| Title |
Comprehensive profiling of biological processes reveals two major prognostic subtypes in breast cancer
|
|---|---|
| Published in |
Tumor Biology, October 2015
|
| DOI | 10.1007/s13277-015-4173-9 |
| Pubmed ID | |
| Authors |
Fei Chen, Sheng Gao, Fengliang Wang, Jingjing Ma, Min Zhang, Mingming Lv, Qian Zhou, Ziyi Fu, Cheng Lu, Hong Yin |
| Abstract |
Heterogeneity is the major obstacle to breast cancer target therapy. Classification of breast cancer with significant biological process may reduce the influence of heterogeneity of intrinsic tumor. We used survival analysis to filter 95 gene sets and classify 638 breast cancer samples into two subtypes based on those gene sets associated with prognosis. Clinical outcome of two subtypes were evaluated with disease-free survival, distant metastasis-free survival, and overall survival levels in three databases and ER+, PR+ HER2+, and TNBC groups. We established a novel classification with 95 prognostic gene sets. In the training and validation cohorts, the subtype 1 was characterized by significant gene sets associated with regulation of metabolic process and enzyme activity and predicted obviously improved clinical outcome than subtype 2, which was enriched by tumor cell division, mitosis, and cell cycle-related gene sets (P < 0.05). When evaluated prognostic impact of subtypes in ER+, PR+ HER2+, and TNBC groups, we found that patients in subtype 1 showed better prognosis in ER+ and PR+ groups (P < 0.05) but had no difference from prognosis of subtype 2 in HER2+ and TNBC groups. These findings may have implications in understanding of breast cancer and filtering effective therapeutic strategies for targeted therapy. |
X Demographics
The data shown below were collected from the profiles of 2 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 Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 22% |
| Other | 1 | 11% |
| Lecturer | 1 | 11% |
| Professor | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Other | 2 | 22% |
| Unknown | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 22% |
| Environmental Science | 1 | 11% |
| Nursing and Health Professions | 1 | 11% |
| Agricultural and Biological Sciences | 1 | 11% |
| Psychology | 1 | 11% |
| Other | 1 | 11% |
| Unknown | 2 | 22% |
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 09 October 2015.
All research outputs
#18,379,687
of 23,613,071 outputs
Outputs from Tumor Biology
#1,244
of 2,614 outputs
Outputs of similar age
#189,512
of 279,209 outputs
Outputs of similar age from Tumor Biology
#84
of 254 outputs
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So far Altmetric has tracked 2,614 research outputs from this source. They receive a mean Attention Score of 2.4. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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