| Title |
Prognostic immune-related gene models for breast cancer: a pooled analysis
|
|---|---|
| Published in |
OncoTargets and therapy, September 2017
|
| DOI | 10.2147/ott.s144015 |
| Pubmed ID | |
| Authors |
Jianli Zhao, Ying Wang, Zengding Lao, Siting Liang, Jingyi Hou, Yunfang Yu, Herui Yao, Na You, Kai Chen |
| Abstract |
Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER-) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER- breast cancer models achieved overall C-indices of 0.62-0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER-, LN+, and LN- breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER- and LN+ tumors. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 4 | 27% |
| Other | 2 | 13% |
| Researcher | 2 | 13% |
| Student > Doctoral Student | 1 | 7% |
| Lecturer > Senior Lecturer | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 5 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 33% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Chemistry | 1 | 7% |
| Nursing and Health Professions | 1 | 7% |
| Unknown | 6 | 40% |
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 12 September 2017.
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#15,478,452
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Outputs from OncoTargets and therapy
#1,050
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Outputs of similar age from OncoTargets and therapy
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