基于炎症相关基因构建子宫内膜癌预后风险模型

doi:10.16288/j.yczz.24-376

遗传 ›› 2025, Vol. 47 ›› Issue (9): 1007-1022.doi: 10.16288/j.yczz.24-376

基于炎症相关基因构建子宫内膜癌预后风险模型

刘鉴瑶¹(), 李越¹, 胡缓缓², 肖姝玥², 谢欣怡², 钟山亮¹, 贡震², 朱晨静¹, 徐寒子¹()

1.南京医科大学附属肿瘤医院，江苏省肿瘤医院,江苏省肿瘤防治研究所，南京 210009
2.南京医科大学附属妇产医院，南京市妇幼保健院，南京 210004

收稿日期:2025-02-08 修回日期:2025-06-05 出版日期:2025-06-06 发布日期:2025-06-06
通讯作者: 徐寒子，博士，副教授，研究方向：妇科肿瘤放射治疗。E-mail: xuhanzi@njmu.edu.cn
作者简介:刘鉴瑶，硕士研究生，专业方向：放射肿瘤学。E-mail: ly24_00@163.com
基金资助:
国家自然科学基金项目(82102850);吴阶平医学基金会临床科研专项基金项目(320.6750.2023.17-1);江苏省自然科学基金项目(BK20241993);江苏省卫生健康委医学科研重点项目(2024-36)

A prognostic risk model construction for endometrial cancer based on inflammation-related genes

Jianyao Liu¹(), Yue Li¹, Huanhuan Hu², Shuyue Xiao², Xinyi Xie², Shanliang Zhong¹, Zhen Gong², Chenjing Zhu¹, Hanzi Xu¹()

1. The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing 210009, China
2. Women's Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210004, China

Received:2025-02-08 Revised:2025-06-05 Published:2025-06-06 Online:2025-06-06
Supported by:
National Natural Science Foundation of China(82102850);Wu Jieping Medical Foundation Special Fund for Clinical Research(320.6750.2023.17-1);Natural Science Foundation of Jiangsu Province, China(BK20241993);Key Medical Research Project of Jiangsu Provincial Health Commission(2024-36)

摘要/Abstract

摘要：

炎症反应参与多种肿瘤的发生与发展，影响肿瘤微环境，不仅促进肿瘤细胞的侵袭和迁移，同时也降低肿瘤治疗的敏感性。炎症被认为是子宫内膜癌(endometrial cancer，EC)发生发展的重要危险因素，但其影响EC发生发展的潜在机制尚不明确。本研究从癌症基因组图谱(The Cancer Genome Atlas，TCGA)数据库中获取EC患者RNA表达谱以及相关临床信息，利用生存分析及最小绝对值收缩和选择算子算法(least absolute shrinkage and selection operator，LASSO)筛选出关键炎症相关基因(inflammation-related genes，IRG)，构建了包含9条非零系数IRG的预后风险评分模型及列线图预测模型。生存分析显示，低风险组患者生存率更高，预后更佳。通过测试集和校正曲线验证两个模型均具有良好的预测性能。然后，从基因表达综合数据库(Gene Expression Omnibus，GEO)中获取EC相关数据集，作为外部验证进一步确认模型的可靠性。接着，通过免疫浸润分析发现高低风险组在9种免疫细胞间具有显著差异，多种免疫细胞与肿瘤的进展及预后相关。同时药物敏感性分析发现1种EC代表性药物他莫昔芬与上述IRG之一存在显著相关关系。综上所述，本研究成功构建了EC风险评分模型和列线图预测模型，有望能更好地预测EC患者总生存期并提供新的治疗靶点。

关键词: 子宫内膜癌, 炎症相关基因, 预后, 免疫浸润, 药物敏感性

Abstract:

Inflammatory responses have been identified as a critical factor in the development and progression of various types of tumors. These responses influence the tumor microenvironment, promoting tumor cell invasion and migration while concomitantly reducing the efficacy of tumor therapy. Inflammation is widely regarded as a significant risk factor for the development of endometrial cancer (EC). However, the precise mechanisms through which it influences the development of EC remain to be elucidated. In this study, we obtain RNA expression profiles of EC patients and related clinical information from The Cancer Genome Atlas (TCGA) database. We then screen key inflammation-related genes using survival analysis and the least absolute value shrinkage and selection operator (LASSO) algorithms. Based on this, we finally construct a prognostic risk scoring model containing nine non-zero coefficient IRGs and an alignment diagram prediction model. Survival analysis demonstrates that patients in the low-risk group exhibit a higher survival rate and more favorable prognosis. The predictive performance of both models was confirmed through the analysis of test sets and calibration curves. Subsequently, we obtain EC-related datasets from the Gene Expression Omnibus (GEO) database to serve as an external validation, thereby further substantiating the reliability of the models. Subsequent immune infiltration analysis revealed significant disparities among nine immune cell types between the high- and low-risk groups, with multiple immune cells correlating with tumor progression and prognosis. Concurrently, we perform drug sensitivity analysis, it reveals a significant correlation between one representative EC drug, tamoxifen, and one of the aforementioned IRGs. In summary, our study successfully constructs a risk score model and a column-line graph prediction model for EC. It is expected that these models will better predict the overall survival and provide new therapeutic targets for EC patients.

Key words: endometrial cancer, inflammation related genes, prognosis, immune infiltration, drug sensitivity

刘鉴瑶, 李越, 胡缓缓, 肖姝玥, 谢欣怡, 钟山亮, 贡震, 朱晨静, 徐寒子. 基于炎症相关基因构建子宫内膜癌预后风险模型[J]. 遗传, 2025, 47(9): 1007-1022.

Jianyao Liu, Yue Li, Huanhuan Hu, Shuyue Xiao, Xinyi Xie, Shanliang Zhong, Zhen Gong, Chenjing Zhu, Hanzi Xu. A prognostic risk model construction for endometrial cancer based on inflammation-related genes[J]. Hereditas(Beijing), 2025, 47(9): 1007-1022.

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参数		所有患者 (n=496)	训练集 (n=248)	测试集 (n=248)	P 值
年龄		63.71 ± 10.97	64.15 ± 10.82	63.28 ± 11.12	0.38
	≤65 岁	286 (57.66)	143 (57.66)	143 (57.66)	1.00
	>65 岁	210 (42.34)	105 (42.34)	105 (42.34)
种族	白种人	337 (67.94)	174 (70.16)	163 (65.73)	0.29
种族	其他	159 (32.06)	74 (29.84)	85 (34.27)
分期	I	319 (65.37)	158 (65.02)	161 (65.71)	0.25
	II	49 (10.04)	30 (12.35)	19 (7.76)
	III	106 (21.72)	47 (19.34)	59 (24.08)
	IV	14 (2.87)	8 (3.29)	6 (2.45)
生存状态	存活	451 (90.73)	223 (89.92)	228 (91.94)	0.43
生存状态	死亡	45 (9.07)	25 (10.08)	20 (8.06)
生存时间 (天)		1,128.94 ±919.02	1,142.05 ± 930.97	1,115.83 ± 908.61	0.75

基于炎症相关基因构建子宫内膜癌预后风险模型

A prognostic risk model construction for endometrial cancer based on inflammation-related genes

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图/表 12

参考文献 35

相关文章 7

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是否转移	风险评分		总计	χ²	P
是否转移	高	低	总计	χ²	P
否	43	57	100	5.709	P=0.017*
是	29	16	45
总计	72	73	145

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