A novel prognosis model for papillary renal cell carcinoma based on Wnt-associated lncRNAs in TCGA database

doi:10.3877/cma.j.issn.1674-3253.2023.03.015

Abstract

Abstract:

Objective

To construct a prognostic model of Wnt associated long non-coding RNAs (LncRNAs) in papillary renal cell carcinoma (pRCC).

Methods

The data for ranscriptome and clinical information of pRCC patients were obtained by The Cancer Genome Atlas (TCGA). Wnt-associated LncRNAs were selected by co-expression network analysis, COX regression, and Lasso analysis to build the risk prognosis model. In addition, the predictive performance of this model is verified by receiver operating characteristic (ROC) curves.

Results

Five Wnt-associated LncRNAs (CT66, PRECSIT, AL352984.1, AC124798.1, and AC011503.2) were chosen to construct a prognostic model of pRCC. Survival analyses showed that the model have predictive validity in TCGA training group (P=0.002), testing group (P=0.013) and combined group (P<0.001). When the risk score was used as an independent prognostic indicator, the area under ROC curve (AUC) for 5-year overall survival in the combined group was 0.708, indicating acceptable predictive performance.

Conclusion

The prognosis model including 5 Wnt-associated LncRNAs is helpful to evaluate the prognosis of pRCC patients.

Key words: Wnt, Palillary renall cell carcinoma, Renal carcinoma, TCGA, Data base, Bioinformatics analysis, Compu-tational biology, Long non-coding RNA(lncRNA), Prognosis model

Guojun Tang, Yude Hong, Chongyu Zhao, Liaoyuan Li. A novel prognosis model for papillary renal cell carcinoma based on Wnt-associated lncRNAs in TCGA database[J]. Chinese Journal of Endourology(Electronic Edition), 2023, 17(03): 270-275.

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URL: https://zhqjmnmwkzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-3253.2023.03.015

https://zhqjmnmwkzz.cma-cmc.com.cn/EN/Y2023/V17/I03/270

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References 26

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组别	例数	年龄			性别		肿瘤分级
组别	例数	≤65岁	>65岁	未知	女	男	Ⅰ	Ⅱ	Ⅲ	Ⅳ	未知
完整组	290	179	108	3	76	214	172	21	52	15	30
测试组	145	94	50	1	42	103	80	10	35	7	13
训练组	145	85	58	2	34	111	92	11	17	8	17
P值		0.369			0.350		0.068

组别	例数	年龄			性别		肿瘤分级
组别	例数	≤65岁	>65岁	未知	女	男	Ⅰ	Ⅱ	Ⅲ	Ⅳ	未知
完整组	290	179	108	3	76	214	172	21	52	15	30
测试组	145	94	50	1	42	103	80	10	35	7	13
训练组	145	85	58	2	34	111	92	11	17	8	17
P值		0.369			0.350		0.068

ID	coef	HR	HR.95L	HR.95H	P值
CT66	2.256	9.542	3.140	29.000	<0.001
PRECSIT	1.298	3.662	1.530	8.768	0.004
AL352984.1	1.901	6.691	1.874	23.897	0.003
AC124798.1	-0.729	0.482	0.278	0.836	0.009
AC011503.2	1.238	3.448	0.736	16.156	0.116

ID	coef	HR	HR.95L	HR.95H	P值
CT66	2.256	9.542	3.140	29.000	<0.001
PRECSIT	1.298	3.662	1.530	8.768	0.004
AL352984.1	1.901	6.691	1.874	23.897	0.003
AC124798.1	-0.729	0.482	0.278	0.836	0.009
AC011503.2	1.238	3.448	0.736	16.156	0.116

分析方法	变量	HR	HR.95L	HR.95H	P值
单因素COX回归分析	年龄	1.0016	0.9738	1.0301	0.914
	性别	0.5267	0.2623	1.0575	0.071
	肿瘤分级	2.3547	1.7449	3.1776	<0.001
	风险评分	1.0183	1.0095	1.0271	<0.001
多因素COX回归分析	年龄	1.0080	0.9797	1.0372	0.583
	性别	0.5994	0.2935	1.2242	0.160
	肿瘤分级	2.2024	1.6172	2.9994	<0.001
	风险评分	1.0125	1.0031	1.0219	0.009

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