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Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis

Overview of attention for article published in Genome Medicine, May 2016
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Mentioned by

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11 tweeters

Citations

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37 Dimensions

Readers on

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78 Mendeley
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Title
Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis
Published in
Genome Medicine, May 2016
DOI 10.1186/s13073-016-0282-3
Pubmed ID
Authors

Deepa Rajamani, Manoj K. Bhasin

Abstract

Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression. Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis. Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities. It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 77 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 22%
Student > Ph. D. Student 14 18%
Student > Master 10 13%
Student > Bachelor 7 9%
Student > Doctoral Student 5 6%
Other 14 18%
Unknown 11 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 21%
Medicine and Dentistry 16 21%
Agricultural and Biological Sciences 12 15%
Computer Science 6 8%
Engineering 2 3%
Other 10 13%
Unknown 16 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 06 May 2016.
All research outputs
#4,619,375
of 17,575,102 outputs
Outputs from Genome Medicine
#804
of 1,171 outputs
Outputs of similar age
#74,025
of 269,679 outputs
Outputs of similar age from Genome Medicine
#5
of 7 outputs
Altmetric has tracked 17,575,102 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,171 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.3. This one is in the 31st percentile – i.e., 31% 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 269,679 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 72% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.