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American Journal of Translational Research logoLink to American Journal of Translational Research
. 2018 Aug 15;10(8):2636–2647.

Survival associated alternative splicing events in diffuse large B-cell lymphoma

Rui Zhang 1, Peng Lin 2, Xia Yang 1, Rong-Quan He 3, Hua-Yu Wu 4, Yi-Wu Dang 1, Yong-Yao Gu 1, Zhi-Gang Peng 3, Zhen-Bo Feng 1, Gang Chen 1
PMCID: PMC6129525  PMID: 30210700

Abstract

Growing evidence has revealed that the initiation of various malignancies is closely associated with alternative splicing (AS) events in certain key oncogenes. However, in diffuse large B-cell lymphoma (DLBCL), there is still a great deal to learn about AS variants. In this study, 33,724 AS variant profiles were obtained from 16,278 genes in 48 DLBCL cases. A total of 10 AS variants were identified as overall survival (OS)- related events via multivariate Cox regression analysis. Notably, alternative donor (AD) sites in AS events in the low-risk group showed a significantly better outcome in DLBCL patients than in the high-risk group (P=0.0002). The area under the curve (AUC) of the receiver-operator characteristic curve (ROC) for ADs in DLBCL was 0.746. Furthermore, 66 related splicing factors were obtained to investigate their potential correlations with AS events. Factors SF1, HNRNPC, HNRNPD, and HNRNPH3 were significantly involved in different OS-related AS variants. Collectively, we constructed valuable prognostic predictors for DLBCL patients and mapped novel splicing networks for further investigation of the underlying mechanisms related to AS variants in DLBCLs.

Keywords: Alternative splicing, diffuse large B-cell lymphoma, prognosis, correlation network

Introduction

Alternative splicing (AS) is a spatiotemporal and pivotal post-transcriptional process. AS determinatively affects over 90% of multi-exon human genes and is important to such mechanisms as cell functions, cell differentiation, organogenesis [1-3]. A single gene can generate diverse mRNA isoforms through exon skipping, the use of alternative splicing sites, and intron retention [4-6]. The AS process can also enhance the diversity of both proteomes and transcriptomes [7,8].

In 2018, the estimated new cases and deaths from non-Hodgkin lymphoma ranked 7th and 9th, respectively, in both sexes [9]. As the most common subtype, diffuse large B-cell lymphoma (DLBCL) accounts for 30%-40% of non-Hodgkin lymphomas in adults [10-14]. Currently, prognoses for DLBCL patients can be slightly improved through the use of rituximab and a regimen consisting of a combination of cyclophosphamide, doxorubicin (hydroxydaunomycin), vincristine (Oncovin®), and prednisolone (a steroid) (CHOP) [15,16]. However, approximately 30% of advanced stage DLBCL patients are still refractory to standard chemotherapy. Almost 50% of relapsed cases fail to respond to high-dose chemotherapy and a great majority of relapsed patients die of lymphoma [17-19]. Thus, to improve prognoses, there is an urgent need for novel treatments that go beyond simple chemotherapy.

The International Prognostic Index developed a prognostic model of DLBCL based on several clinical parameters without molecular insights. However, in DLBCL patients, a growing body of evidence has identified various biological molecular markers and gene signatures that are correlated with prognostic significance. Studies have demonstrated that ICT1, Mad2, and decreased LMR could be unfavorable prognosis biomarkers for DLBCL [20-22]. Inversely, NLR and ERβ1 have been recommended as valuable prognostic markers for DLBCL [20,23].

Meanwhile, the underlying mechanisms of perturbed AS could influence DLBCL. Previous studies reported a smaller mRNA isoform, FOXP1, that was found to be primarily expressed in DLBCL and that represented an adverse factor in B-cell lymphomas [24]. Genes with AS PTPROt could significantly increase G0/G1 arrest via sense cloning of itself [25]. In addition, Leivonen et al. revealed that AS events in DLBCL have a direct impact on prognosis and could help in the discrimination of DLBCL subtypes [26].

In the current study, we systematically evaluated survival profiles of AS in DLBCL. DLBCL splicing networks were mapped for further research on potential mechanisms. All AS events and clinicopathological features of DLBCL were obtained from The Cancer Genome Atlas (TCGA).

Materials and methods

Alternative splicing data

RNA-Seq data for the DLBCL cohort were obtained from TCGA data portal (https://tcga-data.nci.nih.gov/tcga/). Splicing events for each DLBCL sample from TCGA were analyzed utilizing SpliceSeq, a Java application that visualizes and quantitates RNA-Seq reads and transcriptional splicing graphs [27]. The percent spliced in (PSI) value was applied to quantify various AS events from zero to one.

UpSet view and network construction

To analyze and visualize the intersected AS events, an UpSet plot was created using ImageGP (http://www.ehbio.com/ImageGP/). An interaction network of survival-related AS genes was constructed using Cytoscape (v 3.5.1). Meanwhile, potential pathway annotations were obtained from the Database for Annotation, Visualization and Integrated Discovery (DAVID, v 6.8) and the enrichment plot was mapped using ImageGP (http://www.ehbio.com/ImageGP/).

Survival analysis

The clinical parameters of 48 DLBCL patients were obtained from TCGA database. A total of 44 DLBCL patients with an overall survival (OS) time of more than 90 days were finally included in the study. To assess the association between AS events and OS, univariate Cox regression was carried out. Multivariate Cox regression was performed to select the independent prognostic factors (SPSS, v 22.0). The survival ROC package in R (v 3.2.4) was applied to estimate the significance of the prognostic predictors in DLBCL via generating receiver-operator characteristic curves (ROC) with censored data.

Correlations between splicing factor-related events and prognosis-related AS events

We obtained splicing factors from SpliceAid 2 (http://193.206.120.249/splicing_tissue.html). Expression data (level 3) for the splicing factors associated with AS were downloaded from TCGA. Correlations between splicing factor-related events and prognosis-related AS events were evaluated using Spearman’s rank-order correlation. The correlation diagrams and survival curves were generated with GraphPad (v 5.01). P-values of ≤ 0.05 were considered significant.

Results

Number of AS events in the DLBCL cohort from TCGA

In the present study, out of 48 DLBCL cases, 7 AS events were classified, including alternate acceptor (AA) sites, alternate donor (AD) sites, alternate promoter (AP), alternate terminator (AT), exon skip (ES), mutually exclusive exon (ME) and retained intron (RI). As shown in Figure 1A, a total of 33,724 AS events from 16,098 genes were curated. Out of 5,086 genes, the maximum number of ES events was 12,101, and out of 150 genes, the minimum number of ME events was 156. Furthermore, via univariate Cox regression analysis, 1,262 AS events in 971 genes were considered significantly associated with OS in cases of DLBCL (P<0.05) (Figure 1B). We calculated 116 AAs in 110 genes, 84 ADs in 81 genes, 225 APs in 136 genes, 288 ATs in 165 genes, 475 ESs in 411 genes, 9 MEs in 9 genes, and 65 RIs in 59 genes. The intersecting sets in various AS events were performed by UpSet plot. As shown in Figure 1C, most genes that related to OS were collected from ES events. In addition, one single survival-related gene could appear in up to three types of AS events simultaneously. For example, gene SF1 significantly correlated to OS in AP, ES, and RI.

Figure 1.

Figure 1

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Number of AS variants and genes involved in DLBCL. A. A total of 33,724 AS events from 16,278 genes in AA, AD, AP, AT, ES, ME, and RI. B. 1,262 AS events from 971 OS-related genes in AA, AD, AP, AT, ES, ME, and RI. C. UpSet plot of different types of OS-related AS variants.

Underlying OS-related gene pathways

Two hundred and four (204) genes that significantly relate to OS were certified utilizing univariate Cox regression (P<0.01). As shown in Figure 2A, 42 of these selected genes interacted with different genes. For additional study, the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used and gene ontology (GO) enrichment analysis was performed (Figure 2B-E). KEGG pathways indicated that 6 genes including EIF3C, EIF4B, NUP160, RAN, RANGAP1, and RNPS1 co-functioned in the most significant pathway, “RNA transport”. Meanwhile, the GO enrichment annotations showed that these 42 interacting genes were enriched in various crucial pathways, such as mRNA splicing, poly (A) RNA binding, and protein complex binding.

Figure 2.

Figure 2

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Correlation network and enrichment analysis of OS-correlated genes. A. Interacting network of OS-related genes. B. KEGG enrichment analysis of OS-related genes. C. Biological process enrichment analysis of OS-related genes. D. Cellular component enrichment analysis of OS-related genes. E. Molecular function enrichment analysis of OS-related genes.

Potential prognostic roles in DLBCL

In order to achieve some reliable prognostic predictors in DLBCL patients, we chose the five most significant univariate AS events among all seven types. Through the use of multivariate Cox regression analysis,10 AS events, including EIF3C and TYMP in AAs, TOX4 and USB1 in ADs, NHP2L1 and MLF2 in APs, STAT4 and POLR2F in ATs, and COX16 and SS18 in MEs (Table 1), were finally selected from AS events in 35 candidates. Unfortunately, no significant results were calculated in ES or RI events. Meanwhile, a prognostic index (PI) model was built to estimate the outcomes for DLBCL patients. Median PI values divided the patients into high and low-risk groups. As shown in Figure 3A-E, there was a trend toward longer OS for five different AS events in the low-risk group. The median OS days for AAs in the low-risk group were 1,972, and in the high-risk group, it was 651. In the AD low-risk and high-risk groups, the median OS was 4,989 and 352 days, respectively. For ATs, 1,252 days and 391 days represented the median OS in the low-risk and high-risk groups, respectively. Meanwhile, the median OS for both APs and MEs were calculated as 3,553 days and 391 days in the low-risk and high-risk groups, respectively. Moreover, ADs of AS events in the low-risk group indicated a significantly better outcome than in the high-risk group among DLBCL patients (P=0.0002). A combination of 10 events also verified considerable power in predicting good prognostic conditions in the low-risk group (P=0.002) (Figure 3F). The median survival days for the low-risk and high-risk groups were 6,425 and 595 days, respectively. Furthermore, ROC curves were used to compare the efficiencies of these 10 events. Areas under the curve (AUCs) of AAs, ADs, APs, ATs, MEs, and all five types of AS events were estimated (Figure 3G-L); APs showed high prognosis predicting efficiency with AUCs over 0.9.

Table 1.

The ten AS events in the final prognostic model

Splice type Gene symbol AS ID Exons From exon To exon Hazard ratio P-value
AA EIF3C 35828 5.1 4 5.2 1.08E-13 0.0014
TYMP 62854 2.2 1 2.3 1.2554 0.0031
AD TOX4 26588 3.2 3.1 5.1 15.1828 0.0044
USB1 36628 3.2 3.1 4 0.0093 0.0043
AP NHP2L1 62447 1 Null Null 7.2020 0.0213
MLF2 19962 2 Null Null 0.5920 0.0016
AT STAT4 56602 26 Null Null 0.7468 0.0032
POLR2F 62182 11 Null Null 15.5894 0.0004
ME COX16 28177 2|3 1 4 1.51E-09 0.0410
SS18 94799 5|6 3 9 1.0407 0.0235
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Figure 3.

Figure 3

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Kaplan-Meier and ROC curves for prognostic predictors of DLBCLs. A-E. Kaplan-Meier curves for AA, AD, AP, AT, and ME for DLBCL. The red line represents the high-risk group, and the green line represents the low-risk group. F. Kaplan-Meier curves for all five types of AS variants for DLBCL. The red line represents the high-risk group, and the green line represents the low-risk group. G-K. ROC curves with AUC for AA, AD, AP, AT, and ME in DLBCL. L. ROC curves for all five types of AS events.

Network of splicing factor-related events

Splicing factors control different genes by affecting the transcriptome. Meanwhile, mRNA stability is regulated by interactions between trans-splicing and basal splicing factors [28]. In this study, we obtained 66 splicing factors, and 65 of these factors functioned in different types of the 193 AS events. Eventually, eight splicing factor related AS events were selected after univariate regression analysis. For deeper insight into correlations between splicing factor-related AS events and significant univariate AS events, PSI values of 71 significant univariate AS events (P<0.005) and eight splicing factor-related AS events were calculated. As shown in Figure 4A, eight splicing factor-related AS events (blue dots) positively correlated to 63 univariate events (red dots) and negatively correlated to 65 univariate events (green dots) (Table 2). Furthermore, survival analysis of four splicing factors involved in eight AS events were estimated (Figure 4B-E); however, no significant results were obtained.

Figure 4.

Figure 4

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Correlation splicing network and Kaplan-Meier curves for OS splicing factors. A. Splicing network of OS-related AS variants. The blue dots represent the AS events from OS-related splicing factors. The red/green dots represent the AS events that are positively (red lines) or negatively (green lines) associated with AS events from splicing factors. B-E. Kaplan-Meier curves for four splicing factors in DLBCL. The red line represents the high level group and the green line represents the low level group.

Table 2.

Involvement of four splicing factors in various AS events

Splicing factor-related AS events Negatively correlated events Correlation value P-value Positively correlated events Correlation value P-value
HNRNPH3-AD-11928 C20orf24-ES-59292 -0.513 0.0004 OPA3-AT-50488 0.344 0.0222
ARHGDIB-ES-20566 -0.454 0.0020 ARMC10-ME-81154 0.344 0.0223
ELP2-ES-45228 -0.403 0.0110 POLR2F-AT-62182 0.302 0.0463
SHISA5-AD-64692 -0.375 0.0170
OPA3-AT-50489 -0.344 0.0222
SMAP2-AP-1997 -0.339 0.0245
SUZ12-ES-40169 -0.331 0.0300
SSR2-ES-8161 -0.318 0.0356
PRPS1-AT-89829 -0.297 0.0499
HNRNPC-AP-26531 POLR2F-AT-62182 -0.452 0.0021 SMAP2-AP-1997 0.519 0.0003
EIF4B-ES-21918 -0.395 0.0080 MLF2-AP-19962 0.488 0.0008
CCDC106-RI-52130 -0.395 0.0087 ACOT8-ES-59632 0.429 0.0041
MRPL43-AT-12852 -0.363 0.0153 ELP2-ES-45228 0.416 0.0084
CDCP1-AT-64397 -0.358 0.0169 CDCP1-AT-64398 0.358 0.0169
ACYP2-AP-53565 -0.336 0.0259 USB1-AD-36628 0.357 0.0187
STAT4-AT-56603 -0.324 0.0338 ACYP2-AP-53563 0.346 0.0216
LYRM5-AA-20813 -0.363 0.0351 FAM154B-AT-32218 0.335 0.0279
STAT4-AT-56602 0.324 0.0338
DDX17-RI-62240 0.315 0.0375
C20orf24-ES-59292 0.309 0.0416
ECD-ES-12133 0.316 0.0442
STAU2-ES-84164 0.316 0.0471
HNRNPC-AP-26533 SMAP2-AP-1997 -0.519 0.0003 POLR2F-AT-62182 0.452 0.0021
MLF2-AP-19962 -0.488 0.0008 EIF4B-ES-21918 0.395 0.0080
ACOT8-ES-59632 -0.429 0.0041 CCDC106-RI-52130 0.395 0.0087
ELP2-ES-45228 -0.416 0.0084 MRPL43-AT-12852 0.363 0.0153
CDCP1-AT-64398 -0.358 0.0169 CDCP1-AT-64397 0.358 0.0169
USB1-AD-36628 -0.357 0.0187 ACYP2-AP-53565 0.336 0.0259
ACYP2-AP-53563 -0.346 0.0216 STAT4-AT-56603 0.324 0.0338
FAM154B-AT-32218 -0.335 0.0279 LYRM5-AA-20813 0.363 0.0351
STAT4-AT-56602 -0.324 0.0338
DDX17-RI-62240 -0.315 0.0375
C20orf24-ES-59292 -0.309 0.0416
ECD-ES-12133 -0.316 0.0442
STAU2-ES-84164 -0.316 0.0471
SF1-AP-16675 ELP2-ES-45228 -0.546 0.0003 UGP2-ES-53758 0.416 0.0062
ZNF227-ES-50300 -0.508 0.0013 CHN1-AP-56046 0.384 0.0131
USB1-AD-36628 -0.442 0.0030 POLR2F-AT-62182 0.338 0.0268
TOM1-ES-61969 -0.431 0.0039 CLEC2D-ES-20238 0.337 0.0270
PKM-ES-31512 -0.429 0.0041 LYRM5-AA-20813 0.349 0.0430
ECD-ES-12133 -0.435 0.0045 TOX4-AD-26588 0.337 0.0445
ANKMY1-AA-58259 -0.433 0.0047
IDH3A-ES-32029 -0.374 0.0135
EIF3C-AA-35828 -0.372 0.0141
NCAPH2-AD-62845 -0.375 0.0144
SLC25A43-ES-89952 -0.395 0.0154
DDX17-RI-62240 -0.342 0.0247
SHISA5-AD-64692 -0.338 0.0329
ACOT8-ES-59632 -0.321 0.0381
CLUAP1-ES-33589 -0.318 0.0404
VPS41-ES-79292 -0.338 0.0469
STAU2-ES-84164 -0.314 0.0483
SF1-AP-16676 UGP2-ES-53758 -0.416 0.0062 ELP2-ES-45228 0.546 0.0003
CHN1-AP-56046 -0.384 0.0131 ZNF227-ES-50300 0.508 0.0013
POLR2F-AT-62182 -0.338 0.0268 USB1-AD-36628 0.442 0.0030
CLEC2D-ES-20238 -0.337 0.0270 TOM1-ES-61969 0.431 0.0039
LYRM5-AA-20813 -0.349 0.0430 PKM-ES-31512 0.429 0.0041
TOX4-AD-26588 -0.337 0.0445 ECD-ES-12133 0.435 0.0045
ANKMY1-AA-58259 0.433 0.0047
IDH3A-ES-32029 0.374 0.0135
EIF3C-AA-35828 0.372 0.0141
NCAPH2-AD-62845 0.375 0.0144
SLC25A43-ES-89952 0.395 0.0154
DDX17-RI-62240 0.342 0.0247
SHISA5-AD-64692 0.338 0.0329
ACOT8-ES-59632 0.321 0.0381
CLUAP1-ES-33589 0.318 0.0404
VPS41-ES-79292 0.338 0.0469
STAU2-ES-84164 0.314 0.0483
SF1-ES-16682 OPA3-AT-50488 -0.454 0.0020 OPA3-AT-50489 0.454 0.0020
FAM154B-AT-32218 -0.311 0.0424 ANKMY1-AA-58259 0.356 0.0225
THAP7-RI-61211 -0.299 0.0490
HNRNPD-RI-69704 GSTZ1-AP-28583 -0.407 0.0067 TARS-RI-71685 0.528 0.0008
COPS7A-AP-19933 -0.344 0.0240 COPS7A-AP-19932 0.344 0.0240
ATP8B3-AT-46544 -0.332 0.0296 ATP8B3-AT-46543 0.332 0.0296
THAP7-RI-61211 -0.330 0.0306 MLF2-AP-19962 0.323 0.0346
CAPG-AP-54272 -0.322 0.0355 CAPG-AP-54271 0.322 0.0355
NHP2L1-AP-62447 -0.303 0.0480 STAU2-ES-84164 0.325 0.0410
ECD-ES-12133 0.319 0.0419
VPS41-ES-79292 0.34 0.0455
SF1-RI-16680 ANKMY1-AA-58259 -0.448 0.0033 CCDC106-RI-52130 0.442 0.0030
ACYP2-AP-53563 -0.342 0.0229 BIN1-ES-55198 0.372 0.0129
OPA3-AT-50489 -0.330 0.0289 THAP7-RI-61211 0.358 0.0169
ACYP2-AP-53565 0.348 0.0207
BIN1-ES-55184 0.366 0.0240
OPA3-AT-50488 0.33 0.0289
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Discussion

Profound modulatory points in gene expression have significant effects on the development of cancers. As one of the regulatory factors in gene control, AS variants are involved in the reorganization of proteomes and can modulate the levels of a number of oncogenes and tumor inhibitor isoforms [29]. As an identified splicing factor oncoprotein, SF2/ASF amplificates in various cancers and generates the activation of proximal 5’ splicing sites. Up-regulated SF2/ASF also blocks aberrant exon-skipping and manages AS events in the tumor-suppressor BIN1 [30,31]. Meanwhile, accurate FOX2 binding sites have been correlated to alternative exons at silencer or enhancer proteins [32]. Furthermore, the level of splicing factors could be influenced by various cancer pathways; for example, hnRNPA1 and hnRNPA2 are up-regulated by c-Myc [29].

In DLBCL patients, a relatively small number of studies have reported pre-mRNA AS variants to be predictive biomarkers and AS events have also been associated with diagnosis and prognosis. Dysregulated CD44 levels can alter lymph node draining [33]. As an isoform of CD44, CD44H is strongly related to poor survival of DLBCL cohorts, and rituximab could decrease the significance of its prognosis [34]. Meanwhile, four likely splice variants of the STAT3 gene have all been represented in DLBCL, including the ΔS/S and α/β splicing variants [35]. Co-worked terms for S and ΔS could sustain activated B-cell-like DLBCL cell survival and modulate STAT3 targets, such as NFKBIA and NFKBIZ [36]. In addition, p63 directly impact cell apoptosis and differentiation. Isoform TAp63, which is a product of an upstream intronic promoter, has been found to be highly expressed in DLBCL cell lines [37]. Collectively, these results outline the critical role of AS variants in DLBCL. A wide variety of isoforms might be valuable candidates for further prospective research on DLBCL.

In the current study, the functional categories of interacting AS genes were analyzed using KEGG and GO. The KEGG enrichment results revealed that “RNA transport” was the most significant term at the P=0.0004 level. We found several consistent pathways, such as “transcription factor activity, RNA polymerase II core promoter sequence-specific”, “mRNA splicing, via spliceosome”, and “protein complex binding” related to AS events from GO annotations. Hence, in the future, gaining insight into these significant interacting AS-related genes in terms of DLBCL mechanisms will be of interest.

Out of 66 splicing factors, genes SF1, HNRNPC, HNRNPD, and HNRNPH3 were identified as the factors involved in OS correlated AS events. To date, it had been well established that protein SF1 was able to recognize the 3’ splice site by binding the branchpoints of certain introns and repressing transcription. Meanwhile, SF1 played a critical role in the retention of nuclear pre-mRNA and SF1 silencing closely related to AS variants of endogenous transcription [38-40]. In recent years, there has been an increase in the literature on SF1 in various cancers, such as in epithelial ovarian cancer [41] and testicular germ cell tumors [42]. In this study, we discovered that protein SF1 was simultaneously engaged in AP, ES, and RI events in DLBCL. However, several studies have indicated that SF1 was identified in just one type of malignancy and rarely seen in lymphoid malignancies [43,44]. Thus, the role of SF1 requires further investigation; our study offers a novel aspect of SF1 in DLBCL. Furthermore, the remaining factors, HNRNPC, HNRNPD, and HNRNPH3, are members of the heterogeneous nuclear ribonucleoproteins (hnRNPs). AS transcript variants of these three factors were previously revealed. Fawal et al. reported that AUF1/hnRNPD is the partner of anaplastic lymphoma kinase (ALK) [45]. Compared to conventional DLBCL patients, ALK positive DLBCL patients displayed unique clinical features and histological types [46]. Collectively, the aforementioned results imply that these four splicing factors might perform multiple functions and offer an orientation from which we might investigate the underlying mechanisms of AS variants in DLBCL.

Conclusion

Taken together, the results of this study revealed that OS-related AD of AS variants is a potential valuable predictor of DLBCL prognosis. In addition, correlation networks and enrichment analysis were performed to uncover the potential regulatory roles of key splicing factors. Considering this evidence, in DLBCL, oncoproteins spliced by certain AS events or the aberrant transcription of splicing factors would be provided. However, the mechanisms of OS-related AS variants and splicing factors are still not clearly understood. Further investigation needs to be conducted with regard to AS in DLBCL patients.

Acknowledgements

The present study was supported by the National Natural Science Foundation of Guangxi (2017GXNSFAA198107) and the Innovation Project of Graduate Education (YCSW2018103). The authors also wish to thank the public databases, such as The Cancer Genome Atlas, for the use of their data.

Disclosure of conflict of interest

None.

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