Risk evaluation model for kidney transplant rejection using peripheral blood gene expression profiling

doi:10.3877/cma.j.issn.1674-3903.2020.05.001

Abstract

Abstract:

Objective

To develop and validate a risk evaluation model for kidney transplant rejection using bioinformatics methods.

Methods

This study conducted a data-mining and integrated analysis of blood-based gene expression profiling of kidney transplant recipients with rejection from the GEO database. Gene selection was performed progressively by LIMMA analysis and LASSO regression, followed by development of a risk evaluation model for kidney transplant rejection using the selected genes through Logistic regression. The utility of the proposed model was assessed by the ROC, calibration, and decision curve analyses. A P<0.05 was considered statistically significant.

Results

A 15-gene panel was selected according to its ability to evaluate the risk of rejection. The 15-gene panel was then used to establish a risk evaluation model and generate a risk of rejection score(RRS) by the gene expression values. RRS ranged from 0 to 100, when the cut-off of RSS was set to 35.55, the AUC, overall accuracy, sensitivity, specificity, and positive and negative predictive value of this model reached 0.836 (0.812-0.859), 76.3%, 73.0%, 78.0%, 62.9% and 84.9% respectively. The decision curve analysis demonstrated significant advantage of clinical benefit using this model.

Conclusions

RRS generated by this model is capable of quantitatively evaluating the risk of rejection in a non-invasive manner. However, this model requires additional clinical trial study for further validation.

Key words: Kidney transplantation, Bioinformatics, Graft rejection, Risk evaluation model, Peripheral blood, Gene expression profiling

Kiat Shenq Lim, Huimin An, Kun Shao, Quan Zhou, Xianghui Wang, Peijun Zhou. Risk evaluation model for kidney transplant rejection using peripheral blood gene expression profiling[J]. Chinese Journal of Transplantation(Electronic Edition), 2020, 14(05): 273-278.

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URL: https://zhyzzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-3903.2020.05.001

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Figures/Tables 9

References 29

1	Bouamar R, Shuker N, Hesselink D A, et al. Tacrolimus predose concentrations do not predict the risk of acute rejection after renal transplantation: a pooled analysis from three randomized-controlled clinical trials(dagger)[J]. Am J Transplant, 2013, 13(5): 1253-1261.
2	Loupy A, Vernerey D, Tinel C, et al. Subclinical rejection phenotypes at 1 year post-transplant and outcome of kidney allografts[J]. J Am Soc Nephrol, 2015, 26(7): 1721-1731.
3	Parkinson H, Kapushesky M, Shojatalab M, et al. ArrayExpress-a public database of microarray experiments and gene expression profiles[J]. Nucleic Acids Res, 2007, 35(Database issue): D747-D750.
4	Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update[J]. Nucleic Acids Res, 2013, 41(Database issue): D991-D995.
5	Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data[J]. Biostatistics, 2003, 4(2): 249-264.
6	Leek JT, Johnson WE, Parker HS, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments[J]. Bioinformatics, 2012, 28(6): 882-883.
7	Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods[J]. Biostatistics, 2007, 8(1): 118-127.
8	Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7): e47.
9	Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2011, 73(3): 273-282.
10	Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1996, 58(1): 267-288.
11	Vickers AJ, Cronin AM, Elkin EB, et al. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers[J]. BMC Med Inform Decis Mak, 2008, 8: 53.
12	Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models[J]. Med Decis Making, 2006, 26(6): 565-574.
13	Van Loon E, Gazut S, Yazdani S, et al. Development and validation of a peripheral blood mRNA assay for the assessment of antibody-mediated kidney allograft rejection: A multicentre, prospective study[J]. EBioMedicine, 2019, 46: 463-472.
14	Friedewald JJ, Kurian SM, Heilman RL, et al. Development and clinical validity of a novel blood-based molecular biomarker for subclinical acute rejection following kidney transplant[J]. Am J Transplant, 2019, 19(1): 98-109.
15	Gunther OP, Shin H, Ng RT, et al. Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study[J]. Omics, 2014, 18(11): 682-695.
16	Shen-Orr SS, Tibshirani R, Khatri P, et al. Cell type-specific gene expression differences in complex tissues[J]. Nat Methods, 2010, 7(4): 287-289.
17	Kurian SM, Williams AN, Gelbart T, et al. Molecular classifiers for acute kidney transplant rejection in peripheral blood by whole genome gene expression profiling[J]. Am J Transplant, 2014, 14(5): 1164-1172.
18	Li L, Khatri P, Sigdel TK, et al. A peripheral blood diagnostic test for acute rejection in renal transplantation[J]. Am J Transplant, 2012, 12(10): 2710-2718.
19	Modena BD, Kurian SM, Gaber LW, et al. Gene expression in biopsies of acute rejection and interstitial fibrosis/tubular atrophy reveals highly shared mechanisms that correlate with worse long-term outcomes[J]. Am J Transplant, 2016, 16(7): 1982-1998.
20	Einecke G, Reeve J, Sis B, et al. A molecular classifier for predicting future graft loss in late kidney transplant biopsies[J]. J Clin Invest, 2010, 120(6): 1862-1872.
21	Reeve J, Bohmig GA, Eskandary F, et al. Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers[J]. Am J Transplant, 2019, 19(10): 2719-2731.
22	Kanehisa M, Goto S, Hattori M, et al. From genomics to chemical genomics: new developments in KEGG[J]. Nucleic Acids Research, 2006, 34(suppl_1): D354-D357.
23	Nishimura D. BioCarta[J]. Biotech Software & Internet Report, 2001, 2(3): 117-120.
24	Joshi-Tope G, Gillespie M, Vastrik I, et al. Reactome: a knowledgebase of biological pathways[J]. Nucleic Acids Research, 2005, 33(suppl_1): D428-D432.
25	Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: From gene data mining to disease genome sequence analyses[J]. Curr Protoc Bioinformatics, 2016, 54: 1.30.1-1.30.33.
26	Roufosse C, Simmonds N, Clahsen-Van Groningen M, et al. A 2018 reference guide to the Banff classification of renal allograft pathology[J]. Transplantation, 2018, 102(11): 1795-1814.
27	Becker JU, Chang A, Nickeleit V, et al. Banff borderline changes suspicious for acute T cell-mediated rejection: Where do we stand？[J]. Am J Transplant, 2016, 16(9): 2654-2660.
28	First MR, Peddi VR, Mannon R, et al. Investigator assessment of the utility of the TruGraf molecular diagnostic test in clinical practice[J]. Transplant Proc, 2019, 51(3): 729-733.
29	Marsh L, Kurian SM, Rice JC, et al. Application of TruGraf v1: A novel molecular biomarker for managing kidney transplant recipients with stable renal function[J]. Transplant Proc, 2019, 51(3): 722-728.

组别	例数	GSE129166系列^a		GSE107509系列		GSE46474系列		GSE15296系列		GSE14346系列^b
组别	例数	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)
排斥反应组	330	52±14	39	50±15	33	46±12	30	45±14	24	12±5	32
非排斥反应组	644	52±14	39	50±14	36	49± 9	40	50±15	35	11±6	45
t/χ²值	-	-	-	-	-	-	-	-	-	-	-
P值	-	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05

组别	例数	GSE129166系列^a		GSE107509系列		GSE46474系列		GSE15296系列		GSE14346系列^b
组别	例数	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)	年龄(岁)	女性比例(%)
排斥反应组	330	52±14	39	50±15	33	46±12	30	45±14	24	12±5	32
非排斥反应组	644	52±14	39	50±14	36	49± 9	40	50±15	35	11±6	45
t/χ²值	-	-	-	-	-	-	-	-	-	-	-
P值	-	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05	>0.05

序号	基因名	Log₂FC	序号	基因名	Log₂FC
1	ECHDC3	0.3715	26	VSIG4	0.2419
2	DAAM2	0.3662	27	SSBP4	0.2414
3	SMIM1	0.3519	28	DYNLT3	-0.2413
4	FCER1A	-0.3005	29	PKM	-0.2399
5	TIGIT	0.2983	30	INHBB	0.2398
6	ZBTB16	0.2944	31	OLAH	0.2385
7	LAG3	0.2879	32	MCOLN2	0.2364
8	JAKMIP1	0.2867	33	SGK1	-0.2356
9	UTS2	-0.2831	34	PRSS23	0.2345
10	PRNP	-0.2815	35	CXCL8	-0.2339
11	TOGARAM2	0.2797	36	LOC100130872	0.2337
12	FKBP5	0.2789	37	ZNF683	0.2321
13	CD3G	0.2751	38	LINC00260	0.2320
14	GRB10	0.2661	39	GAPT	-0.2298
15	CD9	-0.2659	40	BANF1	-0.2295
16	CD8B	0.2648	41	LOC101928595	0.2263
17	PMAIP1	-0.2576	42	CCR3	-0.2255
18	ITGA6	-0.2568	43	TRAPPC1	-0.2203
19	LOC101929667	0.2510	44	BEX4	-0.2195
20	MIAT	0.2506	45	CCNG1	-0.2181
21	FLVCR1-DT	0.2464	46	TMIGD3	0.2146
22	HCG27	0.2460	47	ADAMTSL4-AS2	0.2143
23	B3GAT1	0.2445	48	LOC100505501	0.2128
24	TPST1	0.2426	49	ADK	-0.2123
25	LOC105373159	0.2421	50	MATK	0.2123

序号	基因名	Log₂FC	序号	基因名	Log₂FC
1	ECHDC3	0.3715	26	VSIG4	0.2419
2	DAAM2	0.3662	27	SSBP4	0.2414
3	SMIM1	0.3519	28	DYNLT3	-0.2413
4	FCER1A	-0.3005	29	PKM	-0.2399
5	TIGIT	0.2983	30	INHBB	0.2398
6	ZBTB16	0.2944	31	OLAH	0.2385
7	LAG3	0.2879	32	MCOLN2	0.2364
8	JAKMIP1	0.2867	33	SGK1	-0.2356
9	UTS2	-0.2831	34	PRSS23	0.2345
10	PRNP	-0.2815	35	CXCL8	-0.2339
11	TOGARAM2	0.2797	36	LOC100130872	0.2337
12	FKBP5	0.2789	37	ZNF683	0.2321
13	CD3G	0.2751	38	LINC00260	0.2320
14	GRB10	0.2661	39	GAPT	-0.2298
15	CD9	-0.2659	40	BANF1	-0.2295
16	CD8B	0.2648	41	LOC101928595	0.2263
17	PMAIP1	-0.2576	42	CCR3	-0.2255
18	ITGA6	-0.2568	43	TRAPPC1	-0.2203
19	LOC101929667	0.2510	44	BEX4	-0.2195
20	MIAT	0.2506	45	CCNG1	-0.2181
21	FLVCR1-DT	0.2464	46	TMIGD3	0.2146
22	HCG27	0.2460	47	ADAMTSL4-AS2	0.2143
23	B3GAT1	0.2445	48	LOC100505501	0.2128
24	TPST1	0.2426	49	ADK	-0.2123
25	LOC105373159	0.2421	50	MATK	0.2123

参数名	类别	系数	标准误	Wald Z	Pr(>\|Z\|)
Intercept	截距	-21.2140	6.72	-3.16	<0.05
SSBP4	蛋白编码基因	2.4402	0.57	4.28	<0.05
CD3G	蛋白编码基因	0.9962	0.28	3.62	<0.05
ECHDC3	蛋白编码基因	0.8759	0.24	3.61	<0.05
GRB10	蛋白编码基因	0.2677	0.30	0.89	>0.05
DAAM2	蛋白编码基因	0.1253	0.27	0.46	>0.05
TIGIT	蛋白编码基因	0.1147	0.29	0.39	>0.05
INHBB	蛋白编码基因	0.0811	0.31	0.27	>0.05
UTS2	蛋白编码基因	-0.2320	0.15	-1.55	>0.05
ADK	蛋白编码基因	-0.2923	0.25	-1.15	>0.05
ITGA6	蛋白编码基因	-0.6918	0.30	-2.31	<0.05
TRAPPC1	蛋白编码基因	-0.8258	0.43	-1.92	>0.05
BANF1	蛋白编码基因	-1.2772	0.47	-2.71	<0.05
LOC105373159	RNA基因	0.7525	0.23	3.24	<0.05
HCG27	RNA基因	0.3946	0.25	1.58	>0.05
FLVCR1-DT	RNA基因	0.2884	0.21	1.37	>0.05

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