Journal for immunotherapy of cancer · 2022
A signature for pan-cancer prognosis based on neutrophil extracellular traps.
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Zhang Yi, Guo Liping, Dai Qichen, Shang Bingqing, Xiao Ting, Di Xuebing, Zhang Kaitai, Feng Lin, Shou Jianzhong, Wang Yipeng
Аннотация
BACKGROUND: Neutrophil extracellular traps (NETs) were originally thought to be formed by neutrophils to trap invading microorganisms as a defense mechanism. Increasing studies have shown that NETs play a pivotal role in tumor progression and diffusion. In this case, transcriptome analysis provides an opportunity to unearth the association between NETs and clinical outcomes of patients with pan-cancer.
METHODS: The transcriptome sequencing data of The Cancer Genome Atlas pan-cancer primary focus was obtained from UCSC Xena, and a 19-gene NETs score was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model based on the expression levels of 69 NETs initial biomarkers we collected from multistudies. In addition, multiple datasets covering multiple cancer types from other databases were collected and used to validate the signature. Gene ontology enrichment analyses were used to annotate the functions of NETs-related pathways. Immunohistochemistry (IHC) was implemented to evaluate the role of NETs-related genes in clinical patients across types of tumors, including lung adenocarcinoma (n=58), colorectal carcinoma (n=93), kidney renal clear cell carcinoma (n=90), and triple-negative breast cancer (n=80).
RESULTS: The NETs score was calculated based on 19-NETs related genes according to the LASSO Cox model. The NETs score was considered a hazardous factor in most cancer types, with a higher score indicating a more adverse outcome. In addition, we found that NETs were significantly correlated to various malignant biological processes, such as the epithelial to mesenchymal transition (R=0.7444, p<0.0001), angiogenesis (R=0.5369, p<0.0001), and tumor cell proliferation (R=0.3835, p<0.0001). Furthermore, in IHC cohorts of a variety of tumors, myeloperoxidase, a gene involved in the model and a classical delegate of NETs formation, was associated with poor clinical outcomes.
CONCLUSIONS: Collectively, these constitutive and complementary biomarkers represented the ability of NETs formation to predict the development of patients' progression. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.
METHODS: The transcriptome sequencing data of The Cancer Genome Atlas pan-cancer primary focus was obtained from UCSC Xena, and a 19-gene NETs score was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model based on the expression levels of 69 NETs initial biomarkers we collected from multistudies. In addition, multiple datasets covering multiple cancer types from other databases were collected and used to validate the signature. Gene ontology enrichment analyses were used to annotate the functions of NETs-related pathways. Immunohistochemistry (IHC) was implemented to evaluate the role of NETs-related genes in clinical patients across types of tumors, including lung adenocarcinoma (n=58), colorectal carcinoma (n=93), kidney renal clear cell carcinoma (n=90), and triple-negative breast cancer (n=80).
RESULTS: The NETs score was calculated based on 19-NETs related genes according to the LASSO Cox model. The NETs score was considered a hazardous factor in most cancer types, with a higher score indicating a more adverse outcome. In addition, we found that NETs were significantly correlated to various malignant biological processes, such as the epithelial to mesenchymal transition (R=0.7444, p<0.0001), angiogenesis (R=0.5369, p<0.0001), and tumor cell proliferation (R=0.3835, p<0.0001). Furthermore, in IHC cohorts of a variety of tumors, myeloperoxidase, a gene involved in the model and a classical delegate of NETs formation, was associated with poor clinical outcomes.
CONCLUSIONS: Collectively, these constitutive and complementary biomarkers represented the ability of NETs formation to predict the development of patients' progression. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.