/YidanSunResearchLab/nf-LongIso.git and Zenodo: https://doi.org/10.5281/zenodo.21049837.

Software↗
PubMed2025

Deciphering the Impact of Temperature on Pleiotropic Consequences of RNA Polymerase Mutations.

Despite occurring in an essential molecule, mutations in RNA polymerase readily emerge and elicit complex pleiotropic effects across different levels of biological organization, which are all modulated by environment. We investigated the impact of temperature on the effects of six mutations on sequence, structure, transcriptome, and organismal traits. We found temperature altered the transcriptomic response and key organismal traits such as growth rate and biofilm formation in a genotype-specific manner. Critically, mechanistic insights into the possible drivers of mutational effects emerged only when examining the relationships between different levels of organization: location of mutations in the tertiary structure and distance to key interacting molecules partly explained the observed transcriptomic differences, which in turn drove the impact of mutations on organismal traits. While falling short of capturing the full complexity of the system, our findings underscore the benefits of integrating insights across multiple biological levels to understand the relationship between environment and mutational effects in molecules with extensive pleiotropic effects.

Mutation↗
PubMed2026

A transcription factor regulatory atlas for activity inference and perturbation prediction.

Inferring transcription factor (TF) activity from transcriptomes and predicting transcriptome-wide responses to TF perturbations remain challenging, in part because available TF-mRNA resources often face a trade-off between precision and coverage and typically lack signed regulatory information. Here, we present TFActProfiler, a TF-mRNA resource and computational framework that learns signed, quantitative TF-mRNA regulatory coefficients by integrating heterogeneous prior evidence (ChIP-based, motif-based, and curated TF-mRNA annotations) with large-scale bulk and single-cell RNA-seq atlases. TFActProfiler contains 2 606 176 signed TF-mRNA interactions and improves TF activity inference in TF knockdown benchmarks relative to widely used regulon resources while retaining broad TF and target coverage. In addition, because the same learned regulatory coefficients can be used to model downstream transcriptional effects, TFActProfiler enables prediction of transcriptome-wide gene expression responses to TF knockdown without training on task-matched perturbation data. When perturbation datasets are available, TFActProfiler can be further refined to achieve performance comparable to state-of-the-art machine-learning baselines. By providing a direction-aware representation of TF-mRNA regulation for both activity inference and perturbation-response modeling, TFActProfiler supports systematic dissection of gene regulatory programs across diverse cellular contexts.

Transcription Factors↗
PubMed2026

Genomic characterization of aggressiveness in pituitary neuroendocrine tumors.

BACKGROUND: Aggressive evolution of PitNETs is rare; metastatic spread is even more. Defining aggressiveness and malignancy is challenging, subsequently hard to predict, and to understand. The aim was to provide a molecular definition of aggressiveness using genomic approaches. METHODS: PitNETs from 206 patients were included. Associations between 9 clinicopathological features of aggressiveness and PitNETs' omics were explored. Omics included transcriptome, DNA methylation, chromosomal alterations, and mutations. Clonal tumor evolution was monitored in 7 patients. RESULTS: Among the 9 clinicopathological features of aggressiveness, only rapid progression, progression after radiotherapy, Ki67/MIB1 proliferation index ≥10%, temozolomide treatment, metastases, and specific death were associated with specific omics signatures, while tumour maximal diameter ≥40 mm, cavernous, and sphenoid invasion were not. The omic signatures associated with these features of aggressiveness overlapped but remained distinct between corticotroph and mammo-somato-thyrotroph lineages. For each lineage, a common signature of aggressiveness was identified, associating a proliferative transcriptome signature and DNA hypermethylation. Alterations in specific genes were associated with aggressive features, including a novel PitNET gene, LRP1B, and known cancer genes (TP53, CDKN2A), while USP8 and GNAS alterations were not. Integration of gene alterations with methylome and transcriptome signatures isolated a subset of molecularly aggressive PitNETs. Molecular signatures were stable during the course of the disease, despite evolution toward aggressiveness and potential clonal divergence. CONCLUSION: This systematic analysis of clinicopathological features of aggressiveness using an integrated multiomic approach establishes a histomolecular definition of aggressiveness in PitNETs. Prospective cohort studies are needed to validate these molecular signatures and establish their prognostic value.

Humans↗
PubMed2026

Genotype-dependent DNA methylation patterns are negatively associated with allelic variation rather than heat-induced gene expression in two contrasting potato genotypes.

Potato (Solanum tuberosum L.) is an important food crop that is sensitive to high temperatures, which cause major changes in the transcriptome and a reduction in yield. In several plant species, DNA methylation has been reported to influence gene expression, particularly under abiotic stress conditions. However, the role of DNA methylation in regulating gene expression in heat-tolerant and heat-sensitive potato genotypes is still poorly understood. In this study, we conducted genome-wide DNA methylome and transcriptome analyses of leaves from two contrasting potato cultivars, Annabelle (moderately heat-tolerant) and Camel (heat-sensitive), before and after heat stress (HS). Genome-wide differential methylation analysis revealed that most identified differentially methylated regions (DMRs) were constitutive, reflecting variation between cultivars rather than being induced by HS. While thousands of heat-responsive differentially expressed genes (DEGs) were identified, only a small fraction coincided with heat-induced DMRs. Despite substantial constitutive DNA methylation and transcriptome differences between the cultivars, we found no consistent association between DMRs and DEGs, indicating that DNA methylation does not play a widespread direct regulatory role in gene expression. Surprisingly, hypermethylated genomic regions were associated with lower alternative allele frequencies, whereas hypomethylated regions showed the opposite trend. These findings indicate that the potato DNA methylome is largely stable under HS and that constitutive DNA methylation variation contributes rather to genetic diversity than to the direct regulation of gene expression.

DNA Methylation↗
PubMed2026

Insights into glandular trichome biology from analysis of organ-specific gene expression programmes in cannabis, hop and tomato.

Glandular trichomes (GTs) are epidermal outgrowths in which diverse specialised (secondary) metabolites are synthesised and stored. Cannabis (Cannabis sativa L.) and its close relative hop (Humulus lupulus L.) have pharmaceutical and industrial significance due to the presence of these metabolites in their GTs. We examined the conservation or divergence of the specific transcriptional programmes underlying GT biology. To achieve this, we generated transcriptome atlases of trichomes, flower, leaf, stem and root for cannabis, hop and tomato. We found that 12.9, 10.1 and 16.8% of cannabis, hop and tomato genes, respectively, were expressed organ/tissue specifically across all organs/tissues. Transcription factors (TFs) on average accounted for 7.5% of the organ-specific transcriptome and likely regulate organ-specific functions. We also conducted weighted gene co-expression network analysis and gene regulatory network (GRN) analysis to identify key regulators of GT function across the species and validated our predictions by DNA affinity purification sequencing for a subset of the cannabis and tomato GT TFs. The GRNs specific to cannabis or hop GTs were enriched for TFs and target genes associated with specialised metabolism, reflecting their species-specific nature. Conversely, the shared GRN components (identified via orthology analysis) were involved in highly conserved processes, such as flavonoid biosynthesis, solute transport and metabolite storage. Together, these GRNs and the associated transcriptome atlases are valuable resources to improve our knowledge of GT function and organ-specific genome regulation.

Solanum lycopersicum↗
PubMed2025

Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infection.

At this stage in the COVID-19 pandemic, most infections are "breakthrough" infections that occur in individuals with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure. To refine long-term vaccine strategies against emerging variants, we examined both innate and adaptive immunity in breakthrough infections. We performed single-cell transcriptomic, proteomic, and functional profiling of primary and breakthrough infections to compare immune responses from unvaccinated and vaccinated individuals during the SARS-CoV-2 Delta wave. Breakthrough infections were characterized by a less activated transcriptomic profile in monocytes and natural killer cells, with induction of pathways limiting monocyte migratory potential and natural killer cell proliferation. Furthermore, we observed a female-specific increase in transcriptomic and proteomic activation of multiple innate immune cell subsets during breakthrough infections. These insights suggest that prior SARS-CoV-2 vaccination prevents overactivation of innate immune responses during breakthrough infections with discernible sex-specific patterns and underscore the potential of harnessing vaccines in mitigating pathologic immune responses resulting from overactivation.

Immunity, Innate↗
PubMed2026

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗
PubMed2026

Proteogenomic features define subtypes of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a biologically heterogeneous B-cell malignancy. Although genomics and transcriptomics have delineated parts of the MCL disease spectrum, proteomics remains largely unexplored. Here, we conducted a comprehensive proteogenomic analysis integrating genomics, transcriptomics, and proteomics on peripheral blood samples from 27 patients with MCL and 4 healthy donors to investigate the translational and posttranslational dimensions of MCL. Our study identified 1296 downregulated and 468 upregulated proteins in MCL cells. The splicing pathways were significantly upregulated at both the mRNA and protein levels, suggesting a critical role for aberrant RNA splicing in MCL pathogenesis. Integration of proteomic data with genetic aberrations revealed immunoglobulin heavy chain variable mutational status and CCND1 mutation are associated with distinctive transcriptomic and proteomic profiles, which correspond to significant differences in clinical outcomes. A multiomics molecular stratification model incorporating proteomic data showed superior predictive power for patient survival compared with single-omics models (concordance index, 0.83 vs 0.74). This study provides, to our knowledge, the first comprehensive proteogenomic profile of MCL, offering novel insights into its molecular mechanisms and clinical behavior. The identification of molecular subtypes and prognostic protein signatures underscores the potential of proteomics to guide precision medicine strategies for MCL.

Humans↗
PubMed2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗
PubMed2024

Identification and analysis of key genes related to efferocytosis in colorectal cancer.

UNLABELLED: The impact of efferocytosis-related genes (ERGs) on the diagnosis of colorectal cancer (CRC) remains unclear. In this study, efferocytosis-associated biomarkers for the diagnosis of CRC were identified by integrating data from transcriptome sequencing and public databases. Finally, the expression of biomarkers was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Our study may provide a reference for CRC diagnosis. BACKGROUND: It has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC). METHODS: In our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS: There were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187. CONCLUSION: Taken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.

Humans↗
PubMed2024

ASXL1 truncating variants in BOS and myeloid leukemia drive shared disruption of Wnt-signaling pathways but have differential isoform usage of RUNX3.

BACKGROUND: Rare variants in epigenes (a.k.a. chromatin modifiers), a class of genes that control epigenetic regulation, are commonly identified in both pediatric neurodevelopmental syndromes and as somatic variants in cancer. However, little is known about the extent of the shared disruption of signaling pathways by the same epigene across different diseases. To address this, we study an epigene, Additional Sex Combs-like 1 (ASXL1), where truncating heterozygous variants cause Bohring-Opitz syndrome (BOS, OMIM #605039), a germline neurodevelopmental disorder, while somatic variants are driver events in acute myeloid leukemia (AML). No BOS patients have been reported to have AML. METHODS: This study explores common pathways dysregulated by ASXL1 variants in patients with BOS and AML. We analyzed whole blood transcriptomic and DNA methylation data from patients with BOS and AML with ASXL1-variant (AML-ASXL1) and examined differential exon usage and cell proportions. RESULTS: Our analyses identified common molecular signatures between BOS and AML-ASXL1 and highlighted key biomarkers, including VANGL2, GRIK5 and GREM2, that are dysregulated across samples with ASXL1 variants, regardless of disease type. Notably, our data revealed significant de-repression of posterior homeobox A (HOXA) genes and upregulation of Wnt-signaling and hematopoietic regulator HOXB4. While we discovered many shared epigenetic and transcriptomic features, we also identified differential splice isoforms in RUNX3 where the long isoform, p46, is preferentially expressed in BOS, while the shorter p44 isoform is expressed in AML-ASXL1. CONCLUSION: Our findings highlight the strong effects of ASXL1 variants that supersede cell-type and even disease states. This is the first direct comparison of transcriptomic and methylation profiles driven by pathogenic variants in a chromatin modifier gene in distinct diseases. Similar to RASopathies, in which pathogenic variants in many genes lead to overlapping phenotypes that can be treated by inhibiting a common pathway, our data identifies common pathways for ASXL1 variants that can be targeted for both disease states. Comparative approaches of high-penetrance genetic variants across cell types and disease states can identify targetable pathways to treat multiple diseases. Finally, our work highlights the connections of epigenes, such as ASXL1, to an underlying stem-cell state in both early development and in malignancy.

Humans↗
PubMed2026

Malignant epithelial states drive immune dysfunction in ampulla of Vater carcinoma.

BACKGROUND: Ampulla of Vater (AoV) carcinoma is a rare malignancy arising at the junction of intestinal and pancreatobiliary epithelium. Its heterogeneous clinical behavior and histological diversity have hindered therapeutic advances, and the cellular basis of this heterogeneity remains unclear. We aimed to construct a single-cell transcriptomic atlas of AoV carcinoma, with a focus on identifying epithelial subtypes and their interactions with the tumor microenvironment (TME). METHODS: We performed single-cell RNA sequencing on eight primary AoV tumors and four matched normal tissues. Comprehensive clustering and transcriptomic analyses identified cell-type composition, epithelial heterogeneity, and tumor-immune interactions. Findings were validated using deconvolution of bulk RNA-seq data from 62 AoV carcinoma patients. Results Malignant epithelial cells were categorized into four distinct subtypes: Int-Wnt, PB-KRAS, Int-Hypoxia, and Cycling stage. PB-KRAS cells exhibited stem-like transcriptional programs and high genomic instability. Deconvolution analysis of bulk RNA-seq data from the independent AoV cohort revealed that enrichment of the PB-KRAS subtype correlated with tumor recurrence and poor survival. Our immune profiling analysis discovered a significant association between PB-KRAS subtype and GZMK+ CD8+ T cells, which are in a pre-dysfunctional state, alongside SPP1+ macrophages exhibiting immunosuppressive traits. Spatial transcriptome data further supports the immunosuppressive natures of TME around PB-KRAS subtype malignant epithelial cells in AoV carcinoma. CONCLUSIONS: Our study presents a single-cell atlas of AoV carcinoma, highlighting the molecular diversity of malignant epithelium and its association with the immune microenvironment. The PB-KRAS subtype emerges as a stem-like, immunosuppressive tumor state associated with poor prognosis, providing insights for future therapeutic targeting.

Ampulla of Vater carcinoma↗
PubMed2025

O-GlcNAcylation of nuclear proteins in the mouse liver exhibit daily oscillations that are influenced by meal timing.

The liver circadian clock and hepatic transcriptome are highly responsive to metabolic signals generated from feeding-fasting rhythm. Previous studies have identified a number of nutrient-sensitive signaling pathways that could interpret metabolic input to regulate rhythmic hepatic biology. Here, we investigated the role of O-GlcNAcylation, a nutrient-sensitive post-translational modification (PTM) in mediating metabolic regulation of rhythmic biology in the liver. We observe daily oscillation of global nuclear protein O-GlcNAcylation in the liver of mice subjected to night-restricted feeding (NRF) using label-free global O-GlcNAc proteomics. Additional site-specific O-GlcNAc analysis by tandem mass tag mass spectrometry further supports temporal differences in O-GlcNAcylation by revealing day-night differences. Proteins involved in gene expression are enriched among rhythmically O-GlcNAcylated proteins, suggesting rhythmic O-GlcNAcylation may directly regulate the hepatic transcriptome. We show that rhythmic O-GlcNAcylation can also indirectly modulate nuclear proteins by interacting with phosphorylation. Several proteins harboring O-GlcNAcylation-phosphorylation interplay motif exhibit rhythmic O-GlcNAcylation and phosphorylation. Specifically, we show that O-GlcNAcylation occurs at a phospho-degron of a key circadian transcriptional activator, circadian locomotor output cycles kaput (CLOCK), thus regulating its stability and transcriptional output. Finally, we report that day-restricted feeding (DRF) in the nocturnal mouse significantly alters O-GlcNAcylation pattern. Whereas global O-GlcNAcylation analysis indicates dampening of global O-GlcNAcylation rhythm in mice fed under DRF, site-specific analysis reveals differential responses of O-GlcNAc sites when timing of food intake is altered. Notably, a substantial number of O-GlcNAcylation sites exhibit inverted day-night profiles when mice are subjected to DRF. This suggests the dysregulation of daily nuclear protein O-GlcNAcylation rhythm may contribute to the disruption in liver transcriptome previously observed in DRF condition. In summary, our results provide new mechanistic insights into metabolic regulation of hepatic transcriptional regulators via interplay between O-GlcNAcylation and phosphorylation and shed light on the deleterious effects of improper mealtimes.

Animals↗
PubMed2024

RNAseq analysis of oocyte maturation from the germinal vesicle stage to metaphase II in pig and human.

During maturation oocytes at the germinal vesicle (GV) stage progress to metaphase II (MII). However, during in vitro maturation a proportion often fail to progress. To understand these processes, we employed RNA sequencing to examine the transcriptome profile of these three groups of oocytes from the pig. We compared our findings with similar public oocyte data from humans. The transcriptomes in oocytes that failed to progress was similar to those that did. We found in both species, the most upregulated genes in MII oocytes were associated with chromosome segregation and cell cycle processes, while the most down regulated genes were relevant to ribosomal and mitochondrial pathways. Moreover, those genes involved in chromosome segregation during GV to MII transition were conserved in pig and human. We also compared MII and GV oocyte transcriptomes at the isoform transcript level in both species. Several thousands of genes (including DTNBP1, MAPK1, RAB35, GOLGA7, ATP1A1 and ATP2B1) identified as not different in expression at a gene transcript level were found to have differences in isoform transcript levels. Many of these genes were involved in ATPase-dependent or GTPase-dependent intracellular transport in pig and human, respectively. In conclusion, our study suggests the failure to progress to MII in vitro may not be regulated at the level of the genome and that many genes are differentially regulated at the isoform level, particular those involved ATPase- or GTPase-dependent intracellular transport.

Humans↗
PubMed2026

Integrative Cross-platform Analysis of Kinase Inhibitor Effects on Statin-relevant Cardioprotective Pathways in Human Cardiomyocytes.

BACKGROUND/AIM: Kinase inhibitors (KIs) can cause cardiotoxicity through mechanisms overlapping with statin cardioprotective pathways, yet their effects on these pathways in cardiomyocytes remain uncertain. We evaluated six literature-defined statin-relevant gene sets using transcriptomic and proteomic data. MATERIALS AND METHODS: Pre-ranked gene set enrichment analysis was performed for 23 KIs in primary cardiac cells (GSE146096; n=319) and iPSC-derived cardiomyocytes (GSE217421; n=541), with cross-platform analysis of 21 KIs by shotgun proteomics (PXD014791; n=300). Pathway-specific concordance was assessed by Spearman correlation with Benjamini-Hochberg correction; protein scores were estimated after adjustment for cell line. RESULTS: KI effects were heterogeneous. The anti-fibrotic pathway showed nominal concordance across the two transcriptomic datasets (ρ=0.495, p=0.016, q=0.098; 91% direction concordance) and significant cell-line-adjusted transcriptomic-proteomic concordance (ρ=0.644, p=0.0016, q=0.0081). Nilotinib reproducibly upregulated NF-κB pathway genes [normalized enrichment score (NES)=+2.29 and +2.18 in discovery and validation], with targeted inter-gene-correlation-adjusted testing supporting higher NF-κB expression than under rosuvastatin (CAMERA p=3.54×10-8). No global cross-omics summary remained significant after harmonizing pathway universes and accounting for repeated pathways. CONCLUSION: KI effects on statin-relevant pathways were pathway-specific. Anti-fibrotic concordance and nilotinib-associated NF-κB upregulation are hypothesis-generating candidates for experimental validation.

Humans↗
PubMed2026

Epigenetic Profiling for Early Detection and Treatment Response Monitoring in Non-Small Cell Lung Cancer: Protocol for a Prospective Translational Biomarker Study.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide and continues to have poor survival outcomes, with most patients diagnosed at advanced stages of disease. In New Zealand, NSCLC contributes substantially to cancer inequities, with Māori communities experiencing disproportionately high incidence and mortality rates. Although low-dose computed tomography screening can improve early detection, major limitations remain, including false-positive findings, overdiagnosis, high infrastructure costs, and limited accessibility for rural and underserved populations. Liquid biopsy approaches using circulating tumor DNA (ctDNA), particularly DNA methylation profiling, have emerged as promising, minimally invasive strategies for improving cancer detection, treatment monitoring, and precision oncology. OBJECTIVE: This study aims to establish integrated genomic and epigenomic predictive and prognostic biomarkers using ctDNA, tumor tissue, and transcriptomic profiling to improve early detection, risk stratification, treatment selection and response prediction, and longitudinal monitoring, with particular emphasis on identifying molecular mechanisms associated with treatment resistance and disease progression. METHODS: This prospective observational translational biomarker study is being conducted through the University of Otago and associated respiratory and oncology services in New Zealand. The study will recruit participants with NSCLC (including squamous and nonsquamous subtypes), individuals referred to fast-track lung nodule assessment clinics, and nonmalignant respiratory controls. Serial peripheral blood sampling will be performed in selected participants at predefined clinical follow-up time points to evaluate treatment response and disease progression. The availability of formalin-fixed paraffin-embedded archival tissues will be recorded, but will not be mandatory for enrollment. Genome-scale DNA methylation profiling will be performed using cell-free reduced representation bisulfite sequencing (cfRRBS), while targeted genomic profiling and transcriptomic analyses will be conducted using targeted sequencing panels and RNA sequencing. Integrative bioinformatic analyses will be used to identify molecular biomarkers associated with early-stage disease, advanced disease, treatment response, and therapeutic resistance. RESULTS: Ethics approval for the study has been obtained from the New Zealand Health and Disability Ethics Committee (2022 EXP 12566). This study commenced in 2022, and recruitment and biospecimen collection are ongoing. The study aims to recruit approximately 450 participants, including patients with NSCLC, individuals referred through respiratory diagnostic pathways, and nonmalignant controls. As of July 31, 2026, 205 participants have been recruited, with recruitment continuing until the target sample size is reached. Molecular and data analyses are ongoing, with additional publications expected as the cohort matures. CONCLUSIONS: This study will generate one of the first integrated genomic, epigenomic, and transcriptomic liquid biopsy datasets for NSCLC in New Zealand. The findings are expected to support the development of sensitive, accessible, and equitable blood-based biomarkers for NSCLC detection and treatment monitoring while also contributing to improved precision oncology approaches and reducing NSCLC inequities among Māori populations.

Humans↗