Reanalysis of single-cell data reveals macrophage subsets associated with the immunotherapy response and prognosis of patients with endometrial cancer

生物 免疫疗法 巨噬细胞 肿瘤相关巨噬细胞 子宫内膜癌 恶性肿瘤 免疫系统 肿瘤微环境 癌症研究 癌症 川地163 免疫学 遗传学 体外
作者
Qianhua Wu,Genyi Jiang,Yihan Sun,Bilan Li
出处
期刊:Experimental Cell Research [Elsevier BV]
卷期号:430 (2): 113736-113736 被引量:18
标识
DOI:10.1016/j.yexcr.2023.113736
摘要

Endometrial cancer (EC) is an aggressive gynecological malignancy with an increased incidence rate. The immune landscape crucially affects immunotherapy efficacy and prognosis in EC patients. Here, we characterized the distinct tumor microenvironment signatures of EC tumors by analyzing single-cell RNA sequencing data from Gene Expression Omnibus and bulk RNA sequencing data from The Cancer Genome Atlas, which were compared with normal endometrium. Three macrophage subsets were identified, and two of them showed tissue-specific distribution. One of the macrophage subsets was dominant in macrophages derived from EC and exhibited characteristic behaviors such as promoting tumor growth and metastasis. One of the other macrophage subsets was mainly found in normal endometrium and served functions related to antigen presentation. We also identified a macrophage subset that was found in both EC and normal endometrial tissue. However, the pathway and cellular cross-talk of this subset were completely different based on the respective origin, suggesting a tumor-related differentiation mechanism of macrophages. Additionally, the tumor-enriched macrophage subset was found to predict immunotherapy responses in EC. Notably, we selected six genes from macrophage subset markers that could predict the survival of EC patients, SCL8A1, TXN, ANXA5, CST3, CD74 and NANS, and constructed a prognostic signature. To verify the signature, we identified immunohistochemistry for the tumor samples of 83 EC patients based on the selected genes and further followed up with the survival of the patients. Our results provide strong evidence that the signature can effectively predict the prognosis of EC patients.
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