A lymph node metastasis‐related protein‐coding genes combining with long noncoding RNA signature for breast cancer survival prediction

2019 ◽  
Vol 234 (11) ◽  
pp. 20036-20045 ◽  
Author(s):  
Yujie Sui ◽  
Chunyan Ju ◽  
Bin Shao
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Noncoding Rna ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Related Protein ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Node Metastasis

Identification of a New Eight-Long Noncoding RNA Molecular Signature for Breast Cancer Survival Prediction

2019 ◽  
Vol 38 (12) ◽  
pp. 1529-1539 ◽  
Author(s):  
Yaqiong Zhang ◽  
Zhaoyun Li ◽  
Meifang Chen ◽  
Hanjun Chen ◽  
Qianyi Zhong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Molecular Signature ◽  
Survival Prediction

scholarly journals Oestrogen receptors β1 and βcx have divergent roles in breast cancer survival and lymph node metastasis

2014 ◽  
Vol 111 (5) ◽  
pp. 918-926 ◽  
Author(s):  
G Rosin ◽  
J de Boniface ◽  
G M Karthik ◽  
J Frisell ◽  
J Bergh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Oestrogen Receptors ◽  
Node Metastasis

scholarly journals Breast cancer survival prediction using seven prognostic biomarker genes

2019 ◽  
Author(s):  
Liu Liu ◽  
Zhilin Chen ◽  
Wenjie Shi ◽  
Hui Liu ◽  
Weiyi Pang
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Prognostic Biomarker ◽  
Breast Cancer Survival ◽  
Survival Prediction

scholarly journals The value of positive lymph nodes ratio combined with negative lymph node count in prediction of breast cancer survival

2017 ◽  
Vol 9 (6) ◽  
pp. 1531-1537 ◽  
Author(s):  
Jing Yang ◽  
Quanyi Long ◽  
Hongjiang Li ◽  
Qing Lv ◽  
Qiuwen Tan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Lymph Node Count ◽  
Negative Lymph Node ◽  
Node Count ◽  
Positive Lymph Nodes

A mechanistic breast cancer survival modelling through the axillary lymph node chain

2012 ◽  
Vol 32 (9) ◽  
pp. 1536-1546 ◽  
Author(s):  
Juliana Cobre ◽  
Gleici S. Castro Perdoná ◽  
Fernanda M. Peria ◽  
Francisco Louzada
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Axillary Lymph Node ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Axillary Lymph

scholarly journals Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods

2021 ◽  
Vol 11 ◽  
Author(s):  
Zongzhen He ◽  
Junying Zhang ◽  
Xiguo Yuan ◽  
Yuanyuan Zhang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Feature Selection ◽  
Somatic Mutations ◽  
Cancer Survival ◽  
Feature Selection Method ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Omics Data ◽  
Selection Methods

Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), methylation, and protein expression data for the prediction of breast cancer survival. Before integration, the maximum relevance minimum redundancy (mRMR) feature selection method was utilized to select features that present high relevance to survival and low redundancy among themselves for each type of data. The experimental results demonstrated that the proposed method achieved the most optimal performance and there was a remarkable improvement in the prediction performance when somatic mutations were included, indicating that somatic mutations are critical for improving breast cancer survival predictions. Moreover, mRMR was superior to other feature selection methods used in previous studies. Furthermore, MKL outperformed the other traditional classifiers in multi-omics data integration. Our analysis indicated that through employing promising omics data such as somatic mutations and harnessing the power of proper feature selection methods and effective integration frameworks, the breast cancer survival predictive accuracy can be further increased, thereby providing a more optimal clinical diagnosis and more effective treatment for breast cancer patients.

scholarly journals eBreCaP: extreme learning-based model for breast cancer survival prediction

2020 ◽  
Vol 14 (3) ◽  
pp. 160-169 ◽  
Author(s):  
Arwinder Dhillon ◽  
Ashima Singh
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction

scholarly journals Transcriptome sequencing uncovers a three–long noncoding RNA signature in predicting breast cancer survival

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Wenna Guo ◽  
Qiang Wang ◽  
Yueping Zhan ◽  
Xijia Chen ◽  
Qi Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Long Noncoding Rna ◽  
Transcriptome Sequencing ◽  
Noncoding Rna ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Rna Signature

scholarly journals Axillary lymph node micrometastases decrease triple-negative early breast cancer survival

2016 ◽  
Vol 115 (9) ◽  
pp. 1024-1031 ◽  
Author(s):  
G Houvenaeghel ◽  
R Sabatier ◽  
F Reyal ◽  
J M Classe ◽  
S Giard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Early Breast Cancer ◽  
Axillary Lymph Node ◽  
Triple Negative ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Axillary Lymph ◽  
Lymph Node Micrometastases
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