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

Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku Tokyo 104-0045, Japan
National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan
Humanome Lab, 2-4-10, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
Author to whom correspondence should be addressed.
Biomolecules 2020, 10(4), 524;
Received: 26 January 2020 / Revised: 25 March 2020 / Accepted: 27 March 2020 / Published: 30 March 2020
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
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. View Full-Text
Keywords: multi-omics analysis; lung cancer; survival-associated genes multi-omics analysis; lung cancer; survival-associated genes
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MDPI and ACS Style

Asada, K.; Kobayashi, K.; Joutard, S.; Tubaki, M.; Takahashi, S.; Takasawa, K.; Komatsu, M.; Kaneko, S.; Sese, J.; Hamamoto, R. Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer. Biomolecules 2020, 10, 524.

AMA Style

Asada K, Kobayashi K, Joutard S, Tubaki M, Takahashi S, Takasawa K, Komatsu M, Kaneko S, Sese J, Hamamoto R. Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer. Biomolecules. 2020; 10(4):524.

Chicago/Turabian Style

Asada, Ken, Kazuma Kobayashi, Samuel Joutard, Masashi Tubaki, Satoshi Takahashi, Ken Takasawa, Masaaki Komatsu, Syuzo Kaneko, Jun Sese, and Ryuji Hamamoto. 2020. "Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer" Biomolecules 10, no. 4: 524.

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