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. 2021 Sep 20;23(9):1232.
doi: 10.3390/e23091232.

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Hui Wen Nies  1 , Mohd Saberi Mohamad  2 , Zalmiyah Zakaria  1 , Weng Howe Chan  1 , Muhammad Akmal Remli  3 , Yong Hui Nies  4
Affiliations

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Hui Wen Nies et al. Entropy (Basel). .

Abstract

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

Keywords: ANOVA; breast cancer; directed random walk; microarray analysis; multiclass; pathway selection; prognostic markers.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the eDRW+.
Figure 2
Figure 2
The flow of the pre-processing step for the gene expression data.
Figure 3
Figure 3
Comparison of the statistical tests employed in DRW (a) and eDRW+ (b).
Figure 4
Figure 4
The calculation of the genes’ weight for eDRW+.
Figure 5
Figure 5
Difference between DRW (a) and eDRW+ (b) in the selection of the pathways.
Figure 6
Figure 6
Overview of the pathway activity inference and pathway selection.
See this image and copyright information in PMC

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