Chinese Medical Sciences Journal ›› 2023, Vol. 38 ›› Issue (3): 191-205.doi: 10.24920/004223
郭金帅1,丁昊1,吴鹏宇1,辛子怡1,李建新1,曹现秀2,马振海()
收稿日期:
2023-03-16
接受日期:
2023-06-12
出版日期:
2023-09-30
发布日期:
2023-07-28
通讯作者:
* 马振海, Email: Jin-Shuai Guo1,Hao Ding1,Peng-Yu Wu1,Zi-Yi Xin1,Jian-Xin Li1,Hyon-Su Jo2,Zhen-Hai Ma()
Received:
2023-03-16
Accepted:
2023-06-12
Published:
2023-09-30
Online:
2023-07-28
Contact:
* Email: 摘要:
目的 肾透明细胞癌(kidney renal clear cell carcinoma,KIRC)是最常见的肾恶性肿瘤之一,死亡率较高。铜死亡是一种新的细胞死亡形式,通过蛋白质脂质化导致蛋白质毒性应激反应和细胞死亡。目前,很少有研究能够充分解释铜死亡相关基因(cuproptosis-related genes,CRGs)在KIRC发生发展中的潜在作用。
方法 利用来自The Cancer Genome Atlas(TCGA)数据库中的KIRC患者的RNA测序数据和相应的临床信息筛选差异表达的CRGs。通过单因素、多因素Cox比例回归分析和LASSO Cox 回归分析构建预后风险模型。使用来自Gene Expression Omnibus(GEO)数据库的数据验证模型。采用Kaplan-Meier(KM)分析和受试者工作特征(ROC)曲线来预测KIRC患者的预后。利用功能富集分析来探索其内部机制。采用单样本基因集富集分析(single-sample gene set enrichment analysis,ssGSEA)、肿瘤免疫功能障碍和排斥(Tumour Immune Dysfunction and Exclusion,TIDE)评分和药物敏感性分析进行免疫相关功能分析。
结果 我们建立了一个由4个CRGs(DBT、DLAT、LIAS和PDHB)组成的预后风险模型来预测KIRC患者的总生存期(overall survival,OS)。生存分析的结果显示,与低风险组患者相比,高风险组患者的OS显著降低。KIRC患者的1、3、5年的ROC曲线下面积(AUC)分别为0.691、0.618和0.614。功能富集分析表明,CRGs在三羧酸循环相关过程和代谢相关途径中显著富集。索拉非尼、阿霉素、恩贝酸和长春瑞滨对高风险组的患者敏感性更高。
结论 我们构建了一个简洁有效的CRGs风险模型来评估KIRC患者的预后,这可能为KIRC提供一个新的诊断和治疗方向。
Jin-Shuai Guo, Hao Ding, Peng-Yu Wu, Zi-Yi Xin, Jian-Xin Li, Hyon-Su Jo, Zhen-Hai Ma. Cuproptosis-Related 4-Gene Risk Model for Predicting Immunotherapy Drug Response and Prognosis of Kidney Renal Clear Cell Carcinoma[J].Chinese Medical Sciences Journal, 2023, 38(3): 191-205.
"
TCGA-KIRC dataset [n(%)] | GEO-GSE29609 [n(%)] | ||
---|---|---|---|
Age | < 60y | 247 (46.0%) | 15 (38.5%) |
≥60y | 290 (54.0%) | 24 (61.5%) | |
Gender | Female | 191 (35.6%) | - |
Male | 346 (64.4%) | - | |
Grade | Grade 1 | 14 (2.6%) | 1 (2.6%) |
Grade 2 | 230 (42.8%) | 12 (30.8%) | |
Grade 3 | 207 (38.6%) | 11 (28.2%) | |
Grade 4 | 78 (14.5%) | 15 (39.4%) | |
unknown | 8 (1.5%) | 0 (0.0%) | |
Stage | Stage Ⅰ | 269 (50.1%) | - |
Stage Ⅱ | 57 (10.6%) | - | |
Stage Ⅲ | 125 (23.3%) | - | |
Stage Ⅳ | 83 (15.4%) | - | |
unknown | 3 (0.6%) | - | |
T | T1 | 275 (51.2%) | 11 (28.2%) |
T2 | 69 (12.9%) | 5 (12.8%) | |
T3 | 182 (33.9%) | 22 (56.4%) | |
T4 | 11 (2.0%) | 1 (2.6%) | |
M | M0 | 426 (79.3%) | 25 (64.1%) |
M1 | 79 (14.7%) | 14 (35.9%) | |
unknown | 32 (6.0%) | 0 (0.0%) | |
N | N0 | 240 (44.7%) | 31 (79.5%) |
N1 | 17 (3.2%) | 8 (20.5%) | |
unknown | 280 (52.1%) | 0 (0.0%) |
"
Characteristics | TCGA-KIRC dataset | GEO-GSE29609 dataset | |||||
---|---|---|---|---|---|---|---|
High risk | Low risk | P value | High risk | Low risk | P value | ||
Age [n (%)] | 0.127 | 0.595 | |||||
<60y | 56 (47.5) | 47 (37.0) | 6 (31.6) | 9 (45.0) | |||
≥60y | 62 (52.5) | 80 (63.0) | 13 (68.4) | 11 (55.0) | |||
Gender [n (%)] | 0.328 | - | |||||
Female | 42 (35.6) | 54 (42.5) | - | - | |||
Male | 76 (64.4) | 73 (57.5) | - | - | |||
Grade [n (%)] | 0.093 | 1 | |||||
G1+G2 | 45 (38.1) | 63 (49.6) | 6 (31.6) | 7 (35.0) | |||
G3+G4 | 73 (61.9) | 64 (50.4) | 13 (68.4) | 13 (65.0) | |||
Stage [n (%)] | 0.038 | - | |||||
Ⅰ+Ⅱ | 55 (46.7) | 77 (60.6) | - | - | |||
Ⅲ+Ⅳ | 63 (53.3) | 50 (39.4) | - | - | |||
T [n (%)] | 0.207 | 0.399 | |||||
T1+T2 | 64 (54.2) | 80 (63.0) | 6 (31.6) | 10 (50.0) | |||
T3+T4 | 54 (45.8) | 47 (37.0) | 13 (68.4) | 10 (50.0) | |||
N [n (%)] | 0.677 | 0.633 | |||||
N0 | 110 (93.2) | 121 (95.3) | 14 (73.7) | 17 (85.0) | |||
N1 | 8 (6.8) | 6 (4.7) | 5 (26.3) | 3 (15.0) | |||
M [n (%)] | 0.008 | 0.121 | |||||
M0 | 90 (76.3) | 114 (89.8) | 15 (78.9) | 10 (50.0) | |||
M1 | 28 (23.7) | 13 (10.2) | 4 (21.1) | 10 (50.0) |
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