↓ Skip to main content

Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines

Overview of attention for article published in Frontiers in Genetics, April 2016
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
3 X users
facebook
1 Facebook page

Readers on

mendeley
32 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines
Published in
Frontiers in Genetics, April 2016
DOI 10.3389/fgene.2016.00042
Pubmed ID
Authors

Darius Wlochowitz, Martin Haubrock, Jetcy Arackal, Annalen Bleckmann, Alexander Wolff, Tim Beißbarth, Edgar Wingender, Mehmet Gültas

Abstract

Transcription factors (TFs) are gene regulatory proteins that are essential for an effective regulation of the transcriptional machinery. Today, it is known that their expression plays an important role in several types of cancer. Computational identification of key players in specific cancer cell lines is still an open challenge in cancer research. In this study, we present a systematic approach which combines colorectal cancer (CRC) cell lines, namely 1638N-T1 and CMT-93, and well-established computational methods in order to compare these cell lines on the level of transcriptional regulation as well as on a pathway level, i.e., the cancer cell-intrinsic pathway repertoire. For this purpose, we firstly applied the Trinity platform to detect signature genes, and then applied analyses of the geneXplain platform to these for detection of upstream transcriptional regulators and their regulatory networks. We created a CRC-specific position weight matrix (PWM) library based on the TRANSFAC database (release 2014.1) to minimize the rate of false predictions in the promoter analyses. Using our proposed workflow, we specifically focused on revealing the similarities and differences in transcriptional regulation between the two CRC cell lines, and report a number of well-known, cancer-associated TFs with significantly enriched binding sites in the promoter regions of the signature genes. We show that, although the signature genes of both cell lines show no overlap, they may still be regulated by common TFs in CRC. Based on our findings, we suggest that canonical Wnt signaling is activated in 1638N-T1, but inhibited in CMT-93 through cross-talks of Wnt signaling with the VDR signaling pathway and/or LXR-related pathways. Furthermore, our findings provide indication of several master regulators being present such as MLK3 and Mapk1 (ERK2) which might be important in cell proliferation, migration, and invasion of 1638N-T1 and CMT-93, respectively. Taken together, we provide new insights into the invasive potential of these cell lines, which can be used for development of effective cancer therapy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 25%
Student > Bachelor 6 19%
Researcher 5 16%
Student > Master 5 16%
Student > Doctoral Student 2 6%
Other 4 13%
Unknown 2 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 31%
Agricultural and Biological Sciences 6 19%
Medicine and Dentistry 5 16%
Computer Science 3 9%
Engineering 2 6%
Other 3 9%
Unknown 3 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 April 2016.
All research outputs
#12,756,718
of 22,860,626 outputs
Outputs from Frontiers in Genetics
#2,582
of 11,880 outputs
Outputs of similar age
#134,899
of 300,859 outputs
Outputs of similar age from Frontiers in Genetics
#23
of 66 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,880 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,859 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.