A pathways-based prediction model for classifying breast cancer subtypes

Wu, Tong; Wang, Yunfeng; Jiang, Ronghui; Lu, Xinliang; Tian, Jing

doi:10.18632/oncotarget.18544

Cited by 19 publications

(11 citation statements)

References 33 publications

(39 reference statements)

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“…With the development of microarray technology and RNA sequencing technology, many studies have used gene expression profiles to classify tumors [ 25 , 26 ]. Gene expression profiles have been used to divide LUAD into subgroups.…”

Section: Discussionmentioning

confidence: 99%

Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes

Zheng¹,

Deng²,

Yang

2020

BioMed Research International

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Background. The acquisition of invasive tumor cell behavior is considered to be the cornerstone of the metastasis cascade. Thus, genetic markers associated with invasiveness can be stratified according to patient prognosis. In this study, we aimed to identify an invasive genetic trait and study its biological relevance in lung adenocarcinoma. Methods. 250 TCGA patients with lung adenocarcinoma were used as the training set, and the remaining 250 TCGA patients, 500 ALL TCGA patients, 226 patients with GSE31210, 83 patients with GSE30219, and 127 patients with GSE50081 were used as the verification data sets. Subtype classification of all TCGA lung adenocarcinoma samples was based on invasion-associated genes using the R package ConsensusClusterPlus. Kaplan-Meier curves, LASSO (least absolute contraction and selection operator) method, and univariate and multivariate Cox analysis were used to develop a molecular model for predicting survival. Results. As a consequence, two molecular subtypes for LUAD were first identified from all TCGA all data sets which were significant on survival time. C1 subtype with poor prognosis has higher clinical characteristics of malignancy, higher mutation frequency of KRAS and TP53, and a lower expression of immune regulatory molecules. 2463 differentially expressed invasion genes between C1 and C2 subtypes were obtained, including 580 upregulation genes and 1883 downregulation genes. Functional enrichment analysis found that upregulated genes were associated with the development of tumor pathways, while downregulated genes were more associated with immunity. Furthermore, 5-invasion gene signature was constructed based on 2463 genes, which was validated in four data sets. This signature divided patients into high-risk and low-risk groups, and the LUDA survival rate of the high-risk group is significantly lower than that of the low-risk group. Multivariate Cox analysis revealed that this gene signature was an independent prognostic factor for LUDA. Compared with other existing models, our model has a higher AUC. Conclusion. In this study, two subtypes were identified. In addition, we developed a 5-gene signature prognostic risk model, which has a good AUC in the training set and independent validation set and is a model with independent clinical characteristics. Therefore, we recommend using this classifier as a molecular diagnostic test to assess the prognostic risk of patients with LUDA.

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Section: Discussionmentioning

confidence: 99%

Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes

Zheng¹,

Deng²,

Yang

2020

BioMed Research International

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“…Copy number variations (24) and single nucleotide polymorphisms (SNPs) (25) were used to train SVM classifiers for bladder, uveal cancer and breast cancer respectively. Wu et al (26) built three SVM classification models based on the identified pathways which effectively classified different breast cancer subtypes.…”

Section: Cancer Classification and Subtypingmentioning

confidence: 99%

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

2018

CGP

675

347

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Abstract. Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that hasMachine learning (ML) "learns" a model from past data in order to predict future data (1). The key process is the learning which is one of the artificial intelligences. Many different statistical, probabilistic, and optimization techniques can be implemented as the learning methods such as the logistic regression, artificial neural networks (ANN), K-nearest neighbor (KNN), decision trees (DT) and Naive Bayes. There are two main types of ML learning -supervised learning and unsupervised learning. The supervised learning builds a model by learning from known classes (labeled training data). In contrast, unsupervised learning methods learn the common features from unknown class data (unlabeled training data).ML algorithms have been used for key feature training and recognition and for group classification. The strength of ML methods is it could detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to complex genomic data, especially in cancer studies. For example, ANN and DT have been used in cancer detection and diagnosis for nearly 20 years (2-3). The clinical implication of cancer heterogeneity and various cancer genomic data available motivate the applications of ML for cancer classification using genomic data.SVM learning is one of many ML methods. Compared to the other ML methods SVM is very powerful at recognizing subtle patterns in complex datasets (4). SVM can be used to recognize handwriting, recognize fraudulent credit cards, identify a speaker, as well as detect face (5). Cancer is a genetic disease where the genomic feature patterns or feature function patterns may represent the cancer subtypes, the outcome prognosis, drug benefit prediction, tumorigenesis drivers, or a tumor-specific biological process. Therefore, the Artificial Intelligence of SVM can help us in recognizing these patterns in a variety of applications. SVM ModelSVM is a powerful method for building a classifier. It aims to create a decision boundary between two classes that enables the prediction of labels from one or more feature vectors (6). This decision boundary, known as the hyperplane, is orientated in such a way that it is as far as 41

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“…EC is hormone-dependent and, like breast cancer, can express markers such as estrogen receptors (ERs), progesterone receptors (PRs) and oncoprotein c-erbB-2 (HER2) (3,4). Breast cancer can be characterized into several subgroups based on immunohistochemistry: Luminal A (ER + and/or PR + , HER2 -, low Ki67); luminal B (ER and/or PR + , HER2 -, high Ki67); non-luminal (ER -, PR -, and HER2 + ); and triple negatives (ER -, PR -, and HER2 -) (5). This classification finds clinical application in terms of prognosis and treatment personalization.…”

Section: Introductionmentioning

confidence: 99%

Triple negative endometrial cancer: Incidence and prognosis in a monoinstitutional series of 220�patients

Porzio

Cordini

et al. 2020

Oncol Lett

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Endometrial cancer (EC) represents the most frequently occuring gynecological tumor worldwide. The aim of the present study was to estimate the prognostic value of triple negative phenotype (TNP) in EC, and any associations with to pathological and clinical characteristics. The present study includes 220 cases of patients with EC who underwent to surgery at the Guglielmo da Saliceto Hospital of Piacenza (Italy) and the expressions of estrogen receptor (ER), progesterone receptor (PR) and oncoprotein c-erbB-2 (HER2) expression were examined. Pearson's Chi-square and Fisher's exact test were used to evaluate the association of TNP cases with variables associated with a worse prognosis. Progression-free survival (PFS) and overall survival (OS) were analyzed with Kaplan-Meier curves. A total of 26 patients (12%) had a TNP, and these cases had a higher percentage of high-risk histology, an advanced stage of disease at the time of diagnosis, with shorter PFS and OS when compared to non-TNP. The present study confirmed that TNP represents prognostic significance in EC.

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A pathways-based prediction model for classifying breast cancer subtypes

Cited by 19 publications

References 33 publications

Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes

Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Triple negative endometrial cancer: Incidence and prognosis in a monoinstitutional series of 220�patients

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