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Cited by 67Lung Cancer Research StudiesGene expression and cancer classificationBioinformatics and Genomic Networks

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

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Asada, Ken, Kobayashi, Kazuma, Joutard, Samuel, Tubaki, Masashi, Takahashi, Satoshi, Takasawa, Ken, Komatsu, Masaaki, Kaneko, Syuzo +2 more·2020·Biomolecules
DOI 10.3390/biom10040524PMC PMC7225957
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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.

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  • Particularly in cancer patient survival subtyping with multi-omics modalities, the sequence of joint representation learning through an AE followed by feature selection with univariate Cox proportional hazard modeling and unsupervised label inference has become popular.

    “Particularly in cancer patient survival subtyping with multi-omics modalities, this sequence of steps has become popular [25–31, 34].”

    From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [29]

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Authors
Asada, K., Kobayashi, K., Joutard, S., Tubaki, M., Takahashi, S., Takasawa, K., Komatsu, M., Kaneko, S., Sese, J., & Hamamoto, R.
Affiliations
  • RIKEN Center for Advanced Intelligence Project (JP)
  • Artificial Intelligence Research Center (JP)
  • National Cancer Center Research Institute (JP)
  • National Cancer Centre Japan (JP)
Corresponding author
Hamamoto, Ryuji
ORCID
Venue
Biomolecules
Publisher
MDPI AG
Published
2020-03-30
Volume
10
Issue
4
Pages
524
ISSN
2218-273X (ISSN-L)
DOI
10.3390/biom10040524
PMC
PMC7225957
OpenAlex
W3013381886
Cited by
67
References
75
Citations by year
2026: 4 · 2025: 8 · 2024: 21 · 2023: 9 · 2022: 11 · 2021: 10 · 2020: 4
FWCI
3.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Biomolecules (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Japan Science and Technology Agency (JPMJCR1689)
  • Japan Society for the Promotion of Science (JP18H04908)
  • Cabinet Office, Government of Japan (the Public/Private R&D Investment Strategic Expansion Program (PRISM))
Topics
  • Health Sciences › Medicine › Oncology › Lung Cancer Research Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
lung adenocarcinoma, patient survival, multi-omics integration, RNA expression, microRNA expression, prognostic gene signature, CD56, RET, MARCH1, SLC7A8, cancer pathways, precision medicine
MeSH terms
Unsupervised Machine Learning, Adenocarcinoma of Lung, Aged, Female, Humans, Male, Middle Aged, Prognosis, Genomics, Kaplan-Meier Estimate, Support Vector Machine

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