基于自噬相关的基因及CT影像特点构建肾细胞癌患者预后模型

李亚县; 吴伟宙; 王琼仁; 詹雄宇; 郑明星; 谭万龙

doi:10.12122/j.issn.1674-4500.2024.10.09

基于自噬相关的基因及CT影像特点构建肾细胞癌患者预后模型

doi: 10.12122/j.issn.1674-4500.2024.10.09

李亚县^{1, 2,},
吴伟宙²,
王琼仁²,
詹雄宇²,
郑明星²,
谭万龙^1, ,

1.
南方医科大学南方医院泌尿外科，广东广州 510515
2.
南方医科大学附属茂名医院(茂名市人民医院)泌尿外科，广东茂名 525000

基金项目:

茂名市科技计划项目 2022163

茂名市科技专项资金项目 2023SZX030

详细信息

作者简介:
李亚县，在读硕士研究生，主任医师，E-mail: littlecounty@126.com

通讯作者:
谭万龙，博士，主任医师，E-mail: twl@smu.edu.cn

计量
- 文章访问数: 39
- HTML全文浏览量: 17
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2024-08-05
- 网络出版日期: 2024-11-02
- 刊出日期: 2024-10-20

Construction of a prognostic model for renal cell carcinoma patients based on autophagyrelated genes and CT imaging characteristics

1.
Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
2.
Department of Urology, Affiliated Maoming Hospital, Southern Medical University (Maoming People's Hospital), Maoming 525000, China

摘要

摘要: 目的探究自噬相关基因在肾细胞癌发展中的作用，基于自噬相关的基因及CT影像特点构建肾细胞癌患者预后模型。方法从TCGA数据库下载肾透明细胞癌患者相关资料，结合自噬相关基因集，探索基于自噬相关基因的肾癌分子亚型及构建个体化自噬评分系统；通过分析TCIA数据库中肾细胞癌患者CT资料，构建自噬相关基因风险模型及影像组学模型预测肾癌患者预后。结果将肾透明细胞癌分为两种新的分子亚型，预后较差的亚型患者伴有大量免疫细胞浸润(P < 0.05)，且临床分级更高、分期更晚(P < 0.05)。高自噬评分患者的肿瘤基因突变频率明显增加、肿瘤体积更大、更晚期、预后更差(P < 0.05)。影像组学联合自噬相关基因模型可较准确预测肾癌患者预后(曲线下面积0.922，95% CI：0.852~0.993)。结论自噬评分高的肾癌患者预后较差，影像组学联合自噬相关基因模型在预测肾癌患者预后中有较好前景。
- 肾细胞癌 /
- 自噬 /
- 影像组学 /
- 预后模型
Abstract: Objective To explore the role of autophagy-related genes in the development of renal cell carcinoma and construct a prognostic model for renal cell carcinoma patients based on autophagy-related genes and CT imaging characteristics. Methods The relevant data of patients with renal clear cell carcinoma were downloaded from the TCGA database. Combined with the autophagy-related gene set, the molecular subtypes of renal carcinoma based on autophagy-related genes and the construction of an individualized autophagy scoring system were explored; by analyzing the CT data of patients with renal cell carcinoma in the TCIA database, the autophagy-related gene risk model and the imaging omics model were constructed to predict the prognosis of patients with renal cancer. Results Renal clear cell carcinoma was divided into two new molecular subtypes. Patients with poor prognosis are accompanied by a large number of immune cell infiltration (P < 0.05), and have higher clinical grades and more advanced stages (P < 0.05). Patients with high autophagy scores have significantly increased tumor gene mutation frequencies, larger tumor volumes, more advanced stages, and worse prognoses (P < 0.05). Radiomics combined with autophagy-related gene model can accurately predict the prognosis of patients with renal cancer (AUC: 0.922, 95% CI: 0.852-0.993). Conclusion Renal cancer patients with high autophagy scores have a poor prognosis. Radiomics combined with autophagy-related gene model has good prospects in predicting the prognosis of patients with renal cancer.
- renal cell carcinoma /
- autophagy /
- radiomics /
- prognostic model

HTML全文

图 1 肾细胞癌患者自噬相关的基因与预后的相关性及其表达情况

Figure 1. Correlation and expression of autophagy-related genes with prognosis in patients with renal cell carcinoma. A: Univariate Cox regression analysis of autophagy-related genes; B: Heat map of the expression of 88 autophagy-related genes with prognostic value in tumor tissues and normal tissues.

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图 2 构建基于自噬相关基因的肾细胞癌分子亚型及临床相关特征分析

Figure 2. Construction of renal cell carcinoma molecular subtypes based on autophagy-related genes and analysis of their relationship with clinically relevant features. A: Heat map of the consistency matrix when K=2, patients with renal clear cell carcinoma were divided into autophagic subtype A and autophagic subtype B; B: Relative change in area under the cumulative distribution function(CDF) curve from K=2 to 9; C: Relative change in the CDF from k=2 to 9; D: Kaplan-Meier survival analysis of patients with subtype A and subtype B; E: Heat map of clinical characteristics of patients with renal clear cell carcinoma molecular subtypes based on autophagy-related genes.

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图 3 不同肾癌分子亚型患者的免疫免疫浸润情况

Figure 3. Immune infiltration in patients with different renal cell carcinoma molecular subtypes. A: ssGSEA evaluated the expression of 23 immune cells in patients with subtype A and subtype B; B: CIBERSORT evaluated the expression of 22 immune cells between subtype A and subtype B.

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图 4 量化ccRCC患者个体化自噬水平及其与基因突变、临床特征的相关性

Figure 4. Quantifying the individualized autophagy level in ccRCC patients and its correlation with gene mutations and clinical characteristics. A: Principal component analysis of autophagy-related genes in ccRCC patients; B: The relation of PCA and autophagy subtypes; C: Kaplan-Meier survival analysis of patients by autophagy score; D: Comparison of autophagy score among autophagic subtypes; E: Waterfall of the top 20 mutated genes in the ccRCC patients with low autophagy score; F: Waterfall of the top 20 mutated genes in the ccRCC patients with high autophagy score; G: CT images of ccRCC patients grouped according to autophagy scores(Left: low autophagy score group patients TCGA-B0-5110; Right: high autophagy score group patients TCGA-B0-4698); H: Heat map of autophagy scores and clinical characteristics.

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图 5 基于自噬相关基因的肾透明细胞癌患者风险模型及影像组学模型的构建与评估

Figure 5. Construction and evaluation of risk model and radiomics model for renal clear cell carcinoma patients based on autophagy-related genes. A: Kaplan-Meier survival analysis of all patients in different autophagy risk groups; B: Heat map of autophagy risk score and clinical characteristics of overall ccRCC patients; ^*P < 0.05, ^***P < 0.001; C: CT images of ccRCC patients grouped according to different autophagy risk groups (Left: low autophagy risk group patients TCGA-B0-5121; Right: high autophagy risk group patients TCGA-B0-5706); D: ROC curve of autophagy risk score prediction model and other clinical characteristics in predicting the 1-year survival of overall ccRCC patients; E: ROC curve of autophagy risk score and radiomics model in predicting 1-year survival of 96 ccRCC patients.; F: Nomogram of 96 ccRCC patients based on autophagy risk score, radiomics and other clinical characteristics.

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图 6 基于自噬相关基因风险模型的免疫浸润及免疫功能分析

Figure 6. Immune infiltration and immune function analysis based on autophagy-related gene risk model. A: Correlation between autophagy risk score and immune cells; B: Analysis of autophagy risk score and immune function; ^*P < 0.05, ^**P < 0.01, ^***P < 0.001; C: Correlation between autophagy risk score and expression of 34 immune checkpoints.

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