ArticleNPJ genomic medicine2020
Whole slide images reflect DNA methylation patterns of human tumors.
Article in NPJ genomic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 37 citations in OpenAlex.
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Nutrition-Based Paternal Influence on Gynecological Diseases in Female Offspring via Epigenetic Mechanisms.Nutrients · 2025Review
- Pathogenomic fingerprinting to identify associations between tumor morphology and epigenetic states.European journal of cancer (Oxford, England : 1990) · 2025Article
- Integrated analysis of histone modification features in clear cell renal cancer for risk stratification and therapeutic prediction.Translational oncology · 2024Article
- FOSL2-mediated transcription of ISG20 induces M2 polarization of macrophages and enhances tumorigenic ability of glioblastoma cells.Journal of neuro-oncology · 2024Article
- A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.Nature cancer · 2024Article
- Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.Nature medicine · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview.Journal of translational medicine · 2024Review
- Cancer drug sensitivity prediction from routine histology images.NPJ precision oncology · 2024Article
- Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach.PloS one · 2024Article
- Leveraging Attention-Based Convolutional Neural Networks for Meningioma Classification in Computational Histopathology.Cancers · 2023Article
- Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer.Cancer research · 2023Article
- Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models.Cell reports methods · 2023Article
- Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Methylation Signature Implicated in Immuno-Suppressive Activities in Tubo-Ovarian High-Grade Serous Carcinoma.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023Article
- Breast Cancer Dataset, Classification and Detection Using Deep Learning.Healthcare (Basel, Switzerland) · 2022Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Review
- Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
DNA methylation is an important epigenetic mechanism regulating gene expression and its role in carcinogenesis has been extensively studied. High-throughput DNA methylation assays have been used broadly in cancer research. Histopathology images are commonly obtained in cancer treatment, given that tissue sampling remains the clinical gold-standard for diagnosis. In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level. We demonstrate that classical machine learning algorithms can associate the DNA methylation profiles of cancer samples with morphometric features extracted from whole slide images. Furthermore, grouping the genes into methylation clusters greatly improves the performance of the models. The well-predicted genes are enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma. Our results provide new insights into the link between histopathological and molecular data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.