Evidence map›Paper›PMID 38438221›Full record

ReviewCold Spring Harbor perspectives in medicine2024

Growth Signaling Networks Orchestrate Cancer Metabolic Networks.

Brendan D Manning, Christian C Dibble

Abstract readReview
In one paragraph

Review in Cold Spring Harbor perspectives in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
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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

2 authors at 2 institutions in 1 country.

Brendan D ManningDepartment of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA bmanning@hsph.harvard.edu.
Christian C DibbleDepartment of Pathology, Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02115, USA.
Beth Israel Deaconess Medical Center · USHarvard University · US

Funding

Tuberous Sclerosis-Pathway and PathogenesisP01CA120964 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI David J. Kwiatkowski · 2007 to 2026
$35.9M
Regulation and Requirements for Coenzyme A BiosynthesisR56DK140239 · NIDDK · BETH ISRAEL DEACONESS MEDICAL CENTER · PI DIBBLE, CHRISTIAN · 2024 to 2024
$676k
NCI NIH HHS P01 CA120964NIDDK NIH HHS R56 DK140239
6 · The paper itself

Abstract

Normal cells grow and divide only when instructed to by signaling pathways stimulated by exogenous growth factors. A nearly ubiquitous feature of cancer cells is their capacity to grow independent of such signals, in an uncontrolled, cell-intrinsic manner. This property arises due to the frequent oncogenic activation of core growth factor signaling pathway components, including receptor tyrosine kinases, PI3K-AKT, RAS-RAF, mTORC1, and MYC, leading to the aberrant propagation of pro-growth signals independent of exogenous growth factors. The growth of both normal and cancer cells requires the acquisition of nutrients and their anabolic conversion to the primary macromolecules underlying biomass production (protein, nucleic acids, and lipids). The core growth factor signaling pathways exert tight regulation of these metabolic processes and the oncogenic activation of these pathways drive the key metabolic properties of cancer cells and tumors. Here, we review the molecular mechanisms through which these growth signaling pathways control and coordinate cancer metabolism.

Indexed as

Metabolic Networks and PathwaysNeoplasmsSignal TransductionAnimalsHumans

Identifiers

PMID38438221
PMCPMC11444256
OpenAlexW4392382058

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.