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PubMed2026

Plasma cell-CD8+ T cell co-enrichment distinguishes immunotherapy-responsive hepatocellular carcinoma subtypes.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterised by significant racial disparities in incidence and outcomes, yet whether these reflect distinct tumour biology or differential distribution of molecular subtypes among immunotherapy patients remains unclear. METHODS: We characterised molecular heterogeneity among 46 patients with HCC of differing background population from the NCI-CLARITY cohort receiving immune checkpoint inhibitor therapy, using transcriptomic and genomic profiling, with validation across multiple independent cohorts. RESULTS: Differential expression analysis comparing African American versus non-African American patients identified 126 genes, of which 55 demonstrated tumour-specific expression across independent validation cohorts with paired tumour-normal samples. Consensus clustering revealed two molecular subtypes with no significant race association, indicating these clusters capture tumour-intrinsic biology rather than ancestry. The genomic landscape showed minimal differences between subtypes. A prognostic signature derived from these expression profiles demonstrated significant risk stratification in the NCI-CLARITY cohort and TCGA-LIHC, but not in Asian cohorts, suggesting population-specific applicability. Immune deconvolution revealed that the two subtypes represent distinct immune microenvironments: one subtype exhibited markedly elevated plasma cell infiltration with strong plasma cell-CD8+T cell correlation suggesting coordinated adaptive immunity, along with elevated tertiary lymphoid structure signatures. The other subtype showed regulatory T cell-macrophage correlation and enrichment for immune-excluded phenotypes. The immune-enriched subtype trended towards higher immunotherapy response rates. CONCLUSIONS: Molecular heterogeneity in HCC reveals distinct tumour-immune ecosystems that transcend racial classification. Tumour immune heterogeneity in HCC reflects distinct molecular patterns, with immune hot tumours characterised by elevated tertiary lymphoid structure signatures and enriched plasma cell and CD8+T cells. These patterns may serve as prognostic biomarkers for immunotherapy patient stratification and demonstrate the value of diverse cohort representation in identifying clinically relevant therapeutic targets.

Gastrointestinal Cancer↗

PubMed2026

Machine Learning and Metabolomics to Characterize Warburg-Like Metabolic Subtypes in Human Retinal Endothelial Cells Exposed to Risk Factors Associated With Proliferative Diabetic Retinopathy.

PURPOSE: High glucose (HG), hypoxia (Hyp), and their combination are major risk factors for proliferative diabetic retinopathy (PDR). Although these conditions induce features of the Warburg-like metabolic reprogramming in human retinal endothelial cells (HRECs), it remains unclear whether they produce distinct metabolic and angiogenic subtypes. This study aimed to characterize the Warburg-like-associated metabolic heterogeneity induced by these PDR-related risk factors and evaluate the ability of supervised machine-learning models to distinguish these subtypes. METHODS: HRECs were cultured under normoglycemic, HG, Hyp (2% O2), and combined HG-Hyp conditions. Untargeted LC-MS/MS metabolomics quantified metabolites spanning carbohydrates, amino acids, nucleotides, and lipids. Principal component analysis (PCA) assessed overall metabolic variation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified metabolic pathways associated with angiogenesis. In vitro angiogenesis assays measured endothelial tube formation and branching. Nine supervised classifiers (decision tree, logistic regression, naïve Bayes, random forest, K-Nearest Neighbors, neural network, gradient boosting, AdaBoost, and Support Vector Machine) were trained on the highest-ranked metabolites selected by the Information Gain Ratio feature-ranking approach. Model performance was evaluated using 10-fold cross-validation, leave-one-out cross-validation (LOOCV), permutation testing, and a classifier stability analysis under biologically meaningful distributional shift using an independent chemically induced hypoxia model (CoCl2). RESULTS: PCA revealed partial separation of metabolic profiles across conditions, indicating different Warburg-like metabolic subtypes. The combined HG-Hyp condition exhibited enhanced angiogenic potential relative to either HG or Hyp alone. KEGG pathway enrichment analysis identified fatty acid biosynthesis and elongation among the most significantly enriched pathways in HRECs under combined HG-Hyp conditions, alongside amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, the pentose phosphate pathway, and glycolysis/gluconeogenesis. Supervised machine-learning classifiers distinguished these metabolic subtypes, with AdaBoost and gradient Boosting showing the most balanced, reproducible performance across 10-fold cross-validation, LOOCV, and permutation testing, and remaining the most reliable classifiers under domain-shift testing (area under the curve = 0.88, P = 0.0061). CONCLUSIONS: In this exploratory analysis, HG, Hyp, and their combination drive metabolically and functionally distinct subtypes of Warburg-like metabolic reprogramming in HRECs, with HG-Hyp in combination producing a highly angiogenic phenotype. Boosting-based ensemble classifiers provide a promising framework for detecting these subtypes even under domain-shift conditions, warranting validation in larger independent datasets. TRANSLATIONAL RELEVANCE: Integrating metabolomics with machine-learning classification offers a strategy to identify Warburg-like metabolic subtypes in retinal endothelial cells, providing insights into angiogenic mechanisms and guiding the development of targeted diagnostics or therapeutics for PDR.

Humans↗

PubMed2025

Coronary Artery Disease-Based Polygenic Risk Score in Early-Onset Acute Myocardial Infarction Subtypes.

BACKGROUND: The coronary artery disease-based polygenic risk score (PRS-CAD) estimates risk of acute myocardial infarction (AMI), but its performance across AMI subtypes in younger individuals, especially women, remains uncertain. OBJECTIVES: The authors assessed PRS-CAD's performance in AMI subtypes. METHODS: We included 2,079 AMI patients aged 18 to 55 years with a 2:1 female-to-male ratio from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young Acute Myocardial Infarction Patients) study and 3,761 controls from the MESA (Multi-Ethnic Study of Atherosclerosis) study. AMI subtypes were classified using the VIRGO taxonomy. We evaluated PRS-CAD's association with AMI subtypes using multinomial logistic regression and with 1-year outcomes in AMI subtypes using Cox regression. RESULTS: PRS-CAD was significantly associated with MI due to coronary artery disease (N = 1,876; OR: 1.82 per 1-SD increase; 95% CI: 1.67-1.97; P < 0.001) but not with MI with nonobstructive coronary artery disease (N = 188; OR: 1.13 per 1-SD increase; 95% CI: 0.96-1.34; P = 0.14). PRS-CAD's performance did not differ by sex. A 1-SD increase in PRS-CAD was associated with higher risk of 1-year hospitalization or death in patients with MI with nonobstructive coronary artery disease (HR: 1.50; 95% CI: 1.08-2.10; P = 0.02) but not in patients with MI due to coronary artery disease (HR: 0.98; 95% CI: 0.91-1.07; P = 0.67). CONCLUSIONS: PRS-CAD's association with AMI varied by subtype but not by sex in young adults, warranting caution in application.

acute myocardial infarction↗

PubMed2026

Pre-Antiretroviral Therapy Vertical HIV-1 Transmission Risk in Uganda Varies by Sex of Child and Maternal Viral Subtype.

We analyzed perinatal transmission in a pre-antiretroviral therapy Ugandan cohort by maternal human immunodeficiency virus type 1 subtype and infant sex in 131 mother-child pairs. Among all children, if the mother was infected with subtype A there was a nearly 3-fold increased risk of perinatal transmission compared with subtype D (risk ratio [RR], 2.96 [95% confidence interval (CI), 1.46-6.01]; P = .008). When stratifying infants by both sex and maternal subtype, significantly more female (56.3% [9 of 16]) than male (9.1% [1 of 11]) infants born to mothers with subtype A were infected (RR, 6.19 [95% CI, .91-42.12]; P = .02). In contrast, among infants born to mothers with subtype D, transmission rates were comparable across sex (RR, 1.59 [95% CI, .57-4.41]; P = .39).

Humans↗

PubMed2026

The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups.

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2314 transcriptomes (15 cohorts, age: 0.8-90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (early T cell precursor [ETP]-like) definition and resolved the LMO2 γδ-like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene-expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine-learning classifier based on ALLCatchR, our B-cell acute lymphoblastic leukemia (B-ALL) classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n = 203). Testing the classifier on a second hold-out data set (n = 265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene-expression cluster markedly enriched (P < 0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts and accounted for 8.9% of adults and 39.5% of patients aged >50 years. We extended ALLCatchR into ALLCatchR2, a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts.

Journal Article↗

PubMed2026

Subtype-specific clinical significance of RRM1 and RRM2 expression in non-small cell lung cancer: a TCGA-based analysis.

BACKGROUND: Non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), exhibits significant molecular heterogeneity. Ribonucleotide reductase (RNR), composed of RRM1 and RRM2, is essential for DNA synthesis and repair, but its subtype-specific clinical significance in NSCLC remains unclear. OBJECTIVE: To investigate the clinical and prognostic significance of RRM1 and RRM2 expression in NSCLC, with a focus on subtype-specific differences between LUAD and LUSC. METHODS: We analyzed RNA expression and clinical data from 980 NSCLC patients in The Cancer Genome Atlas (TCGA). Associations with clinicopathologic characteristics, overall survival, and oncogenic driver alterations were assessed. RESULTS: In LUAD, high RRM2 expression was significantly associated with advanced pathologic stage (p = 0.004), nodal involvement (p = 0.005), higher T stage (p = 0.030), and gender (p = 0.046). In LUSC, RRM2 was associated with age (p = 0.008), pathologic stage (p = 0.006), and N stage (p = 0.001). RRM1 showed no significant associations with stage-related parameters in either subtype. Correlation analyses revealed modest associations between RRM1 and multiple oncogenic drivers, whereas RRM2 showed stronger subtype-specific correlations, particularly with KRAS/BRAF in LUAD and CDKN2A/SOX2 in LUSC. Kaplan-Meier analysis demonstrated that high expression of both RRM1 and RRM2 was associated with poorer overall survival in LUAD, but not in LUSC. However, neither marker remained significant after adjustment for clinicopathological variables in multivariate analysis. CONCLUSION: RRM2 is associated with tumor progression in both NSCLC subtypes, while the prognostic associations of RRM1 and RRM2 are confined to LUAD. Although neither marker demonstrated independent prognostic significance in multivariate analysis, the findings support subtype-dependent roles of RNR components and highlight the potential biological and therapeutic relevance of nucleotide metabolism pathways in LUAD.

Carcinoma, Non-Small-Cell Lung↗

PubMed1979

Preparation of sera for subtyping of influenza A viruses by immunofluorescence.

The conditions for preparation of type-specific and subtype-specific influenza A virus reagents to be used in the immunofluorescence technique have been evaluated. Type A-specific antibodies were prepared by passing an antivirion hyperimmune serum through an immunoadsorbent column containing antigens from disrupted virions of a different influenza A virus subtype. The type-specific antibodies were recovered from the immunoadsorbent by desorption with 3 M NaI. For subtype determination, antisera against the various hemagglutinins were used. Such sera could be prepared by removal of irrelevant influenza A virus antibodies from sera directed against purified virions and isolated peplomers, respectively. This was performed by passing the antisera through immunoadsorbent columns containing antigens from disrupted virions of appropriate strains. However, attempts to obtain a hemagglutin-specific antiserum from a serum directed against allantoic fluid virus suspension failed with this procedure. Antisera obtained after immunization with purified hemagglutinin were also elaborated. These sera were shown to be superior for subtyping of influenza A virus infections by immunofluorescence, but could not a priori be regarded as subtype-specific. The usefulness of subtype-specific sera has been demonstrated on clinical specimens for rapid virus diagnosis.

Animals↗

PubMed2026

Clinical outcomes and genomic features of uncommon EGFR exon 19 deletion subtypes in osimertinib-treated non-small cell lung cancer.

BACKGROUND: Epidermal growth factor receptor (EGFR) exon 19 deletion subtypes may be associated with differential survival outcomes following EGFR-tyrosine kinase inhibitor treatment. However, evidence remains scarce, particularly regarding osimertinib, and the underlying biological mechanisms are poorly understood. We aimed to compare survival outcomes among EGFR exon 19 deletion subtypes in patients with non-small cell lung cancer (NSCLC) treated with osimertinib. METHODS: In this multicenter retrospective study, patients with NSCLC were stratified according to exon 19 deletion subtypes. Whole-exome sequencing data from the American Association for Cancer Research Genomics Evidence Neoplasia Information Exchange registry and Memorial Sloan Kettering Clinicogenomic Harmonized Oncologic Real-World Dataset were analyzed to investigate co-occurring genomic alterations. RESULTS: Overall, 111 patients with advanced EGFR exon 19 deletion-positive NSCLC were analyzed and 86.5% received osimertinib as first-line therapy. Patients with non-E746_A750del (n = 25) had shorter progression-free survival (PFS) than those with E746_A750del (n = 86) (median: 14.3 vs. 20.6 months; p < 0.05). Among non-E746_A750del subtypes, L747_A750delinsP (n = 4) had a particularly poor prognosis, with significantly worse survival than those with E746_A750del (median PFS: 3.5 vs. 20.6 months; p < 0.001, and median overall survival: 11.8 vs. 48.5 months; p < 0.001). In public database analyses, non-E746_A750del had a higher rate of RBM10 co-mutations, whereas L747_A750delinsP was characterized by frequent CDKN2A/B homozygous deletions and MYC amplifications. CONCLUSIONS: Non-E746_A750del was associated with poorer outcomes, with L747_A750delinsP potentially being a high-risk subtype. Differences in co-occurring genomic alterations may contribute to the prognostic heterogeneity among exon 19 deletion subtypes.

Humans↗

PubMed1975

Subtypes of hepatitis B antigen among patients and symptomless carriers.

A study of the distribution of subtypes ad and ay among sera from hepatitis B antigen-positive subjects in North West England and North Wales revealed a marked contrast between symptomless carriers among whom ad predominated and patients with acute hepatitis the majority of whom were ay. Those with hepatitis associated with drug addiction or other forms of "needle transmission" were almost all of subtype ay. On the other hand in cases of "sporadic" hepatitis without evidence of parenteral exposure subtypes ad and ay are about equally distributed. These findings are similar to those reported from other countries in Northern Europe and North America. Although geographical and social factors clearly affect the distribution of the two subtypes it is suggested that the virus of subtype ay may be more readily transmitted than subtype ad by parenteral routes involving small amounts of blood.

Acute Disease↗

PubMed2025

HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.

MOTIVATION: Accurate tumor subtype diagnosis is crucial for precision oncology, yet current methodologies face significant challenges. These include balancing model accuracy with interpretability and the high costs of generating multi-omics data in clinical settings. Moreover, there is a lack of validated models capable of classifying hierarchical tumor subtypes across a comprehensive pan-cancer cohort. RESULTS: We present a graph neural network, HallmarkGraph, the first biologically informed model developed to classify hierarchical tumor subtypes in human cancer. Inspired by cancer hallmarks, the model's architecture integrates transcriptome profiles and gene regulatory interactions to perform multi-label classification. We evaluate the model on a comprehensive pan-cancer cohort comprising 11 476 samples from 26 primary cancers with 405 subtypes up to eight levels. The model demonstrates exceptional performance, achieving 5-fold cross-validation accuracy between 85% and 99% for tumor subtypes labeled with increasing details of genomic information. It also shows good generalizability on a validation dataset of 887 samples, assessed using three metrics that consider tumor subtypes at individual, combined, and sample levels. Benchmarking and ablation experiments show that hallmark-based embeddings slightly influence model performance, while the integrated multilayer perceptron plays a significant role in determining classifier accuracy. Additionally, we use the SHAP method to link cancer hallmarks with genes, identifying key features that influence model decisions. Our findings present a biologically informed machine learning framework capable of tracking tumor transcriptomic trajectories and distinguishing inter- and intra-tumor heterogeneity in pan-cancer. This approach holds promise for enhancing cancer diagnostics. AVAILABILITY AND IMPLEMENTATION: HallmarkGraph is accessible at https://github.com/laixn/HallmarkGraph.

Humans↗

PubMed2025

Pyramidal neurons proportionately alter the identity and survival of specific cortical interneuron subtypes.

The mammalian cerebral cortex comprises a complex neuronal network that maintains a precise balance between excitatory pyramidal neurons and inhibitory interneurons. Accumulating evidence indicates that specific interneuron subtypes form stereotyped microcircuits with distinct pyramidal neuron classes. Here we show that pyramidal neurons play an active role in this process by promoting the survival and terminal differentiation of their associated interneuron subtypes. In wild-type cortex, interneuron subtype abundance mirrors the prevalence of their pyramidal neuron partners. In Fezf2 mutants, which lack layer 5b pyramidal neurons and are expanded in layer 6 intratelencephalic neurons, corresponding subtype-specific shifts occur through two distinct mechanisms: somatostatin interneurons adjust their programmed cell death, whereas parvalbumin interneurons switch their subtype identity. Silencing neuronal activity or blocking vesicular release in L5b pyramidal neurons revealed that their communication with interneurons does not require voltage-gated synaptic activity and engages both tetanus toxin-sensitive and -insensitive pathways. Moreover, a targeted bioinformatic screen for ligand-receptor pairs displaying subtype-specific expression and reduced expression of pyramidal neuron-derived ligand in Fezf2 mutants identified candidate secreted factors and adhesion molecules. These findings reveal distinct, pyramidal neuron-driven mechanisms for sculpting interneuron diversity and integrating them into local cortical circuits.

Journal Article↗

PubMed2025

Phenotypic and Genetic Associations Between Cardiovascular Disease Subtypes and Alzheimer's Disease.

BACKGROUND: Cardiovascular disease (CVD) and Alzheimer's disease (AD) are major public health concerns that share overlapping risk factors and potential mechanistic pathways. While vascular contributions to cognitive decline are well-documented, the specific relationships between AD and different CVD subtypes remain poorly understood. METHODS: We examined associations between AD and 11 CVD subtypes using logistic regression models in two large biobanks: the UK Biobank (n = 502,133) and the All of Us Research Program (n = 287,011). Models were adjusted for demographic, lifestyle, and clinical covariates. We also explored genetic overlap between AD and CVD traits through colocalization of significant single nucleotide polymorphisms (SNPs) (p < 5×10-8) using genome-wide association study (GWAS) data. RESULTS: Most CVD subtypes were significantly associated with AD in both cohorts. Hypotension had the strongest and most consistent association, followed by hypertension and cerebral infarction. Acute myocardial infarction was the only subtype not significantly linked to AD. Genetic analyses revealed shared loci between AD and CVD-related traits, particularly in regions near APOE, MAPT, and genes influencing myocardial structure and vascular function. CONCLUSIONS: This study identifies subtype-specific CVD associations with AD across two diverse cohorts and highlights shared genetic architecture underlying heart-brain interactions. These findings underscore the importance of vascular health in AD risk and suggest that certain CVD subtypes, especially hypotension, may play underrecognized roles in cognitive decline.

Alzheimer’s disease↗

PubMed2026

Incidence patterns and genetic validation of primary glaucoma subtypes among 1 million adults in China and the UK.

BACKGROUND/AIMS: Primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) are distinct diseases, yet many glaucoma cases in population-based datasets lack subtype specification. We assessed incidence patterns of glaucoma subtypes in China and the UK and used genetic evidence to infer the likely subtype composition of cases recorded as unspecified glaucoma. METHODS: Incident primary glaucoma was identified from linked inpatient records in the prospective China Kadoorie Biobank (CKB; n=512 504) and UK Biobank (UKB; n=492 329) studies. Cohort-specific phenotyping algorithms defined POAG, PACG and unspecified glaucoma. Adjusted incidence rates were estimated by direct standardisation. To support subtype inference, polygenic risk scores (PRSs) were constructed using ancestry-specific genome-wide association studies, including a new East Asian PACG meta-analysis, and tested for association with glaucoma phenotypes using multivariable logistic regression. RESULTS: Over 12 years of follow-up, 1658 primary glaucoma cases were identified in CKB and 7643 in UKB. Most (>68%) cases lacked subtype specification. Incidence increased with age and was twofold higher among women for PACG in both cohorts and for unspecified glaucoma in CKB. In UKB, POAG incidence was fivefold higher among Black than White participants, with a similar but attenuated pattern for unspecified glaucoma. PRS analyses indicated that unspecified glaucoma closely aligned with PACG in CKB but was more heterogeneous in UKB. CONCLUSION: Healthcare-recorded incidence patterns for POAG and PACG were consistent with established demographic risk factors, whereas unspecified glaucoma showed differences in subtype composition between populations. Integrating epidemiological and genetic evidence improves interpretation of glaucoma phenotypes when detailed clinical information is unavailable.

Epidemiology↗

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↗

PubMed1977

Subtyping of hepatitis B surface antigen and antibody by radioimmunoassay.

The hepatitis B surface antigen (HBSAg) has been shown to possess distinct subtypes adw, ayw, adr, and ayr). A commercially available solid phase radioimmunoassay for antibody to HBSAg (Ausab, Abbott Laboratories North Chicago, Ill.) has been modified to detect the subtypes of HBSAg as well as the subtype-specific anti-HBS reactivities to detect the subtypes of HBSAg as well as the subtype-specific anti-HBS reactivities (anti-d, anti-y, and anti-w). This method has the advantages of general availability, ease of performance, and increased sensitivity over conventional subtyping methods of agar gel diffusion and counterelectrophoresis.

Agar↗

PubMed2026

Circadian-rhythm-based dynamics of the secretome in molecular subtypes of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) lacks a reliable diagnostic biomarker, largely due to its asymptomatic onset and frequent late-stage detection, resulting in a poor 5-year survival rate. Identifying biomarkers for timely diagnosis is critical. Cancer cells display distinct protein regulation changes that drive disease hallmarks, and characterizing these across PDAC subtypes may provide insights into disease progression. RESEARCH DESIGN AND METHODS: In this study, a label-free quantitative (LFQ) proteomics approach using mass spectrometry (MS) was employed to profile circadian rhythm-regulated proteins in the secretome of PDAC cell lines. RESULTS: LFQ analysis revealed rhythmic protein regulation patterns, reflecting temporal control of biological processes in PDAC. Upregulated pathways included signal transduction, glycolysis, angiogenesis, and protein synthesis, indicating enhanced metabolic and proliferative activity. Downregulated immune pathways suggested potential immune modulation. Comparative analysis revealed subtype-specific patterns: the quasi-mesenchymal subtype exhibited higher levels of metabolic and extracellular matrix (ECM) remodeling proteins, while the classical subtype showed higher levels of ECM-degrading proteins, consistent with known phenotypic differences. CONCLUSION: These findings highlight rhythmically regulated proteins as potential subtype-specific markers in PDAC and provide a basis for future validation studies. Mass spectrometry proteomics data are available via the ProteomeXchange Consortium (PRIDE: PXD054693).

Humans↗

PubMed2026

Shared genetic architecture between ADHD and intelligence varies across ADHD subtypes.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition frequently accompanied by cognitive difficulties. Although previous genetic studies have demonstrated substantial overlap between ADHD and intelligence, most have treated ADHD as a single phenotype. However, whether this shared genetic architecture differs across ADHD subtypes remains unclear. METHODS: We conducted a genome-wide cross-trait analysis integrating large-scale genome-wide association study (GWAS) datasets of overall ADHD, its subtypes-childhood ADHD, persistent ADHD, and late-diagnosed ADHD-and intelligence (total N > 300,000). Genome-wide genetic correlations, polygenic overlap, local genetic correlations, and variant-level associations between ADHD phenotypes and intelligence were evaluated to characterize their shared genetic architecture. Shared variants were identified through cross-trait enrichment analyses and subsequently mapped to genes for functional annotation and gene-set enrichment. Bidirectional associations were evaluated using two-sample Mendelian randomization with sensitivity analyses. Additional GWAS datasets were used to validate the robustness of shared loci by assessing the consistency of effect directions. RESULTS: All ADHD phenotypes showed significant negative genetic correlations with intelligence (rg ranging from -0.3442 to -0.4205). Despite these modest genome-wide correlations, cross-trait analyses revealed substantial genetic overlap, including polygenic overlap, local genetic correlations, and variant-level associations. We identified 184 loci jointly associated with ADHD traits and intelligence, including 64 novel loci, whereas no shared loci were detected for persistent ADHD under the current analysis. Functional annotation revealed biologically distinct enrichment patterns across subtypes: childhood ADHD loci were linked to early neurodevelopmental processes, while late-diagnosed ADHD loci were enriched in synapse-related and neuronal signaling pathways. Mendelian randomization analyses suggested bidirectional associations, with stronger evidence supporting a directional association from intelligence to ADHD risk. Furthermore, these shared loci showed largely consistent effect directions across additional GWAS datasets, providing support for the robustness of the findings. CONCLUSIONS: The shared genetic architecture between ADHD and intelligence varies across ADHD subtypes, highlighting distinct biological pathways underlying cognitive heterogeneity in ADHD. These findings suggest that the relationship between ADHD liability and general cognitive ability is not uniform across ADHD subtypes and may inform future research on risk stratification and early identification in child and adolescent psychiatry.

Humans↗

PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗