ArticleCell2018
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.
Article in Cell, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,515 papers, 1 of them a synthesis that pooled it.
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Who cites it
1,515 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- The tumor microenvironment in pancreatic cancer: from composition to therapeutic targeting.Biochemical Society transactions · 2026Review
- Increasing Cancer Stemness Drives Prostate Cancer Progression, Plasticity, Therapy Resistance and Poor Patient Survival.bioRxiv : the preprint server for biology · 2026Article
- Transposable elements and homotypic niches drive immune dynamics and resistance in melanoma epigenetic-based immunotherapy.Science advances · 2026Article
- A multidimensional pan-cancer analysis of CD47 and its role in promoting malignant phenotype in pancreatic adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Comprehensive Pan-Cancer Analysis of ABCA1: Insights From Multi-Omics Data and Exploratory Validation in Esophageal Squamous Cell Carcinoma.Cell biochemistry and function · 2026Article
- Serum metabolomics profiling in head and neck squamous cell carcinoma: a multicentre diagnostic study.EBioMedicine · 2026Article
1,455 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.
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