Table_1_Immune Cell Infiltration as Signatures for the Diagnosis and Prognosis of Malignant Gynecological Tumors.DOCX

Background 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. Methods 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. Results 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. Conclusion 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.研究背景：恶性妇科肿瘤是全球范围内女性癌症相关死亡的主要诱因，涵盖子宫癌肉瘤、子宫内膜癌、宫颈癌、卵巢癌及乳腺癌。本研究旨在明确免疫细胞浸润与恶性妇科肿瘤之间的关联，并构建用于诊断与预后的特征模型。 研究方法：我们从癌症基因组图谱（TCGA）数据库中获取了恶性妇科肿瘤的RNA测序（RNA-seq）转录组数据。随后，采用“CIBERSORT”算法计算恶性妇科肿瘤中22种免疫细胞的浸润水平。为构建诊断与预后特征模型，本研究应用了逐步回归与最小绝对收缩和选择算子（LASSO）分析，并进一步鉴定了列线图与免疫亚型。 研究结果：值得注意的是，免疫细胞浸润在肿瘤发生与发展过程中发挥着关键作用。正常组织与肿瘤组织的免疫细胞分布存在显著差异。静息自然杀伤（NK）细胞、M0型巨噬细胞及M1型巨噬细胞参与了诊断模型的构建，其受试者工作特征曲线下面积（AUC）值达0.898。LASSO分析鉴定出包含CD8阳性T细胞、活化NK细胞、单核细胞、M2型巨噬细胞、静息肥大细胞及中性粒细胞的风险特征模型，证实了该风险特征的预后价值。本研究通过一致性聚类鉴定出两种免疫亚型，其中免疫亚型3呈现最高的风险水平。 研究结论：本研究基于免疫细胞浸润特征成功鉴定出诊断与预后特征模型。因此，本研究为恶性妇科肿瘤的早期诊断与有效治疗提供了坚实的理论基础。