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

Abstract

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.

Fig 1. The AUC, AUPR, and accuracies of EMT features versus random features using DM data with the core EMT network.

Gaussian kernel is used to estimate the density functions based on results from 30 times 10-fold cross-validation. For each cross-validation fold, EMT features and random features are tested on the same training and cross-validation samples. Each row in the figure corresponds to one feature selection algorithm. The last row corresponds to using all EMT features. The p-values of paired t-tests are provided in each sub-figure.

Fig 2. The comparison of prediction performance between FSFs and individually selected features for different feature selection algorithms.

The boxplot is based on the results from 30 times stratified 10-fold cross-validation.

Fig 3. EMT single-omics signatures can stratify test samples into significantly different prognostic groups.

The signature is selected by addDA2 algorithm using DM data.

Fig 4. EMT multi-omics signatures can stratify test samples into significantly different prognostic groups, when the corresponding single-omics signatures cannot.

The signature consists of both GE and DM single-omics signatures selected by t-test.