Cancer is an aggressive disease and a heavy burden to public health. Cancer cells exhibit adaptability in reprogramming their metabolism to support tumor initiation, progression, metastasis and resistance to therapies. With the development of high-throughput technologies, multi-omics data have been produced to identify biomarkers to facilitate the risk assessment, early detection, prognosis, and treatment response prediction of tumors.
This research topic summarizes the latest breakthroughs in identifying novel tumor-related biomarkers through the comprehensive analysis of omics-data. The objective is to augment diagnostic precision, prognostic efficacy, and the potency of immunotherapy regimens. These biomarkers encompass a broad spectrum of molecular entities, such as RNAs (mRNA, noncoding RNA), metabolites, peptides, and proteins, which are assayed using omics data including but not limited to transcriptomicsics, metabolomics, lipidomics, glycomics, genomics, proteomics.
Identifying singular or synergistic combinations of these novel biomarkers holds immense potential as a crucial indicator, illuminating the intrinsic relationship with tumorigenesis, thereby propelling personalized therapeutic strategies and significantly improving clinical outcomes. Furthermore, exploring new immunotherapeutic drugs for tumor-related biomarkers stands to revolutionize clinical medication in personalized medicine by AI or molecular docking model.
This research topic emphasizes novel biomarkers in translational research and clinical application, including research, review, case report, et al. Specifically, this topic should include but are not limited to the following:
1. Screening diagnostic/prognostic/therapeutic biomarkers by omics data.
2. Key biomarkers in the process of tumor metastasis.
3. Clinical cases of using novel biomarkers in translational research and clinical management.
4. Screening immunotherapy drugs for tumor-related biomarkers by AI or docking model.
5. Online tools for screening biomarkers to evaluate prognostic risk.
Cancer is an aggressive disease and a heavy burden to public health. Cancer cells exhibit adaptability in reprogramming their metabolism to support tumor initiation, progression, metastasis and resistance to therapies. With the development of high-throughput technologies, multi-omics data have been produced to identify biomarkers to facilitate the risk assessment, early detection, prognosis, and treatment response prediction of tumors.
This research topic summarizes the latest breakthroughs in identifying novel tumor-related biomarkers through the comprehensive analysis of omics-data. The objective is to augment diagnostic precision, prognostic efficacy, and the potency of immunotherapy regimens. These biomarkers encompass a broad spectrum of molecular entities, such as RNAs (mRNA, noncoding RNA), metabolites, peptides, and proteins, which are assayed using omics data including but not limited to transcriptomicsics, metabolomics, lipidomics, glycomics, genomics, proteomics.
Identifying singular or synergistic combinations of these novel biomarkers holds immense potential as a crucial indicator, illuminating the intrinsic relationship with tumorigenesis, thereby propelling personalized therapeutic strategies and significantly improving clinical outcomes. Furthermore, exploring new immunotherapeutic drugs for tumor-related biomarkers stands to revolutionize clinical medication in personalized medicine by AI or molecular docking model.
This research topic emphasizes novel biomarkers in translational research and clinical application, including research, review, case report, et al. Specifically, this topic should include but are not limited to the following:
1. Screening diagnostic/prognostic/therapeutic biomarkers by omics data.
2. Key biomarkers in the process of tumor metastasis.
3. Clinical cases of using novel biomarkers in translational research and clinical management.
4. Screening immunotherapy drugs for tumor-related biomarkers by AI or docking model.
5. Online tools for screening biomarkers to evaluate prognostic risk.