Cancer is a complex and heterogeneous disease, with a wide range of biological and clinical characteristics. The use of genomic biomarkers has become an important tool for the diagnosis, prognosis, and treatment of cancer. Genomic biomarkers are specific alterations in the genome of cancer cells that can be used to identify the molecular subtypes of cancer and predict the response to therapy, and there is increasing evidence that genomics biomarkers can aid traditional pathology to improve clinical management and patient outcomes. Numerous potential biomarkers have come to light from the technological advances in genomics and analytics, driven by the digital revolution. Discovering, evaluating, sorting out, and eventually applying these biomarkers in the clinical setting is one aspect that translational genomics has been working on. Efficient and low-cost biomarker research methods and technologies are also important to promote translational genomics development.
Despite some of the cancer genomics biomarkers have translated into clinical practice and progress has been made with the implementation of improved technologies (mass spectrometry, arrays, and deep sequencing), several kinds of cancers with occult onsets, such as pancreatic cancer and ovarian cancer, are still short of biomarkers for early diagnosis. Moreover, the concept of cancer management demands more biomarkers for measuring disease progression, allowing better targeting of treatments and avoiding drug resistance. Meanwhile, as the number of potential biomarkers has been growing rapidly, computational approaches are essential for the analysis of big data and the evaluation of potential biomarkers. In addition, there is a growing focus on the importance of data sharing and collaboration in the field of translational genomics. Collaborative efforts are underway to collect and analyze large datasets of genomic and clinical data, which can be used to identify new biomarkers and improve our understanding of the molecular basis of cancer. Therefore, large discovery and validation studies are required to investigate the mechanisms relating to cancer development, progression, and metastasis, identify new cancer-related genomics biomarkers, and foster translation to the clinics.
In this research topic, we aim to gather original research articles, reviews, perspectives, and opinions that reveal markers and their molecular mechanisms, providing the necessary theoretical basis and clinical evidence for their application in cancer management. Topics include but are not limited to:
• Utilizing research findings achieved at the molecular, genomic, and cellular levels to guide therapeutic strategies for the enhancement of clinical outcomes.
• Novel computational or bioinformatics methods to identify potential biomarkers for cancer diagnosis and prognosis.
• Using multiple approaches, such as multi-omics technology, multi-modal data, and the combination of wet lab analysis and in silico methods, to study the diagnosis, treatment, and pathogenesis of cancer and optimize cancer patients’ management.
• Novel biomarkers discovery, identification, and validation, such as miRNA, lncRNA, DNA methylation, and cfDNA fragment omics.
• Development of new technologies and platforms for the identification and analysis of genomic biomarkers.
• Integration of genomic biomarkers with other clinical, pathological, and radiological parameters to improve cancer management.
Please note: Bioinformatic studies are welcome, however, these should not be based solely on analysis of publicly available datasets such as TCGA. It is essential to have an independent validation cohort on alternative datasets for statistically significant confirmation of the findings communicated and sufficient patient cases.
Cancer is a complex and heterogeneous disease, with a wide range of biological and clinical characteristics. The use of genomic biomarkers has become an important tool for the diagnosis, prognosis, and treatment of cancer. Genomic biomarkers are specific alterations in the genome of cancer cells that can be used to identify the molecular subtypes of cancer and predict the response to therapy, and there is increasing evidence that genomics biomarkers can aid traditional pathology to improve clinical management and patient outcomes. Numerous potential biomarkers have come to light from the technological advances in genomics and analytics, driven by the digital revolution. Discovering, evaluating, sorting out, and eventually applying these biomarkers in the clinical setting is one aspect that translational genomics has been working on. Efficient and low-cost biomarker research methods and technologies are also important to promote translational genomics development.
Despite some of the cancer genomics biomarkers have translated into clinical practice and progress has been made with the implementation of improved technologies (mass spectrometry, arrays, and deep sequencing), several kinds of cancers with occult onsets, such as pancreatic cancer and ovarian cancer, are still short of biomarkers for early diagnosis. Moreover, the concept of cancer management demands more biomarkers for measuring disease progression, allowing better targeting of treatments and avoiding drug resistance. Meanwhile, as the number of potential biomarkers has been growing rapidly, computational approaches are essential for the analysis of big data and the evaluation of potential biomarkers. In addition, there is a growing focus on the importance of data sharing and collaboration in the field of translational genomics. Collaborative efforts are underway to collect and analyze large datasets of genomic and clinical data, which can be used to identify new biomarkers and improve our understanding of the molecular basis of cancer. Therefore, large discovery and validation studies are required to investigate the mechanisms relating to cancer development, progression, and metastasis, identify new cancer-related genomics biomarkers, and foster translation to the clinics.
In this research topic, we aim to gather original research articles, reviews, perspectives, and opinions that reveal markers and their molecular mechanisms, providing the necessary theoretical basis and clinical evidence for their application in cancer management. Topics include but are not limited to:
• Utilizing research findings achieved at the molecular, genomic, and cellular levels to guide therapeutic strategies for the enhancement of clinical outcomes.
• Novel computational or bioinformatics methods to identify potential biomarkers for cancer diagnosis and prognosis.
• Using multiple approaches, such as multi-omics technology, multi-modal data, and the combination of wet lab analysis and in silico methods, to study the diagnosis, treatment, and pathogenesis of cancer and optimize cancer patients’ management.
• Novel biomarkers discovery, identification, and validation, such as miRNA, lncRNA, DNA methylation, and cfDNA fragment omics.
• Development of new technologies and platforms for the identification and analysis of genomic biomarkers.
• Integration of genomic biomarkers with other clinical, pathological, and radiological parameters to improve cancer management.
Please note: Bioinformatic studies are welcome, however, these should not be based solely on analysis of publicly available datasets such as TCGA. It is essential to have an independent validation cohort on alternative datasets for statistically significant confirmation of the findings communicated and sufficient patient cases.