| 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. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 16% |
| Student > Ph. D. Student | 3 | 16% |
| Student > Doctoral Student | 3 | 16% |
| Student > Master | 2 | 11% |
| Lecturer | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 6 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 26% |
| Biochemistry, Genetics and Molecular Biology | 3 | 16% |
| Immunology and Microbiology | 1 | 5% |
| Nursing and Health Professions | 1 | 5% |
| Psychology | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 7 | 37% |
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
#15,494,712
of 23,026,672 outputs
Outputs from BMC Cancer
#4,155
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Outputs of similar age
#213,303
of 333,594 outputs
Outputs of similar age from BMC Cancer
#115
of 232 outputs
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