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. 2020 Mar 30;10(4):524.
doi: 10.3390/biom10040524.

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

Ken Asada  1   2 , Kazuma Kobayashi  1   2 , Samuel Joutard  1   2 , Masashi Tubaki  3 , Satoshi Takahashi  1   2 , Ken Takasawa  1   2 , Masaaki Komatsu  1   2 , Syuzo Kaneko  2 , Jun Sese  2   4 , Ryuji Hamamoto  1   2
Affiliations

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

Ken Asada et al. Biomolecules. .

Abstract

Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.

Keywords: lung cancer; multi-omics analysis; survival-associated genes.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overall workflow for classification of lung-cancer subtypes. (A) Multi-omics analysis pipeline. (B) Clustering result of elbow method. (C) Clustering results of the Silhouette index and Calinski–Harabasz criterion. (D) Clustering result of K-means clustering. Red dot represents S1, and blue dot represents S2 subtype in Figure 1E. (E) Kaplan–Meier plot using patient labels obtained from Figure 1D.
Figure 2
Figure 2
Copy number variation analysis in two subtypes. Red box represents low-risk subtype, and blue box represents high-risk subtype.
Figure 3
Figure 3
Subtype-specific signaling pathways obtained from GSEA. The left represents pathway names, and the right represents gene ranks.
Figure 4
Figure 4
Newly identified survival-associated genes. The red line represents high-expression subtype, and the blue line represents low-expression subtype.
Figure 5
Figure 5
Co-expression analysis of ERO1B. Correlation analysis was performed with Pearson correlation test against ERO1B gene.
See this image and copyright information in PMC

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