Identification of Nephrogenic Therapeutic Biomarkers of Wilms Tumor Using Machine Learning

Hindawi Journal of Oncology Volume 2021, Article ID 6471169, 14 pages https://doi.org/10.1155/2021/6471169

Research Article Identification of Nephrogenic Therapeutic Biomarkers of Wilms Tumor Using Machine Learning

Hanxiang Liu,1 Chaozhi Tang,2 and Yi Yang 1

1Pediatric Urology, Shengjing Hospital of China Medical University, Shenyang 110001, China 2Department of Urology, !e First Aﬃliated Hospital of China Medical University, Shenyang 110001, China

Correspondence should be addressed to Yi Yang; [email protected]

Received 16 April 2021; Revised 22 June 2021; Accepted 24 July 2021; Published 10 August 2021

Academic Editor: Zhixiong Liu

Copyright © 2021 Hanxiang Liu et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Wilms tumor is the most common renal malignancy in children, with a survival rate of more than 90%; however, treatment outcomes for certain patient subgroups, such as those with bilateral and recurrent diseases, remain significantly below this survival rate. )erefore, it remains essential to identify new biomarkers and develop effective therapeutic strategies. Based on the )erapeutically Applicable Research to Generate Effective Treatments and Gene Expression Omnibus RNA microarray datasets, we have identified eight differentially expressed genes in Wilms tumors as renal-specific in 33 randomly selected adult tumors. )e risk model, constructed using survival forest and multivariate Cox regression, can effectively predict the prognosis; the risk score is an independent prognostic factor in Wilms tumor. Gene set enrichment analysis showed that most of the signature genes were involved in regulating human development-related pathways. At the same time, patients in the high-risk group exhibited more sensitive immunological and chemotherapeutic properties than those in the low-risk group. )ese results provide new insights into personalized and precise Wilms tumor treatment strategies.

1. Introduction Although the guidelines of the International Society of Pediatric Oncology (SIOP) and the American Pediatric Wilms tumor is the most common malignant kidney tumor Oncology Group (COG) differ regarding the treatment and and the fourth most common cancer in children [1] with an risk stratification strategies, their therapies follow relatively onset age between 3 and 5 years. Approximately, 650 new simple methods, with very similar outcomes and overall cases are annually reported in the United States [2]. His- survival rates >85% [4, 5]. Surgery is usually the first line of tologically, Wilms tumor mimics the various stages of stem treatment for Wilms tumor, followed by systemic chemo- cells in the kidney, suggesting the abnormal differentiation therapy; however, in some cases, chemotherapy may proceed of pluripotent mesenchymal stem cells. )e classic Wilms nephrectomy. Radiotherapy and chemotherapy can effec- tumor comprises three cell types, namely, blastoderm, tively improve the survival rate of patients with advanced stroma, and epithelium, which do not coexist in all cases. Wilms tumors; however, they may also increase the risk of Meanwhile, Wilms tumors can be divided into two histo- secondary malignant tumors years later [6, 7]. logic types, namely, “favorable” and “unfavorable.” Ap- Chemotherapeutic agents including actinomycin, proximately, 90% of all cases show a “favorable” histology, adriamycin, and vincristine can increase the risk of sec- including the three abovementioned cell types, and usually ondary malignant tumors, are associated with specific have a good prognosis [3]. Conversely, cases with an “un- toxicities, may interfere with hearing (carboplatin) and favorable” histology show a higher degree of dysregulation, cardiac function (doxorubicin), and may induce peripheral which is defined as a polychromatic, polymorphic nucleus neuropathy (vincristine) [5]. )erefore, identifying new that is three times the size of neighboring cells with an biomarkers for Wilms tumors, reducing the intensity of abnormal mitotic morphology. treatment for children with a low risk of recurrence and 2 Journal of Oncology developing strategies to reduce their toxicity are urgently carcinoma (KIRP) was downloaded from the UALCAN needed. (http://ualcan.path.uab.edu) database. In this study, we have identified unique biomarkers of Wilms tumor using the )erapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene 2.2. Differentially Expressed Gene Analysis. We used the edgr Expression Omnibus (GEO) RNA-seq datasets and clinical package at the same time to control the normal and tumor information and verified them using adult kidney tumors. samples of TARGET, GSE66405, and GSE73209 after log2 p . Furthermore, we systematically analyzed the risk charac- normalization and used |log2FC| > 1 and < 0 05, as the teristics constructed by these biomarkers to predict the threshold values to screen for differentially expressed genes. prognosis of patients with Wilms tumor and determine their In addition, we analyzed the intersection of the three feasibility for immunotherapy and chemotherapy. datasets to obtain 48 differentially expressed genes and used GO (gene ontology) and KEGG (Kyoto Encyclopedia of 2. Materials and Methods Genes and Genomes) for functional enrichment analysis.

2.1. Datasets. )e RNA-seq transcriptome data, methylation 2.3. Random Survival Forest. To identify the differentially data, and clinical information of the patients were down- expressed genes with significant impacts on the prognosis of loaded from the TARGET database (https://ocg.cancer.gov/ Wilms tumors, a univariate Cox proportional hazard re- programs/target), which contains a total of six cases: normal gression model was established for each DEG using survival samples, 130 cancer samples, 117 methylated samples, and data. We used the coxph function in the Survival R package 125 patients with complete clinical information. )e and found that a screening criterion of p < 0.05 would not be microarray data of the other two datasets, GSE73209 and sufficient to screen the target gene; therefore, we set the GSE66405, were downloaded from the GEO database. threshold to p < 0.9. We used the randomSurvivalForest GSE66405 contains 28 Wilms tumor samples and four algorithm to rank the importance of prognostic-related normal kidney tissue samples. GSE73209 includes 32 Wilms genes (ntree < 1,000, which indicates that the number of tumor samples and four fetal kidney tissues for genetic random forest CHAID decision trees is lower than 1,000). difference analysis. All datasets were processed using the We identified genes with a relative importance >0.3, as the log2 normalization standard. )e TCGA pan-cancer RNA final feature. sequencing data were downloaded from the UCSC Xena database (https://xenabrowser.net/datapages/), and 18 cancers containing both normal and tumor samples were used 2.4. Multivariate Regression Was Used to Build a Predictive for the verification of differentially expressed genes in the Model. We performed multiple regression analysis on the Wilms tumors. )e comparison of the methylation levels eight genes obtained from the random forest algorithm and between the normal and tumor samples of kidney renal clear then obtained their coefficients, p value, and Z score. )e cell carcinoma (KIRC) and kidney renal papillary cell final risk model is as follows:

risk score � 0.59190 × UMOD expression + 0.76363 × NAT8 expression + 0.05435 × CLDN19 expression − 0.02884 × TMPRSS2 expression − 0.66439× (1) DEFB1 expression − 0.59586 × KCNJ1 expression − 0.39844 × AQP2 expression− 0.16687 × HMGCS2 expression.

2.5. Gene Set Enrichment Analysis (GSEA) of Risk Models. on the GDSC training set and verified the accuracy of the According to the risk score calculated using the above formula, prediction. )e half-maximal inhibitory concentration the TARGET patients were divided into high- and low-risk (IC50) of the sample was estimated using ridge regression. groups, based on the median value. To analyze the significantly Second, based on the data on the immune response of enriched pathways between them, we used h.all.v7.4.symbols of melanoma patients who have recently received checkpoint GSEA 4.0.1. gmt [Hallmarks], c2.cp.kegg.v7.4.symbols.gmt blockade treatment against cytotoxic T lymphocyte antigen [Curated], and c5.all.v7.4.symbols.gmt [Gene ontology] as 4 (CTLA-4) and programmed death receptor 1 (PD-1) [8], reference datasets, and a p < 0.05 was used as the screening we used the subgraph method to predict the responsiveness threshold. In addition, the gene set related to development was of the high- and low-risk groups to immunotherapy [9]. also obtained from the GSEA database (http://www.gsea- msigdb.org/gsea/index.jsp). 2.7. Prediction of Drug Susceptibility of Signature Genes. We downloaded the Compound activity: DTP NCI-60 and 2.6. Chemical Prediction and Immune Prediction. We used the RNA: RNA-seq data from the CellMinerCDB (Version: the R package “pRRophetic” to predict the chemotherapy 2020.4) database (https://discover.nci.nih.gov/cellminer/ response of each sample through a 10-fold crossover based home.do). Only clinical trials and FDA-approved drugs Journal of Oncology 3 were retained, the correlation between the signature genes between the age, sex, stage, histological type, and risk score and drug sensitivity was checked, and a Spearman corre- from the perspective of single factors and multiple factors. lation coefficient >0.3 and a p < 0.05 were used as the Differences in sex, stage, and risk score were statistically screening thresholds. significant as indicated using the univariate and multivariate regression analyses (p < 0.05), suggesting that they are op- posite prognostic factors for patients with Wilms tumor. In 2.8. Statistical Analysis. All statistical tests were performed addition, we used the R software package timeROC to using R version 4.0.3. )e Wilcoxon test was used for the perform receiver operating characteristic (ROC) analysis of comparison between two groups of data. A nonparametric the prognostic classification of the risk score and analyzed test was used for Spearman correlation analysis within the the classification efficiency of the 2-year, 3-year, and 5-year study, and log-rank test was used for the survival analysis of prognostic predictions. )e ROC curve showed that the 2- the Kaplan–Meier curve. Bonferroni correction was used to year, 3-year, and 5-year AUCs were 0.654, 0.678, and 0.685, adjust the immunotherapy response in the high- and low- respectively. Similarly, the 5-year risk score AUC of patients risk groups. All tests were two-sided, and statistical signif- with Wilms tumors was better than that of the sex, age, stage, icance was set at a p value < 0.05. and histology (Figure 3(e)). 3. Results 3.4. Chemical Prediction and Immune Prediction. To further 3.1. Identification of Differential Genes in Wilms Tumors. explore the possibility and applicability of the risk model First, in order to screen the important differentially constructed in a clinical setting, based on the recent ac- expressed genes, we used the edgr package to compare the ceptance of cytotoxic T lymphocyte antigen 4 (CTLA-4) and normal and tumor samples using TARGET and GSE73209 programmed death receptor 1 (PD-1) checkpoint blockade datasets after log2 normalization, and |log2FC| > 1 and of the immune response data of melanoma patients treated, p < 0.05 were used as the threshold values to obtain 1,121 the subgraph method was used to predict the responsiveness differentially expressed genes. By intersecting these with of the high- and low-risk groups to immunotherapy. Pa- 2,567 genes derived from GSE66405, 48 differentially tients in the high-risk group were more likely to be treated expressed genes were obtained (Figures 1 and 2(a)). with CTLA-4. Second, considering that chemotherapy is the most common treatment for patients with Wilms tumor after surgery (Figure 3(f)), we trained the prediction model 3.2. Random Survival Forest. To identify genes with signif- on the GDSC cell line dataset through ridge regression and icant effects on prognosis of the 48 differentially expressed evaluated it using a 10-fold cross-validation satisfactory genes, we used the randomForestSRC R software package for forecast accuracy. We found that the high- and low-risk functional selection. We identified genes with a relative groups showed highly consistent responsiveness to some importance >0.3 as the final feature. Figure 2(b) depicts the commonly used chemotherapeutic agents against Wilms relationship between the error rate and the number of tumors, that is, in the prediction of the sensitivity to vin- classification trees, and Figure 2(c) shows the final order of blastine, vinorelbine, mitomycin C, doxorubicin, gemcita- relative importance of the first eight genes. Figures 2(d)–2(f) bine, and cisplatin. Furthermore, the high-risk group show the expression of these eight genes, HMGCS2, UMOD, patients had a significantly higher response sensitivity than DEFB1, NAT8, KCNJ1, TMPRSS2, CLDN19, and AQP2, in the low-risk group patients (Figure 3(g); p < 0.05). the normal and cancer tissues of the TARGET, GSE66405, and GSE73209 datasets. Compared with the normal tissues, these eight genes were consistently downregulated in cancer 3.5. Gene Set Enrichment Analysis of the High- and Low-Risk tissues and were expressed as a significant positive corre- Groups. Next, explain these differences in drug sensitivity lation (Figure 2(g)). between the high- and low-risk groups, we performed Gene Set Enrichment Analysis (GSEA) on the high- and low-risk groups to characterize the differences in the functional 3.3. Construction of a Prognostic Risk Model Based on the pathways between the two groups. Compared with the low- Differentially Expressed Genes. Following the identification risk group, the high-risk group showed significant activation of eight genes using random survival forest, we conducted of the ERAD pathway and ubiquitin-dependent ERAD multiple regression analysis to construct a risk model for the pathway, while in developmental cell growth, morphogen- prognostic evaluation of Wilms tumor patients. Table 1 esis, hedgehog signaling, late estrogen response, MAPK shows the coefficients, p value, Z score, raw importance, signaling pathway, and calcium signaling pathway were and relative importance of these eight genes. significantly inhibited (Figure 4). )e high- and low-risk According to the median risk score, patients with Wilms groups manifested significant abnormalities in the devel- tumors were divided into high- and low-risk groups. )e opment-related pathways. We collected the six major human survival rate of patients with low-risk scores was much developmental signaling pathways in the GSEA dataset and higher than that of patients in the high-risk group quantified their scores in each Wilms tumor sample using (Figure 3(a)). At the same time, to evaluate the relationship the ssGSEA algorithm. )e results showed that the high-risk between the validity of the risk model and the clinical group was associated with Wnt/β-catenin signaling, Notch characteristics, we analyzed the predictive relationship signaling, and hedgehog signaling, compared to the low-risk 4 Journal of Oncology

Screening 1121 differentially expressed genes in TARGET

Screening 267 differentially expressed Screening 2566 differentially genes in GSE73209 expressed genes in GSE66405

Obtaining 48 common differentially expressed genes

Random survival forest

Building a risk model for 8 common differentially expressed genes

Functional enrichment analysis between The expression of signature in the high- and low-risk groups model verified in adult renal tumors

Prediction of immunotherapy and Methylation analysis for genes chemotherapy between high- and of signature in the model low-risk groups

Prediction of sensitive chemotherapeutic agents of genes of signature in the mode

Figure 1: Flow chart of this study. group. )e scores of signaling, TGF-β signaling, Hippo available for the majority of the samples in KICH, KIRC, and signaling, and ESC pluripotency pathway were low. Further, KIRP; KICH has no normal methylation data). )e meth- Spearman correlation analysis showed that the risk score was ylation modification results of KIRC and KIRP show that the significantly negatively correlated with the ssGSEA scores of methylation level of the promoter region of AQP2, CLDN19, these six developmental signaling pathways (Figure 4). DEFB1, KCNJ1, HMGCS2, and TMPRSS2 is elevated compared to the normal samples, which indicates their hypermethylation. )e methylation level in the promoter 3.6. Expression of Signature Genes in Pan-Cancer. We region of NAT8 was significantly reduced, which indicates downloaded 33 types of TCGA cancers from UCSC Xena to hypomethylation (Figure 6). At the same time, we compared examine the expression patterns of the signature genes of the methylation modification levels of the high- and low-risk adult tumors. )e signature genes were significantly groups in the TARGETdataset and found that in addition to downregulated in most tumors. Apart from DEFB1 and TMPRSS2, for the low-risk group, the high-risk group had a TMPRSS2, which were upregulated in KICH, the other genes higher level of methylation modifications. )e mRNA ex- were significantly downregulated in KICH, KIRC, and KIRP. pression levels of AQP2, CLDN19, and DEFB1 were sig- At the same time, the expression of AQP2, CLDN19, DEFB1, nificantly negatively correlated with the methylation KCNJ1, NAT8, and UMOD in the normal tissues of KICH, modifications, while the mRNA expression levels of KIRC, and KIRP was much higher than that in the normal TMPRSS2 and UMOD were significantly positively corre- tissues of other tumors. )erefore, these may be unique signs lated with the methylation modifications, while the rela- of renal origin (Figure 5). tionship between the mRNA expression level of other genes and the methylation modifications remains nebulous 3.7. Methylation of Signature Genes Was Explored in Adult (Figure 7). Renal Cancer. To further clarify the reasons for the general low expression of the above-mentioned signature genes in adult renal tumors, we used the genes of the query signature, 3.8. Prediction of the Sensitivity of Signature Genes in CellMiner AQP2, CLDN19, DEFB1, KCNJ1, NAT8, HMGCS2, and Database. To further explore the possibility of clinical ap- TMPRSS2 (the average beta value for “UMOD” is not plication of the signature genes and promote a more precise Journal of Oncology 5

TARGET GSE73209 HMGCS2 UMOD DEFB1 0.54 NAT8 7 KCNJ1 97 TMPRSS2 735 CLDN19 AQP2 48 PDZK1IP1 FOLR3 FOLR1 331 115 ELF5 0.52 SLC34A1 PRODH EMX1 2072 SLC36A2 KNG1 AGXT2 C1orf116 GSE66405 CYP4A11 ATP6V0A4 0.50 FXYD2 TMEM213 Error rate Error SLC12A1 SCNN1G TMEM207 CLCNKA SLC12A3 CASR ACSM2B 0.48 RAB17 ITLN1 PTGER1 CLDN8 CDH16 FXYD4 SLC22A11 CLDN10 CALB1 0.46 PLA2G4F GCGR CLCNKB

0 200 400 600 800 1000 −0.01 0.00 0.01 0.02 0.03 0.04 Number of trees Variable importance (a) (b)

HMGCS2 UMOD UMOD 2.5 NAT8 AQP2 DEFB1 2 HMGCS2 NAT8 1.5 CLDN19 TMPRSS2 KCNJ1 1 DEFB1 TMPRSS2 KCNJ1

CLDN19 Tumor

AQP2 Normal 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Variable relative importance (c) (d)

HMGCS2 DEFB1 4 3.6 KCNJ1 NAT8 AQP2 3.5 3.4 CLDN19 NAT8 KCNJ1 CLDN19 3.2 3 TMPRSS2 TMPRSS2 HMGCS2 DEFB1 3 2.5 UMOD AQP2 2.8 UMOD Normal Tumor Tumor Normal (e) (f) Figure 2: Continued. 6 Journal of Oncology

NAT8 DEFB1 2.4 30 0.6 1.2 1.8 1.8 1.2 2.4 −1 0.6 0 0 0.6 KCNJ1 2.4 1.2

1.8 1.8

1.2 2.4 UMOD

0.6 3 0

3 0

2.4 0.6 HMGCS2 1.8 1.2

1.2 1.8 0.6 2.4 TMPRSS2 0 0 1 1.2 0.6 0.6 1.2 AQP2 0 2.4 1.8 CLDN19 (g)

Figure 2: Identification of differential genes in Wilms tumor. (a) Venn diagram, (b) error rate, and variation importance of the 48 differentially expressed genes using dimensionality reduction analysis of random survival forest. (c) Target genes with a relative importance > 0.3 of the random survival forest. Heat map of the eight differentially expressed genes finally identified in normal and tumor tissues in (d) TARGET, (e) GSE66405, and (f) GSE73209.

Table 1: )e coefficients, p value, Z score, raw importance, and relative importance of these eight genes. Gene Coef z p Raw importance Relative importance UMOD 0.5919 1.612 0.1071 0.02239931 0.57317073 TMPRSS2 −0.02884 −0.048 0.9614 0.01098428 0.35772358 NAT8 0.76363 2.174 0.0297 0.01378419 0.41056911 KCNJ1 −0.59586 −1.271 0.2036 0.01270730 0.39024390 HMGCS2 −0.16687 −0.467 0.6403 0.045014 1 DEFB1 −0.66439 −1.755 0.0793 0.01550722 0.44308943 CLDN19 0.05435 0.158 0.8741 0.01076889 0.35365854 AQP2 −0.39844 −1.396 0.1628 0.00818436 0.30487805 Coef: coefficient; p: p value; z: Z score. treatment, we used the CellMiner database to evaluate the with Wilms tumor into groups of significantly different risk, impact of the target genes on drug sensitivity. Drug sensi- to effectively predict prognosis, demonstrating that the risk tivity was measured using the Z score. )e higher the score, score is an independent prognostic factor for Wilms tumor. the more sensitive the cells were to the drug treatment Further verification of GSEA and pan-cancer signature gene (Figure 8). Our results showed that the expression of AQP2 expression demonstrates that the signature genes are spe- and CLDN19 was increased, which is associated with the cifically of kidney origin and are associated with pathways sensitivity to imiquimod. )e increased expression of that regulate human development. In particular, develop- DEFB1 is associated with the sensitivity to carboplatin and mental cell growth is involved in morphogenesis, hedgehog idarubicin, while the increased expression of TMPRSS2 and signaling, Wnt/β-catenin signaling, and Notch signaling. HNGCS2 is associated with resistance to cisplatin. Our results showed that as the risk score increased, the corresponding ssGSEA score of development-related path- 4. Discussion ways decreased; that is, the risk score is significantly negatively correlated with the development-related pathways. It )e etiology of Wilms tumor remains unclear; however, it has been reported that an excessive activation of develop- may be influenced by genetic changes. For example, some ment-related pathways, such as Wnt/β-catenin signaling genetic biomarkers associated with Wilms tumors have been [11–13] and hedgehog signaling [14], can lead to the pro- identified in approximately 1/3 of all Wilms tumors include gression or metastasis of Wilms tumors. Our results also WT1, CTNNB1, and WTX mutations or the loss of het- demonstrated that compared with normal tissues, in patients erozygosity of the 1p, 1q, 11p15, and 16q chromosomes [10] with Wilms tumor, whether in high- or low-risk groups, affecting the normal embryonic development of the geni- Wnt/β-catenin, Notch, hedgehog, and hippo signaling tourinary tract. )erefore, Wilms tumor can also be clas- pathways have different degrees of activation. However, the sified as a developmental disease. In this study, we used low-risk group patients had a higher degree of activation of machine learning methods to screen the differentially the abovementioned pathways than the high-risk group expressed genes from multiple datasets and identified eight patients. )erefore, it is difficult to associate the activation of genes that affect the survival of patients with Wilms tumor. these pathways with an absolute prognosis equivalent of Further, the risk model constructed can also classify patients Wilms tumor. We believe that in the high-risk group, the Journal of Oncology 7

1.00 p value Hazard ratio p value Hazard ratio 0.75

0.50 Gender 0.041 1.785 (1.023−3.114) Gender 0.008 2.186 (1.222−3.911)

0.25 p = 2.487e – 04 Age 0.709 0.981 (0.886−1.086) Age 0.582 0.966 (0.856−1.091) Survival probability 0.00 012345678910111213 Time (years) Stage 0.015 1.481 (1.078−2.033) Stage 0.009 1.573 (1.118−2.213)

High risk 6253373025191411753221 Low risk 635649424136323019114200 Risk Histologic 0.667 1.137 (0.633−2.042) Histologic 0.480 1.251 (0.672−2.329) 012345678910111213 Time (years) Risk score <0.001 1.739 (1.414−2.138) Risk score <0.001 1.681 (1.343−2.104) Risk High risk 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 123 Low risk Hazard ratio Hazard ratio (a) (b) (c) 1.0 1.0 1 P = 0.052 High risk_p 0.8 0.8 0.8 Low risk_p 0.6 0.6 0.6 High risk_b 0.4 0.4 0.4 Low risk_b True positive rate True positive rate 0.2 0.2 0.2

0.0 0.0 p value 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 PD1−R PD1−noR False positive rate False positive rate CTLA4−R CTAL4−noR Five years (AUC = 0.685) Risk score (AUC = 0.685) p value Three years (AUC = 0.678) Gender (AUC = 0.583) Nominal p value Two years (AUC = 0.654) Age (AUC = 0.476) Bonferroni corrected Stage (AUC = 0.631) Histologic (AUC = 0.513) (d) (e) (f) Vinblastine Vinorelbine Mitomycin. C Doxorubicin Gemcitabine Cisplatin P = 0.0024 P = 0.0129 P = 0.0251 P = 0.0020 P = 0.0003 P = 0.0122 4.5 0.0 0 −1.0 −3.0 −3.5 −0.5 4.0

−3.5 −1.5 −2 −1.0 3.5 −4.0 −4.0 −2.0 −1.5 Estimated IC50

Estimated IC50 Estimated IC50 Estimated IC50 Estimated IC50 Estimated IC50 3.0 −4 −4.5 −4.5 −2.0 −2.5 2.5

−5.0 −2.5 −5.0 −3.0 Low risk Low risk Low risk Low risk Low risk Low risk High risk High risk High risk High risk High risk High risk (g)

Figure 3: Clinical characteristics and immunochemical predictive analysis of the risk model. (a) Analysis of the Kaplan–Meier survival curve based on the risk model. (b) Univariate and (c) multivariate analysis of the risk score and clinical characteristics. (d) )e high- and low-risk group responsiveness to treatment is based on the data of the immune response of melanoma patients who had recently received checkpoint blockade treatment against cytotoxic T lymphocyte antigen 4 (CTLA-4) and programmed death receptor 1 (PD-1). (e) )e chemical prediction response of the high- and low-risk group patients is based on the trained model used on the GDSC cell line dataset. 8 Journal of Oncology

Enrichment plot: GOBP_ERAD_PATHWAY Enrichment plot: GOBP_UBIQUITIN_DEPENDENT_ERAD_PATHWAY Enrichment plot: GOBP_DEVELOPMENTAL_CELL_GROWTH

0.0 NES = 2.07 0.5 NES = 2.02 0.4 P = 0.0 P = 0.002 –0.1 0.4 0.3 –0.2 0.3 –0.3 0.2 NES = –1.70 0.2 –0.4 P = 0.0 0.1 0.1 Enrichment score (ES)

Enrichment score (ES) –0.5 Enrichment score (ES) 0.0 0.0 –0.6

2.5 2.5 2.5 “h” (positively correlated) “h” (positively correlated) “h” (positively correlated) 2.0 2.0 2.0 1.5 1.5 1.5 1.0 Zero cross at 12330 1.0 Zero cross at 12330 1.0 Zero cross at 12330 0.5 0.5 0.5 0.0 0.0 0.0 “i” (negatively correlated) “i” (negatively correlated) –0.5 “i” (negatively correlated) –0.5 –0.5 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 Ranked list metric (signal2noise) Ranked list metric (signal2noise) Ranked list metric (signal2noise) Rank in ordered dataset Rank in ordered dataset Rank in ordered dataset

Enrichment profile Enrichment profile Enrichment profile Hits Hits Hits Ranking metric scores Ranking metric scores Ranking metric scores (a) (b) (c)

Enrichment plot: GOBP_DEVELOPMENTAL_GROWTH_ Enrichment plot: HALLMARK_HEDGEHOG_SIGNALING Enrichment plot: HALLMARK_ESTROGEN_RESPONSE_LATE INVOLVED_IN_MORPHOGENESIS 0.0 0.0 0.0 –0.1 –0.1 –0.1 –0.2 –0.2 –0.2 –0.3 –0.3 NES = –1.70 –0.3 P = 0.0 –0.4 NES = –1.50 –0.4 –0.4 P = 0.03 NES = –1.47 Enrichment score (ES) –0.5 Enrichment score (ES) –0.5 P = Enrichment score (ES) –0.5 0.03 –0.6 –0.6

“h” (positively correlated) 2.5 2.5 2 “h” (positively correlated) “h” (positively correlated) 2.0 2.0 1.5 1 Zero cross at 12330 1.5 1.0 Zero cross at 12330 1.0 Zero cross at 12330 0 0.5 0.5 “i” (negatively correlated) 0.0 0.0 “i” (negatively correlated) –0.5 “i” (negatively correlated) –0.5 Ranked list metric (signal2noise) 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 Ranked list metric (signal2noise) Rank in ordered dataset Ranked list metric (signal2noise) Rank in ordered dataset Rank in ordered dataset Enrichment profile Enrichment profile Enrichment profile Hits Hits Hits Ranking metric scores Ranking metric scores Ranking metric scores (d) (e) (f)

Enrichment plot: KEGG_MAPK_SIGNALING_PATHWAY Enrichment plot: KEGG_CALCIUM_SIGNALING_PATHWAY

0.0 0.0 –0.1 –0.1

–0.2 –0.2 –0.3 –0.3 NES = –1.53 –0.4 –0.4 P = 0.001 NES = –1.43 Enrichment score (ES) –0.5 P = 0.011 Enrichment score (ES) –0.5 –0.6

2.5 2.5 “h” (positively correlated) “h” (positively correlated) 2.0 2.0 1.5 1.5 1.0 Zero cross at 12330 1.0 Zero cross at 12330 0.5 0.5 0.0 0.0 “i” (negatively correlated) –0.5 “i” (negatively correlated) –0.5 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 Ranked list metric (signal2noise) Ranked list metric (signal2noise) Rank in ordered dataset Rank in ordered dataset

Enrichment profile Enrichment profile Hits Hits Ranking metric scores Ranking metric scores (g) (h) High risk Low risk

2 WNT_BETA catenin signaling∗∗ 1 NOTCH signaling∗ HEDGEHOG signaling∗ 0 TGF_BETA signaling 1 HIPPO signaling ESC pluripotency pathways∗ 2 (i) 1.0 r = –0.2555 0.8 r = –0.2924 1.0 r = –0.2572 P = 0.004 P = 0.0009 P = 0.0038 0.8 0.6 0.9 0.6 0.4 0.8 0.4

0.2 WNT BETA 0.2 0.7 catenin_signaling Hedgehog signaling 0.0 0.0 TGF BETA signaling 0.6 0 246810 0 246810 0 246810 Risk score Riskscore Riskscore (j) (k) (l) Figure 4: Continued. Journal of Oncology 9

1.0 r = –0.224 0.8 r = –0.2903 0.4 r = –0.2998 P = 0.012 P = 0.001 P = 0.0007 0.8 0.6 0.3 0.6 0.2 0.4 0.4 0.1 0.2

0.2 Notch signaling Hippo signaling 0.0 2 46810 0.0 0.0 –0.1 0 246810 0 246810 ESC pluripotency pathway Risk score Riskscore Riskscore (m) (n) (o)

Figure 4: Gene set enrichment analysis. (a–h) Gene set enrichment analysis of the high- and low-risk groups. (i) Heat map of the ssGSEA scores in six developmental pathways of the high- and low-risk groups. (j–o) Correlation analysis of the risk score and ssGSEA score of each developmental pathway. NES: normalized enrichment score. ∗p < 0.05 and ∗∗p < 0.01.

12 ∗∗∗ ∗∗∗ ∗ ∗∗∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗

6 9

4 6

3 2 AQP2 expression CLDN19 expression

0 0

GBM

KIRP

KIRC

LIHC

GBM KIRP

ESCA

LUSC

KIRC LIHC

KICH

BLCA

STAD ESCA

LUSC

BRCA KICH

BLCA PRAD

READ

UCEC

STAD HNSC

BRCA

LUAD

CHOL

PRAD THCA READ

UCEC HNSC

LUAD

COAD CHOL

THCA

COAD Cancer type Cancer type

Type Type Normal Normal Tumor Tumor (a) (b)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

10 10

5 5 DEFB1 expression HMGCS2 expression

0 0

GBM

KIRP

KIRC

LIHC

ESCA

LUSC

KICH

BLCA

STAD

BRCA

PRAD

READ

GBM

KIRP

UCEC

HNSC

LUAD

CHOL

KIRC

THCA

LIHC

ESCA

COAD

LUSC

KICH

BLCA

STAD

BRCA

PRAD

READ

UCEC

HNSC

LUAD

CHOL

THCA

COAD Cancer type Cancer type

Type Type Normal Normal Tumor Tumor (c) (d) Figure 5: Continued. 10 Journal of Oncology

∗∗∗ ∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗ ∗∗∗ ∗ 12.5 ∗ ∗∗∗ ∗∗∗ ∗ ∗∗ ∗∗∗ ∗∗∗ ∗ ∗∗∗ ∗ ∗∗∗ ∗∗

7.5 10.0 7.5 5.0 5.0 2.5 NAT8 expression 2.5 KCNJ1 expression

0.0 0.0

GBM

KIRP

KIRC

LIHC

ESCA

LUSC

KICH

BLCA

STAD GBM BRCA

KIRP

PRAD

READ

UCEC HNSC

KIRC

LIHC

LUAD

CHOL THCA

ESCA

LUSC

KICH COAD

BLCA

STAD

BRCA

PRAD

READ

UCEC

HNSC

LUAD

CHOL

THCA

COAD Cancer type Cancer type

Type Type Normal Normal Tumor Tumor (e) (f)

10.0 ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗∗ ∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗

7.5 10

5.0 5

2.5 UMOD expression TMPRSS2 expression 0 0.0

GBM

KIRP

KIRC

LIHC

ESCA

LUSC

KICH

BLCA

STAD

BRCA

PRAD

READ

UCEC

HNSC

LUAD

CHOL

THCA

GBM

KIRP

COAD

KIRC

LIHC

ESCA

LUSC

KICH

BLCA

STAD

BRCA

PRAD

READ

UCEC

HNSC

LUAD

CHOL

THCA COAD Cancer type Cancer type

Type Type Normal Normal Tumor Tumor (g) (h)

Figure 5: Verification of the expression of eight differentially expressed genes in pan-cancer. (a–h) AQP2, CLDN19, DEFB1, HMGCS2, KCNJ1, NAT8, TMPRSS2, and UMOD expression in pan-cancer; ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. regulation of the activated ERAD pathway, the ubiquitin- reprogrammed between different generations of mammals dependent ERAD pathway, may be one of the main un- [18]. Although only hundreds of imprinted genes have been derlying reasons for the occurrence of Wilms tumors. described to date, it has been demonstrated that imprinting ERAD consists of a series of spatiotemporal coupled plays a key role in several biological processes, including activities that mediate the recognition of proteins, cyto- development, growth, cell cycle [19], heredity, and circu- plasmic delocalization (also called reversal), and the ER- lation. Epigenetic analysis of cell-free DNA (cfDNA) isolated dependent ubiquitin-dependent proteasomal degradation of from the blood of patients with Wilms tumor can be used to selected proteins, at physiological levels. In this state, ERAD predict tumor prognosis and monitor responses to treatment maintains the quality of the secreted proteome by degrading and can help diagnose different cancers [20–22]. Pritchard- proteins with mutations, transcription and translation er- Jones et al. have analyzed the methylation of cfDNA rors, or inability to assemble into natural oligomers. extracted from the blood of children with or without Wilms However, an overly strict ERAD system can have cata- tumor and found that the genomic region (DMR-2) close to strophic consequences. )e ΔF508 mutant form of the cystic the PRRT1 gene was relatively highly methylated before fibrosis transmembrane conductance regulator (CFTR), the treatment in children with Wilms tumor. In addition, the most common cause of cystic fibrosis, is retained, despite its level of epitope mutations in this genomic region is sig- local function being effectively degraded by ERAD [15, 16]. nificantly increased following the preoperative chemother- )e inability of proteins to mature and transport to the apy phase and maintained immediately after the operation plasma membrane can lead to severe chloride transport [23]. Similarly, of the eight signature genes in our study, defects, which manifest as the thickening of mucus in the seven had abnormally high methylation levels in the priming lungs, pancreas, and other organs. Pharmacological treat- region in adult tumors KIRC and KIRP, while the meth- ment can selectively damage ERAD and promote the ylation level of NAT8 in the priming region was significantly maturation of functional mutant proteins, which can be reduced. However, this methylation modification is likely to effective against cystic fibrosis and other diseases associated be the reason for their significant downregulation in tumors. with protein maturity changes [17]. Although the TARGET dataset does not contain the DNA methylation at CpG dinucleotides is the most well- methylation data of normal samples, we compared the characterized epigenetic marker, which has been largely mRNA and methylation data of the eight signature genes in Journal of Oncology 11

Promoter methylation level Promoter methylation level Promoter methylation level Promoter methylation level of AQP2 in KIRC of AQP2 in KIRP of CLDN19 in KIRC of CLDN19 in KIRP 1 0.8 1 0.9 P < 0.001 P < 0.001 P < 0.001 0.9 P < 0.001 0.8 0.9 0.7 0.8 0.8 0.7 0.7 0.6 0.6 0.6 0.7 0.5 Beta value Beta value

Beta value 0.5 0.4 Beta value 0.6 0.5 0.3 0.4 0.5 0.4 0.2 Normal Primary tumor Normal Primary tumor Normal Primary tumor Normal Primary tumor (n = 160) (n = 324) (n = 45) (n = 275) (n = 160) (n = 324) (n = 45) (n = 275) TCGA samples TCGA samples TCGA samples TCGA samples

(a) (b) Promoter methylation level Promoter methylation level Promoter methylation level Promoter methylation level of DEFB1 in KIRC of DEFB1 in KIRP of HMGCS2 in KIRC of HMGCS2 in KIRP 1 P < 0.001 1 P < 0.001 0.7 P < 0.001 0.8 P < 0.001 0.6 0.8 0.9 0.6 0.8 0.5 0.6 0.7 0.4 0.4 0.6 0.3 Beta value Beta value Beta value 0.4 0.2 Beta value 0.5 0.2 0.2 0.4 0.1 0 Normal Primary tumor Normal Primary tumor Normal Primary tumor Normal Primary tumor (n = 160) (n = 324) (n = 45) (n = 275) (n = 160) (n = 324) (n = 45) (n = 275) TCGA samples TCGA samples TCGA samples TCGA samples

(c) (d) Promoter methylation level Promoter methylation level Promoter methylation level Promoter methylation level of KCNJ1 in KIRC of KCNJ1 in KIRP of NAT8 in KIRC of NAT8 in KIRP 1 P < 0.001 1 P < 0.001 0.8 P < 0.001 0.7 P < 0.001 0.6 0.9 0.9 0.6 0.5 0.8 0.8 0.4 0.4 0.7 0.7 0.3 0.2 Beta value Beta value 0.2 Beta value 0.6 Beta value 0.6 0.1 0.5 0.5 0 0 Normal Primary tumor Normal Primary tumor Normal Primary tumor Normal Primary tumor (n = 160) (n = 324) (n = 45) (n = 275) (n = 160) (n = 324) (n = 45) (n = 275) TCGA samples TCGA samples TCGA samples TCGA samples

(e) (f) Promoter methylation level Promoter methylation level of TMPRSS2 in KIRC of TMPRSS2 in KIRP 0.6 P < 0.001 0.8 P < 0.001 0.5 0.4 0.6 0.3 0.4 0.2

Beta value 0.2 0.1 Beta value 0 0 Normal Primary tumor Normal Primary tumor (n = 160) (n = 324) (n = 45) (n = 275) TCGA samples TCGA samples

(g)

Figure 6: )e methylation of the signature genes in adult renal cancer. (a–g) Methylation levels of AQP2, CLDN19, DEFB1, HMGCS2, KCNJ1, NAT8, and TMPRSS2 in normal and tumor tissues of KIRC and KIRP. KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma. the high- and low-risk groups. )e methylation modifica- treat genital warts. It was the first small-molecule immuno- tions and mRNA expression were negatively correlated, oncology drug approved by the FDA for the treatment of while at the same time, the correlation analysis between the basal cell carcinoma [24, 25]. Imiquimod targets toll-like mRNA expression and the methylation levels also verified receptor 7 (TLR7), which is a pattern recognition receptor that the methylation modifications of the signature genes (PRR) that binds to conserved PAMPs, such as double- negatively regulate the mRNA expression. stranded RNA, lipopolysaccharide, or unmethylated CpG- In addition, in terms of molecular targeted therapy DNA. Most TLRs are expressed on the cell surface, but prediction, our results show that the increased expression of TLR3, 7, 8, and 9 are mainly located in endosomes. )e small AQP2 and CLDN19 is associated with the sensitivity to TLR8 agonist motolimod (VTX-2337) exhibits antitumor imiquimod, an imidazoline derivative commonly used to activity in recurrent or metastatic head and neck squamous 12 Journal of Oncology

P < 0.05 P > 0.05 P > 0.05 P > 0.05 0.9 0.80 0.7 0.8 0.8 0.75 0.6 0.7 0.7 0.70 0.5 0.6 AQP2 beta value 0.65 DEFB1 beta value CLDN19 beta value 0.4 0.6 HMGCS2 beta value 0.5 Low risk High risk Low risk High risk Low risk High risk Low risk High risk (a) (b) (c) (d) P < 0.05 P < 0.05 P > 0.05 P > 0.05 0.8 0.7 0.8 0.6 0.6 0.7 0.7 0.5 0.5 0.4 0.6 0.6 0.4 KCNJ1 beta value NAT8 beta value 0.3 UMOD beta value TMPRSS2 beta value 0.3 0.2 Low risk High risk Low risk High risk Low risk High risk Low risk High risk (e) (f) (g) (h) Cor = –0.358 Cor = –0.472 Cor = –0.223 Cor = –0.091 8 8 8 8 P < 0.0001 P < 0.0001 P = 0.016 P = 0.320 6 6 6 6

4 4 4 4

2 2 2 2 AQP2 expression DEFB1 expression 0 CLDN19 expression 0 0 HMGCS2 expression 0 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.4 0.5 0.6 0.7 0.8 0.9 0.0 0.2 0.4 0.6 0.8 0.3 0.4 0.5 0.6 0.7 0.8 AQP2 methylation CLDN19 methylation DEFB1 methylation HMGCS2 methylation (i) (j) (k) (l) Cor = –0.163 Cor = 0.0156 Cor = 0.269 Cor = 0.159 8 P = 0.080 6 P = 0.868 P = 0.0033 P = 0.086 8 6 6 6 4 4 4 4 2 2 2 2 KCNJ1 expression NAT8 expression

0 UMOD expression 0.4 0.5 0.6 0.7 0.8 0.9 0 TMPRSS2 expression 0 0 0.4 0.5 0.6 0.7 0.8 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 KCNJ1 methylation NAT8 methylation TMPRSS2 methylation UMOD methylation (m) (n) (o) (p)

Figure 7: Methylation comparison between the high- and low-risk groups and correlation analysis of the mRNA expression and methylation level. (a–h) Comparison between the high- and low-risk groups regarding the methylation of AQP2, CLDN19, DEFB1, HMGCS2, KCNJ1, NAT8, TMPRSS2, and UMOD. (i–p) Correlation analysis of the mRNA expression and methylation level of AQP2, CLDN19, DEFB1, HMGCS2, KCNJ1, NAT8, TMPRSS2, and UMOD; Spearman correlation. cell carcinoma (HNSCC) by stimulating natural killer cells However, despite the risk model we constructed, which and enhancing antibody-dependent cell-mediated toxicity. consists of eight genes, the prognosis and clinical features, Motolimod combined with cetuximab (anti-EGFR anti- immunology and chemotherapy, the correlation between body), or conventional chemotherapy, can decrease the mRNA expression and methylation modiﬁcations, the number of Tregs in the tumor microenvironment and in- regulation of the developmental pathways, and the decrease in the number of circulating EGFR-speciﬁc CD8+ velopment of molecularly targeted drugs have achieved T cells. Compared with cetuximab or chemotherapy, the good predictive performance [28, 29]. Since the data on progression-free survival (PFS) and overall survival rates Wilms tumors with complete follow-up information is were increased [26, 27]. Imiquimod, motolimod, and resi- limited, the prognosis and clinical characteristics of pa- quimod (targeting TLR7 and TLR8) are currently under- tients with Wilms tumor are also limited in the TARGET going a series of clinical trials (NCT03276832, NCT0397626, database; therefore, studies with larger samples are urgently NCT02126579, and NCT01204684) for the treatment of needed. However, our results suggest these eight gene solid tumors, usually as adjuvants for vaccination. )erefore, markers as potential prognostic biomarkers thereby pro- the development of vaccines for AQP2 can further serve the viding new insights into novel therapeutic strategies for clinical application of Wilms tumor treatments. Wilms tumors. Journal of Oncology 13

AQP2, imiquimod CLDN19, imiquimod DEFB1, carboplatin Cor = 0.551, p < 0.001 Cor = 0.394, p = 0.002 Cor = 0.390, p = 0.002 3 4 4 2

2 2 1 0 0 0 −1 0.0 0.1 0.2 0.3 0.4 0.00 0.25 0.50 0.75 0 246 (a) (b) (c) DEFB1, idarubicin HMGCS2, cisplatin TMPRSS2, cisplatin Cor = 0.368, p = 0.004 Cor = −0.351, p = 0.006 Cor = −0.350, p = 0.006 2 2 2 1 1 1 0 0 0 −1 −1 −1 −2 0 2460 24 0 123 (d) (e) (f) TMPRSS2, carboplatin DEFB1, cisplatin Cor = −0.331, p = 0.010 Cor = 0.327, p = 0.011 3 2

2 1

1 0

0 −1 −1 0 123 0 246 (g) (h)

Figure 8: Prediction of the sensitivity induced by the signature genes using CellMiner database. (a–h) )e scatter plot indicates the correlation between the target gene expression and drug sensitivity (the Z score of the CellMiner interface) for the Spearman correlation test using NCI-60 cell line data.

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