SEARCH · PubMed Health

Results for “Copy Number Variation (CNV)”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 19 recordsLinked to original sources

PubMed2026

Comparative genomics reveals lineage-associated structural variation and diversification in a barley fungal pathogen.

Leaf rust, caused by Puccinia hordei, is a major barley disease worldwide. Despite repeated shifts in virulence, contrasting reproductive histories, and emerging fungicide insensitivity, the genomic basis of its diversification and adaptation remains poorly understood. In this study, we generated haplotype-resolved, chromosome-level genome assemblies for two isolates with contrasting virulence and analyzed 41 Australian isolates collected over 54 yr (1966-2020), integrating comparative and population genomics, mating-type gene phylogenies, chromosome-specific k-mer profiling, genome-wide copy-number variation (CNV) analysis, and gene-expression analysis. We identified a structurally dynamic chromosome characterized by repeat-associated rearrangements, structural variation, and lineage-associated CNV, representing the first evidence in a rust fungus of chromosome-scale structural diversification of this extent. Population analyses distinguished clonally expanded lineages from recombination-associated lineages, with mating-type gene phylogenies providing further support for lineage differentiation. More recently collected isolates showed increased duplication-associated variation, and CNV boundaries were associated with structural-variant breakpoints. We also identified lineage-associated amplification of Cyp51, with increased copy number associated with higher transcript abundance, supporting a potential role in fungicide adaptation. Overall, our findings highlight structural variation, contrasting reproductive histories, and lineage-associated CNV as important contributors to diversification in P. hordei, providing insights for future rust pathogen surveillance and management strategies.

Cyp51 gene↗

PubMed2026

Cross-Platform Concordance in DNA Methylation Based Classification of CNS Tumors.

DNA methylation profiling enables precise classification of pediatric central nervous system (CNS) tumors. Oxford Nanopore Technologies (ONT) offers same-day, single-sample methylation readouts, but its concordance with Illumina EPIC arrays in routine diagnostic tasks remains incompletely defined. We profiled 23 pediatric tumors (18 CNS, 5 non-CNS) by EPIC arrays and ONT. Methylation profiles from both platforms were classified with crossNN (brain model or pan-cancer model); ONT data were additionally classified with Rapid-CNS2 and Sturgeon. We compared (i) classifier agreement with integrated histology (w/o NGS) at family/class levels, (ii) pass-rate above platform-specific score cutoffs, (iii) cross-platform concordance of copy-number variation (CNV), and MGMT promoter methylation status. In CNS cases, ONT and EPIC methylation profiles demonstrated strong correlation, except for a single outlier (P2), which was excluded from further analysis. Comparative assessment of the two platforms showed that: (a) Molecular classification of CNS tumors using the crossNN classifier was consistent with histology (w/o NGS) at the family level in all cases. (b) Copy-number profiles showed high concordance between platforms. (c) MGMT promoter methylation status matched in 94% of cases (16/17). When comparing ONT-specific analysis pipelines using the ONT data, the Rapid-CNS2 pipeline yielded the most reliable class level assignments with 94% (16/17) concordance with the histopathological diagnosis, which marginally exceeded the crossNN and sturgeon classifiers. In non-CNS tumors, the pan-cancer model produced low-confidence outputs with poor agreement with histology (w/o NGS) (only 1/5 concordant), indicating limited readiness for these entities. In conclusion, ONT enables same-day, clinically reliable family-level CNS tumor classification with high concordance to arrays, while EPIC retains a modest class-level edge. A key limitation of ONT is its reliance on fresh-frozen DNA and on classifiers originally built around array-derived CpG sites, rather than on models developed natively from ONT data.

DNA methylation↗

PubMed2026

Exploring precision risk in pediatric vesicoureteral reflux: Innate immune gene variations and reflux outcomes in the RIVUR cohort.

INTRODUCTION: Children with vesicoureteral reflux (VUR) are at increased risk for morbidity from recurrent urinary tract infections (UTIs), yet the factors influencing spontaneous VUR resolution remain poorly defined. This study evaluates whether genetic variations in key urinary innate immune effectors (DEFA1A3, DMBT1, and RNASE7) influences VUR resolution and interacts with prophylaxis to alter clinical response. METHODS: We conducted a secondary analysis of 303 RIVUR participants with available DEFA1A3 and DMBT1 copy number variation (CNV) data and RNASE7 rs1263872 genotype. Primary outcomes were (1) VUR improvement (decrease in grade) and (2) VUR resolution at study exit. Multivariable logistic regression models included genotype, treatment, and their interactions, adjusting for age, sex, baseline grade (high vs low), laterality, bowel/bladder dysfunction, and any UTI. Internal validation used 2000-sample bootstrap with bias-corrected and accelerated confidence intervals and influence diagnostics. RESULTS: Clinical covariates did not significantly predict VUR improvement. Children with DEFA1A3 CNV >5 had higher odds of improvement (OR 2.36, 95% CI 1.12-4.96, p = 0.023), an effect that remained significant in bootstrap analyses. High-grade VUR was associated with lower odds of resolution (OR 0.34, 95% CI 0.12-0.94, p = 0.038). A significant interaction was observed between prophylaxis and high DMBT1 copy number for VUR resolution (interaction OR 2.99, 95% CI 1.11-8.04, p = 0.031); no interaction was seen for improvement. RNASE7 rs1263872 was not associated with either outcome. CONCLUSION: Innate immune gene variation may contribute to heterogeneity in VUR outcomes. High DEFA1A3 copy number was associated with reflux improvement and a DMBT1-prophylaxis interaction was associated with reflux resolution. The results of this study is hypothesis-generating and prompt further evaluation to assess whether a subset of children may experience structural benefit from prophylaxis or have a more favorable natural history based on their innate immune genotype.

Humans↗

PubMed2025

The CAP/ACMG CYCGH proficiency testing program: 10 years in review.

PURPOSE: The College of American Pathologists has offered proficiency testing (PT) for the detection of copy-number variations (CNV) in the constitutional setting (CYCGH) since 2008. We review and summarize data from the CYCGH PT program, including participant performance over time, changes made to the program, and ungraded challenges. METHODS: The PT challenges from 2011 through 2021 (22 total mailings) and changes to the program over time were reviewed. Laboratory enrollment and performance were assessed. RESULTS: Overall participation has increased over time, and laboratories have maintained a high level of proficiency. The major changes to the program have occurred twice during the time span examined. Reasons for challenges not meeting consensus were varied. The use of ungraded challenges was also discussed. CONCLUSION: The CYCGH PT program is challenging because it assesses both analytical performance and interpretation as a single analyte. The program has evolved over time to address the changes in the field of CNV detection. During this time, additional technologies with the ability to detect CNVs have emerged, and the possibility of developing a platform-agnostic CNV PT program is being explored.

Humans↗

PubMed2026

Methylome Profiling of Cartilage Tumors: A Promising New Diagnostic Tool?

DNA methylation and copy number variation (CNV) profiling has emerged as a promising tool for the classification of bone and soft tissue tumors. We evaluated its utility in cartilage tumors, where distinguishing low-grade from high-grade conventional central chondrosarcomas (CSs) and atypical cartilaginous tumors (ACTs) from enchondromas (ECs) is a frequent diagnostic challenge, particularly on biopsy material. We analyzed 214 chondrogenic tumors, including ECs, ACTs, conventional CSs, dedifferentiated chondrosarcomas (DDCSs), and clear cell CSs, and determined their IDH1/2 mutation status. Unsupervised dimensionality reduction of genome-wide DNA methylation patterns revealed 4 clusters among IDH-mutant (MUT) tumors (IDH-MUT-1: mostly ECs and ACTs and some high-grade CSs; IDH-MUT-2: predominantly high-grade CSs; IDH-MUT-3: largely DDCSs; and IDH-MUT-SB: distinct skull base group with a markedly different methylation pattern) and 2 clusters among IDH-wild-type (WT) tumors (IDH-WT-1 and IDH-WT-2: both primarily high-grade CSs, with IDH-WT-2 showing higher tumor grade and more extensive CNVs). Clear cell CSs formed a separate cluster. The amount of CNVs, including loss of CDKN2A, increased with tumor grade, reflecting increased genomic instability during chondrosarcoma progression. Supervised classifiers trained separately, both on methylation and CNV data, and distinguished low-grade and high-grade cartilaginous tumors with area under the curve values of 0.87 to 0.97 and 85% to 90% accuracy. Furthermore, we tested whether DDCSs can be distinguished from metastatic carcinomas and other high-grade sarcomas of the bone. Across 246 reference samples, a supervised classifier achieved 97.2% accuracy (area under the curve, 99.8%) and correctly identified 30 of 32 DDCSs (93.8%). These results indicate that DNA methylation and CNV data analysis provide a valuable tool for distinguishing most low- and high-grade CSs, with additional utility also in differentiating DDCS from morphologic mimics.

cartilaginous tumors↗

PubMed2026

Overexpression of a subset of long intergenic noncoding RNAs in uterine serous carcinoma predicts poor prognosis.

The evaluation and prediction of uterine serous carcinoma (USC), a type of endometrial cancer that is more severe than endometrioid adenocarcinoma, remain challenging. Long noncoding RNAs (lncRNAs) are frequently dysregulated in human cancers. This study assessed the expression patterns and prognostic values of long intergenic noncoding RNAs (lincRNAs) in USC. RNA sequencing, copy number variation (CNV), and clinical data from The Cancer Genome Atlas were used to investigate various lncRNAs in endometrial cancer. LincRNAs, a major subclass of lncRNAs, exhibit specific expression patterns modulated by CNVs and act as predictors of poor prognosis, survival, and recurrence in USC. Functional analyses were conducted to investigate the roles of lncRNAs in USC. Finally, the expression of these lincRNAs was verified in 32 pairs of USCs collected from the hospital over 3 years. A series of lincRNAs were found to be specifically expressed in USC compared with other lncRNAs and regulated by CNV. Moreover, these specific upregulated lincRNAs, particularly ENSG00000281406, ENSG00000226791, ENSG00000269903, and ENSG00000204277, demonstrated poor prognoses for survival and recurrence in USC. Functionally, our analysis showed that ENSG00000281406 positively correlated with the Wnt signaling pathway, whereas ENSG00000226791, ENSG00000269903, and ENSG00000204277 negatively correlated with the T-cell receptor signaling pathway. Importantly, we confirmed that ENSG00000204277 negatively correlated with CD8+ T-cell immune infiltration in USC. Our results highlight that these lincRNAs can serve as new biomarkers for the prognostic prediction of USC. In particular, ENSG00000204277 may be used as a therapeutic target for USC.

Humans↗

PubMed2026

A stratified urine-based molecular diagnostic and prognostic model for non-muscle-invasive bladder cancer management.

BACKGROUND: Non-muscle-invasive bladder cancer (NMIBC) is characterized by a high recurrence rate requiring lifelong cystoscopic surveillance. Existing urine-based molecular assays mainly rely on mutations or methylation, which fail to capture large-scale genomic instability. Copy number variation (CNV) profiling offers complementary information on tumor evolution and aggressiveness, but its application in urinary diagnosis remains limited. We aimed to integrate CNV and DNA methylation signals from urinary DNA to establish a noninvasive and biologically informed stratified diagnostic model for NMIBC recurrence surveillance and risk stratification. METHODS: Urine samples were prospectively collected from 91 patients (75 evaluable) between June 2021 and August 2023. Shallow whole-genome sequencing (sWGS) was used to detect CNVs at chromosomal arm and focal gene levels, while ONECUT2 promoter methylation was quantified by qPCR. Diagnostic and prognostic performance was evaluated by ROC analysis, Kaplan-Meier survival, and stratified recurrence assessment. RESULTS: We evaluated a stratified diagnostic model combining CNV and ONECUT2 methylation testing in a cohort of 79 patients. CNV analysis alone showed high specificity (0.923) for NMIBC diagnosis. A combined model, using CNV as an initial screen followed by ONECUT2 methylation testing in CNV-positive cases, achieved a sensitivity of 0.783, specificity of 0.981, and a negative predictive value (NPV) of 0.911. This approach reduced the number of required ONECUT2 tests by 35% and identified a high proportion of true-negative patients (98.1%), which may help reduce unnecessary cystoscopy procedures. The model also demonstrated significant prognostic value, with the molecularly defined high-risk group showing significantly shorter recurrence-free survival (RFS) than the low-risk group (median RFS: 4.33 months vs. not reached; p < 0.001). Additional, in patients with initially negative cystoscopy after urine sample collection, the model demonstrated a predictive accuracy of 0.922 for recurrence, with molecular positivity observed a median of 9.6 months prior to clinical diagnosis. CONCLUSIONS: Integrating CNV and DNA methylation profiling from urinary DNA provides a powerful and noninvasive molecular framework for NMIBC surveillance. By combining early epigenetic changes with genomic instability signals, this approach enhances recurrence risk assessment and enables earlier detection compared with conventional cystoscopy. It offers a practical route toward personalized and adaptive post-treatment monitoring of NMIBC. TRIAL REGISTRATION: NCT04994197.

Humans↗

PubMed2025

Novel genetic determinants contribute to hearing loss in a central European cohort with enlarged vestibular aqueduct.

BACKGROUND: The enlarged vestibular aqueduct (EVA) is the most commonly detected inner ear malformation. Biallelic pathogenic variants in the SLC26A4 gene, coding for the anion exchanger pendrin, are frequently involved in determining Pendred syndrome and nonsyndromic autosomal recessive hearing loss DFNB4 in EVA patients. In Caucasian cohorts, the genetic determinants of EVA remain unknown in approximately 50% of cases. We have recruited a cohort of 32 Austrian patients with hearing loss and EVA to define the prevalence and type of pathogenic sequence alterations in SLC26A4 and discover novel EVA-associated genes. METHODS: Sanger sequencing, single nucleotide polymorphism (SNP) assays, copy number variation (CNV) testing, and Exome Sequencing (ES) were employed for gene analysis. Cell-based functional and molecular assays were used to discriminate between gene variants with and without impact on protein function. RESULTS: SLC26A4 biallelic variants were detected in 5/32 patients (16%) and monoallelic variants in 5/32 patients (16%). The pathogenicity of the uncharacterized SLC26A4 protein variants was assigned or excluded based on their ion transport function and cellular abundance. The monoallelic or biallelic Caucasian EVA haplotype was detected in 7/32 (22%) patients, but its pathogenicity could not be confirmed. X-linked pathogenic variants in POU3F4 (2/32, 6%) and biallelic pathogenic variants in GJB2 (2/32, 6%) were also found. No CNV of SLC26A4 and STRC genes was detected. ES of eleven undiagnosed patients with bilateral EVA detected rare sequence variants in six EVA-unrelated genes (monoallelic variants in SCD5, REST, EDNRB, TJP2, TMC1, and two variants in CDH23) in five patients (5/11, 45%). Cell-based assays showed that the TJP2 variant leads to a mislocalized protein product forming dimers with the wild-type, supporting autosomal dominant pathogenicity. The genetic causes of hearing loss and EVA remained unidentified in (14/32) 44% of patients. CONCLUSIONS: The present investigation confirms the role of SLC26A4 in determining hearing loss with EVA, identifies novel genes in this pathophysiological context, highlights the importance of functional testing to exclude or assign pathogenicity of a given gene variant, proposes a possible diagnostic workflow, suggests a novel pathomechanism of disease for TJP2, and highlights voids of knowledge that deserve further investigation.

Humans↗

PubMed2026

A cfDNA fragmentomics classifier for noninvasive differentiation of benign and malignant renal masses.

Noninvasive differentiation of malignant and benign renal masses remains a major clinical challenge, particularly for radiologically indeterminate lesions. Here, we developed and validated a plasma cell-free DNA (cfDNA) fragmentomics-based machine learning classifier for renal mass characterization. The model was trained on 331 participants (171 cancer, 160 benign) and independently validated on 144 participants (73 cancer, 71 benign). Three cfDNA fragmentation features, including copy number variation (CNV), fragmentation-based methylation (FRAGMA), and nucleosome footprint (NF), derived from low-pass whole-genome sequencing, were integrated into an ensemble framework. The model achieved strong discriminative performance, with area under the curve (AUC) values of 0.956 in the training cohort and 0.946 in the validation cohort, outperforming individual feature-based models. At a predefined operating threshold corresponding to 90% sensitivity, specificity reached 0.90 and 0.87, respectively. Notably, most cancer samples exhibited low tumor fraction (TF < 3%), yet the model maintained robust performance in low-TF samples (AUCs: 0.952 and 0.941, respectively). Performance remained consistent across tumor stage, grade, and histological subtypes. The classifier also demonstrated potential clinical utility in diagnostically challenging settings, including lipid-poor angiomyolipoma and oncocytoma, with 12 of 13 oncocytoma samples correctly classified in an independent cohort. In addition, the model correctly identified 85.3% of benign masses > 4 cm, for which surgical intervention is more commonly considered, and 84.6% of malignant tumors ≤ 4 cm, for which management can be challenging. Collectively, these findings support cfDNA fragmentomics as a promising noninvasive liquid biopsy approach for renal mass evaluation and clinical decision-making.

Humans↗

PubMed2026

Age-associated epigenomic heterogeneity in papillary tumors of the pineal region: a multicenter YoungNOA investigation.

BACKGROUND: Papillary tumors of the pineal region (PTPR) are rare CNS neoplasms with adult and pediatric presentations, but whether age defines distinct molecular biology is unclear. METHODS: We assembled a multicenter retrospective cohort of 86 histologically confirmed PTPR with genome-wide DNA methylation data, comprising 62 adult and 24 pediatric tumors. Molecular subgroup, array platform, sex, and tumor purity were incorporated into multivariable models. Analyses included DNA methylation class assignment, differential methylation, copy-number variation (CNV), epigenetic mitotic-clock scores, methylation-based tumor microenvironment deconvolution, and descriptive survival evaluation. RESULTS: Adult and pediatric tumors mapped within the established PTPR-A and PTPR-B methylation framework rather than forming age-defined methylation classes. Pediatric tumors were enriched for PTPR-B (22 of 24 tumors [91.7%]) compared with adult tumors (39 of 62 [62.9%]). After adjustment for methylation-based subgroup as well as technical and biological covariates, 2,923 CpG probes were associated with age at a false discovery rate (FDR) threshold below 0.05, and 530 also met the prespecified effect-size threshold. Global methylation summaries were similar between age groups. CNV patterns were dominated by molecular subgroup; adjusted genomic CNV load was not independently associated with pediatric age. In contrast, epiTOC2 intrinsic rate score and the methylation signature represented by the first principal component (PC1) showed age-associated effects independent of molecular subgroup. Methylation-based deconvolution suggested a limited microenvironmental signal, with neutrophil fraction showing the most consistent adjusted association. CONCLUSIONS: Adult and pediatric PTPR share the established PTPR-A/PTPR-B framework. Pediatric tumors, particularly within PTPR-B, showed age-associated DNA methylation differences and higher epigenetic mitotic-clock (epiTOC2) scores in this retrospective cohort. These tissue-level associations do not establish clinical risk or treatment implications and require prospective clinical annotation and orthogonal validation.

Humans↗

PubMed2025

The MTORC1 signaling pathway related gene POLR3G serves as a potential prognostic biomarker in Hepatocellular Carcinoma.

This study aims to investigate the prognostic significance and potential biological functions of the MTORC1 signaling pathway-associated gene POLR3G in Hepatocellular carcinoma (HCC). A prognostic risk model for HCC was developed by integrating HCC-related datasets and associated clinical data obtained from The Cancer Genome Atlas (TCGA) database. The GSVA website was employed to analyze the model genes across pan-cancer datasets, focusing on copy number variations (CNV), single nucleotide variations (SNV), methylation differences, drug sensitivity and immune cell infiltration profiles. Subsequently, we examined the expression levels and prognostic significance of POLR3G in HCC. Utilizing Spearman correlation analysis, we identified genes associated with POLR3G. Furthermore, Gene Set Enrichment Analysis (GSEA) was employed to elucidate the potential signaling pathways in which POLR3G may be involved. The relationship between POLR3G expression and immune cell abundance in HCC samples was assessed using the ssGSEA algorithm. Finally, the impact of POLR3G on HCC cell proliferation was validated through CCK-8 and EDU cell proliferation assays. Through univariate Cox regression analysis and LASSO regression analysis, we established a prognostic risk model for HCC comprising 13 genes. The analysis revealed that individuals categorized in the low-risk group had a markedly improved overall survival probability relative to those in the high-risk group. POLR3G exhibited a markedly elevated expression in HCC tissues when compared to adjacent normal tissues. The expression of POLR3G was correlated with tumor grade, and elevated POLR3G expression was associated with poor prognosis in HCC patients. Furthermore, the expression level of POLR3G was found to be correlated with the level of immune cell infiltration. Knockdown of POLR3G significantly inhibited the proliferative capacity of hepatocellular carcinoma cells. The findings suggest that POLR3G may serve as a potential biomarker influencing the prognosis of hepatocellular carcinoma patients by modulating the tumor immune microenvironment.

Humans↗

PubMed2026

Dual-dimensional profiling of host genomic variations and HPV integration in PD-L1-stratified cervical cancer via Oxford Nanopore Technology.

BACKGROUND: The integration of human papillomavirus (HPV) DNA into the host genome is a key step in the development of HPV-associated cervical cancer (CC). However, the genomic characteristics of host genomic variations and HPV integration within the context of programmed death-ligand 1 (PD-L1) expression stratification have not been systematically investigated. METHODS: Whole-genome sequencing was performed using Oxford Nanopore Technology (ONT) on six samples (three from the high PD-L1 expression group and three from the low PD-L1 expression group). The characteristics of host genomic variations under different PD-L1 expression stratifications were explored, including structural variations (SV), copy number variations (CNV), single nucleotide polymorphisms (SNP), and insertion-deletions (Indel). Subsequently, the distribution features of HPV integration sites were analyzed, different integration types were identified, and pathway analysis was conducted. RESULTS: Whole-genome SV analysis revealed that the total number of SVs and the composition of mutation types were similar between the high and low PD-L1 expression groups, with insertions (INS) and deletions (DEL) predominating in both. These variations were primarily enriched in intergenic regions and introns. In the low PD-L1 expression group, integration events were observed at multiple chromosomal loci, with the most frequent integration occurring in the KLF5 gene region on chromosome 13. No frequently integrated loci were identified in the high PD-L1 expression group. Additionally, four distinct HPV integration breakpoint patterns were preliminarily identified and analyzed. CONCLUSION: PD-L1 expression stratification did not significantly alter the overall genomic instability of the host. However, differences were observed in the distribution patterns of HPV integration sites. These findings provide new insights into the genomic heterogeneity of CC under different PD-L1 expression backgrounds and may lay the groundwork for future research exploring stratified immunotherapy based on HPV integration features.

Humans↗

PubMed2026

Integrated pan-cancer profiling highlights OSR2 as a prognostic indicator and immune-associated biomarker.

BACKGROUND: Odd-skipped-related 2 (OSR2), encoded by the OSR2 gene, has been reported to function as a checkpoint associated with CD8⁺ T-cell exhaustion in the tumor microenvironment of solid malignancies, suggesting its potential as a therapeutic target to improve immunotherapeutic responses. Nevertheless, the molecular and clinical significance of OSR2 across diverse cancer types has not yet been systematically investigated, and its pan-cancer expression profile, prognostic implications, and associations with tumor immunity remain to be fully elucidated. METHODS: In this study, we integrated datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) portal, and the Human Protein Atlas to construct a systematic pan-cancer profile of OSR2. The prognostic value of OSR2 was comprehensively assessed using univariate Cox regression, survival analysis, and receiver operating characteristic (ROC) curve analysis. In addition, we performed an in-depth analysis of the relationships between OSR2 and multiple molecular and immunological features, including copy number variation (CNV), DNA methylation, tumor mutational burden (TMB), microsatellite instability (MSI), immune-related gene expression, immune cell infiltration, and drug sensitivity, with the aim of exploring its potential immunological associations with the tumor microenvironment. RESULTS: OSR2 expression was significantly upregulated or downregulated in the majority of tumor tissues relative to normal counterparts and exhibited distinct cancer-type-specific patterns across clinical stages. CNV alterations and aberrant DNA methylation were closely associated with abnormal OSR2 mRNA expression in multiple cancers. Prognostic analyses indicated that OSR2 expression was significantly associated with overall survival, disease-specific survival, disease-free interval, and progression-free interval across multiple cancer types, showing either risk-associated or protective associations in a tumor-context-dependent manner. Furthermore, OSR2 expression showed strong associations with immune cell infiltration, particularly T-cell subsets, and was significantly correlated with the expression of multiple immune checkpoint-related genes across diverse malignancies. OSR2 expression was also closely associated with TMB, MSI, and sensitivity to multiple anticancer agents. CONCLUSION: Taken together, these findings suggest that OSR2 is associated with prognosis and immune-related features across multiple cancer types. OSR2 may be linked to features of the tumor immune microenvironment through its relationships with immune cell infiltration, immune checkpoint gene expression, and genomic instability, and thus may serve as a candidate biomarker for further investigation in cancer immunotherapy.

CD8⁺ T-cell↗

PubMed2026

Non-invasive strategy for gastric cancer detection: Integration of cell-free DNA fragmentomics and protein biomarkers.

Gastric cancer (GC) ranks as the fifth most common cancer worldwide, however, accurate and non-invasive diagnostic modalities for GC remain limited. Cell-free DNA (cfDNA) fragmentomics has emerged as a promising tool for cancer cell detection. Here we develop a gastric cancer detection model, named GaFraD model. The GaFraD model uses four cfDNA fragmentomics features, including fragment size ratio (FSR), copy number variation (CNV), 9-bp end motif (Motif), and fragment size at transcription start sites (TF). This model achieves an area under the receiver-operating characteristic curve (AUC) of 0.970 (95% CI: 0.944 - 0.990), a sensitivity of 95.0% and a specificity of 80.9%. By combining the GaFraD model and conventional protein biomarkers CA19-9 and PG-I/PG-II, the CONFIRM model was generated. The CONFIRM model attained an AUC of 0.986 (95% CI: 0.966 - 1.000), a sensitivity of 95.0% and a specificity of 95.6% in detecting GC. Moreover, the CONFIRM model achieved remarkable performance (AUC = 0.983, sensitivity 95.6%, specificity 94.2%) in distinguishing patients with early-stage GC from controls. Our work showed the high discriminatory power in distinguishing GC patients from controls, indicating the clinical potential of using cfDNA fragmentomics combined with protein biomarkers for non-invasive GC detection. The results of the study provide a new avenue for early, accurate, and non-invasive clinical diagnosis of GC.

Cell-free DNA↗

PubMed2026

Integrated Clinicopathologic and Multiomic Profiling Reveals MEIS1-Rearranged Sarcoma as a Distinct Entity With 2 Prognostic Subgroups.

Sarcomas with MEIS1 fusions represent a rare, recently recognized group of mesenchymal neoplasms with a predilection for genitourinary and gynecologic sites. A subset exhibits skeletal muscle differentiation resembling spindle cell rhabdomyosarcoma. Existing literature is limited to case reports and small series, with scant comprehensive clinicopathologic, molecular, and outcome data. In this study, we analyzed a multi-institutional cohort of 20 MEIS1-rearranged sarcomas using integrated clinicopathologic review, genomic profiling, and DNA methylation analysis. The tumors occurred in 17 females and 3 males (median age, 41 years; range, 6-58 years), arising mainly in the uterus/vagina (n = 12), vulva/perineum (n = 4), bone (n = 2), and kidney (n = 2), with a median size of 9 cm (range, 2.5-20 cm). Histology showed mostly bland spindle cells in fascicles/storiform patterns, alternating cellularity, fibromyxoid stroma, prominent vascularity, and adipose metaplasia (45%). A subset of cases featured high-grade morphology with epithelioid cells and increased mitotic activity. Skeletal muscle markers were variably positive in 9 cases. Fusions involved MEIS1 with NCOA2 (16/20), NCOA1 (3/20), or FOXO1 (1/20). Recurrent additional genomic alterations included CTNNB1 mutations (31.6%) and MDM2 amplification (15%). DNA methylation profiling showed that MEIS1-rearranged sarcomas formed a unifying cluster comprising 2 subgroups, regardless of rhabdomyosarcomatous phenotype, clearly separated from other mesenchymal neoplasms, including various rhabdomyosarcoma subtypes and uterine sarcomas. The 2 DNA methylation (Meth) subgroups correlated with differences in genome-wide copy number variation (CNV) status (Meth-CNV high vs Meth-CNV low), with Meth-CNV high tumors characterized by high mitotic rate, frequent tumor necrosis, recurrent co-occurring CTNNB1 and MDM2 alterations, and recurrent chromosomal arm-level changes. Most importantly, this subgroup exhibited significantly worse overall survival (P = .027) and disease-free survival (median, 5 vs 99 months; P = .017). This study establishes MEIS1-rearranged sarcoma as a distinct entity with generally indolent but potentially aggressive behavior. The 2 methylation/CNV subgroups provide potential utility for prognostic stratification and highlight actionable molecular targets in high-risk cases.

Humans↗

PubMed2026

CRISPR/Cpf1-mediated knockout of FLG in human induced pluripotent stem cells generates a model for studying epidermal barrier dysfunction.

Loss of filaggrin (FLG) function impairs skin barrier formation and contributes to common inflammatory skin diseases. In this study, we established a FLG knockout human induced pluripotent stem cell (iPSC) line based on KOLF2.1 J using CRISPR/Cas12a (Cpf1)-mediated genome editing. A guide RNA targeting exon 2 introduced a homozygous mutation, which was confirmed by sequencing. The edited cells maintained typical pluripotent stem cell morphology, expressed key undifferentiated markers, and retained the ability to differentiate into all three germ layers. Karyotype and copy number variation (CNV) analyses confirmed genomic stability and parental origin; the cells were free of mycoplasma. This cell line enables studies of FLG-associated skin biology and pathology.

Humans↗

PubMed2026

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell↗

PubMed2025

PARTAGE: Parallel analysis of replication timing and gene expression.

The human genome is partitioned into functional compartments that replicate at specific times during the S-phase. This temporal program, referred to as replication timing (RT), is co-regulated with the 3D genome organization, is cell type-specific, and changes during development in coordination with gene expression. Moreover, RT alterations are linked to abnormal gene expression, genome instability, and structural variation in multiple diseases, including cancer. However, mechanistic links between RT, large-scale 3D genome architecture, and transcriptional regulation remain poorly understood. A major limitation is that current approaches require the separate profiling of RT and transcriptomes from independent batches of samples, obscuring the complex co-regulation between the epigenome and transcriptome. Here, we developed PARTAGE, a multiomics approach that enables joint profiling of copy number variation (CNV), RT, and gene expression from the same sample, providing a more accurate integrative view of the complex relationships between RT and gene regulation.

Journal Article↗