Frontiers in immunology · 2022
Infiltration Patterns of Cervical Epithelial Microenvironment Cells During Carcinogenesis.
СтатьяОбзорПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Zhang Jianwei, Meng Silu, Zhang Xiuqing, Shao Kang, Lin Cong
Аннотация
BACKGROUND: Local cellular microenvironment plays a crucial role in the HPV-induced cervical malignant transformation. Characterization of the dynamic infiltration changes of microenvironment cells during cervical carcinogenesis would contribute to a better understanding of involved mechanisms.
METHODS: Three public gene expression datasets of cervical squamous epithelium samples were collected and combined. We applied seven up-to-date computational methods for infiltrating estimation and compared their results (CD4+ and CD8+ T cells) to the known fraction. After benchmarking the applied methods, the cell filtration patterns were determined and clustered through fuzzy c-means algorithm.
RESULTS: Most methods displayed better performance in predicting the abundance of CD4+ T cell than that of CD8+ T cell. The infiltration patterns of 33 microenvironment cell types (including 31 immune cells and 2 non-immune cells) were determined, and five immune cell clusters with distinct features were then derived. Meanwhile, opposite changes in abundance were observed between the activated and resting state of some immune cells from the progression perspective.
CONCLUSIONS: Based on characteristics and evaluation performance of different methods, as well as previous findings, for the first time we provide a comprehensive overview of the infiltration patterns of microenvironment cells throughout cervical cancer progression.
METHODS: Three public gene expression datasets of cervical squamous epithelium samples were collected and combined. We applied seven up-to-date computational methods for infiltrating estimation and compared their results (CD4+ and CD8+ T cells) to the known fraction. After benchmarking the applied methods, the cell filtration patterns were determined and clustered through fuzzy c-means algorithm.
RESULTS: Most methods displayed better performance in predicting the abundance of CD4+ T cell than that of CD8+ T cell. The infiltration patterns of 33 microenvironment cell types (including 31 immune cells and 2 non-immune cells) were determined, and five immune cell clusters with distinct features were then derived. Meanwhile, opposite changes in abundance were observed between the activated and resting state of some immune cells from the progression perspective.
CONCLUSIONS: Based on characteristics and evaluation performance of different methods, as well as previous findings, for the first time we provide a comprehensive overview of the infiltration patterns of microenvironment cells throughout cervical cancer progression.