Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Lewis, Joshua E.; Kemp, Melissa L.

doi:10.1038/s41467-021-22989-1

Cited by 121 publications

(71 citation statements)

References 89 publications

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“…In most of the reviewed studies, the different omics datasets were separately analyzed and only combined for the final interpretation of the metabolic changes. In this scenario, the development and implementation of novel computational tools, focused on the integrated analysis of different omics datasets that enable the assessment of the interplay between the different components of a biological system, would be greatly valuable [162,163]. Furthermore, although some studies included a vast number of samples [61,63,101,107,109], a major limitation in the majority of these studies was the lack of an external cohort of PCa patients/samples for confirming the reproducibility and robustness of the results.…”

Section: Future Perspectives and Conclusionmentioning

confidence: 99%

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Gómez-Cebrián

Poveda

Pineda‐Lucena³

et al. 2022

Cancers

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Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

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Section: Future Perspectives and Conclusionmentioning

confidence: 99%

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Gómez-Cebrián

Poveda

Pineda‐Lucena³

et al. 2022

Cancers

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“…In particular, GCNs of 17 human cancers and 46 human tissues have been generated and used to gain insights into disease mechanisms by identifying the key biological components of the cancers or tissues ( Arif et al., 2021 ; Lee et al., 2018 ; Uhlen et al., 2017 ). GEMs are reconstructed by incorporating all biochemical reactions and transport processes in a cell or tissue and have been extensively used to discover potential biomarkers and drug targets, as well as to reveal the mode of action of a drug ( Lewis and Kemp, 2021 ; Mardinoglu et al., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

et al. 2021

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Summary Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genome-scale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.

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“…As a result of this study, the high accuracy (AUC about 0.906 ± 0.004) was reported in the model used to integrate data from different omics fields. In addition, this method played an important role in identifying subgroups of patients as well as metabolic biomarkers in resistance to ionizing radiation (Lewis and Kemp, 2021).…”

Section: In Multi-omics Data Analyses Of Cancermentioning

confidence: 99%

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

et al. 2022

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Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Cited by 121 publications

References 89 publications

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

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