基于4个铜死亡基因的风险模型在肾透明细胞癌中的预后价值及免疫治疗药物研究

doi:10.24920/004223

摘要/Abstract

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。

关键词: 铜死亡, 风险模型, 肾透明细胞癌, 预后, 药物敏感性

Abstract:

Background Kidney renal clear cell carcinoma (KIRC) is one of the most common renal malignancies with a high mortality rate. Cuproptosis, a novel form of cell death, is strongly linked to mitochondrial metabolism and is mediated by protein lipoylation, leading to a proteotoxic stress response and cell death. To date, few studies have ellucidated the holistic role of cuproptosis-related genes (CRGs) in the pathogenesis of KIRC.
Methods We comprehensively and completely analyzed the RNA sequencing data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We screened for differentially expressed CRGs and constructed a prognostic risk model using univariate and multivariate Cox proportional regression analyses. Kaplan-Meier analysis was performed and receiver operating characteristic (ROC) curves were plotted to predict the prognosis of KIRC patients. Functional enrichment analysis was utilized to explore the internal mechanisms. Immune-related functions were analyzed using single-sample gene set enrichment analysis (ssGSEA), tumour immune dysfunction and exclusion (TIDE) scores, and drug sensitivity analysis.
Results We established a concise prognostic risk model consisting of four CRGs (DBT, DLAT, LIAS and PDHB) to predict the overall survival (OS) in KIRC patients. The results of the survival analysis indicated a significantly lower OS in the high-risk group as compared to the patients in the low-risk group. The area under the time-dependent ROC curve (AUC) at 1, 3, and 5 year was 0.691, 0.618, and 0.614 in KIRC. Functional enrichment analysis demonstrated that CRGs were significantly enriched in tricarboxylic acid (TCA) cycle-related processes and metabolism-related pathways. Sorafenib, doxorubicin, embelin, and vinorelbine were more sensitive in the high-risk group.
Conclusions We constructed a concise CRGs risk model to evaluate the prognosis of KIRC patients and this may be a new direction for the diagnosis and treatment of KIRC.

Key words: cuproptosis, risk model, kidney renal clear cell carcinoma, prognosis, drug sensitivity

基金资助: 辽宁省自然科学基金(20170540254)

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.

图/表 9

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		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
Characteristics	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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