ArticleGenome research2017
Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics.
Article in Genome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04931446 (Research on Neuroendocrine Neoplasm in a Single Center Based on Multi-omics Integrated Analysis), which is not on this map. Cited by 19 papers.
What it found
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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.
Research on Neuroendocrine Neoplasm in a Single Center Based on Multi-omics Integrated Analysis
Who cites it
19 citing papers in PubMed.
- KSTITCH links cellular morphology and gene expression in spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- Scaling the glassy dynamics of active particles: Tunable fragility and reentrance.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer.NPJ breast cancer · 2025Article
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- ncRNAs-mediated high expression of TICRR promotes tumor cell proliferation and migration and is correlated with poor prognosis and tumor immune infiltration of hepatocellular carcinoma.Molecular therapy. Nucleic acids · 2022Article
- In vivo 3D profiling of site-specific human cancer cell morphotypes in zebrafish.The Journal of cell biology · 2022Article
- Cell Architecture-Dependent Constraints: Critical Safeguards to Carcinogenesis.International journal of molecular sciences · 2022Review
- Identification of phenotype-specific networks from paired gene expression-cell shape imaging data.Genome research · 2022Article
- Insights Into the Biogenesis and Emerging Functions of Lipid Droplets From Unbiased Molecular Profiling Approaches.Frontiers in cell and developmental biology · 2022Review
- Joint analysis of expression levels and histological images identifies genes associated with tissue morphology.Nature communications · 2021Article
- Evaluation of Cervical High-Grade Squamous Intraepithelial Lesions-Correlated Markers as Triage Strategy for Colposcopy After Co-Testing.OncoTargets and therapy · 2021Article
- Computational Methods for Single-Cell Imaging and Omics Data Integration.Frontiers in molecular biosciences · 2021Review
- Airway mechanical compression: its role in asthma pathogenesis and progression.European respiratory review : an official journal of the European Respiratory Society · 2020Review
- KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens.Molecular systems biology · 2020Article
- Imaging cell morphology and physiology using X-rays.Biochemical Society transactions · 2019Review
- Article
- Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem.Oncotarget · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The associations between clinical phenotypes (tumor grade, survival) and cell phenotypes, such as shape, signaling activity, and gene expression, are the basis for cancer pathology, but the mechanisms explaining these relationships are not always clear. The generation of large data sets containing information regarding cell phenotypes and clinical data provides an opportunity to describe these mechanisms. Here, we develop an image-omics approach to integrate quantitative cell imaging data, gene expression, and protein-protein interaction data to systematically describe a "shape-gene network" that couples specific aspects of breast cancer cell shape to signaling and transcriptional events. The actions of this network converge on NF-κB, and support the idea that NF-κB is responsive to mechanical stimuli. By integrating RNAi screening data, we identify components of the shape-gene network that regulate NF-κB in response to cell shape changes. This network was also used to generate metagene models that predict NF-κB activity and aspects of morphology such as cell area, elongation, and protrusiveness. Critically, these metagenes also have predictive value regarding tumor grade and patient outcomes. Taken together, these data strongly suggest that changes in cell shape, driven by gene expression and/or mechanical forces, can promote breast cancer progression by modulating NF-κB activation. Our findings highlight the importance of integrating phenotypic data at the molecular level (signaling and gene expression) with those at the cellular and tissue levels to better understand breast cancer oncogenesis.
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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.