Epithelial-Mesenchymal Transition Regulatory Network-Based Feature Selection in Lung Cancer Prognosis Prediction

Shao, Borong; Conrad, Tim

doi:10.1007/978-3-319-31744-1_13

Cited by 6 publications

(9 citation statements)

References 21 publications

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“…The Cancer Genome Atlas (TCGA) offers a comprehensive data portal that contains multiple types of molecular data for more than 30 types of cancer, which enables studies to be carried out such as prognosis prediction [6], cancer subtypes classification [7,8] and cancer driver genes identification [8]. This study focuses on the prognosis prediction in lung adenocarcinoma (LUAD) and it is a substantial extension of our previous work [9].…”

Section: Introductionmentioning

confidence: 99%

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

Shao

Carlo

Conrad

2017

Genomics Comput Biol

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Epithelial mesenchymal transition (EMT) process has been shown as highly relevant to cancer prognosis. However, although different biological network-based biomarker identification methods have been proposed to predict cancer prognosis, EMT network has not been directly used for this purpose. In this study, we constructed an EMT regulatory network consisting of 87 molecules and tried to select features that are useful for prognosis prediction in Lung Adenocarcinoma (LUAD). To incorporate multiple molecular profiles, we obtained four types of molecular data including mRNA-Seq, copy number alteration (CNA), DNA methylation, and miRNA-Seq data from The Cancer Genome Atlas. The data were mapped to the EMT network in three alternative ways: mRNA-Seq and miRNA-Seq, DNA methylation, and CNA and miRNA-Seq. Each mapping was employed to extract five different sets of features using discretization and network-based biomarker identification methods. Each feature set was then used to predict prognosis with SVM and logistic regression classifiers. We measured the prediction accuracy with AUC and AUPR values using 10 times 10-fold cross validation. For a more comprehensive evaluation, we also measured the prediction accuracies of clinical features, EMT plus clinical features, randomly picked 87 molecules from each data mapping, and using all molecules from each data type. Counter-intuitively, EMT features do not always outperform randomly selected features and the prediction accuracies of the five feature sets are mostly not significantly different. Clinical features are shown to give the highest prediction accuracies. In addition, the prediction accuracies of both EMT features and random features are comparable as using all features (more than 17,000) from each data type.

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Section: Introductionmentioning

confidence: 99%

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

Shao

Carlo

Conrad

2017

Genomics Comput Biol

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“…This is 55 achieved by assigning each node an initial score based on t-test and then multiplying it 56 with the random walk kernel. The p-step random walk kernel is used as a similarity 57 measure to capture the relatedness of two nodes in the network. It is defined as:…”

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confidence: 99%

“…Previously we employed EMT networks for selecting lung cancer prognostic 366 signatures [39,57]. However, [57] used mRNA expression and miRNA expression data 367 only.…”

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confidence: 99%

“…Previously we employed EMT networks for selecting lung cancer prognostic 366 signatures [39,57]. However, [57] used mRNA expression and miRNA expression data 367 only. [39] employed three data levels for feature selection but obtained no significant 368 improvement in predictions.…”

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confidence: 99%

“…Besides, we reviewed the network used in [39] and 370 removed the edges which denote associations rather than direct gene regulations. What 371 also distinguishes this study from our previous work is the employment of 10 372 representative feature selection algorithms, instead of decomposing the network into 373 network motifs [39,57]. We have selected EMT signatures on three data levels with 374 different network sizes, compared with the features selected from the whole data 375 dimensions, and derived prognostic rules from EMT signatures.…”

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confidence: 99%

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EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma

Shao

Bjaanæs

Helland

et al. 2018

Preprint

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Various feature selection algorithms have been proposed to identify cancer prognostic biomarkers. In recent years, however, their reproducibility is criticized. The performance of feature selection algorithms is shown to be affected by the datasets, underlying networks and evaluation metrics. One of the causes is the curse of dimensionality, which makes it hard to select the features that generalize well on independent data. Even the integration of biological networks does not mitigate this issue because the networks are large and many of their components are not relevant for the phenotype of interest. With the availability of multi-omics data, integrative approaches are being developed to build more robust predictive models. In this scenario, the higher data dimensions create greater challenges.We proposed a phenotype relevant network-based feature selection (PRNFS) framework and demonstrated its advantages in lung cancer prognosis prediction. We constructed cancer prognosis relevant networks based on epithelial mesenchymal transition (EMT) and integrated them with different types of omics data for feature selection. With less than 2.5% of the total dimensionality, we obtained EMT prognostic signatures that achieved remarkable prediction performance (average AUC values >0.8), very significant sample stratifications, and meaningful biological interpretations. In addition to finding EMT signatures from different omics data levels, we combined these single-omics signatures into multi-omics signatures, which improved sample stratifications significantly. Both single-and multi-omics EMT signatures were tested on independent multi-omics lung cancer datasets and significant sample stratifications were obtained.

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Integrating Biological Context into the Analysis of Gene Expression Data

Perscheid

Uflacker

2019

Advances in Intelligent Systems and Computing

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Epithelial-Mesenchymal Transition Regulatory Network-Based Feature Selection in Lung Cancer Prognosis Prediction

Cited by 6 publications

References 21 publications

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma

Integrating Biological Context into the Analysis of Gene Expression Data

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