Identification of Multi-Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi-omics Data

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152

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Identification of Multi-Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi-omics Data

76.2K

views

152

authors

19

articles

Editors

5

Chunquan Li

The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan

Dechao Bu

Institute of Computing Technology, Chinese Academy of Sciences (CAS)

DECHEN Lin LIN

Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center

Sun Liang

Shandong First Medical University

Masaharu Hazawa

Kanazawa University

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Cancer is the second leading cause of death globally. Biomarkers play an increasingly important role in predicting cancer risk and in the clinical management of cancer patients. With the development of high-throughput profiling technologies, we could obtain a great amount of cancer-related data, including genetic variation, transcriptome, proteomics, and protein modification omics data. These multidimensional omics data provides a panoramic analysis of cancers. The coding and noncoding genes could format a competing endogenous RNA (ceRNA) network, thus involved in cancer initiation and progression. Computational methods are commonly used in systems biology and the integration of experimental research studies. The application of computational biology facilitated the screening of cancer biomarkers using multi-omics data. Analysis based on the integration of multiple omics data (such as protein expression, ncRNA expression, DNA methylation, etc.) could provide a comprehensive perspective for cancer molecular-level analysis. It would greatly promote the development of tumour precision medicine. Increasing evidence shows that multi-biomarkers (including abnormal gene expression, genetic variation, DNA methylation etc.) are potential prognostic/diagnostic biomarkers and therapeutic targets for cancer.

This Research Topic aims at promoting the understanding of multi-omics markers in tumours and provide new insights for the application of bioinformatics algorithms.

We welcome Original Research articles, Methods, Reviews, Mini Reviews, Perspectives and Opinions on the sub-themes below:

• Genetic and epigenetic traits as prognostic and diagnostic biomarkers for cancer

• The potential of ncRNA as biomarkers in cancer

• Somatic and cell-free DNA methylation as a universal biomarker in cancer

• Identifying novel diagnostic and prognostic biomarkers using multi-omics data

• Integration of multi-omics data in cancer diagnosis and prognosis

• Proteomics for cancer biomarker discovery

• Multi-biomarker in classification and staging of cancer

• Cancer-related diagnostic and prognostic multi-biomarker

• Identification multi-biomarker for cancer diagnosis and prognosis based on biomolecular network

• Application of computational biology in the screening of biomarkers using omics data

• Machine learning methods in the computational biology of cancer

Disclaimer: Authors must be aware that the validation of predictions for biomarkers is the standard for the journal section.

Descriptive studies will not be considered for review unless they are extended to provide meaningful insights into gene/protein function and/or the biology of the subject described. Brief Research Reports, Data Reports, Genome Announcements, Systematic Reviews, and Case Reports will not undergo the peer-review process. Studies that fall into the following categories will also not be considered for review:

• Comparative transcriptomic analyses that report only a collection of differentially expressed genes, some of which have been validated by qPCR under different conditions or treatments;
• Re-analysis of existing genomic and transcriptomic data that attempts to identify a set of diagnostic or prognostic markers for disease.
• Descriptive studies that merely define gene families using basic phylogenetics and assign cursory functional attributions (e.g. expression profiles, hormone or metabolites levels, promoter analysis, informatic parameters).
• Studies consisting of publicly available data to develop predictive models.
• Descriptive studies that only report sequencing data.

Cancer is the second leading cause of death globally. Biomarkers play an increasingly important role in predicting cancer risk and in the clinical management of cancer patients. With the development of high-throughput profiling technologies, we could obtain a great amount of cancer-related data, including genetic variation, transcriptome, proteomics, and protein modification omics data. These multidimensional omics data provides a panoramic analysis of cancers. The coding and noncoding genes could format a competing endogenous RNA (ceRNA) network, thus involved in cancer initiation and progression. Computational methods are commonly used in systems biology and the integration of experimental research studies. The application of computational biology facilitated the screening of cancer biomarkers using multi-omics data. Analysis based on the integration of multiple omics data (such as protein expression, ncRNA expression, DNA methylation, etc.) could provide a comprehensive perspective for cancer molecular-level analysis. It would greatly promote the development of tumour precision medicine. Increasing evidence shows that multi-biomarkers (including abnormal gene expression, genetic variation, DNA methylation etc.) are potential prognostic/diagnostic biomarkers and therapeutic targets for cancer.

This Research Topic aims at promoting the understanding of multi-omics markers in tumours and provide new insights for the application of bioinformatics algorithms.

We welcome Original Research articles, Methods, Reviews, Mini Reviews, Perspectives and Opinions on the sub-themes below:

• Genetic and epigenetic traits as prognostic and diagnostic biomarkers for cancer

• The potential of ncRNA as biomarkers in cancer

• Somatic and cell-free DNA methylation as a universal biomarker in cancer

• Identifying novel diagnostic and prognostic biomarkers using multi-omics data

• Integration of multi-omics data in cancer diagnosis and prognosis

• Proteomics for cancer biomarker discovery

• Multi-biomarker in classification and staging of cancer

• Cancer-related diagnostic and prognostic multi-biomarker

• Identification multi-biomarker for cancer diagnosis and prognosis based on biomolecular network

• Application of computational biology in the screening of biomarkers using omics data

• Machine learning methods in the computational biology of cancer

Disclaimer: Authors must be aware that the validation of predictions for biomarkers is the standard for the journal section.

Descriptive studies will not be considered for review unless they are extended to provide meaningful insights into gene/protein function and/or the biology of the subject described. Brief Research Reports, Data Reports, Genome Announcements, Systematic Reviews, and Case Reports will not undergo the peer-review process. Studies that fall into the following categories will also not be considered for review:

• Comparative transcriptomic analyses that report only a collection of differentially expressed genes, some of which have been validated by qPCR under different conditions or treatments;
• Re-analysis of existing genomic and transcriptomic data that attempts to identify a set of diagnostic or prognostic markers for disease.
• Descriptive studies that merely define gene families using basic phylogenetics and assign cursory functional attributions (e.g. expression profiles, hormone or metabolites levels, promoter analysis, informatic parameters).
• Studies consisting of publicly available data to develop predictive models.
• Descriptive studies that only report sequencing data.

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Editors

Chunquan Li

The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan

Dechao Bu

Institute of Computing Technology, Chinese Academy of Sciences (CAS)

DECHEN Lin LIN

Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center

Sun Liang

Shandong First Medical University

Masaharu Hazawa

Kanazawa University

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Published In

Frontiers in Genetics

Computational Genomics

Frontiers in Plant Science

Computational Genomics

Frontiers in Bioengineering and Biotechnology

Computational Genomics

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