ArticleGenome research2024
Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer.
Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Biomarker discovery and patient stratification in pancreatic cancer using incomplete multi-omics data.PLoS computational biology · 2026Article
- Single-cell and spatial transcriptomics analysis of osteoarthritis: pathway regulation, cell interaction networks, and therapeutic translation.Journal of translational medicine · 2026Review
- Frizzled-1 amplification promotes fibrosis in the gastric tumor microenvironment through the activation of fibroblasts via the SLIT2/ROBO1 axis.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Extracellular matrix stiffness conditions glioblastoma cells for long-term migration: Mechanical memory as a driver of invasion and recurrence in glioblastoma.Neuro-oncology · 2026Review
- Uncovering a fibroblast differentiation-based keloid classification by integration of single-cell and bulk RNA sequencing.Communications biology · 2025Article
- Integrative single-cell transcriptomic analysis deciphers heterogeneous characteristics of gastrointestinal tract cancer.Clinical and translational medicine · 2025Article
- PUF60-Regulated Isoform Switching of MAZ Modulates Gastric Cancer Cell Migration.Cancer medicine · 2025Article
- High expression ofBiochemistry and biophysics reports · 2025Article
- Deciphering aging-associated prognosis and heterogeneity in gastric cancer through a machine learning-driven approach.iScience · 2025Article
- Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".Applied intelligence (Dordrecht, Netherlands) · 2025Article
- Investigation of c-Fos/c-Jun Signaling Pathways in Periostracum Cicadae's Inhibition of EMT in Gastric Tissue.Pharmaceuticals (Basel, Switzerland) · 2025Article
- CXCL Gene Clusters Regulated by Enhancer-Mediated DNA Looping Alteration in Pancreatic Cancer Cells.Journal of cellular and molecular medicine · 2025Article
- Smad2 Cooperating with TGIF2 Contributes to EMT and Cancer Stem Cells Properties in Pancreatic Cancer via Co-Targeting SOX2.International journal of biological sciences · 2025Article
- Epigenetic Regulation of DNA Methylation and RNA Interference in Gastric Cancer: A 2024 Update.Biomedicines · 2024Review
- MicroRNAs provide negative feedback and stability in gene regulatory network models of cell-state transitions.Frontiers in epigenetics and epigenomics · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Gastric cancer (GC) is the fifth most common cancer worldwide and is a heterogeneous disease. Among GC subtypes, the mesenchymal phenotype (Mes-like) is more invasive than the epithelial phenotype (Epi-like). Although gene expression of the epithelial-to-mesenchymal transition (EMT) has been studied, the regulatory landscape shaping this process is not fully understood. Here we use ATAC-seq and RNA-seq data from a compendium of GC cell lines and primary tumors to detect drivers of regulatory state changes and their transcriptional responses. Using the ATAC-seq data, we developed a machine learning approach to determine the transcription factors (TFs) regulating the subtypes of GC. We identified TFs driving the mesenchymal (RUNX2, ZEB1, SNAI2, AP-1 dimer) and the epithelial (GATA4, GATA6, KLF5, HNF4A, FOXA2, GRHL2) states in GC. We identified DNA copy number alterations associated with dysregulation of these TFs, specifically deletion of
Indexed as
Identifiers
What OpenQuestion holds
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.