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

Highlight

Kidney renal clear cell carcinoma (KIRC) is the most common pathological type of renal cell carcinoma. This study adds to the understanding of the molecular mechanisms of KIRC and demonstrates the potential relationship between cuproptosis, the immune microenvironment, and the prognosis of KIRC patients based on bioinformatics.

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

Funding:

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.

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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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Cuproptosis-Related 4-Gene Risk Model for Predicting Immunotherapy Drug Response and Prognosis of Kidney Renal Clear Cell Carcinoma

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