Liu Qi-Fang, Feng Zi-Yi, Jiang Li-Li, Xu Tong-Tong, Li Si-Man, Liu Kui-Ran
Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
Department of Plastic Surgery, The First Hospital of China Medical University, Shenyang, China.
Front Cell Dev Biol. 2021 Jun 17;9:702451. doi: 10.3389/fcell.2021.702451. eCollection 2021.
Malignant gynecological tumors are the main cause of cancer-related deaths in women worldwide and include uterine carcinosarcomas, endometrial cancer, cervical cancer, ovarian cancer, and breast cancer. This study aims to determine the association between immune cell infiltration and malignant gynecological tumors and construct signatures for diagnosis and prognosis. We acquired malignant gynecological tumor RNA-seq transcriptome data from the TCGA database. Next, the "CIBERSORT" algorithm calculated the infiltration of 22 immune cells in malignant gynecological tumors. To construct diagnosis and prognosis signatures, step-wise regression and LASSO analyses were applied, and nomogram and immune subtypes were further identified. Notably, Immune cell infiltration plays a significant role in tumorigenesis and development. There are obvious differences in the distribution of immune cells in normal, and tumor tissues. Resting NK cells, M0 Macrophages, and M1 Macrophages participated in the construction of the diagnostic model, with an AUC value of 0.898. LASSO analyses identified a risk signature including T cells CD8, activated NK cells, Monocytes, M2 Macrophages, resting Mast cells, and Neutrophils, proving the prognostic value for the risk signature. We identified two subtypes according to consensus clustering, where immune subtype 3 presented the highest risk. We identified diagnostic and prognostic signatures based on immune cell infiltration. Thus, this study provided a strong basis for the early diagnosis and effective treatment of malignant gynecological tumors.
恶性妇科肿瘤是全球女性癌症相关死亡的主要原因，包括子宫癌肉瘤、子宫内膜癌、宫颈癌、卵巢癌和乳腺癌。本研究旨在确定免疫细胞浸润与恶性妇科肿瘤之间的关联，并构建诊断和预后特征。我们从TCGA数据库获取了恶性妇科肿瘤的RNA测序转录组数据。接下来，使用“CIBERSORT”算法计算22种免疫细胞在恶性妇科肿瘤中的浸润情况。为构建诊断和预后特征，应用逐步回归和LASSO分析，并进一步确定列线图和免疫亚型。值得注意的是，免疫细胞浸润在肿瘤发生和发展中起重要作用。正常组织和肿瘤组织中免疫细胞的分布存在明显差异。静息自然杀伤细胞、M0巨噬细胞和M1巨噬细胞参与了诊断模型的构建，AUC值为0.898。LASSO分析确定了一个风险特征，包括T细胞CD8、活化自然杀伤细胞、单核细胞、M2巨噬细胞、静息肥大细胞和中性粒细胞，证明了该风险特征的预后价值。根据一致性聚类我们确定了两个亚型，其中免疫亚型3的风险最高。我们基于免疫细胞浸润确定了诊断和预后特征。因此，本研究为恶性妇科肿瘤的早期诊断和有效治疗提供了有力依据。
