Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine

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articles

Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine

74.2K

views

214

authors

27

articles

Editorial

05 July 2023

Editorial: Computational methods for multi-omics data analysis in cancer precision medicine

Moein Piroozkhah

,

Radman Mazloomnejad

,

Zahra Salehi

and

Ehsan Nazemalhosseini-Mojarad

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Editors

2

Ehsan Nazemalhosseini-Mojarad

Research Institute For Gastroenterology & Liver Diseases, Shahid Beheshti University of Medical Sciences

Institute of Bioimaging and Molecular Physiology, Department of Biomedical Sciences, National Research Council (CNR)

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Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels:
i) DNA with genome-wide association studies (GWAS),
ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs)
iii) mRNA.

The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.

This Research Topic aims to publish papers that extract research related to these concepts. Articles extracted from bioinformatics databases and also have a laboratory confirmation take first score. However, high quality purely bioinformatic papers are also accepted. Topics include but not limited to:

• GWAS to investigate genes associated with cancer and other comorbidities or lifestyle risk factors such as (but not limited to) obesity, hypertension, neurological diseases, sedentary life, and smoking.
• Novel Computational methods to identify potential biomarkers for cancer diagnosis and prognosis
• Integrative analysis of multi-omics data from public datasets (i.e., TCGA, GEO, and UKBB)
• Combination of wet lab analysis and in silico methods to improve knowledge in cancer patients’ management
• In-silico protein-ligand binding prediction for targeted therapy (i.e., molecular docking) of cancer

Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels:
i) DNA with genome-wide association studies (GWAS),
ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs)
iii) mRNA.

The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.

This Research Topic aims to publish papers that extract research related to these concepts. Articles extracted from bioinformatics databases and also have a laboratory confirmation take first score. However, high quality purely bioinformatic papers are also accepted. Topics include but not limited to:

• GWAS to investigate genes associated with cancer and other comorbidities or lifestyle risk factors such as (but not limited to) obesity, hypertension, neurological diseases, sedentary life, and smoking.
• Novel Computational methods to identify potential biomarkers for cancer diagnosis and prognosis
• Integrative analysis of multi-omics data from public datasets (i.e., TCGA, GEO, and UKBB)
• Combination of wet lab analysis and in silico methods to improve knowledge in cancer patients’ management
• In-silico protein-ligand binding prediction for targeted therapy (i.e., molecular docking) of cancer

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Editors

Ehsan Nazemalhosseini-Mojarad

Research Institute For Gastroenterology & Liver Diseases, Shahid Beheshti University of Medical Sciences

Institute of Bioimaging and Molecular Physiology, Department of Biomedical Sciences, National Research Council (CNR)

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Published In

Frontiers in Genetics

Cancer Genetics and Oncogenomics

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