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

Heo, Yong Jin; Hwa, Chanwoong; Lee, Gang-Hee; Park, Jaemin; An, Joon-Yong

doi:10.14348/molcells.2021.0042

Cited by 111 publications

(58 citation statements)

References 87 publications

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“…In recent years, the rapid development of high-throughput sequencing technologies enables researchers to obtain multi-omics molecular profiles of various cancer types. To better understand the molecular and clinical characteristics of cancers, it is crucial to use multi-omics data fusion methods [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

Leng¹,

Zheng

Wen³

et al. 2022

Genome Biol

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Background A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples. Results In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods’ strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks. Conclusions Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo.

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Section: Resultsmentioning

confidence: 99%

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

Leng¹,

Zheng

Wen³

et al. 2022

Genome Biol

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show abstract

“…However, the application of precision medicine for the prevention, diagnosis, and treatment of tumors is far from satisfactory (Lu and Zhan, 2018). Multi-omics approaches are novel frameworks that can integrate multiple omics datasets generated from the same patients (Heo et al, 2021); thus, an increasing number of studies have tried to characterize the molecular and clinical features of cancers from a multi-omics perspective (Sun and Hu, 2016).…”

Section: Introductionmentioning

confidence: 99%

MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis

Leng

et al. 2022

Front. Genet.

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In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

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“…In recent years, new advances in and the widespread application of omics technologies have enabled cost-efficient, high-throughput, in-depth analyses of transcripts, proteins, and other molecules, creating an unprecedented body of knowledge [28] and enabling advances in personalized medicine [29], cancer research [30], and rare diseases [31,32]. Herein, we reviewed the literature on DM1, since the number of omics studies far exceeds those involving Myotonic Dystrophy Type 2 (DM2), focusing particularly on the tools used to elucidate the pathological mechanisms involved, to broaden the scope of our understanding of this complex multisystem disorder.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Complex Molecular Pathogenesis of Myotonic Dystrophy Type 1 through Omics Studies

Espinosa-Espinosa

González-Barriga

López-Castel

et al. 2022

IJMS

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Omics studies are crucial to improve our understanding of myotonic dystrophy type 1 (DM1), the most common muscular dystrophy in adults. Employing tissue samples and cell lines derived from patients and animal models, omics approaches have revealed the myriad alterations in gene and microRNA expression, alternative splicing, 3′ polyadenylation, CpG methylation, and proteins levels, among others, that contribute to this complex multisystem disease. In addition, omics characterization of drug candidate treatment experiments provides crucial insight into the degree of therapeutic rescue and off-target effects that can be achieved. Finally, several innovative technologies such as single-cell sequencing and artificial intelligence will have a significant impact on future DM1 research.

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

Cited by 111 publications

References 87 publications

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis

Deciphering the Complex Molecular Pathogenesis of Myotonic Dystrophy Type 1 through Omics Studies

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