Evidence map›Paper›PMID 39420135›Full record

ArticleInterdisciplinary sciences, computational life sciences2025

Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks.

Abicumaran Uthamacumaran

Abstract read
PubMed Publisher
In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Abicumaran UthamacumaranDepartment of Physics (Alumni), Concordia University, Montréal, H4B 1R6, Canada. a_utham@live.concordia.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pediatric glioblastoma is a complex dynamical disease that is difficult to treat due to its multiple adaptive behaviors driven largely by phenotypic plasticity. Integrated data science and network theory pipelines offer novel approaches to studying glioblastoma cell fate dynamics, particularly phenotypic transitions over time. Here we used various single-cell trajectory inference algorithms to infer signaling dynamics regulating pediatric glioblastoma-immune cell networks. We identified GATA2, PTPRZ1, TPT1, MTRNR2L1/2, OLIG1/2, SOX11, FXYD6, SEZ6L, PDGFRA, EGFR, S100B, WNT, TNF

Indexed as

Brain NeoplasmsCell DifferentiationGlioblastomaReceptor-Like Protein Tyrosine Phosphatases, Class 5ChildFeedback, PhysiologicalGene Expression Regulation, NeoplasticHumansSignal TransductionPTPRZ1 protein, humanReceptor-Like Protein Tyrosine Phosphatases, Class 5Artificial intelligenceAttractorCancerCellular decision-makingCyberneticsData scienceDynamicsNetworksPrecision oncologySystems medicine

Identifiers

PMID39420135

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.