Cited by 295Bioinformatics and Genomic NetworksCancer Genomics and DiagnosticsGene expression and cancer classification

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Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

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Heo, Yong Jin, Hwa, Chanwoong, Lee, Gang-Hee, Park, Jae-Min, An, Joon-Yong·2021·Molecules and Cells

DOI 10.14348/molcells.2021.0042PMC PMC8334347

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Abstract

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

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Fig. 1 Overview of multi-omics approaches in cancer research. The integration of omics datasets is a crucial step in multi-omics studies. Datasets such as somatic mutations, CNV, gene expression, methylation, and proteome datasets are merged using various computational frameworks with distinct methods. The integration enables the comparison of molecular features across multiple viewpoints and the clustering of patients with relevant clinical features. Possible outcomes include enhanced identification of clinical subtypes, understanding of cancer pathophysiology, prediction of potential drug targets, and clinical decision support.CC BY-NC-SA · PubMed Central

Fig. 2 Latest findings in cancer multi-omics research. Multi-omics approaches integrate various high-throughput sequencing datasets across a range of molecular layers. Biological features are subject to multi-view clustering methods and account for distinct subtypes of cancer patients based on relevant clinical features.CC BY-NC-SA · PubMed Central

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Authors: Heo, Y. J., Hwa, C., Lee, G.-H., Park, J.-M., & An, J.-Y.
Affiliations: Korea University (KR)
ORCID: Heo, Yong Jin
Hwa, Chanwoong
Lee, Gang-Hee
Park, Jae-Min
An, Joon-Yong
Venue: Molecules and Cells
Publisher: Elsevier BV
Published: 2021-07
Volume: 44
Issue: 7
Pages: 433-443
ISSN: 0219-1032 (ISSN-L)
1016-8478
DOI: 10.14348/molcells.2021.0042
PMC: PMC8334347
OpenAlex: W3180571536
Cited by: 294
References: 88
Citations by year: 2026: 66 · 2025: 81 · 2024: 75 · 2023: 56 · 2022: 15
FWCI: 3.86
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-sa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-sa/3.0/ (vor, 8 d)
Locations: Molecules and Cells (publishedVersion, cc-by-nc-sa)
PubMed Central (publishedVersion, cc-by-nc-sa)
PubMed (publishedVersion)
Funders: National Research Foundation of Korea (2019M3E5D3073568)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: multi-omics integration, cancer subtyping, multi-view clustering, biological networks, survival prediction, therapeutic outcome prediction, drug development, cancer genomics
MeSH terms: Humans, Neoplasms, Survival Analysis, Computational Biology, Biomedical Research, Metabolomics

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Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

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