Evidence map›Paper›PMID 33562960›Full record

ArticleThe journal of physical chemistry. B2021

Modular Reorganization of Signaling Networks during the Development of Colon Adenoma and Carcinoma.

Klára Schulc, Zsolt T Nagy, Sebestyén Kamp, János Molnár, Daniel V Veres, Peter Csermely, Borbála M Kovács

Open access · hybridAbstract read
In one paragraph

Article in The journal of physical chemistry. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 58% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

7 authors at 1 institution in 1 country.

Klára SchulcDepartment of Molecular Biology, Semmelweis University, Budapest 1085, Hungary.
Zsolt T NagyDepartment of Molecular Biology, Semmelweis University, Budapest 1085, Hungary.
Sebestyén KampTurbine Ltd, Budapest, Hungary.
János MolnárTurbine Ltd, Budapest, Hungary.
Daniel V VeresDepartment of Molecular Biology, Semmelweis University, Budapest 1085, Hungary.
Peter CsermelyDepartment of Molecular Biology, Semmelweis University, Budapest 1085, Hungary.ORCID 0000-0001-9234-0659
Borbála M KovácsDepartment of Molecular Biology, Semmelweis University, Budapest 1085, Hungary.
Semmelweis University · HU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Network science is an emerging tool in systems biology and oncology, providing novel, system-level insight into the development of cancer. The aim of this project was to study the signaling networks in the process of oncogenesis to explore the adaptive mechanisms taking part in the cancerous transformation of healthy cells. For this purpose, colon cancer proved to be an excellent candidate as the preliminary phase, and adenoma has a long evolution time. In our work, transcriptomic data have been collected from normal colon, colon adenoma, and colon cancer samples to calculating link (i.e., network edge) weights as approximative proxies for protein abundances, and link weights were included in the Human Cancer Signaling Network. Here we show that the adenoma phase clearly differs from the normal and cancer states in terms of a more scattered link weight distribution and enlarged network diameter. Modular analysis shows the rearrangement of the apoptosis- and the cell-cycle-related modules, whose pathway enrichment analysis supports the relevance of targeted therapy. Our work enriches the system-wide assessment of cancer development, showing specific changes for the adenoma state.

Indexed as

AdenomaCarcinomaColonic NeoplasmsHumansSignal Transduction

Identifiers

PMID33562960
PMCPMC8023713
OpenAlexW3127776692

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.