Adipose tissue, once considered a passive energy store, is now recognized as a metabolically and immunologically active organ that plays a crucial role in cancer development and progression. Obesity-driven changes in adipose tissue lead to a chronic, low-grade inflammatory state characterized by altered populations of immune cells such as macrophages, T cells, and innate lymphoid cells. These immunological shifts contribute to a pro-tumorigenic environment that facilitates tumor initiation, immune evasion, and metastasis.

In particular, the immunological landscape of adipose tissue, especially in the context of obesity, has been shown to interact dynamically with the tumor microenvironment, influencing immune surveillance, inflammation, and response to therapy. Furthermore, adipocytes release a wide range of bioactive molecules, including adipokines, cytokines, and metabolites, which can directly or indirectly modulate both local and systemic immune responses.

This special issue explores the complex intersection between cancer, immunology, adipose biology, and inflammation. We welcome contributions that examine the immunological functions of adipose tissue in cancer and investigate how obesity-associated inflammation impacts tumor immunity and therapy outcomes.

In addition, artificial intelligence (AI) is now emerging as a powerful tool to decipher these complex and multidimensional interactions. Through machine learning, multi-omics integration, and predictive modeling, AI enables researchers to identify novel biomarkers, stratify patient risk based on immune-adipose signatures, and personalize immunotherapeutic strategies, particularly in obese cancer patients who may respond differently to treatment.

The ultimate goal is to bridge basic science, translational research, and precision medicine by integrating insights from adipose tissue immunology, inflammation, tumor microenvironment studies, and AI-enabled analytics.

Keywords: Adipose tissue, immunometabolism, tumor microenvironment, inflammation lipid metabolism, machine learning