scholarly journals Identification of Small and Discriminative Gene Signatures for Chemosensitivity Prediction in Breast Cancer

2011 ◽  
Vol 02 (02) ◽  
pp. 196-202
Author(s):  
Wei Hu
Keyword(s):  
Breast Cancer ◽  
Gene Signatures ◽  
Discriminative Gene

scholarly journals Prognostic Value of Gene Signatures and Proliferation in Lymph-Node-Negative Breast Cancer

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e90642 ◽  
Author(s):  
Kristin Jonsdottir ◽  
Jörg Assmus ◽  
Aida Slewa ◽  
Einar Gudlaugsson ◽  
Ivar Skaland ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Prognostic Value ◽  
Node Negative ◽  
Gene Signatures ◽  
Node Negative Breast Cancer

Transcriptomics based prediction of metastasis in TNBC patients: Challenges in cross-platforms validation

2021 ◽  
Author(s):  
Naorem Leimarembi Devi ◽  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Gajendra P. S. Raghava
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Lymph Node Status ◽  
P Value ◽  
Gene Signatures ◽  
Node Metastasis ◽  
Microarray Datasets

Triple-negative breast cancer (TNBC) is more prone to metastasis and recurrence than other breast cancer subtypes. This study aimed to identify genes that can act as diagnostic biomarkers for predicting lymph node metastasis in TNBC patients. The transcriptomic data of TNBC with or without lymph node metastasis was acquired from TCGA, and the differentially expressed genes were identified. Further, logistic-regression method has been used to identify the top 15 genes (or 15 gene signatures) based on their ability to predict metastasis (AUC>0.65). These 15 gene signatures were used to develop machine learning techniques based prediction models; Gaussian Naive Bayes classifier outperformed other with AUC>0.80 on both training and validation datasets. The best model failed drastically on nine independent microarray datasets obtained from GEO. We investigated the reason for the failure of our best model, and it was observed that the certain genes in 15 gene signatures were showing opposite regulating trends, i.e., genes are upregulated in TCGA-TNBC patients while it is downregulated on other microarray datasets or vice-versa. In conclusion, the 15 gene signatures may act as diagnostic markers for the detection of lymph node metastatic status in TCGA dataset, but quite challenging across multiple platforms. We also identified the prognostic potential of the 15 selected genes and found that overexpression of ZNRF2, FRZB, and TCEAL4 was associated with poor survival with HR>2.3 and p-value≤0.05. In order to provide services to the scientific community, we developed a webserver named 'MTNBCPred' for the prediction of metastatic and non-metastatic lymph node status of TNBC patients (http://webs.iiitd.edu.in/raghava/mtnbcpred/ ).

Risk stratification of triple-negative breast cancer with core gene signatures associated with chemoresponse and prognosis

2019 ◽  
Vol 178 (1) ◽  
pp. 185-197 ◽  
Author(s):  
Eun-Kyu Kim ◽  
Ae Kyung Park ◽  
Eunyoung Ko ◽  
Woong-Yang Park ◽  
Kyung-Min Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Stratification ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Core Gene ◽  
Gene Signatures
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