↓ Skip to main content

Biomarker correlation network in colorectal carcinoma by tumor anatomic location

Overview of attention for article published in BMC Bioinformatics, June 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
5 X users

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
49 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
Biomarker correlation network in colorectal carcinoma by tumor anatomic location
Published in
BMC Bioinformatics, June 2017
DOI 10.1186/s12859-017-1718-5
Pubmed ID
Authors

Reiko Nishihara, Kimberly Glass, Kosuke Mima, Tsuyoshi Hamada, Jonathan A. Nowak, Zhi Rong Qian, Peter Kraft, Edward L. Giovannucci, Charles S. Fuchs, Andrew T. Chan, John Quackenbush, Shuji Ogino, Jukka-Pekka Onnela

Abstract

Colorectal carcinoma evolves through a multitude of molecular events including somatic mutations, epigenetic alterations, and aberrant protein expression, influenced by host immune reactions. One way to interrogate the complex carcinogenic process and interactions between aberrant events is to model a biomarker correlation network. Such a network analysis integrates multidimensional tumor biomarker data to identify key molecular events and pathways that are central to an underlying biological process. Due to embryological, physiological, and microbial differences, proximal and distal colorectal cancers have distinct sets of molecular pathological signatures. Given these differences, we hypothesized that a biomarker correlation network might vary by tumor location. We performed network analyses of 54 biomarkers, including major mutational events, microsatellite instability (MSI), epigenetic features, protein expression status, and immune reactions using data from 1380 colorectal cancer cases: 690 cases with proximal colon cancer and 690 cases with distal colorectal cancer matched by age and sex. Edges were defined by statistically significant correlations between biomarkers using Spearman correlation analyses. We found that the proximal colon cancer network formed a denser network (total number of edges, n = 173) than the distal colorectal cancer network (n = 95) (P < 0.0001 in permutation tests). The value of the average clustering coefficient was 0.50 in the proximal colon cancer network and 0.30 in the distal colorectal cancer network, indicating the greater clustering tendency of the proximal colon cancer network. In particular, MSI was a key hub, highly connected with other biomarkers in proximal colon cancer, but not in distal colorectal cancer. Among patients with non-MSI-high cancer, BRAF mutation status emerged as a distinct marker with higher connectivity in the network of proximal colon cancer, but not in distal colorectal cancer. In proximal colon cancer, tumor biomarkers tended to be correlated with each other, and MSI and BRAF mutation functioned as key molecular characteristics during the carcinogenesis. Our findings highlight the importance of considering multiple correlated pathways for therapeutic targets especially in proximal colon cancer.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Ph. D. Student 7 14%
Student > Bachelor 5 10%
Student > Master 4 8%
Professor 3 6%
Other 9 18%
Unknown 11 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 22%
Agricultural and Biological Sciences 7 14%
Medicine and Dentistry 6 12%
Computer Science 4 8%
Immunology and Microbiology 4 8%
Other 4 8%
Unknown 13 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 30 October 2020.
All research outputs
#14,431,072
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#4,561
of 7,400 outputs
Outputs of similar age
#171,956
of 318,076 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 116 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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 318,076 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.