Evidence map›Paper›PMID 31156157›Full record

ArticleIn silico biology2019

Lower connectivity of tumor coexpression networks is not specific to cancer.

Ertuğrul Dalgıç, Özlen Konu, Zehra Safi Öz, Christina Chan

Abstract read
In one paragraph

Article in In silico biology, 2019. 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. 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

4 authors.

Ertuğrul DalgıçDepartment of Medical Biology, Zonguldak Bülent Ecevit University School of Medicine, Zonguldak, Turkey.
Özlen KonuDepartment of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey.
Zehra Safi ÖzDepartment of Medical Biology, Zonguldak Bülent Ecevit University School of Medicine, Zonguldak, Turkey.
Christina ChanDepartment of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global level network analysis of molecular links is necessary for systems level view of complex diseases like cancer. Using genome-wide expression datasets, we constructed and compared gene co-expression based specific networks of pre-cancerous tumors (adenoma) and cancerous tumors (carcinoma) with paired normal networks to assess for any possible changes in network connectivity. Previously, loss of connectivity was reported as a characteristics of cancer samples. Here, we observed that pre-cancerous conditions also had significantly less connections than paired normal samples. We observed a loss of connectivity trend for colorectal adenoma, aldosterone producing adenoma and uterine leiomyoma. We also showed that the loss of connectivity trend is not specific to positive or negative correlation based networks. Differential hub genes, which were the most highly differentially less connected genes in tumor, were mostly different between different datasets. No common gene list could be defined which underlies the lower connectivity of tumor specific networks. Connectivity of colorectal cancer methylation targets was different from other genes. Extracellular space related terms were enriched in negative correlation based differential hubs and common methylation targets of colorectal carcinoma. Our results indicate a systems level change of lower connectivity as cells transform to not only cancer but also pre-cancerous conditions. This systems level behavior could not be attributed to a group of genes.

Indexed as

Gene Expression Regulation, NeoplasticGene Regulatory NetworksColorectal NeoplasmsComputational BiologyDNA MethylationGene Expression ProfilingHumansMutationNeoplasm GradingNeoplasmsOrgan SpecificityColorectal cancergene co-expressionnetwork analysissystems biology

Identifiers

PMID31156157
PMCPMC6597990

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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.