Cancer is one of the main causes of human death and great efforts have been made in the research field to understand the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors in order to find effective treatments. However, most cancer drugs developed so far are effective only in a subgroup of patients because of the huge heterogeneity found in individual tumor. There are still large gaps in our understanding of the optimal treatment approaches and the pathogenic molecular mechanisms of cancer. In addition, the diagnostic tools for tumors are still dominated by approaches that were developed years ago which ignore the heterogeneity in different patients despite the advances in medical research. Therefore, there is an urgent need for the development of molecular characterization and precision diagnosis for cancer.
During the past decade, advances in high-throughput technologies have dramatically decreased the cost of large-scale multi-omics dataset generation. The development of new omics tools (such as genomics, transcriptomics, proteomics, metabolomics and metagenomics) has opened up new possibilities to study cancer in a multi-omics, high-throughput and genome-wide manner with high accuracy. Systems biology utilizes mathematical and computational approaches to generate models as platforms which could be used to investigate the changes of the system by the integration of omics datasets. In this context, systems biology together with current multi-omics technologies can accelerate the understanding of the molecular mechanisms and the emergence of personalized and precision diagnosis of cancer. The aim of this Research Topic is to cover promising, recent, and novel research trends in the molecular characterization and diagnosis of cancer. Areas to be covered in this Research Topic may include, but are not limited to, systematic characterization and development of biomarkers for stratification and/or diagnosis of cancer subtypes or individual patient based on single or multi-omics profile.
Cancer is one of the main causes of human death and great efforts have been made in the research field to understand the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors in order to find effective treatments. However, most cancer drugs developed so far are effective only in a subgroup of patients because of the huge heterogeneity found in individual tumor. There are still large gaps in our understanding of the optimal treatment approaches and the pathogenic molecular mechanisms of cancer. In addition, the diagnostic tools for tumors are still dominated by approaches that were developed years ago which ignore the heterogeneity in different patients despite the advances in medical research. Therefore, there is an urgent need for the development of molecular characterization and precision diagnosis for cancer.
During the past decade, advances in high-throughput technologies have dramatically decreased the cost of large-scale multi-omics dataset generation. The development of new omics tools (such as genomics, transcriptomics, proteomics, metabolomics and metagenomics) has opened up new possibilities to study cancer in a multi-omics, high-throughput and genome-wide manner with high accuracy. Systems biology utilizes mathematical and computational approaches to generate models as platforms which could be used to investigate the changes of the system by the integration of omics datasets. In this context, systems biology together with current multi-omics technologies can accelerate the understanding of the molecular mechanisms and the emergence of personalized and precision diagnosis of cancer. The aim of this Research Topic is to cover promising, recent, and novel research trends in the molecular characterization and diagnosis of cancer. Areas to be covered in this Research Topic may include, but are not limited to, systematic characterization and development of biomarkers for stratification and/or diagnosis of cancer subtypes or individual patient based on single or multi-omics profile.