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PubMed2026

Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.

AIMS: To preliminarily characterize metabolic molecular subtypes of cerebral thromboemboli and evaluate their clinical significance in anterior circulation acute ischemic stroke due to large vessel occlusion (AIS-LVO). METHODS: Untargeted metabolomics was performed on thromboemboli retrieved from 36 patients with anterior circulation AIS-LVO using ultra-performance coupled liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Unsupervised hierarchical clustering was employed to identify distinct metabolic molecular subtypes, and their associations with stroke etiology, radiographic severity, and functional outcomes were analyzed. RESULTS: Two distinct thrombus metabolic molecular subtypes (C1 and C2) were identified based on 12 metabolites significantly associated with both short-term (7-day ∆NIHSS) and long-term (90-day mRS) functional outcomes. The C1 subtype, predominantly cardioembolic, exhibited enhanced lipid metabolism, whereas the C2 subtype, primarily atherothrombotic, demonstrated increased folate metabolism. Patients with C1 thromboemboli presented more severe admission ischemic lesions (as indicated by ASPECTS) and experienced poorer short-term and long-term outcomes. A six-metabolite signature derived from LASSO regression was identified for exploratory discrimination of thrombus metabolic subtypes, etiological subtypes, and 90-day outcomes. CONCLUSION: This preliminary exploratory study identifies two metabolically distinct thrombus molecular subtypes with clinical implications in anterior circulation AIS-LVO, providing a novel basis for risk stratification and personalized secondary prevention and warrants further investigation.

Humans↗

PubMed2026

DIA proteomics of FFPE renal biopsies reveals two molecular subtypes of lupus nephritis and identifies APOL1 as candidate biomarker for stratification.

INTRODUCTION: Lupus nephritis (LN) exhibits substantial clinical and pathological heterogeneity. We aimed to define proteomics-based molecular subtypes of LN and identify candidate biomarkers for subtype discrimination. METHODS: We analysed formalin-fixed paraffin-embedded (FFPE) renal biopsy specimens from 292 patients with biopsy-proven LN from four tertiary hospitals using data-independent acquisition (DIA)-liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics. Molecular subtypes were identified by non-negative matrix factorisation. Differential proteins, functional enrichment, immune pathway activity, protein-protein interaction networks and subtype-associated clinical/pathological features were evaluated. Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were used to identify key subtype-related features and derive a protein panel distinguishing proliferative (class III/IV) from membranous (class V) LN. RESULTS: Two stable molecular subtypes were identified, with 1002 differential proteins between them. Subtype_2 was enriched for interferon-related innate immunity, complement activation, phagocytosis-endocytosis-lysosome pathways and ribosome biogenesis/RNA metabolism, whereas Subtype_1 was characterised by keratinisation and epithelial structural remodelling. Subtype_2 was associated with higher serum creatinine, lower estimated glomerular filtration rate and higher chronicity index. APOL1 showed discriminatory value between subtypes, and serum ELISA demonstrated a consistent pattern with FFPE proteomic findings. A five-protein LASSO panel achieved an area under the curve of approximately 0.76 for distinguishing class III/IV from class V LN. CONCLUSION: DIA-based proteomic profiling of FFPE renal biopsies identifies biologically and clinically relevant LN molecular subtypes and may support tissue-informed classification and risk stratification.

Humans↗

PubMed2026

Differential Effectiveness of Adjuvant Endocrine Therapy According to Menopausal Status, Body Mass Index, and Molecular Subtype in Hormone Receptor-Positive Breast Cancer.

The effectiveness of adjuvant endocrine therapy for hormone receptor-positive (HR+) breast cancer (BC) varies according to menopausal status, body mass index (BMI), and tumor biology. We evaluated the association between selective estrogen receptor modulators (SERMs), aromatase inhibitors (AIs), and BC-specific mortality according to menopausal status, BMI, and molecular subtype in a nationwide Korean cohort. We analyzed data from 31,030 patients with HR+ BC who were registered in the Korean Breast Cancer Society Registry, diagnosed between 2000 and 2008, and followed through 2013. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for BC-specific mortality after adjusting for demographic and clinical factors. Of the 31,030 patients, 19,634 received SERM therapy, and 3,354 received AI therapy. SERM use was associated with lower BC-specific mortality in premenopausal women (HR, 0.75; 95% CI, 0.63-0.91), whereas AI therapy was more strongly associated with lower BC-specific mortality among postmenopausal women (HR, 0.76; 95% CI, 0.61-0.94). Lower BC-specific mortality was observed among patients with a BMI ≥ 23 kg/m² who received SERM (HR, 0.84; 95% CI, 0.72-0.98) or AI therapy (HR, 0.78; 95% CI, 0.62-0.99). The strongest association with lower BC-specific mortality was observed in postmenopausal women with luminal B tumors (HR, 0.59; 95% CI, 0.42-0.83). The association between adjuvant endocrine therapy and BC-specific mortality differed according to menopausal status, BMI, and molecular subtype. These findings suggest that menopausal status, BMI, and molecular subtype are important considerations when evaluating endocrine treatment strategies.

Aromatase Inhibitors↗

PubMed2025

TCGA molecular subtypes in endometriosis-associated ovarian cancer: a systematic review and meta-analysis.

BACKGROUND: Endometriosis-associated ovarian cancer (EAOC) mainly includes endometrioid ovarian cancer (ENOC) and clear cell ovarian cancer (CCOC). The Cancer Genome Atlas (TCGA) revealed four molecular subtypes of endometrial cancer (EC) in 2013, which have been proven pivotal in the diagnostic, prognostic and therapeutic domains of EC. Existing evidence indicates that EC and EAOC molecular analysis have similar significance. This review aims to investigate the distribution, staging and prognostic characteristics of molecular subtypes in EAOC. METHODS: PubMed, Embase and Web of Science were systematically searched from January 2013 to December 2023 using predefined keywords. Patient characteristics, including stage and prognostic characteristics, were extracted from the selected studies. Data analysis was carried out using Stata 14MP. RESULTS: A total of 6 studies involving 1,133 patients with ENOC and 4 studies comprising 377 patients with CCOC were included. ENOC had a higher frequency of the POLE mutation (POLEmut) subtype (odds ratio (OR) = 2.29, 95% CI: 1.03-5.11, p = 0.043) and the mismatch repair deficient (MMRd) subtype (OR = 3.54, 95% CI: 2.05-6.11, p = 0.000) than CCOC; ENOC had a lower frequency of the no specific molecular profile (NSMP) subtype (OR = 0.55, 95% CI: 0.41-0.73, p = 0.000) and the p53 abnormal (p53abn) subtype (OR = 0.97, 95% CI: 0.67-1.42, p = 0.893). The hazard ratios (HR) of the p53abn subtype in ENOC were disease-free survival (DFS) (HR = 3.25, 95% CI: 1.46-7.21, p = 0.004) and progression-free survival (PFS) (HR = 4.11, 95% CI: 2.86-5.92, p = 0.000). The DFS of the p53abn subtype in CCOC was calculated (HR = 5.52, 95% CI: 3.43-8.90, p = 0.000). CONCLUSION: The TCGA subtypes of EC may exhibit similarities in prognosis between ENOC and CCOC.

Humans↗

PubMed2026

Multiomics Integration Identifies a Molecular Subtype of Intrahepatic Cholangiocarcinoma With Enhanced Benefit From Adjuvant Therapy.

Intrahepatic cholangiocarcinoma (iCCA) is a molecularly heterogeneous liver cancer with a poor prognosis. Improved stratification is needed to guide postoperative therapy. In this study, we applied integrative multiomics analysis to classify iCCA and identify biomarkers predictive of adjuvant treatment benefit. Using publicly available datasets (including whole exome sequencing, RNA sequencing, proteomics, and phosphoproteomics from FU-iCCA cohort and a transcriptomic cohort GSE244807), we defined 3 robust molecular subtypes of iCCA. These subtypes exhibited distinct genomic alterations, pathway activation, and immune microenvironments, with significant differences in overall survival (OS). Through protein-protein interaction network analysis and consensus feature selection using 10 clustering algorithms, we prioritized 8 marker genes distinguishing the subtypes. A Cox proportional-hazards model constructed from these markers stratified patients into high- and low-risk groups. High-risk iCCA, characterized by elevated expression of markers such as CLDN18, MUC1, and MUC5AC, had significantly worse OS in the absence of adjuvant therapy. Notably, in an independent validation of 174 patients with iCCA who underwent resection (single-center cohort), high expression of any of these 3 markers were associated with markedly prolonged OS in patients who received adjuvant chemotherapy or chemoembolization, compared with those who did not. In contrast, marker-negative patients showed no clear benefit from adjuvant therapy. In conclusion, our multiomics approach identified a high-risk, mucin-enriched subtype of iCCA. CLDN18, MUC1, and MUC5AC emerge as candidate predictive biomarkers for adjuvant chemotherapy benefit in iCCA, warranting prospective validation to improve personalized postoperative management.

Humans↗

PubMed2025

Deep Learning on Histologic Slides Accurately Predicts Consensus Molecular Subtypes and Spatial Heterogeneity in Colon Cancer.

Colon cancer (CC) is the third most prevalent cancer type. It is highly heterogeneous, particularly in terms of molecular profiles, which have both prognostic and predictive impacts on the treatment efficacy. However, CC treatment in adjuvant situations is currently guided solely by T and N staging. In this context, consensus molecular subtypes (CMSs) were introduced to stratify patients with CC based on molecular profiles. Recent studies have shown that CMS can be heterogeneous in CC, leading to a worse prognosis. This study focused on predicting CMS and its heterogeneity in CC using deep learning on digitized hematoxylin and eosin ± saffron-stained whole-slide images. Data and whole-slide images of 1996 patients from the PETACC-8, The Cancer Genome Atlas-COAD, and PRODIGE-13 cohorts were used. The model is trained to predict a 4-dimensional CMS vector, reflecting intratumor heterogeneity (ITH). It comprises a self-supervised model for embedding image patches into vectors and a weakly supervised model predicting CMS calls. Ground-truth CMS scores are obtained with the CMSclassifier package. Interpretability analyses are performed at the slide and patch levels. For homogeneous tumors, the model trained on PETACC-8 achieves 93.0% (±1.4%) macroaverage area under the curve in internal cross-validation and 94.4% macroaverage area under the curve in external validation over PRODIGE-13, whereas the The Cancer Genome Atlas-COAD model reaches 85.4% (±3.0%) in cross-validation and 92.4% over PRODIGE-13. The trained models also provide spatial distributions of CMS across tumor slides and associate specific histologic features with each CMS. Finally, the models are able to predict ITH. The results show that a deep learning model trained on routine histology slides is capable of providing an efficient and robust method for predicting CMS and characterizing a patient's ITH, paving the way for the routine consideration of CMS/ITH in clinical decision making in the adjuvant setting.

Humans↗

PubMed2026

RNF43 Mutations Are Associated With the Classical Molecular Subtype, Vigorous Antitumor Immune Responses, and Prolonged Survival in Pancreatic Adenocarcinoma.

RNF43 mutations were correlated with microsatellite status in colorectal cancer and with fewer and later recurrences in pancreatic ductal adenocarcinoma (PDAC). Here, we undertake a detailed assessment of RNF43 mutations in PDAC. A total of 313 PDACs (308 microsatellite stable [MSS] and 5 microsatellite-instable [MSI] cases) underwent next-generation sequencing (Oncomine Tumor Mutation Load assay; Thermo Fisher). Spatial analyses (NanoString) classified PDACs according to their transcriptomic and proteomic immune signaling. Fluorescent imaging was used to define spatial compartments (tumor: pancytokeratin+/CD45- and leukocytes: pancytokeratin-/CD45+). Each of 20 PDACs with RNF43 mutations (RNF43mut) and without RNF43 mutations (RNF43wt) underwent multiplex immunofluorescence analysis to determine immune status. A total of 153 PDACs (22 RNF43mut and 131 RNF43wt cases) underwent bulk RNA sequencing to assign into molecular subtypes. Overall, 24 RNF43 mutations were identified (22 MSS PDACs and 2 MSI PDACs). The incidence of RNF43 mutations in MSS PDACs (7.1%) was consistent with The Cancer Genome Atlas (6.7%). However, RNF43 mutations were more frequent among MSI PDACs (40%). Additionally, RNF43mut had differential frequencies of other mutations (including Wnt pathway genes), higher tumor mutational burden values (5.5 mut/mb vs 1.67 mut/mb; P < .01), and significantly longer overall survival (47 vs 18 months; P < .0001) than RNF43wt. Moreover, RNF43mut exhibited significantly higher densities of CD8+ T lymphocytes, dendritic cells, and B lymphocytes (P < .001) and an upregulation of ITGAX, CD11c, CD8, and HLA-DR compared with RNF43wt. Patients with RNF43mut PDACs were more often of the classical molecular subtype (20/22, 90.9%). RNF43mut PDACs showed high tumor mutational burden values, suggesting increased neoantigen load coupled with an abundance of antigen-presenting immune cells and an upregulation of immune determinants promoting antigen presentation. All this contributes to stronger antitumor immune responses and improved clinical outcomes.

Humans↗

PubMed2026

Integrating molecular subtypes, genomics and functional dependencies to identify context-specific therapeutic vulnerabilities in small cell lung cancer.

Small cell lung cancer is one of the most aggressive malignancies, characterized by rapid tumor growth, early metastatic spread and extremely poor survival. Although most patients initially respond to platinum-based chemotherapy, relapse is almost inevitable and treatment options at recurrence remain limited. The recent introduction of immune checkpoint inhibitors has provided only modest clinical benefit, largely due to the fact that these tumors are immunologically cold. These limitations highlight the urgent need to better understand the molecular features of small cell lung cancer in order to identify more effective therapeutic strategies. In this review, we summarize current knowledge of the molecular landscape of small cell lung cancer, with particular emphasis on transcriptome-based classifications that have identified four major molecular subtypes defined by distinct transcriptional regulators and gene expression programs. We discuss how these classifications have improved the biological understanding of the disease and stimulated efforts to develop subtype-specific therapeutic strategies. At the same time, we highlight important limitations of this framework, including the remarkable transcriptional plasticity of tumor cells, which allows dynamic transitions between subtypes and may contribute to therapeutic resistance. To address these challenges, we examine additional molecular features that may represent more stable vulnerabilities, including recurrent genomic alterations, such as the widespread loss of tumor suppressor genes or oncogene amplifications through extrachromosomal DNA. We also discuss emerging approaches aimed at identifying novel context-specific cancer dependencies, including genome-scale functional screens in vitro and in vivo and genetic restraint analyses. Finally, we consider the growing potential of liquid biopsy strategies, which exploit the high level of circulating tumor DNA in patients with this disease to detect clinically relevant genomic alterations and monitor tumor evolution. Overall, this review highlights both the opportunities and challenges associated with molecular stratification in small cell lung cancer. The integration of transcriptional classifications with genomic and functional approaches may help identify more robust therapeutic vulnerabilities and guide the development of more effective treatments for this highly aggressive disease.

Cancer vulnerabilities↗

PubMed2026

Lymphangiogenesis-related gene signature-based risk model for prognostic assessment of cervical cancer: immune-metabolic characterization and molecular subtype analysis.

BACKGROUND: Lymphangiogenesis promotes tumor dissemination and may shape the immune contexture of cervical cancer, yet lymphangiogenesis-related prognostic stratification and its immunometabolic implications remain insufficiently defined in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). METHODS: TCGA-CESC transcriptomes and clinical data were obtained from UCSC Xena and integrated with normal cervix tissues from the Genotype-Tissue Expression Project after batch correction. Prognostic LYMRGs were first identified from the differentially expressed set using univariable Cox proportional hazards analysis. Candidate genes were then reduced using an L1-regularized Cox model (Least Absolute Shrinkage and Selection Operator), and the remaining markers were entered into a multivariable Cox regression to obtain the final coefficients and compute an individualized risk score. The model's prognostic value was further assessed in an independent Gene Expression Omnibus dataset. In addition, expression patterns of the signature genes were leveraged for molecular subtyping of TCGA samples via non-negative matrix factorization (NMF). Immune infiltration and immunotherapy-associated characteristics were interrogated through a multi-algorithm strategy (single-sample gene set enrichment analysis, CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore . Additional analyses included pathway enrichment (GSEA/GO/KEGG), drug sensitivity prediction (pRRophetic/CellMiner), and ceRNA network analysis. RESULTS: A six-gene LYMRG signature robustly stratified survival. High-risk patients had significantly worse overall survival in The Cancer Genome Atlas with AUCs of 0.819/0.801/0.801 at 1/3/5 years, and in GSE52903 (P = 0.001) with AUCs of 0.733/0.719/0.725. NMF identified two subtypes with distinct prognosis (P = 0.01) and divergent immune landscapes. Risk groups and subtypes exhibited consistent differences in immune infiltration, checkpoint expression, TIDE/IPS patterns, and pathway enrichment. Predicted chemosensitivity differed by risk group, and the ceRNA network suggested candidate upstream lncRNA regulators of the signature. CONCLUSION: A lymphangiogenesis-related six-gene model enables clinically meaningful prognostic stratification of CESC and links lymphangiogenesis programs to distinct tumor immune phenotypes and therapeutic vulnerabilities.

cancer↗

PubMed2026

Sodium Overload-Related Molecular Subtypes and a Four-Gene Prognostic Signature Predict Survival, Immune Landscape, and Therapeutic Response in Acute Myeloid Leukemia.

Sodium overload has recently emerged as a critical metabolic stressor involved in cancer progression; however, its molecular characteristics and clinical relevance in acute myeloid leukemia (AML) remain unexplored. RNA-seq data sets, clinical annotations, and mutational profiles of AML patients were annotations from The Cancer Genome Atlas and integrated with Genotype-Tissue Expression normal samples. Sodium overload-related genes (SORGs) were obtained from GeneCards. Differentially expressed SORGs (DESORGs) screened by applying the limma statistical model, followed by univariate Cox proportional hazards regression, consensus clustering, functional enrichment, immune infiltration analysis, and pathway evaluation. A prognostic signature was developed through least absolute shrinkage and selection operator regression followed by multivariate Cox modeling. The model's performance was further verified in two external GEO data sets (GSE71014 and GSE37642). Nomogram construction, subgroup analysis, tumor mutational burden (TMB) assessment, drug sensitivity prediction, transcription factor (TF) analysis, and competing endogenous RNA (ceRNA) network analyses were also performed. A total of 57 DESORGs were identified, and 2 sodium overload-related molecular subtypes exhibited distinct survival, immune infiltration, and inflammatory pathway activation. A robust four-gene signature (DOCK1, GABRE, HTR7, ACSM1) stratified patients into high- and low-risk categories with significantly different survival across training and validation cohorts. High-risk patients displayed increased immune infiltration, higher TMB, reduced sensitivity to multiple chemotherapeutic drugs, and inferior predicted response to PD-L1 blockade. TF and ceRNA networks revealed multilayered transcriptional and post-transcriptional regulation of the signature genes. This study identifies sodium overload-related molecular heterogeneity in AML and establishes a validated four-gene prognostic signature that integrates genomic, immunologic, and therapeutic features, offering potential utility for personalized risk assessment and treatment optimization.

Humans↗

PubMed2026

Identification of molecular subtypes in clear cell renal cell carcinoma based on chromatin regulators and tumor immune microenvironment profiling.

In the histological classification of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) accounts for the highest proportion and is the most common subtype. Despite advances in management, it continues to be associated with considerable incidence and mortality. Although surgery and systemic therapies are available, their efficacy is constrained by pronounced intratumoral heterogeneity and treatment resistance. Identifying robust biomarkers and clarifying the underlying biological mechanisms are therefore essential to improving diagnosis, risk stratification and therapeutic decision-making. In this work, we identified two ccRCC molecular subtypes displaying divergent chromatin regulator (CR) profiles and different clinical prognoses. Using the genes differentially expressed between these subgroups, we constructed a CR-related score (CRS) that effectively stratified patients according to survival. More analysis concluded that the low expression of CR was more linked with the immune-activated tumors, which encompassed the immune pathway enrichment, as well as the elevation of numerous immune cell subtypes. Moreover, elevated CRS was associated with improved immunotherapy responsiveness. Drug-sensitivity analyses nominated several candidate agents, and SMARCD3 knockdown in 786-O cells inhibited proliferation and migration and reduced sensitivity to masitinib. Collectively, these findings support the prognostic and therapeutic relevance of CR-related states in ccRCC and provide a framework for future experimental validation of chromatin-regulated tumor-immune interactions.

Humans↗

PubMed2026

Germline determinants of risk and molecular subtype in young-onset lung cancer.

Young-onset lung cancer is enriched for never-smoking and oncogene-driven tumors, yet its inherited genetic basis remains poorly defined. We performed germline whole-genome sequencing in 251 young-onset lung cancer cases (median age 37), which we jointly analyzed with never-smoking cases (n=196; median age 68) and cancer-free controls (n=1,883). We identified enrichments of rare deleterious coding variants across 55 cancer-related gene sets, including EGFR/ERBB2 signaling and genes implicated by prior lung cancer GWAS. Exome-wide analyses of rare coding variants affirmed TP53 as a penetrant lung cancer predisposition gene (odds ratio [OR]=36.1, p=1.02x10-7) and discovered two novel exome-wide significant tumor subtype-dependent associations: IREB2 in cases with fusion-driven tumors (p=1.39x10-6) and SMAD6 in fusion-negative tumors (p=2.05x10-6). Structural variants contributed distinct risk, with enrichment in constrained, lung-expressed genes (OR=5.79, p=5.8x10-5) and very large germline deletions being markedly enriched in cases with fusion-driven tumors. Polygenic risk scores for lung cancer were inversely correlated with rare variant burden, consistent with additive risk from rare and common variants. Collectively, these findings delineate a complex germline architecture underlying susceptibility and molecular subtype in young-onset lung cancer.

Journal Article↗

PubMed2026

Molecular subtyping of adrenocortical carcinoma reveals distinct subtypes with prognostic and therapeutic implications.

Adrenocortical carcinoma (ACC) is a rare but aggressive malignancy with poor survival and limited treatment options. To comprehensively characterize its molecular landscape and identify clinically relevant subtypes, we performed an integrated genomic analysis - including whole-exome sequencing, RNA sequencing, and copy number variation profiling - on 61 Chinese patients with ACC. We identified recurrent mutations in TP53 (25%), CTNNB1 (15%), ZNRF3 (10%), and MEN1 (8%). Unsupervised clustering of transcriptomic data revealed four distinct molecular subtypes: cortisol-driven (CD, 14%), immune-suppressed (IS, 40%), cell cycle-altered (CCA, 22%), and immunomodulatory (IM, 24%). The CD subtype exhibited steroidogenic pathway activation; the IS subtype showed T cell receptor downregulation and the worst disease-free survival; the CCA subtype was marked by chromosomal instability and cell cycle gene overexpression; and the IM subtype displayed enriched immune signaling and favorable outcomes. Copy number analysis further uncovered focal amplifications (e.g. TERT, CDK4) and HLA-II deletions. This study establishes a novel molecular classification of ACC, providing a framework for subtype-specific therapeutic strategies, such as CDK4/6 inhibition for CCA and immunotherapy for IM tumors, while highlighting the clinical challenges of immune-cold IS tumors.

Humans↗

PubMed2026

First identification and molecular subtyping of Blastocystis spp. in donkeys in Aksaray province, Türkiye.

Blastocystis is a common intestinal protist worldwide that can infect humans and animals. Although its molecular epidemiology in Türkiye is mostly focused primarily on humans and livestock, equids have received limited attention despite their traditional roles and frequent contact with humans and other animals in rural environments. This study aimed to determine the molecular prevalence and subtype (ST) distribution of Blastocystis spp. in donkeys in Aksaray Province, providing the first molecular data on donkeys in Türkiye. A total of 182 fresh fecal samples were collected from donkeys in nine villages within Aksaray province. Genomic DNA was extracted, and the small subunit ribosomal RNA (SSU rRNA) gene fragment of Blastocystis spp. was amplified via PCR analysis. Positive isolates were sequenced bidirectionally for identification and subsequent phylogenetic analysis of Blastocystis in donkeys. The overall molecular prevalence of Blastocystis spp. in donkeys was 4.4% (8/182). The infection rate was higher in young donkeys (under 3 years old; 8.33%) than in adults (3 years or older; 2.46%). However, this difference was not statistically significant. Sequence analysis of the positive PCR products revealed the presence of one known livestock-specific subtype, ST10. Phylogenetic analysis showed that the ST10 isolates characterized in this study clustered with isolates identified from different hosts. This study provides the first molecular data on Blastocystis presence in donkeys in Türkiye. The exclusive detection of ST10 suggests potential cross-species transmission, likely facilitated by the traditional practice of co-housing donkeys with other animals in confined barns. These findings indicate that donkeys may contribute to Blastocystis transmission, underscoring the importance of a "One Health" approach in future epidemiological surveillance.

Animals↗

PubMed2026

IGH::FENDRR and specific KRAS mutations define a novel B-ALL molecular subtype with poor chemotherapy response.

Large-scale sequencing efforts have defined up to 27 diagnostic subtypes in B-cell precursor acute lymphoblastic leukemia (B-ALL), leaving few samples unclassified. Extended genomic and transcriptomic profiling in routine diagnostics broadens the sample collection, enabling identification of novel subtypes. We analyzed 4857 patients with B-ALL from 3 cohorts and identified a group of 20 patients (age, 18-66 years; median, 34 years) characterized by a previously undescribed IGH::FENDRR rearrangement exclusive to this subtype (n = 17/20), KRAS p.A146T/V/P mutations (n = 17/20 vs n = 86/4857; P< .001), and distinct DNA methylation/gene expression profiles, including overexpression of the lncRNA FENDRR and the transcription factor FOXF1 (FOXF1/FENDRR) as well as JAK/STAT and RAS/MAPK signaling signatures. A gene expression machine learning classifier accurately identified FOXF1/FENDRR cases in 2 independent cohorts. Patients treated according to German Multicenter Study Group for Adult ALL (GMALL)/Group for Research in Adult ALL (GRAALL) protocols showed very poor chemotherapy response with 8 of 13 exhibiting induction failure or minimal residual disease (MRD) ≥10-3 and 8 of 12 remaining MRD positive after first consolidation/salvage. Intensification including blinatumomab (n = 10) and/or allogeneic stem cell transplantation (n = 12) resulted in ongoing molecular remission in 13 of 16 patients. FOXF1/FENDRR represents a novel B-ALL subtype which might benefit from early immunotherapeutic treatment or targeted interventions.

Humans↗

PubMed2026

Anatomical location defines distinct molecular subtypes of mucosal melanoma.

BACKGROUND: Mucosal melanoma (MM) is a rare and aggressive melanoma subtype that is understudied. The relationships between anatomical location, genomic alterations, stage at presentation, and survival remain incompletely characterized. METHODS: We carried out a retrospective single tertiary center study of 105 patients with histologically confirmed MM diagnosed between 1996 and 2025. Clinical and genomic data were analyzed to evaluate associations between anatomical location, mutational profile, stage at presentation, and survival outcomes, including melanoma-specific mortality. RESULTS: Lower-body tumors arising in the anus or genital areas were enriched for KIT and splicing factor 3 subunit B1 alterations, whereas NRAS mutations were distributed across anatomical regions. Among the two most common mutated genes, NRAS-mutant tumors were more likely than KIT-mutant tumors to present with metastatic disease [53% versus 19%; P = 0.046, odds ratio (OR) 4.7, 95% confidence interval (CI) 1.15-19.41]. Lower-body tumors were associated with worse overall survival (OS) than upper-body tumors (median 2.81 versus 8.40 years; OR = 0.05) and with higher melanoma-specific mortality. In multivariable analyses, upper-body location remained independently associated with improved OS (hazard ratio 0.14, 95% CI 0.05-0.36, P < 0.001). CONCLUSIONS: Anatomical location of MMs and genomic alterations define biologically and clinically distinct subtypes.

KIT mutation↗

PubMed2025

Development and validation of a novel risk stratification signature derived from migrasome and tumor microenvironment-related genes for molecular subtyping and improving clinical outcomes in head and neck squamous cell carcinoma.

BACKGROUND: The tumor microenvironment (TME) and migrasomes released by tumor cells significantly influence carcinogenesis and immune evasion. However, our understanding of the prognostic and therapeutic implications of migrasome and tumor microenvironment-related genes (mtmRGs) in head and neck squamous cell carcinoma (HNSCC) remains limited. METHODS: We explored the relationship between mtmRGs and HNSCC prognosis by utilizing The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Subsequently, we developed an innovative prognostic signature, and assessed its prognostic significance using the Kaplan-Meier method, time-dependent receiver operating characteristic (ROC), and Cox regression analyses. To explore the underlying mechanisms, we conducted gene set variation analysis (GSVA), gene set enrichment analysis (GESA), and immune infiltration analysis. A nomogram was developed to estimate the overall survival (OS) rates for HNSCC patients. Lastly, we chose P4HA1, which was part of the signature, for additional experimental validation in vitro and in vivo. RESULTS: The mtmRGs signature effectively classifies HNSCC patients into two distinct risk subgroups, with the high-risk cohort demonstrating significantly poorer OS. The risk score serves as an independent prognostic factor for HNSCC patients; those with lower risk scores are more likely to exhibit favorable responses to immunotherapy, particularly with CTLA4 inhibitors. Furthermore, a lower risk score is significantly correlated with the sensitivity of HNSCC patients to cyclophosphamide, gemcitabine, and axitinib. CONCLUSION: This study presents an innovative gene signature associated with mtmRGs, which may be utilized both for predicting survival and directing personalized chemotherapy and immunotherapy regiments for patients with HNSCC.

Humans↗

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↗