Pathogenomic Fingerprinting to Identify Associations Between Tumor Morphology and Epigenetic States DOI Creative Commons
Shayan Monabbati, Germán Corredor, Tilak Pathak

et al.

European Journal of Cancer, Journal Year: 2025, Volume and Issue: 221, P. 115429 - 115429

Published: April 14, 2025

Measuring the chromatin state of a tumor provides powerful map its epigenetic commitments; however, as these are generally bulk measurements, it has not yet been possible to connect changes in accessibility pathological signatures complex tumors. In parallel, recent advances computational pathology have enabled identification spatial features and immune cells within oral cavity tumors their microenvironment. Here, we present pathogenomic fingerprinting (PaGeFin), novel method that integrates morphological with states using ATAC-seq. This framework links morphologic features, offering insights into progression evasion across Morphologic describing relationships between lymphocyte prognostic squamous cell carcinoma (OSCC) were identified through AI-driven analysis. These pathomic spatially colocalized epigenome 4 distinct sections OSCC key pinpointed regions responsible for critical function peak locations enrichment analysis, highlighting loci CD27+ memory B cells, helper CD4+ T cytotoxic CD8 naïve likely drive distribution lymphocytes microenvironment promote aggressive behavior. Gene Ontology analysis revealed CTLA4, CD79A, CD3D, CCR7 genes embedded regions. approach is first assess correlation context cancer.

Language: Английский

Integrating single-cell and bulk RNA sequencing data to characterize the heterogeneity of glycan-lipid metabolism polarization in hepatocellular carcinoma DOI Creative Commons
Peng Lin,

Qiong Qin,

Xiang-Yu Gan

et al.

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: March 22, 2025

Hepatocellular carcinoma (HCC) is high heterogeneity and remains an unmet medical challenge, but their metabolic has not been fully uncovered required clinical applicable translational strategies. By analyzing the RNA sequencing data in in-house cohort public HCC cohorts, we identified a subtype of associated with multi-omics features prognosis. Multi-omics alterations clinicopathological information between different subtypes were analyzed. Gene signature, radiomics, contrast-enhanced ultrasound (CEUS), serum biomarkers tested as potential surrogate methods for throughput technology-based subtyping. Single-cell analyses employed to evaluate immune characteristics changes subtypes. utilizing metabolic-related pathways, two heterogeneous subtypes, glycan-HCC lipid-HCC, distinct Kaplan–Meier restricted mean survival time revealed worse overall glycan-HCCs. And glycan-HCCs characterized genomic instability, proliferation-related pathways activation exhausted microenvironment. Furthermore, developed gene signatures, CEUS determination, which showed substantial agreement high-throughput-based classification. RNA-seq multifaceted distortion, including exhaustion T cells enriched SPP1 + macrophages. Collectively, our analysis demonstrated HCCs enabled development translation strategies, thus promoting understanding applications about metabolism heterogeneity.

Language: Английский

Citations

0
Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks DOI Creative Commons
Kun Mei,

Zikang Feng,

Hui Liu

et al.

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 10, 2025

The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment GGN using computed tomography (CT) imaging integrated with a neural network to enhance precision clinical decision-making in planning therapeutic interventions. multicenter retrospective analyzed data quantify mismatch rates approaches across varying statuses. Regions interest (ROIs) within CT lung window level were manually delineated ITK-SNAP software, enabling extraction relevant features, including morphological descriptors, first-order statistical parameters, texture attributes, high-order characteristics. Feature selection was performed Lasso algorithm identify most predictive variables, which subsequently incorporated into radiomics-based model. architecture combined 3D convolutional (CNN) random rotations for augmentation employed pre-trained parameters optimize model weights. radiomics-integrated exhibited high performance, achieving an area under subject operating characteristic curve (AUC) 0.85, validation set AUCs 0.66 0.71. Additionally, predicted rate between lobectomy sublobectomy 21.48%, representing 35.57% reduction, while decreased by 13.66%, reaching 10.73% CONCLUSION: network-enhanced provides robust tool assessing GGNs. Its application significantly reduces decision-making, contributing more precise treatment strategies.

Language: Английский

Citations

0
Diagnostic Meta-Analysis of 18F-FDG PET/CT Metabolic Parameters for Early Prediction of Pathological Response in NSCLCTreated with Neoadjuvant Immuno(chemo)Therapy DOI
Hongsheng Deng, Zichen Deng, Yi Zhao

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Pathogenomic Fingerprinting to Identify Associations Between Tumor Morphology and Epigenetic States DOI Creative Commons
Shayan Monabbati, Germán Corredor, Tilak Pathak

et al.

European Journal of Cancer, Journal Year: 2025, Volume and Issue: 221, P. 115429 - 115429

Published: April 14, 2025

Measuring the chromatin state of a tumor provides powerful map its epigenetic commitments; however, as these are generally bulk measurements, it has not yet been possible to connect changes in accessibility pathological signatures complex tumors. In parallel, recent advances computational pathology have enabled identification spatial features and immune cells within oral cavity tumors their microenvironment. Here, we present pathogenomic fingerprinting (PaGeFin), novel method that integrates morphological with states using ATAC-seq. This framework links morphologic features, offering insights into progression evasion across Morphologic describing relationships between lymphocyte prognostic squamous cell carcinoma (OSCC) were identified through AI-driven analysis. These pathomic spatially colocalized epigenome 4 distinct sections OSCC key pinpointed regions responsible for critical function peak locations enrichment analysis, highlighting loci CD27+ memory B cells, helper CD4+ T cytotoxic CD8 naïve likely drive distribution lymphocytes microenvironment promote aggressive behavior. Gene Ontology analysis revealed CTLA4, CD79A, CD3D, CCR7 genes embedded regions. approach is first assess correlation context cancer.

Language: Английский

Citations

0