About this Research Topic
Cancer has become one of the leading causes of mortality around the globe. Activating the innate immune signal pathway and inducing the anti-tumor immune response plays a key role in the efficacy of tumor treatment, especially in preventing the recurrence of residual tumor cells. With the development of high-throughput sequencing technology, multi-omics data for cancer become accessible. These data have given researchers increasing opportunities to explore genetic risk, regulatory mechanism, and protein function of immune micro-environment in cancers. However, it is still a big challenge to utilize these data effectively and to mine knowledge from them. Artificial intelligence algorithms / statistical methods have shown great potential to deal with omics data and reveal the mechanism of immune function in cancer.
For this Research Topic, we would like to bring together recent advances on novel immune-related biomarkers based on multi-omics studies. We welcome submissions of Original Research, Review, and Mini-Reviews. Topics of interest include, but are not limited to, the following aspects:
• Integrating multi-omics data in the identification of immune-associated cancer biomarkers based on machine learning.
• Statistical methods and applications for integrating multi-omics data to identify immune-associated biomarkers for cancer.
• Identification of immune-related molecular biomarkers for cancers based on single-cell data.
• Identification of immune-associated biomarkers in cancer immune microenvironment based on multi-omics data.
• Immune cells or genes function analysis based on multi-omics data.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
For this Research Topic, we would like to bring together recent advances on novel immune-related biomarkers based on multi-omics studies. We welcome submissions of Original Research, Review, and Mini-Reviews. Topics of interest include, but are not limited to, the following aspects:
• Integrating multi-omics data in the identification of immune-associated cancer biomarkers based on machine learning.
• Statistical methods and applications for integrating multi-omics data to identify immune-associated biomarkers for cancer.
• Identification of immune-related molecular biomarkers for cancers based on single-cell data.
• Identification of immune-associated biomarkers in cancer immune microenvironment based on multi-omics data.
• Immune cells or genes function analysis based on multi-omics data.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
