Abstract
Glioblastoma multiforme (GBM) is the most aggressive primary central
nervous system malignant tumor. The median survival of GBM patients is
12–15 months, and the 5 years survival rate is less than 5%. More novel
molecular biomarkers are still urgently required to elucidate the
mechanisms or improve the prognosis of GBM. This study aimed to explore
novel biomarkers for GBM prognosis prediction. The gene expression
profiles from The Cancer Genome Atlas (TCGA) and Gene Expression
Omnibus (GEO) datasets of GBM were downloaded. A total of 2241
overlapping differentially expressed genes (DEGs) were identified from
TCGA and [37]GSE7696 datasets. By univariate COX regression survival
analysis, 292 survival-related genes were found among these DEGs (p <
0.05). Functional enrichment analysis was performed based on these
survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3,
IGFBP2, and TIMP4) was further selected by multivariable Cox regression
analysis and a prognostic model of this five-gene signature was
constructed. Based on this risk score system, patients in the high-risk
group had significantly poorer survival results than those in the
low-risk group. Moreover, with the assistance of GEPIA
[38]http://gepia.cancer-pku.cn/, all five genes were found to be
differentially expressed in GBM tissues compared with normal brain
tissues. Furthermore, the co-expression network of the five genes was
constructed based on weighted gene co-expression network analysis
(WGCNA). Finally, this five-gene signature was further validated in
other datasets. In conclusion, our study identified five novel
biomarkers that have potential in the prognosis prediction of GBM.
Keywords: glioblastoma, differentially expressed genes, gene signature,
prognosis, TCGA, GEO
Introduction
Glioblastoma multiforme (GBM) is the most common and aggressive primary
central nervous system malignant tumor with high morbidity and
mortality. According to genomic abnormalities and gene expression, GBM
can be divided into four molecular subtypes: classical, mesenchymal,
neural, and proneural, which lay a foundation for understanding its
inherent heterogeneity ([39]Verhaak et al., 2010; [40]Ma et al., 2018).
In the United States, the incidence of GBM is 2.96 cases/100,000
population/year ([41]Jhanwar-Uniyal et al., 2015). Although there are
several treatment options, including surgery, radiotherapy and
chemotherapy, the median survival of GBM patients remains 12–15 months,
and the 5 years survival rate is less than 5% ([42]Wen and Kesari,
2008; [43]Ostrom et al., 2013).
With the development of next-generation sequencing technologies, many
specific molecular signatures have been identified to better understand
the molecular pathogenesis of GBM ([44]Aldape et al., 2015). As a
result, many potential diagnostic and prognostic biomarkers have been
discovered that enable a more specific classification and a more
precise outcome prediction of GBM. Some molecular markers including
MGMT (O6-methylguanine DNA methyltransferase), IDH (isocitrate
dehydrogenase), EGFR (epidermal growth factor receptor), and PTEN
(phosphatase and tensin homolog) have been routinely tested in GBM
patients clinically ([45]van den Bent et al., 2017; [46]Binabaj et al.,
2018). More importantly, these molecular signatures have contributed to
personalized therapeutic approaches and targeted anti-GBM therapies
([47]Huang et al., 2017; [48]Szopa et al., 2017). However, considering
the poor prognosis of GBM, novel molecular biomarkers and new
therapeutic strategies are still urgently required to elucidate the
mechanisms of GBM or increase overall patient survival.
Previous studies have shown that gene expression profile analysis could
detect gene signatures to predict the outcome for malignancy tumors
([49]Luo et al., 2018; [50]Mao et al., 2018; [51]Zeng et al., 2018).
[52]Shergalis et al. (2018) discovered that 20 genes were overexpressed
and correlated with poor survival outcomes in GBM patients by
bioinformatics analysis using data from The Cancer Genome Atlas (TCGA)
project. [53]Bao et al. (2014) identified a nine-gene signature to
predict the prognosis of glioma patients based on mRNA expression
profiling from the Chinese Glioma Genome Atlas (CGGA) database.
Therefore, it is necessary to understand the development and
progression of GBM by identifying GBM-related genes and to investigate
of their potential clinical roles and molecular mechanisms.
In this study, RNA-Seq data from TCGA and microarray data from the Gene
Expression Omnibus (GEO) database of GBM were downloaded. Based on the
overlapping differentially expressed genes (DEGs), the genes related to
prognosis were screened. By using Cox regression, we developed a
five-gene signature based risk score to demonstrate the association
between gene expression and the prognosis of GBM. Moreover, we
validated this signature in the GEO dataset and TCGA array dataset.
These results might be able to provide new reference for the prognostic
predication of GBM.
Materials and Methods
Data Source
The GBM RNA sequencing (RNA-seq) dataset and corresponding clinical
follow-up information were downloaded from TCGA database (March, 2018).
Subtype data of GBM were downloaded from UCSC Xena^[54]1. A total of
159 patients, including 154 samples of primary GBM patients and five
samples of normal brain tissue were extracted for subsequent analysis.
Gene expression microarray data [55]GSE7696 ([56]Lambiv et al., 2011),
including 71 samples of primary GBM patients and four samples of normal
brain tissue, were downloaded from the National Center of Biotechnology
Information (NCBI) Gene Expression Omnibus^[57]2. The dataset was based
on the [58]GPL570 platform of [HG-U133_Plus_2] Affymetrix Human Genome
U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA, United States).
Differential Expression Analyses
Then, gene profiles were standard normalized within and among samples,
respectively. Because the numerical distribution of RPKM (reads per
kilo-base per million mapped reads) is too wide, the final expression
level of a gene was defined as the log[2](x + 1) of the raw expression
level. Next, the DEGs between the tumor and normal samples were
calculated by the limma package (Padj < 0.05 and | log[2]FC| > 1). The
Venn diagram was produced by the VennDiagram R package ([59]Chen and
Boutros, 2011).
Identification and Selection of Survival-Related Genes
Only the patients with detailed follow-up times were extracted for
subsequent survival analyses. Univariate Cox regression survival
analysis using the Survival package in R was performed to identify
survival-related genes ([60]Yang et al., 2016). Genes were selected
with a p-value of less than 0.05.
Go and KEGG Annotation of Survival-Related Genes
Gene Ontology (GO) enrichment and KEGG (Kyoto Encyclopedia of Genes and
Genomes) analysis were performed on the survival-related genes
([61]Ogata et al., 1999; [62]Wanggou et al., 2016; [63]Li et al.,
2018). DAVID (The Database for Annotation, Visualization, and
Integrated Discovery) ([64]Dennis et al., 2003) software and the
clusterProfiler package ([65]Yu et al., 2012) in R were used to
annotate and visualize GO terms and KEGG pathways.
Gene Signature Identification and Risk Score System Establishment
Based on the top 100 survival-related genes in TCGA dataset,
multivariable Cox proportional hazard regression analysis was performed
to establish a risk score formula ([66]O’Quigley and Moreau, 1986). As
previously reported, a prognosis risk score formula could be
constructed on the basis of a linear combination of the expression
level (exp) multiplied by a regression coefficient (β) derived from the
multivariate cox regression model.
[MATH: Risk Score(RS) =expPTPRN*βPTPRN+expRGS14*βRGS14+expG6PC3*βG6PC3+expIGFBP2*βIGFBP2+expTIMP4*βTIMP4 :MATH]
Based on the formula, the risk score of each GBM patient was
calculated, and then GBM patients were divided into high-risk score and
low-risk score groups. The receiver operating characteristic (ROC)
curve analysis was conducted using the R package “pROC.” After choosing
an optimal cut-off point with the maximal sensitivity and specificity,
the survival differences between the low-risk and high-risk groups were
assessed by the Kaplan–Meier analysis with log-rank test. Similarly, to
evaluate the predictive power of the five-gene signature in internal
dataset, we assessed the gene signature within each subtype (classical,
mesenchymal, neural, and proneural).
Analysis in GEPIA and Exploring Co-expression by WGCNA
The expression levels of the five genes were acquired with the
assistance of GEPIA^[67]3, which is a newly developed interactive web
server for analyzing the RNA sequencing expression data of 23 types of
cancers and normal samples from TCGA and the GTEx projects according to
the standard processing pipeline ([68]Tang et al., 2017).
To explore the regulatory network of the five genes, all the overlapped
DEGs were analyzed by WGCNA ([69]Ahn et al., 2016; [70]Chen et al.,
2018). Finally, the co-expression network of the five genes was
constructed based on WGCNA and visualized by Cytoscape 3.6.1
([71]Shannon et al., 2003).
Validation of the Five-Gene Prognostic Signature by the GEO Dataset and TCGA
Microarray Dataset
Dataset [72]GSE13041 from the GEO and TCGA microarray dataset were used
to validate this five-gene prognostic signature ([73]Lee et al., 2008).
The [74]GSE13041 dataset including 188 samples of GBM patients and the
TCGA microarray dataset including 498 samples of GBM patients were both
based on the Affymetrix Human Genome U133A Array platform ([75]GPL97).
The ROC curves and Kaplan–Meier analyses were used to validate the
prognostic value of the five-gene for GBM patients.
Results
Differentially Expressed Genes (DEGs) in TCGA and [76]GSE7696
Altogether, 4473 DEGs in TCGA dataset ([77]Figure 1A) and 5789 DEGs in
the [78]GSE7696 dataset ([79]Figure 1B) were screened by the limma
package. The 2241 overlapping DEGs were screened for further analysis
([80]Figure 1C).
FIGURE 1.
[81]FIGURE 1
[82]Open in a new tab
Identification of DEGs among TCGA and GEO datasets of GBM. (A) Volcano
plots of DEGs in TCGA dataset. (B) Volcano plots of DEGs in [83]GSE7696
dataset. (C) The Venn diagram of overlapping DEGs among TCGA and
[84]GSE7696 datasets.
Survival-Related Genes in GBM
In TCGA dataset, every overlapped DEG was evaluated by univariate Cox
regression survival analysis. Altogether, 292 significantly changed
genes were considered -survival-related genes by the threshold of p <
0.05. The top 100 survival-related genes are shown in [85]Supplementary
Table 1.
Go and KEGG Analysis of Survival-Related Genes
For the “biological processes” (BP), negative regulation of catalytic
activity, regulation of cell shape, negative regulation of monocyte
chemotaxis, long-term synaptic potentiation and insulin secretion
involved in cellular response to glucose stimulus were the commonly
enriched categories ([86]Figure 2A). For the “cellular component” (CC),
the enriched categories were correlated with focal adhesion,
extracellular space, synaptic vesicle membrane, extracellular exosome,
and endoplasmic reticulum ([87]Figure 2B). For the “molecular function”
(MF), those genes mainly showed enrichment in calcium ion binding,
phospholipase inhibitor activity, calcium-dependent protein binding,
calcium-dependent phospholipid binding, and signal transducer activity
([88]Figure 2C). KEGG pathway enrichment analysis suggested that
glycosaminoglycan degradation was the most significant pathway. These
genes also participated in following pathways: proteoglycans in cancer,
lysosome, and regulation of the actin cytoskeleton ([89]Figure 2D).
FIGURE 2.
[90]FIGURE 2
[91]Open in a new tab
The most significantly enriched GO annotations and KEGG pathways of
genes related to survival. The length of the bars represents the number
of genes, and the color of the bars corresponds to the p-value
according to legend. (A) Top 5 significantly enriched biological
process. (B) Top 5 significantly enriched cellular component. (C) Top 5
significantly enriched molecular function. (D) Top 5 significantly
enriched KEGG pathways.
Risk Score System Based on Five-Gene Signature
After multivariate Cox regression analysis was conducted for these 100
genes, five genes (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) were
selected as signature genes that can optimally predict the overall
survival of patients with GBM ([92]Table 1). To comprehensively
investigate the association between these five genes and the prognosis
of GBM, a five-gene survival risk score system was established based on
their Cox coefficients.
Table 1.
Information about the five genes screened to build the risk score
system.
Genes Coefficient HR 95% CI P-value
PTPRN 0.50894 1.66353 1.4010–1.9753 6.35e-09
RGS14 0.54671 1.72757 1.2026–2.4816 0.00309
G6PC3 1.20753 3.34520 1.9960–5.6063 4.57e-06
IGFBP2 0.25845 1.29492 1.1096–1.5112 0.00104
TIMP4 -0.20684 0.81315 0.6951–0.9513 0.00976
[93]Open in a new tab
[MATH: Risk Score(RS) =0.50894*expPTPRN+0.54671*expRGS14+1.20753*expG6PC3+0.25845*expIGFBP2−0.20684*expTIMP4 :MATH]
Then, the risk score for each patient was calculated in TCGA dataset
and ranked according to the risk scores. Thus, patients were divided
into a high-risk group (n = 75) and a low-risk group (n = 76). The
survival time of GBM patients was adversely associated with their risk
scores ([94]Figure 3A). A remarkably lower expression was noted for
TIMP4 in the high-risk groups, while a higher expression was observed
for the other genes in the high-risk groups ([95]Figure 3B). The
Kaplan–Meier analysis and log-rank test showed that patients in the
low-risk group had a significantly positive overall survival time
compared to the high-risk group (p = 7.055906e-11) ([96]Figure 3C).
FIGURE 3.
[97]FIGURE 3
[98]Open in a new tab
Risk score analysis, expression distribution and survival analysis of
the five-gene signature in TCGA dataset. (A) The five-gene signature
risk score distribution. (B) The heat-map of the five-gene expression
profiles. Red indicates a higher expression and green indicates a lower
expression. Blue bar: low-risk group. Red bar: high-risk group. (C)
Kaplan–Meier analysis using the median risk score cut-off which divided
patients into low-risk and high-risk groups.
Moreover, ROC analysis was performed for this risk score system.
[99]Figure 4A shows that the area under the ROC Curves (AUC) was 0.704.
The optimal cutoff point was selected as 8.421. With this cutoff point,
the patients were further divided into a high-risk group and a low-risk
group. The Kaplan–Meier analysis and log-rank test further indicated a
significant difference in overall survival between the two groups (p =
1.075619e-11) ([100]Figure 4B). Similarly, with different cutoff
points, the patients in each subtype were divided into a high-risk
group and a low-risk group. The Kaplan–Meier analysis and log-rank test
also indicated a significant difference between the two groups in each
subtype ([101]Figure 5A–[102]D).
FIGURE 4.
[103]FIGURE 4
[104]Open in a new tab
ROC and Kaplan–Meier analysis of the five-gene signature in TCGA
dataset. (A) ROC analysis of the sensitivity and specificity of the
survival time according to the five-gene signature based on risk score.
(B) Kaplan–Meier analysis of the five-gene signature based risk score.
Patients were divided into low-risk and high-risk groups based on the
optimal cut-off point.
FIGURE 5.
[105]FIGURE 5
[106]Open in a new tab
Kaplan–Meier analysis of the five-gene signature in different molecular
subtypes of glioblastoma. Classical (A), mesenchymal (B), neural (C),
and proneural (D).
Analysis in GEPIA and Exploring Co-expression by WGCNA
Based on the results derived from GEPIA, the expression of G6PC3,
IGFBP2, and TIMP4 were significantly up-regulated in GBM, while the
expression of PTPRN and RGS14 were significantly down-regulated
([107]Figure 6). By using GEPIA, the selected five genes were verified
as DEGs in GBM with amplified normal sample sizes.
FIGURE 6.
[108]FIGURE 6
[109]Open in a new tab
Comparisons of the expression of the five genes between GBM and non-GBM
tissues in TCGA and GTEx based on GEPIA. The Y axis represents the log2
(TPM + 1) for gene expression. The gray bar indicates the non-GBM
tissues, and the red bar shows the GBM tissues. These figures were
derived from GEPIA. TPM: transcripts per kilobase million. ^∗p < 0.05.
The co-expressed genes of the five genes were determined by WGCNA.
Finally, 129 genes were discovered to be co-expressed with PTPRN, 41
genes were co-expressed with IGFBP2, 10 genes with RGS14 and 1 gene
with TIMP4. However, no gene was co-expressed with G6PC3. The
co-expression network of the four genes is visualized by WGCNA in
[110]Figure 7.
FIGURE 7.
[111]FIGURE 7
[112]Open in a new tab
The co-expression network of the five-gene signature. Red diamonds
showed the key genes and green nodes are genes which co-expressed with
the key genes.
Validation of the Five-Gene Prognostic Signature by GEO Dataset and TCGA
Microarray Dataset
The [113]GSE13041 dataset including 188 GBM patients and the TCGA
microarray dataset including 498 GBM patients were used for the
validation of the five-gene signature separately. Similarly, the risk
score for each patient was calculated. ROC analyses were used to
identify the optimal cutoff points ([114]Figure 8A,C). Then, we divided
the patients into a high-risk group and a low-risk group using the
selected optimal cut-off points, respectively. The Kaplan–Meier
analyses suggested a significantly prolonged survival time in the
low-risk patients compared to that in the high-risk patients (p =
3.480445e-06 and p = 0.00011) ([115]Figure 8B,D).
FIGURE 8.
[116]FIGURE 8
[117]Open in a new tab
ROC and Kaplan–Meier analyses of the five-gene signature in validation
datasets. (A) ROC analysis of the [118]GSE13041 dataset. (B)
Kaplan–Meier analysis of the [119]GSE13041 dataset. (C) ROC analysis of
the TCGA microarray dataset. (D) Kaplan–Meier analysis of the TCGA
microarray dataset.
Discussion
GBM is the most aggressive brain tumor associated with poor prognosis.
By analyzing TCGA and [120]GSE7696 datasets, we identified 2241
significantly overlapping DEGs. A total of 292 survival-related DEGs
were selected from the overlapping DEGs. Functional analyses
demonstrated that these genes are mainly associated with following
pathways: glycosaminoglycan degradation, proteoglycans in cancer,
lysosome, and regulation of the actin cytoskeleton. More importantly,
based on multivariate Cox regression analysis of TCGA dataset, five
genes which could predict overall survival were screen out, namely
PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4. According to their Cox
coefficients derived from cox regression, a risk score system based on
the five genes was established. Additionally, after identifying the
optimal cut-off point by ROC analysis, patients were classified into
high-risk and low-risk groups. This five-gene signature was further
successfully validated as a prognostic marker in each subtype of GBM,
another independent GEO dataset ([121]GSE13041) and TCGA microarray
dataset. Furthermore, differential expression analysis of the five
genes in GEPIA validated that three genes (G6PC3, IGFBP2, and TIMP4)
were significantly up-regulated and two genes (PTPRN and RGS14) were
significantly down-regulated in GBM. Co-expression network analysis
revealed the regulation network of the five genes. These results
suggest that these genes may play an important role in the molecular
pathogenesis, progression and prognosis of GBM.
Based on GO and KEGG enrichment analyses of the survival-related DEGs
among different studies, “negative regulation of catalytic activity”
was the most significant enrichment in BP. This indicated that
inhibiting the catalytic activity of some genes may be critical for
cancer progression. Coincidentally, [122]Zhao et al. (2009) found that
IDH1 mutation could inhibit IDH1 catalytic activity and contribute to
the tumorigenesis of glioma. Other BPs such as regulation of cell shape
and negative regulation of monocyte chemotaxis were also enriched. For
the CC category, focal adhesion was the most significant enrichment
which has been shown to be as a major determinant of cell migration and
an essential process in tumor invasion ([123]Garzon-Muvdi et al.,
2012). The following three kinds of CCs, extracellular space, synaptic
vesicle membrane and extracellular exosome, may also play important
roles in tumor development and its micro-environmental manipulation
([124]Wei et al., 2017). Regarding the MF category, calcium ion binding
was the most affected MF. Ca^2+-mediated cell connectivity and
plasticity are unique features of the central nervous system, and the
Ca^2+/calmodulin-dependent process is able to regulate cell cycle
progression and inhibit proliferation of malignant glioma ([125]Cheng
et al., 1995; [126]Liu et al., 2011). For KEGG pathway enrichment
analysis, glycosaminoglycan degradation was the most significant
pathway. Extracellular proteoglycans play critical roles in driving
oncogenic pathways in tumor cells and promoting critical
tumor-microenvironment interactions ([127]Wade et al., 2013). The other
KEGG pathways, proteoglycans in cancer, lysosome, and regulation of
actin cytoskeleton, were also closely related to oncogenesis ([128]Liu
et al., 2012; [129]Terakawa et al., 2013; [130]Wade et al., 2013).
The five-gene signature provides a wealth of potential biological and
therapeutic information about GBM. PTPRN (protein tyrosine phosphatase,
receptor type N), located on the long arm of human chromosome 2 (2q35)
([131]Lan et al., 1996), is an integral transmembrane protein of dense
core vesicles and plays an important role in the secretion of hormones
and neurotransmitters ([132]Xu et al., 2016). PTPRN has been confirmed
to be negatively related to the survival of hepatocellular carcinoma
patients and closely related to liver tumorigenesis ([133]Zhangyuan et
al., 2018). Moreover, the hypermethylation of PTPRN is also associated
with shorter survival in ovarian cancer patients ([134]Bauerschlag et
al., 2011). A high expression of PTPRN in small cell lung cancer is
associated with tumor growth and proliferation. Interestingly,
Shergalis et al. also found that a high PTPRN expression is strongly
associated with a poor prognosis in GBM patients, which was consistent
with our finding ([135]Shergalis et al., 2018). RGS14 is a member of
the regulator of the G-protein signaling (RGS) protein family and is
highly expressed in the caudate nucleus of the brain, spleen and thymus
([136]Cho et al., 2005; [137]Gerber et al., 2016). Previous study found
that RGS14 is important for centrosome function, transcriptional
regulation and stress-induced cellular responses ([138]Cho et al.,
2005). However, little work has been done to elucidate the role of
RGS14 in cancer. Interestingly, PTPRN and RGS14 expressed at low levels
in GBM tissue, but their increased expression was associated with poor
prognosis. The reason may be that they have different functions in
normal and tumor tissues. More work is needed elucidate their functions
in GBM. G6PC3, namely, glucose-6–phosphatase isoform β, is a catalysis
subunit of- G6PC ([139]Gao et al., 2017). G6PC (glucose-6–phosphatase)
is a key enzyme that regulates glucose homeostasis and glycogenolysis,
which has been reported as a specific enzyme regulating proliferation
and invasiveness in several tumors, such as liver, kidney and ovarian
cancer ([140]Gao et al., 2017). Furthermore, a previous study revealed
that G6PC is a key enzyme regulating glioblastoma invasion ([141]Abbadi
et al., 2014). Our study demonstrated that G6PC3 was significantly
up-regulated in GBM samples compared with normal brain tissue, and the
high expression of G6PC3 was closely related to a poor prognosis in GBM
patients. IGFBP2 (Insulin-like growth factor binding protein 2), an
important member of the Insulin-like growth factor binding protein
family, modulates cell growth, differentiation, migration, and invasion
in neoplasms ([142]Fukushima and Kataoka, 2007). IGFBP2 is involved in
immunosuppressive activities and is a potential immunotherapeutic
target for GBM ([143]Cai et al., 2018). Our study confirmed that IGFBP2
was significantly up-regulated in GBM and predicted a worse outcome for
patients, which was consistent with the previous study ([144]Cai et
al., 2018). TIMP4 is a member of tissue inhibitors of matrix
metalloproteinases (TIMPs), which are involved in several processes of
tumorigenesis including proliferation, migration, and invasion
([145]Boufraqech et al., 2016). A high-expression of TIMP4 has been
found in patients with breast, cervical, and prostate cancers, whereas
a low expression has been observed in patients with pancreatic cancer
([146]Boufraqech et al., 2016). Interestingly, our study found that
TIMP4 was high-expressed in GBM patients, however, its high expression
was associated with a good prognosis in patients with GBM. More work is
also needed elucidate its functions in GBM. In summary, the five-gene
signature not only is robust for predicting the overall survival for
GBM, but also has promising practical value in the treatment of GBM.
There are some limitations in our work. First of all, there were only
very limited normal samples included in our differential expression
analyses, which might neglect some potential mRNAs. Moreover, the
efficiency of the five-gene signature should be confirmed in more GBM
patients. Furthermore, the molecular mechanisms how the five-gene
signature affected the prognosis of GBM patients should be further
elucidated by a series of experiments.
Conclusion
In conclusion, our study identified five novel biomarkers that have
potential for the prognosis prediction in GBM. Moreover, our findings
provide new insights into the pathogenesis and prognosis of GBM.
Author Contributions
WY and XJ conceived and designed the study. GT, QZ, YC, HL, XF, and ZW
performed the analysis procedures. GT, WY, and XJ analyzed the results.
WY and XJ wrote the manuscript. All authors contributed to the editing
of the manuscript.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Acknowledgments