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The tumor microenvironment (TME) plays a critical role in tumor proliferation, progression, and therapeutic responses. TME is a complex network of cancer cells, stromal cells, and, most importantly, infiltrating immune cells. Cancer cells regulate numerous biological functions through direct or indirect interaction with TME components. Emerging evidence suggests that TME crucially influences the response to both chemotherapy and immunotherapy. As scientific research has entered the big data era with the fast development of high-throughput sequencing technologies, machine learning has been gradually widely applied to extract important knowledge from big data bioinformatics. Thus, characterizing the TME landscape in cancer and identifying different immune-related TME phenotypes using machine learning-based bioinformatics analyses, in vitro experiments, and in vivo experiments are of great interest and significance.

As the major part of TME, immunosuppressive cells, including tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs), and regulatory T cells (Tregs) have been shown to participate in tumor migration and invasion, all of which are associated with poor prognosis and resistance to therapy. Currently, targeted therapies of TAMs, MDSCs, CAFs, and Tregs emerge as potent immunotherapy options with promising results in both preclinical and clinical studies. Therefore, it is also important to explore novel subtypes of TAMs, MDSCs, CAFs, and Tregs involved in the modulation of the TME to improve targeted therapy based on large-scale bioinformatics analysis.

The goal of this Research Topic is to provide a forum for advancing knowledge about the contribution of TME in the development of tumorigenesis via integrative bioinformatics analysis and in vitro or in vivo experiments. New biomarkers related to TME will be discovered through machine learning in order to propose new immunotherapeutic strategies. We welcome manuscripts in the following subtopics:

1) Application of machine learning for predictive and prognostic models related to TME,

2) Exploring novel molecular subtypes of TAMs, MDSCs, CAFs, and Tregs at single-cell sequencing level and RNA sequencing level,

3) Exploring novel molecular subtypes related to TME at single-cell sequencing level and RNA sequencing level,

4) Unveiling novel biomarkers related to TME to predict immunotherapy response,

5) Exploring the influence of TME on patient prognosis through multi-omics data analysis,

Note: This article collection does not accept only bioinformatics analysis studies that are not accompanied by real-world cohort, in vitro, or in vivo experiments for validation.

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