Integrated network analysis and machine learning approach for the identification of key genes of triple‐negative breast cancer

2018 ◽  
Vol 120 (4) ◽  
pp. 6154-6167 ◽  
Author(s):  
Leimarembi Devi Naorem ◽  
Mathavan Muthaiyan ◽  
Amouda Venkatesan
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Network Analysis ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Learning Approach ◽  
Integrated Network ◽  
Machine Learning Approach ◽  
Key Genes

Novel key genes in triple‐negative breast cancer identified by weighted gene co‐expression network analysis

2019 ◽  
Vol 120 (10) ◽  
pp. 16900-16912 ◽  
Author(s):  
Jian Chen ◽  
Xiaojun Qian ◽  
Yifu He ◽  
Xinghua Han ◽  
Yueyin Pan
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Key Genes

scholarly journals A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

2020 ◽  
Author(s):  
Jean-Philippe Villemin ◽  
Claudio Lorenzi ◽  
Andrew Oldfield ◽  
Marie-Sarah Cabrillac ◽  
William Ritchie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cell Lines ◽  
Cell Invasion ◽  
Triple Negative ◽  
Splice Variants ◽  
Breast Cancer Cell Lines ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Alternative Splice Variants

ABSTRACTBackgroundBreast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers.ResultsTranscriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype.ConclusionsUsing a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

scholarly journals Coexpression Network Analysis of Genes Related to the Characteristics of Tumor Stemness in Triple-Negative Breast Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Huan-dan Suo ◽  
Zuo Tao ◽  
Lei Zhang ◽  
Zi-ning Jin ◽  
Xiao-ying Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Stem Cell ◽  
Network Analysis ◽  
Triple Negative Breast Cancer ◽  
Functional Annotation ◽  
Triple Negative ◽  
Nuclear Division ◽  
Key Genes ◽  
Coexpression Network Analysis

Cancer stem cells (CSCs) are subsets of cells with the ability of self-renewal and differentiation in neoplasm, which are considered to be related to tumor heterogeneity. It has been reported that CSCs act on tumorigenesis and tumor biology of triple-negative breast cancer (TNBC). However, the key genes that cause TNBC showing stem cell characteristics are still unclear. We combined the RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database and mRNA expression-based stemness index (mRNAsi) to further analyze mRNAsi with regard to molecular subtypes, tumor depth, and pathological staging characteristics of breast cancer (BC). Secondly, we extract the differential gene expression of tumor vs. normal group and TNBC vs. other subtypes of BC group, respectively, and intersect them to achieve precise results. We used a weighted gene coexpression network analysis (WGCNA) to screen significant gene modules and the functions of selected genes including BIRC5, CDC25A, KIF18B, KIF2C, ORC1, RAD54L, and TPX2 were carried out through gene ontology (GO) functional annotation. The Oncomine, bc-GenExMiner v4.4, GeneMANIA, Kaplan-Meier Plotter (KM-plotter), and GEPIA were used to verify the expression level and functions of key genes. In this study, we found that TNBC had the highest stem cell characteristics in BC compared with other subtypes. The lower the mRNAsi score, the better the overall survival and treatment outcome. Seven key genes of TNBC were screened and functional annotation indicated that there were strong correlations between them, relating to nuclear division, organelle fission, mitotic nuclear division, and other events that determine cell fate. Among these genes, we found four genes that were highly associated with adverse survival events. Seven key genes identified in this study were found to be closely related to the maintenance of TNBC stemness, and the overexpression of four showed earlier recurrence. The overall survival (OS) curves of all key genes between differential expression level crossed at around nine-year follow-up, which was consistent with the trend of the OS curve related to mRNAsi. These findings may provide new ideas for screening therapeutic targets in order to depress TNBC stemness.

scholarly journals A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jean-Philippe Villemin ◽  
Claudio Lorenzi ◽  
Marie-Sarah Cabrillac ◽  
Andrew Oldfield ◽  
William Ritchie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cell Lines ◽  
Cell Invasion ◽  
Triple Negative ◽  
Splice Variants ◽  
Breast Cancer Cell Lines ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Alternative Splice Variants

Abstract Background Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers. Results Transcriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype. Conclusions Using a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

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