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Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma

Overview of attention for article published in BMC Cancer, March 2018
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3 tweeters

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13 Dimensions

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Title
Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma
Published in
BMC Cancer, March 2018
DOI 10.1186/s12885-018-4176-1
Pubmed ID
Authors

Peng Wu, Jia-Li Liu, Shi-Mei Pei, Chang-Peng Wu, Kai Yang, Shu-Peng Wang, Song Wu

Abstract

Renal cell carcinoma (RCC) account for over 80% of renal malignancies. The most common type of RCC can be classified into three subtypes including clear cell, papillary and chromophobe. ccRCC (the Clear Cell Renal Cell Carcinoma) is the most frequent form and shows variations in genetics and behavior. To improve accuracy and personalized care and increase the cure rate of cancer, molecular typing for individuals is necessary. We adopted the genome, transcriptome and methylation HMK450 data of ccRCC in The Cancer Genome Atlas Network in this research. Consensus Clustering algorithm was used to cluster the expression data and three subtypes were found. To further validate our results, we analyzed an independent data set and arrived at a consistent conclusion. Next, we characterized the subtype by unifying genomic and clinical dimensions of ccRCC molecular stratification. We also implemented GSEA between the malignant subtype and the other subtypes to explore latent pathway varieties and WGCNA to discover intratumoral gene interaction network. Moreover, the epigenetic state changes between subgroups on methylation data are discovered and Kaplan-Meier survival analysis was performed to delve the relation between specific genes and prognosis. We found a subtype of poor prognosis in clear cell renal cell carcinoma, which is abnormally upregulated in focal adhesions and cytoskeleton related pathways, and the expression of core genes in the pathways are negatively correlated with patient outcomes. Our work of classification schema could provide an applicable framework of molecular typing to ccRCC patients which has implications to influence treatment decisions, judge biological mechanisms involved in ccRCC tumor progression, and potential future drug discovery.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Student > Master 2 15%
Student > Doctoral Student 2 15%
Student > Bachelor 1 8%
Unspecified 1 8%
Other 0 0%
Unknown 3 23%
Readers by discipline Count As %
Medicine and Dentistry 5 38%
Biochemistry, Genetics and Molecular Biology 2 15%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Neuroscience 1 8%
Unspecified 1 8%
Other 0 0%
Unknown 3 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 August 2018.
All research outputs
#8,431,271
of 13,451,706 outputs
Outputs from BMC Cancer
#2,458
of 5,061 outputs
Outputs of similar age
#161,576
of 272,005 outputs
Outputs of similar age from BMC Cancer
#1
of 2 outputs
Altmetric has tracked 13,451,706 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,061 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 41st percentile – i.e., 41% 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 272,005 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them