Cancer is a complex disease that is typically caused by a combination of genetic, environmental, and lifestyle factors, necessitating advanced approaches to diagnosis, treatment, and management. The immune system plays a pivotal role in the development and progression of cancer. Integrative analysis of data from various sources (e.g., omics data, clinical data, and pathological data) can provide more comprehensive information for characterizing and exploring cancer immunity than using a single modality alone. Recent advances in multimodal profiling have enabled researchers to gain a more systematic understanding of the immune dynamics and functions of tumors. However, novel pathways and biomarkers associated with cancer immunity, as well as the underlying molecular mechanisms, still need to be further explored to better promote the clinical management of cancers.
This research topic aims to systematically explore cancer immunity through multimodal approaches to improve clinical management. A more comprehensive view of cancer and its associated immune profile can be obtained by combining data from various sources, such as genomic, transcriptomic, proteomic, epigenetic, metabolomic, clinical, and pathological data. The advantages of multimodal strategies can facilitate the improvement of cancer diagnosis, prognosis, and treatment by identifying novel biomarkers and gaining deeper insights into the underlying molecular mechanisms. Furthermore, it may benefit the detection and characterization of new therapeutic targets and the prediction of patient outcomes. Additionally, exploring multimodal data (e.g., bulk and single-cell multi-omics data, clinical information) can aid the development of personalized treatments for cancer patients and the guidance of corresponding clinical decisions. We anticipate that this research topic will provide a better understanding of cancer immunity and will enhance the clinical management of cancer.
We welcome the submission of Original Research, Review, Mini Review, Perspective, and Clinical Trials, covering, but not limited to, the following sub-topics:
• Novel immune-related biomarkers identified from multimodal data integration for cancer diagnosis, prognosis, and treatment.
• Mechanisms underlying the functions of immune cells in cancer development and progression.
• New therapeutic targets associated with cancer immunity based on multimodal strategies.
• Dissection of immune cell heterogeneity with bulk and single-cell multi-omics data of cancer.
• Bioinformatics analysis with experimental validation to identify novel biomarkers for cancer management.
Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this topic.
Topic editor Dr. Geng Chen is employed by Stemirna Therapeutics Co., Ltd., Shanghai, China, and declares no competing interests with regard to the Research Topic subject. Topic Editor Dr. Ye Shi is the founder of deepGeneAI Co., Ltd., Shanghai, China, and declares no competing interests with regard to the Research Topic subject. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Cancer is a complex disease that is typically caused by a combination of genetic, environmental, and lifestyle factors, necessitating advanced approaches to diagnosis, treatment, and management. The immune system plays a pivotal role in the development and progression of cancer. Integrative analysis of data from various sources (e.g., omics data, clinical data, and pathological data) can provide more comprehensive information for characterizing and exploring cancer immunity than using a single modality alone. Recent advances in multimodal profiling have enabled researchers to gain a more systematic understanding of the immune dynamics and functions of tumors. However, novel pathways and biomarkers associated with cancer immunity, as well as the underlying molecular mechanisms, still need to be further explored to better promote the clinical management of cancers.
This research topic aims to systematically explore cancer immunity through multimodal approaches to improve clinical management. A more comprehensive view of cancer and its associated immune profile can be obtained by combining data from various sources, such as genomic, transcriptomic, proteomic, epigenetic, metabolomic, clinical, and pathological data. The advantages of multimodal strategies can facilitate the improvement of cancer diagnosis, prognosis, and treatment by identifying novel biomarkers and gaining deeper insights into the underlying molecular mechanisms. Furthermore, it may benefit the detection and characterization of new therapeutic targets and the prediction of patient outcomes. Additionally, exploring multimodal data (e.g., bulk and single-cell multi-omics data, clinical information) can aid the development of personalized treatments for cancer patients and the guidance of corresponding clinical decisions. We anticipate that this research topic will provide a better understanding of cancer immunity and will enhance the clinical management of cancer.
We welcome the submission of Original Research, Review, Mini Review, Perspective, and Clinical Trials, covering, but not limited to, the following sub-topics:
• Novel immune-related biomarkers identified from multimodal data integration for cancer diagnosis, prognosis, and treatment.
• Mechanisms underlying the functions of immune cells in cancer development and progression.
• New therapeutic targets associated with cancer immunity based on multimodal strategies.
• Dissection of immune cell heterogeneity with bulk and single-cell multi-omics data of cancer.
• Bioinformatics analysis with experimental validation to identify novel biomarkers for cancer management.
Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this topic.
Topic editor Dr. Geng Chen is employed by Stemirna Therapeutics Co., Ltd., Shanghai, China, and declares no competing interests with regard to the Research Topic subject. Topic Editor Dr. Ye Shi is the founder of deepGeneAI Co., Ltd., Shanghai, China, and declares no competing interests with regard to the Research Topic subject. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
