ArticleThe journal of physical chemistry. B2016
A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes.
Article in The journal of physical chemistry. B, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 13 citations in OpenAlex.
- Advances in Glioblastoma Therapy: An Update on Current Approaches.Brain sciences · 2023Review
- Understanding Glioblastoma Signaling, Heterogeneity, Invasiveness, and Drug Delivery Barriers.International journal of molecular sciences · 2023Review
- Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.Genome medicine · 2022Article
- Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures.Biomolecules · 2020Article
- Personalized disease signatures through information-theoretic compaction of big cancer data.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.Proteomics · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 3 countries.
Funding
Abstract
We describe a thermodynamic-motivated, information theoretic analysis of proteomic data collected from a series of 8 glioblastoma multiforme (GBM) tumors. GBMs are considered here as prototypes of heterogeneous cancers. That heterogeneity is viewed here as manifesting in different unbalanced biological processes that are associated with thermodynamic-like constraints. The analysis yields a molecular description of a stable steady state that is common across all tumors. It also resolves molecular descriptions of unbalanced processes that are shared by several tumors, such as hyperactivated phosphoprotein signaling networks. Further, it resolves unbalanced processes that provide unique classifiers of tumor subgroups. The results of the theoretical interpretation are compared against those of statistical multivariate methods and are shown to provide a superior level of resolution for identifying unbalanced processes in GBM tumors. The identification of specific constraints for each GBM tumor suggests tumor-specific combination therapies that may reverse this imbalance.
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.